1
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Liu M, Zhao Z, Wang C, Sang S, Cui Y, Lv C, Yang X, Zhang N, Xiong K, Chen B, Dong Q, Liu K, Gu Y. Harnessing genetic interactions for prediction of immune checkpoint inhibitors response signature in cancer cells. Cancer Lett 2024; 594:216991. [PMID: 38797232 DOI: 10.1016/j.canlet.2024.216991] [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: 01/10/2024] [Revised: 05/20/2024] [Accepted: 05/23/2024] [Indexed: 05/29/2024]
Abstract
Genetic interactions (GIs) refer to two altered genes having a combined effect that is not seen individually. They play a crucial role in influencing drug efficacy. We utilized CGIdb 2.0 (http://www.medsysbio.org/CGIdb2/), an updated database of comprehensively published GIs information, encompassing synthetic lethality (SL), synthetic viability (SV), and chemical-genetic interactions. CGIdb 2.0 elucidates GIs relationships between or within protein complex models by integrating protein-protein physical interactions. Additionally, we introduced GENIUS (GENetic Interactions mediated drUg Signature) to leverage GIs for identifying the response signature of immune checkpoint inhibitors (ICIs). GENIUS identified high MAP4K4 expression as a resistant signature and high HERC4 expression as a sensitive signature for ICIs treatment. Melanoma patients with high expression of MAP4K4 were associated with decreased efficacy and poorer survival following ICIs treatment. Conversely, overexpression of HERC4 in melanoma patients correlated with a positive response to ICIs. Notably, HERC4 enhances sensitivity to immunotherapy by facilitating antigen presentation. Analyses of immune cell infiltration and single-cell data revealed that B cells expressing MAP4K4 may contribute to resistance to ICIs in melanoma. Overall, CGIdb 2.0, provides integrated GIs data, thus serving as a crucial tool for exploring drug effects.
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Affiliation(s)
- Mingyue Liu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhangxiang Zhao
- Clinical Research Center (CRC), Medical Pathology Center (MPC), Cancer Early Detection and Treatment Center (CEDTC), Chongqing University Three Gorges Hospital, Chongqing University, Wanzhou, Chongqing, China
| | - Chengyu Wang
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education and School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
| | - Shaocong Sang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yanrui Cui
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chen Lv
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xiuqi Yang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Nan Zhang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Kai Xiong
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Bo Chen
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Qi Dong
- Department of Biochemistry and Molecular Biology, Harbin Medical University, Harbin, China
| | - Kaidong Liu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yunyan Gu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
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2
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Wooller SK, Pearl LH, Pearl FMG. Identifying actionable synthetically lethal cancer gene pairs using mutual exclusivity. FEBS Lett 2024. [PMID: 38977941 DOI: 10.1002/1873-3468.14950] [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: 08/03/2023] [Revised: 04/25/2024] [Accepted: 05/09/2024] [Indexed: 07/10/2024]
Abstract
Mutually exclusive loss-of-function alterations in gene pairs are those that occur together less frequently than may be expected and may denote a synthetically lethal relationship (SSL) between the genes. SSLs can be exploited therapeutically to selectively kill cancer cells. Here, we analysed mutation, copy number variation, and methylation levels in samples from The Cancer Genome Atlas, using the hypergeometric and the Poisson binomial tests to identify mutually exclusive inactivated genes. We focused on gene pairs where one is an inactivated tumour suppressor and the other a gene whose protein product can be inhibited by known drugs. This provided an abundance of potential targeted therapeutics and repositioning opportunities for several cancers. These data are available on the MexDrugs website, https://bioinformaticslab.sussex.ac.uk/mexdrugs.
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Affiliation(s)
- Sarah K Wooller
- Bioinformatics Lab, School of Life Sciences, University of Sussex, Brighton, UK
| | - Laurence H Pearl
- Genome Damage Stability Centre, School of Life Sciences, University of Sussex, Brighton, UK
| | - Frances M G Pearl
- Bioinformatics Lab, School of Life Sciences, University of Sussex, Brighton, UK
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3
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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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4
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Du P, Fan R, Zhang N, Wu C, Zhang Y. Advances in Integrated Multi-omics Analysis for Drug-Target Identification. Biomolecules 2024; 14:692. [PMID: 38927095 PMCID: PMC11201992 DOI: 10.3390/biom14060692] [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: 05/11/2024] [Revised: 06/08/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
As an essential component of modern drug discovery, the role of drug-target identification is growing increasingly prominent. Additionally, single-omics technologies have been widely utilized in the process of discovering drug targets. However, it is difficult for any single-omics level to clearly expound the causal connection between drugs and how they give rise to the emergence of complex phenotypes. With the progress of large-scale sequencing and the development of high-throughput technologies, the tendency in drug-target identification has shifted towards integrated multi-omics techniques, gradually replacing traditional single-omics techniques. Herein, this review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification, highlights the common multi-omics analysis strategies, briefly summarizes the selection of multi-omics analysis tools, and explores the challenges of existing multi-omics analyses, as well as the applications of multi-omics technology in drug-target identification.
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Affiliation(s)
- Peiling Du
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China; (P.D.); (R.F.); (N.Z.); (C.W.)
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, China
| | - Rui Fan
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China; (P.D.); (R.F.); (N.Z.); (C.W.)
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, China
| | - Nana Zhang
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China; (P.D.); (R.F.); (N.Z.); (C.W.)
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, China
| | - Chenyuan Wu
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China; (P.D.); (R.F.); (N.Z.); (C.W.)
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, China
| | - Yingqian Zhang
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China; (P.D.); (R.F.); (N.Z.); (C.W.)
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, China
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5
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Li X, Feng X, Zhou J, Luo Y, Chen X, Zhao J, Chen H, Xiong G, Luo G. A muti-modal feature fusion method based on deep learning for predicting immunotherapy response. J Theor Biol 2024; 586:111816. [PMID: 38589007 DOI: 10.1016/j.jtbi.2024.111816] [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: 10/21/2023] [Revised: 03/28/2024] [Accepted: 04/03/2024] [Indexed: 04/10/2024]
Abstract
Immune checkpoint therapy (ICT) has greatly improved the survival of cancer patients in the past few years, but only a small number of patients respond to ICT. To predict ICT response, we developed a multi-modal feature fusion model based on deep learning (MFMDL). This model utilizes graph neural networks to map gene-gene relationships in gene networks to low dimensional vector spaces, and then fuses biological pathway features and immune cell infiltration features to make robust predictions of ICT. We used five datasets to validate the predictive performance of the MFMDL. These five datasets span multiple types of cancer, including melanoma, lung cancer, and gastric cancer. We found that the prediction performance of multi-modal feature fusion model based on deep learning is superior to other traditional ICT biomarkers, such as ICT targets or tumor microenvironment-associated markers. In addition, we also conducted ablation experiments to demonstrate the necessity of fusing different modal features, which can improve the prediction accuracy of the model.
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Affiliation(s)
- Xiong Li
- School of Software, East China Jiaotong University, Nanchang 330013, China
| | - Xuan Feng
- School of Software, East China Jiaotong University, Nanchang 330013, China
| | - Juan Zhou
- School of Software, East China Jiaotong University, Nanchang 330013, China
| | - Yuchao Luo
- School of Software, East China Jiaotong University, Nanchang 330013, China
| | - Xiao Chen
- School of Software, East China Jiaotong University, Nanchang 330013, China
| | - Jiapeng Zhao
- School of Software, East China Jiaotong University, Nanchang 330013, China
| | - Haowen Chen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
| | - Guoming Xiong
- School of Software, East China Jiaotong University, Nanchang 330013, China
| | - Guoliang Luo
- School of Software, East China Jiaotong University, Nanchang 330013, China
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6
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Roohani Y, Huang K, Leskovec J. Predicting transcriptional outcomes of novel multigene perturbations with GEARS. Nat Biotechnol 2024; 42:927-935. [PMID: 37592036 PMCID: PMC11180609 DOI: 10.1038/s41587-023-01905-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 07/12/2023] [Indexed: 08/19/2023]
Abstract
Understanding cellular responses to genetic perturbation is central to numerous biomedical applications, from identifying genetic interactions involved in cancer to developing methods for regenerative medicine. However, the combinatorial explosion in the number of possible multigene perturbations severely limits experimental interrogation. Here, we present graph-enhanced gene activation and repression simulator (GEARS), a method that integrates deep learning with a knowledge graph of gene-gene relationships to predict transcriptional responses to both single and multigene perturbations using single-cell RNA-sequencing data from perturbational screens. GEARS is able to predict outcomes of perturbing combinations consisting of genes that were never experimentally perturbed. GEARS exhibited 40% higher precision than existing approaches in predicting four distinct genetic interaction subtypes in a combinatorial perturbation screen and identified the strongest interactions twice as well as prior approaches. Overall, GEARS can predict phenotypically distinct effects of multigene perturbations and thus guide the design of perturbational experiments.
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Affiliation(s)
- Yusuf Roohani
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Kexin Huang
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA, USA.
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7
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Sinha S, Vegesna R, Mukherjee S, Kammula AV, Dhruba SR, Wu W, Kerr DL, Nair NU, Jones MG, Yosef N, Stroganov OV, Grishagin I, Aldape KD, Blakely CM, Jiang P, Thomas CJ, Benes CH, Bivona TG, Schäffer AA, Ruppin E. PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors. NATURE CANCER 2024; 5:938-952. [PMID: 38637658 DOI: 10.1038/s43018-024-00756-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 03/08/2024] [Indexed: 04/20/2024]
Abstract
Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.
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Affiliation(s)
- Sanju Sinha
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA.
- NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, San Diego, CA, USA.
| | - Rahulsimham Vegesna
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Sumit Mukherjee
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Ashwin V Kammula
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
- University of Maryland, College Park, MD, USA
| | | | - Wei Wu
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - D Lucas Kerr
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Nishanth Ulhas Nair
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Matthew G Jones
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
- Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA, USA
- Whitehead Institute, Cambridge, MA, USA
| | - Nir Yosef
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
| | | | - Ivan Grishagin
- Rancho BioSciences, San Diego, CA, USA
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA
| | - Kenneth D Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Collin M Blakely
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Peng Jiang
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Craig J Thomas
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA
- Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Cyril H Benes
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Trever G Bivona
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub Investigator, San Francisco, CA, USA
| | | | - Eytan Ruppin
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA.
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8
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Hernando-Calvo A, Han M, Ayodele O, Wang BX, Bruce JP, Abbas-Aghababazadeh F, Vila-Casadesús M, Sanz-Garcia E, Yang SYC, Berman HK, Vivancos A, Lam B, Lungu I, Salawu A, Stayner LA, Haibe-Kains B, Bedard PL, Avery L, Razak ARA, Pugh TJ, Spreafico A, Siu LL, Hansen AR. A Phase II, Open-Label, Randomized Trial of Durvalumab With Olaparib or Cediranib in Patients With Mismatch Repair-Proficient Colorectal or Pancreatic Cancer. Clin Colorectal Cancer 2024:S1533-0028(24)00032-X. [PMID: 38960798 DOI: 10.1016/j.clcc.2024.05.002] [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: 02/28/2024] [Revised: 05/04/2024] [Accepted: 05/08/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND The use of immunotherapy in mismatch repair proficient colorectal cancer (pMMR-CRC) or pancreatic adenocarcinoma (PDAC) is associated with limited efficacy. DAPPER (NCT03851614) is a phase 2, basket study randomizing patients with pMMR CRC or PDAC to durvalumab with olaparib (durvalumab + olaparib) or durvalumab with cediranib (durvalumab + cediranib). METHODS PDAC or pMMR-CRC patients were randomized to either durvalumab+olaparib (arm A), or durvalumab + cediranib (arm B). Co-primary endpoints included pharmacodynamic immune changes in the tumor microenvironment (TME) and safety. Objective response rate, progression-free survival (PFS) and overall survival (OS) were determined. Paired tumor samples were analyzed by multiplexed immunohistochemistry and RNA-sequencing. RESULTS A total of 31 metastatic pMMR-CRC patients were randomized to arm A (n = 16) or B (n = 15). In 28 evaluable patients, 3 patients had stable disease (SD) (2 patients treated with durvalumab + olaparib and 1 patient treated with durvalumab + cediranib) while 25 had progressive disease (PD). Among patients with PDAC (n = 19), 9 patients were randomized to arm A and 10 patients were randomized to arm B. In 18 evaluable patients, 1 patient had a partial response (unconfirmed) with durvalumab + cediranib, 1 patient had SD with durvalumab + olaparib while 16 had PD. Safety profile was manageable and no grade 4-5 treatment-related adverse events were observed in either arm A or B. No significant changes were observed for CD3+/CD8+ immune infiltration in on-treatment biopsies as compared to baseline for pMMR-CRC and PDAC independent of treatment arms. Increased tumor-infiltrating lymphocytes at baseline, low baseline CD68+ cells and different immune gene expression signatures at baseline were associated with outcomes. CONCLUSIONS In patients with pMMR-CRC or PDAC, durvalumab + olaparib and durvalumab + cediranib showed limited antitumor activity. Different immune components of the TME were associated with treatment outcomes.
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Affiliation(s)
- Alberto Hernando-Calvo
- Department of Medicine, Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Ming Han
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Olubukola Ayodele
- Department of Medicine, Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Ben X Wang
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Jeffrey P Bruce
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | | | | | - Enrique Sanz-Garcia
- Department of Medicine, Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - S Y Cindy Yang
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Hal K Berman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Ana Vivancos
- Vall d'Hebron Institute of Oncology, Barcelona, Spain
| | - Bernard Lam
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Ilinca Lungu
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Abdulazeez Salawu
- Department of Medicine, Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Lee-Anne Stayner
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Ontario Institute for Cancer Research, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada; Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Philippe L Bedard
- Department of Medicine, Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Lisa Avery
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Albiruni R A Razak
- Department of Medicine, Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Trevor J Pugh
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Ontario Institute for Cancer Research, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Anna Spreafico
- Department of Medicine, Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Lillian L Siu
- Department of Medicine, Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Aaron R Hansen
- Department of Medicine, Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada.
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9
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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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10
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Acharya D, Mukhopadhyay A. A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology. Brief Funct Genomics 2024:elae013. [PMID: 38600757 DOI: 10.1093/bfgp/elae013] [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: 10/13/2023] [Revised: 03/12/2024] [Accepted: 03/22/2024] [Indexed: 04/12/2024] Open
Abstract
Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact: anirban@klyuniv.ac.in.
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Affiliation(s)
- Debabrata Acharya
- Department of Computer Science & Engineering, University of Kalyani, Kalyani-741235, West Bengal, India
| | - Anirban Mukhopadhyay
- Department of Computer Science & Engineering, University of Kalyani, Kalyani-741235, West Bengal, India
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11
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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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12
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Nair NU, Schäffer AA, Gertz EM, Cheng K, Zerbib J, Sahu AD, Leor G, Shulman ED, Aldape KD, Ben-David U, Ruppin E. Chromosome 7 to the rescue: overcoming chromosome 10 loss in gliomas. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.17.576103. [PMID: 38313282 PMCID: PMC10836086 DOI: 10.1101/2024.01.17.576103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
The co-occurrence of chromosome 10 loss and chromosome 7 gain in gliomas is the most frequent loss-gain co-aneuploidy pair in human cancers, a phenomenon that has been investigated without resolution since the late 1980s. Expanding beyond previous gene-centric studies, we investigate the co-occurrence in a genome-wide manner taking an evolutionary perspective. First, by mining large tumor aneuploidy data, we predict that the more likely order is 10 loss followed by 7 gain. Second, by analyzing extensive genomic and transcriptomic data from both patients and cell lines, we find that this co-occurrence can be explained by functional rescue interactions that are highly enriched on 7, which can possibly compensate for any detrimental consequences arising from the loss of 10. Finally, by analyzing transcriptomic data from normal, non-cancerous, human brain tissues, we provide a plausible reason why this co-occurrence happens preferentially in cancers originating in certain regions of the brain.
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13
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Liu Y, Altreuter J, Bodapati S, Cristea S, Wong CJ, Wu CJ, Michor F. Predicting patient outcomes after treatment with immune checkpoint blockade: A review of biomarkers derived from diverse data modalities. CELL GENOMICS 2024; 4:100444. [PMID: 38190106 PMCID: PMC10794784 DOI: 10.1016/j.xgen.2023.100444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 07/12/2023] [Accepted: 10/24/2023] [Indexed: 01/09/2024]
Abstract
Immune checkpoint blockade (ICB) therapy targeting cytotoxic T-lymphocyte-associated protein 4, programmed death 1, and programmed death ligand 1 has shown durable remission and clinical success across different cancer types. However, patient outcomes vary among disease indications. Studies have identified prognostic biomarkers associated with immunotherapy response and patient outcomes derived from diverse data types, including next-generation bulk and single-cell DNA, RNA, T cell and B cell receptor sequencing data, liquid biopsies, and clinical imaging. Owing to inter- and intra-tumor heterogeneity and the immune system's complexity, these biomarkers have diverse efficacy in clinical trials of ICB. Here, we review the genetic and genomic signatures and image features of ICB studies for pan-cancer applications and specific indications. We discuss the advantages and disadvantages of computational approaches for predicting immunotherapy effectiveness and patient outcomes. We also elucidate the challenges of immunotherapy prognostication and the discovery of novel immunotherapy targets.
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Affiliation(s)
- Yang Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Jennifer Altreuter
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Sudheshna Bodapati
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Simona Cristea
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Cheryl J Wong
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 20115, USA
| | - Catherine J Wu
- Harvard Medical School, Boston, MA 02115, USA; The Eli and Edythe Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Franziska Michor
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 20115, USA; The Eli and Edythe Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02138, USA; The Ludwig Center at Harvard, Boston, MA 02115, USA.
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14
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Staheli JP, Neal ML, Navare A, Mast FD, Aitchison JD. Predicting host-based, synthetic lethal antiviral targets from omics data. NAR MOLECULAR MEDICINE 2024; 1:ugad001. [PMID: 38994440 PMCID: PMC11233254 DOI: 10.1093/narmme/ugad001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 12/08/2023] [Accepted: 01/03/2024] [Indexed: 07/13/2024]
Abstract
Traditional antiviral therapies often have limited effectiveness due to toxicity and the emergence of drug resistance. Host-based antivirals are an alternative, but can cause nonspecific effects. Recent evidence shows that virus-infected cells can be selectively eliminated by targeting synthetic lethal (SL) partners of proteins disrupted by viral infection. Thus, we hypothesized that genes depleted in CRISPR knockout (KO) screens of virus-infected cells may be enriched in SL partners of proteins altered by infection. To investigate this, we established a computational pipeline predicting antiviral SL drug targets. First, we identified SARS-CoV-2-induced changes in gene products via a large compendium of omics data. Second, we identified SL partners for each altered gene product. Last, we screened CRISPR KO data for SL partners required for cell viability in infected cells. Despite differences in virus-induced alterations detected by various omics data, they share many predicted SL targets, with significant enrichment in CRISPR KO-depleted datasets. Our comparison of SARS-CoV-2 and influenza infection data revealed potential broad-spectrum, host-based antiviral SL targets. This suggests that CRISPR KO data are replete with common antiviral targets due to their SL relationship with virus-altered states and that such targets can be revealed from analysis of omics datasets and SL predictions.
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Affiliation(s)
- Jeannette P Staheli
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
| | - Maxwell L Neal
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
| | - Arti Navare
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
| | - Fred D Mast
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
| | - John D Aitchison
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
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15
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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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16
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Liu M, Dong Q, Chen B, Liu K, Zhao Z, Wang Y, Zhuang S, Han H, Shi X, Jin Z, Hui Y, Gu Y. Synthetic viability induces resistance to immune checkpoint inhibitors in cancer cells. Br J Cancer 2023; 129:1339-1349. [PMID: 37620409 PMCID: PMC10575993 DOI: 10.1038/s41416-023-02404-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: 02/03/2023] [Revised: 08/05/2023] [Accepted: 08/11/2023] [Indexed: 08/26/2023] Open
Abstract
BACKGROUND Immune checkpoint inhibitors (ICI) have revolutionized the treatment for multiple cancers. However, most of patients encounter resistance. Synthetic viability (SV) between genes could induce resistance. In this study, we established SV signature to predict the efficacy of ICI treatment for melanoma. METHODS We collected features and predicted SV gene pairs by random forest classifier. This work prioritized SV gene pairs based on CRISPR/Cas9 screens. SV gene pairs signature were constructed to predict the response to ICI for melanoma patients. RESULTS This study predicted robust SV gene pairs based on 14 features. Filtered by CRISPR/Cas9 screens, we identified 1,861 SV gene pairs, which were also related with prognosis across multiple cancer types. Next, we constructed the six SV pairs signature to predict resistance to ICI for melanoma patients. This study applied the six SV pairs signature to divide melanoma patients into high-risk and low-risk. High-risk melanoma patients were associated with worse response after ICI treatment. Immune landscape analysis revealed that high-risk melanoma patients had lower natural killer cells and CD8+ T cells infiltration. CONCLUSIONS In summary, the 14 features classifier accurately predicted robust SV gene pairs for cancer. The six SV pairs signature could predict resistance to ICI.
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Affiliation(s)
- Mingyue Liu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Qi Dong
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Bo Chen
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Kaidong Liu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhangxiang Zhao
- The Sino-Russian Medical Research Center of Jinan University, The Institute of Chronic Disease of Jinan University, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yuquan Wang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuping Zhuang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Huiming Han
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xingyang Shi
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zixin Jin
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yang Hui
- Department of Biochemistry and Molecular Biology, Harbin Medical University, Harbin, China.
| | - Yunyan Gu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
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17
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Naqash AR, McCallen JD, Mi E, Iivanainen S, Marie MA, Gramenitskaya D, Clark J, Koivunen JP, Macherla S, Jonnalagadda S, Polsani S, Jiwani RA, Hafiz M, Muzaffar M, Brunetti L, Stroud CRG, Walker PR, Wang K, Chung Y, Ruppin E, Lee SH, Yang LV, Pinato DJ, Lee JS, Cortellini A. Increased interleukin-6/C-reactive protein levels are associated with the upregulation of the adenosine pathway and serve as potential markers of therapeutic resistance to immune checkpoint inhibitor-based therapies in non-small cell lung cancer. J Immunother Cancer 2023; 11:e007310. [PMID: 37852738 PMCID: PMC10603340 DOI: 10.1136/jitc-2023-007310] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/13/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Systemic immune activation, hallmarked by C-reactive protein (CRP) and interleukin-6 (IL-6), can modulate antitumor immune responses. In this study, we evaluated the role of IL-6 and CRP in the stratification of patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs). We also interrogated the underlying immunosuppressive mechanisms driven by the IL-6/CRP axis. METHODS In cohort A (n=308), we estimated the association of baseline CRP with objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) in patients with NSCLC treated with ICIs alone or with chemo-immunotherapy (Chemo-ICI). Baseline tumor bulk RNA sequencing (RNA-seq) of lung adenocarcinomas (LUADs) treated with pembrolizumab (cohort B, n=59) was used to evaluate differential expression of purine metabolism, as well as correlate IL-6 expression with PFS. CODEFACS approach was applied to deconvolve cohort B to characterize the tumor microenvironment by reconstructing the cell-type-specific transcriptome from bulk expression. Using the LUAD cohort from The Cancer Genome Atlas (TCGA) we explored the correlation between IL-6 expression and adenosine gene signatures. In a third cohort (cohort C, n=18), plasma concentrations of CRP, adenosine 2a receptor (A2aR), and IL-6 were measured using ELISA. RESULTS In cohort A, 67.2% of patients had a baseline CRP≥10 mg/L (CRP-H). Patients with CRP-H achieved shorter OS (8.6 vs 14.8 months; p=0.006), shorter PFS (3.3 vs 6.6 months; p=0.013), and lower ORR (24.7% vs 46.3%; p=0.015). After adjusting for relevant clinical variables, CRP-H was confirmed as an independent predictor of increased risk of death (HR 1.51, 95% CI: 1.09 to 2.11) and lower probability of achieving disease response (OR 0.34, 95% CI: 0.13 to 0.89). In cohort B, RNA-seq analysis demonstrated higher IL-6 expression on tumor cells of non-responders, along with a shorter PFS (p<0.05) and enrichment of the purinergic pathway. Within the TCGA LUAD cohort, tumor IL-6 expression strongly correlated with the adenosine signature (R=0.65; p<2.2e-16). Plasma analysis in cohort C demonstrated that CRP-H patients had a greater median baseline level of A2aR (6.0 ng/mL vs 1.3 ng/mL; p=0.01). CONCLUSIONS This study demonstrates CRP as a readily available blood-based prognostic biomarker in ICI-treated NSCLC. Additionally, we elucidate a potential link of the CRP/IL-6 axis with the immunosuppressive adenosine signature pathway that could drive inferior outcomes to ICIs in NSCLC and also offer novel therapeutic avenues.
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Affiliation(s)
- Abdul Rafeh Naqash
- Stephenson Cancer Center, The University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
- Hematology / Oncology Division, East Carolina University, Greenville, South Carolina, USA
| | - Justin D McCallen
- Department of Internal Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Brody School of Medicine, East Carolina University, Greenville, NC, USA
| | - Emma Mi
- Department of Surgery and Cancer, Hammersmith Hospital Campus, Imperial College London, London, UK
| | - Sanna Iivanainen
- Oncology and Radiation Department, Oulu University Hospital, University of Oulu, MRC Oulu, Oulu, Finland
| | - Mona A Marie
- Hematology / Oncology Division, East Carolina University, Greenville, South Carolina, USA
| | - Daria Gramenitskaya
- Department of Surgery and Cancer, Hammersmith Hospital Campus, Imperial College London, London, UK
| | - James Clark
- Department of Surgery and Cancer, Hammersmith Hospital Campus, Imperial College London, London, UK
| | - Jussi Pekka Koivunen
- Oncology and Radiation Department, Oulu University Hospital, University of Oulu, MRC Oulu, Oulu, Finland
| | - Shravanti Macherla
- Hematology / Oncology Division, East Carolina University, Greenville, South Carolina, USA
| | - Sweta Jonnalagadda
- Hematology / Oncology Division, East Carolina University, Greenville, South Carolina, USA
| | - Shanker Polsani
- Hematology / Oncology Division, East Carolina University, Greenville, South Carolina, USA
| | - Rahim Ali Jiwani
- Department of Internal Medicine, East Carolina University, Greenville, NC, USA
| | - Maida Hafiz
- Division of Pulmonary Critical Care, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
- Division of Pulmonary and Critical Care, East Carolina University, Greenville, NC, USA
| | - Mahvish Muzaffar
- Hematology / Oncology Division, East Carolina University, Greenville, South Carolina, USA
| | - Leonardo Brunetti
- Operative Research Unit of Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, Roma, Italy, Italy
| | | | - Paul R Walker
- Hematology / Oncology Division, East Carolina University, Greenville, South Carolina, USA
- Circulogene, Birmingham, Alabama, USA
| | - Kun Wang
- Cancer Data Science Lab, National Cancer Institute, National Institute of Health, Bethesda, Maryland, USA
| | - Youngmin Chung
- Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Reuplic of Korea
| | - Eytan Ruppin
- Cancer Data Science Lab, National Cancer Institute, National Institute of Health, Bethesda, Maryland, USA
| | - Se-Hoon Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Health Sciences and Technology, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Li V Yang
- Hematology / Oncology Division, East Carolina University, Greenville, South Carolina, USA
| | - David J Pinato
- Department of Surgery and Cancer, Hammersmith Hospital Campus, Imperial College London, London, UK
- Division of Oncology, Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy
| | - Joo Sang Lee
- Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Reuplic of Korea
- Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Digital Health, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Alessio Cortellini
- Department of Surgery and Cancer, Hammersmith Hospital Campus, Imperial College London, London, UK
- Operative Research Unit of Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, Roma, Italy, Italy
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18
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Payá-Milans M, Peña-Chilet M, Loucera C, Esteban-Medina M, Dopazo J. Functional Profiling of Soft Tissue Sarcoma Using Mechanistic Models. Int J Mol Sci 2023; 24:14732. [PMID: 37834179 PMCID: PMC10572617 DOI: 10.3390/ijms241914732] [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: 08/10/2023] [Revised: 09/08/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023] Open
Abstract
Soft tissue sarcoma is an umbrella term for a group of rare cancers that are difficult to treat. In addition to surgery, neoadjuvant chemotherapy has shown the potential to downstage tumors and prevent micrometastases. However, finding effective therapeutic targets remains a research challenge. Here, a previously developed computational approach called mechanistic models of signaling pathways has been employed to unravel the impact of observed changes at the gene expression level on the ultimate functional behavior of cells. In the context of such a mechanistic model, RNA-Seq counts sourced from the Recount3 resource, from The Cancer Genome Atlas (TCGA) Sarcoma project, and non-diseased sarcomagenic tissues from the Genotype-Tissue Expression (GTEx) project were utilized to investigate signal transduction activity through signaling pathways. This approach provides a precise view of the relationship between sarcoma patient survival and the signaling landscape in tumors and their environment. Despite the distinct regulatory alterations observed in each sarcoma subtype, this study identified 13 signaling circuits, or elementary sub-pathways triggering specific cell functions, present across all subtypes, belonging to eight signaling pathways, which served as predictors for patient survival. Additionally, nine signaling circuits from five signaling pathways that highlighted the modifications tumor samples underwent in comparison to normal tissues were found. These results describe the protective role of the immune system, suggesting an anti-tumorigenic effect in the tumor microenvironment, in the process of tumor cell detachment and migration, or the dysregulation of ion homeostasis. Also, the analysis of signaling circuit intermediary proteins suggests multiple strategies for therapy.
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Affiliation(s)
- Miriam Payá-Milans
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain; (M.P.-M.); (M.P.-C.); (C.L.); (M.E.-M.)
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, 41013 Seville, Spain
- Institute of Biomedicine of Seville, IBiS/University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Sevilla, Spain
| | - María Peña-Chilet
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain; (M.P.-M.); (M.P.-C.); (C.L.); (M.E.-M.)
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, 41013 Seville, Spain
- Institute of Biomedicine of Seville, IBiS/University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Sevilla, Spain
| | - Carlos Loucera
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain; (M.P.-M.); (M.P.-C.); (C.L.); (M.E.-M.)
- Institute of Biomedicine of Seville, IBiS/University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Sevilla, Spain
| | - Marina Esteban-Medina
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain; (M.P.-M.); (M.P.-C.); (C.L.); (M.E.-M.)
- Institute of Biomedicine of Seville, IBiS/University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Sevilla, Spain
| | - Joaquín Dopazo
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain; (M.P.-M.); (M.P.-C.); (C.L.); (M.E.-M.)
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, 41013 Seville, Spain
- Institute of Biomedicine of Seville, IBiS/University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Sevilla, Spain
- FPS/ELIXIR-ES, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocío, 41013 Sevilla, Spain
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19
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Hoang DT, Dinstag G, Hermida LC, Ben-Zvi DS, Elis E, Caley K, Sammut SJ, Sinha S, Sinha N, Dampier CH, Stossel C, Patil T, Rajan A, Lassoued W, Strauss J, Bailey S, Allen C, Redman J, Beker T, Jiang P, Golan T, Wilkinson S, Sowalsky AG, Pine SR, Caldas C, Gulley JL, Aldape K, Aharonov R, Stone EA, Ruppin E. Prediction of cancer treatment response from histopathology images through imputed transcriptomics. RESEARCH SQUARE 2023:rs.3.rs-3193270. [PMID: 37790315 PMCID: PMC10543028 DOI: 10.21203/rs.3.rs-3193270/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Our key contribution is showing that ENLIGHT-DeepPT successfully predicts true responders in five independent patients' cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, which needs to be trained and tested on the same cohort. ENLIGHT-DeepPT future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries.
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Affiliation(s)
- Danh-Tai Hoang
- Biological Data Science Institute, College of Science, Australian National University, Canberra, ACT, Australia
| | | | - Leandro C. Hermida
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA, USA
- Tumor Microenvironment Center, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | - Katherine Caley
- Biological Data Science Institute, College of Science, Australian National University, Canberra, ACT, Australia
| | - Stephen-John Sammut
- Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, United Kingdom
- The Royal Marsden Hospital NHS Foundation Trust, London, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - Sanju Sinha
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Neelam Sinha
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Christopher H. Dampier
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Chani Stossel
- Oncology Institute, Sheba Medical Center at Tel-Hashomer, Tel Aviv University, Tel Aviv, Israel
| | - Tejas Patil
- Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Arun Rajan
- Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Wiem Lassoued
- Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Julius Strauss
- Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Shania Bailey
- Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Clint Allen
- Surgical Oncology Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Jason Redman
- Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | | | - Peng Jiang
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Talia Golan
- Oncology Institute, Sheba Medical Center at Tel-Hashomer, Tel Aviv University, Tel Aviv, Israel
| | - Scott Wilkinson
- Laboratory of Genitourinary Cancer Pathogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Adam G. Sowalsky
- Laboratory of Genitourinary Cancer Pathogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Sharon R. Pine
- Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Carlos Caldas
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - James L. Gulley
- Genitourinary Malignancy Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Kenneth Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | | | - Eric A. Stone
- Biological Data Science Institute, College of Science, Australian National University, Canberra, ACT, Australia
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
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20
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Wu L, Jin Y, Zhao X, Tang K, Zhao Y, Tong L, Yu X, Xiong K, Luo C, Zhu J, Wang F, Zeng Z, Pan D. Tumor aerobic glycolysis confers immune evasion through modulating sensitivity to T cell-mediated bystander killing via TNF-α. Cell Metab 2023; 35:1580-1596.e9. [PMID: 37506695 DOI: 10.1016/j.cmet.2023.07.001] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 05/09/2023] [Accepted: 07/02/2023] [Indexed: 07/30/2023]
Abstract
Metabolic reprogramming toward glycolysis is a hallmark of cancer malignancy. The molecular mechanisms by which the tumor glycolysis pathway promotes immune evasion remain to be elucidated. Here, by performing genome-wide CRISPR screens in murine tumor cells co-cultured with cytotoxic T cells (CTLs), we identified that deficiency of two important glycolysis enzymes, Glut1 (glucose transporter 1) and Gpi1 (glucose-6-phosphate isomerase 1), resulted in enhanced killing of tumor cells by CTLs. Mechanistically, Glut1 inactivation causes metabolic rewiring toward oxidative phosphorylation, which generates an excessive amount of reactive oxygen species (ROS). Accumulated ROS potentiate tumor cell death mediated by tumor necrosis factor alpha (TNF-α) in a caspase-8- and Fadd-dependent manner. Genetic and pharmacological inactivation of Glut1 sensitizes tumors to anti-tumor immunity and synergizes with anti-PD-1 therapy through the TNF-α pathway. The mechanistic interplay between tumor-intrinsic glycolysis and TNF-α-induced killing provides new therapeutic strategies to enhance anti-tumor immunity.
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Affiliation(s)
- Lijian Wu
- Department of Basic Medical Sciences, Tsinghua University, Beijing 100084, China
| | - Yiteng Jin
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100084, China
| | - Xi Zhao
- Department of Basic Medical Sciences, Tsinghua University, Beijing 100084, China
| | - Kaiyang Tang
- Department of Basic Medical Sciences, Tsinghua University, Beijing 100084, China
| | - Yaoning Zhao
- Department of Basic Medical Sciences, Tsinghua University, Beijing 100084, China
| | - Linjie Tong
- Department of Basic Medical Sciences, Tsinghua University, Beijing 100084, China
| | - Xuerong Yu
- Department of Basic Medical Sciences, Tsinghua University, Beijing 100084, China
| | - Ke Xiong
- Department of Basic Medical Sciences, Tsinghua University, Beijing 100084, China
| | - Ce Luo
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100084, China
| | - Jiajun Zhu
- Department of Basic Medical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Science (CLS), Beijing 100084, China
| | - Fubing Wang
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan 430071, China.
| | - Zexian Zeng
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100084, China; Tsinghua-Peking Center for Life Science (CLS), Beijing 100084, China.
| | - Deng Pan
- Department of Basic Medical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Science (CLS), Beijing 100084, China.
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21
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Lu X, Chen G, Li J, Hu X, Sun F. MAGCN: A Multiple Attention Graph Convolution Networks for Predicting Synthetic Lethality. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2681-2689. [PMID: 36374879 DOI: 10.1109/tcbb.2022.3221736] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Synthetic lethality (SL) is a potential cancer therapeutic strategy and drug discovery. Computational approaches to identify synthetic lethality genes have become an effective complement to wet experiments which are time consuming and costly. Graph convolutional networks (GCN) has been utilized to such prediction task as be good at capturing the neighborhood dependency in a graph. However, it is still a lack of the mechanism of aggregating the complementary neighboring information from various heterogeneous graphs. Here, we propose the Multiple Attention Graph Convolution Networks for predicting synthetic lethality (MAGCN). First, we obtain the functional similarity features and topological structure features of genes from different data sources respectively, such as Gene Ontology data and Protein-Protein Interaction. Then, graph convolutional network is utilized to accumulate the knowledge from neighbor nodes according to synthetic lethal associations. Meanwhile, we propose a multiple graphs attention model and construct a multiple graphs attention network to learn the contribution factors of different graphs to generate embedded representation by aggregating these graphs. Finally, the generated feature matrix is decoded to predict potential synthetic lethal interaction. Experimental results show that MAGCN is superior to other baseline methods. Case study demonstrates the ability of MAGCN to predict human SL gene pairs.
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22
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Staheli JP, Neal ML, Navare A, Mast FD, Aitchison JD. Predicting host-based, synthetic lethal antiviral targets from omics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.15.553430. [PMID: 37645861 PMCID: PMC10462099 DOI: 10.1101/2023.08.15.553430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Traditional antiviral therapies often have limited effectiveness due to toxicity and development of drug resistance. Host-based antivirals, while an alternative, may lead to non-specific effects. Recent evidence shows that virus-infected cells can be selectively eliminated by targeting synthetic lethal (SL) partners of proteins disrupted by viral infection. Thus, we hypothesized that genes depleted in CRISPR KO screens of virus-infected cells may be enriched in SL partners of proteins altered by infection. To investigate this, we established a computational pipeline predicting SL drug targets of viral infections. First, we identified SARS-CoV-2-induced changes in gene products via a large compendium of omics data. Second, we identified SL partners for each altered gene product. Last, we screened CRISPR KO data for SL partners required for cell viability in infected cells. Despite differences in virus-induced alterations detected by various omics data, they share many predicted SL targets, with significant enrichment in CRISPR KO-depleted datasets. Comparing data from SARS-CoV-2 and influenza infections, we found possible broad-spectrum, host-based antiviral SL targets. This suggests that CRISPR KO data are replete with common antiviral targets due to their SL relationship with virus-altered states and that such targets can be revealed from analysis of omics datasets and SL predictions.
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Affiliation(s)
- Jeannette P. Staheli
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, 98101, USA
| | - Maxwell L. Neal
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, 98101, USA
| | - Arti Navare
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, 98101, USA
| | - Fred D. Mast
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, 98101, USA
| | - John D. Aitchison
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, 98101, USA
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23
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Wang Q, Jiang S, Wu Y, Zhang Y, Huang M, Qiu Y, Luo X. Prognostic and clinicopathological role of RACK1 for cancer patients: a systematic review and meta-analysis. PeerJ 2023; 11:e15873. [PMID: 37601269 PMCID: PMC10434108 DOI: 10.7717/peerj.15873] [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: 04/21/2023] [Accepted: 07/18/2023] [Indexed: 08/22/2023] Open
Abstract
Background The receptor for activated C kinase 1 (RACK1) expression is associated with clinicopathological characteristics and the prognosis of various cancers; however, the conclusions are controversial. As a result, this study aimed to explore the clinicopathological and prognostic values of RACK1 expression in patients with cancer. Methodology PubMed, Embase, Web of Science, Cochrane Library, and Scopus were comprehensively explored from their inception to April 20, 2023, for selecting studies on the clinicopathological and prognostic role of RACK1 in patients with cancer that met the criteria for inclusion in this review. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were used to assess the prognosis-predictive value of RACK1 expression, while pooled odds ratios (ORs) and 95% CIs were used to evaluate the correlation between RACK1 expression and the clinicopathological characteristics of patients with cancer. The quality of the included studies was evaluated using the Newcastle-Ottawa Scale. Results Twenty-two studies (13 on prognosis and 20 on clinicopathological characteristics) were included in this systematic review and meta-analysis. The findings indicated that high RACK1 expression was significantly associated with poor overall survival (HR = 1.62; 95% CI, 1.13-2.33; P = 0.009; I2 = 89%) and reversely correlated with disease-free survival/recurrence-free survival (HR = 1.87; 95% CI, 1.22-2.88; P = 0.004; I2 = 0%). Furthermore, increased RACK1 expression was significantly associated with lymphatic invasion/N+ stage (OR = 1.74; 95% CI, 1.04-2.90; P = 0.04; I2 = 79%) of tumors. Conclusions RACK1 may be a global predictive marker of poor prognosis in patients with cancer and unfavorable clinicopathological characteristics. However, further clinical studies are required to validate these findings.
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Affiliation(s)
- Qiuhao Wang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Sixin Jiang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Yuqi Wu
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - You Zhang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Mei Huang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Yan Qiu
- Laboratory of Pathology, Clinical Research Center for Breast, Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaobo Luo
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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24
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Li C, Zhang B, Schaafsma E, Reuben A, Wang L, Turk MJ, Zhang J, Cheng C. TimiGP: Inferring cell-cell interactions and prognostic associations in the tumor immune microenvironment through gene pairs. Cell Rep Med 2023; 4:101121. [PMID: 37467716 PMCID: PMC10394258 DOI: 10.1016/j.xcrm.2023.101121] [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: 12/02/2022] [Revised: 04/11/2023] [Accepted: 06/21/2023] [Indexed: 07/21/2023]
Abstract
Determining the prognostic association of different immune cell types in the tumor microenvironment is critical for understanding cancer biology and developing new therapeutic strategies. However, this is challenging in certain cancer types, where the abundance of different immune subsets is highly correlated. In this study, we develop a computational method named TimiGP to overcome this challenge. Based on bulk gene expression and survival data, TimiGP infers cell-cell interactions that reveal the association between immune cell relative abundance and prognosis. As demonstrated in metastatic melanoma, TimiGP prioritizes immune cells critical in prognosis based on the identified cell-cell interactions. Highly consistent results are obtained by TimiGP when applied to seven independent melanoma datasets and when different cell-type marker sets are used as inputs. Additionally, TimiGP can leverage single-cell RNA sequencing data to delineate the tumor immune microenvironment at high resolutions across a wide range of cancer types.
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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
| | - Baoyi Zhang
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX 77030, USA
| | - Evelien Schaafsma
- Department of Microbiology and Immunology, Dartmouth College, Hanover, NH 03755, 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
| | - Mary Jo Turk
- Department of Microbiology and Immunology, The Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA; Norris Cotton Cancer Center, The Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, 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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25
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Kwon EJ, Mashelkar KK, Seo J, Shin YZ, Sung K, Jang SC, Cheon SW, Lee H, Lee HW, Kim G, Han BW, Lee SK, Jeong LS, Cha HJ. In Silico Discovery of 5'-Modified 7-Deoxy-7-ethynyl-4'-thioadenosine as a HASPIN Inhibitor and Its Synergistic Anticancer Effect with the PLK1 Inhibitor. ACS CENTRAL SCIENCE 2023; 9:1140-1149. [PMID: 37396870 PMCID: PMC10311661 DOI: 10.1021/acscentsci.3c00332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Indexed: 07/04/2023]
Abstract
Despite genetic perturbations resulting in embryo lethality for most mitotic kinases, loss of the histone H3 mitotic kinase HASPIN reveals no adverse effect in mice models, establishing HASPIN as a promising target for anticancer therapy. However, developing a HASPIN inhibitor from conventional pharmacophores poses a technical challenge as this atypical kinase shares slight similarities with eukaryotic protein kinases. Chemically modifying a cytotoxic 4'-thioadenosine analogue through high genotoxicity yielded several novel nongenotoxic kinase inhibitors. In silico apporoaches utilizing transcriptomic and chemical similarities with known compounds and KINOMEscan profiles unveiled the HASPIN inhibitor LJ4827. LJ4827's specificity and potency as a HASPIN inhibitor were verified through in vitro kinase assay and X-ray crystallography. HASPIN inhibition by LJ4827 reduced histone H3 phosphorylation and impeded Aurora B recruitment in cancer cell centromeres but not in noncancer cells. Through transcriptome analysis of lung cancer patients, PLK1 was determined as a druggable synergistic partner to complement HASPIN inhibition. Chemical or genetic PLK1 perturbation with LJ4827 effectuated pronounced lung cancer cytotoxicity in vitro and in vivo. Therefore, LJ4827 is a novel anticancer therapeutic for selectively impeding cancer mitosis through potent HASPIN inhibition, and simultaneous HASPIN and PLK1 interference is a promising therapeutic strategy for lung cancer.
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Affiliation(s)
- Eun-Ji Kwon
- College
of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
| | | | - Juhee Seo
- College
of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
| | - Yoon-Ze Shin
- College
of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
| | - Kisu Sung
- College
of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
| | - Sung Chul Jang
- College
of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
- Natural Products
Research Institute, Seoul National University, Seoul 08826, Republic of Korea
| | - Sang Won Cheon
- College
of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
| | - Haeseung Lee
- College
of Pharmacy, Pusan National University, Busan 46241, Republic of Korea
- Research
Institute for Drug Development, Pusan National
University, Busan 46241, Republic
of Korea
| | - Hyuk Woo Lee
- Future
Medicine Company, Limited, Seongnam, Gyeonggi-do 13449, Republic of Korea
| | - Gyudong Kim
- College
of Pharmacy, and Research Institute of Drug Development, Chonnam National University, Gwangju 61469, Republic of Korea
| | - Byung Woo Han
- College
of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
- Research
Institute of Pharmaceutical Sciences, Seoul
National University, Seoul 08826, Republic
of Korea
| | - Sang Kook Lee
- College
of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
- Natural Products
Research Institute, Seoul National University, Seoul 08826, Republic of Korea
| | - Lak Shin Jeong
- College
of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
- Research
Institute of Pharmaceutical Sciences, Seoul
National University, Seoul 08826, Republic
of Korea
- Future
Medicine Company, Limited, Seongnam, Gyeonggi-do 13449, Republic of Korea
| | - Hyuk-Jin Cha
- College
of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
- Research
Institute of Pharmaceutical Sciences, Seoul
National University, Seoul 08826, Republic
of Korea
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26
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Piper M, Kluger H, Ruppin E, Hu-Lieskovan S. Immune Resistance Mechanisms and the Road to Personalized Immunotherapy. Am Soc Clin Oncol Educ Book 2023; 43:e390290. [PMID: 37459578 DOI: 10.1200/edbk_390290] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
What does the future of cancer immunotherapy look like and how do we get there? Find out where we've been and where we're headed in A Report on Resistance: The Road to personalized immunotherapy.
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Affiliation(s)
- Miles Piper
- School of Medicine, University of Utah, Salt Lake City, UT
| | | | - Eytan Ruppin
- Center for Cancer Research, National Cancer Institute, Bethesda, MD
| | - Siwen Hu-Lieskovan
- School of Medicine, University of Utah, Salt Lake City, UT
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT
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27
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Wiecek AJ, Cutty SJ, Kornai D, Parreno-Centeno M, Gourmet LE, Tagliazucchi GM, Jacobson DH, Zhang P, Xiong L, Bond GL, Barr AR, Secrier M. Genomic hallmarks and therapeutic implications of G0 cell cycle arrest in cancer. Genome Biol 2023; 24:128. [PMID: 37221612 PMCID: PMC10204193 DOI: 10.1186/s13059-023-02963-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 05/07/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Therapy resistance in cancer is often driven by a subpopulation of cells that are temporarily arrested in a non-proliferative G0 state, which is difficult to capture and whose mutational drivers remain largely unknown. RESULTS We develop methodology to robustly identify this state from transcriptomic signals and characterise its prevalence and genomic constraints in solid primary tumours. We show that G0 arrest preferentially emerges in the context of more stable, less mutated genomes which maintain TP53 integrity and lack the hallmarks of DNA damage repair deficiency, while presenting increased APOBEC mutagenesis. We employ machine learning to uncover novel genomic dependencies of this process and validate the role of the centrosomal gene CEP89 as a modulator of proliferation and G0 arrest capacity. Lastly, we demonstrate that G0 arrest underlies unfavourable responses to various therapies exploiting cell cycle, kinase signalling and epigenetic mechanisms in single-cell data. CONCLUSIONS We propose a G0 arrest transcriptional signature that is linked with therapeutic resistance and can be used to further study and clinically track this state.
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Affiliation(s)
- Anna J. Wiecek
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Stephen J. Cutty
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - Daniel Kornai
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Mario Parreno-Centeno
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Lucie E. Gourmet
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, UK
| | | | - Daniel H. Jacobson
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, UK
- UCL Cancer Institute, Paul O’Gorman Building, University College London, London, UK
| | - Ping Zhang
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Lingyun Xiong
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Gareth L. Bond
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, UK
| | - Alexis R. Barr
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
- Cell Cycle Control Team, MRC London Institute of Medical Sciences (LMS), London, UK
| | - Maria Secrier
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, UK
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28
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Gomari D, Achkar IW, Benedetti E, Tabling J, Halama A, Krumsiek J. piTracer - Automatic reconstruction of molecular cascades for the identification of synergistic drug targets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.08.535933. [PMID: 37066188 PMCID: PMC10104160 DOI: 10.1101/2023.04.08.535933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Cancer cells frequently undergo metabolic reprogramming as a mechanism of resistance against chemotherapeutic drugs. Metabolomic profiling provides a direct readout of metabolic changes and can thus be used to identify these tumor escape mechanisms. Here, we introduce piTracer, a computational tool that uses multi-scale molecular networks to identify potential combination therapies from pre- and post-treatment metabolomics data. We first demonstrate piTracer’s core ability to reconstruct cellular cascades by inspecting well-characterized molecular pathways and previously studied associations between genetic variants and metabolite levels. We then apply a new gene ranking algorithm on differential metabolomic profiles from human breast cancer cells after glutaminase inhibition. Four of the automatically identified gene targets were experimentally tested by simultaneous inhibition of the respective targets and glutaminase. Of these combination treatments, two were be confirmed to induce synthetic lethality in the cell line. In summary, piTracer integrates the molecular monitoring of escape mechanisms into comprehensive pathway networks to accelerate drug target identification. The tool is open source and can be accessed at https://github.com/krumsieklab/pitracer .
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29
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Li X, Wei S, Deng L, Tao H, Liu M, Zhao Z, Du X, Li Y, Hou J. Sex-biased molecular differences in lung adenocarcinoma are ethnic and smoking specific. BMC Pulm Med 2023; 23:99. [PMID: 36964522 PMCID: PMC10039609 DOI: 10.1186/s12890-023-02387-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 03/14/2023] [Indexed: 03/26/2023] Open
Abstract
BACKGROUND Sex-related differences in cancer epidemiology, tumor biology, immune system activity, and pharmacogenomics have been suggested to be important considerations for precision cancer control. Here we elucidated systematically sex biases in genetic variants, gene expression profiles, and immunological landscapes of lung adenocarcinoma patients (LUADs) with different ancestry and smoking status. METHODS Somatic mutation and mRNA expression data of Asian and Non-Asian LUADs were obtained from public databases. Sex-biased genetic mutations, gene expression, biological pathways, and immune infiltration were identified in the context of smoking status and race. RESULTS Among nonsmokers, male-biased mutations were prevalent in Asian LUADs, while few sex-biased mutations were detected in Non-Asian LUADs. EGFR was the only mutation whose frequency was significantly higher in females than males in both Asian and Non-Asian nonsmokers. More genes exhibited sex-biased expression in Non-Asian LUADs compared to Asian LUADs. Moreover, genes distinctly expressed in females were mainly related to immune-related pathways, whereas those in males were more involved in activation of DNA repair, E2F_targets, and MYC_targets pathways. We also detected sex-specific immune infiltration in the context of genetic variation. In EGFR-mutant LUADs, males had a significantly increased infiltration of CD8 + T cells, whereas resting CD4 + memory T cells were more abundant in females. Additionally, in KRAS-mutant LUADs, CD8 + and CD4 + T cells were more abundant in females than males. In addition, we detected all female patients with high SCGB3A2 expression were exclusively sensitive to immunotherapy, while this phenomenon was not observed in male patients. CONCLUSIONS Our findings provided evidence that sex-related molecular and cellular components are involved in shaping tumor distinct genetic and immune features, which might have important impact on personalized targeted and immune therapy.
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Affiliation(s)
- Xuetao Li
- Department of Oncology, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430070, China
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
| | - Shuquan Wei
- Department of Pulmonary and Critical Care Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China
| | - Liaoyuan Deng
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
| | - HongYan Tao
- Department of Pulmonary Diseases, The Second Affiliated Hospital of Lanzhou University, Lanzhou, 730000, Gansu, China
| | - Mingkai Liu
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
| | - Ziwen Zhao
- Department of Pulmonary and Critical Care Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China
| | - Xin Du
- Department of Hematology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China.
| | - Yujun Li
- Department of Pulmonary and Critical Care Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China.
| | - Jun Hou
- Center for Medical Research On Innovation and Translation, Institute of Clinical Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China.
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30
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Silvestri M, Nghia Vu T, Nichetti F, Niger M, Di Cosimo S, De Braud F, Pruneri G, Pawitan Y, Calza S, Cappelletti V. Comprehensive transcriptomic analysis to identify biological and clinical differences in cholangiocarcinoma. Cancer Med 2023; 12:10156-10168. [PMID: 36938752 PMCID: PMC10166943 DOI: 10.1002/cam4.5719] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/07/2023] [Accepted: 02/09/2023] [Indexed: 03/21/2023] Open
Abstract
BACKGROUND Cholangiocarcinoma (CC) is a rare and aggressive disease with limited therapeutic options and a poor prognosis. All available public records of cohorts reporting transcriptomic data on intrahepatic cholangiocarcinoma (ICC) and extrahepatic cholangiocarcinoma (ECC) were collected with the aim to provide a comprehensive gene expression-based classification with clinical relevance. METHODS A total of 543 patients with primary tumor tissues profiled by RNAseq and microarray platforms from seven public datasets were used as a discovery set to identify distinct biological subgroups. Group predictors developed on the discovery sets were applied to a single cohort of 131 patients profiled with RNAseq for validation and assessment of clinical relevance leveraging machine learning techniques. RESULTS By unsupervised clustering analysis of gene expression data we identified both in the ICC and ECC discovery datasets four subgroups characterized by a distinct type of immune infiltrate and signaling pathways. We next developed class predictors using short gene list signatures and identified in an independent dataset subgroups of ICC tumors at different prognosis. CONCLUSIONS The developed class-predictor allows identification of CC subgroups with specific biological features and clinical behavior at single-sample level. Such results represent the starting point for a complete molecular characterization of CC, including integration of genomics data to develop in clinical practice.
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Affiliation(s)
- Marco Silvestri
- Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.,Unit of Biostatistics, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Trung Nghia Vu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Federico Nichetti
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.,Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Monica Niger
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Serena Di Cosimo
- Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Filippo De Braud
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Giancarlo Pruneri
- Department Pathology and Laboratory Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Yudi Pawitan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Stefano Calza
- Unit of Biostatistics, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Vera Cappelletti
- Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
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31
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Zhao L, Qi X, Chen Y, Qiao Y, Bu D, Wu Y, Luo Y, Wang S, Zhang R, Zhao Y. Biological knowledge graph-guided investigation of immune therapy response in cancer with graph neural network. Brief Bioinform 2023; 24:6995380. [PMID: 36682018 DOI: 10.1093/bib/bbad023] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 12/18/2022] [Accepted: 01/07/2023] [Indexed: 01/23/2023] Open
Abstract
The determination of transcriptome profiles that mediate immune therapy in cancer remains a major clinical and biological challenge. Despite responses induced by immune-check points inhibitors (ICIs) in diverse tumor types and all the big breakthroughs in cancer immunotherapy, most patients with solid tumors do not respond to ICI therapies. It still remains a big challenge to predict the ICI treatment response. Here, we propose a framework with multiple prior knowledge networks guided for immune checkpoints inhibitors prediction-DeepOmix-ICI (or ICInet for short). ICInet can predict the immune therapy response by leveraging geometric deep learning and prior biological knowledge graphs of gene-gene interactions. Here, we demonstrate more than 600 ICI-treated patients with ICI response data and gene expression profile to apply on ICInet. ICInet was used for ICI therapy responses prediciton across different cancer types-melanoma, gastric cancer and bladder cancer, which includes 7 cohorts from different data sources. ICInet is able to robustly generalize into multiple cancer types. Moreover, the performance of ICInet in those cancer types can outperform other ICI biomarkers in the clinic. Our model [area under the curve (AUC = 0.85)] generally outperformed other measures, including tumor mutational burden (AUC = 0.62) and programmed cell death ligand-1 score (AUC = 0.74). Therefore, our study presents a prior-knowledge guided deep learning method to effectively select immunotherapy-response-associated biomarkers, thereby improving the prediction of immunotherapy response for precision oncology.
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Affiliation(s)
- Lianhe Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Xiaoning Qi
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Chen
- The First People's Hospital of Yunnan Province, Kunming, 650032, Yunnan, China
| | - Yixuan Qiao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dechao Bu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Yang Wu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Yufan Luo
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Sheng Wang
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Yi Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
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32
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Tsakiroglou M, Evans A, Pirmohamed M. Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis. Front Genet 2023; 14:1100352. [PMID: 36968610 PMCID: PMC10036914 DOI: 10.3389/fgene.2023.1100352] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 02/20/2023] [Indexed: 03/12/2023] Open
Abstract
Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.
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Affiliation(s)
- Maria Tsakiroglou
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- *Correspondence: Maria Tsakiroglou,
| | - Anthony Evans
- Computational Biology Facility, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Munir Pirmohamed
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
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33
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Nair NU, Jiang Q, Wei JS, Misra VA, Morrow B, Kesserwan C, Hermida LC, Lee JS, Mian I, Zhang J, Lebensohn A, Miettinen M, Sengupta M, Khan J, Ruppin E, Hassan R. Genomic and transcriptomic analyses identify a prognostic gene signature and predict response to therapy in pleural and peritoneal mesothelioma. Cell Rep Med 2023; 4:100938. [PMID: 36773602 PMCID: PMC9975319 DOI: 10.1016/j.xcrm.2023.100938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 08/23/2022] [Accepted: 01/19/2023] [Indexed: 02/12/2023]
Abstract
Malignant mesothelioma is an aggressive cancer with limited treatment options and poor prognosis. A better understanding of mesothelioma genomics and transcriptomics could advance therapies. Here, we present a mesothelioma cohort of 122 patients along with their germline and tumor whole-exome and tumor RNA sequencing data as well as phenotypic and drug response information. We identify a 48-gene prognostic signature that is highly predictive of mesothelioma patient survival, including CCNB1, the expression of which is highly predictive of patient survival on its own. In addition, we analyze the transcriptomics data to study the tumor immune microenvironment and identify synthetic-lethality-based signatures predictive of response to therapy. This germline and somatic whole-exome sequencing as well as transcriptomics data from the same patient are a valuable resource to address important biological questions, including prognostic biomarkers and determinants of treatment response in mesothelioma.
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Affiliation(s)
- Nishanth Ulhas Nair
- Cancer Data Science Laboratory, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD 20892, USA
| | - Qun Jiang
- Thoracic and GI Malignancies Branch, CCR, NCI, NIH, Bethesda, MD 20892, USA
| | | | | | - Betsy Morrow
- Thoracic and GI Malignancies Branch, CCR, NCI, NIH, Bethesda, MD 20892, USA
| | | | - Leandro C Hermida
- Cancer Data Science Laboratory, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD 20892, USA; Tumor Microenvironment Center, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joo Sang Lee
- Cancer Data Science Laboratory, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD 20892, USA; School of Medicine and Department of Artificial Intelligence, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Idrees Mian
- Thoracic and GI Malignancies Branch, CCR, NCI, NIH, Bethesda, MD 20892, USA
| | - Jingli Zhang
- Thoracic and GI Malignancies Branch, CCR, NCI, NIH, Bethesda, MD 20892, USA
| | | | | | - Manjistha Sengupta
- Thoracic and GI Malignancies Branch, CCR, NCI, NIH, Bethesda, MD 20892, USA
| | - Javed Khan
- Genetics Branch, CCR, NCI, NIH, Bethesda, MD 20892, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD 20892, USA.
| | - Raffit Hassan
- Thoracic and GI Malignancies Branch, CCR, NCI, NIH, Bethesda, MD 20892, USA.
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34
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Song X, Xiong A, Wu F, Li X, Wang J, Jiang T, Chen P, Zhang X, Zhao Z, Liu H, Cheng L, Zhao C, Wang Z, Pan C, Cui X, Xu T, Luo H, Zhou C. Spatial multi-omics revealed the impact of tumor ecosystem heterogeneity on immunotherapy efficacy in patients with advanced non-small cell lung cancer treated with bispecific antibody. J Immunother Cancer 2023; 11:e006234. [PMID: 36854570 PMCID: PMC9980352 DOI: 10.1136/jitc-2022-006234] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 03/02/2023] Open
Abstract
BACKGROUND Immunotherapy for malignant tumors has made great progress, but many patients do not benefit from it. The complex intratumoral heterogeneity (ITH) hindered the in-depth exploration of immunotherapy. Conventional bulk sequencing has masked intratumor complexity, preventing a more detailed discovery of the impact of ITH on treatment efficacy. Hence, we initiated this study to explore ITH at the multi-omics spatial level and to seek prognostic biomarkers of immunotherapy efficacy considering the presence of ITH. METHODS Using the segmentation strategy of digital spatial profiling (DSP), we obtained differential information on tumor and stromal regions at the proteomic and transcriptomic levels. Based on the consideration of ITH, signatures constructed by candidate proteins in different regions were used to predict the efficacy of immunotherapy. RESULTS Eighteen patients treated with a bispecific antibody (bsAb)-KN046 were enrolled in this study. The tumor and stromal areas of the same samples exhibited distinct features. Signatures consisting of 11 and 18 differentially expressed DSP markers from the tumor and stromal areas, respectively, were associated with treatment response. Furthermore, the spatially resolved signature identified from the stromal areas showed greater predictive power for bsAb immunotherapy response (area under the curve=0.838). Subsequently, our stromal signature was validated in an independent cohort of patients with non-small cell lung cancer undergoing immunotherapy. CONCLUSION We deciphered ITH at the spatial level and demonstrated for the first time that genetic information in the stromal region can better predict the efficacy of bsAb treatment. TRIAL REGISTRATION NUMBER NCT03838848.
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Affiliation(s)
- Xinyu Song
- School of Medicine, Tongji University, Shanghai, China
- Department of Medical Oncology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Anwen Xiong
- Department of Medical Oncology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Fengying Wu
- Department of Medical Oncology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Xuefei Li
- Department of Lung Cancer and Immunology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Jing Wang
- Clinical research center, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Tao Jiang
- Department of Medical Oncology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Peixin Chen
- School of Medicine, Tongji University, Shanghai, China
| | | | - Zhikai Zhao
- Department of Pathology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Huifang Liu
- Department of Pathology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Lei Cheng
- Department of Lung Cancer and Immunology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Chao Zhao
- Department of Lung Cancer and Immunology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Zhehai Wang
- Department of Medical Oncology, Shandong Cancer Hospital, Jinan, Shandong, China
| | - Chaohu Pan
- Shenzhen Engineering Center for Translational Medicine of Precision Cancer Immunodiagnosis and Therapy, YuceBio Technology Co Ltd, Shenzhen, Guangdong, China
| | - Xiaoli Cui
- Shenzhen Engineering Center for Translational Medicine of Precision Cancer Immunodiagnosis and Therapy, YuceBio Technology Co Ltd, Shenzhen, Guangdong, China
| | - Ting Xu
- Alphamab Biopharmaceuticals, Suzhou, Jiangsu, China
| | - Haitao Luo
- Shenzhen Engineering Center for Translational Medicine of Precision Cancer Immunodiagnosis and Therapy, YuceBio Technology Co Ltd, Shenzhen, Guangdong, China
| | - Caicun Zhou
- School of Medicine, Tongji University, Shanghai, China
- Department of Medical Oncology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
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35
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Dinstag G, Shulman ED, Elis E, Ben-Zvi DS, Tirosh O, Maimon E, Meilijson I, Elalouf E, Temkin B, Vitkovsky P, Schiff E, Hoang DT, Sinha S, Nair NU, Lee JS, Schäffer AA, Ronai Z, Juric D, Apolo AB, Dahut WL, Lipkowitz S, Berger R, Kurzrock R, Papanicolau-Sengos A, Karzai F, Gilbert MR, Aldape K, Rajagopal PS, Beker T, Ruppin E, Aharonov R. Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome. MED 2023; 4:15-30.e8. [PMID: 36513065 PMCID: PMC10029756 DOI: 10.1016/j.medj.2022.11.001] [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: 04/11/2022] [Revised: 08/30/2022] [Accepted: 10/31/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. METHODS We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort. FINDINGS Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. CONCLUSIONS ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. FUNDING This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.
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Affiliation(s)
| | | | | | | | | | | | - Isaac Meilijson
- Pangea Biomed Ltd., Tel Aviv, Israel; Tel Aviv University, Tel Aviv, Israel
| | | | | | | | | | - Danh-Tai Hoang
- Biological Data Science Institute, College of Science, The Australian National University, Canberra, ACT, Australia
| | - Sanju Sinha
- Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nishanth Ulhas Nair
- Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Joo Sang Lee
- Department of Precision Medicine, School of Medicine & Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea
| | - Alejandro A Schäffer
- Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ze'ev Ronai
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Dejan Juric
- Department of Medicine, Massachusetts General Hospital Cancer Center, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Andrea B Apolo
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - William L Dahut
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stanley Lipkowitz
- Women's Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Raanan Berger
- Cancer Center, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Razelle Kurzrock
- Worldwide Innovative Network (WIN) for Personalized Cancer Therapy, Chevilly-Larue, France
| | | | - Fatima Karzai
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mark R Gilbert
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kenneth Aldape
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Padma S Rajagopal
- Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Women's Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Eytan Ruppin
- Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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Oncosuppressive miRNAs loaded in lipid nanoparticles potentiate targeted therapies in BRAF-mutant melanoma by inhibiting core escape pathways of resistance. Oncogene 2023; 42:293-307. [PMID: 36418472 PMCID: PMC9684877 DOI: 10.1038/s41388-022-02547-9] [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: 06/11/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 11/27/2022]
Abstract
BRAF-mutated melanoma relapsing after targeted therapies is an aggressive disease with unmet clinical need. Hence the need to identify novel combination therapies able to overcome drug resistance. miRNAs have emerged as orchestrators of non-genetic mechanisms adopted by melanoma cells to challenge therapies. In this context we previously identified a subset of oncosuppressor miRNAs downregulated in drug-resistant melanomas. Here we demonstrate that lipid nanoparticles co-encapsulating two of them, miR-199-5p and miR-204-5p, inhibit tumor growth both in vitro and in vivo in combination with target therapy and block the development of drug resistance. Mechanistically they act by directly reducing melanoma cell growth and also indirectly by hampering the recruitment and reprogramming of pro-tumoral macrophages. Molecularly, we demonstrate that the effects on macrophages are mediated by the dysregulation of a newly identified miR-204-5p-miR-199b-5p/CCL5 axis. Finally, we unveiled that M2 macrophages programs are molecular signatures of resistance and predict response to therapy in patients. Overall, these findings have strong translational implications to propose new combination therapies making use of RNA therapeutics for metastatic melanoma patients.
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37
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Berger R, Dinstag G, Tirosh O, Schiff E, Kleiner D, Aldape KD, Ruppin E, Beker T, Kurzrock R. Fibrolamellar carcinoma transcriptomic-based treatment prediction: complete response after nivolumab and ipilimumab. J Immunother Cancer 2022; 10:jitc-2022-005620. [PMID: 36600603 PMCID: PMC9743402 DOI: 10.1136/jitc-2022-005620] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/14/2022] [Indexed: 12/13/2022] Open
Abstract
Fibrolamellar carcinoma (FLC) is a rare cancer of the liver that most commonly affects children and young adults. There is no clear standard of care for the disease, whose response to treatment seems to be very different from that of hepatocellular carcinoma. We present a case of FLC in a patient in her mid 30s that recurred and persisted despite resection and multiple lines of treatment. Following transcriptomic analysis, a combination of ipilimumab (anti-CTLA4) and nivolumab (anti-PD-1) led to complete remission, although common biomarkers for immune checkpoint blockade were all negative in this case. The patient is still in remission. Here, combined checkpoint blockade guided by novel transcriptomic analysis led to complete remission after failure of several lines of treatment.
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Affiliation(s)
- Raanan Berger
- Oncology, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | | | | | | | - David Kleiner
- Laboratory of Pathology, Center for Cancer Research, NCI, Bethesda, Maryland, USA
| | - Kenneth D Aldape
- Laboratory of Pathology, Center for Cancer Research, NCI, Bethesda, Maryland, USA
| | - Eytan Ruppin
- Cancer Data Science Lab, Center for Cancer Research, NCI, Bethesda, Maryland, USA
| | | | - Razelle Kurzrock
- Clinical Cancer Center, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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38
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ACAP1 Deficiency Predicts Inferior Immunotherapy Response in Solid Tumors. Cancers (Basel) 2022; 14:cancers14235951. [PMID: 36497434 PMCID: PMC9740925 DOI: 10.3390/cancers14235951] [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: 09/14/2022] [Revised: 11/20/2022] [Accepted: 11/29/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND ACAP1 plays a key role in endocytic recycling, which is essential for the normal function of lymphocytes. However, the expression and function of ACAP1 in lymphocytes have rarely been studied. METHODS Large-scale genomic data, including multiple bulk RNA-sequencing datasets, single-cell sequencing datasets, and immunotherapy cohorts, were exploited to comprehensively characterize ACAP1 expression, regulation, and function. Gene set enrichment analysis (GSEA) was used to uncover the pathways associated with ACAP1 expression. Eight algorithms, including TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, xCELL, MCPCOUNTER, EPIC, and TIDE, were applied to estimate the infiltrating level of immune cells. Western blotting, qPCR, and ChIP-PCR were used to validate the findings from bioinformatic analyses. A T-cell co-culture killing assay was used to investigate the function of ACAP1 in lymphocytes. RESULTS ACAP1 was highly expressed in immune-related tissues and cells and minimally in other tissues. Moreover, single-cell sequencing analysis in tumor samples revealed that ACAP1 is expressed primarily in tumor-infiltrating lymphocytes (TILs), including T, B, and NK cells. ACAP1 expression is negatively regulated by promoter DNA methylation, with its promoter hypo-methylated in immune cells but hyper-methylated in other cells. Furthermore, SPI1 binds to the ACAP1 promoter and positively regulates its expression in immune cells. ACAP1 levels positively correlate with the infiltrating levels of TILs, especially CD8+ T cells, across a broad range of solid cancer types. ACAP1 deficiency is associated with poor prognosis and immunotherapeutic response in multiple cancer types treated with checkpoint blockade therapy (ICT). Functionally, the depletion of ACAP1 by RNA interference significantly impairs the T cell-mediated killing of tumor cells. CONCLUSIONS Our study demonstrates that ACAP1 is essential for the normal function of TILs, and its deficiency indicates an immunologically "cold" status of tumors that are resistant to ICT.
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Cao Z, Shu Y, Wang J, Wang C, Feng T, Yang L, Shao J, Zou L. Super enhancers: Pathogenic roles and potential therapeutic targets for acute myeloid leukemia (AML). Genes Dis 2022; 9:1466-1477. [PMID: 36157504 PMCID: PMC9485276 DOI: 10.1016/j.gendis.2022.01.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 01/06/2022] [Accepted: 01/13/2022] [Indexed: 11/04/2022] Open
Abstract
Acute myeloid leukemia (AML) is a malignant hematological tumor with disordered oncogenes/tumor suppressor genes and limited treatments. The potent anti-cancer effects of bromodomain and extra-terminal domain (BET) inhibitors, targeting the key component of super enhancers, in early clinical trials on AML patients, implies the critical role of super enhancers in AML. Here, we review the concept and characteristic of super enhancer, and then summarize the current researches about super enhancers in AML pathogenesis, diagnosis and classification, followed by illustrate the potential super enhancer-related targets and drugs, and propose the future directions of super enhancers in AML. This information provides integrated insight into the roles of super enhancers in this disease.
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Affiliation(s)
- Ziyang Cao
- Clinical Research Unit, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai 200062, PR China
- Institute of Pediatric Infection, Immunity, Critical Care Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai 200062, PR China
| | - Yi Shu
- Center for Clinical Molecular Laboratory Medicine of Children's Hospital of Chongqing Medical University, Chongqing 400014, PR China
| | - Jinxia Wang
- Clinical Research Unit, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai 200062, PR China
- Institute of Pediatric Infection, Immunity, Critical Care Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai 200062, PR China
| | - Chunxia Wang
- Clinical Research Unit, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai 200062, PR China
- Institute of Pediatric Infection, Immunity, Critical Care Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai 200062, PR China
| | - Tienan Feng
- Clinical Research Unit, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai 200062, PR China
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, PR China
| | - Li Yang
- Department of Hematology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, PR China
| | - Jingbo Shao
- Department of Hematology/Oncology, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai 200062, PR China
| | - Lin Zou
- Clinical Research Unit, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai 200062, PR China
- Institute of Pediatric Infection, Immunity, Critical Care Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai 200062, PR China
- Center for Clinical Molecular Laboratory Medicine of Children's Hospital of Chongqing Medical University, Chongqing 400014, PR China
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40
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Jiang P, Sinha S, Aldape K, Hannenhalli S, Sahinalp C, Ruppin E. Big data in basic and translational cancer research. Nat Rev Cancer 2022; 22:625-639. [PMID: 36064595 PMCID: PMC9443637 DOI: 10.1038/s41568-022-00502-0] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/26/2022] [Indexed: 02/07/2023]
Abstract
Historically, the primary focus of cancer research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen the rapid accumulation of large-scale cancer omics data catalysed by breakthroughs in high-throughput technologies. This fast data growth has given rise to an evolving concept of 'big data' in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements. Further advances will require a concerted effort among data scientists, clinicians, biologists and policymakers. Here, we review the current state of the art and future challenges for harnessing big data to advance cancer research and treatment.
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Affiliation(s)
- Peng Jiang
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Sanju Sinha
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kenneth Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sridhar Hannenhalli
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Cenk Sahinalp
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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41
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Tang YC, Powell RT, Gottlieb A. Molecular pathways enhance drug response prediction using transfer learning from cell lines to tumors and patient-derived xenografts. Sci Rep 2022; 12:16109. [PMID: 36168036 PMCID: PMC9515168 DOI: 10.1038/s41598-022-20646-1] [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: 09/12/2022] [Accepted: 09/16/2022] [Indexed: 11/24/2022] Open
Abstract
Computational models have been successful in predicting drug sensitivity in cancer cell line data, creating an opportunity to guide precision medicine. However, translating these models to tumors remains challenging. We propose a new transfer learning workflow that transfers drug sensitivity predicting models from large-scale cancer cell lines to both tumors and patient derived xenografts based on molecular pathways derived from genomic features. We further compute feature importance to identify pathways most important to drug response prediction. We obtained good performance on tumors (AUROC = 0.77) and patient derived xenografts from triple negative breast cancers (RMSE = 0.11). Using feature importance, we highlight the association between ER-Golgi trafficking pathway in everolimus sensitivity within breast cancer patients and the role of class II histone deacetylases and interlukine-12 in response to drugs for triple-negative breast cancer. Pathway information support transfer of drug response prediction models from cell lines to tumors and can provide biological interpretation underlying the predictions, serving as a steppingstone towards usage in clinical setting.
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Affiliation(s)
- Yi-Ching Tang
- grid.267308.80000 0000 9206 2401Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Reid T. Powell
- grid.264756.40000 0004 4687 2082Center for Translational Cancer Research, Texas A&M University, Houston, TX 77030 USA
| | - Assaf Gottlieb
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect. Biomedicines 2022; 10:biomedicines10092318. [PMID: 36140419 PMCID: PMC9496268 DOI: 10.3390/biomedicines10092318] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/14/2022] [Accepted: 09/16/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: Emergence of methods interrogating gene expression at high throughput gave birth to quantitative transcriptomics, but also posed a question of inter-comparison of expression profiles obtained using different equipment and protocols and/or in different series of experiments. Addressing this issue is challenging, because all of the above variables can dramatically influence gene expression signals and, therefore, cause a plethora of peculiar features in the transcriptomic profiles. Millions of transcriptomic profiles were obtained and deposited in public databases of which the usefulness is however strongly limited due to the inter-comparison issues; (2) Methods: Dozens of methods and software packages that can be generally classified as either flexible or predefined format harmonizers have been proposed, but none has become to the date the gold standard for unification of this type of Big Data; (3) Results: However, recent developments evidence that platform/protocol/batch bias can be efficiently reduced not only for the comparisons of limited transcriptomic datasets. Instead, instruments were proposed for transforming gene expression profiles into the universal, uniformly shaped format that can support multiple inter-comparisons for reasonable calculation costs. This forms a basement for universal indexing of all or most of all types of RNA sequencing and microarray hybridization profiles; (4) Conclusions: In this paper, we attempted to overview the landscape of modern approaches and methods in transcriptomic harmonization and focused on the practical aspects of their application.
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Yin X, Liao H, Yun H, Lin N, Li S, Xiang Y, Ma X. Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer. Semin Cancer Biol 2022; 86:146-159. [PMID: 35963564 DOI: 10.1016/j.semcancer.2022.08.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/06/2022] [Accepted: 08/08/2022] [Indexed: 11/26/2022]
Abstract
Lung cancer accounts for the main proportion of malignancy-related deaths and most patients are diagnosed at an advanced stage. Immunotherapy and targeted therapy have great advances in application in clinics to treat lung cancer patients, yet the efficacy is unstable. The response rate of these therapies varies among patients. Some biomarkers have been proposed to predict the outcomes of immunotherapy and targeted therapy, including programmed cell death-ligand 1 (PD-L1) expression and oncogene mutations. Nevertheless, the detection tests are invasive, time-consuming, and have high demands on tumor tissue. The predictive performance of conventional biomarkers is also unsatisfactory. Therefore, novel biomarkers are needed to effectively predict the outcomes of immunotherapy and targeted therapy. The application of artificial intelligence (AI) can be a possible solution, as it has several advantages. AI can help identify features that are unable to be used by humans and perform repetitive tasks. By combining AI methods with radiomics, pathology, genomics, transcriptomics, proteomics, and clinical data, the integrated model has shown predictive value in immunotherapy and targeted therapy, which significantly improves the precision treatment of lung cancer patients. Herein, we reviewed the application of AI in predicting the outcomes of immunotherapy and targeted therapy in lung cancer patients, and discussed the challenges and future directions in this field.
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Affiliation(s)
- Xiaomeng Yin
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Hu Liao
- Department of Thoracic Surgery, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Hong Yun
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Nan Lin
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Shen Li
- West China School of Medicine, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Yu Xiang
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xuelei Ma
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
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Liu Z, Lin D, Zhou Y, Zhang L, Yang C, Guo B, Xia F, Li Y, Chen D, Wang C, Chen Z, Leng C, Xiao Z. Exploring synthetic lethal network for the precision treatment of clear cell renal cell carcinoma. Sci Rep 2022; 12:13222. [PMID: 35918352 PMCID: PMC9345903 DOI: 10.1038/s41598-022-16657-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 07/13/2022] [Indexed: 11/29/2022] Open
Abstract
The emerging targeted therapies have revolutionized the treatment of advanced clear cell renal cell carcinoma (ccRCC) over the past 15 years. Nevertheless, lack of personalized treatment limits the development of effective clinical guidelines and improvement of patient prognosis. In this study, large-scale genomic profiles from ccRCC cohorts were explored for integrative analysis. A credible method was developed to identify synthetic lethality (SL) pairs and a list of 72 candidate pairs was determined, which might be utilized to selectively eliminate tumors with genetic aberrations using SL partners of specific mutations. Further analysis identified BRD4 and PRKDC as novel medical targets for patients with BAP1 mutations. After mapping these target genes to the comprehensive drug datasets, two agents (BI-2536 and PI-103) were found to have considerable therapeutic potentials in the BAP1 mutant tumors. Overall, our findings provided insight into the overview of ccRCC mutation patterns and offered novel opportunities for improving individualized cancer treatment.
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Affiliation(s)
- Zhicheng Liu
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Dongxu Lin
- Department and Institute of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Yi Zhou
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Linmeng Zhang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Chen Yang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Bin Guo
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Feng Xia
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Yan Li
- Department of Immunology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China
| | - Danyang Chen
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Cun Wang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Zhong Chen
- Department and Institute of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
| | - Chao Leng
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
| | - Zhenyu Xiao
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
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45
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He L, Fan Y, Zhang Y, Tu T, Zhang Q, Yuan F, Cheng C. Single-cell transcriptomic analysis reveals circadian rhythm disruption associated with poor prognosis and drug-resistance in lung adenocarcinoma. J Pineal Res 2022; 73:e12803. [PMID: 35436363 DOI: 10.1111/jpi.12803] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 02/20/2022] [Accepted: 04/12/2022] [Indexed: 11/26/2022]
Abstract
Circadian rhythm disruption (CRD) represents a major contributor to tumor proliferation. Nonetheless, the role of CRD in the clinical prediction of cancer outcomes has not been well studied. In this study, we developed a computational algorithm, which was implemented in an open-source R package CRDscore, to define the intratumoral status of circadian disruption in three representative single-cell RNA-seq data sets of lung adenocarcinoma. We found that the malignant cells with high CRDscore were characterized by activation of glycolysis and epithelial-mesenchymal transition pathways. Furthermore, cell communication analysis indicated that CRD played a pivotal role in T cell exhaustion, which may be responsible for the poor prognosis of the malignancy. We then validated the findings with public bulk transcriptome datasets involving 22 cancer types. Cox regression analysis revealed that the CRDscore was a valuable prognostic biomarker. A model containing 23 circadian-related genes performed well in predicting immunotherapeutic outcomes in 14 independent cohorts. Importantly, decreased CRDscore was detect by RNA sequencing on H1299 cells with melatonin treatment. Meanwhile, the cells downregulated the expression level of SNAIL and TWIST, which contributed to an invasive phenotype. In conclusion, this study provides a novel computational framework for characterizing CRD status using single-cell transcriptomic data and further confirmed the molecular mechanisms underlying metabolic reprogramming and T cell exhaustion under CRD. The better understanding of the mechanisms may provide new possibilities for incorporating "anticancer approaches based on circadian clocks" into the treatment protocols of precision medicine.
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Affiliation(s)
- Lei He
- Department of Blood Transfusion, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yixian Fan
- Department of Biochemistry and Molecular Biology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yue Zhang
- Department of Blood Transfusion, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tongtao Tu
- Department of Blood Transfusion, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Quan Zhang
- Department of Laboratory Medicine, Hubei Provincial Hospital of Integrated Chinese and Western Medicine, Wuhan, China
| | - Fahu Yuan
- School of Medicine, Jianghan University, Wuhan, China
| | - Chao Cheng
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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46
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Napoli GC, Figg WD, Chau CH. Functional Drug Screening in the Era of Precision Medicine. Front Med (Lausanne) 2022; 9:912641. [PMID: 35879922 PMCID: PMC9307928 DOI: 10.3389/fmed.2022.912641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 06/16/2022] [Indexed: 11/13/2022] Open
Abstract
The focus of precision medicine is providing the right treatment to each unique patient. This scientific movement has incited monumental advances in oncology including the approval of effective, targeted agnostic therapies. Yet, precision oncology has focused largely on genomics in the treatment decision making process, and several recent clinical trials demonstrate that genomics is not the only variable to be considered. Drug screening in three dimensional (3D) models, including patient derived organoids, organs on a chip, xenografts, and 3D-bioprinted models provide a functional medicine perspective and necessary complement to genomic testing. In this review, we discuss the practicality of various 3D drug screening models and each model's ability to capture the patient's tumor microenvironment. We highlight the potential for enhancing precision medicine that personalized functional drug testing holds in combination with genomic testing and emerging mathematical models.
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Affiliation(s)
| | | | - Cindy H. Chau
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
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47
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Network-based machine learning approach to predict immunotherapy response in cancer patients. Nat Commun 2022; 13:3703. [PMID: 35764641 PMCID: PMC9240063 DOI: 10.1038/s41467-022-31535-6] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 06/22/2022] [Indexed: 11/08/2022] Open
Abstract
Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over the past several years. However, only a minority of patients respond to ICI treatment (~30% in solid tumors), and current ICI-response-associated biomarkers often fail to predict the ICI treatment response. Here, we present a machine learning (ML) framework that leverages network-based analyses to identify ICI treatment biomarkers (NetBio) that can make robust predictions. We curate more than 700 ICI-treated patient samples with clinical outcomes and transcriptomic data, and observe that NetBio-based predictions accurately predict ICI treatment responses in three different cancer types—melanoma, gastric cancer, and bladder cancer. Moreover, the NetBio-based prediction is superior to predictions based on other conventional ICI treatment biomarkers, such as ICI targets or tumor microenvironment-associated markers. This work presents a network-based method to effectively select immunotherapy-response-associated biomarkers that can make robust ML-based predictions for precision oncology. Identifying biomarkers for response to immunotherapy in cancer remains challenging. Here, the authors develop an approach based on network biology and machine learning -NetBio- to identify molecular biomarkers of response to immunotherapy across different cancer types and cohorts.
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48
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Kim CG, Sim NS, Kim JE, Yun KH, Lee YH, Kim SH, Baek W, Han YD, Kim SK, Kim JH, Koh YW, Jung I, Shin SJ, Rha SY, Ahn JH, Kim HS. Phase II clinical trial of Eribulin-gemcitabine combination therapy in previously treated patients with advanced liposarcoma or leiomyosarcoma. Clin Cancer Res 2022; 28:3225-3234. [PMID: 35583824 DOI: 10.1158/1078-0432.ccr-22-0518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/08/2022] [Accepted: 05/16/2022] [Indexed: 11/16/2022]
Abstract
PURPOSE Monotherapy with eribulin or gemcitabine has been found to be moderately effective in treating soft-tissue sarcomas (STS). In this study, we evaluated the efficacy and safety of eribulin-gemcitabine combination therapy for the two most common histologic types of STS, liposarcoma and leiomyosarcoma. PATIENTS AND METHODS In this non-randomized, multicenter, phase II study, we included patients with progressive disease who had received one or two courses of chemotherapy that included doxorubicin. Patients were administered 1.4 mg/m2 eribulin and 1,000 mg/m2 gemcitabine on days 1 and 8 every 3 weeks. The primary endpoint was progression-free survival rate at 12 weeks (PFSR12wks), with null and alternative hypotheses of PFSR12wks {less than or equal to}20.0% and {greater than or equal to}40.0%, respectively. Exploratory biomarker analyses with next-generation sequencing (NGS) were performed on pretreatment tumor samples. RESULTS Among the 37 patients included, the overall PFSR12wks was 73.0%, achieving the primary endpoint. The objective response rate, disease control rate, median progression-free survival, and median overall survival were 16.2%, 78.4%, 5.6 months, and 31.9 months, respectively, without differences according to histologic type. New safety signals and treatment-related deaths were not documented. NGS-based transcriptome analysis revealed that functional enrichment in the transforming growth factor-ß pathway was mostly associated with a poor outcome, whereas single genetic alterations largely failed to predict treatment outcome. CONCLUSIONS Eribulin-gemcitabine combination therapy showed promising activity and an acceptable safety profile in patients with liposarcoma or leiomyosarcoma. Gene expression profiling with pathway enrichment analysis would have possibilities to have predictive value for survival outcome, necessitating further investigation to confirm.
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Affiliation(s)
- Chang Gon Kim
- Yonsei University College of Medicine, Seoul, Korea (South), Republic of
| | - Nam Suk Sim
- Yonsei University College of Medicine, Severance Hospital, Korea (South), Republic of
| | - Jeong Eun Kim
- Asan Medical Center, , University of Ulsan College of Medicine, Seoul, Korea (South), Republic of
| | - Kum-Hee Yun
- Yonsei Cancer Center, Seoul, Korea (South), Republic of
| | - Young Han Lee
- Yonsei University College of medicine, Seoul, Korea (South), Republic of
| | - Seung Hyun Kim
- Yonsei University College of Medicine, Seoul, Korea (South), Republic of
| | - Wooyeol Baek
- Yonsei University College of Medicine, Korea (South), Republic of
| | - Yoon Dae Han
- Yonsei University College of Medicine, Seoul, Korea (South), Republic of
| | - Sang Kyum Kim
- Yonsei University College of Medicine, Seoul, Korea (South), Republic of
| | - Jee Hung Kim
- Gangnam Severance Hospital, Seoul, Korea (South), Republic of
| | - Yoon Woo Koh
- Yonsei University College of Medicine, Seoul, Korea (South), Republic of
| | | | - Su-Jin Shin
- Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (South), Republic of
| | - Sun Young Rha
- Yonsei University College of Medicine, Seoul, Korea (South), Republic of
| | - Jin-Hee Ahn
- Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea (South), Republic of
| | - Hyo Song Kim
- Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Korea (South), Republic of
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49
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Li X, Dowling EK, Yan G, Dereli Z, Bozorgui B, Imanirad P, Elnaggar JH, Luna A, Menter DG, Pilié PG, Yap TA, Kopetz S, Sander C, Korkut A. Precision combination therapies based on recurrent oncogenic co-alterations. Cancer Discov 2022; 12:1542-1559. [PMID: 35412613 PMCID: PMC9524464 DOI: 10.1158/2159-8290.cd-21-0832] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 12/28/2021] [Accepted: 03/23/2022] [Indexed: 11/16/2022]
Abstract
Cancer cells depend on multiple driver alterations whose oncogenic effects can be suppressed by drug combinations. Here, we provide a comprehensive resource of precision combination therapies tailored to oncogenic co-alterations that are recurrent across patient cohorts. To generate the resource, we developed Recurrent Features Leveraged for Combination Therapy (REFLECT), which integrates machine learning and cancer informatics algorithms. Using multi-omic data, the method maps recurrent co-alteration signatures in patient cohorts to combination therapies. We validated the REFLECT pipeline using data from patient-derived xenografts, in vitro drug screens, and a combination therapy clinical trial. These validations demonstrate that REFLECT-selected combination therapies have significantly improved efficacy, synergy, and survival outcomes. In patient cohorts with immunotherapy response markers, DNA repair aberrations, and HER2 activation, we have identified therapeutically actionable and recurrent co-alteration signatures. REFLECT provides a resource and framework to design combination therapies tailored to tumor cohorts in data-driven clinical trials and pre-clinical studies.
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Affiliation(s)
- Xubin Li
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | | | - Gonghong Yan
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Zeynep Dereli
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Behnaz Bozorgui
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Parisa Imanirad
- Department of Systems Biology, and The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jacob H. Elnaggar
- Department of Microbiology, Immunology, and Parasitology, Louisiana State University Health Sciences Center, New Orleans, LA 70112
| | - Augustin Luna
- cBio Center, Department of Data Sciences, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - David G. Menter
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Patrick G. Pilié
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Timothy A. Yap
- Department of Investigational Cancer Therapeutics (Phase I Program), The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Scott Kopetz
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Chris Sander
- cBio Center, Department of Data Sciences, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Anil Korkut
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Corresponding Author: Anil Korkut, Bioinformatics & Comp Biology, Phone: 718-300-0666, , 1515 Holcombe Blvd., Houston, Texas 77030-4009
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50
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Wang J, Zhang Q, Han J, Zhao Y, Zhao C, Yan B, Dai C, Wu L, Wen Y, Zhang Y, Leng D, Wang Z, Yang X, He S, Bo X. Computational methods, databases and tools for synthetic lethality prediction. Brief Bioinform 2022; 23:6555403. [PMID: 35352098 PMCID: PMC9116379 DOI: 10.1093/bib/bbac106] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/15/2022] [Accepted: 03/02/2022] [Indexed: 12/17/2022] Open
Abstract
Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.
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Affiliation(s)
- Jing Wang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Qinglong Zhang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Junshan Han
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yanpeng Zhao
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Caiyun Zhao
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Bowei Yan
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Chong Dai
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Lianlian Wu
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yuqi Wen
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yixin Zhang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Dongjin Leng
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Zhongming Wang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaoxi Yang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Song He
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
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