Pan-cancer analysis of the interplay between mutational signatures and cellular signaling

Cancer is a multi-faceted disease with intricate relationships between mutagenic processes, alterations in cellular signaling, and the tissue microenvironment. To date, these processes have been largely studied in isolation. A systematic understanding of how they interact and influence each other is lacking. Here, we present a framework for systematically characterizing the interaction between pairs of mutational signatures and between signatures and signaling pathway alterations. We applied this framework to large-scale data from TCGA and PCAWG and identified multiple positive and negative interactions, both cross֊tissue and tissue֊specific, that provide new insights into the molecular routes observed in tumorigenesis and their respective drivers. This framework allows for a more fine-grained dissection of common and distinct etiology of mutational signatures. We further identified several interactions with both positive and negative impacts on patient survival, demonstrating their clinical relevance and potential for improving personalized cancer care.

Tumor Mutation Burden as a Cornerstone in Precision Oncology Landscapes: Effect of Panel Size and Uncertainty in Cutoffs

Tumor mutation burden (TMB) has profound implications for personalized cancer therapy, particularly immunotherapy. However, the size of the panel and the cutoff values for an accurate determination of TMB are still controversial. In this study, a pan-cancer analysis was performed on 22 cancer types from The Cancer Genome Atlas. The efficiency of gene panels of different sizes and the effect of cutoff values in accurate TMB determination was assessed on a large cohort using Whole Exome Sequencing data (n = 9929 patients) as the gold standard. Gene panels of four different sizes (i.e., 0.44-2.54 Mb) were selected for comparative analyses. The heterogeneity of TMB within and between cancer types is observed to be very high, and it becomes possible to obtain the exact TMB value as the size of the panel increases. In panels with limited size, it is particularly difficult to recognize patients with low TMB. In addition, the use of a general TMB cutoff can be quite misleading. The optimal cutoff value varies between 5 and 20, depending on the TMB distribution of the different tumor types. The use of comprehensive gene panels and the optimization of TMB cutoff values for different cancer types can make TMB a robust biomarker in precision oncology. Moreover, optimization of TMB can help accelerate translational medicine research, and by extension, delivery of personalized cancer care in the future.

Tumor Mutation Burden-Related Histopathologic Features for Predicting Overall Survival in Gliomas Using Graph Deep Learning

Tumor mutation burden (TMB) is a potential biomarker for evaluating the prognosis and response to immune checkpoint inhibitors, but its costly and time-consuming method of measurement limits its widespread application. This study aimed to identify the TMB-related histopathologic features from hematoxylin and eosin slides and explore their prognostic value in gliomas. TMB-related features were detected using a graph convolutional neural network from whole-slide images of patients from The Cancer Genome Atlas data set (619 patients), and the correlation between features and TMB was evaluated in an external validation set (237 patients). TMB-related features were used for predicting overall survival (OS) of patients to investigate whether these features have potential for prognostic prediction. Moreover, biological pathways underlying the prognostic value of the features were further explored. Histopathologic features derived from whole-slide images were significantly associated with patient TMB (P = 0.007 in the external validation set). TMB-related features showed excellent performance for OS prediction, and patients with lower-grade gliomas could be further stratified into different risk groups according to the features (P = 0.00013; hazard ratio, 4.004). Pathways involved in the cell cycle and execution of immune response were enriched in patients with higher OS risk. The TMB-related features could be used to estimate TMB and aid in prognostic risk stratification of patients with glioma with dysregulated biological pathways.

The role of the genomic mutation signature and tumor mutation burden on relapse risk prediction in head and neck squamous cell carcinoma after concurrent chemoradiotherapy

Personalized genetic profiling has focused on improving treatment efficacy and predicting risk stratification by identifying mutated genes and selecting targeted agents according to genetic testing. Therefore, we evaluated the role of genetic profiling and tumor mutation burden (TMB) using next-generation sequencing in patients with head and neck squamous cell carcinoma (HNSC). The relapse mutation signature (RMS) and chromatin remodeling mutation signature (CRMS) were explored to predict the risk of relapse in patients with HNSC treated with concurrent chemoradiotherapy (CCRT) with platinum-based chemotherapy. Patients in the high RMS and CRMS groups showed significantly shorter relapse-free survival than those in the low RMS and CRMS groups, respectively (p < 0.001 and p = 0.006). Multivariate Cox regression analysis showed that extranodal extension, CCRT response, and three somatic mutation profiles (TMB, RMS, and CRMS) were independent risk predictors for HNSC relapse. The predictive nomogram showed satisfactory performance in predicting relapse-free survival in patients with HNSC treated with CCRT.

The development of a novel signature based on the m6A RNA methylation regulator-related ceRNA network to predict prognosis and therapy response in sarcomas

Background: N⁶ methyladenosine (m⁶A)-related noncoding RNAs (including lncRNAs and miRNAs) are closely related to the development of cancer. However, the gene signature and prognostic value of m⁶A regulators and m⁶A-associated RNAs in regulating sarcoma (SARC) development and progression remain largely unexplored. Therefore, further research is required.

Methods: We obtained expression data for RNA sequencing (RNA-seq) and miRNAs of SARC from The Cancer Genome Atlas (TCGA) datasets. Correlation analysis and two target gene prediction databases (miRTarBase and LncBase v.2) were used to deduce m⁶A-related miRNAs and lncRNAs, and Cytoscape software was used to construct ceRNA-regulating networks. Based on univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, an m⁶A-associated RNA risk signature (m6Ascore) model was established. Prognostic differences between subgroups were explored using Kaplan–Meier (KM) analysis. Risk score-related biological phenotypes were analyzed in terms of functional enrichment, tumor immune signature, and tumor mutation signature. Finally, potential immunotherapy features and drug sensitivity predictions for this model were also discussed.

Results: A total of 16 miRNAs, 104 lncRNAs, and 11 mRNAs were incorporated into the ceRNA network. The risk score was obtained based on RP11-283I3.6, hsa-miR-455-3p, and CBLL1. Patients were divided into two risk groups using the risk score, with patients in the low-risk group having longer overall survival (OS) than those in the high-risk group. The receiver operating characteristic (ROC) curves indicated that risk characteristic performed well in predicting the prognosis of patients with SARC. In addition, lower m6Ascore was also positively correlated with the abundance of immune cells such as monocytes and mast cells activated, and several immune checkpoint genes were highly expressed in the low-m6Ascore group. According to our analysis, lower m6Ascore may lead to better immunotherapy response and OS outcomes. The risk signature was significantly associated with the chemosensitivity of SARC. Finally, a nomogram was constructed to predict the OS in patients with SARC. The concordance index (C-index) for the nomogram was 0.744 (95% CI: 0.707–0.784). The decision curve analysis (DCA), calibration plot, and ROC curve all showed that this nomogram had good predictive performance.

Conclusion: This m6Ascore risk model based on m⁶A RNA methylation regulator-related RNAs may be promising for clinical prediction of prognosis and might contain potential biomarkers for treatment response prediction for SARC patients.

Immune landscape and risk prediction based on pyroptosis-related molecular subtypes in triple-negative breast cancer

The survival outcome of triple-negative breast cancer (TNBC) remains poor, with difficulties still existing in prognosis assessment and patient stratification. Pyroptosis, a newly discovered form of programmed cell death, is involved in cancer pathogenesis and progression. The role of pyroptosis in the tumor microenvironment (TME) of TNBC has not been fully elucidated. In this study, we disclosed global alterations in 58 pyroptosis-related genes at somatic mutation and transcriptional levels in TNBC samples collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Based on the expression patterns of genes related to pyroptosis, we identified two molecular subtypes that harbored different TME characteristics and survival outcomes. Then, based on differentially expressed genes between two subtypes, we established a 12-gene score with robust efficacy in predicting short- and long-term overall survival of TNBC. Patients at low risk exhibited a significantly better prognosis, more antitumor immune cell infiltration, and higher expression of immune checkpoints including PD-1, PD-L1, CTLA-4, and LAG3. The comprehensive analysis of the immune landscape in TNBC indicated that alterations in pyroptosis-related genes were closely related to the formation of the immune microenvironment and the intensity of the anticancer response. The 12-gene score provided new information on the risk stratification and immunotherapy strategy for highly heterogeneous patients with TNBC.

Molecular Subtypes Based on Cell Differentiation Trajectories in Head and Neck Squamous Cell Carcinoma: Differential Prognosis and Immunotherapeutic Responses

Objective

Head and neck squamous cell carcinoma (HNSCC) is one of the most common and lethal malignant tumors. We aimed to investigate the HNSCC cell differentiation trajectories and the corresponding clinical relevance.

Methods

Based on HNSCC cell differentiation-related genes (HDRGs) identified by single-cell sequencing analysis, the molecular subtypes and corresponding immunity, metabolism, and stemness characteristics of 866 HNSCC cases were comprehensively analyzed. Machine-learning strategies were used to develop a HNSCC cell differentiation score (HCDscore) in order to quantify the unique heterogeneity of individual samples. We also assessed the prognostic value and biological characteristics of HCDscore using the multi-omics data.

Results

HNSCCs were stratified into three distinct molecular subtypes based on HDRGs: active stroma (Cluster-A), active metabolism (Cluster-B), and active immune (Cluster-C) types. The three molecular subtypes had different characteristics in terms of biological phenotype, genome and epigenetics, prognosis, immunotherapy and chemotherapy responses. We then demonstrated the correlations between HCDscore and the immune microenvironment, subtypes, carcinogenic biological processes, genetic variation, and prognosis. The low-HCDscore group was characterized by activation of immunity, enhanced response to anti-PD-1/PD-L1 immunotherapy, and better survival compared to the high-HCDscore group. Finally, by integrating the HCDscore with prognostic clinicopathological characteristics, a nomogram with strong predictive performance and high accuracy was constructed.

Conclusions

This study revealed that the cell differentiation trajectories in HNSCC played a nonnegligible role in patient prognosis, biological characteristics, and immune responses. Evaluating cancer cell differentiation will help to develop more effective immunotherapy, metabolic therapy, and chemotherapy strategies.

Understanding resistance to immune checkpoint inhibitors in advanced breast cancer

INTRODUCTION

The addition of immune checkpoint inhibitors (ICIs) to frontline chemotherapy has improved survival for patients with advanced triple-negative breast cancer (TNBC) expressing programmed death-ligand 1 (PD-L1). Nonetheless, most patients develop resistance, with outcomes remaining poor for this population. Moreover, unsatisfactory activity has been observed with ICIs in PD-L1-negative TNBC and in other breast cancer (BC) subtypes, warranting a deeper understanding of resistance to ICIs in BC.

AREAS COVERED

We discuss the immune landscape of distinct BC subtypes, review the clinical activity of immunotherapy in BC, and highlight strategies under development to overcome resistance to ICIs.

EXPERT OPINION

Activity and resistance to ICIs in BC are strongly related to the intrinsic immunophenotype of the tumor tissue. Several promising biomarkers reflecting the immunological state of BC are emerging, with only PD-L1 expression currently adopted into clinical practice. However, limitations make of PD-L1 a sub-optimal biomarker for patient selection, which require efforts to integrate this marker with other immunological features. Concomitantly, a wide variety of drug combinations designed to overcome immune-resistance are being evaluated, with some encouraging signals observed in early-phase trials. Combination strategies tailored to patient and tumor immunophenotype may allow to overcome resistance and fully exploit the potential of ICIs.