Computational immunogenomic approaches to predict response to cancer immunotherapies

Mesenchymal-like immune-altered is the fourth robust triple-negative breast cancer molecular subtype

BACKGROUND

Robust molecular subtyping of triple-negative breast cancer (TNBC) is a prerequisite for the success of precision medicine. Today, there is a clear consensus on three TNBC molecular subtypes: luminal androgen receptor (LAR), basal-like immune-activated (BLIA), and basal-like immune-suppressed (BLIS). However, the debate about the robustness of other subtypes is still open.

METHODS

An unprecedented number (n = 1942) of TNBC patient data was collected. Microarray- and RNAseq-based cohorts were independently investigated. Unsupervised analyses were conducted using k-means consensus clustering. Clusters of patients were then functionally annotated using different approaches. Prediction of response to chemotherapy and targeted therapies, immune checkpoint blockade, and radiotherapy were also screened for each TNBC subtype.

RESULTS

Four TNBC subtypes were identified in the cohort: LAR (19.36%); mesenchymal stem-like (MSL/MES) (17.35%); BLIA (31.06%); and BLIS (32.23%). Regarding the MSL/MES subtype, we suggest renaming it to mesenchymal-like immune-altered (MLIA) to emphasize its specific histological background and nature of immune response. Treatment response prediction results show, among other things, that despite immune activation, immune checkpoint blockade is probably less or completely ineffective in MLIA, possibly caused by mesenchymal background and/or an enrichment in dysfunctional cytotoxic T lymphocytes. TNBC subtyping results were included in the bc-GenExMiner v5.0 webtool ( http://bcgenex.ico.unicancer.fr ).

CONCLUSION

The mesenchymal TNBC subtype is characterized by an exhausted and altered immune response, and resistance to immune checkpoint inhibitors. Consensus for molecular classification of TNBC subtyping and prediction of cancer treatment responses helps usher in the era of precision medicine for TNBC patients.

Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond

Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.

Challenges and opportunities in cancer immunotherapy: a Society for Immunotherapy of Cancer (SITC) strategic vision

Cancer immunotherapy has flourished over the last 10-15 years, transforming the practice of oncology and providing long-term clinical benefit to some patients. During this time, three distinct classes of immune checkpoint inhibitors, chimeric antigen receptor-T cell therapies specific for two targets, and two distinct classes of bispecific T cell engagers, a vaccine, and an oncolytic virus have joined cytokines as a standard of cancer care. At the same time, scientific progress has delivered vast amounts of new knowledge. For example, advances in technologies such as single-cell sequencing and spatial transcriptomics have provided deep insights into the immunobiology of the tumor microenvironment. With this rapid clinical and scientific progress, the field of cancer immunotherapy is currently at a critical inflection point, with potential for exponential growth over the next decade. Recognizing this, the Society for Immunotherapy of Cancer convened a diverse group of experts in cancer immunotherapy representing academia, the pharmaceutical and biotechnology industries, patient advocacy, and the regulatory community to identify current opportunities and challenges with the goal of prioritizing areas with the highest potential for clinical impact. The consensus group identified seven high-priority areas of current opportunity for the field: mechanisms of antitumor activity and toxicity; mechanisms of drug resistance; biomarkers and biospecimens; unique aspects of novel therapeutics; host and environmental interactions; premalignant immunity, immune interception, and immunoprevention; and clinical trial design, endpoints, and conduct. Additionally, potential roadblocks to progress were discussed, and several topics were identified as cross-cutting tools for optimization, each with potential to impact multiple scientific priority areas. These cross-cutting tools include preclinical models, data curation and sharing, biopsies and biospecimens, diversification of funding sources, definitions and standards, and patient engagement. Finally, three key guiding principles were identified that will both optimize and maximize progress in the field. These include engaging the patient community; cultivating diversity, equity, inclusion, and accessibility; and leveraging the power of artificial intelligence to accelerate progress. Here, we present the outcomes of these discussions as a strategic vision to galvanize the field for the next decade of exponential progress in cancer immunotherapy.

Malignant Melanoma: An Overview, New Perspectives, and Vitamin D Signaling

Melanoma, originating through malignant transformation of melanin-producing melanocytes, is a formidable malignancy, characterized by local invasiveness, recurrence, early metastasis, resistance to therapy, and a high mortality rate. This review discusses etiologic and risk factors for melanoma, diagnostic and prognostic tools, including recent advances in molecular biology, omics, and bioinformatics, and provides an overview of its therapy. Since the incidence of melanoma is rising and mortality remains unacceptably high, we discuss its inherent properties, including melanogenesis, that make this disease resilient to treatment and propose to use AI to solve the above complex and multidimensional problems. We provide an overview on vitamin D and its anticancerogenic properties, and report recent advances in this field that can provide solutions for the prevention and/or therapy of melanoma. Experimental papers and clinicopathological studies on the role of vitamin D status and signaling pathways initiated by its active metabolites in melanoma prognosis and therapy are reviewed. We conclude that vitamin D signaling, defined by specific nuclear receptors and selective activation by specific vitamin D hydroxyderivatives, can provide a benefit for new or existing therapeutic approaches. We propose to target vitamin D signaling with the use of computational biology and AI tools to provide a solution to the melanoma problem.

Hand in hand to successful immunotherapy: CD8+ T cells and M1-like macrophages swap the baton

Cancer immunotherapy is a pillar of clinical oncology but only achieves long-term remissions in a minority of cases. In this issue, van Elsas et al. show that effective immunotherapy requires a series of processes orchestrated by CD8+ T cells that result in the recruitment and local activation of M1-like macrophages.

Germline variants alter immune surveillance

Germline-derived epitopes shape tumor development through immunoediting.

Germline-mediated immunoediting sculpts breast cancer subtypes and metastatic proclivity

Tumors with the same diagnosis can have different molecular profiles and response to treatment. It remains unclear when and why these differences arise. Somatic genomic aberrations occur within the context of a highly variable germline genome. Interrogating 5870 breast cancer lesions, we demonstrated that germline-derived epitopes in recurrently amplified genes influence somatic evolution by mediating immunoediting. Individuals with a high germline-epitope burden in human epidermal growth factor receptor 2 (HER2/ERBB2) are less likely to develop HER2-positive breast cancer compared with other subtypes. The same holds true for recurrent amplicons defining three aggressive estrogen receptor (ER)-positive subgroups. Tumors that overcome such immune-mediated negative selection are more aggressive and demonstrate an "immune cold" phenotype. These data show that the germline genome plays a role in dictating somatic evolution.

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy

Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable, but is hindered by the limited performance of existing biomarkers. Here, we leveraged in-silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We tested 90 biomarker candidates, including various cellular and molecular species, by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pre-treatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

Tumor biomarkers for diagnosis, prognosis and targeted therapy

Tumor biomarkers, the substances which are produced by tumors or the body's responses to tumors during tumorigenesis and progression, have been demonstrated to possess critical and encouraging value in screening and early diagnosis, prognosis prediction, recurrence detection, and therapeutic efficacy monitoring of cancers. Over the past decades, continuous progress has been made in exploring and discovering novel, sensitive, specific, and accurate tumor biomarkers, which has significantly promoted personalized medicine and improved the outcomes of cancer patients, especially advances in molecular biology technologies developed for the detection of tumor biomarkers. Herein, we summarize the discovery and development of tumor biomarkers, including the history of tumor biomarkers, the conventional and innovative technologies used for biomarker discovery and detection, the classification of tumor biomarkers based on tissue origins, and the application of tumor biomarkers in clinical cancer management. In particular, we highlight the recent advancements in biomarker-based anticancer-targeted therapies which are emerging as breakthroughs and promising cancer therapeutic strategies. We also discuss limitations and challenges that need to be addressed and provide insights and perspectives to turn challenges into opportunities in this field. Collectively, the discovery and application of multiple tumor biomarkers emphasized in this review may provide guidance on improved precision medicine, broaden horizons in future research directions, and expedite the clinical classification of cancer patients according to their molecular biomarkers rather than organs of origin.

Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning

Background

Proliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates single-cell RNA sequencing (scRNA-seq), Non-negative Matrix Factorization (NMF), machine learning, and AlphaFold 2 methods to explore the molecular level of PDR.

Methods

We analyzed scRNA-seq data from PDR patients and healthy controls to identify distinct cellular subtypes and gene expression patterns. NMF was used to define specific transcriptional programs in PDR. The oxidative stress-related genes (ORGs) identified within Meta-Program 1 were utilized to construct a predictive model using twelve machine learning algorithms. Furthermore, we employed AlphaFold 2 for the prediction of protein structures, complementing this with molecular docking to validate the structural foundation of potential therapeutic targets. We also analyzed protein-protein interaction (PPI) networks and the interplay among key ORGs.

Results

Our scRNA-seq analysis revealed five major cell types and 14 subcell types in PDR patients, with significant differences in gene expression compared to those in controls. We identified three key meta-programs underscoring the role of microglia in the pathogenesis of PDR. Three critical ORGs (ALKBH1, PSIP1, and ATP13A2) were identified, with the best-performing predictive model demonstrating high accuracy (AUC of 0.989 in the training cohort and 0.833 in the validation cohort). Moreover, AlphaFold 2 predictions combined with molecular docking revealed that resveratrol has a strong affinity for ALKBH1, indicating its potential as a targeted therapeutic agent. PPI network analysis, revealed a complex network of interactions among the hub ORGs and other genes, suggesting a collective role in PDR pathogenesis.

Conclusion

This study provides insights into the cellular and molecular aspects of PDR, identifying potential biomarkers and therapeutic targets using advanced technological approaches.

Special Issue "Cancer Immunotherapy: Tumor Microenvironment, Biomarker Discovery and Immune Resistance"

Since the launch of this Special Issue entitled "Cancer Immunotherapy: Tumor Microenvironment, Biomarker Discovery and Immune Resistance", the field of cancer immunotherapy has continued to witness rapid growth in the development of novel agents, improvements in our understanding of mechanisms of response and resistance, and the maturation of emerging technologies such as artificial intelligence, machine learning, single-cell sequencing and spatial profiling [...].

Identifying specific TLS-associated genes as potential biomarkers for predicting prognosis and evaluating the efficacy of immunotherapy in soft tissue sarcoma

Background

The tertiary lymphatic structure (TLS) is an important component of the tumor immune microenvironment and has important significance in patient prognosis and response to immune therapy. However, the underlying mechanism of TLS in soft tissue sarcoma remains unclear.

Methods

A total of 256 RNAseq and 7 single-cell sequencing samples were collected from TCGA-SARC and GSE212527 cohorts. Based on published TLS-related gene sets, four TLS scores were established by GSVA algorithm. The immune cell infiltration was calculated via TIMER2.0 and "MCPcounter" algorithms. In addition, the univariate, LASSO, and multivariate-Cox analyses were used to select TLS-related and prognosis-significant hub genes. Single-cell sequencing dataset, clinical immunohistochemical, and cell experiments were utilized to validate the hub genes.

Results

In this study, four TLS-related scores were identified, and the total-gene TLS score more accurately reflected the infiltration level of TLS in STS. We further established two hub genes (DUSP9 and TNFSF14) prognosis markers and risk scores associated with soft tissue sarcoma prognosis and immune therapy response. Flow cytometry analysis showed that the amount of CD3, CD8, CD19, and CD11c positive immune cell infiltration in the tumor tissue dedifferentiated liposarcoma patients was significantly higher than that of liposarcoma patients. Cytological experiments showed that soft tissue sarcoma cell lines overexpressing TNFSF14 could inhibit the proliferation and migration of sarcoma cells.

Conclusion

This study systematically explored the TLS and related genes from the perspectives of bioinformatics, clinical features and cytology experiments. The total-gene TLS score, risk score and TNFSF14 hub gene may be useful biomarkers for predicting the prognosis and immunotherapy efficacy of soft tissue sarcoma.

MOCS, a novel classifier system integrated multimoics analysis refining molecular subtypes and prognosis for skin melanoma

PURPOSE

The present investigation focuses on Skin Cutaneous Melanoma (SKCM), a melanocytic carcinoma characterized by marked aggression, significant heterogeneity, and a complex etiological background, factors which collectively contribute to the challenge in prognostic determinations. We defined a novel classifier system specifically tailored for SKCM based on multiomics.

METHODS

We collected 423 SKCM samples with multi omics datasets to perform a consensus cluster analysis using 10 machine learning algorithms and verified in 2 independent cohorts. Clinical features, biological characteristics, immune infiltration pattern, therapeutic response and mutation landscape were compared between subtypes.

RESULTS

Based on consensus clustering algorithms, we identified two Multi-Omics-Based-Cancer-Subtypes (MOCS) in SKCM in TCGA project and validated in GSE19234 and GSE65904 cohorts. MOCS2 emerged as a subtype with poor prognosis, characterized by a complex immune microenvironment, dysfunctional anti-tumor immune state, high cancer stemness index, and genomic instability. MOCS2 exhibited resistance to chemotherapy agents like erlotinib and sunitinib while sensitive to rapamycin, NSC87877, MG132, and FH355. Additionally, ELSPBP1 was identified as the target involving in glycolysis and M2 macrophage infiltration in SKCM.

CONCLUSIONS

MOCS classification could stably predict prognosis of SKCM; patients with a high cancer stemness index combined with genomic instability may be predisposed to an immune exhaustion state.Communicated by Ramaswamy H. Sarma.

Single cell sequencing revealed the mechanism of CRYAB in glioma and its diagnostic and prognostic value

Background

We explored the characteristics of single-cell differentiation data in glioblastoma and established prognostic markers based on CRYAB to predict the prognosis of glioblastoma patients. Aberrant expression of CRYAB is associated with invasive behavior in various tumors, including glioblastoma. However, the specific role and mechanisms of CRYAB in glioblastoma are still unclear.

Methods

We assessed RNA-seq and microarray data from TCGA and GEO databases, combined with scRNA-seq data on glioma patients from GEO. Utilizing the Seurat R package, we identified distinct survival-related gene clusters in the scRNA-seq data. Prognostic pivotal genes were discovered through single-factor Cox analysis, and a prognostic model was established using LASSO and stepwise regression algorithms. Moreover, we investigated the predictive potential of these genes in the immune microenvironment and their applicability in immunotherapy. Finally, in vitro experiments confirmed the functional significance of the high-risk gene CRYAB.

Results

By analyzing the ScRNA-seq data, we identified 28 cell clusters representing seven cell types. After dimensionality reduction and clustering analysis, we obtained four subpopulations within the oligodendrocyte lineage based on their differentiation trajectory. Using CRYAB as a marker gene for the terminal-stage subpopulation, we found that its expression was associated with poor prognosis. In vitro experiments demonstrated that knocking out CRYAB in U87 and LN229 cells reduced cell viability, proliferation, and invasiveness.

Conclusion

The risk model based on CRYAB holds promise in accurately predicting glioblastoma. A comprehensive study of the specific mechanisms of CRYAB in glioblastoma would contribute to understanding its response to immunotherapy. Targeting the CRYAB gene may be beneficial for glioblastoma patients.