Mutational and putative neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma

Integrative multi-omics analysis unveils stemness-associated molecular subtypes in prostate cancer and pan-cancer: prognostic and therapeutic significance

BACKGROUND

Prostate cancer (PCA) is the fifth leading cause of cancer-related deaths worldwide, with limited treatment options in the advanced stages. The immunosuppressive tumor microenvironment (TME) of PCA results in lower sensitivity to immunotherapy. Although molecular subtyping is expected to offer important clues for precision treatment of PCA, there is currently a shortage of dependable and effective molecular typing methods available for clinical practice. Therefore, we aim to propose a novel stemness-based classification approach to guide personalized clinical treatments, including immunotherapy.

METHODS

An integrative multi-omics analysis of PCA was performed to evaluate stemness-level heterogeneities. Unsupervised hierarchical clustering was used to classify PCAs based on stemness signature genes. To make stemness-based patient classification more clinically applicable, a stemness subtype predictor was jointly developed by using four PCA datasets and 76 machine learning algorithms.

RESULTS

We identified stemness signatures of PCA comprising 18 signaling pathways, by which we classified PCA samples into three stemness subtypes via unsupervised hierarchical clustering: low stemness (LS), medium stemness (MS), and high stemness (HS) subtypes. HS patients are sensitive to androgen deprivation therapy, taxanes, and immunotherapy and have the highest stemness, malignancy, tumor mutation load (TMB) levels, worst prognosis, and immunosuppression. LS patients are sensitive to platinum-based chemotherapy but resistant to immunotherapy and have the lowest stemness, malignancy, and TMB levels, best prognosis, and the highest immune infiltration. MS patients represent an intermediate status of stemness, malignancy, and TMB levels with a moderate prognosis. We further demonstrated that these three stemness subtypes are conserved across pan-tumor. Additionally, the 9-gene stemness subtype predictor we developed has a comparable capability to 18 signaling pathways to make tumor diagnosis and to predict tumor recurrence, metastasis, progression, prognosis, and efficacy of different treatments.

CONCLUSIONS

The three stemness subtypes we identified have the potential to be a powerful tool for clinical tumor molecular classification in PCA and pan-cancer, and to guide the selection of immunotherapy or other sensitive treatments for tumor patients.

Adoptive cell therapy in paediatric extracranial solid tumours: current approaches and future challenges

Immunotherapy has ignited hope to cure paediatric solid tumours that resist traditional therapies. Among the most promising methods is adoptive cell therapy (ACT). Particularly, ACT using T cells equipped with chimeric antigen receptors (CARs) has moved into the spotlight in clinical studies. However, the efficacy of ACT is challenged by ACT-intrinsic factors, like lack of activation or T cell exhaustion, as well as immune evasion strategies of paediatric solid tumours, such as their highly immunosuppressive microenvironment. Novel strategies, including ACT using innate-like lymphocytes, innovative cell engineering techniques, and ACT combination therapies, are being developed and will be crucial to overcome these challenges. Here, we discuss the main classes of ACT for the treatment of paediatric extracranial solid tumours, reflect on the available preclinical and clinical evidence supporting promising strategies, and address the challenges that ACT is still facing. Ultimately, we highlight state-of-the-art developments and opportunities for new therapeutic options, which hold great potential for improving outcomes in this challenging patient population.

Integration of bioinformatics and machine learning strategies identifies APM-related gene signatures to predict clinical outcomes and therapeutic responses for breast cancer patients

BACKGROUND

Tumor antigenicity and efficiency of antigen presentation jointly influence tumor immunogenicity, which largely determines the effectiveness of immune checkpoint blockade (ICB). However, the role of altered antigen processing and presentation machinery (APM) in breast cancer (BRCA) has not been fully elucidated.

METHODS

A series of bioinformatic analyses and machine learning strategies were performed to construct APM-related gene signatures to guide personalized treatment for BRCA patients. A single-sample gene set enrichment analysis (ssGSEA) algorithm and weighted gene co-expression network analysis (WGCNA) were combined to screen for BRCA-specific APM-related genes. The non-negative matrix factorization (NMF) algorithm was used to divide the cohort into different clusters and the fgsea algorithm was applied to investigate the altered signaling pathways. Random survival forest (RSF) and the least absolute shrinkage and selection operator (Lasso) Cox regression analysis were combined to construct an APM-related risk score (APMrs) signature to predict overall survival. Furthermore, a nomogram and decision tree were generated to improve predictive accuracy and risk stratification for individual patients. Based on Tumor Immune Dysfunction and Exclusion (TIDE) method, random forest (RF) and Lasso logistic regression model were combined to establish an APM-related immunotherapeutic response score (APMis). Finally, immune infiltration, immunomodulators, mutational patterns, and potentially applicable drugs were comprehensively analyzed in different APM-related risk groups. IHC staining was used to assess the expression of APM-related genes in clinical samples.

RESULTS

In this study, APMrs and APMis showed favorable performances in risk stratification and therapeutic prediction for BRCA patients. APMrs exhibited more powerful prognostic capacity and accurate survival prediction compared to conventional clinicopathological features. APMrs was closely associated with distinct mutational patterns, immune cell infiltration and immunomodulators expression. Furthermore, the two APM-related gene signatures were independently validated in external cohorts with prognosis or immunotherapeutic responses. Potential applicable drugs and targets were mined in the APMrs-high group. APM-related genes were further validated in our in-house samples.

CONCLUSION

The APM-related gene signatures established in our study could improve the personalized assessment of survival risk and guide ICB decision-making for BRCA patients.

Microdissection of cancer-associated fibroblast infiltration subtypes unveils the secreted SERPINE2 contributing to immunosuppressive microenvironment and immuotherapeutic resistance in gastric cancer: A large-scale study integrating bulk and single-cell transcriptome profiling

In the era of immunotherapy, the suboptimal response rate and the development of acquired resistance among the initial beneficiaries continue to present significant challenges across multiple malignancies, including gastric cancer (GC). Considering that the interactions of tumor stroma, especially the cancer-associated fibroblasts (CAFs), with immune and tumor cells, play indispensable roles in tumor progression, tumor microenvironment remodeling and therapeutic responsiveness, in-depth exploration on the roles of CAFs and pivotal mediators of their functions may provide novel clues to increase the effectiveness of current immunotherapeutic drugs and further achieve synergistic antitumor response. Herein, through the consensus clustering of canonical biomarkers, three GC subclasses with different abundance of CAFs were virtually microdissected in four integrated bulk cohorts encompassing 2148 GC patients from 11 independent datasets. An extensive immunogenomic analysis revealed that tumors with high CAFs infiltration were characterized with unfavorable outcomes, aggressive phenotypes, decreased tumor immunogenicity, high risk of immune evasion and thus immunotherapeutic resistance. By leveraging large-scale single-cell transcriptomic profiling, a series of CAF-secreted proteins were identified, among which the SERPINE2 was confirmed to be restrictively enriched in stromal fibroblasts of GC tissues and contribute to promoting a protumor milieu and fostering an immunosuppressive microenvironment via bioinformatics computations and tissue microarray analysis. Moreover, pan-cancer investigations generalized the immunological roles of SERPINE2, especially in pan-gastrointestinal malignancies, with multiple real-world immunotherapy cohorts further confirming its implications on predicting immunotherapeutic efficacy. In conclusion, these findings suggest that the CAF-derived SERPINE2 is a promising immune-oncology target with therapeutic implications to further synergize the immunotherapeutic combinations.

Tumor-Infiltrating Lymphocytes and Adoptive Cell Therapy: State of the Art in Colorectal, Breast and Lung Cancer

Our knowledge of tumor-infiltrating lymphocytes (TILs) is dramatically expanding. These cells have proven prognostic and therapeutic value for many cancer outcomes and potential to treat also disseminated breast, colorectal, or lung cancer. However, the therapeutical outcome of TILs is negatively affected by tumor mutational burden and neoantigens. On the other hand, it can be improved in combination with checkpoint blockade therapy. This knowledge and rapid detection techniques alongside gene editing allow us to classify and modify T cells in many ways. Hence, to tailor them precisely to the patient´s needs as to program T cell receptors to recognize specific tumor-associated neoantigens and to insert them into lymphocytes or to select tumor neoantigen-specific T cells, for the development of vaccines that recognize tumor-specific antigens in tumors or metastases. Further studies and clinical trials in the field are needed for an even better-detailed understanding of TILs interactions and aiming in the fight against multiple cancers.

Multi-omics indicators of long-term survival benefits after immune checkpoint inhibitor therapy

Molecular indicators of long-term survival (LTS) in response to immune-checkpoint inhibitor (ICI) treatment have the potential to provide both mechanistic and therapeutic insights. In this study, we construct predictive models of LTS following ICI therapy based on data from 158 clinical trials involving 21,023 patients of 25 cancer types with available 1-year overall survival (OS) rates. We present evidence for the use of 1-year OS rate as a surrogate for LTS. Based on these and corresponding TCGA multi-omics data, total neoantigen, metabolism score, CD8+ T cell, and MHC_score were identified as predictive biomarkers. These were integrated into a Gaussian process regression model that estimates "long-term survival predictive score of immunotherapy" (iLSPS). We found that iLSPS outperformed the predictive capabilities of individual biomarkers and successfully predicted LTS of patient groups with melanoma and lung cancer. Our study explores the feasibility of modeling LTS based on multi-omics indicators and machine-learning methods.

Spatial heterogeneity of T cell repertoire across NSCLC tumors, tumor edges, adjacent and distant lung tissues

Background

A better understanding of T cells in lung cancer and their distribution across tumor-adjacent lungs and peripheral blood is needed to improve efficacy and minimize toxicity from immunotherapy to lung cancer patients.

Methods

Here, we performed CDR3β TCR sequencing of 136 samples from 20 patients with early-stage NSCLC including peripheral blood mononuclear cells, tumors, tumor edges (<1 cm from tumor), as well as adjacent lungs 1 cm, 2 cm, 5 cm, and 10 cm away from the tumor to gain insight into the spatial heterogeneity of T cells across the lungs in patients with NSCLC. PD-L1, CD4, and CD8 expression was assessed using immunohistochemical staining, and genomic features were derived by targeted sequencing of 1,021 cancer-related genes. Multiplex immunohistochemistry against PD-1, CTLA4, LAG3, and TIM3 was performed on four patients to assess T cell exhaustion.

Results

Our study reveals a decreasing gradient in TIL Tumor Infiltrating Lymphocytes homology with tumor edge, adjacent lungs, and peripheral blood but no discernible distance-associated patterns of T cell trafficking within the adjacent lung itself. Furthermore, we show a decrease in pathogen-specific TCRs in regions with high T cell clonality and PD-L1 expression.

Conclusions

Exclusion in T exhaustion cells at play across the lungs of patients with NSCLC may potentially be the mechanism for lung cancer occurrence.

Carcinogen exposure enhances cancer immunogenicity by blocking the development of an immunosuppressive tumor microenvironment

Carcinogen exposure is strongly associated with enhanced cancer immunogenicity. Increased tumor mutational burden and resulting neoantigen generation have been proposed to link carcinogen exposure and cancer immunogenicity. However, the neoantigen-independent immunological impact of carcinogen exposure on cancer is unknown. Here, we demonstrate that chemical carcinogen-exposed cancer cells fail to establish an immunosuppressive tumor microenvironment (TME), resulting in their T cell-mediated rejection in vivo. A chemical carcinogen-treated breast cancer cell clone that lacked any additional coding region mutations (i.e., neoantigen) was rejected in mice in a T cell-dependent manner. Strikingly, the coinjection of carcinogen- and control-treated cancer cells prevented this rejection, suggesting that the loss of immunosuppressive TME was the dominant cause of rejection. Reduced M-CSF expression by carcinogen-treated cancer cells significantly suppressed tumor-associated macrophages (TAMs) and resulted in the loss of an immunosuppressive TME. Single-cell analysis of human lung cancers revealed a significant reduction in the immunosuppressive TAMs in former smokers compared with individuals who had never smoked. These findings demonstrate that carcinogen exposure impairs the development of an immunosuppressive TME and indicate a novel link between carcinogens and cancer immunogenicity.

Hub gene identification and molecular subtype construction for Helicobacter pylori in gastric cancer via machine learning methods and NMF algorithm

Helicobacter pylori (HP) is a gram-negative and spiral-shaped bacterium colonizing the human stomach and has been recognized as the risk factor of gastritis, peptic ulcer disease, and gastric cancer (GC). Moreover, it was recently identified as a class I carcinogen, which affects the occurrence and progression of GC via inducing various oncogenic pathways. Therefore, identifying the HP-related key genes is crucial for understanding the oncogenic mechanisms and improving the outcomes of GC patients. We retrieved the list of HP-related gene sets from the Molecular Signatures Database. Based on the HP-related genes, unsupervised non-negative matrix factorization (NMF) clustering method was conducted to stratify TCGA-STAD, GSE15459, GSE84433 samples into two clusters with distinct clinical outcomes and immune infiltration characterization. Subsequently, two machine learning (ML) strategies, including support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF), were employed to determine twelve hub HP-related genes. Beyond that, receiver operating characteristic and Kaplan-Meier curves further confirmed the diagnostic value and prognostic significance of hub genes. Finally, expression of HP-related hub genes was tested by qRT-PCR array and immunohistochemical images. Additionally, functional pathway enrichment analysis indicated that these hub genes were implicated in the genesis and progression of GC by activating or inhibiting the classical cancer-associated pathways, such as epithelial-mesenchymal transition, cell cycle, apoptosis, RAS/MAPK, etc. In the present study, we constructed a novel HP-related tumor classification in different datasets, and screened out twelve hub genes via performing the ML algorithms, which may contribute to the molecular diagnosis and personalized therapy of GC.

Formin protein FMNL1 is a biomarker for tumor-infiltrating immune cells and associated with well immunotherapeutic response

Background: Increased studies on the basis of bulk RNA-sequencing (RNA-seq) data of cancer identify numbers of immune-related genes which may play potential regulatory roles in the tumor microenvironment (TME) without in-depth validation. Methods: In the current study, the immunological correlation and cell subpopulation expression pattern of FMNL1 were analyzed using public data. In addition, the cell subpopulation expression pattern of FMNL1 was also deeply validated using single-cell RNA-sequencing (scRNA-seq) and multiplexed quantitative immunofluorescence (mQIF). Results: Bulk FMNL1 mRNA was related to better prognosis in hepatocellular carcinoma (HCC) and was able to identify immuno-hot tumor in not only HCC but also multiple cancer types. Bulk FMNL1 mRNA also predicted the response to immunotherapy in multiple cancers. Further validation using scRNA-seq and mQIF revealed that FMNL1 was a biomarker for immune cells. Conclusions: FMNL1 is a biomarker for immune cells in not only hepatocellular carcinoma, but also multiple cancer types. Moreover, immune infiltration analysis using the bulk RNA-seq data would be further validated using scRNA-seq and/or mQIF to describe the cell subpopulation expression pattern in tumor tissues for more in-depth and appropriate understanding.

Chromatin accessibility dynamics in colorectal cancer liver metastasis: Uncovering the liver tropism at single cell resolution

Tumor metastasis causes over 90% of cancer related death and no currently available therapies target it. However, there is limited understanding regarding the epigenetic regulation of genes during this complex process. Here by integrating single-cell ATAC-seq (scATAC-seq), single-cell RNA-seq (scRNA-seq), microarray, bulk RNA-seq, immunohistochemistry (IHC) staining, as well as proteomics datasets from paired primary and liver metastatic colorectal cancer (CRC) patient-derived xenograft (PDX) model and patients, we discovered that liver metastatic CRC cells lose their colon-specific chromatin accessible sites yet gain liver-specific ones. Importantly, we observed elevated accessibility of HNF4A, a liver-specific transcription factor, in liver metastatic CRC cells. Subsequently, we performed clustering analysis of liver metastatic CRC cells together with cells involved in liver development, revealing significant heterogeneity among the liver metastatic CRC cells. Over 50% of the liver metastatic CRC cells exhibited characteristics similar to those of erythroid progenitors and hepatocytes, showing increased expression of genes involved in oxidative phosphorylation and glycolysis. Moreover, our discovery further revealed that the MHC and IFN response genes in these cells exhibit moderate epigenetic activity, which is significantly associated with the low objective response rates in checkpoint blockade immunotherapy. Our findings uncovered the critical roles of HNF4A and the cell populations within liver metastatic CRC cells might serve as crucial therapeutic targets for addressing liver metastasis and improving the immunotherapy response in patients with CRC.

A tumor-associated endothelial signature score model in immunotherapy and prognosis across pan-cancers

Background: The tumor-associated endothelial cell (TAE) component plays a vital role in tumor immunity. However, systematic tumor-associated endothelial-related gene assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers have not been explored. Herein, we investigated a TAE gene risk model to predict CIT responses and patient survival in a pan-cancer analysis. Methods: We analyzed publicly available datasets of tumor samples with gene expression and clinical information, including gastric cancer, metastatic urothelial cancer, metastatic melanoma, non-small cell lung cancer, primary bladder cancer, and renal cell carcinoma. We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm. Results: The model demonstrated a high predictive accuracy in both training and validation cohorts. The response rate of the high score group to immunotherapy in the training cohort was significantly higher than that of the low score group, with CIT response rates of 51% and 27%, respectively. The survival analysis showed that the prognosis of the high score group was significantly better than that of the low score group (all p < 0·001). Tumor-associated endothelial gene signature scores positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may benefit patients in the high score group. The analysis of TAE scores across 33 human cancers revealed that the TAE model could reflect immune cell infiltration and predict the survival of cancer patients. Conclusion: The TAE signature model could represent a CIT response prediction model with a prognostic value in multiple cancer types.

Immune-related interaction perturbation networks unravel biological peculiars and clinical significance of glioblastoma

The immune system is an interacting network of plentiful molecules that could better characterize the relationship between immunity and cancer. This study aims to investigate the behavioral patterns of immune-related interaction perturbation networks in glioblastoma. An immune-related interaction-perturbation framework was introduced to characterize four heterogeneous subtypes using RNA-seq data of TCGA/CGGA glioblastoma tissues and GTEx normal brain tissues. The stability and robustness of the four subtypes were validated in public datasets and our in-house cohort. In the four subtypes, C1 was an inflammatory subtype with high immune infiltration, low tumor purity, and potential response to immunotherapy; C2, an invasive subtype, was featured with dismal prognosis, telomerase reverse transcriptase promoter mutations, moderate levels of immunity, and stromal constituents, as well as sensitivity to receptor tyrosine kinase signaling inhibitors; C3 was a proliferative subtype with high tumor purity, immune-desert microenvironment, sensitivity to phosphatidylinositol 3'-kinase signaling inhibitor and DNA replication inhibitors, and potential resistance to immunotherapy; C4, a synaptogenesis subtype with the best prognosis, exhibited high synaptogenesis-related gene expression, prevalent isocitrate dehydrogenase mutations, and potential sensitivity to radiotherapy and chemotherapy. Overall, this study provided an attractive platform from the perspective of immune-related interaction perturbation networks, which might advance the tailored management of glioblastoma.

Antitumor effect of neoantigen-reactive T cells combined with PD1 inhibitor therapy in mouse lung cancer

PURPOSE

Neoantigens produced from mutations in tumors are important targets of T-cell-based immunotherapy and immune checkpoint blockade has been approved for treating multiple solid tumors. We investigated the potential benefit of adoptive neoantigen-reactive T (NRT) cells in combination with programmed cell death protein 1 inhibitor (anti-PD1) for treating lung cancer in a mouse model.

METHODS

NRT cells were prepared by co-culturing T cells and neoantigen-RNA vaccine-induced dendritic cells. Then, adoptive NRT cells in combination with anti-PD1 were administered to tumor-bearing mice. Pre- and post-therapy cytokine secretion, antitumor efficacy, and tumor microenvironment (TME) changes were determined both in vitro and in vivo.

RESULTS

We successfully generated NRT cells based on the 5 neoantigen epitopes identified in this study. NRT cells exhibited an enhanced cytotoxic phenotype in vitro and the combination therapy led to attenuated tumor growth. In addition, this combination strategy downregulated the expression of the inhibitory marker PD-1 on tumor-infiltrating T cells and promoted the trafficking of tumor-specific T cells to the tumor sites.

CONCLUSION

The adoptive transfer of NRT cells in association with anti-PD1 therapy can exert an antitumor effect on lung cancer, and is a feasible, effective, and novel immunotherapy regimen for treating solid tumors.

Identification of the relationship between single-cell N6-methyladenosine regulators and the infiltrating immune cells in esophageal carcinoma

Background

N6-methyladenosine (m⁶A) RNA methylation plays a crucial role in important genomic processes in a variety of malignancies. However, the characterization of m⁶A with infiltrating immune cells in the tumor microenvironment (TME) in esophageal squamous carcinoma (ESCC) remains unknown.

Methods

The single-cell transcriptome data from five ESCC patients in our hospital were analyzed, and TME clusters associated with prognosis and immune checkpoint genes were investigated. Cell isolation and qPCR were conducted to validate the gene characterization in different cells.

Results

According to distinct biological processes and marker genes, macrophages, T cells, and B cells clustered into three to four different subgroups. In addition, we demonstrated that m⁶A RNA methylation regulators were strongly related to the clinical and biological features of ESCC. Analysis of transcriptome data revealed that m⁶A-mediated TME cell subsets had high predictive value and showed a close relationship with immune checkpoint genes. The validation results from qPCR demonstrated the characteristics of essential genes. CellChat analysis revealed that RNA from TME cells m⁶A methylation-associated cell subtypes had substantial and diversified interactions with cancer cells. Further investigation revealed that MIF- (CD74+CXCR4) and MIF- (CD74⁺CD44) ligand-receptor pairings facilitated communication between m⁶A-associated subtypes of TME cells and cancer cells.

Conclusion

Overall, our study demonstrated for the first time the function of m⁶A methylation-mediated intercellular communication in the microenvironment of tumors in controlling tumor development and anti-tumor immune regulation in ESCC.

A luminescence-based method to assess antigen presentation and antigen-specific T cell responses for in vitro screening of immunomodulatory checkpoints and therapeutics

Investigations into the strength of antigen-specific responses in vitro is becoming increasingly relevant for decision making in early-phase research of novel immunotherapeutic approaches, including adoptive cell but also immune checkpoint inhibitor (ICI)-based therapies. In the latter, antigen-specific rapid and high throughput tools to investigate MHC/antigen-specific T cell receptor (TCR) activation haven't been implemented yet. Here, we present a simple and rapid luminescence-based approach using the human papillomavirus 16 (HPV16) E711-20 peptide as model antigen and E7-TCR transgenic Jurkat.NFAT-luciferase reporter cells. Upon E7 peptide pulsing of HLA-A2+ cell lines and macrophages, an effector to target ratio dependent increase in luminescence compared to non-pulsed cells was observed after co-incubation with E7-TCR expressing Jurkat, but not with parental cells. Analogous experiments with cells expressing full-length HPV16 identified that E7-specific activation of Jurkat cells enabled detection of endogenous antigen processing and MHC-I presentation. As proof of concept, overexpression of established checkpoints/inhibitory molecules (e.g., PD-L1 or HLA-G) significantly reduced the E7-specific TCR-induced luminescence, an effect that could be restored after treatment with corresponding targeting antagonistic antibodies. Altogether, the luminescence-based method described here represents an alternative approach for the rapid evaluation of MHC-dependent antigen-specific T cell responses in vitro. It can be used as a rapid tool to evaluate the impact of the immunosuppressive tumor microenvironment or novel ICI in triggering effective T cell responses, as well as speeding up the development of novel therapeutics within the immune-oncology field.

Aggrephagy-related patterns in tumor microenvironment, prognosis, and immunotherapy for acute myeloid leukemia: a comprehensive single-cell RNA sequencing analysis

Acute myeloid leukemia (AML) is a complex mixed entity composed of malignant tumor cells, immune cells and stromal cells, with intra-tumor and inter-tumor heterogeneity. Single-cell RNA sequencing enables a comprehensive study of the highly complex tumor microenvironment, which is conducive to exploring the evolutionary trajectory of tumor cells. Herein, we carried out comprehensive analyses of aggrephagy-related cell clusters based on single-cell sequencing for patients with acute myeloid leukemia. A total of 11 specific cell types (T, NK, CMP, Myeloid, GMP, MEP, Promono, Plasma, HSC, B, and Erythroid cells) using t-SNE dimension reduction analysis. Several aggrephagy-related genes were highly expressed in the 11 specific cell types. Using Monocle analysis and NMF clustering analysis, six aggrephagy-related CD8+ T clusters, six aggrephagy-related NK clusters, and six aggrephagy-related Mac clusters were identified. We also evaluated the ligand-receptor links and Cell-cell communication using CellChat package and CellChatDB database. Furthermore, the transcription factors (TFs) of aggrephagy-mediated cell clusters for AML were assessed through pySCENIC package. Prognostic analysis of the aggrephagy-related cell clusters based on R package revealed the differences in prognosis of aggrephagy-mediated cell clusters. Immunotherapy of the aggrephagy-related cell clusters was investigated using TIDE algorithm and public immunotherapy cohorts. Our study revealed the significance of aggrephagy-related patterns in tumor microenvironment, prognosis, and immunotherapy for AML.

The landscape of T cell antigens for cancer immunotherapy

The remarkable capacity of immunotherapies to induce durable regression in some patients with metastatic cancer relies heavily on T cell recognition of tumor-presented antigens. As checkpoint-blockade therapy has limited efficacy, tumor antigens have the potential to be exploited for complementary treatments, many of which are already in clinical trials. The surge of interest in this topic has led to the expansion of the tumor antigen landscape with the emergence of new antigen categories. Nonetheless, how different antigens compare in their ability to elicit efficient and safe clinical responses remains largely unknown. Here, we review known cancer peptide antigens, their attributes and the relevant clinical data and discuss future directions.

MYC Induces Immunotherapy and IFNγ Resistance Through Downregulation of JAK2

Immunotherapy has revolutionized the treatment of advanced melanoma. Because the pathways mediating resistance to immunotherapy are largely unknown, we conducted transcriptome profiling of preimmunotherapy tumor biopsies from patients with melanoma that received PD-1 blockade or adoptive cell therapy with tumor-infiltrating lymphocytes. We identified two melanoma-intrinsic, mutually exclusive gene programs, which were controlled by IFNγ and MYC, and the association with immunotherapy outcome. MYC-overexpressing melanoma cells exhibited lower IFNγ responsiveness, which was linked with JAK2 downregulation. Luciferase activity assays, under the control of JAK2 promoter, demonstrated reduced activity in MYC-overexpressing cells, which was partly reversible upon mutagenesis of a MYC E-box binding site in the JAK2 promoter. Moreover, silencing of MYC or its cofactor MAX with siRNA increased JAK2 expression and IFNγ responsiveness of melanomas, while concomitantly enhancing the effector functions of T cells coincubated with MYC-overexpressing cells. Thus, we propose that MYC plays a pivotal role in immunotherapy resistance through downregulation of JAK2.

Mechanisms of immune modulation in the tumor microenvironment and implications for targeted therapy

The efficacy of cancer therapies is limited to a great extent by immunosuppressive mechanisms within the tumor microenvironment (TME). Numerous immune escape mechanisms have been identified. These include not only processes associated with tumor, immune or stromal cells, but also humoral, metabolic, genetic and epigenetic factors within the TME. The identification of immune escape mechanisms has enabled the development of small molecules, nanomedicines, immune checkpoint inhibitors, adoptive cell and epigenetic therapies that can reprogram the TME and shift the host immune response towards promoting an antitumor effect. These approaches have translated into series of breakthroughs in cancer therapies, some of which have already been implemented in clinical practice. In the present article the authors provide an overview of some of the most important mechanisms of immunosuppression within the TME and the implications for targeted therapies against different cancers.

PANoptosis-based molecular subtyping and HPAN-index predicts therapeutic response and survival in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a highly prevalent and fatal cancer. The role of PANoptosis, a novel form of programmed cell death, in HCC is yet to be fully understood. This study focuses on identifying and analyzing PANoptosis-associated differentially expressed genes in HCC (HPAN_DEGs), aiming to enhance our understanding of HCC pathogenesis and potential treatment strategies.

Methods

We analyzed HCC differentially expressed genes from TCGA and IGCG databases and mapped them to the PANoptosis gene set, identifying 69 HPAN_DEGs. These genes underwent enrichment analyses, and consensus clustering analysis was used to determine three distinct HCC subgroups based on their expression profiles. The immune characteristics and mutation landscape of these subgroups were evaluated, and drug sensitivity was predicted using the HPAN-index and relevant databases.

Results

The HPAN_DEGs were mainly enriched in pathways associated with the cell cycle, DNA damage, Drug metabolism, Cytokines, and Immune receptors. We identified three HCC subtypes (Cluster_1, SFN+PDK4-; Cluster_2, SFN-PDK4+; Cluster_3, SFN/PDK4 intermediate expression) based on the expression profiles of the 69 HPAN_DEGs. These subtypes exhibited distinct clinical outcomes, immune characteristics, and mutation landscapes. The HPAN-index, generated by machine learning using the expression levels of 69 HPAN_DEGs, was identified as an independent prognostic factor for HCC. Moreover, the high HPAN-index group exhibited a high response to immunotherapy, while the low HPAN-index group showed sensitivity to small molecule targeted drugs. Notably, we observed that the YWHAB gene plays a significant role in Sorafenib resistance.

Conclusion

This study identified 69 HPAN_DEGs crucial to tumor growth, immune infiltration, and drug resistance in HCC. Additionally, we discovered three distinct HCC subtypes and constructed an HPAN-index to predict immunotherapeutic response and drug sensitivity. Our findings underscore the role of YWHAB in Sorafenib resistance, presenting valuable insights for personalized therapeutic strategy development in HCC.

Ex vivo modulation of intact tumor fragments with anti-PD-1 and anti-CTLA-4 influences the expansion and specificity of tumor-infiltrating lymphocytes

Checkpoint inhibition (CPI) therapy and adoptive cell therapy with autologous tumor-infiltrating lymphocytes (TIL-based ACT) are the two most effective immunotherapies for the treatment of metastatic melanoma. While CPI has been the dominating therapy in the past decade, TIL-based ACT is beneficial for individuals even after progression on previous immunotherapies. Given that notable differences in response have been made when used as a subsequent treatment, we investigated how the qualities of TILs changed when the ex vivo microenvironment of intact tumor fragments were modulated with checkpoint inhibitors targeting programmed death receptor 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4). Initially, we show that unmodified TILs from CPI-resistant individuals can be produced, are overwhelmingly terminally differentiated, and are capable of responding to tumor. We then investigate these properties in ex vivo checkpoint modulated TILs finding that that they retain these qualities. Lastly, we confirmed the specificity of the TILs to the highest responding tumor antigens, and identified this reactivity resides largely in CD39+CD69+ terminally differentiated populations. Overall, we found that anti-PD-1 will alter the proliferative capacity while anti-CTLA4 will influence breadth of antigen specificity.

Neoantigen-specific CD8 T cells with high structural avidity preferentially reside in and eliminate tumors

The success of cancer immunotherapy depends in part on the strength of antigen recognition by T cells. Here, we characterize the T cell receptor (TCR) functional (antigen sensitivity) and structural (monomeric pMHC-TCR off-rates) avidities of 371 CD8 T cell clones specific for neoantigens, tumor-associated antigens (TAAs) or viral antigens isolated from tumors or blood of patients and healthy donors. T cells from tumors exhibit stronger functional and structural avidity than their blood counterparts. Relative to TAA, neoantigen-specific T cells are of higher structural avidity and, consistently, are preferentially detected in tumors. Effective tumor infiltration in mice models is associated with high structural avidity and CXCR3 expression. Based on TCR biophysicochemical properties, we derive and apply an in silico model predicting TCR structural avidity and validate the enrichment in high avidity T cells in patients' tumors. These observations indicate a direct relationship between neoantigen recognition, T cell functionality and tumor infiltration. These results delineate a rational approach to identify potent T cells for personalized cancer immunotherapy.

An estrogen response-related signature predicts response to immunotherapy in melanoma

Background

Estrogen/estrogen receptor signaling influences the tumor microenvironment and affects the efficacy of immunotherapy in some tumors, including melanoma. This study aimed to construct an estrogen response-related gene signature for predicting response to immunotherapy in melanoma.

Methods

RNA sequencing data of 4 immunotherapy-treated melanoma datasets and TCGA melanoma was obtained from open access repository. Differential expression analysis and pathway analysis were performed between immunotherapy responders and non-responders. Using dataset GSE91061 as the training group, a multivariate logistic regression model was built from estrogen response-related differential expression genes to predict the response to immunotherapy. The other 3 datasets of immunotherapy-treated melanoma were used as the validation group. The correlation was also examined between the prediction score from the model and immune cell infiltration estimated by xCell in the immunotherapy-treated and TCGA melanoma cases.

Results

"Hallmark Estrogen Response Late" was significantly downregulated in immunotherapy responders. 11 estrogen response-related genes were significantly differentially expressed between immunotherapy responders and non-responders, and were included in the multivariate logistic regression model. The AUC was 0.888 in the training group and 0.654-0.720 in the validation group. A higher 11-gene signature score was significantly correlated to increased infiltration of CD8+ T cells (rho=0.32, p=0.02). TCGA melanoma with a high signature score showed a significantly higher proportion of immune-enriched/fibrotic and immune-enriched/non-fibrotic microenvironment subtypes (p<0.001)-subtypes with better response to immunotherapy-and significantly better progression-free interval (p=0.021).

Conclusion

In this study, we identified and verified an 11-gene signature that could predict response to immunotherapy in melanoma and was correlated with tumor-infiltrating lymphocytes. Our study suggests targeting estrogen-related pathways may serve as a combination strategy for immunotherapy in melanoma.

Systematic analysis of the aberrances and functional implications of cuproptosis in cancer

Cuproptosis is a novel form of cell death driven by a copper-dependent proteotoxic stress response whose comprehensive landscape in tumors remains unclear. Here, we comprehensively characterized cuproptosis-related genes (CRGs) across 33 cancers using multi-omic data from The Cancer Genome Atlas (TCGA), showing complicated and diverse results in different cancers. We also explored the relationships between CRGs and cancer metabolic patterns, pathway activity, and tumor microenvironment (TME), suggesting that they played critical roles in tumor progression and TME cell infiltration. We further established the cuproptosis potential index (CPI) to reveal the functional roles of cuproptosis, and characterized multi-omic molecular features associated with cuproptosis. In clinical applications, we performed a combined analysis of the sensitivity of CRGs and CPI to drug response and immunotherapy. This study provides a rich resource for understanding cuproptosis, offering a broad molecular perspective for future functional and therapeutic studies of multiple cancer pathways mediated by cuproptosis.

A prognostic signature consisting of N6-methyladenosine modified mRNAs demonstrates clinical potential in prediction of biochemical recurrence and guidance on precision therapy in prostate cancer

Novel biomarkers are urgently needed to improve the prediction of clinical outcomes and guide personalized treatment for prostate cancer (PCa) patients. However, the role of N6-methyladenosine (m6A) modifications in PCa initiation and progression remains largely elusive. In our study, we collected benign Prostate Hyperplasia (BPH), localized PCa, and metastatic PCa samples from patients and performed methylated RNA immunoprecipitation sequencing (MeRIP-Seq) to map m6A-methylated mRNAs. Furthermore, we developed a prognostic signature based on 239 differentially methylated RNAs and the TCGA-PRAD dataset, which can be used to calculate an m6A-modified mRNA (MMM) score for a PCa patient, validated by independent multi-center cohorts. Our findings revealed that differential m6A modifications were positively correlated with altered expressions of mapped m6A-modified mRNAs. Higher MMM scores were associated with shorter times to biochemical recurrence (BCR) in PCa patients, and the MMM scoring system outperformed three well-established signatures in nine independent validation cohorts, as demonstrated by Kaplan-Meier survival analysis, C-index and ROC. Patients who did not respond to androgen receptor signaling inhibitor (ARSI) therapy and immunotherapy were found to have high MMM scores. Two hub genes, TLE1 and PFKL, were confirmed to have m6A sites through MeRIP-qPCR, and their knockdown promoted PCa cell invasion. Bioinformatics analysis of single-cell databases identified cell types with high transcript abundance levels of these two genes. In summary, our study is the first to perform transcriptome-wide m6A mapping in prostate tissues. The translational potential of a prognostic signature, comprising m6A-methylated mRNAs, in predicting clinical outcomes and therapy responses for PCa patients, is demonstrated.

Prioritizing exhausted T cell marker genes highlights immune subtypes in pan-cancer

Exhausted T (TEX) cells are main immunotherapy targets in cancer, but it lacks a general identification method to characterize TEX cell in disease. To assess the characterization of TEX cell, we extract signature of TEX cell from large cancer and chronic infection cohorts. Based on single-cell transcriptomes, a systematic T cell exhaustion prediction (TEXP) model is designed to define TEX cell in cancer and chronic infection. We then prioritize 42 marker genes, including HAVCR2, PDCD1, TOX, TIGIT and LAG3, which are associated with T cell exhaustion. TEXP could identify high TEX and low TEX subtypes in pan-cancer of TCGA. The high TEX subtypes are characterized by high immune score, immune cell infiltration, high expression of TEX marker genes and poor prognosis. In summary, TEXP and marker genes provide a resource for understanding the function of TEX cell, with implications for immune prediction and immunotherapy in chronic infection and cancer.

An immune indicator based on BTK and DPEP2 identifies hot and cold tumors and clinical treatment outcomes in lung adenocarcinoma

In lung adenocarcinoma (LUAD), immune heterogeneity of hot and cold tumors has been recognized as one of the major factors affecting immunotherapy and other common treatments. However, there is still a lack of biomarkers that can effectively identify the immunophenotype of cold and hot tumors. First, the immune signatures were obtained based on literature mining, including macrophage/monocyte, IFN-γ response, TGF-β response, IL12 response, lymphocyte activation, and ECM/Dve/immune response. Subsequently, LUAD patients were further clustered into different immune phenotypes based on these immune signatures. Next, the key genes related to the immune phenotypes were screened by WGCNA analysis, univariate analysis, and lasso-cox analysis, and the risk signature was established via the key genes. In additional, we compared the clinicopathological characteristics, drug sensitivity, the abundance of immune infiltration, and the efficacy of immunotherapy and commonly used therapies between patients in the high- and low-risk groups in LUAD. LUAD patients were divided into immune hot phenotype and immune cold phenotype groups. The clinical presentation showed that patients with the immune hot phenotype had higher immunoactivity (including higher MHC, CYT, immune, stromal, ESTIMATE scores, higher abundance of immune cell infiltration, higher abundance of TIL, and enrichment of immune-enriched subtypes) and better survival outcomes than those with the immune cold phenotype. Subsequently, WGCNA analysis, univariate analysis, and lasso-cox analysis identified the genes highly associated with the immune phenotype: BTK and DPEP2. The risk signature, consisting of BTK and DPEP2, is highly correlated with the immune phenotype. High-risk scores were enriched in patients with immune cold phenotype and low-risk scores were enriched in patients with immune hot phenotype. Compared to the high-risk group, the low-risk group had better clinical performance, higher drug sensitivity, and a higher degree of immunoactivity, as well as better efficacy in receiving immunotherapy and common adjuvant therapy. This study developed an immune indicator consisting of BTK and DPEP2 based on the heterogeneity of hot and cold Immunophenotypes of the tumor microenvironment. This indicator has good efficacy in predicting prognosis and assessing the efficacy of immunotherapy, chemotherapy, and radiotherapy. It has the potential to facilitate personalized and precise treatment of LUAD in the future.

Neoepitope load, T cell signatures and PD-L2 as combined biomarker strategy for response to checkpoint inhibition immunotherapy

Immune checkpoint inhibition for the treatment of cancer has provided a breakthrough in oncology, and several new checkpoint inhibition pathways are currently being investigated regarding their potential to provide additional clinical benefit. However, only a fraction of patients respond to such treatment modalities, and there is an urgent need to identify biomarkers to rationally select patients that will benefit from treatment. In this study, we explore different tumor associated characteristics for their association with favorable clinical outcome in a diverse cohort of cancer patients treated with checkpoint inhibitors. We studied 29 patients in a basket trial comprising 12 different tumor types, treated with 10 different checkpoint inhibition regimens. Our analysis revealed that even across this diverse cohort, patients achieving clinical benefit had significantly higher neoepitope load, higher expression of T cell signatures, and higher PD-L2 expression, which also correlated with improved progression-free and overall survival. Importantly, the combination of biomarkers serves as a better predictor than each of the biomarkers alone. Basket trials are frequently used in modern immunotherapy trial design, and here we identify a set of biomarkers of potential relevance across multiple cancer types, allowing for the selection of patients that most likely will benefit from immune checkpoint inhibition.

In silico designed mRNA vaccines targeting CA-125 neoantigen in breast and ovarian cancer

Somatic mutation-derived neoantigens are associated with patient survival in breast and ovarian cancer. These neoantigens are targets for cancer, as shown by the implementation of neoepitope peptides as cancer vaccines. The success of cost-effective multi-epitope mRNA vaccines against SARS-Cov-2 in the pandemic established a model for reverse vaccinology. In this study, we aimed to develop an in silico pipeline designing an mRNA vaccine of the CA-125 neoantigen against breast and ovarian cancer, respectively. Using immuno-bioinformatics tools, we predicted cytotoxic CD8+ T cell epitopes based on somatic mutation-driven neoantigens of CA-125 in breast or ovarian cancer, constructed a self-adjuvant mRNA vaccine with CD40L and MHC-I -targeting domain to enhance cross-presentation of neoepitopes by dendritic cells. With an in silico ImmSim algorithm, we estimated the immune responses post-immunization, showing IFN-γ and CD8+ T cell response. The strategy described in this study may be scaled up and implemented to design precision multi-epitope mRNA vaccines by targeting multiple neoantigens.

Epigenetically regulated lncRNAs dissect the intratumoural heterogeneity and facilitate immune evasion of glioblastomas

Background: Glioblastomas are the most common and malignant central nervous system (CNS) tumors that occupied a highly heterogeneous tumor microenvironment (TIME). Long noncoding RNAs (lncRNAs), whose expression can be modified by DNA methylation, are emerging as critical regulators in the immune system. However, knowledge about the epigenetic changes in lncRNAs and their contribution to the immune heterogeneity of glioma is still lacking. Methods: In this study, we integrated paired methylome and transcriptome datasets of glioblastomas and identified 2 robust immune subtypes based on lncRNA methylation features. The immune characteristics of glioma subtypes were compared. Furthermore, immune-related lncRNAs were identified and their relationships with immune evasion were evaluated. Results: Glioma immunophenotypes exhibited distinct immune-related characteristics as well as clinical and epigenetic features. 149 epigenetically regulated (ER) lncRNAs were recognized that possessed inverse variation in epigenetic and transcriptional levels between glioma subtypes. Immune-related lncRNAs were further identified through the investigation of their correlation with immune cell infiltrations and immune-related pathways. In particular, the 'Hot' glioma subtype with higher immunoactivity while a worse survival outcome was found to character immune evasion features. We finally prioritized candidate ER lncRNAs associated with immune evasion markers and response to glioma immunotherapy. Among them, CD109-AS1 and LINC02447 were validated as novel immunoevasive biomarkers for glioma through in vitro experiments. Conclusion: In summary, our study systematically reveals the crosstalk among DNA methylation, lncRNA, and immune regulation in glioblastomas, and will facilitate the development of epigenetic immunotherapy approaches.

Current Trends in Neoantigen-Based Cancer Vaccines

Cancer immunotherapies are treatments that use drugs or cells to activate patients’ own immune systems against cancer cells. Among them, cancer vaccines have recently been rapidly developed. Based on tumor-specific antigens referred to as neoantigens, these vaccines can be in various forms such as messenger (m)RNA and synthetic peptides to activate cytotoxic T cells and act with or without dendritic cells. Growing evidence suggests that neoantigen-based cancer vaccines possess a very promising future, yet the processes of immune recognition and activation to relay identification of a neoantigen through the histocompatibility complex (MHC) and T-cell receptor (TCR) remain unclear. Here, we describe features of neoantigens and the biological process of validating neoantigens, along with a discussion of recent progress in the scientific development and clinical applications of neoantigen-based cancer vaccines.

SECTM1 is upregulated in immuno-hot tumors and predicts immunotherapeutic efficacy in multiple cancers

Immune checkpoint inhibitors (ICIs) have transformed the management of advanced cancers. However, many patients could not benefit from ICIs therapy, and thus several biomarkers for therapeutic prediction have been uncovered. In this research, more than ten public and in-house cohorts were used to explore the predictive value and immunological correlations of secreted and transmembrane 1 (SECTM1) in cancers. SECTM1 expression was enhanced in tumors from patients with well immunotherapeutic responses in multiple cancers. In addition, SECTM1 was immuno-correlated in pan-cancer and enhanced in immuno-hot tumors. In vitro assays revealed that SECTM1 was upregulated by the IFN-γ/STAT1 signaling. Moreover, analysis of in-house immunotherapy cohorts suggested both tumor-expressed and circulating SECTM1 are promising biomarkers to predict therapeutic responses. Overall, this study reveals that SECTM1 is a biomarker of benefit to ICIs in cancer patients. Further studies including large-scale patients are needed to establish its utilization as a biomarker of benefit to ICIs.

Identification of a pyroptosis-based model for predicting clinical outcomes from immunotherapy in patients with metastatic melanoma

Immunotherapy has greatly improved outcomes for patients with advanced melanoma, but good predictive biomarkers remain lacking in clinical practice. Although increasing evidence has revealed a vital role of pyroptosis in the tumor microenvironment (TME), it remains unclear for pyroptosis as a predictive biomarker for immunotherapy in melanoma. RNA sequencing data and annotated clinical information of melanoma patients were obtained from four published immunotherapy datasets. LASSO regression analysis was conducted to develop a pyroptosis-based model for quantifying a pyroptosis score in each tumor. Based on four clinical cohorts, we evaluated the predictive capability of the model using multiple immunotherapeutic outcomes, including clinical benefits, overall survival (OS), and progression-free survival (PFS). Furthermore, we depicted the distinctive TME features associated with pyroptosis. Compared with the group with low pyroptosis scores, the group with high pyroptosis scores consistently achieved better durable clinical benefits in four independent cohorts and the meta-cohort. ROC analysis validated that the pyroptosis-based model was a reliable biomarker for predicting clinical benefits from immunotherapy in melanoma. Survival analyses showed that the group with high pyroptosis scores harbored more favorable OS and PFS than those with low pyroptosis scores. Molecular analysis revealed that tumors with high pyroptosis scores displayed a typical immune-inflamed phenotype in TME, including enrichment of immunostimulatory pathways, increased level of tumor-infiltrating lymphocytes, upregulation of immune effectors, and activation of the antitumor immune response. Our findings suggested that the pyroptosis-related model associated with multiple immune-inflamed characteristics might be a reliable tool for predicting clinical benefit and survival outcomes from immunotherapy in melanoma.

Clustering by antigen-presenting genes reveals immune landscapes and predicts response to checkpoint immunotherapy

Immune checkpoint blockade (ICB) has demonstrated efficacy by reinvigorating immune cytotoxicity against tumors. However, the mechanisms underlying how ICB induces responses in a subset of patients remain unclear. Using bulk and single-cell transcriptomic cohorts of melanoma patients receiving ICB, we proposed a clustering model based on the expression of an antigen-presenting machinery (APM) signature consisting of 23 genes in a forward-selection manner. We characterized four APM clusters associated with distinct immune characteristics, cancer hallmarks, and patient prognosis in melanoma. The model predicts differential regulation of APM genes during ICB, which shaped ICB responsiveness. Surprisingly, while immunogenically hot tumors with high baseline APM expression prior to treatment are correlated with a better response to ICB than cold tumors with low APM expression, a subset of hot tumors with the highest pre-ICB APM expression fail to upregulate APM expression during treatment. In addition, they undergo immunoediting and display infiltration of exhausted T cells. In comparison, tumors associated with the best patient prognosis demonstrate significant APM upregulation and immune infiltration following ICB. They also show infiltration of tissue-resident memory T cells, shaping prolonged antitumor immunity. Using only pre-treatment transcriptomic data, our model predicts the dynamic APM-mediated tumor-immune interactions in response to ICB and provides insights into the immune escape mechanisms in hot tumors that compromise the ICB efficacy. We highlight the prognostic value of APM expression in predicting immune response in chronic diseases.

Cross-talk between cuproptosis and ferroptosis regulators defines the tumor microenvironment for the prediction of prognosis and therapies in lung adenocarcinoma

Cuproptosis, a newly identified form of programmed cell death, plays vital roles in tumorigenesis. However, the interconnectivity of cuproptosis and ferroptosis is poorly understood. In our study, we explored genomic alterations in 1162 lung adenocarcinoma (LUAD) samples from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) cohort to comprehensively evaluate the cuproptosis regulators. We systematically performed a pancancer genomic analysis by depicting the molecular correlations between the cuproptosis and ferroptosis regulators in 33 cancer types, indicating cross-talk between cuproptosis and ferroptosis regulators at the multiomic level. We successfully identified three distinct clusters based on cuproptosis and ferroptosis regulators, termed CuFeclusters, as well as the three distinct cuproptosis/ferroptosis gene subsets. The tumor microenvironment cell-infiltrating characteristics of three CuFeclusters were highly consistent with the three immune phenotypes of tumors. Furthermore, a CuFescore was constructed and validated to predict the cuproptosis/ferroptosis pathways in individuals and the response to chemotherapeutic drugs and immunotherapy. The CuFescore was significantly associated with the expression of miRNA and the regulation of post-transcription. Thus, our research established an applied scoring scheme, based on the regulators of cuproptosis/ferroptosis to identify LUAD patients who are candidates for immunotherapy and to predict patient sensitivity to chemotherapeutic drugs.

The mutation in splicing factor genes correlates with unfavorable prognosis, genomic instability, anti-tumor immunosuppression and increased immunotherapy response in pan-cancer

Splicing abnormality resulting from somatic mutations in key splicing factor genes (SFG) has been detected in various cancers. Hence, an in-depth study of splicing factor genes mutations' impact on pan-cancer is meaningful. This study investigated associations of splicing factor genes mutations with clinical features, tumor progression phenotypes, genomic integrity, anti-tumor immune responses, and immunotherapy response in 12 common cancer types from the TCGA database. Compared to SFG-wildtype cancers, SFG-mutated cancers displayed worse survival prognosis, higher tumor mutation burden and aneuploidy levels, higher expression of immunosuppressive signatures, and higher levels of tumor stemness, proliferation potential, and intratumor heterogeneity (ITH). However, splicing factor genes-mutated cancers showed higher response rates to immune checkpoint inhibitors than splicing factor genes-wildtype cancers in six cancer cohorts. Single-cell data analysis confirmed that splicing factor genes mutations were associated with increased tumor stemness, proliferation capacity, PD-L1 expression, intratumor heterogeneity, and aneuploidy levels. Our data suggest that the mutation in key splicing factor genes correlates with unfavorable clinical outcomes and disease progression, genomic instability, anti-tumor immunosuppression, and increased immunotherapy response in pan-cancer. Thus, the splicing factor genes mutation is an adverse prognostic factor and a positive marker for immunotherapy response in cancer.

Systematic evaluation of the predictive gene expression signatures of immune checkpoint inhibitors in metastatic melanoma

Advances in immunotherapy, including immune checkpoint inhibitors (ICIs), have transformed the standard of care for many types of cancer including melanoma. ICIs have improved the overall outcome of melanoma patients; however, a significant proportion of patients suffer from primary or secondary tumor resistance. Therefore, there is an urgent need to develop predictive biomarkers to better select patients for ICI therapy. Numerous biomarkers that predict the response of melanoma to ICIs have been investigated, including biomarker signatures based on genomics or transcriptomics. Most of these predictive biomarkers have not been systematically evaluated across different cohorts to determine the reproducibility of these signatures in metastatic melanoma. We evaluated 28 previously published predictive biomarkers of ICIs based on gene expression signatures in eight previously published studies with available RNA-sequencing data in public repositories. We found that signatures related to IFN-γ-responsive genes, T and B cell markers, and chemokines in the tumor immune microenvironment are generally predictive of response to ICIs in these patients. In addition, we identified that these predictive biomarkers have higher predictive values in on-treatment samples as compared to pretreatment samples in metastatic melanoma. The most frequently overlapping genes among the top 18 predictive signatures were CXCL10, CXCL9, PRF1, RANTES, IFNG, HLA-DRA, GZMB, and CD8A. From gene set enrichment analysis and cell type deconvolution, we estimated that the tumors of responders were enriched with infiltrating cytotoxic T-cells and other immune cells and the upregulation of genes related to interferon-γ signaling. Conversely, the tumors of non-responders were enriched with stromal-related cell types such as fibroblasts and myofibroblasts, as well as enrichment with T helper 17 cell types across all cohorts. In summary, our approach of validating and integrating multi-omics data can help guide future biomarker development in the field of ICIs and serve the quest for a more personalized therapeutic approach for melanoma patients.

[Molecular mechanisms of impaired antigenic presentation as a cause of tumor escape from immune surveillance]

The review summarizes data on the features of antigen presentation in tumor cells. The molecular mechanisms of the antitumor immune response are considered with an emphasis on the ability of tumor cells to avoid the action of immune surveillance. The features of expression of MHC molecules depending on treatment regimens are provided. Ways to improve existing and create new treatment regimens aimed at elimination of tumor cells because of antitumor immune response are discussed.

Multi-omics pan-cancer study of cuproptosis core gene FDX1 and its role in kidney renal clear cell carcinoma

Background

The mechanism of copper-induced cellular death was newly discovered and termed cuproptosis. Inducing cuproptosis in cancer cells is well anticipated for its curative potential in treating tumor diseases. However, ferredoxin 1 (FDX1), the core regulatory gene in cuproptosis, is rarely studied, and the regulation of FDX1 in tumor biology remains obscure. A comprehensive pan-cancer analysis of FDX1 is needed.

Methods

Thirty-three types of tumors were included with paired normal tissues in The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets. The interaction between transcription, protein, phosphorylation, and promoter methylation levels was analyzed. Survival, immune infiltration, single-cell FDX1 expression, FDX1-related tumor mutational burden (TMB), microsatellite instability (MSI), stemness, tumor immune dysfunction and exclusion (TIDE), and immunotherapy-related analyses were performed. FDX1 protein expression was assessed by kidney renal clear cell carcinoma (KIRC) tissue microarray immunohistochemistry. The function of FDX1 in KIRC was further explored by experiments in 786-O cell lines in vitro.

Results

FDX1 is highly expressed in 15 tumor types and lowly expressed in 11 tumor types. The corresponding changes in protein expression, phosphorylation, and promoter methylation level of FDX1 have been described in several tumors. Survival analysis showed that FDX1 was related to favorable or poor overall survival in eight tumors and progression-free survival in nine tumors. Immune infiltration and single-cell analysis indicated the indispensable role of FDX1 expression in macrophages and monocytes. Multiple established immunotherapy cohorts suggested that FDX1 may be a potential predictor of treatment effects for tumor patients. Tissue microarray analysis showed decreased FDX1 expression in KIRC patients' tumor tissues. Knockdown of FDX1 resulted in the downregulation of cuproptosis in kidney renal clear tumor cells. Mechanistically, the FDX1-associated gene expression signature in KIRC is related to the enrichment of genes involved in the tricarboxylic acid (TCA) cycle, NOTCH pathway, etc. Several NOTCH pathway genes were differentially expressed in the high- and low-FDX1 groups in KIRC.

Conclusion

Our analysis showed that the central regulatory gene of cuproptosis, FDX1, has differential expression and modification levels in various tumors, which is associated with cellular function, immune modulation, and disease prognosis. Thus, FDX1-dependent cuproptosis may serve as a brand-new target in future therapeutic approaches against tumors.

Epigenetic modulation of neuroblastoma enhances T cell and NK cell immunogenicity by inducing a tumor-cell lineage switch

BACKGROUND

Immunotherapy in high-risk neuroblastoma (HR-NBL) does not live up to its full potential due to inadequate (adaptive) immune engagement caused by the extensive immunomodulatory capacity of HR-NBL. We aimed to tackle one of the most notable immunomodulatory processes in neuroblastoma (NBL), absence of major histocompatibility complex class I (MHC-I) surface expression, a process greatly limiting cytotoxic T cell engagement. We and others have previously shown that MHC-I expression can be induced by cytokine-driven immune modulation. Here, we aimed to identify tolerable pharmacological repurposing strategies to upregulate MHC-I expression and therewith enhance T cell immunogenicity in NBL.

METHODS

Drug repurposing libraries were screened to identify compounds enhancing MHC-I surface expression in NBL cells using high-throughput flow cytometry analyses optimized for adherent cells. The effect of positive hits was confirmed in a panel of NBL cell lines and patient-derived organoids. Compound-treated NBL cell lines and organoids were cocultured with preferentially expressed antigen of melanoma (PRAME)-reactive tumor-specific T cells and healthy-donor natural killer (NK) cells to determine the in vitro effect on T cell and NK cell cytotoxicity. Additional immunomodulatory effects of histone deacetylase inhibitors (HDACi) were identified by transcriptome and translatome analysis of treated organoids.

RESULTS

Drug library screening revealed MHC-I upregulation by inhibitor of apoptosis inhibitor (IAPi)- and HDACi drug classes. The effect of IAPi was limited due to repression of nuclear factor kappa B (NFκB) pathway activity in NBL, while the MHC-I-modulating effect of HDACi was widely translatable to a panel of NBL cell lines and patient-derived organoids. Pretreatment of NBL cells with the HDACi entinostat enhanced the cytotoxic capacity of tumor-specific T cells against NBL in vitro, which coincided with increased expression of additional players regulating T cell cytotoxicity (eg, TAP1/2 and immunoproteasome subunits). Moreover, MICA and MICB, important in NK cell cytotoxicity, were also increased by entinostat exposure. Intriguingly, this increase in immunogenicity was accompanied by a shift toward a more mesenchymal NBL cell lineage.

CONCLUSIONS

This study indicates the potential of combining (immuno)therapy with HDACi to enhance both T cell-driven and NKcell-driven immune responses in patients with HR-NBL.

Decoding Strategies to Evade Immunoregulators Galectin-1, -3, and -9 and Their Ligands as Novel Therapeutics in Cancer Immunotherapy

Galectins are a family of ß-galactoside-binding proteins that play a variety of roles in normal physiology. In cancer, their expression levels are typically elevated and often associated with poor prognosis. They are known to fuel a variety of cancer progression pathways through their glycan-binding interactions with cancer, stromal, and immune cell surfaces. Of the 15 galectins in mammals, galectin (Gal)-1, -3, and -9 are particularly notable for their critical roles in tumor immune escape. While these galectins play integral roles in promoting cancer progression, they are also instrumental in regulating the survival, differentiation, and function of anti-tumor T cells that compromise anti-tumor immunity and weaken novel immunotherapies. To this end, there has been a surge in the development of new strategies to inhibit their pro-malignancy characteristics, particularly in reversing tumor immunosuppression through galectin-glycan ligand-targeting methods. This review examines some new approaches to evading Gal-1, -3, and -9-ligand interactions to interfere with their tumor-promoting and immunoregulating activities. Whether using neutralizing antibodies, synthetic peptides, glyco-metabolic modifiers, competitive inhibitors, vaccines, gene editing, exo-glycan modification, or chimeric antigen receptor (CAR)-T cells, these methods offer new hope of synergizing their inhibitory effects with current immunotherapeutic methods and yielding highly effective, durable responses.

Surveying lncRNA-lncRNA cooperations reveals dominant effect on tumor immunity cross cancers

Long non-coding RNAs (lncRNAs) can crosstalk with each other by post-transcriptionally co-regulating genes involved in the same or similar functions; however, the regulatory principles and biological insights in tumor-immune are still unclear. Here, we show a multiple-step model to identify lncRNA-lncRNA immune cooperation based on co-regulating functional modules by integrating multi-omics data across 20 cancer types. Moreover, lncRNA immune cooperative networks (LICNs) are constructed, which are likely to modulate tumor-immune microenvironment by regulating immune-related functions. We highlight conserved and rewired network hubs which can regulate interactions between immune cells and tumor cells by targeting ligands and activating or inhibitory receptors such as PDCD1, CTLA4 and CD86. Immune cooperative lncRNAs (IC-lncRNAs) playing central roles in many cancers also tend to target known anticancer drug targets. In addition, these IC-lncRNAs tend to be highly expressed in immune cell populations and are significantly correlated with immune cell infiltration. The similar immune mechanisms cross cancers are revealed by the LICNs. Finally, we identify two subtypes of skin cutaneous melanoma with different immune context and prognosis based on IC-lncRNAs. In summary, this study contributes to a comprehensive understanding of the cooperative behaviours of lncRNAs and accelerating discovery of lncRNA-based biomarkers in cancer.

Comprehensive Pan-Cancer Analyses of Immunogenic Cell Death as a Biomarker in Predicting Prognosis and Therapeutic Response

Immunogenic cell death (ICD), a form of regulated cell death, is related to anticancer therapy. Due to the absence of widely accepted markers, characterizing ICD-related phenotypes across cancer types remained unexplored. Here, we defined the ICD score to delineate the ICD landscape across 33 cancerous types and 31 normal tissue types based on transcriptomic, proteomic and epigenetics data from multiple databases. We found that ICD score showed cancer type-specific association with genomic and immune features. Importantly, the ICD score had the potential to predict therapy response and patient prognosis in multiple cancer types. We also developed an ICD-related prognostic model by machine learning and cox regression analysis. Single-cell level analysis revealed intra-tumor ICD state heterogeneity and communication between ICD-based clusters of T cells and other immune cells in the tumor microenvironment in colon cancer. For the first time, we identified IGF2BP3 as a potential ICD regulator in colon cancer. In conclusion, our study provides a comprehensive framework for evaluating the relation between ICD and clinical relevance, gaining insights into identification of ICD as a potential cancer-related biomarker and therapeutic target.

ACAP1 Deficiency Predicts Inferior Immunotherapy Response in Solid Tumors

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.

Integration of cancer stemness and neoantigen load to predict responsiveness to anti-PD1/PDL1 therapy

Background: Anti-programmed cell death 1/programmed cell death ligand 1 (PD1/PDL1) therapy is an important part of comprehensive cancer therapy. However, many patients suffer from non-response to therapy. Tumor neoantigen burden (TNB) and cancer stemness play essential roles in the responsiveness to therapy. Therefore, the identification of drug candidates for anti-PD1/PDL1 therapy remains an unmet need.

Methods: Three anti-PD1/PDL1 therapy cohorts were obtained from GEO database and published literatures. Cancer immune characteristics were analyzed using CIBERSORTX, GSVA, and ESTIMATE. WGCNA was employed to identify the gene modules correlated with cancer TNB and stemness. A machine-learning method was used to construct the immunotherapy resistance score (TSIRS). Pharmacogenomic analysis was conducted to explore the potential alternative drugs for anti-PD1/PDL1 therapy resistant patients. CCK-8 assay, EdU assay and wound healing assay were used to validate the effect of the predicted drug on cancer cells.

Results: The therapy response and non-response cancer groups have different microenvironment features. TSIRS was developed based on tumor neoantigen and stemness. TSIRS can effectively predict the outcomes of patients with anti-PD1/PDL1 therapy in training, validation and meta cohorts. Meanwhile, TSIRS can reflect the characteristics of tumor microenvironment during anti-PD1/PDL1 therapy. PF-4708671 is identified as a potential alternative drug for patients with resistance to anti-PD1/PDL1 therapy. It possesses significant inhibitive effect on the proliferation and migration of BGC-823 cells.

Conclusion: TSIRS is an effective tool in the identification of candidate patients who will be benefit from anti-PD1/PDL1 therapy. Small molecule drug PF-4708671 has the potential to be used in anti-PD1/PDL1 therapy resistant patients.

Tumor Microenvironment Characterization in Breast Cancer Identifies Prognostic Pathway Signatures

Breast cancer is one of the most common female malignancies worldwide. Due to its early metastases formation and a high degree of malignancy, the 10 year-survival rate of metastatic breast cancer does not exceed 30%. Thus, more precise biomarkers are urgently needed. In our study, we first estimated the tumor microenvironment (TME) infiltration using the xCell algorithm. Based on TME infiltration, the three main TME clusters were identified using consensus clustering. Our results showed that the three main TME clusters cause significant differences in survival rates and TME infiltration patterns (log-rank test, p = 0.006). Then, multiple machine learning algorithms were used to develop a nine-pathway-based TME-related risk model to predict the prognosis of breast cancer (BRCA) patients (the immune-related pathway-based risk score, defined as IPRS). Based on the IPRS, BRCA patients were divided into two subgroups, and patients in the IPRS-low group presented significantly better overall survival (OS) rates than the IPRS-high group (log-rank test, p < 0.0001). Correlation analysis revealed that the IPRS-low group was characterized by increases in immune-related scores (cytolytic activity (CYT), major histocompatibility complex (MHC), T cell-inflamed immune gene expression profile (GEP), ESTIMATE, immune, and stromal scores) while exhibiting decreases in tumor purity, suggesting IPRS-low patients may have a strong immune response. Additionally, the gene-set enrichment analysis (GSEA) result confirmed that the IPRS-low patients were significantly enriched in several immune-associated signaling pathways. Furthermore, multivariate Cox analysis revealed that the IPRS was an independent prognostic biomarker after adjustment by clinicopathologic characteristics. The prognostic value of the IPRS model was further validated in three external validation cohorts. Altogether, our findings demonstrated that the IPRS was a powerful predictor to screen out certain populations with better prognosis in breast cancer and may serve as a potential biomarker guiding clinical treatment decisions.

Tumor-infiltrating lymphocytes for treatment of solid tumors: It takes two to tango?

Tumor-infiltrating lymphocytes (TILs), frontline soldiers of the adaptive immune system, are recruited into the tumor site to fight against tumors. However, their small number and reduced activity limit their ability to overcome the tumor. Enhancement of TILs number and activity against tumors has been of interest for a long time. A lack of knowledge about the tumor microenvironment (TME) has limited success in primary TIL therapies. Although the advent of engineered T cells has revolutionized the immunotherapy methods of hematologic cancers, the heterogeneity of solid tumors warrants the application of TILs with a wide range of specificity. Recent advances in understanding TME, immune exhaustion, and immune checkpoints have paved the way for TIL therapy regimens. Nowadays, TIL therapy has regained attention as a safe personalized immunotherapy, and currently, several clinical trials are evaluating the efficacy of TIL therapy in patients who have failed conventional immunotherapies. Gaining favorable outcomes following TIL therapy of patients with metastatic melanoma, cervical cancer, ovarian cancer, and breast cancer has raised hope in patients with refractory solid tumors, too. Nevertheless, TIL therapy procedures face several challenges, such as high cost, timely expansion, and technical challenges in selecting and activating the cells. Herein, we reviewed the recent advances in the TIL therapy of solid tumors and discussed the challenges and perspectives.

Genomic landscape of the immunogenicity regulation in skin melanomas with diverse tumor mutation burden

Skin melanoma cells are tightly interconnected with their tumor microenvironment (TME), which influences their initiation, progression, and sensitivity/resistance to therapeutic interventions. An immune-active TME favors patient response to immune checkpoint inhibition (ICI), but not all patients respond to therapy. Here, we assessed differential gene expression in primary and metastatic tumors from the TCGA-SKCM dataset, compared to normal skin samples from the GTEx project and validated key findings across 4 independent GEO datasets, as well as using immunohistochemistry in independent patient cohorts. We focused our attention on examining the expression of various immune receptors, immune-cell fractions, immune-related signatures and mutational signatures across cutaneous melanomas with diverse tumor mutation burdens (TMB). Globally, the expression of most immunoreceptors correlated with patient survival, but did not differ between TMBhigh and TMBlow tumors. Melanomas were enriched in "naive T-cell", "effector memory T-cell", "exhausted T-cell", "resting Treg T-cell" and "Th1-like" signatures, irrespective of their BRAF, NF1 or RAS mutational status. Somatic mutations in IDO1 and HLA-DRA were frequent and could be involved in hindering patient response to ICI therapies. We finally analyzed transcriptome profiles of ICI-treated patients and associated their response with high levels of IFNγ, Merck18, CD274, CD8, and low levels of myeloid-derived suppressor cells (MDSCs), cancer-associated fibroblasts (CAFs) and M2 macrophages, irrespective of their TMB status. Overall, our findings highlight the importance of pre-existing T-cell immunity in ICI therapeutic outcomes in skin melanoma and suggest that TMBlow patients could also benefit from such therapies.