Revealing the contribution of somatic gene mutations to shaping tumor immune microenvironment.

Interaction between tumor cells and immune cells determined highly heterogeneous microenvironments across patients, leading to substantial variation in clinical benefits from immunotherapy. Somatic gene mutations were found not only to elicit adaptive immunity but also to influence the composition of tumor immune microenvironment and various processes of antitumor immunity. However, due to an incomplete view of associations between gene mutations and immunophenotypes, how tumor cells shape the immune microenvironment and further determine the clinical benefit of immunotherapy is still unclear. To address this, we proposed a computational approach, inference of mutation effect on immunophenotype by integrated gene set enrichment analysis (MEIGSEA), for tracing back the genomic factor responsible for differences in immunophenotypes. MEIGSEA was demonstrated to accurately identify the previous confirmed immune-associated gene mutations, and systematic evaluation in simulation data further supported its performance. We used MEIGSEA to investigate the influence of driver gene mutations on the infiltration of 22 immune cell types across 19 cancers from The Cancer Genome Atlas. The top associated gene mutations with infiltration of CD8 T cells, such as CASP8, KRAS and EGFR, also showed extensive impact on other immune components; meanwhile, immune effector cells shared critical gene mutations that collaboratively contribute to shaping distinct tumor immune microenvironment. Furthermore, we highlighted the predictive capacity of gene mutations that are positively associated with CD8 T cells for the clinical benefit of immunotherapy. Taken together, we present a computational framework to help illustrate the potential of somatic gene mutations in shaping the tumor immune microenvironment.

Dissecting cellular states of infiltrating microenvironment cells in melanoma by integrating single-cell and bulk transcriptome analysis.

BACKGROUND: Cellular states of different immune cells can affect the activity of the whole immune microenvironment. METHODS: Here, leveraging reference profiles of microenvironment cell states that were constructed based on single-cell RNA-seq data of melanoma, we dissected the composition of microenvironment cell states across 463 skin cutaneous melanoma (SKCM) bulk samples through CIBERSORT-based deconvolution of gene expression profiles and revealed high heterogeneity of their distribution. Correspondence analysis on the estimated cellular fractions of melanoma bulk samples was performed to identify immune phenotypes. Based on the publicly available clinical survival and therapy data, we analyzed the relationship between immune phenotypes and clinical outcomes of melanoma. RESULTS: By analysis of the relationships among those cell states, we further identified three distinct tumor microenvironment immune phenotypes: "immune hot/active", "immune cold-suppressive" and "immune cold-exhausted". They were characterized by markedly different patterns of cell states: most notably the CD8 T Cytotoxic state, CD8 T Mixed state, B non-regulatory state and cancer-associated fibroblasts (CAFs), depicting distinct types of antitumor immune response (or immune activity). These phenotypes had prognostic significance for progression-free survival and implications in response to immune therapy in an independent cohort of anti-PD1 treated melanoma patients. CONCLUSIONS: The proposed strategy of leveraging single-cell data to dissect the composition of microenvironment cell states in individual bulk tumors can also extend to other cancer types, and our results highlight the importance of microenvironment cell states for the understanding of tumor immunity.

Systematic investigation of the prognostic impact of clonal status of somatic mutations across multiple cancer types.

Tumors are genetically heterogeneous and many mutations are actually present in subclonal populations. The clonal status of mutations is valuable for accurate prognosis, clinical management. The aim of this study was to identify the clonal status of somatic mutations and systematically evaluate their prognostic values across various cancer types. We totally identified 227 clonal and 432 subclonal mutations contributed to prognosis and demonstrated the importance of clonal status in improving mutation-related clinical guidance. We further developed a customized multi-step approach to identify gene-specific prognostic patterns of clonal status at pan-cancer level and found some cancer-specific prognostic patterns. The 'subclonal-dependent risk' subpattern was one of the most common subpatterns, it usually accompanied by high genomic in-stability and high extent of intra-tumor heterogeneity and could be used to improve the accuracy of prognostic analysis. Our results revealed the importance of clonal status, especially subclonal mutation in clinical survival.

Revealing the functions of clonal driver gene mutations in patients based on evolutionary dependencies.

The clonal mutations in driver genes enable cells to gradually acquire growth advantage in tumor development. Therefore, revealing the functions of clonal driver gene mutations is important. Here, we proposed the method FCMP that considered evolutionary dependencies to analyze the functions of clonal driver gene mutations in a single patient. Applying our method to five cancer types from The Cancer Genome Atlas, we identified specific functions and common functions of clonal driver gene mutations. We found that the clonal driver gene mutations in the same patient played multiple functions. We also found that clonal mutations in the same driver gene performed different functions in different patients. These findings suggested that the clonal driver gene mutations showed strong tumor heterogeneity. In the pan-cancer analysis, the immune-related functions for clonal driver gene mutations were shared by multiple cancer types. In addition, clonal mutations in some driver genes predicted the survival of patients in cancers.

Identifying mutual exclusivity across cancer genomes: computational approaches to discover genetic interaction and reveal tumor vulnerability.

Systematic sequencing of cancer genomes has revealed prevalent heterogeneity, with patients harboring various combinatorial patterns of genetic alteration. In particular, a phenomenon that a group of genes exhibits mutually exclusive patterns has been widespread across cancers, covering a broad spectrum of crucial cancer pathways. Recently, there is considerable evidence showing that, mutual exclusivity reflects alternative functions in tumor initiation and progression, or suggests adverse effects of their concurrence. Given its importance, numerous computational approaches have been proposed to study mutual exclusivity using genomic profiles alone, or by integrating networks and phenotypes. Some of them have been routinely used to explore genetic associations, which lead to a deeper understanding of carcinogenic mechanisms and reveals unexpected tumor vulnerabilities. Here, we present an overview of mutual exclusivity from the perspective of cancer genome. We describe the common hypothesis underlying mutual exclusivity, summarize the strategies for the identification of significant mutually exclusive patterns, compare the performance of representative algorithms from simulated data sets and discuss their common confounders.

Breast cancer prognosis signature: linking risk stratification to disease subtypes.

Breast cancer is a very complex and heterogeneous disease with variable molecular mechanisms of carcinogenesis and clinical behaviors. The identification of prognostic risk factors may enable effective diagnosis and treatment of breast cancer. In particular, numerous gene-expression-based prognostic signatures were developed and some of them have already been applied into clinical trials and practice. In this study, we summarized several representative gene-expression-based signatures with significant prognostic value and separately assessed their ability of prognosis prediction in their originally targeted populations of breast cancer. Notably, many of the collected signatures were originally designed to predict the outcomes of estrogen receptor positive (ER+) patients or the whole breast cancer cohort; there are no typical signatures used for the prognostic prediction in a specific population of patients with the intrinsic subtype. We thus attempted to identify subtype-specific prognostic signatures via a computational framework for analyzing multi-omics profiles and patient survival. For both the discovery and an independent data set, we confirmed that subtype-specific signature is a strong and significant independent prognostic factor in the corresponding cohort. These results indicate that the subtype-specific prognostic signature has a much higher resolution in the risk stratification, which may lead to improved therapies and precision medicine for patients with breast cancer.

CellMarker: a manually curated resource of cell markers in human and mouse.

One of the most fundamental questions in biology is what types of cells form different tissues and organs in a functionally coordinated fashion. Larger-scale single-cell sequencing and biology experiment studies are now rapidly opening up new ways to track this question by revealing substantial cell markers for distinguishing different cell types in tissues. Here, we developed the CellMarker database (http://biocc.hrbmu.edu.cn/CellMarker/ or http://bio-bigdata.hrbmu.edu.cn/CellMarker/), aiming to provide a comprehensive and accurate resource of cell markers for various cell types in tissues of human and mouse. By manually curating over 100 000 published papers, 4124 entries including the cell marker information, tissue type, cell type, cancer information and source, were recorded. At last, 13 605 cell markers of 467 cell types in 158 human tissues/sub-tissues and 9148 cell makers of 389 cell types in 81 mouse tissues/sub-tissues were collected and deposited in CellMarker. CellMarker provides a user-friendly interface for browsing, searching and downloading markers of diverse cell types of different tissues. Furthermore, a summarized marker prevalence in each cell type is graphically and intuitively presented through a vivid statistical graph. We believe that CellMarker is a comprehensive and valuable resource for cell researches in precisely identifying and characterizing cells, especially at the single-cell level.

Comprehensive analysis of long noncoding RNA (lncRNA)-chromatin interactions reveals lncRNA functions dependent on binding diverse regulatory elements.

Over the past decade, thousands of long noncoding RNAs (lncRNAs) have been identified, many of which play crucial roles in normal physiology and human disease. LncRNAs can interact with chromatin and then recruit protein complexes to remodel chromatin states, thus regulating gene expression. However, how lncRNA-chromatin interactions contribute to their biological functions is largely unknown. Here, we collected and constructed an atlas of 188,647 lncRNA-chromatin interactions in human and mouse. All lncRNAs showed diverse epigenetic modification patterns at their binding sites, especially the marks of enhancer activity. Functional analysis of lncRNA target genes further revealed that lncRNAs could exert their functions by binding to both promoter and distal regulatory elements, especially the distal regulatory elements. Intriguingly, many important pathways were observed to be widely regulated by lncRNAs through distal binding. For example, NEAT1, a cancer lncRNA, controls 13.3% of genes in the PI3K-AKT signaling pathway by interacting with distal regulatory elements. In addition, "two-gene" signatures composed of a lncRNA and its distal target genes, such as HOTAIR-CRIM1, provided significant clinical benefits relative to the lncRNA alone. In summary, our findings underscored that lncRNA-distal interactions were essential for lncRNA functions, which would provide new clues to understand the molecular mechanisms of lncRNAs in complex disease.

DiseaseEnhancer: a resource of human disease-associated enhancer catalog.

Large-scale sequencing studies discovered substantial genetic variants occurring in enhancers which regulate genes via long range chromatin interactions. Importantly, such variants could affect enhancer regulation by changing transcription factor bindings or enhancer hijacking, and in turn, make an essential contribution to disease progression. To facilitate better usage of published data and exploring enhancer deregulation in various human diseases, we created DiseaseEnhancer (http://biocc.hrbmu.edu.cn/DiseaseEnhancer/), a manually curated database for disease-associated enhancers. As of July 2017, DiseaseEnhancer includes 847 disease-associated enhancers in 143 human diseases. Database features include basic enhancer information (i.e. genomic location and target genes); disease types; associated variants on the enhancer and their mediated phenotypes (i.e. gain/loss of enhancer and the alterations of transcription factor bindings). We also include a feature on our website to export any query results into a file and download the full database. DiseaseEnhancer provides a promising avenue for researchers to facilitate the understanding of enhancer deregulation in disease pathogenesis, and identify new biomarkers for disease diagnosis and therapy.

Revealing clonality and subclonality of driver genes for clinical survival benefits in breast cancer.

PURPOSE: Genomic studies have revealed that genomic aberrations play important roles in the progression of this disease. The aim of this study was to evaluate the associations between clinical survival outcomes of the clonality and subclonality status of driver genes in breast cancer. METHODS: We performed an integrated analysis to infer the clonal status of 55 driver genes in breast cancer data from TCGA. We used the chi-squared test to assess the relations between clonality of driver gene mutations and clinicopathological factors. The Kaplan-Meier method was performed for the visualization and the differences between survival curves were calculated by log-rank test. Univariate and multivariate Cox proportional hazards regression models were used to adjust for clinicopathological factors. RESULTS: We identified a high proportion of clonal mutations in these driver genes. Among them, there were 17 genes showing significant associations between their clonality and multiple clinicopathologic factors. Performing survival analysis on BRCA patients with clonal or subclonal driver gene mutations, we found that clonal ERBB2, FOXA1, and KMT2C mutations and subclonal GATA3 and RB1 mutations predicted shorter overall survival compared with those with wild type. Furthermore, clonal ERBB2 and FOXA1 mutations and subclonal GATA3 and RB1 mutations independently predicted for shorter overall survival after adjusting for clinicopathological factors. By longitudinal analysis, the clonality of ERBB2, FOXA1, GATA3, and RB1 significantly predicted patients' outcome within some specific BRCA tumor stages and histological subtypes. CONCLUSIONS: In summary, these clonal or subclonal mutations of driver genes have implications for diagnosis, prognosis, and treatment with BRCA patients.

A comprehensive overview of lncRNA annotation resources.

Long noncoding RNAs (lncRNAs) are emerging as a class of important regulators participating in various biological functions and disease processes. With the widespread application of next-generation sequencing technologies, large numbers of lncRNAs have been identified, producing plenty of lncRNA annotation resources in different contexts. However, at present, we lack a comprehensive overview of these lncRNA annotation resources. In this study, we reviewed 24 currently available lncRNA annotation resources referring to > 205 000 lncRNAs in over 50 tissues and cell lines. We characterized these annotation resources from different aspects, including exon structure, expression, histone modification and function. We found many distinct properties among these annotation resources. Especially, these resources showed diverse chromatin signatures, remarkable tissue and cell type dependence and functional specificity. Our results suggested the incompleteness and complementarity of current lncRNA annotations and the necessity of integration of multiple resources to comprehensively characterize lncRNAs. Finally, we developed 'LNCat' (lncRNA atlas, freely available at http://biocc.hrbmu.edu.cn/LNCat/), a user-friendly database that provides a genome browser of lncRNA structures, visualization of different resources from multiple angles and download of different combinations of lncRNA annotations, and supports rapid exploration, comparison and integration of lncRNA annotation resources. Overall, our study provides a comprehensive comparison of numerous lncRNA annotations, and can facilitate understanding of lncRNAs in human disease.

'Traffic light rules': Chromatin states direct miRNA-mediated network motifs running by integrating epigenome and regulatome.

BACKGROUND: Epigenetic marks can cooperatively regulate chromatin accessibility and in turn facilitate or impede the binding of regulatory factors to various elements, suggesting their important roles in regulatory circuits. However, it remains elusive as to how epigenetic marks cooperate in the operations of regulatory network. METHODS: Here, we systematically characterized chromatin states of 26 epigenetic marks on different elements of protein-coding genes and miRNAs. We comprehensively analyzed, by using an integrative regulatory network, how cooperation among epigenetic, transcriptional, and post-transcriptional regulations came about. RESULTS: We observed extensive cooperation of epigenetic marks on local functional elements and complex epigenetic patterns corresponding to different biological functions. By identifying the significantly epigenetic state-modified motifs, we found that multiple combinations of epigenetic states were associated with a specific type of motif. Interestingly, miRNA-mediated motifs were linked to stable epigenetic states of downstream targets. Changes in epigenetic states of downstream targets in miRNA-mediated motifs can buffer the effects of upstream regulator on target genes, suggesting that miRNA-mediated motifs require the cooperation of epigenetic marks. CONCLUSIONS: Overall, epigenetic marks are involved in the running of regulatory motifs in the way traffic lights control traffic flows and hence should be part of the architecture of complex regulatory circuits. GENERAL SIGNIFICANCE: We demonstrated a detailed analysis of the cooperation of multiple epigenetic marks and how epigenetic regulation was organized into a human regulatory network. The findings form a basis for further understanding of the complicated roles of epigenetic marks on regulatory circuits.

Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals.

High heterogeneity in genome and phenotype of cancer populations made it difficult to apply population-based common driver genes to the diagnosis and treatment of cancer individuals. Characterizing and identifying the personalized driver mechanism for glioblastoma multiforme (GBM) individuals were pivotal for the realization of precision medicine. We proposed an integrative method to identify the personalized driver gene sets by integrating the profiles of gene expression and genetic alterations in cancer individuals. This method coupled genetic algorithm and random walk to identify the optimal gene sets that could explain abnormality of transcriptome phenotype to the maximum extent. The personalized driver gene sets were identified for 99 GBM individuals using our method. We found that genomic alterations in between one and seven driver genes could maximally and cumulatively explain the dysfunction of cancer hallmarks across GBM individuals. The driver gene sets were distinct even in GBM individuals with significantly similar transcriptomic phenotypes. Our method identified MCM4 with rare genetic alterations as previously unknown oncogenic genes, the high expression of which were significantly associated with poor GBM prognosis. The functional experiments confirmed that knockdown of MCM4 could significantly inhibit proliferation, invasion, migration, and clone formation of the GBM cell lines U251 and U118MG, and overexpression of MCM4 significantly promoted the proliferation, invasion, migration, and clone formation of the GBM cell line U87MG. Our method could dissect the personalized driver genetic alteration sets that are pivotal for developing targeted therapy strategies and precision medicine. Our method could be extended to identify key drivers from other levels and could be applied to more cancer types.

Decoding the associations between cell functional states in head and neck cancer based on single-cell transcriptome.

OBJECTIVES: Systematically identifying cancer cell functional states, especially their associations, is key to understanding the pathogenesis of cancers. MATERIALS AND METHODS: Here, we systematically identified six cancer-related states, including epithelial-mesenchymal transition (EMT), immune response, epithelial differentiation, stress, G1/S and G2/M phases, in head and neck squamous cell carcinoma (HNSCC) based on single-cell RNA-sequencing (scRNA-seq). RESULTS AND CONCLUSION: We defined the association patterns between these functional states and found the patterns were correlated with the state activity. Particularly, immune response and EMT were negatively, positively, or non-significantly correlated in samples with the highest immune response activity, the lowest activity of the two states, or with the highest EMT activity, respectively. Combining scRNA-seq data of immune cells and four independent HNSCC cohorts, we found the negative relationship between EMT and immune response was correlated with an activated immune microenvironment and a longer survival, while the non-significant relationship was correlated with an immunosuppressed microenvironment and a poor prognosis. Collectively, our results provide insight into the association patterns between functional states in HNSCC, and may facilitate the elucidation of the interactions between cancer cells and immune system during cancer progression.

Identifying Key Somatic Copy Number Alterations Driving Dysregulation of Cancer Hallmarks in Lower-Grade Glioma.

Somatic copy-number alterations (SCNAs) are major contributors to cancer development that are pervasive and highly heterogeneous in human cancers. However, the driver roles of SCNAs in cancer are insufficiently characterized. We combined network propagation and linear regression models to design an integrative strategy to identify driver SCNAs and dissect the functional roles of SCNAs by integrating profiles of copy number and gene expression in lower-grade glioma (LGG). We applied our strategy to 511 LGG patients and identified 98 driver genes that dysregulated 29 cancer hallmark signatures, forming 143 active gene-hallmark pairs. We found that these active gene-hallmark pairs could stratify LGG patients into four subtypes with significantly different survival times. The two new subtypes with similar poorest prognoses were driven by two different gene sets (one including EGFR, CDKN2A, CDKN2B, INFA8, and INFA5, and the other including CDK4, AVIL, and DTX3), respectively. The SCNAs of the two gene sets could disorder the same cancer hallmark signature in a mutually exclusive manner (including E2F_TARGETS and G2M_CHECKPOINT). Compared with previous methods, our strategy could not only capture the known cancer genes and directly dissect the functional roles of their SCNAs in LGG, but also discover the functions of new driver genes in LGG, such as IFNA5, IFNA8, and DTX3. Additionally, our method can be applied to a variety of cancer types to explore the pathogenesis of driver SCNAs and improve the treatment and diagnosis of cancer.

Models for Predicting Sentinel and Non-sentinel Lymph Nodes Based on Pre-operative Ultrasonic Breast Imaging to Optimize Axillary Strategies.

Axillary strategy decisions have become more complex and controversial in considering minimally traumatic therapy instead of sentinel lymph node biopsy, axillary lymph node dissection or regional nodal irradiation for people with breast cancer. The purpose of this study was to noninvasively predict sentinel lymph node (SLN) and non-sentinel lymph node (NSLN) status based on pre-operative sonographic and clinicopathologic features to determine optimal decisions regarding axillary therapy. In total, 701 patients with breast cancer from two independent centers were retrospectively analyzed. The SLN model (SLNM) for predicting SLN status and the NSLN model (NSLNM) for predicting NSLN status were trained based on a training set using the random-forest algorithm, and their performance was validated using an independent external test set. A receiver operating characteristic curve was drawn to obtain the area under the curve, which was used to assess performance. The area under the curve for the SLNM in the training and test, respectively, was 94.2% and 83.0%, and for the NSLNM, 99.5% and 92.7%. The SLNM and NSLNM accurately predicted that 61.46% (319/519) and 17.53% (91/519), respectively, of our participants were non-metastatic. The overall benefit of the three models was 78.99% in our participants. The two models for predicting SLN and NSLN status showed excellent application potential in optimizing axillary strategies.

Transcriptomic heterogeneity of driver gene mutations reveals novel mutual exclusivity and improves exploration of functional associations.

BACKGROUND: Lung adenocarcinoma (LUAD), as the most common subtype of lung cancer, is the leading cause of cancer deaths in the world. The accumulation of driver gene mutations enables cancer cells to gradually acquire growth advantage. Therefore, it is important to understand the functions and interactions of driver gene mutations in cancer progression. METHODS: We obtained gene mutation data and gene expression profile of 506 LUAD tumors from The Cancer Genome Atlas (TCGA). The subtypes of tumors with driver gene mutations were identified by consensus cluster analysis. RESULTS: We found 21 significantly mutually exclusive pairs consisting of 20 genes among 506 LUAD patients. Because of the increased transcriptomic heterogeneity of mutations, we identified subtypes among tumors with non-silent mutations in driver genes. There were 494 mutually exclusive pairs found among driver gene mutations within different subtypes. Furthermore, we identified functions of mutually exclusive pairs based on the hypothesis of functional redundancy of mutual exclusivity. These mutually exclusive pairs were significantly enriched in nuclear division and humoral immune response, which played crucial roles in cancer initiation and progression. We also found 79 mutually exclusive triples among subtypes of tumors with driver gene mutations, which were key roles in cell motility and cellular chemical homeostasis. In addition, two mutually exclusive triples and one mutually exclusive triple were associated with the overall survival and disease-specific survival of LUAD patients, respectively. CONCLUSIONS: We revealed novel mutual exclusivity and generated a comprehensive functional landscape of driver gene mutations, which could offer a new perspective to understand the mechanisms of cancer development and identify potential biomarkers for LUAD therapy.

Nomograms for Predicting Axillary Lymph Node Status Reconciled With Preoperative Breast Ultrasound Images.

INTRODUCTION: The axillary lymph node (ALN) status of breast cancer patients is an important prognostic indicator. The use of primary breast mass features for the prediction of ALN status is rare. Two nomograms based on preoperative ultrasound (US) images of breast tumors and ALNs were developed for the prediction of ALN status. METHODS: A total of 743 breast cancer cases collected from 2016 to 2019 at the Second Affiliated Hospital of Harbin Medical University were randomly divided into a training set (n = 523) and a test set (n = 220). A primary tumor feature model (PTFM) and ALN feature model (ALNFM) were separately generated based on tumor features alone, and a combination of features was used for the prediction of ALN status. Logistic regression analysis was used to construct the nomograms. A receiver operating characteristic curve was plotted to obtain the area under the curve (AUC) to evaluate accuracy, and bias-corrected AUC values and calibration curves were obtained by bootstrap resampling for internal and external verification. Decision curve analysis was applied to assess the clinical utility of the models. RESULTS: The AUCs of the PTFM were 0.69 and 0.67 for the training and test sets, respectively, and the bias-corrected AUCs of the PTFM were 0.67 and 0.67, respectively. Moreover, the AUCs of the ALNFM were 0.86 and 0.84, respectively, and the bias-corrected AUCs were 0.85 and 0.81, respectively. Compared with the PTFM, the ALNFM showed significantly improved prediction accuracy (p < 0.001). Both the calibration and decision curves of the ALNFM nomogram indicated greater accuracy and clinical practicality. When the US tumor size was ≤21.5 mm, the Spe was 0.96 and 0.92 in the training and test sets, respectively. When the US tumor size was greater than 21.5 mm, the Sen was 0.85 in the training set and 0.87 in the test set. Our further research showed that when the US tumor size was larger than 35 mm, the Sen was 0.90 in the training set and 0.93 in the test set. CONCLUSION: The ALNFM could effectively predict ALN status based on US images especially for different US tumor size.

Prognostic impact of a lymphocyte activation-associated gene signature in GBM based on transcriptome analysis.

BACKGROUND: Glioblastoma multiforme (GBM) is a highly, malignant tumor of the primary central nervous system. Patients diagnosed with this type of tumor have a poor prognosis. Lymphocyte activation plays important roles in the development of cancers and its therapeutic treatments. OBJECTIVE: We sought to identify an efficient lymphocyte activation-associated gene signature that could predict the progression and prognosis of GBM. METHODS: We used univariate Cox proportional hazards regression and stepwise regression algorithm to develop a lymphocyte activation-associated gene signature in the training dataset (TCGA, n = 525). Then, the signature was validated in two datasets, including GSE16011 (n = 150) and GSE13041 (n = 191) using the Kaplan Meier method. Univariate and multivariate Cox proportional hazards regression models were used to adjust for clinicopathological factors. RESULTS: We identified a lymphocyte activation-associated gene signature (TCF3, IGFBP2, TYRO3 and NOD2) in the training dataset and classified the patients into high-risk and low-risk groups with significant differences in overall survival (median survival 15.33 months vs 12.57 months, HR = 1.55, 95% CI [1.28-1.87], log-rank test P < 0.001). This signature showed similar prognostic values in the other two datasets. Further, univariate and multivariate Cox proportional hazards regression models analysis indicated that the signature was an independent prognostic factor for GBM patients. Moreover, we determined that there were differences in lymphocyte activity between the high- and low-risk groups of GBM patients among all datasets. Furthermore, the lymphocyte activation-associated gene signature could significantly predict the survival of patients with certain features, including IDH-wildtype patients and patients undergoing radiotherapy. In addition, the signature may also improve the prognostic power of age. CONCLUSIONS: In summary, our results suggested that the lymphocyte activation-associated gene signature is a promising factor for the survival of patients, which is helpful for the prognosis of GBM patients.

Analysis of Mutations and Dysregulated Pathways Unravels Carcinogenic Effect and Clinical Actionability of Mutational Processes.

Somatic mutations accumulate over time in cancer cells as a consequence of mutational processes. However, the role of mutational processes in carcinogenesis remains poorly understood. Here, we infer the causal relationship between mutational processes and somatic mutations in 5,828 samples spanning 34 cancer subtypes. We found most mutational processes cause abundant recurrent mutations in cancer genes, while exceptionally ultraviolet exposure and altered activity of the error-prone polymerase bring a large number of recurrent non-driver mutations. Furthermore, some mutations are specifically induced by a certain mutational process, such as IDH1 p.R132H which is mainly caused by spontaneous deamination of 5-methylcytosine. At the pathway level, clock-like mutational processes extensively trigger mutations to dysregulate cancer signal transduction pathways. In addition, APOBEC mutational process destroys DNA double-strand break repair pathway, and bladder cancer patients with high APOBEC activity, though with homologous recombination proficient, show a significantly longer overall survival with platinum regimens. These findings help to understand how mutational processes act on the genome to promote carcinogenesis, and further, presents novel insights for cancer prevention and treatment, as our results showing, APOBEC mutagenesis and HRD synergistically contributed to the clinical benefits of platinum-based treatment.