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Chen S, Huang M, Zhang L, Huang Q, Wang Y, Liang Y. Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis. Comput Struct Biotechnol J 2024; 23:369-383. [PMID: 38226313 PMCID: PMC10788202 DOI: 10.1016/j.csbj.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/29/2023] [Accepted: 12/02/2023] [Indexed: 01/17/2024] Open
Abstract
Background Inflammatory responses influence the outcome of immunotherapy and tumorigenesis by modulating host immunity. However, systematic inflammatory response assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers remain unexplored. Here, we investigated an inflammatory response score model to predict CIT responses and patient survival in a pan-cancer analysis. Methods We retrieved 12 CIT response gene expression datasets from the Gene Expression Omnibus database (GSE78220, GSE19423, GSE100797, GSE126044, GSE35640, GSE67501, GSE115821 and GSE168204), Tumor Immune Dysfunction and Exclusion database (PRJEB23709, PRJEB25780 and phs000452.v2.p1), European Genome-phenome Archive database (EGAD00001005738), and IMvigor210 cohort. The tumor samples from six cancers types: metastatic urothelial cancer, metastatic melanoma, gastric cancer, primary bladder cancer, renal cell carcinoma, and non-small cell lung cancer.We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm. Findings The model had high predictive accuracy in both the training and validation cohorts. During sub-group analysis, area under the curve (AUC) values of 0.82, 0.80, 0.71, 0.7, 0.67, and 0.64 were obtained for the non-small cell lung cancer, gastric cancer, metastatic urothelial cancer, primary bladder cancer, metastatic melanoma, and renal cell carcinoma cohorts, respectively. CIT response rates were higher in the high-scoring training cohort subjects (51%) than the low-scoring subjects (27%). The five-year survival rates in the high- and low score groups of the training cohorts were 62% and 21%, respectively, while those of the validation cohorts were 54% and 22%, respectively (P < 0·001 in all cases). Inflammatory response signature score derived from on-treatment tumor specimens are highly predictive of response to CIT in patients with metastatic melanoma. A significant correlation was observed between the inflammatory response scores and tumor purity. Regardless of the tumor purity, patients in the low score group had a significantly poorer prognosis than those in the high score group. Immune cell infiltration analysis indicated that in the high score cohort, tumor-infiltrating lymphocytes were significantly enriched, particularly effector and natural killer cells. Inflammatory response scores were positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may have benefited patients with high scores. Analysis of signature scores across different cancer types from The Cancer Genome Atlas revealed that the prognostic performance of inflammatory response scores for survival in patients who have not undergone immunotherapy can be affected by tumor purity. Interleukin 21 (IL21) had the highest weight in the inflammatory response model, suggesting its vital role in the prediction mode. Since the number of metastatic melanoma patients (n = 429) was relatively large among CIT cohorts, we further performed a co-culture experiment using a melanoma cell line and CD8 + T cell populations generated from peripheral blood monocytes. The results showed that IL21 therapy combined with anti-PD1 (programmed cell death 1) antibodies (trepril monoclonal antibodies) significantly enhanced the cytotoxic activity of CD8 + T cells against the melanoma cell line. Conclusion In this study, we developed an inflammatory response gene signature model that predicts patient survival and immunotherapy response in multiple malignancies. We further found that the predictive performance in the non-small cell lung cancer and gastric cancer group had the highest value among the six different malignancy subgroups. When compared with existing signatures, the inflammatory response gene signature scores for on-treatment samples were more robust predictors of the response to CIT in metastatic melanoma.
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Affiliation(s)
- Shuzhao Chen
- Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong, China
- Department of Thyroid and Breast Surgery, Clinical Research Center, The First Affiliated Hospital of Shantou University Medical College (SUMC), Shantou, Guangdong, China
| | - Mayan Huang
- Department of Pathology, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
| | - Limei Zhang
- Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong, China
| | - Qianqian Huang
- Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong, China
| | - Yun Wang
- Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong, China
| | - Yang Liang
- Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong, China
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Yang Y, Chen X, Pan J, Ning H, Zhang Y, Bo Y, Ren X, Li J, Qin S, Wang D, Chen MM, Zhang Z. Pan-cancer single-cell dissection reveals phenotypically distinct B cell subtypes. Cell 2024; 187:4790-4811.e22. [PMID: 39047727 DOI: 10.1016/j.cell.2024.06.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 04/25/2024] [Accepted: 06/26/2024] [Indexed: 07/27/2024]
Abstract
Characterizing the compositional and phenotypic characteristics of tumor-infiltrating B cells (TIBs) is important for advancing our understanding of their role in cancer development. Here, we establish a comprehensive resource of human B cells by integrating single-cell RNA sequencing data of B cells from 649 patients across 19 major cancer types. We demonstrate substantial heterogeneity in their total abundance and subtype composition and observe immunoglobulin G (IgG)-skewness of antibody-secreting cell isotypes. Moreover, we identify stress-response memory B cells and tumor-associated atypical B cells (TAABs), two tumor-enriched subpopulations with prognostic potential, shared in a pan-cancer manner. In particular, TAABs, characterized by a high clonal expansion level and proliferative capacity as well as by close interactions with activated CD4 T cells in tumors, are predictive of immunotherapy response. Our integrative resource depicts distinct clinically relevant TIB subsets, laying a foundation for further exploration of functional commonality and diversity of B cells in cancer.
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Affiliation(s)
- Yu Yang
- Biomedical Pioneering Innovation Center (BIOPIC), Academy for Advanced Interdisciplinary Studies, and School of Life Sciences, Peking University, Beijing 100871, China
| | - Xueyan Chen
- Biomedical Pioneering Innovation Center (BIOPIC), Academy for Advanced Interdisciplinary Studies, and School of Life Sciences, Peking University, Beijing 100871, China
| | - Jieying Pan
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Huiheng Ning
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Yaojun Zhang
- State Key Laboratory of Oncology in South China, Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Yufei Bo
- Biomedical Pioneering Innovation Center (BIOPIC), Academy for Advanced Interdisciplinary Studies, and School of Life Sciences, Peking University, Beijing 100871, China
| | - Xianwen Ren
- Biomedical Pioneering Innovation Center (BIOPIC), Academy for Advanced Interdisciplinary Studies, and School of Life Sciences, Peking University, Beijing 100871, China
| | - Jiesheng Li
- Biomedical Pioneering Innovation Center (BIOPIC), Academy for Advanced Interdisciplinary Studies, and School of Life Sciences, Peking University, Beijing 100871, China
| | - Shishang Qin
- Biomedical Pioneering Innovation Center (BIOPIC), Academy for Advanced Interdisciplinary Studies, and School of Life Sciences, Peking University, Beijing 100871, China
| | - Dongfang Wang
- Biomedical Pioneering Innovation Center (BIOPIC), Academy for Advanced Interdisciplinary Studies, and School of Life Sciences, Peking University, Beijing 100871, China.
| | - Min-Min Chen
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, China.
| | - Zemin Zhang
- Biomedical Pioneering Innovation Center (BIOPIC), Academy for Advanced Interdisciplinary Studies, and School of Life Sciences, Peking University, Beijing 100871, China.
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Kim J, Yong SH, Jang G, Kim Y, Park R, Koh HH, Kim S, Oh CM, Lee SH. Spatial profiling of non-small cell lung cancer provides insights into tumorigenesis and immunotherapy response. Commun Biol 2024; 7:930. [PMID: 39095464 PMCID: PMC11297140 DOI: 10.1038/s42003-024-06568-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 07/05/2024] [Indexed: 08/04/2024] Open
Abstract
Lung cancer is the second most common cancer worldwide and a leading cause of cancer-related deaths. Despite advances in targeted therapy and immunotherapy, the prognosis remains unfavorable, especially in metastatic cases. This study aims to identify molecular changes in non-small cell lung cancer (NSCLC) patients based on their response to treatment. Using tumor and matched immune cell rich peritumoral tissues, we perform a retrospective, comprehensive spatial transcriptomic analysis of a proven malignant NSCLC sample treated with immune checkpoint inhibitor (ICI). In addition to T cells, other immune cell types, such as B cells and macrophages, were also activated in responders to ICI treatment. In particular, B cells and B cell-mediated immunity pathways are consistently found to be activated. Analysis of the histologic subgroup (lung squamous cell carcinoma, LUSC; lung adenocarcinoma, LUAD) of NSCLC also confirms activation of B cell mediated immunity. Analysis of B cell subtypes shows that B cell subtypes were more activated in immune cell-rich tissues near tumor tissue. Furthermore, increased expression of B cell immunity-related genes is associated with better prognosis. These findings provide insight into predicting ICI treatment responses and identifying appropriate candidates for immunotherapy in NSCLC patients.
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Affiliation(s)
- Joon Kim
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, Korea
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seung Hyun Yong
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Gyuho Jang
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, Korea
| | - Yumin Kim
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, Korea
| | - Raekil Park
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, Korea
| | - Hyun-Hee Koh
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sehui Kim
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
- Department of Pathology, Korea University Guro Hospital, Seoul, Republic of Korea.
| | - Chang-Myung Oh
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, Korea.
| | - Sang Hoon Lee
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
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Wang Z, Zhang J, Zuo C, Chen H, Wang L, Xie Y, Ma H, Min S, Wang X, Lian C. Identification and validation of tryptophan-related gene signatures to predict prognosis and immunotherapy response in lung adenocarcinoma reveals a critical role for PTTG1. Front Immunol 2024; 15:1386427. [PMID: 39144144 PMCID: PMC11321965 DOI: 10.3389/fimmu.2024.1386427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 07/15/2024] [Indexed: 08/16/2024] Open
Abstract
Introduction Tryptophan metabolism is strongly associated with immunosuppression and may influence lung adenocarcinoma prognosis as well as tumor microenvironment alterations. Methods Sequencing datasets were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Two different clusters were identified by consensus clustering, and prognostic models were established based on differentially expressed genes (DEGs) in the two clusters. We investigated differences in mutational landscapes, enrichment pathways, immune cell infiltration, and immunotherapy between high- and low-risk scoring groups. Single-cell sequencing data from Bischoff et al. were used to identify and quantify tryptophan metabolism, and model genes were comprehensively analyzed. Finally, PTTG1 was analyzed at the pan-cancer level by the pan-TCGA cohort. Results Risk score was defined as an independent prognostic factor for lung adenocarcinoma and was effective in predicting immunotherapy response in patients with lung adenocarcinoma. PTTG1 is one of the key genes, and knockdown of PTTG1 in vitro decreases lung adenocarcinoma cell proliferation and migration and promotes apoptosis and down-regulation of tryptophan metabolism regulators in lung adenocarcinoma cells. Discussion Our study revealed the pattern and molecular features of tryptophan metabolism in lung adenocarcinoma patients, established a model of tryptophan metabolism-associated lung adenocarcinoma prognosis, and explored the roles of PTTG1 in lung adenocarcinoma progression, EMT process, and tryptophan metabolism.
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Affiliation(s)
- Ziqiang Wang
- Anhui Province Key Laboratory of Clinical and Preclinical Research in Respiratory Disease, Molecular Diagnosis Center, Joint Research Center for Regional Diseases of Institute of Health and Medicine (IHM), First Affiliated Hospital of Bengbu Medical University, Bengbu, China
- Research Center of Clinical Laboratory Science, Bengbu Medical University, Bengbu, China
| | - Jing Zhang
- Department of Genetics, School of Life Sciences, Bengbu Medical University, Bengbu, China
| | - Chao Zuo
- Department of Clinical Laboratory, Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Huili Chen
- Research Center of Clinical Laboratory Science, Bengbu Medical University, Bengbu, China
| | - Luyao Wang
- Department of Genetics, School of Life Sciences, Bengbu Medical University, Bengbu, China
| | - Yiluo Xie
- Department of Clinical Medicine, Bengbu Medical University, Bengbu, China
| | - Hongyu Ma
- Department of Clinical Medicine, Bengbu Medical University, Bengbu, China
| | - Shengping Min
- Anhui Province Key Laboratory of Clinical and Preclinical Research in Respiratory Disease, Molecular Diagnosis Center, Joint Research Center for Regional Diseases of Institute of Health and Medicine (IHM), First Affiliated Hospital of Bengbu Medical University, Bengbu, China
| | - Xiaojing Wang
- Anhui Province Key Laboratory of Clinical and Preclinical Research in Respiratory Disease, Molecular Diagnosis Center, Joint Research Center for Regional Diseases of Institute of Health and Medicine (IHM), First Affiliated Hospital of Bengbu Medical University, Bengbu, China
| | - Chaoqun Lian
- Research Center of Clinical Laboratory Science, Bengbu Medical University, Bengbu, China
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Lin H, Fu L, Zhou X, Yu A, Chen Y, Liao W, Shu G, Zhang L, Tan L, Liang H, Wang Z, Deng Q, Wang J, Jin M, Chen Z, Wei J, Cao J, Chen W, Li X, Li P, Lu J, Luo J. LRP1 induces anti-PD-1 resistance by modulating the DLL4-NOTCH2-CCL2 axis and redirecting M2-like macrophage polarisation in bladder cancer. Cancer Lett 2024; 593:216807. [PMID: 38462037 DOI: 10.1016/j.canlet.2024.216807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/23/2024] [Accepted: 03/06/2024] [Indexed: 03/12/2024]
Abstract
The tumour microenvironment (TME) drives bladder cancer (BLCA) progression. Targeting the TME has emerged as a promising strategy for BLCA treatment in recent years. Furthermore, checkpoint blockade therapies are only beneficial for a minority of patients with BLCA, and drug resistance is a barrier to achieving significant clinical effects of anti-programmed cell death protein-1 (PD-1)/programmed death protein ligand-1 (PD-L1) therapy. In this study, higher low-density lipoprotein receptor-related protein 1 (LRP1) levels were related to a poorer prognosis for patients with various cancers, including those with higher grades and later stages of BLCA. Enrichment analysis demonstrated that LRP1 plays a role in the epithelial-mesenchymal transition (EMT), NOTCH signalling pathway, and ubiquitination. LRP1 knockdown in BLCA cells delayed BLCA progression both in vivo and in vitro. Furthermore, LRP1 knockdown suppressed EMT, reduced DLL4-NOTCH2 signalling activity, and downregulated M2-like macrophage polarisation. Patients with BLCA and higher LRP1 levels responded weakly to anti-PD-1 therapy in the IMvigor210 cohort. Moreover, LRP1 knockdown enhanced the therapeutic effects of anti-PD-1 in mice. Taken together, our findings suggest that LRP1 is a potential target for improving the efficacy of anti-PD-1/PD-L1 therapy by preventing EMT and M2-like macrophage polarisation by blocking the DLL4-NOTCH2 axis.
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Affiliation(s)
- Hansen Lin
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Liangmin Fu
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xinwei Zhou
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Anze Yu
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yuhang Chen
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Wuyuan Liao
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Guannan Shu
- Department of Urology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, 510623, China
| | - Lizhen Zhang
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Lei Tan
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hui Liang
- Department of Urology, Affiliated Longhua People's Hospital, Southern Medical University, Shenzhen, Guangdong, China
| | - Zhu Wang
- Department of Urology, Affiliated Longhua People's Hospital, Southern Medical University, Shenzhen, Guangdong, China
| | - Qiong Deng
- Department of Urology, Affiliated Longhua People's Hospital, Southern Medical University, Shenzhen, Guangdong, China
| | - Jieyan Wang
- Department of Urology, Affiliated Longhua People's Hospital, Southern Medical University, Shenzhen, Guangdong, China
| | - Meiyu Jin
- Department of Urology, Affiliated Longhua People's Hospital, Southern Medical University, Shenzhen, Guangdong, China
| | - Zhenhua Chen
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jinhuan Wei
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiazheng Cao
- Department of Urology, Jiangmen Central Hospital, Haibang Street 23, Pengjiang District, Jiangmen, 529030, Guangdong Province, China
| | - Wei Chen
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiaofei Li
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| | - Pengju Li
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| | - Jun Lu
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| | - Junhang Luo
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
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Ye B, Hongting G, Zhuang W, Chen C, Yi S, Tang X, Jiang A, Zhong Y. Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature. J Cell Mol Med 2024; 28:e18564. [PMID: 39046884 PMCID: PMC11268368 DOI: 10.1111/jcmm.18564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 06/03/2024] [Accepted: 06/27/2024] [Indexed: 07/27/2024] Open
Abstract
Lung adenocarcinoma (LUAD) is a leading cause of cancer-related deaths, and improving prognostic accuracy is vital for personalised treatment approaches, especially in the context of immunotherapy. In this study, we constructed an artificial intelligence (AI)-driven stemness-related gene signature (SRS) that deciphered LUAD prognosis and immunotherapy response. CytoTRACE analysis of single-cell RNA sequencing data identified genes associated with stemness in LUAD epithelial cells. An AI network integrating traditional regression, machine learning, and deep learning algorithms constructed the SRS based on genes associated with stemness. Subsequently, we conducted a comprehensive exploration of the connection between SRS and both intrinsic and extrinsic immune environments using multi-omics data. Experimental validation through siRNA knockdown in LUAD cell lines, followed by assessments of proliferation, migration, and invasion, confirmed the functional role of CKS1B, a top SRS gene. The SRS demonstrated high precision in predicting LUAD prognosis and likelihood of benefiting from immunotherapy. High-risk groups classified by the SRS exhibited decreased immunogenicity and reduced immune cell infiltration, indicating challenges for immunotherapy. Conversely, in vitro experiments revealed CKS1B knockdown significantly impaired aggressive cancer phenotypes like proliferation, migration, and invasion of LUAD cells, highlighting its pivotal role. These results underscore a close association between stemness and tumour immunity, offering predictive insights into the immune landscape and immunotherapy responses in LUAD. The newly established SRS holds promise as a valuable tool for selecting LUAD populations likely to benefit from future clinical stratification efforts.
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Affiliation(s)
- Bicheng Ye
- School of Clinical MedicineYangzhou Polytechnic CollegeYangzhouChina
| | - Ge Hongting
- Department of Respiratory and Critical Care MedicineHuai'an Hospital Affiliated to Yangzhou University (The Fifth People's Hospital of Huai'an)Huai'anChina
| | - Wen Zhuang
- Huai'an Second People's Hospital Affiliated to Xuzhou Medical UniversityHuai'anJiangsuChina
| | - Cheng Chen
- School of Clinical MedicineYangzhou Polytechnic CollegeYangzhouChina
| | - Shulin Yi
- School of Clinical MedicineYangzhou Polytechnic CollegeYangzhouChina
| | - Xinyan Tang
- Department of NursingJiangsu Vocational College of MedicineYanchengChina
| | - Aimin Jiang
- Department of Urology, Changhai HospitalNaval Medical University (Second Military Medical University)ShanghaiChina
| | - Yating Zhong
- Department of OncologyShuyang County Hospital of Traditional Chinese MedicineSuqianChina
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Wang H, Shui X, Zhang Z, He M, Tai S, Lin Y. Construction of m7G RNA modification-related prognostic model and prediction of immune therapy response in hepatocellular carcinoma. Transl Cancer Res 2024; 13:2799-2811. [PMID: 38988942 PMCID: PMC11231774 DOI: 10.21037/tcr-24-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/28/2024] [Indexed: 07/12/2024]
Abstract
Background RNA plays an important role in tumorigenesis. Changes in RNA may cause changes in the biological function. The N7-methylguanosine (m7G) methylation modification performs an integral function in tumor progression as the most widely existed RNA modification. Hepatocellular carcinoma (HCC) is among the greatest threats to human health worldwide. Low detection rates remain the main cause of advanced disease progression. Therefore, finding significant biomarkers for prognosis prediction and immune therapy response in HCC is valuable and urgently needed. Methods RNA expression and clinical data were acquired from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Different subtypes screening was finished by consensus cluster. Different expression was performed by R software. The results were validated by western blot (WB) methods. Genes with HCC prognostic potential were identified utilizing least absolute shrinkage and selection operator (LASSO) analyses. A prognosis model was established with the help of the risk score that we calculated. Related genes screening and protein-protein interactions (PPI) network construction were performed using the GeneMANIA database. Functional annotation was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) databases. In addition, gene set enrichment analysis (GSEA) of key genes and immune infiltration status were both done by R software. Finally, the immune infiltration was performed by cibersort method and single sample GSEA (ssGSEA) method. The response of immune therapy was validated by Tumor Immune Dysfunction and Exclusion database (TIDE) and the immune therapy cohort in GEO database. Results We found that two different subtypes related with m7G RNA modification and four genes associated with m7G RNA modification were differentially expressed in the TCGA-Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Additionally, to examine the value of these four genes in the HCC patients' prognoses according to the LASSO, we selected three genes, including WDR4, AGO2, and NCBP2, as prognostic related genes. Premised on the expression of these three genes, a risk score model and nomogram were constructed to provide a prediction of the HCC patients' prognoses. We performed functional annotation and created a PPI network based on the three genes (WDR4, NCBP2, and AGO2). Using R software, we performed the GSEA and immune regulation analyses. Finally, we predicted the relationship between the gene expression and the response of immune therapy. Conclusions Our study suggests that high expression of m7G RNA modification subtype is related with poor prognosis and immune response. WDR4, AGO2, and NCBP2 are key regulators of m7G RNA modification which can be clinically promising biomarkers that can be used to treat HCC. In addition, our risk score model was shown to have a strong link to OS in patients with HCC.
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Affiliation(s)
- Haoran Wang
- Department of Hepatobiliary Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xian Shui
- Department of Hepatobiliary Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zheng Zhang
- Department of Hepatobiliary Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Meng He
- Department of Hepatobiliary Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Sheng Tai
- Department of Hepatobiliary Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yujia Lin
- Department of Hepatobiliary Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin, China
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Li C, Hong W, Reuben A, Wang L, Maitra A, Zhang J, Cheng C. TimiGP-Response: the pan-cancer immune landscape associated with response to immunotherapy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.21.600089. [PMID: 38979334 PMCID: PMC11230183 DOI: 10.1101/2024.06.21.600089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Accumulating evidence suggests that the tumor immune microenvironment (TIME) significantly influences the response to immunotherapy, yet this complex relationship remains elusive. To address this issue, we developed TimiGP-Response (TIME Illustration based on Gene Pairing designed for immunotherapy Response), a computational framework leveraging single-cell and bulk transcriptomic data, along with response information, to construct cell-cell interaction networks associated with responders and estimate the role of immune cells in treatment response. This framework was showcased in triple-negative breast cancer treated with immune checkpoint inhibitors targeting the PD-1:PD-L1 interaction, and orthogonally validated with imaging mass cytometry. As a result, we identified CD8+ GZMB+ T cells associated with responders and its interaction with regulatory T cells emerged as a potential feature for selecting patients who may benefit from these therapies. Subsequently, we analyzed 3,410 patients with seven cancer types (melanoma, non-small cell lung cancer, renal cell carcinoma, metastatic urothelial carcinoma, hepatocellular carcinoma, breast cancer, and esophageal cancer) treated with various immunotherapies and combination therapies, as well as several chemo- and targeted therapies as controls. Using TimiGP-Response, we depicted the pan-cancer immune landscape associated with immunotherapy response at different resolutions. At the TIME level, CD8 T cells and CD4 memory T cells were associated with responders, while anti-inflammatory (M2) macrophages and mast cells were linked to non-responders across most cancer types and datasets. Given that T cells are the primary targets of these immunotherapies and our TIME analysis highlights their importance in response to treatment, we portrayed the pan-caner landscape on 40 T cell subtypes. Notably, CD8+ and CD4+ GZMK+ effector memory T cells emerged as crucial across all cancer types and treatments, while IL-17-producing CD8+ T cells were top candidates associated with immunotherapy non-responders. In summary, this study provides a computational method to study the association between TIME and response across the pan-cancer immune landscape, offering resources and insights into immune cell interactions and their impact on treatment efficacy.
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Affiliation(s)
- Chenyang Li
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center UTHealth Houston, Houston, TX 77030, USA
| | - Wei Hong
- Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Alexandre Reuben
- Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center UTHealth Houston, Houston, TX 77030, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center UTHealth Houston, Houston, TX 77030, USA
| | - Anirban Maitra
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Sheikh Ahmed Center for Pancreatic Cancer Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jianjun Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center UTHealth Houston, Houston, TX 77030, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Lung Cancer Genomics Program, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Lung Cancer Interception Program, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Chao Cheng
- Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
- The Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX 77030, USA
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9
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Xia H, Zhang H, Ruan Z, Zhang H, Sun L, Chen H, Zhou Y, Zhang L, Bian D, Zhu X, Zhang J, Sun F, Yu H, Song N, Liu X, Zhu Y, Zhang H, He W, Chen J, Yang J, Chen G, Xie S, Tang D, Zhang X, Duan L, Zhao D, Li Q, Zhang P, Jiang G. Neoadjuvant camrelizumab (an anti-PD-1 antibody) plus chemotherapy or apatinib (a VEGFR-2 inhibitor) for initially unresectable stage II-III non-small-cell lung cancer: a multicentre, two-arm, phase 2 exploratory study. Signal Transduct Target Ther 2024; 9:145. [PMID: 38871690 PMCID: PMC11176298 DOI: 10.1038/s41392-024-01861-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 04/29/2024] [Accepted: 05/10/2024] [Indexed: 06/15/2024] Open
Abstract
This multicentre, two-arm, phase 2 study aimed to explore the efficacy and safety of neoadjuvant camrelizumab plus chemotherapy or apatinib in patients with initially unresectable stage II-III non-small-cell lung cancer (NSCLC). Eligible patients regardless of PD-L1 expression received neoadjuvant camrelizumab 200 mg and platinum-doublet chemotherapy every 3 weeks (arm A) or those with PD-L1-positive tumors received neoadjuvant camrelizumab and apatinib 250 mg once daily (arm B), for 2-4 cycles, followed by surgery. The primary endpoint was major pathological response (MPR) rate. Thirty patients in arm A and 21 in arm B were enrolled. Surgery rates were 50.0% (15/30) in arm A and 42.9% (9/21) in arm B, with all patients achieving R0 resections. Of these patients, the MPR and pathological complete response rates were both 20.0% (95% CI 4.3-48.1) in arm A and were 55.6% (95% CI 21.2-86.3) and 11.1% (95% CI 0.3-48.2) in arm B, respectively. The corresponding objective response rates were 33.3% (95% CI 11.8-61.6) and 55.6% (95% CI 21.2-86.3). With a median follow-up of 22.4 months (95% CI 19.0-26.0), the median event-free survival was not reached (NR; 95% CI 13.6-NR) in arm A and 16.8 months (95% CI 8.6-NR) in arm B. Grade 3 or above treatment-related adverse events occurred in eight (26.7%) patients in arm A and three (14.3%) in arm B. Biomarker analysis showed baseline TYROBP expression was predictive of treatment response in arm B. Neoadjuvant camrelizumab plus chemotherapy or apatinib exhibits preliminary efficacy and manageable toxicity in patients with initially unresectable stage II-III NSCLC.
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Affiliation(s)
- Haoran Xia
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Han Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zheng Ruan
- Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huibiao Zhang
- Department of Thoracic Surgery, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Liangdong Sun
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Hezhong Chen
- Department of Thoracic Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Yongxin Zhou
- Department of Thoracic-Cardiovascular Surgery, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Lele Zhang
- Central Laboratory, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Dongliang Bian
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xinsheng Zhu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jing Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Fenghuan Sun
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Huansha Yu
- Experimental Animal Center, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Nan Song
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiaogang Liu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yuming Zhu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Haiping Zhang
- Department of Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Wenxin He
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jian Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jie Yang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Guohan Chen
- Department of Thoracic Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Shiliang Xie
- Department of Thoracic-Cardiovascular Surgery, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Dongfang Tang
- Department of Thoracic Surgery, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Xiaomiao Zhang
- Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Liang Duan
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Deping Zhao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
| | - Qinchuan Li
- Department of Thoracic Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
| | - Peng Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
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10
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Guo Z, Cao B, Hu Z, Wu J, Zhou W, Zhang W, Shi Z. Immunotherapy, prognostic, and tumor biomarker based on pancancer analysis, SMARCD3. Aging (Albany NY) 2024; 16:10074-10107. [PMID: 38862250 PMCID: PMC11210247 DOI: 10.18632/aging.205921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 04/10/2024] [Indexed: 06/13/2024]
Abstract
BACKGROUND SMARCD3 has recently been shown to be an important gene affecting cancer, playing an important role in medulloblastoma and pancreatic ductal adenocarcinoma. Therefore, we conducted this research to investigate the potential involvement of SMARCD3 across cancers and to offer recommendations for future studies. METHODS Utilizing information on 33 malignancies in the UCSC Xena database, SMARCD3 expression and its prognostic value were assessed. The tumor microenvironment was evaluated with the "CIBERSORT" and "ESTIMATE" algorithms. SMARCD3 and immune-related genes were analyzed using the TISIDB website. The pathways related to the target genes were examined using GSEA. MSI (microsatellite instability), TMB (tumor mutational burden), and immunotherapy analysis were used to evaluate the impact of target genes on the response to immunotherapy. RESULTS There is heterogeneity in terms of the expression and prognostic value of SMARCD3 among various cancers, but it is a risk factor for many cancers including uterine corpus endometrial cancer (UCEC), renal clear cell carcinoma (KIRC), and gastric adenocarcinoma (STAD). GSEA revealed that SMARCD3 is related to chromatin remodeling and transcriptional activation, lipid metabolism, and the activities of various immune cells. The TMB and MSI analyses suggested that SMARCD3 affects the immune response efficiency of KIRC, LUAD and STAD. Immunotherapy analysis suggested that SMARCD3 may be a potential immunotherapy target. RT-qPCR demonstrated the variation in SMARCD3 expression in KIRC, LUAD, and STAD. CONCLUSION Our study revealed that SMARCD3 affects the prognosis and immunotherapy response of some tumors, providing a direction for further research on this gene.
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Affiliation(s)
- Zishun Guo
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Bingji Cao
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - Zhuozheng Hu
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Jiajun Wu
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Weijun Zhou
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Wenxiong Zhang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Zhihua Shi
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
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11
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Luo L, Jiang M, Wu H, Liu Y, Wang H, Zhou C, Ren S, Chen X, Jiang T, Xu C. SIRPG expression positively associates with an inflamed tumor microenvironment and response to PD-1 blockade. Cancer Immunol Immunother 2024; 73:147. [PMID: 38833156 PMCID: PMC11150346 DOI: 10.1007/s00262-024-03737-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 05/15/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND This study aimed to investigate the relationship between signal regulatory protein gamma (SIRPG) and tumor immune microenvironment phenotypes or T cell mediated-adaptive antitumor immunity, and its predictive value for response to PD-1 blockade in cancers. METHODS Pan-cancer analysis of SIRPG expression and immune deconvolution was performed using transcriptomic data across 33 tumor types. Transcriptomic and clinical data from 157 patients with non-small-cell lung cancer (NSCLC) and melanoma received PD-1 blockade were analyzed. Expression characteristics of SIRPG were investigated using single-cell RNA sequencing (scRNA-seq) data of 103,599 cells. The effect of SIRPG expression was evaluated via SIRPG knockdown or overexpression in Jurkat T cells. RESULTS The results showed that most cancers with high SIRPG expression had significantly higher abundance of T cells, B cells, NK cells, M1 macrophages and cytotoxic lymphocytes and increased expression level of immunomodulatory factors regulating immune cell recruitment, antigen presentation, T cell activation and cytotoxicity, but markedly lower abundance of neutrophils, M2 macrophages, and myeloid-derived suppressor cells. High SIRPG expression was associated with favorable response to PD-1 blockade in both NSCLC and melanoma. scRNA-seq data suggested SIRPG was mainly expressed in CD8+ exhausted T and CD4+ regulatory T cells, and positively associated with immune checkpoint expression including PDCD1 and CTLA4. In vitro test showed SIRPG expression in T cells could facilitate expression of PDCD1 and CTLA4. CONCLUSION High SIRPG expression is associated with an inflamed immune phenotype in cancers and favorable response to PD-1 blockade, suggesting it would be a promising predictive biomarker for PD-1 blockade and novel immunotherapeutic target.
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Affiliation(s)
- Libo Luo
- Department of Medical Oncology, Shanghai Pulmonary Hospital and Thoracic Cancer Institute, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China
| | - Minlin Jiang
- Department of Medical Oncology, Shanghai Pulmonary Hospital and Thoracic Cancer Institute, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China
| | - Hong Wu
- Department of Oncology and Cancer Institute, Department of Laboratory Medicine and Sichuan Provincial Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32 1st Ring Road, Chengdu, 610072, China
| | - Yiqiang Liu
- Department of Oncology and Cancer Institute, Department of Laboratory Medicine and Sichuan Provincial Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32 1st Ring Road, Chengdu, 610072, China
| | - Haowei Wang
- Department of Medical Oncology, Shanghai Pulmonary Hospital and Thoracic Cancer Institute, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China
| | - Caicun Zhou
- Department of Medical Oncology, Shanghai Pulmonary Hospital and Thoracic Cancer Institute, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China
| | - Shengxiang Ren
- Department of Medical Oncology, Shanghai Pulmonary Hospital and Thoracic Cancer Institute, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China
| | - Xiaoxia Chen
- Department of Medical Oncology, Shanghai Pulmonary Hospital and Thoracic Cancer Institute, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China.
| | - Tao Jiang
- Department of Medical Oncology, Shanghai Pulmonary Hospital and Thoracic Cancer Institute, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China.
| | - Chuan Xu
- Department of Oncology and Cancer Institute, Department of Laboratory Medicine and Sichuan Provincial Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32 1st Ring Road, Chengdu, 610072, China.
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12
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Liu C, Xie J, Lin B, Tian W, Wu Y, Xin S, Hong L, Li X, Liu L, Jin Y, Tang H, Deng X, Zou Y, Zheng S, Fang W, Cheng J, Dai X, Bao X, Zhao P. Pan-Cancer Single-Cell and Spatial-Resolved Profiling Reveals the Immunosuppressive Role of APOE+ Macrophages in Immune Checkpoint Inhibitor Therapy. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2401061. [PMID: 38569519 PMCID: PMC11186051 DOI: 10.1002/advs.202401061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/13/2024] [Indexed: 04/05/2024]
Abstract
The heterogeneity of macrophages influences the response to immune checkpoint inhibitor (ICI) therapy. However, few studies explore the impact of APOE+ macrophages on ICI therapy using single-cell RNA sequencing (scRNA-seq) and machine learning methods. The scRNA-seq and bulk RNA-seq data are Integrated to construct an M.Sig model for predicting ICI response based on the distinct molecular signatures of macrophage and machine learning algorithms. Comprehensive single-cell analysis as well as in vivo and in vitro experiments are applied to explore the potential mechanisms of the APOE+ macrophage in affecting ICI response. The M.Sig model shows clear advantages in predicting the efficacy and prognosis of ICI therapy in pan-cancer patients. The proportion of APOE+ macrophages is higher in ICI non-responders of triple-negative breast cancer compared with responders, and the interaction and longer distance between APOE+ macrophages and CD8+ exhausted T (Tex) cells affecting ICI response is confirmed by multiplex immunohistochemistry. In a 4T1 tumor-bearing mice model, the APOE inhibitor combined with ICI treatment shows the best efficacy. The M.Sig model using real-world immunotherapy data accurately predicts the ICI response of pan-cancer, which may be associated with the interaction between APOE+ macrophages and CD8+ Tex cells.
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Affiliation(s)
- Chuan Liu
- Department of Medical OncologyThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310003China
| | - Jindong Xie
- State Key Laboratory of Oncology in South ChinaGuangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhou510060China
| | - Bo Lin
- College of Computer Science and TechnologyZhejiang UniversityHangzhou310053China
- Innovation Centre for InformationBinjiang Institute of Zhejiang UniversityHangzhou310053China
| | - Weihong Tian
- Changzhou Third People's HospitalChangzhou Medical CenterNanjing Medical UniversityChangzhou213000China
| | - Yifan Wu
- School of softwareZhejiang UniversityNingbo315100China
| | - Shan Xin
- Department of GeneticsYale School of medicineNew HavenCT06510USA
| | - Libing Hong
- Department of Medical OncologyThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310003China
| | - Xin Li
- Department Chronic Inflammation and CancerGerman Cancer Research Center (DKFZ)69120HeidelbergGermany
| | - Lulu Liu
- Department of Medical OncologyThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310003China
| | - Yuzhi Jin
- Department of Medical OncologyThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310003China
| | - Hailin Tang
- State Key Laboratory of Oncology in South ChinaGuangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhou510060China
| | - Xinpei Deng
- State Key Laboratory of Oncology in South ChinaGuangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhou510060China
| | - Yutian Zou
- State Key Laboratory of Oncology in South ChinaGuangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhou510060China
| | - Shaoquan Zheng
- Breast Disease CenterThe First Affiliated HospitalSun Yat‐Sen UniversityGuangzhou510060China
| | - Weijia Fang
- Department of Medical OncologyThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310003China
| | - Jinlin Cheng
- State Key Laboratory for Diagnosis and Treatment of Infectious DiseasesNational Clinical Research Center for Infectious DiseasesNational Medical Center for Infectious DiseasesCollaborative Innovation Center for Diagnosis and Treatment of Infectious DiseasesThe First Affiliated HospitalZhejiang University School of MedicineZhejiang UniversityHangzhou310003China
| | - Xiaomeng Dai
- Department of Medical OncologyThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310003China
| | - Xuanwen Bao
- Department of Medical OncologyThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310003China
| | - Peng Zhao
- Department of Medical OncologyThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310003China
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13
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Zhang P, Yang Z, Liu Z, Zhang G, Zhang L, Zhang Z, Fan J. Deciphering lung adenocarcinoma evolution: Integrative single-cell genomics identifies the prognostic lung progression associated signature. J Cell Mol Med 2024; 28:e18408. [PMID: 38837585 DOI: 10.1111/jcmm.18408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/22/2024] [Accepted: 04/27/2024] [Indexed: 06/07/2024] Open
Abstract
We employed single-cell analysis techniques, specifically the inferCNV method, to dissect the complex progression of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) through minimally invasive adenocarcinoma (MIA) to invasive adenocarcinoma (IAC). This approach enabled the identification of Cluster 6, which was significantly associated with LUAD progression. Our comprehensive analysis included intercellular interaction, transcription factor regulatory networks, trajectory analysis, and gene set variation analysis (GSVA), leading to the development of the lung progression associated signature (LPAS). Interestingly, we discovered that the LPAS not only accurately predicts the prognosis of LUAD patients but also forecasts genomic alterations, distinguishes between 'cold' and 'hot' tumours, and identifies potential candidates suitable for immunotherapy. PSMB1, identified within Cluster 6, was experimentally shown to significantly enhance cancer cell invasion and migration, highlighting the clinical relevance of LPAS in predicting LUAD progression and providing a potential target for therapeutic intervention. Our findings suggest that LPAS offers a novel biomarker for LUAD patient stratification, with significant implications for improving prognostic accuracy and guiding treatment decisions.
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Affiliation(s)
- Pengpeng Zhang
- Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Zijun Yang
- Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Zuo Liu
- Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Ge Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lianmin Zhang
- Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Zhenfa Zhang
- Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Jun Fan
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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14
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Liu H, Sima X, Xiao B, Gulizeba H, Zhao S, Zhou T, Huang Y. Integrated analysis of single-cell and bulk RNA sequencing data reveals a myeloid cell-related regulon predicting neoadjuvant immunotherapy response across cancers. J Transl Med 2024; 22:486. [PMID: 38773508 PMCID: PMC11110189 DOI: 10.1186/s12967-024-05123-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/20/2024] [Indexed: 05/23/2024] Open
Abstract
BACKGROUND Immunotherapy has brought about a paradigm shift in the treatment of cancer. However, the majority of patients exhibit resistance or become refractory to immunotherapy, and the underlying mechanisms remain to be explored. METHODS Sing-cell RNA sequencing (scRNA‑seq) datasets derived from 1 pretreatment and 1 posttreatment achieving pathological complete response (pCR) patient with lung adenocarcinoma (LUAD) who received neoadjuvant immunotherapy were collected, and pySCENIC was used to find the gene regulatory network (GRN) between cell types and immune checkpoint inhibitor (ICI) response. A regulon predicting ICI response was identified and validated using large‑scale pan-cancer data, including a colorectal cancer scRNA‑seq dataset, a breast cancer scRNA‑seq dataset, The Cancer Genome Atlas (TCGA) pan-cancer cohort, and 5 ICI transcriptomic cohorts. Symphony reference mapping was performed to construct the myeloid cell map. RESULTS Thirteen major cluster cell types were identified by comparing pretreatment and posttreatment patients, and the fraction of myeloid cells was higher in the posttreatment group (19.0% vs. 11.8%). A PPARG regulon (containing 23 target genes) was associated with ICI response, and its function was validated by a colorectal cancer scRNA‑seq dataset, a breast cancer scRNA‑seq dataset, TCGA pan-cancer cohort, and 5 ICI transcriptomic cohorts. Additionally, a myeloid cell map was developed, and cluster I, II, and III myeloid cells with high expression of PPARG were identified. Moreover, we constructed a website called PPARG ( https://pparg.online/PPARG/ or http://43.134.20.130:3838/PPARG/ ), which provides a powerful discovery tool and resource value for researchers. CONCLUSIONS The PPARG regulon is a predictor of ICI response. The myeloid cell map enables the identification of PPARG subclusters in public scRNA-seq datasets and provides a powerful discovery tool and resource value.
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Affiliation(s)
- Hong Liu
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, P. R. China
| | - Xiaoxian Sima
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, P. R. China
| | - Bijing Xiao
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, P. R. China
| | - Haimiti Gulizeba
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, P. R. China
| | - Shen Zhao
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, P. R. China.
| | - Ting Zhou
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, P. R. China.
| | - Yan Huang
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, P. R. China.
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15
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Liu Y, Zhang X, Pang Z, Wang Y, Zheng H, Wang G, Wang K, Du J. Prediction of prognosis and immunotherapy efficacy based on metabolic landscape in lung adenocarcinoma by bulk, single-cell RNA sequencing and Mendelian randomization analyses. Aging (Albany NY) 2024; 16:8772-8809. [PMID: 38771130 PMCID: PMC11164486 DOI: 10.18632/aging.205838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 04/16/2024] [Indexed: 05/22/2024]
Abstract
Immunotherapy has been a remarkable clinical advancement in cancer treatment, but only a few patients benefit from it. Metabolic reprogramming is tightly associated with immunotherapy efficacy and clinical outcomes. However, comprehensively analyzing their relationship is still lacking in lung adenocarcinoma (LUAD). Herein, we evaluated 84 metabolic pathways in TCGA-LUAD by ssGSEA. A matrix of metabolic pathway pairs was generated and a metabolic pathway-pair score (MPPS) model was established by univariable, LASSO, multivariable Cox regression analyses. The differences of metabolic reprogramming, tumor microenvironment (TME), tumor mutation burden and drug sensitivity in different MPPS groups were further explored. WGCNA and 117 machine learning algorithms were performed to identify MPPS-related genes. Single-cell RNA sequencing and in vitro experiments were used to explore the role of C1QTNF6 on TME. The results showed MPPS model accurately predicted prognosis and immunotherapy efficacy of LUAD patients regardless of sequencing platforms. High-MPPS group had worse prognosis, immunotherapy efficacy and lower immune cells infiltration, immune-related genes expression and cancer-immunity cycle scores than low-MPPS group. Seven MPPS-related genes were identified, of which C1QTNF6 was mainly expressed in fibroblasts. High C1QTNF6 expression in fibroblasts was associated with more infiltration of M2 macrophage, Treg cells and less infiltration of NK cells, memory CD8+ T cells. In vitro experiments validated silencing C1QTNF6 in fibroblasts could inhibit M2 macrophage polarization and migration. The study depicted the metabolic landscape of LUAD and constructed a MPPS model to accurately predict prognosis and immunotherapy efficacy. C1QTNF6 was a promising target to regulate M2 macrophage polarization and migration.
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Affiliation(s)
- Yong Liu
- Institute of Oncology, Shandong Provincial Hospital, Shandong University, Jinan 250021, Shandong, China
| | - Xiangwei Zhang
- Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
| | - Zhaofei Pang
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
| | - Yadong Wang
- Institute of Oncology, Shandong Provincial Hospital, Shandong University, Jinan 250021, Shandong, China
| | - Haotian Zheng
- Institute of Oncology, Shandong Provincial Hospital, Shandong University, Jinan 250021, Shandong, China
| | - Guanghui Wang
- Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
- Institute of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
| | - Kai Wang
- Institute of Oncology, Shandong Provincial Hospital, Shandong University, Jinan 250021, Shandong, China
| | - Jiajun Du
- Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
- Institute of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
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16
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Hu YM, Zhao F, Graff JN, Chen C, Zhao X, Thomas GV, Wu H, Kardosh A, Mills GB, Alumkal JJ, Moran AE, Xia Z. Androgen receptor activity inversely correlates with immune cell infiltration and immunotherapy response across multiple cancer lineages. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.08.593181. [PMID: 38798471 PMCID: PMC11118439 DOI: 10.1101/2024.05.08.593181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
There is now increasing recognition of the important role of androgen receptor (AR) in modulating immune function. To gain a comprehensive understanding of the effects of AR activity on cancer immunity, we employed a computational approach to profile AR activity in 33 human tumor types using RNA-Seq datasets from The Cancer Genome Atlas. Our pan-cancer analysis revealed that the genes most negatively correlated with AR activity across cancers are involved in active immune system processes. Importantly, we observed a significant negative correlation between AR activity and IFNγ pathway activity at the pan-cancer level. Indeed, using a matched biopsy dataset from subjects with prostate cancer before and after AR-targeted treatment, we verified that inhibiting AR enriches immune cell abundances and is associated with higher IFNγ pathway activity. Furthermore, by analyzing immunotherapy datasets in multiple cancers, our results demonstrate that low AR activity was significantly associated with a favorable response to immunotherapy. Together, our data provide a comprehensive assessment of the relationship between AR signaling and tumor immunity.
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Affiliation(s)
- Ya-Mei Hu
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Faming Zhao
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Julie N. Graff
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- VA Portland Health Care System, Portland, OR, USA
| | - Canping Chen
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Xiyue Zhao
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - George V. Thomas
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Department of Pathology & Laboratory Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Hui Wu
- Division of Biomaterial and Biomedical Sciences, Department of Oral Rehabilitation and Biosciences, Oregon Health & Science University, Portland, OR, USA
| | - Adel Kardosh
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Gordon B. Mills
- Division of Oncological Sciences, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Joshi J. Alumkal
- Department of Internal Medicine, Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | - Amy E. Moran
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University, Portland, OR, USA
| | - Zheng Xia
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Center for Biomedical Data Science, Oregon Health & Science University, Portland, OR, USA
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Spagnolo CC, Pepe F, Ciappina G, Nucera F, Ruggeri P, Squeri A, Speranza D, Silvestris N, Malapelle U, Santarpia M. Circulating biomarkers as predictors of response to immune checkpoint inhibitors in NSCLC: Are we on the right path? Crit Rev Oncol Hematol 2024; 197:104332. [PMID: 38580184 DOI: 10.1016/j.critrevonc.2024.104332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 03/25/2024] [Indexed: 04/07/2024] Open
Abstract
Immune checkpoints inhibitors (ICIs) have markedly improved the therapeutic management of advanced NSCLC and, more recently, they have demonstrated efficacy also in the early-stage disease. Despite better survival outcomes with ICIs compared to standard chemotherapy, a large proportion of patients can derive limited clinical benefit from these agents. So far, few predictive biomarkers, including the programmed death-ligand 1 (PD-L1), have been introduced in clinical practice. Therefore, there is an urgent need to identify novel biomarkers to select patients for immunotherapy, to improve efficacy and avoid unnecessary toxicity. A deeper understanding of the mechanisms involved in antitumor immunity and advances in the field of liquid biopsy have led to the identification of a wide range of circulating biomarkers that could potentially predict response to immunotherapy. Herein, we provide an updated overview of these circulating biomarkers, focusing on emerging data from clinical studies and describing modern technologies used for their detection.
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Affiliation(s)
- Calogera Claudia Spagnolo
- Medical Oncology Unit, Department of Human Pathology "G. Barresi", University of Messina, Messina 98122, Italy
| | - Francesco Pepe
- Department of Public Health, University of Naples Federico II, Via S. Pansini, Naples 80131, Italy
| | - Giuliana Ciappina
- Medical Oncology Unit, Department of Human Pathology "G. Barresi", University of Messina, Messina 98122, Italy
| | - Francesco Nucera
- Respiratory Medicine Unit, Department of Biomedical Sciences, Dentistry and Morphological and Functional Imaging (BIOMORF), University of Messina, Messina 98122, Italy
| | - Paolo Ruggeri
- Respiratory Medicine Unit, Department of Biomedical Sciences, Dentistry and Morphological and Functional Imaging (BIOMORF), University of Messina, Messina 98122, Italy
| | - Andrea Squeri
- Medical Oncology Unit, Department of Human Pathology "G. Barresi", University of Messina, Messina 98122, Italy
| | - Desirèe Speranza
- Medical Oncology Unit, Department of Human Pathology "G. Barresi", University of Messina, Messina 98122, Italy
| | - Nicola Silvestris
- Medical Oncology Unit, Department of Human Pathology "G. Barresi", University of Messina, Messina 98122, Italy
| | - Umberto Malapelle
- Department of Public Health, University of Naples Federico II, Via S. Pansini, Naples 80131, Italy
| | - Mariacarmela Santarpia
- Medical Oncology Unit, Department of Human Pathology "G. Barresi", University of Messina, Messina 98122, Italy.
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18
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Mei J, Cai Y, Xu R, Li Q, Chu J, Luo Z, Sun Y, Shi Y, Xu J, Li D, Liang S, Jiang Y, Liu J, Qian Z, Zhou J, Wan M, Yang Y, Zhu Y, Zhang Y, Yin Y. Conserved immuno-collagenic subtypes predict response to immune checkpoint blockade. Cancer Commun (Lond) 2024; 44:554-575. [PMID: 38507505 PMCID: PMC11110954 DOI: 10.1002/cac2.12538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 03/06/2024] [Accepted: 03/10/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Immune checkpoint blockade (ICB) has revolutionized the treatment of various cancer types. Despite significant preclinical advancements in understanding mechanisms, identifying the molecular basis and predictive biomarkers for clinical ICB responses remains challenging. Recent evidence, both preclinical and clinical, underscores the pivotal role of the extracellular matrix (ECM) in modulating immune cell infiltration and behaviors. This study aimed to create an innovative classifier that leverages ECM characteristics to enhance the effectiveness of ICB therapy. METHODS We analyzed transcriptomic collagen activity and immune signatures in 649 patients with cancer undergoing ICB therapy. This analysis led to the identification of three distinct immuno-collagenic subtypes predictive of ICB responses. We validated these subtypes using the transcriptome data from 9,363 cancer patients from The Cancer Genome Atlas (TCGA) dataset and 1,084 in-house samples. Additionally, novel therapeutic targets were identified based on these established immuno-collagenic subtypes. RESULTS Our categorization divided tumors into three subtypes: "soft & hot" (low collagen activity and high immune infiltration), "armored & cold" (high collagen activity and low immune infiltration), and "quiescent" (low collagen activity and immune infiltration). Notably, "soft & hot" tumors exhibited the most robust response to ICB therapy across various cancer types. Mechanistically, inhibiting collagen augmented the response to ICB in preclinical models. Furthermore, these subtypes demonstrated associations with immune activity and prognostic predictive potential across multiple cancer types. Additionally, an unbiased approach identified B7 homolog 3 (B7-H3), an available drug target, as strongly expressed in "armored & cold" tumors, relating with poor prognosis. CONCLUSION This study introduces histopathology-based universal immuno-collagenic subtypes capable of predicting ICB responses across diverse cancer types. These findings offer insights that could contribute to tailoring personalized immunotherapeutic strategies for patients with cancer.
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Affiliation(s)
- Jie Mei
- Department of OncologyThe First Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsuP. R. China
- The First Clinical Medicine CollegeNanjing Medical UniversityNanjingJiangsuP. R. China
| | - Yun Cai
- Departments of GynecologyWuxi Maternal and Child Health Care Hospital, Wuxi Medical Center, Nanjing Medical UniversityWuxiJiangsuP. R. China
| | - Rui Xu
- Department of OncologyThe First Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsuP. R. China
- The First Clinical Medicine CollegeNanjing Medical UniversityNanjingJiangsuP. R. China
| | - Qing Li
- Departments of OncologyXuzhou Central HospitalThe Xuzhou School of Clinical Medicine of Nanjing Medical UniversityXuzhouJiangsuP. R. China
| | - Jiahui Chu
- Department of OncologyThe First Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsuP. R. China
- The First Clinical Medicine CollegeNanjing Medical UniversityNanjingJiangsuP. R. China
| | - Zhiwen Luo
- Department of Sports MedicineHuashan Hospital Affiliated to Fudan UniversityShanghaiP. R. China
| | - Yaying Sun
- Department of Sports MedicineShanghai General HospitalShanghai Jiao Tong University School of MedicineShanghaiP. R. China
| | - Yuxin Shi
- Departments of OncologyThe Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical UniversityWuxiJiangsuP. R. China
| | - Junying Xu
- Departments of OncologyThe Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical UniversityWuxiJiangsuP. R. China
| | - Di Li
- Shanghai Outdo Biotech Co., Ltd., National Engineering Center for BiochipShanghaiP. R. China
| | - Shuai Liang
- Departments of OncologyThe Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical UniversityWuxiJiangsuP. R. China
| | - Ying Jiang
- Departments of GynecologyWuxi Maternity and Child Health Care HospitalAffiliated Women's Hospital of Jiangnan UniversityWuxiJiangsuP. R. China
| | - Jiayu Liu
- Departments of GynecologyWuxi Maternity and Child Health Care HospitalAffiliated Women's Hospital of Jiangnan UniversityWuxiJiangsuP. R. China
| | - Zhiwen Qian
- Departments of GynecologyWuxi Maternal and Child Health Care Hospital, Wuxi Medical Center, Nanjing Medical UniversityWuxiJiangsuP. R. China
| | - Jiaofeng Zhou
- Department of PhysiologySchool of Basic Medical SciencesNanjing Medical UniversityNanjingJiangsuP. R. China
| | - Mengyun Wan
- Department of PhysiologySchool of Basic Medical SciencesNanjing Medical UniversityNanjingJiangsuP. R. China
| | - Yunlong Yang
- Department of Cellular and Genetic MedicineSchool of Basic Medical Sciences, Fudan UniversityShanghaiP. R. China
| | - Yichao Zhu
- Department of PhysiologySchool of Basic Medical SciencesNanjing Medical UniversityNanjingJiangsuP. R. China
| | - Yan Zhang
- Departments of GynecologyWuxi Maternal and Child Health Care Hospital, Wuxi Medical Center, Nanjing Medical UniversityWuxiJiangsuP. R. China
- Departments of GynecologyWuxi Maternity and Child Health Care HospitalAffiliated Women's Hospital of Jiangnan UniversityWuxiJiangsuP. R. China
| | - Yongmei Yin
- Department of OncologyThe First Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsuP. R. China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Personalized Cancer Medicine, Nanjing Medical UniversityNanjingJiangsuP. R. China
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19
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Dai L, Tan Q, Li L, Lou N, Zheng C, Yang J, Huang L, Wang S, Luo R, Fan G, Xie T, Yao J, Zhang Z, Tang L, Shi Y, Han X. High-Throughput Antigen Microarray Identifies Longitudinal Prognostic Autoantibody for Chemoimmunotherapy in Advanced Non-Small Cell Lung Cancer. Mol Cell Proteomics 2024; 23:100749. [PMID: 38513890 PMCID: PMC11070596 DOI: 10.1016/j.mcpro.2024.100749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 02/03/2024] [Accepted: 03/18/2024] [Indexed: 03/23/2024] Open
Abstract
Chemoimmunotherapy has evolved as a standard treatment for advanced non-small cell lung cancer (aNSCLC). However, inevitable drug resistance has limited its efficacy, highlighting the urgent need for biomarkers of chemoimmunotherapy. A three-phase strategy to discover, verify, and validate longitudinal predictive autoantibodies (AAbs) for aNSCLC before and after chemoimmunotherapy was employed. A total of 528 plasma samples from 267 aNSCLC patients before and after anti-PD1 immunotherapy were collected, plus 30 independent formalin-fixed paraffin-embedded samples. Candidate AAbs were firstly selected using a HuProt high-density microarray containing 21,000 proteins in the discovery phase, followed by validation using an aNSCLC-focused microarray. Longitudinal predictive AAbs were chosen for ELISA based on responders versus non-responders comparison and progression-free survival (PFS) survival analysis. Prognostic markers were also validated using immunohistochemistry and publicly available immunotherapy datasets. We identified and validated a panel of two AAbs (MAX and DHX29) as pre-treatment biomarkers and another panel of two AAbs (MAX and TAPBP) as on-treatment predictive markers in aNSCLC patients undergoing chemoimmunotherapy. All three AAbs exhibited a positive correlation with early responses and PFS (p < 0.05). The kinetics of MAX AAb showed an increasing trend in responders (p < 0.05) and a tendency to initially increase and then decrease in non-responders (p < 0.05). Importantly, MAX protein and mRNA levels effectively discriminated PFS (p < 0.05) in aNSCLC patients treated with immunotherapy. Our results present a longitudinal analysis of changes in prognostic AAbs in aNSCLC patients undergoing chemoimmunotherapy.
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Affiliation(s)
- Liyuan Dai
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Qiaoyun Tan
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Lin Li
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Ning Lou
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Cuiling Zheng
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Jianliang Yang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Liling Huang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Shasha Wang
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Rongrong Luo
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Guangyu Fan
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Tongji Xie
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Jiarui Yao
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Zhishang Zhang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Le Tang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Yuankai Shi
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China.
| | - Xiaohong Han
- Clinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Clinical PK & PD Investigation for Innovative Drugs, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
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20
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Sun J, Guo H, Nie Y, Zhou S, Zeng Y, Sun Y. Deciphering the heterogeneity dominated by tumor-associated macrophages for survival prognostication and prediction of immunotherapy response in lung adenocarcinoma. Sci Rep 2024; 14:9276. [PMID: 38653742 DOI: 10.1038/s41598-024-60132-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 04/19/2024] [Indexed: 04/25/2024] Open
Abstract
Tumor-associated macrophages (TAMs) are a specific subset of macrophages that reside inside the tumor microenvironment. The dynamic interplay between TAMs and tumor cells plays a crucial role in the treatment response and prognosis of lung adenocarcinoma (LUAD). The study aimed to examine the association between TAMs and LUAD to advance the development of targeted strategies and immunotherapeutic approaches for treating this type of lung cancer. The study employed single-cell mRNA sequencing data to characterize the immune cell composition of LUAD and delineate distinct subpopulations of TAMs. The "BayesPrism" and "Seurat" R packages were employed to examine the association between these subgroups and immunotherapy and clinical features to identify novel immunotherapy biomarkers. Furthermore, a predictive signature was generated to forecast patient prognosis by examining the gene expression profile of immunotherapy-associated TAMs subsets and using 104 machine-learning techniques. A comprehensive investigation has shown the existence of a hitherto unidentified subgroup of TAMs known as RGS1 + TAMs, which has been found to have a strong correlation with the efficacy of immunotherapy and the occurrence of tumor metastasis in LUAD patients. CD83 was identified CD83 as a distinct biomarker for the expression of RGS1 + TAMs, showcasing its potential utility as an indicator for immunotherapeutic interventions. Furthermore, the prognostic capacity of the RTMscore signature, encompassing three specific mRNA (NR4A2, MMP14, and NPC2), demonstrated enhanced robustness when contrasted against the comprehensive collection of 104 features outlined in the published study. CD83 has potential as an immunotherapeutic biomarker. Meanwhile, The RTMscore signature established in the present study might be beneficial for survival prognostication.
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Affiliation(s)
- Jiazheng Sun
- Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hehua Guo
- Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yalan Nie
- Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Sirui Zhou
- Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yulan Zeng
- Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Yalu Sun
- Affiliated Hospital of Jining Medical University, Jining, China.
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21
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Zheng K, Hai Y, Chen H, Zhang Y, Hu X, Ni K. Tumor immune dysfunction and exclusion subtypes in bladder cancer and pan-cancer: a novel molecular subtyping strategy and immunotherapeutic prediction model. J Transl Med 2024; 22:365. [PMID: 38632658 PMCID: PMC11025237 DOI: 10.1186/s12967-024-05186-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 04/09/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Molecular subtyping is expected to enable precise treatment. However, reliable subtyping strategies for clinical application remains defective and controversial. Given the significance of tumor immune dysfunction and exclusion (TIDE), we aimed to develop a novel TIDE-based subtyping strategy to guide personalized immunotherapy in the bladder cancer (BC). METHODS Transcriptome data of BC was used to evaluate the heterogeneity and the status of TIDE patterns. Subsequently, consensus clustering was applied to classify BC patients based on TIDE marker-genes. Patients' clinicopathological, molecular features and signaling pathways of the different TIDE subtypes were well characterized. We also utilize the deconvolution algorithms to analyze the tumor microenvironment, and further explore the sensitivity and mechanisms of each subtype to immunotherapy. Furthermore, BC patient clinical information, real-world BC samples and urine samples were collected for the validation of our findings, which were used for RNA-seq analysis, H&E staining, immunohistochemistry and immunofluorescence staining, and enzyme-linked immunosorbent assay. Finally, we also explored the conservation of our novel TIDE subtypes in pan-cancers. RESULTS We identified 69 TIDE biomarker genes and classified BC samples into three subtypes using consensus clustering. Subtype I showed the lowest TIDE status and malignancy with the best prognosis and highest sensitivity to immune checkpoint blockade (ICB) treatment, which was enriched of metabolic related signaling pathways. Subtype III represented the highest TIDE status and malignancy with the poorest prognosis and resistance to ICB treatment, resulting from its inhibitory immune microenvironment and T cell terminal exhaustion. Subtype II was in a transitional state with intermediate TIDE level, malignancy, and prognosis. We further confirmed the existence and characteristics of our novel TIDE subtypes using real-world BC samples and collected patient clinical data. This subtyping method was proved to be more efficient than previous known methods in identifying non-responders to immunotherapy. We also propose that combining our TIDE subtypes with known biomarkers can potentially improve the sensitivity and specificity of these biomarkers. Moreover, besides guiding ICB treatment, this classification approach can assist in selecting the frontline or recommended drugs. Finally, we confirmed that the TIDE subtypes are conserved across the pan-tumors. CONCLUSIONS Our novel TIDE-based subtyping method can serve as a powerful clinical tool for BC and pan-cancer patients, and potentially guiding personalized therapy decisions for selecting potential beneficiaries and excluding resistant patients of ICB therapy.
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Affiliation(s)
- Kun Zheng
- Department of Urology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Youlong Hai
- Department of Urology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Hongqi Chen
- Department of Urology, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, 215200, Jiangsu, China
| | - Yukun Zhang
- Beijing University of Chinese Medicine East Hospital, Zaozhuang Hospital, Zaozhuang, 277000, Shandong, China
| | - Xiaoyong Hu
- Department of Urology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China.
| | - Kai Ni
- Department of Urology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China.
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22
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Deng Y, Xia L, Zhang J, Deng S, Wang M, Wei S, Li K, Lai H, Yang Y, Bai Y, Liu Y, Luo L, Yang Z, Chen Y, Kang R, Gan F, Pu Q, Mei J, Ma L, Lin F, Guo C, Liao H, Zhu Y, Liu Z, Liu C, Hu Y, Yuan Y, Zha Z, Yuan G, Zhang G, Chen L, Cheng Q, Shen S, Liu L. Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages. Cell Rep Med 2024; 5:101489. [PMID: 38554705 PMCID: PMC11031428 DOI: 10.1016/j.xcrm.2024.101489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 01/26/2024] [Accepted: 03/06/2024] [Indexed: 04/02/2024]
Abstract
Lung adenocarcinoma is a type of cancer that exhibits a wide range of clinical radiological manifestations, from ground-glass opacity (GGO) to pure solid nodules, which vary greatly in terms of their biological characteristics. Our current understanding of this heterogeneity is limited. To address this gap, we analyze 58 lung adenocarcinoma patients via machine learning, single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing, and we identify six lung multicellular ecotypes (LMEs) correlating with distinct radiological patterns and cancer cell states. Notably, GGO-associated neoantigens in early-stage cancers are recognized by CD8+ T cells, indicating an immune-active environment, while solid nodules feature an immune-suppressive LME with exhausted CD8+ T cells, driven by specific stromal cells such as CTHCR1+ fibroblasts. This study also highlights EGFR(L858R) neoantigens in GGO samples, suggesting potential CD8+ T cell activation. Our findings offer valuable insights into lung adenocarcinoma heterogeneity, suggesting avenues for targeted therapies in early-stage disease.
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Affiliation(s)
- Yulan Deng
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Liang Xia
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Jian Zhang
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Senyi Deng
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Mengyao Wang
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China; Faculty of Dentistry, The University of Hong Kong, Prince Philip Dental Hospital, Sai Ying Pun, Hong Kong, China
| | - Shiyou Wei
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Kaixiu Li
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China; Faculty of Dentistry, The University of Hong Kong, Prince Philip Dental Hospital, Sai Ying Pun, Hong Kong, China
| | - Hongjin Lai
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yunhao Yang
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yuquan Bai
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yongcheng Liu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Lanzhi Luo
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Zhenyu Yang
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yaohui Chen
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Ran Kang
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Fanyi Gan
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Qiang Pu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Jiandong Mei
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Lin Ma
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Feng Lin
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Chenglin Guo
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Hu Liao
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yunke Zhu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Zheng Liu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Chengwu Liu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yang Hu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yong Yuan
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Zhengyu Zha
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Gang Yuan
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Gao Zhang
- Faculty of Dentistry, The University of Hong Kong, Prince Philip Dental Hospital, Sai Ying Pun, Hong Kong, China
| | - Luonan Chen
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China; Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
| | - Qing Cheng
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Shensi Shen
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China.
| | - Lunxu Liu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China.
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23
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Xia Y, Li X, Bie N, Pan W, Miao YR, Yang M, Gao Y, Chen C, Liu H, Gan L, Guo AY. A method for predicting drugs that can boost the efficacy of immune checkpoint blockade. Nat Immunol 2024; 25:659-670. [PMID: 38499799 DOI: 10.1038/s41590-024-01789-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 02/13/2024] [Indexed: 03/20/2024]
Abstract
Combination therapy is a promising therapeutic strategy to enhance the efficacy of immune checkpoint blockade (ICB); however, predicting drugs for effective combination is challenging. Here we developed a general data-driven method called CM-Drug for screening compounds that can boost ICB treatment efficacy based on core and minor gene sets identified between responsive and nonresponsive samples in ICB therapy. The CM-Drug method was validated using melanoma and lung cancer mouse models, with combined therapeutic efficacy demonstrated in eight of nine predicted compounds. Among these compounds, taltirelin had the strongest synergistic effect. Mechanistic analysis and experimental verification demonstrated that taltirelin can stimulate CD8+ T cells and is mediated by the induction of thyroid-stimulating hormone. This study provides an effective and general method for predicting and evaluating drugs for combination therapy and identifies candidate compounds for future ICB combination therapy.
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Affiliation(s)
- Yun Xia
- Department of Thoracic Surgery, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Xin Li
- National Engineering Research Center for Nanomedicine, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Nana Bie
- National Engineering Research Center for Nanomedicine, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Wen Pan
- Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Ya-Ru Miao
- Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Mei Yang
- Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Gao
- Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Chuang Chen
- Department of Breast and Thyroid Surgery, Central Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hanqing Liu
- Department of Breast and Thyroid Surgery, Central Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lu Gan
- National Engineering Research Center for Nanomedicine, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
| | - An-Yuan Guo
- Department of Thoracic Surgery, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
- Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
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24
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Xu L, Chen Y, Liu L, Hu X, He C, Zhou Y, Ding X, Luo M, Yan J, Liu Q, Li H, Lai D, Zou Z. Tumor-associated macrophage subtypes on cancer immunity along with prognostic analysis and SPP1-mediated interactions between tumor cells and macrophages. PLoS Genet 2024; 20:e1011235. [PMID: 38648200 PMCID: PMC11034676 DOI: 10.1371/journal.pgen.1011235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 03/25/2024] [Indexed: 04/25/2024] Open
Abstract
Tumor-associated macrophages (TAM) subtypes have been shown to impact cancer prognosis and resistance to immunotherapy. However, there is still a lack of systematic investigation into their molecular characteristics and clinical relevance in different cancer types. Single-cell RNA sequencing data from three different tumor types were used to cluster and type macrophages. Functional analysis and communication of TAM subpopulations were performed by Gene Ontology-Biological Process and CellChat respectively. Differential expression of characteristic genes in subpopulations was calculated using zscore as well as edgeR and Wilcoxon rank sum tests, and subsequently gene enrichment analysis of characteristic genes and anti-PD-1 resistance was performed by the REACTOME database. We revealed the heterogeneity of TAM, and identified eleven subtypes and their impact on prognosis. These subtypes expressed different molecular functions respectively, such as being involved in T cell activation, apoptosis and differentiation, or regulating viral bioprocesses or responses to viruses. The SPP1 pathway was identified as a critical mediator of communication between TAM subpopulations, as well as between TAM and epithelial cells. Macrophages with high expression of SPP1 resulted in poorer survival. By in vitro study, we showed SPP1 mediated the interactions between TAM clusters and between TAM and tumor cells. SPP1 promoted the tumor-promoting ability of TAM, and increased PDL1 expression and stemness of tumor cells. Inhibition of SPP1 attenuated N-cadherin and β-catenin expression and the activation of AKT and STAT3 pathway in tumor cells. Additionally, we found that several subpopulations could decrease the sensitivity of anti-PD-1 therapy in melanoma. SPP1 signal was a critical pathway of communication between macrophage subtypes. Some specific macrophage subtypes were associated with immunotherapy resistance and prognosis in some cancer types.
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Affiliation(s)
- Liu Xu
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
| | - Yibing Chen
- Genetic and Prenatal Diagnosis Center, Department of Gynecology and Obstetrics, First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
| | - Lingling Liu
- Department of Hematology, The Third Affiliated Hospital of Sun Yat-sen University & Sun Yat-sen Institute of Hematology, Guangzhou, China
| | - Xinyu Hu
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
| | - Chengsi He
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
| | - Yuan Zhou
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
| | - Xinyi Ding
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
| | - Minhua Luo
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
| | - Jiajing Yan
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
| | - Quentin Liu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Hongsheng Li
- Department of Breast Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Dongming Lai
- Shenshan Medical Center and Department of Gastrointestinal Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhengzhi Zou
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
- Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou, China
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25
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Zhang P, Pei S, Zhou G, Zhang M, Zhang L, Zhang Z. Purine metabolism in lung adenocarcinoma: A single-cell analysis revealing prognostic and immunotherapeutic insights. J Cell Mol Med 2024; 28:e18284. [PMID: 38597415 PMCID: PMC11005461 DOI: 10.1111/jcmm.18284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/19/2024] [Accepted: 03/22/2024] [Indexed: 04/11/2024] Open
Abstract
Lung adenocarcinoma (LUAD) is a prevalent subtype of lung cancer, yet the contribution of purine metabolism (PM) to its pathogenesis remains poorly elucidated. PM, a critical component of intracellular nucleotide synthesis and energy metabolism, is hypothesized to exert a significant influence on LUAD development. Herein, we employed single-cell analysis to investigate the role of PM within the tumour microenvironment (TME) of LUAD. PM scoring (PMS) across distinct cell types was determined using AUCell, UCell, singscore and AddModuleScore algorithms. Subsequently, we explored communication networks among cells within high- and low-PMS groups, establishing a robust PM-associated signature (PAS) utilizing a comprehensive dataset comprising LUAD samples from TCGA and five GEO datasets. Our findings revealed that the high-PMS group exhibited intensified cell interactions, while the PAS, constructed using PM-related genes, demonstrated precise prognostic predictive capability. Notably, analysis across the TCGA dataset and five GEO datasets indicated that low-PAS patients exhibited a superior prognosis. Furthermore, the low-PAS group displayed increased immune cell infiltration and elevated CD8A expression, coupled with reduced PD-L1 expression. Moreover, data from eight publicly available immunotherapy cohorts suggested enhanced immunotherapy outcomes in the low-PAS group. These results underscore a close association between PAS and tumour immunity, offering predictive insights into genomic alterations, chemotherapy drug sensitivity and immunotherapy responses in LUAD. The newly established PAS holds promise as a valuable tool for selecting LUAD populations likely to benefit from future clinical stratification efforts.
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Affiliation(s)
- Pengpeng Zhang
- Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for CancerTianjin Medical University Cancer Institute and HospitalTianjinChina
- Department of Thoracic SurgeryThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Shengbin Pei
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Guangyao Zhou
- Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for CancerTianjin Medical University Cancer Institute and HospitalTianjinChina
| | - Mengzhe Zhang
- Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for CancerTianjin Medical University Cancer Institute and HospitalTianjinChina
| | - Lianmin Zhang
- Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for CancerTianjin Medical University Cancer Institute and HospitalTianjinChina
| | - Zhenfa Zhang
- Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for CancerTianjin Medical University Cancer Institute and HospitalTianjinChina
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26
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Ren Y, Wu R, Li C, Liu L, Li L, Weng S, Xu H, Xing Z, Zhang Y, Wang L, Liu Z, Han X. Single-cell RNA sequencing integrated with bulk RNA sequencing analysis identifies a tumor immune microenvironment-related lncRNA signature in lung adenocarcinoma. BMC Biol 2024; 22:69. [PMID: 38519942 PMCID: PMC10960411 DOI: 10.1186/s12915-024-01866-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 03/13/2024] [Indexed: 03/25/2024] Open
Abstract
BACKGROUND Recently, long non-coding RNAs (lncRNAs) have been demonstrated as essential roles in tumor immune microenvironments (TIME). Nevertheless, researches on the clinical significance of TIME-related lncRNAs are limited in lung adenocarcinoma (LUAD). METHODS Single-cell RNA sequencing and bulk RNA sequencing data are integrated to identify TIME-related lncRNAs. A total of 1368 LUAD patients are enrolled from 6 independent datasets. An integrative machine learning framework is introduced to develop a TIME-related lncRNA signature (TRLS). RESULTS This study identified TIME-related lncRNAs from integrated analysis of single‑cell and bulk RNA sequencing data. According to these lncRNAs, a TIME-related lncRNA signature was developed and validated from an integrative procedure in six independent cohorts. TRLS exhibited a robust and reliable performance in predicting overall survival. Superior prediction performance barged TRLS to the forefront from comparison with general clinical features, molecular characters, and published signatures. Moreover, patients with low TRLS displayed abundant immune cell infiltration and active lipid metabolism, while patients with high TRLS harbored significant genomic alterations, high PD-L1 expression, and elevated DNA damage repair (DDR) relevance. Notably, subclass mapping analysis of nine immunotherapeutic cohorts demonstrated that patients with high TRLS were more sensitive to immunotherapy. CONCLUSIONS This study developed a promising tool based on TIME-related lncRNAs, which might contribute to tailored treatment and prognosis management of LUAD patients.
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Affiliation(s)
- Yuqing Ren
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Ruhao Wu
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Chunwei Li
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Long Liu
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shanxi, China
| | - Lifeng Li
- Cancer Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Siyuan Weng
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Hui Xu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Zhe Xing
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Libo Wang
- Department of Pancreatic Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
| | - Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
| | - Xinwei Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.
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27
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Mason M, Lapuente-Santana Ó, Halkola AS, Wang W, Mall R, Xiao X, Kaufman J, Fu J, Pfeil J, Banerjee J, Chung V, Chang H, Chasalow SD, Lin HY, Chai R, Yu T, Finotello F, Mirtti T, Mäyränpää MI, Bao J, Verschuren EW, Ahmed EI, Ceccarelli M, Miller LD, Monaco G, Hendrickx WRL, Sherif S, Yang L, Tang M, Gu SS, Zhang W, Zhang Y, Zeng Z, Das Sahu A, Liu Y, Yang W, Bedognetti D, Tang J, Eduati F, Laajala TD, Geese WJ, Guinney J, Szustakowski JD, Vincent BG, Carbone DP. A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer. J Transl Med 2024; 22:190. [PMID: 38383458 PMCID: PMC10880244 DOI: 10.1186/s12967-023-04705-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 11/05/2023] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.
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Affiliation(s)
- Mike Mason
- Bristol Myers Squibb, Princeton, NJ, USA
| | - Óscar Lapuente-Santana
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Anni S Halkola
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
| | - Wenyu Wang
- Faculty of Medicine, Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar
- Department of Immunology, St. Jude Children's Research Hospital, P.O. Box 38105, Memphis, TN, USA
- Biotechnology Research Center, Technology Innovation Institute, P.O. Box 9639, Abu Dhabi, United Arab Emirates
| | - Xu Xiao
- School of Informatics, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Jacob Kaufman
- Department of Medicine, Duke University, Durham, NC, USA
- The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Jingxin Fu
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | | | - Han Chang
- Bristol Myers Squibb, Princeton, NJ, USA
| | | | | | | | | | - Francesca Finotello
- Institute of Molecular Biology, University of Innsbruck, Innsbruck, Austria
- Digital Science Center (DiSC), University of Innsbruck, Innsbruck, Austria
| | - Tuomas Mirtti
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- iCAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
- Department of Biomedical Engineering, School of Medicine, Emory University, Atlanta, GA, USA
| | - Mikko I Mäyränpää
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Jie Bao
- Faculty of Medicine, Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Emmy W Verschuren
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Eiman I Ahmed
- Human Immunology Department, Sidra Medicine, P.O. Box 26999, Doha, Qatar
| | - Michele Ceccarelli
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples "Federico II", 80125, Naples, Italy
- BIOGEM Institute of Molecular Biology and Genetics, Via Camporeale, Ariano Irpino, Italy
| | - Lance D Miller
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Atrium Health Wake Forest Baptist Comprehensive Cancer Center, Winston-Salem, NC, USA
| | - Gianni Monaco
- BIOGEM Institute of Molecular Biology and Genetics, Via Camporeale, Ariano Irpino, Italy
| | - Wouter R L Hendrickx
- Human Immunology Department, Sidra Medicine, P.O. Box 26999, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, P.O. Box 26999, Doha, Qatar
| | - Shimaa Sherif
- Human Immunology Department, Sidra Medicine, P.O. Box 26999, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, P.O. Box 26999, Doha, Qatar
| | - Lin Yang
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ming Tang
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | - Yi Zhang
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Zexian Zeng
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Yang Liu
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Davide Bedognetti
- Human Immunology Department, Sidra Medicine, P.O. Box 26999, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, P.O. Box 26999, Doha, Qatar
- Department of Internal Medicine and Medical Specialties, University of Genoa, Genoa, Italy
| | - Jing Tang
- Faculty of Medicine, Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Teemu D Laajala
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- iCAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
- FICAN West Cancer Centre, University of Turku and Turku University Hospital, Turku, Finland
- Department of Pharmacology, Anschutz Medical Campus, University of Colorado, Denver, CO, USA
| | | | | | | | - Benjamin G Vincent
- Department of Medicine, Division of Hematology, Department of Microbiology and Immunology, Curriculum in Bioinformatics and Computational Biology, Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - David P Carbone
- The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA.
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Zhang W, Dang R, Liu H, Dai L, Liu H, Adegboro AA, Zhang Y, Li W, Peng K, Hong J, Li X. Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients. Sci Rep 2024; 14:4173. [PMID: 38378721 PMCID: PMC10879095 DOI: 10.1038/s41598-024-54643-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/14/2024] [Indexed: 02/22/2024] Open
Abstract
Glioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment. The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. After collecting 18 RCD-related signatures from the opening literature, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. Here, based on bulk and single-cell sequencing samples, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed an RCD gene pair scoring system, named RCD.GP signature, showing a reliable and robust performance in predicting the prognosis of glioblastoma. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified that the SLC43A3 highly expressed in glioma grades and glioma cell lines through qRT-PCR. Our study provided comprehensive insights into the RCD roles in glioma, developed a robust RCD gene pair signature for predicting the prognosis of glioma patients, constructed a machine learning framework for screening the core candidates and identified the SLC43A3 as an oncogenic role and a prediction biomarker in glioblastoma.
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Affiliation(s)
- Wei Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Ruiyue Dang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Hongyi Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Luohuan Dai
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Hongwei Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Abraham Ayodeji Adegboro
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Yihao Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Wang Li
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Kang Peng
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Jidong Hong
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, China.
| | - Xuejun Li
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China.
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Li Z, Liu G, Yang X, Shu M, Jin W, Tong Y, Liu X, Wang Y, Yuan J, Yang Y. An atlas of cell-type-specific interactome networks across 44 human tumor types. Genome Med 2024; 16:30. [PMID: 38347596 PMCID: PMC10860273 DOI: 10.1186/s13073-024-01303-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/06/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Biological processes are controlled by groups of genes acting in concert. Investigating gene-gene interactions within different cell types can help researchers understand the regulatory mechanisms behind human complex diseases, such as tumors. METHODS We collected extensive single-cell RNA-seq data from tumors, involving 563 patients with 44 different tumor types. Through our analysis, we identified various cell types in tumors and created an atlas of different immune cell subsets across different tumor types. Using the SCINET method, we reconstructed interactome networks specific to different cell types. Diverse functional data was then integrated to gain biological insights into the networks, including somatic mutation patterns and gene functional annotation. Additionally, genes with prognostic relevance within the networks were also identified. We also examined cell-cell communications to investigate how gene interactions modulate cell-cell interactions. RESULTS We developed a data portal called CellNetdb for researchers to study cell-type-specific interactome networks. Our findings indicate that these networks can be used to identify genes with topological specificity in different cell types. We also found that prognostic genes can deconvolved into cell types through analyzing network connectivity. Additionally, we identified commonalities and differences in cell-type-specific networks across different tumor types. Our results suggest that these networks can be used to prioritize risk genes. CONCLUSIONS This study presented CellNetdb, a comprehensive repository featuring an atlas of cell-type-specific interactome networks across 44 human tumor types. The findings underscore the utility of these networks in delineating the intricacies of tumor microenvironments and advancing the understanding of molecular mechanisms underpinning human tumors.
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Affiliation(s)
- Zekun Li
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Gerui Liu
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Xiaoxiao Yang
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Meng Shu
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Wen Jin
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Yang Tong
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Xiaochuan Liu
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Yuting Wang
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Jiapei Yuan
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300020, China.
- Tianjin Institutes of Health Science, Tianjin, 301600, China.
| | - Yang Yang
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China.
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China.
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Wu Y, Zhou Z, Qi Q, Xu S, Chen L, Wang F. Anoikis-related gene signature is associated with immune infiltration and predicts the prognosis of non-small cell lung cancer. Aging (Albany NY) 2024; 16:2908-2933. [PMID: 38329444 PMCID: PMC10911374 DOI: 10.18632/aging.205522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 12/26/2023] [Indexed: 02/09/2024]
Abstract
Non-small cell lung cancer (NSCLC) is the most common histological type of lung cancer. With the in-depth exploration of cell death manners, numerous studies found that anoikis is an important mechanism that associated with treatment. Therefore, we aimed to explore the prognostic value and treatment guidance of anoikis in NSCLC patients. In the current study, we first constructed a prognostic model based on the anoikis-related genes based on bulk RNA-sequencing and single-cell RNA-sequencing (scRNA-seq) dataset. Then, immuno-correlations of anoikis-related risk scores (ARGRS) were analyzed. In addition, HMGA1, a risky gene in ARGRS, was further explored to define its expression and immuno-correlation. Results showed that patients with higher ARGRS had worse clinical outcomes. Moreover, the five genes in the prognostic model were all highly expressed on tumor cells. Moreover, further analysis found that the ARGRS was negatively correlated with ImmuneScore, but positively with tumor purity. Besides, patients in the ARGRS-high group had lower levels of immunological characteristics, such as the immune-related signaling pathways and subpopulations. Additionally, in the immunotherapy cohorts, patients with the ARGRS-high phenotype were more resistant to immunotherapy and tended to not achieve remission after treatment. Last, HMGA1 was chosen as the representative biomarker, and analysis of the in-house cohort showed that HMGA1 was highly expressed in tumor tissues and correlated with decreased T cell infiltration. To sum up, ARGRS was correlated with a desert tumor microenvironment and identified immune-cold tumors, which can be a novel biomarker for the recognition of immunological characteristics and an immunotherapeutic response in NSCLC.
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Affiliation(s)
- Yixuan Wu
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China
- Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China
| | - Zhou Zhou
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China
- Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China
| | - Qianyi Qi
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China
- Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China
| | - Shirong Xu
- Department of Laboratory Medicine, Taizhou Second People’s Hospital, Taizhou 225511, China
| | - Lin Chen
- Nantong Institute of Liver Diseases, Nantong Third People’s Hospital Affiliated Nantong Hospital 3 of Nantong University, Nantong 226006, China
| | - Feng Wang
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China
- Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China
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Lee J, Kim D, Kong J, Ha D, Kim I, Park M, Lee K, Im SH, Kim S. Cell-cell communication network-based interpretable machine learning predicts cancer patient response to immune checkpoint inhibitors. SCIENCE ADVANCES 2024; 10:eadj0785. [PMID: 38295179 PMCID: PMC10830106 DOI: 10.1126/sciadv.adj0785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 12/28/2023] [Indexed: 02/02/2024]
Abstract
Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, only some patients respond to ICIs, and current biomarkers for ICI efficacy have limited performance. Here, we devised an interpretable machine learning (ML) model trained using patient-specific cell-cell communication networks (CCNs) decoded from the patient's bulk tumor transcriptome. The model could (i) predict ICI efficacy for patients across four cancer types (median AUROC: 0.79) and (ii) identify key communication pathways with crucial players responsible for patient response or resistance to ICIs by analyzing more than 700 ICI-treated patient samples from 11 cohorts. The model prioritized chemotaxis communication of immune-related cells and growth factor communication of structural cells as the key biological processes underlying response and resistance to ICIs, respectively. We confirmed the key communication pathways and players at the single-cell level in patients with melanoma. Our network-based ML approach can be used to expand ICIs' clinical benefits in cancer patients.
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Affiliation(s)
- Juhun Lee
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea
| | - Donghyo Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea
| | - JungHo Kong
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea
| | - Doyeon Ha
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea
| | - Inhae Kim
- ImmunoBiome Inc., Pohang 166-20, Korea
| | - Minhyuk Park
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea
| | - Kwanghwan Lee
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea
| | - Sin-Hyeog Im
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea
- ImmunoBiome Inc., Pohang 166-20, Korea
- Institute of Convergence Science, Yonsei University, Seoul 120-749, Korea
| | - Sanguk Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea
- Institute of Convergence Science, Yonsei University, Seoul 120-749, Korea
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Hu X, Hu Z, Zhang H, Zhang N, Feng H, Jia X, Zhang C, Cheng Q. Deciphering the tumor-suppressive role of PSMB9 in melanoma through multi-omics and single-cell transcriptome analyses. Cancer Lett 2024; 581:216466. [PMID: 37944578 DOI: 10.1016/j.canlet.2023.216466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/14/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023]
Abstract
Skin cutaneous melanoma (SKCM) poses a significant challenge in skin cancers. Recent immunotherapy breakthroughs have revolutionized melanoma treamtment, yet tumor heterogeneity persists as an obstacle. Epigenetic modifications orchestrated by DNA methylation contributed to tumorigenesis, thus potentially unveiling melanoma prognosis. Here, we identified an interferon-gamma (IFN-g) sensitive subtype, which possesses favorable outcomes, robust infiltration CD8+T cells, and IFN-g score in bulk RNA-seq profile. Subsequently, we established an IFN-g sensitivity signature based on machine learning. We validated that PSMB9 is strongly correlated with immunotherapy response in both methylation and expression cohorts in this 10-probe signature. We assumed that PSMB9 acts as a putative melanoma suppressor, for its activation of CD8+T cell; capacity to modulate IFN-γ secretion; and dynamics altering IFN-g receptors in bulk tissue. We performed single-cell RNA-seq on immunotherapy patients' tissue to uncover the nuanced role of PSMB9 in activating CD8T + cells, enhancing IFN-g, and influencing malignant cells receptors and transcriptional factors. Overexpress PSMB9 in two SKCM cell lines to mimic the hypomethylated state to approve our conjecture. Strong cell proliferation and migration inhibition were detected on both cells, indicating that PSMB9 is present in tumor cells and that high expression is detrimental to tumor growth and migration. Overall, comprehensive integrated analysis shows that PSMB9 emerges as a vital prognostic marker, acting predictive potential regarding immunotherapy in melanoma. This evidence not only reveals the multifaceted impact of PSMB9 on both malignant and immune cells but also serves as a prospective target for undergoing immunotherapeutic strategies in the future.
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Affiliation(s)
- Xing Hu
- Department of Dermatology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, 410000, China
| | - Zhengang Hu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Hao Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China; Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, Chongqing, 400016, China
| | - Nan Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, Chongqing, 400016, China; College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Hao Feng
- Department of Dermatology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, 410000, China
| | - Xiaomin Jia
- Department of Pathology, Lhasa People's Hospital, Lhasa, Tibet Autonomous Region, 850001, China
| | - Chi Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Quan Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
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Wang G, Zheng H, Xiang Y, Wang Y, Wang K, Ren X, Du J. Identifying the critical oncogenic mechanism of LDHA based on a prognostic model of T-cell synthetic drivers. Int Immunopharmacol 2024; 126:111265. [PMID: 38000233 DOI: 10.1016/j.intimp.2023.111265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/16/2023] [Indexed: 11/26/2023]
Abstract
BACKGROUND Despite its early success, immunotherapy focused on removing T-cell inhibition does not achieve the desired effect in most patients. New strategies that target antigen-driven T-cell activation are needed to improve immunotherapy outcomes. However, a comprehensive analysis of synthetic drivers of T-cells is greatly lacking in lung adenocarcinoma (LUAD) and other types of tumors. METHODS We comprehensively evaluated the patterns of LUAD patients based on T -cell synthetic drivers by unsupervised clustering analysis. A risk model was constructed using Lasso Cox regression analysis. The predicted survival and immunotherapy efficacy of the model was validated by independent cohorts. Finally, single-cell sequencing analysis, and a series of in vitro experiments were conducted to explore the role of lactate dehydrogenase A (LDHA) in the malignant progression of LUAD. RESULTS Patients in the high-risk group were characterized by survival disadvantage, a "cold" immune phenotype, and by not having benefitted from immunotherapy. LDHA was shown to promote LUAD cell proliferation, cell cycle, invasion, and migration. Secondly, we found that LDHA induced NF-κB pathway activation, tyrosine kinase inhibitor resistance and immunosuppressant microenvironment. Finally, LDHA was found to be highly expressed in fibroblasts, which may be involved in promoting TKI resistance and mediating the immune escape. CONCLUSION This study revealed that the T-cell synthetic driver-associated prognostic model developed herein significantly predicted prognosis and immunotherapy efficacy in LUAD. We further investigated the role of LDHA in the malignant phenotype of tumor cells and tumor microenvironment remodeling, providing a promising and novel therapeutic strategy for LUAD.
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Affiliation(s)
- Guanghui Wang
- Institute of Oncology, Shandong Provincial Hospital, Shandong University, Jinan, China; Department of Thoracic Surgery, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, Jinan, China
| | - Haotian Zheng
- Institute of Oncology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Yunzhi Xiang
- Institute of Oncology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Yadong Wang
- Institute of Oncology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Kai Wang
- Institute of Oncology, Shandong Provincial Hospital, Shandong University, Jinan, China; Department of Healthcare Respiratory Medicine, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Xiaoyang Ren
- Department of Thoracic Surgery, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, Jinan, China
| | - Jiajun Du
- Institute of Oncology, Shandong Provincial Hospital, Shandong University, Jinan, China; Department of Thoracic Surgery, Shandong Provincial Hospital, Shandong University, Jinan, China.
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Ni Y, Jiang M, Wu Y, Xiao P, Wu A, Xia W, Tang C, Yang X, Tian K, Chen H, Huang R. Anoikis-related CTNND1 is associated with immuno-suppressive tumor microenvironment and predicts unfavorable immunotherapeutic outcome in non-small cell lung cancer. J Cancer 2024; 15:317-331. [PMID: 38169514 PMCID: PMC10758022 DOI: 10.7150/jca.89542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 11/08/2023] [Indexed: 01/05/2024] Open
Abstract
Background: Immunotherapy has greatly changed the treatment of advanced non-small cell lung cancer (NSCLC). Anoikis is a programmed cell death process associated with cancer. However, the correlation between anoikis-related genes and the tumor microenvironment (TME) features and immunotherapeutic outcome in NSCLC has not been fully explored. Methods: The bulk and single-cell transcriptome data of NSCLC were downloaded from TCGA and GEO databases. The distribution of anoikis-related genes on different cell types at the single-cell level was analyzed, and these genes specifically expressed by tumor cells and immunotherapy-related were further extracted. Next, the candidate gene CTNND1 was identified and its correlations with the TME features and immunotherapeutic outcome in NSCLC were explored in multiple public cohorts. Finally, an in-house cohort was used to determine the CTNND1 expression and immuno-correlation in NSCLC. Results: At single-cell atlas, we found that anoikis-related genes expressed specifically in tumor cells of NSCLC. By intersecting anoikis-related genes, immunotherapy-associated genes, and the genes expressed in tumor cells, we obtained a special biomarker CTNND1. In addition, cell-cell communication analysis revealed that CTNND1+ tumor cells communicated with immune subpopulations frequently. Moreover, we found that high expression of CTNND1 was related to immuno-suppressive status of NSCLC. The expression of CTNND1 and its immuno-correlation were also validated, and the results showed that CTNND1 was highly expressed in NSCLC tissues and tumors with high CTNND1 expression accompanied with low CD8+ T cells infiltration. Conclusions: Overall, our study reported that CTNND1 can be considered as a novel biomarker for the predication of immunotherapeutic responses and a potential target for NSCLC therapy.
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Affiliation(s)
- Yingchen Ni
- Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, China
| | - Mengna Jiang
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Yixuan Wu
- Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, China
| | - Pei Xiao
- Center for Non-Communicable Disease Management, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Anqi Wu
- Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, China
| | - Weiyi Xia
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Can Tang
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Xu Yang
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Kai Tian
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Hong Chen
- Department of Respiratory Medicine, Nantong Fourth People's Hospital, Nantong, 226000, China
| | - Rongrong Huang
- Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, China
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Peng X, Wu H, Zhang B, Xu C, Lang J. A Novel Nucleic Acid Sensing-related Genes Signature for Predicting Immunotherapy Efficacy and Prognosis of Lung Adenocarcinoma. Curr Cancer Drug Targets 2024; 24:425-444. [PMID: 37592781 DOI: 10.2174/1568009623666230817101843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 06/23/2023] [Accepted: 07/10/2023] [Indexed: 08/19/2023]
Abstract
BACKGROUND As a novel pillar for lung adenocarcinoma (LUAD) treatment, immunotherapy has limited efficiency in LUAD patients. The nucleic acid sensing (NAS) pathways are critical in the anti-tumor immune response, but their role in LUAD remains controversial. OBJECTIVE The study aims to develop a classification system to identify immune subtypes of LUAD based on nucleic acid sensing-related genes so that it can help screen patients who may respond to immunotherapy. METHODS We performed a comprehensive bioinformatics analysis of the NAS molecule expression profiles across multiple public datasets. Using qRT-PCR to verify the NAS genes in multiple lung cancer cell lines. Molecular docking was performed to screen drug candidates. RESULTS The NAS-activated subgroup and NAS-suppressed subgroup were validated based on the different patterns of gene expression and pathways enrichment. The NAS-activated subgroup displayed a stronger immune infiltration and better prognosis of patients. Moreover, we constructed a seven nucleic acid sensing-related risk score (NASRS) model for the convenience of clinical application. The predictive values of NASRS in prognosis and immunotherapy were subsequently fully validated in the lung adenocarcinoma dataset and the uroepithelial carcinoma dataset. Additionally, five potential drugs binding to the core target of the NAS signature were predicted through molecular docking. CONCLUSION We found a significant correlation between nucleic acid sensing function and the immune treatment efficiency in LUAD. The NASRS can be used as a robust biomarker for the predicting of prognosis and immunotherapy efficiency and may help in clinical decisions for LUAD patients.
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Affiliation(s)
- Xinhao Peng
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Hong Wu
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- Department of Oncology & Cancer Institute, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- Department of Laboratory Medicine and Sichuan Provincial Key Laboratory for Human Disease Gene Study, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Biqin Zhang
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Chuan Xu
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- Department of Oncology & Cancer Institute, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- Department of Laboratory Medicine and Sichuan Provincial Key Laboratory for Human Disease Gene Study, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Jinyi Lang
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
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Xue D, Zhu T, Lin H, Guo P, Li M, Yu M, Yang F, Yang S, Chen X. Transcriptome dysregulation in hyper-progressive disease samples with immune checkpoint blockade. Chin Med J (Engl) 2023; 136:3019-3021. [PMID: 37649413 PMCID: PMC10752450 DOI: 10.1097/cm9.0000000000002550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Indexed: 09/01/2023] Open
Affiliation(s)
- Dan Xue
- Department of Respiratory Medicine, Fujian Medical University Union Hospital, Fuzhou, Fujian 350000, China
- Department of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, Fujian 350000, China
| | - Tengteng Zhu
- Hongqiao International Institute of Medicine, Shanghai Tongren Hospital/Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hongguang Lin
- Department of Respiratory Medicine, Fujian Medical University Union Hospital, Fuzhou, Fujian 350000, China
- Department of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, Fujian 350000, China
| | - Peilin Guo
- Department of Respiratory Medicine, Fujian Medical University Union Hospital, Fuzhou, Fujian 350000, China
| | - Mengling Li
- Department of Respiratory Medicine, Fujian Medical University Union Hospital, Fuzhou, Fujian 350000, China
| | - Mei'e Yu
- Department of Respiratory Medicine, Fujian Medical University Union Hospital, Fuzhou, Fujian 350000, China
| | - Fan Yang
- Department of Respiratory Medicine, Fujian Medical University Union Hospital, Fuzhou, Fujian 350000, China
| | - Sheng Yang
- Department of Oncology, Fujian Medical University Union Hospital, Fuzhou, Fujian 350000, China
| | - Xiangqi Chen
- Department of Respiratory Medicine, Fujian Medical University Union Hospital, Fuzhou, Fujian 350000, China
- Department of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, Fujian 350000, China
- Fujian Key Laboratory of Translational Research in Cancer and Neurodegenerative Diseases, Fuzhou, Fujian 350122, China
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Zheng X, Han L, Guan J, Chen C, Zhang Y, Zhang J, Zhang Y, Liu S, Su J, Liu M, Huang H. TICRR serves as a prognostic biomarker in lung adenocarcinoma with implications in RNA epigenetic modification, DDR pathway, and RNA metabolism. Front Oncol 2023; 13:1274439. [PMID: 38152367 PMCID: PMC10751343 DOI: 10.3389/fonc.2023.1274439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/28/2023] [Indexed: 12/29/2023] Open
Abstract
Purpose TOPBP1 interacting checkpoint and replication regulator (TICRR), a hub gene of the Cdk2-mediated initiation step of DNA replication, has been shown an essential role in tumorigenesis by accelerating the DNA replication of tumor cells. Methods RT-qPCR was used to detect the mRNA expression of TICRR in LUAD tumors and adjacent normal tissues. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database of LUAD were acquired to analyze the critical role of TICRR expression in survival prognosis and clinicopathology characters in LUAD. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed using the R package. The correlation of TICRR expression with immune cell infiltration, RNA epigenetic modification, DNA damage repair (DDR) pathway, and cell metabolism of LUAD was further explored to verify significant conclusions. Results TICRR was significantly upregulated in most cancer types, including LUAD, lung squamous cell carcinoma (LUSC), and others. Cox regression analysis indicated the overexpression of TICRR was associated with poor survival in several cancers. In LUAD, TICRR expression was positively correlated with tumor stage and was increased in smoking, male, and high tumor mutational burden (TMB) patients. Enrichment analysis revealed that TICRR could influence tumor proliferation and prognosis via activating pathways involving cell cycle, DNA repair, DNA replication, cysteine metabolism, oxidative phosphorylation, and ubiquitin-mediated proteolysis pathways. Interestingly, high TICRR expression correlated with DDR pathway signature (34 genes), 37 m6A/m5C regulated genes, and some metabolism-regulated genes. Silencing the TICRR gene affects cysteine metabolism and modifies cancer-related pathways, with decreased cell cycle and increased B/T cell receptor signaling. Our TICRR risk model accurately predicts LUAD patient prognosis, validated across GEO datasets, and is integrated with clinical characteristics via a nomogram, facilitating personalized treatment strategies and enhancing patient management. Conclusions Taken together, TICRR has emerged as a promising prognostic biomarker in lung adenocarcinoma (LUAD), with implications in immune activation, cell cycle regulation, RNA modification, and tumor energy metabolism. These findings suggest that TICRR could serve as a viable therapeutic target and a reliable prognostic indicator for LUAD.
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Affiliation(s)
- Xunbo Zheng
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, China
- Department of Respiratory and Critical Illness Medicine, the First Hospital of Putian City, Putian, China
| | - Li Han
- Beijing ChosenMed Clinical Laboratory Co. Ltd., Beijing, China
| | - Jun Guan
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, China
- Department of Cardiothoracic Surgery, the First Hospital of Putian City, Putian, China
| | - Chenteng Chen
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, China
- Department of Respiratory and Critical Illness Medicine, the First Hospital of Putian City, Putian, China
| | - Yue Zhang
- Beijing ChosenMed Clinical Laboratory Co. Ltd., Beijing, China
| | - Jiali Zhang
- Beijing ChosenMed Clinical Laboratory Co. Ltd., Beijing, China
| | - Yiran Zhang
- Beijing ChosenMed Clinical Laboratory Co. Ltd., Beijing, China
| | - Siyao Liu
- Beijing ChosenMed Clinical Laboratory Co. Ltd., Beijing, China
| | - Junyan Su
- Beijing ChosenMed Clinical Laboratory Co. Ltd., Beijing, China
| | - Mengyuan Liu
- Beijing ChosenMed Clinical Laboratory Co. Ltd., Beijing, China
| | - Hanxing Huang
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, China
- Department of Pathology, the First Hospital of Putian City, Putian, China
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Xie J, Deng W, Deng X, Liang JY, Tang Y, Huang J, Tang H, Zou Y, Zhou H, Xie X. Single-cell histone chaperones patterns guide intercellular communication of tumor microenvironment that contribute to breast cancer metastases. Cancer Cell Int 2023; 23:311. [PMID: 38057779 DOI: 10.1186/s12935-023-03166-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 11/26/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Histone chaperones (HCs) are crucial for governing genome stability and gene expression in multiple cancers. However, the functioning of HCs in the tumor microenvironment (TME) is still not clearly understood. METHODS Self-tested single-cell RNA-seq data derived from 6 breast cancer (BC) patients with brain and liver metastases were reanalyzed by nonnegative matrix factorization (NMF) algorithm for 36 HCs. TME subclusters were observed with BC and immunotherapy public cohorts to assess their prognosis and immune response. The biological effect of HSPA8, one of the HCs, was verified by transwell assay and wound-healing assays. RESULTS Cells including fibroblasts, macrophages, B cells, and T cells, were classified into various subclusters based on marker genes. Additionally, it showed that HCs might be strongly associated with biological and clinical features of BC metastases, along with the pseudotime trajectory of each TME cell type. Besides, the results of bulk-seq analysis revealed that TME cell subclusters mediated by HCs distinguished significant prognostic value for BC patients and were relevant to patients' immunotherapy responses, especially for B cells and macrophages. In particular, CellChat analysis exhibited that HCs-related TME cell subclusters revealed extensive and diverse interactions with malignant cells. Finally, transwell and wound-healing assays exhibited that HSPA8 deficiency inhibited BC cell migration and invasion. CONCLUSIONS Collectively, our study first dissected HCs-guided intercellular communication of TME that contribute to BC metastases.
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Affiliation(s)
- Jindong Xie
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, 651 East Dongfeng Road, Guangzhou, 510060, China
| | - Wei Deng
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, 651 East Dongfeng Road, Guangzhou, 510060, China
| | - Xinpei Deng
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, 651 East Dongfeng Road, Guangzhou, 510060, China
| | - Jie-Ying Liang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510000, China
| | - Yuhui Tang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, 651 East Dongfeng Road, Guangzhou, 510060, China
| | - Jun Huang
- College of Basic Medicine, Shaoyang University, Shaoyang, China
| | - Hailin Tang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, 651 East Dongfeng Road, Guangzhou, 510060, China
| | - Yutian Zou
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, 651 East Dongfeng Road, Guangzhou, 510060, China.
| | - Huamao Zhou
- The Affiliated Nanhua Hospital, Hengyang Medical School, University of South China, Hengyang, China.
| | - Xiaoming Xie
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, 651 East Dongfeng Road, Guangzhou, 510060, China.
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Zhao YQ, Zhang HH, Wu J, Li L, Li J, Zhong H, Jin Y, Lei TY, Zhao XY, Xu B, Song QB, He J. Prediction of Tumor Microenvironment Characteristics and Treatment Response in Lung Squamous Cell Carcinoma by Pseudogene OR7E47P-related Immune Genes. Curr Med Sci 2023; 43:1133-1150. [PMID: 38015361 DOI: 10.1007/s11596-023-2798-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 09/22/2023] [Indexed: 11/29/2023]
Abstract
OBJECTIVE Pseudogenes are initially regarded as nonfunctional genomic sequences, but some pseudogenes regulate tumor initiation and progression by interacting with other genes to modulate their transcriptional activities. Olfactory receptor family 7 subfamily E member 47 pseudogene (OR7E47P) is expressed broadly in lung tissues and has been identified as a positive regulator in the tumor microenvironment (TME) of lung adenocarcinoma (LUAD). This study aimed to elucidate the correlation between OR7E47P and tumor immunity in lung squamous cell carcinoma (LUSC). METHODS Clinical and molecular information from The Cancer Genome Atlas (TCGA) LUSC cohort was used to identify OR7E47P-related immune genes (ORIGs) by weighted gene correlation network analysis (WGCNA). Based on the ORIGs, 2 OR7E47P clusters were identified using non-negative matrix factorization (NMF) clustering, and the stability of the clustering was tested by an extreme gradient boosting classifier (XGBoost). LASSO-Cox and stepwise regressions were applied to further select prognostic ORIGs and to construct a predictive model (ORPScore) for immunotherapy. The Botling cohorts and 8 immunotherapy cohorts (the Samstein, Braun, Jung, Gide, IMvigor210, Lauss, Van Allen, and Cho cohorts) were included as independent validation cohorts. RESULTS OR7E47P expression was positively correlated with immune cell infiltration and enrichment of immune-related pathways in LUSC. A total of 57 ORIGs were identified to classify the patients into 2 OR7E47P clusters (Cluster 1 and Cluster 2) with distinct immune, mutation, and stromal programs. Compared to Cluster 1, Cluster 2 had more infiltration by immune and stromal cells, lower mutation rates of driver genes, and higher expression of immune-related proteins. The clustering performed well in the internal and 5 external validation cohorts. Based on the 7 ORIGs (HOPX, STX2, WFS, DUSP22, SLFN13, GGCT, and CCSER2), the ORPScore was constructed to predict the prognosis and the treatment response. In addition, the ORPScore was a better prognostic factor and correlated positively with the immunotherapeutic response in cancer patients. The area under the curve values ranged from 0.584 to 0.805 in the 6 independent immunotherapy cohorts. CONCLUSION Our study suggests a significant correlation between OR7E47P and TME modulation in LUSC. ORIGs can be applied to molecularly stratify patients, and the ORPScore may serve as a biomarker for clinical decision-making regarding individualized prognostication and immunotherapy.
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Affiliation(s)
- Ya-Qi Zhao
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, China
- Department of Medical Oncology, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China
| | - Hao-Han Zhang
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Jie Wu
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Lan Li
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Jing Li
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Hao Zhong
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Yan Jin
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Tian-Yu Lei
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Xin-Yi Zhao
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Bin Xu
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Qi-Bin Song
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
| | - Jie He
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100032, China.
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Liu Y, Yuan Y, Chen T, Xiao H, Zhang X, Zhang F. Identification of aneuploidy-related gene signature to predict survival in head and neck squamous cell carcinomas. Aging (Albany NY) 2023; 15:13100-13117. [PMID: 37988195 DOI: 10.18632/aging.205221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/15/2023] [Indexed: 11/23/2023]
Abstract
BACKGROUND To parse the characteristics of aneuploidy related riskscore (ARS) model in head and neck squamous cell carcinomas (HNSC) and their predictive ability on patient prognosis. METHODS Molecular subtyping of HNSC specimens was clustered by Copy Number Variation (CNV) data from The Cancer Genome Atlas (TCGA) dataset applying consistent clustering, followed by immune condition evaluation, differentially expressed genes (DEGs) analysis and DEGs function annotation. Weighted gene co-expression network analysis (WGCNA), protein-protein interaction, Univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analysis were implemented to construct an ARS model. A nomogram for clinic practice was designed by rms package. Immunotherapy evaluation and drug sensitivity prediction were also carried out. RESULTS We stratified HNSC patients into three different molecular subgroups, with the best prognosis in C1 cluster among 3 clusters. C1 cluster displayed greatest immune infiltration status. The most DEGs between C1 and C2 groups, mainly enriched in cell cycle and immune function. We constructed a nine-gene ARS model (ICOS, IL21R, CCR7, SELL, CYTIP, ZAP70, CCR4, S1PR4 and CD79A) that effectively differentiates between high- and low-risk patients. Patients in low ARS group showed a higher sensitivity to immunotherapy. A nomogram built by integrating ARS and clinic-pathological characteristics helped predict clinic survival benefit. Drug sensitivity evaluation found that 4/9 inhibitor drugs (MK-8776, AZD5438, PD-0332991, PHA-665752) acted on the cell cycle. CONCLUSIONS We classified 3 molecular subtypes for HNSC patients and established an ARS prognostic model, which offered a prospective direction for prognosis in HNSC.
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Affiliation(s)
- Yu Liu
- Department of Pharmacy, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Yonghua Yuan
- Research Center for Pharmacodynamic Evaluation Engineering Technology of Chongqing, College of Pharmacy, Chongqing Medical University, Chongqing 400016, China
| | - Tao Chen
- Department of Pharmacy, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Hongyi Xiao
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xiangyu Zhang
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Fujun Zhang
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
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Zheng K, Hai Y, Xi Y, Zhang Y, Liu Z, Chen W, Hu X, Zou X, Hao J. Integrative multi-omics analysis unveils stemness-associated molecular subtypes in prostate cancer and pan-cancer: prognostic and therapeutic significance. J Transl Med 2023; 21:789. [PMID: 37936202 PMCID: PMC10629187 DOI: 10.1186/s12967-023-04683-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 10/29/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND Prostate cancer (PCA) is the fifth leading cause of cancer-related deaths worldwide, with limited treatment options in the advanced stages. The immunosuppressive tumor microenvironment (TME) of PCA results in lower sensitivity to immunotherapy. Although molecular subtyping is expected to offer important clues for precision treatment of PCA, there is currently a shortage of dependable and effective molecular typing methods available for clinical practice. Therefore, we aim to propose a novel stemness-based classification approach to guide personalized clinical treatments, including immunotherapy. METHODS An integrative multi-omics analysis of PCA was performed to evaluate stemness-level heterogeneities. Unsupervised hierarchical clustering was used to classify PCAs based on stemness signature genes. To make stemness-based patient classification more clinically applicable, a stemness subtype predictor was jointly developed by using four PCA datasets and 76 machine learning algorithms. RESULTS We identified stemness signatures of PCA comprising 18 signaling pathways, by which we classified PCA samples into three stemness subtypes via unsupervised hierarchical clustering: low stemness (LS), medium stemness (MS), and high stemness (HS) subtypes. HS patients are sensitive to androgen deprivation therapy, taxanes, and immunotherapy and have the highest stemness, malignancy, tumor mutation load (TMB) levels, worst prognosis, and immunosuppression. LS patients are sensitive to platinum-based chemotherapy but resistant to immunotherapy and have the lowest stemness, malignancy, and TMB levels, best prognosis, and the highest immune infiltration. MS patients represent an intermediate status of stemness, malignancy, and TMB levels with a moderate prognosis. We further demonstrated that these three stemness subtypes are conserved across pan-tumor. Additionally, the 9-gene stemness subtype predictor we developed has a comparable capability to 18 signaling pathways to make tumor diagnosis and to predict tumor recurrence, metastasis, progression, prognosis, and efficacy of different treatments. CONCLUSIONS The three stemness subtypes we identified have the potential to be a powerful tool for clinical tumor molecular classification in PCA and pan-cancer, and to guide the selection of immunotherapy or other sensitive treatments for tumor patients.
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Affiliation(s)
- Kun Zheng
- Department of Urology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Youlong Hai
- Department of Urology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Yue Xi
- Department of Reproductive Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan, 250013, Shandong, China
| | - Yukun Zhang
- Beijing University of Chinese Medicine East Hospital, Zaozhuang Hospital, Zaozhuang, 277000, Shandong, China
| | - Zheqi Liu
- Department of Oral and Maxillofacial Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Wantao Chen
- Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, National Clinical Research Center of Stomatology, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Xiaoyong Hu
- Department of Urology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China.
| | - Xin Zou
- Jinshan Hospital Center for Tumor Diagnosis & Therapy, Jinshan Hospital, Fudan University, Shanghai, 201508, China.
- Department of Pathology, Jinshan Hospital, Fudan University, Shanghai, 201508, China.
| | - Jie Hao
- Institute of Clinical Science, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
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Li Z, Liu Y, Guo P, Wei Y. Construction and validation of a novel angiogenesis pattern to predict prognosis and immunotherapy efficacy in colorectal cancer. Aging (Albany NY) 2023; 15:12413-12450. [PMID: 37938164 PMCID: PMC10683615 DOI: 10.18632/aging.205189] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 10/02/2023] [Indexed: 11/09/2023]
Abstract
BACKGROUND Evidence suggests that the tumor microenvironment (TME) affects the tumor active response to immunotherapy. Tumor angiogenesis is closely related to the TME. Nonetheless, the effects of angiogenesis on the TME of colorectal cancer (CRC) remain unknown. METHODS We comprehensively assessed the angiogenesis patterns in CRC based on 36 angiogenesis-related genes (ARGs). Subsequently, we evaluated the prognostic values and therapeutic sensitivities of angiogenesis patterns using multiple methods. We then performed the machine learning algorithm and functional experiments to identify the prognostic key ARGs. Ultimately, the regulation of gut microbiota on the expression of ARGs was further investigated by using whole genome sequencing. RESULTS Two angiogenesis clusters were identified and angiogenesis cluster B was characterized by increased stromal and immunity activation with unfavorable odds of survival. Further, an ARG_score including 9 ARGs to predict recurrence-free survival (RFS) was established and its predominant predictive ability was confirmed. The low ARG_score patients were characterized by a high mutation burden, high microsatellite instability, and immune activation with better prognosis. Moreover, patients with high KLK10 expression were associated with a hot tumor immune microenvironment, poorer immune checkpoint blocking treatment, and shorter survival. The in vitro experiments also indicated that Fusobacterium nucleatum (F.n) infection significantly induced KLK10 expression in CRC. CONCLUSIONS The quantification of angiogenesis patterns could contribute to predict TME characteristics, prognosis, and individualized immunotherapy strategies. Furthermore, our findings suggest that F.n may influence CRC progression through ARGs, which could serve as a clinical biomarker and therapeutic target for F.n-infected CRC patients.
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Affiliation(s)
- Zhiyong Li
- Department of Emergency Surgery, Peking University People’s Hospital, Xicheng, Beijing 100044, China
| | - Yang Liu
- Department of Pancreatic and Gastrointestinal Surgery Division, Ningbo Second Hospital, Ningbo, Zhejiang 315010, China
| | - Peng Guo
- Department of Emergency Surgery, Peking University People’s Hospital, Xicheng, Beijing 100044, China
- Department of Emergency Medicine, Peking University People’s Hospital, Xicheng, Beijing 100044, China
- Laboratory of Surgery Oncology, Peking University People’s Hospital, Xicheng, Beijing 100044, China
| | - Yunwei Wei
- Department of Pancreatic and Gastrointestinal Surgery Division, Ningbo Second Hospital, Ningbo, Zhejiang 315010, China
- Ningbo Key Laboratory of Intestinal Microecology and Human Major Diseases, Ningbo, Zhejiang 315010, China
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Zhao J, Dong Y, Bai H, Bai F, Yan X, Duan J, Wan R, Xu J, Fei K, Wang J, Wang Z. Multi-omics indicators of long-term survival benefits after immune checkpoint inhibitor therapy. CELL REPORTS METHODS 2023; 3:100596. [PMID: 37738982 PMCID: PMC10626191 DOI: 10.1016/j.crmeth.2023.100596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 05/08/2023] [Accepted: 08/30/2023] [Indexed: 09/24/2023]
Abstract
Molecular indicators of long-term survival (LTS) in response to immune-checkpoint inhibitor (ICI) treatment have the potential to provide both mechanistic and therapeutic insights. In this study, we construct predictive models of LTS following ICI therapy based on data from 158 clinical trials involving 21,023 patients of 25 cancer types with available 1-year overall survival (OS) rates. We present evidence for the use of 1-year OS rate as a surrogate for LTS. Based on these and corresponding TCGA multi-omics data, total neoantigen, metabolism score, CD8+ T cell, and MHC_score were identified as predictive biomarkers. These were integrated into a Gaussian process regression model that estimates "long-term survival predictive score of immunotherapy" (iLSPS). We found that iLSPS outperformed the predictive capabilities of individual biomarkers and successfully predicted LTS of patient groups with melanoma and lung cancer. Our study explores the feasibility of modeling LTS based on multi-omics indicators and machine-learning methods.
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Affiliation(s)
- Jie Zhao
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Yiting Dong
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Hua Bai
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Fan Bai
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing 100021, China
| | - Xiaoyan Yan
- Clinical Research Institute, Peking University, Beijing 100021, China
| | - Jianchun Duan
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Rui Wan
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Jiachen Xu
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Kailun Fei
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Jie Wang
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China.
| | - Zhijie Wang
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China.
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Lee DH, Ahn H, Sim HI, Choi E, Choi S, Jo Y, Yun B, Song HK, Oh SJ, Denda-Nagai K, Park CS, Irimura T, Park Y, Jin HS. A CRISPR activation screen identifies MUC-21 as critical for resistance to NK and T cell-mediated cytotoxicity. J Exp Clin Cancer Res 2023; 42:272. [PMID: 37858248 PMCID: PMC10588101 DOI: 10.1186/s13046-023-02840-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 09/24/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND Immunotherapy has significantly advanced cancer treatments, but many patients do not respond to it, partly due to immunosuppressive mechanisms used by tumor cells. These cells employ immunosuppressive ligands to evade detection and elimination by the immune system. Therefore, the discovery and characterization of novel immunosuppressive ligands that facilitate immune evasion are crucial for developing more potent anti-cancer therapies. METHODS We conducted gain-of-function screens using a CRISPRa (CRISPR activation) library that covered the entire human transmembrane sub-genome to identify surface molecules capable of hindering NK-mediated cytotoxicity. The immunosuppressive role and mechanism of MUC21 were validated using NK and T cell mediated cytotoxicity assays. Bioinformatics tools were employed to assess the clinical implications of mucin-21 (MUC21) in cancer cell immunity. RESULTS Our genetic screens revealed that MUC21 expression on cancer cell surfaces inhibits both the cytotoxic activity of NK cells and antibody-dependent cellular cytotoxicity, but not affecting complement-dependent cytotoxicity. Additionally, MUC21 expression hinders T cell activation by impeding antigen recognition, thereby diminishing the effectiveness of the immune checkpoint inhibitor, anti-PD-L1. Moreover, MUC21 expression suppress the antitumor function of both CAR-T cells and CAR-NK cells. Mechanistically, MUC21 facilitates immune evasion by creating steric hindrance, preventing interactions between cancer and immune cells. Bioinformatics analysis revealed elevated MUC21 expression in lung cancer, which correlated with reduced infiltration and activation of cytotoxic immune cells. Intriguingly, MUC21 expression was higher in non-small cell lung cancer (NSCLC) tumors that were non-responsive to anti-PD-(L)1 treatment compared to responsive tumors. CONCLUSIONS These findings indicate that surface MUC21 serves as a potent immunosuppressive ligand, shielding cancer cells from NK and CD8+T cell attacks. This suggests that inhibiting MUC21 could be a promising strategy to improve cancer immunotherapy.
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Affiliation(s)
- Dong-Hee Lee
- Department of Convergence Medicine, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea
| | - Hyejin Ahn
- Department of Convergence Medicine, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea
| | - Hye-In Sim
- Chemical and Biological Integrative Research Center, Biomedical Research Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, South Korea
- Department of Life Sciences, Korea University, Seoul, 02481, South Korea
| | - Eunji Choi
- Department of Convergence Medicine, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea
| | - Seunghyun Choi
- Chemical and Biological Integrative Research Center, Biomedical Research Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, South Korea
- Department of Life Sciences, Korea University, Seoul, 02481, South Korea
| | - Yunju Jo
- Chemical and Biological Integrative Research Center, Biomedical Research Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, South Korea
- Department of Life Sciences, Korea University, Seoul, 02481, South Korea
| | - Bohwan Yun
- Department of Convergence Medicine, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea
| | - Hyun Kyu Song
- Department of Life Sciences, Korea University, Seoul, 02481, South Korea
| | - Soo Jin Oh
- Department of Convergence Medicine, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea
| | - Kaori Denda-Nagai
- Division of Glycobiologics, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Chan-Sik Park
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea
| | - Tatsuro Irimura
- Division of Glycobiologics, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Yoon Park
- Chemical and Biological Integrative Research Center, Biomedical Research Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, South Korea.
| | - Hyung-Seung Jin
- Department of Convergence Medicine, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea.
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Fan G, Xie T, Tan Q, Lou N, Wang S, Han X, Shi Y. An immunosuppressive subtype of senescent tumor cells predicted worse immunotherapy response in lung adenocarcinoma. iScience 2023; 26:107894. [PMID: 37766998 PMCID: PMC10520875 DOI: 10.1016/j.isci.2023.107894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 08/14/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
Senescent tumor cells (STCs) can induce immunosuppression, promoting tumor progression and therapy resistance. However, the specific characteristics of immunosuppressive STC have not been thoroughly investigated. This study aimed to characterize and elucidate the immunosuppressive phenotype of STC in lung adenocarcinoma by employing single-cell and bulk transcriptomics, as well as serum proteomics profiling. We identified senescence-related genes specific to tumors and identified Cluster10 of STC as the immunomodulatory subtype. Cluster10 exhibited a distinct secretome dominated by cytokines such as CXCL1, CXCL2, and CXCL8 and showed activation of transcription factors associated with cytokine secretion, including NFKB1, RELA, and STAT3. Notably, Cluster10 demonstrated the highest degree of intercellular communication among all cell types, with interactions as LGALS9-TIM3 and MIF-CD74. Furthermore, Cluster10 showed significant associations with poor prognosis and diminished response to immunotherapy. Analysis of serum proteomics data from our in-house cohort identified CXCL8 as a potential marker for predicting immunotherapeutic outcomes.
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Affiliation(s)
- Guangyu Fan
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Tongji Xie
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Qiaoyun Tan
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, N0.109,Machang Road, Jianghan District, Wuhan 430024, China
| | - Ning Lou
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Shasha Wang
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Xiaohong Han
- Clinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Clinical PK & PD Investigation for Innovative Drugs, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1, Shuaifuyuan, Dongcheng District, Beijing 100730, China
| | - Yuankai Shi
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
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Wang Y, Pan J, An F, Chen K, Chen J, Nie H, Zhu Y, Qian Z, Zhan Q. GBP2 is a prognostic biomarker and associated with immunotherapeutic responses in gastric cancer. BMC Cancer 2023; 23:925. [PMID: 37784054 PMCID: PMC10544588 DOI: 10.1186/s12885-023-11308-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 08/17/2023] [Indexed: 10/04/2023] Open
Abstract
BACKGROUND The interferon-induced protein known as guanylate-binding protein 2 (GBP2) has been linked to multiple different cancer types as an oncogenic gene. Although the role of GBP2 in cancer has been preliminarily explored, it is unclear how this protein interacts with tumor immunity in gastric cancer. METHODS The expression, prognostic value, immune-correlations of GBP2 in gastric cancer was explored in multiple public and in-house cohorts. In addition, the pan-cancer analysis was performed to investigate the immunological role of GBP2 based on The Cancer Genome Atlas (TCGA) dataset, and the predictive value of GBP2 for immunotherapy was also examined in multiple public cohorts. RESULTS GBP2 was highly expressed in tumor tissues and associated with poor prognosis in gastric cancer. In addition, GBP2 was associated with the immune-hot phenotype. To be more specific, GBP2 was positively related to immuno-modulators, tumor-infiltrating immune cells (TIICs), immunotherapy biomarkers, and even well immunotherapeutic response. In addition to gastric cancer, GBP2 was expected to be an indicator of high immunogenicity in most cancer types. Importantly, GBP2 could predict the immunotherapeutic responses in at least four different cancer types, including melanoma, urothelial carcinoma, non-small cell lung cancer, and breast cancer. CONCLUSIONS To sum up, GBP2 expression is a promising pan-cancer biomarker for estimating the immunological characteristics of tumors and may be utilized to detect immuno-hot tumors in gastric cancer.
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Affiliation(s)
- Yunfei Wang
- Departments of Gastroenterology, Wuxi People's Hospital, Wuxi Medical Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Nanjing Medical University, Wuxi, Jiangsu, 214023, China
| | - Jiadong Pan
- Departments of Gastroenterology, The Third People's Hospital of Kunshan, Suzhou, 215300, China
| | - Fangmei An
- Departments of Gastroenterology, Wuxi People's Hospital, Wuxi Medical Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Nanjing Medical University, Wuxi, Jiangsu, 214023, China
| | - Ke Chen
- Departments of Gastroenterology, Wuxi People's Hospital, Wuxi Medical Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Nanjing Medical University, Wuxi, Jiangsu, 214023, China
| | - Jiawei Chen
- Departments of Gastroenterology, Wuxi People's Hospital, Wuxi Medical Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Nanjing Medical University, Wuxi, Jiangsu, 214023, China
| | - He Nie
- Departments of Gastroenterology, Wuxi People's Hospital, Wuxi Medical Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Nanjing Medical University, Wuxi, Jiangsu, 214023, China
| | - Yanping Zhu
- Department of Clinical Laboratory, Changshu Medicine Examination Institute, Changshu, 215500, China
| | - Zhengtao Qian
- Department of Clinical Laboratory, Changshu Medicine Examination Institute, Changshu, 215500, China.
| | - Qiang Zhan
- Departments of Gastroenterology, Wuxi People's Hospital, Wuxi Medical Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Nanjing Medical University, Wuxi, Jiangsu, 214023, China.
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Lu G, Wang H, Xu R, Xu J, An F, Xu H, Nie H, Mei J, Zhan Q, Zhang Q. Formin protein FMNL1 is a biomarker for tumor-infiltrating immune cells and associated with well immunotherapeutic response. J Cancer 2023; 14:2978-2989. [PMID: 37859818 PMCID: PMC10583584 DOI: 10.7150/jca.86965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 09/03/2023] [Indexed: 10/21/2023] Open
Abstract
Background: Increased studies on the basis of bulk RNA-sequencing (RNA-seq) data of cancer identify numbers of immune-related genes which may play potential regulatory roles in the tumor microenvironment (TME) without in-depth validation. Methods: In the current study, the immunological correlation and cell subpopulation expression pattern of FMNL1 were analyzed using public data. In addition, the cell subpopulation expression pattern of FMNL1 was also deeply validated using single-cell RNA-sequencing (scRNA-seq) and multiplexed quantitative immunofluorescence (mQIF). Results: Bulk FMNL1 mRNA was related to better prognosis in hepatocellular carcinoma (HCC) and was able to identify immuno-hot tumor in not only HCC but also multiple cancer types. Bulk FMNL1 mRNA also predicted the response to immunotherapy in multiple cancers. Further validation using scRNA-seq and mQIF revealed that FMNL1 was a biomarker for immune cells. Conclusions: FMNL1 is a biomarker for immune cells in not only hepatocellular carcinoma, but also multiple cancer types. Moreover, immune infiltration analysis using the bulk RNA-seq data would be further validated using scRNA-seq and/or mQIF to describe the cell subpopulation expression pattern in tumor tissues for more in-depth and appropriate understanding.
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Affiliation(s)
- Guomin Lu
- Departments of Gastroenterology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu 214023, China
| | - Hui Wang
- Departments of Gastroenterology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu 214023, China
| | - Rui Xu
- The First College of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Junying Xu
- Departments of Oncology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu 214023, China
| | - Fangmei An
- Departments of Gastroenterology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu 214023, China
| | - Haoran Xu
- Departments of Gastroenterology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu 214023, China
| | - He Nie
- Departments of Gastroenterology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu 214023, China
| | - Jie Mei
- Departments of Oncology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu 214023, China
| | - Qiang Zhan
- Departments of Gastroenterology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu 214023, China
| | - Qinglin Zhang
- Departments of Gastroenterology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu 214023, China
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Lai EY, Huang YT. Genome-wide multimediator analyses using the generalized Berk-Jones statistics with the composite test. Bioinformatics 2023; 39:btad544. [PMID: 37665751 PMCID: PMC10500087 DOI: 10.1093/bioinformatics/btad544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 07/18/2023] [Accepted: 09/01/2023] [Indexed: 09/06/2023] Open
Abstract
MOTIVATION Mediation analysis is performed to evaluate the effects of a hypothetical causal mechanism that marks the progression from an exposure, through mediators, to an outcome. In the age of high-throughput technologies, it has become routine to assess numerous potential mechanisms at the genome or proteome scales. Alongside this, the necessity to address issues related to multiple testing has also arisen. In a sparse scenario where only a few genes or proteins are causally involved, conventional methods for assessing mediation effects lose statistical power because the composite null distribution behind this experiment cannot be attained. The power loss hence decreases the true mechanisms identified after multiple testing corrections. To fairly delineate a uniform distribution under the composite null, Huang (Genome-wide analyses of sparse mediation effects under composite null hypotheses. Ann Appl Stat 2019a;13:60-84; AoAS) proposed the composite test to provide adjusted P-values for single-mediator analyses. RESULTS Our contribution is to extend the method to multimediator analyses, which are commonly encountered in genomic studies and also flexible to various biological interests. Using the generalized Berk-Jones statistics with the composite test, we proposed a multivariate approach that favors dense and diverse mediation effects, a decorrelation approach that favors sparse and consistent effects, and a hybrid approach that captures the edges of both approaches. Our analysis suite has been implemented as an R package MACtest. The utility is demonstrated by analyzing the lung adenocarcinoma datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium. We further investigate the genes and networks whose expression may be regulated by smoking-induced epigenetic aberrations. AVAILABILITY AND IMPLEMENTATION An R package MACtest is available on https://github.com/roqe/MACtest.
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Affiliation(s)
- En-Yu Lai
- Institute of Statistical Science, Academia Sinica, Nankang, Taipei 11529, Taiwan
| | - Yen-Tsung Huang
- Institute of Statistical Science, Academia Sinica, Nankang, Taipei 11529, Taiwan
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Wang X, Qiao Z, Aramini B, Lin D, Li X, Fan J. Potential biomarkers for immunotherapy in non-small-cell lung cancer. Cancer Metastasis Rev 2023; 42:661-675. [PMID: 37121931 DOI: 10.1007/s10555-022-10074-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 12/09/2022] [Indexed: 05/02/2023]
Abstract
For individuals with advanced or metastatic non-small cell lung cancer (NSCLC), the primary treatment is platinum-based doublet chemotherapy. Immune checkpoint inhibitors (ICIs), primarily PD-1/PD-L1 and CTLA-4, have been found to be effective in patients with NSCLC who have no EGFR/ALK mutations. Furthermore, ICIs are considered a standard therapy. The quantity of fresh immunogenic antigens discovered by cytotoxic T cells was measured by PD-L1 expression and tumor mutational burden (TMB), which were the first biomarkers assessed in clinical trials. However, immunotherapy did not have response efficacy markers similar to targeted therapy, highlighting the significance of newly developed biomarkers. This investigation aims to review the research on immunotherapy for NSCLC, focusing primarily on the impact of biomarkers on efficacy prediction to determine whether biomarkers may be utilized to evaluate the effectiveness of immunotherapy.
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Affiliation(s)
- Xing Wang
- Department of Thoracic Surgery, Shanghai General Hospital, Shanghai, China
| | - Ziyun Qiao
- Department of Thoracic Surgery, Shanghai General Hospital, Shanghai, China
| | - Beatrice Aramini
- Division of Thoracic Surgery, Department of Experimental, Diagnostic and Specialty Medicine-DIMES of the Alma Mater Studiorum, G.B. Morgagni-L. Pierantoni Hospital, University of Bologna, Forlì, Italy
| | - Dong Lin
- Department of Thoracic Surgery, Shanghai General Hospital, Shanghai, China
| | - Xiaolong Li
- Department of Thoracic Surgery, Shanghai General Hospital, Shanghai, China
| | - Jiang Fan
- Department of Thoracic Surgery, Shanghai General Hospital, Shanghai, China.
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Chen S, Zhang L, Huang M, Liang Y, Wang Y. A tumor-associated endothelial signature score model in immunotherapy and prognosis across pan-cancers. Front Pharmacol 2023; 14:1190660. [PMID: 37719845 PMCID: PMC10500301 DOI: 10.3389/fphar.2023.1190660] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/14/2023] [Indexed: 09/19/2023] Open
Abstract
Background: The tumor-associated endothelial cell (TAE) component plays a vital role in tumor immunity. However, systematic tumor-associated endothelial-related gene assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers have not been explored. Herein, we investigated a TAE gene risk model to predict CIT responses and patient survival in a pan-cancer analysis. Methods: We analyzed publicly available datasets of tumor samples with gene expression and clinical information, including gastric cancer, metastatic urothelial cancer, metastatic melanoma, non-small cell lung cancer, primary bladder cancer, and renal cell carcinoma. We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm. Results: The model demonstrated a high predictive accuracy in both training and validation cohorts. The response rate of the high score group to immunotherapy in the training cohort was significantly higher than that of the low score group, with CIT response rates of 51% and 27%, respectively. The survival analysis showed that the prognosis of the high score group was significantly better than that of the low score group (all p < 0·001). Tumor-associated endothelial gene signature scores positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may benefit patients in the high score group. The analysis of TAE scores across 33 human cancers revealed that the TAE model could reflect immune cell infiltration and predict the survival of cancer patients. Conclusion: The TAE signature model could represent a CIT response prediction model with a prognostic value in multiple cancer types.
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Affiliation(s)
- Shuzhao Chen
- Department of Hematologic Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Limei Zhang
- Department of Hematologic Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Mayan Huang
- Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Yang Liang
- Department of Hematologic Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Yun Wang
- Department of Hematologic Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
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