Clinical and genomic characterization of mutational signatures across human cancers.

Mutational signatures, the generic patterns of mutations, are the footprints of both endogenous and exogenous factors that have influenced cancer development. To date, dozens of mutational signatures have been discerned through computational methods. However, the etiology, mutational properties, clonality, immunology and prognostic value of mutation signatures across cancer types are poorly understood. To address this, we extensively characterized mutational signatures across 8836 cancer samples spanning 42 cancer types. We confirmed and extended clinical and genomic features associated with mutation signatures. Mutation distribution analysis showed that most mutation processes were depleted in exons and APOBEC signatures (SBS2 and SBS13), the Pol-η related signature (SBS9) and SBS40 tended to contribute clustered mutations. We observed that age-related signatures (SBS1 and SBS5) and SBS40 tended to induce mutations affecting cancer genes and subclonal drivers posted by specific signatures (eg, mismatch repair deficiency-related signature SBS44) were unlikely subjected to positive selection. We also revealed early mutation signatures (eg, UV light exposure-related signature SBS7a) and signatures (eg, reactive oxygen species-related signature SBS18) predominated in the late stage of tumorigenesis. Comprehensive association analysis of mutation processes with microenvironment revealed that APOBEC- and mismatch repair deficiency-related signatures were positively associated with immune parameters, while age-related signatures showed negative correlations. In addition, prognostic association analysis showed that many signatures were favorable (eg, SBS9) or adverse factors (eg, SBS18) of patient survival. Our findings enhance appreciation of the role of mutational signatures in tumor evolution and underline their potential in immunotherapy guidance and prognostic prediction.

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.

Applicability of Anticancer Drugs for the Triple-Negative Breast Cancer Based on Homologous Recombination Repair Deficiency.

Triple-negative breast cancer (TNBC) is a highly aggressive disease with historically poor outcomes, primarily due to the lack of effective targeted therapies. Here, we established a drug sensitivity prediction model based on the homologous recombination deficiency (HRD) using 83 TNBC patients from TCGA. Through analyzing the effect of HRD status on response efficacy of anticancer drugs and elucidating its related mechanisms of action, we found rucaparib (PARP inhibitor) and doxorubicin (anthracycline) sensitive in HR-deficient patients, while paclitaxel sensitive in the HR-proficient. Further, we identified a HRD signature based on gene expression data and constructed a transcriptomic HRD score, for analyzing the functional association between anticancer drug perturbation and HRD. The results revealed that CHIR99021 (GSK3 inhibitor) and doxorubicin have similar expression perturbation patterns with HRD, and talazoparib (PARP inhibitor) could kill tumor cells by reversing the HRD activity. Genomic characteristics indicated that doxorubicin inhibited tumor cells growth by hindering the process of DNA damage repair, while the resistance of cisplatin was related to the activation of angiogenesis and epithelial-mesenchymal transition. The negative correlation of HRD signature score could interpret the association of doxorubicin pIC50 with worse chemotherapy response and shorter survival of TNBC patients. In summary, these findings explain the applicability of anticancer drugs in TNBC and underscore the importance of HRD in promoting personalized treatment development.

IDH clonal heterogeneity segregates a subgroup of non-1p/19q codeleted gliomas with unfavourable clinical outcome.

AIMS: Diffuse gliomas (DGs) are classified into three major molecular subgroups following the revised World Health Organisation (WHO) classification criteria based on their IDH mutation and 1p/19q codeletion status. However, substantial biological heterogeneity and differences in the clinical course are apparent within each subgroup, which remain to be resolved. We sought to assess the clonal status of somatic mutations and explore whether additional molecular subgroups exist within DG. METHODS: A computational framework that integrates the variant allele frequency, local copy number and tumour purity was used to infer the clonality of somatic mutations in 876 DGs from The Cancer Genome Atlas (TCGA). We performed an unsupervised cluster analysis to identify molecular subgroups and characterised their clinical and biological significance. RESULTS: DGs showed widespread genetic intratumoural heterogeneity (ITH), with nearly all driver genes harbouring subclonal mutations, even for known glioma initiating event IDH1 (17.1%). Gliomas with subclonal IDH mutation and without 1p/19q codeletion showed shorter overall and disease-specific survival, higher ITH and exhibited differences in genomic patterns, transcript levels and proliferative potential, when compared with IDH clonal mutation and no 1p/19q codeletion gliomas. We defined a refined stratification system based on the current WHO glioma molecular classification, which showed close correlations with patients' clinical outcomes. CONCLUSIONS: For the first time, we integrated the clonal status of somatic mutations into cancer genomic classification and highlighted the necessity of considering IDH clonal architectures in glioma precision stratification.

Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis.

BACKGROUND: Bioinformatics provides a valuable tool to explore the molecular mechanisms underlying pathogenesis of hepatocellular carcinoma (HCC). To improve prognosis of patients, identification of robust biomarkers associated with the pathogenic pathways of HCC remains an urgent research priority. METHODS: We employed the Robust Rank Aggregation method to integrate nine qualified HCC datasets from the Gene Expression Omnibus. A robust set of differentially expressed genes (DEGs) between tumor and normal tissue samples were screened. Weighted gene co-expression network analysis was applied to cluster DEGs and the key modules related to clinical traits identified. Based on network topology analysis, novel risk genes derived from key modules were mined and biological verification performed. The potential functions of these risk genes were further explored with the aid of miRNA-mRNA regulatory networks. Finally, the prognostic ability of these genes was assessed by constructing a clinical prediction model. RESULTS: Two key modules showed significant association with clinical traits. In combination with protein-protein interaction analysis, 29 hub genes were identified. Among these genes, 19 from one module showed a pattern of upregulation in HCC and were associated with the tumor node metastasis stage, and 10 from the other module displayed the opposite trend. Survival analyses indicated that all these genes were significantly related to patient prognosis. Based on the miRNA-mRNA regulatory network, 29 genes strongly linked to tumor activity were identified. Notably, five of the novel risk genes, ABAT, DAO, PCK2, SLC27A2, and HAO1, have rarely been reported in previous studies. Gene set enrichment analysis for each gene revealed regulatory roles in proliferation and prognosis of HCC. Least absolute shrinkage and selection operator regression analysis further validated DAO, PCK2, and HAO1 as prognostic factors in an external HCC dataset. CONCLUSION: Analysis of multiple datasets combined with global network information presents a successful approach to uncover the complex biological mechanisms of HCC. More importantly, this novel integrated strategy facilitates identification of risk hub genes as candidate biomarkers for HCC, which could effectively guide clinical treatments.

Revealing the subtyping of non-small cell lung cancer based on genomic evolutionary patterns by multi-region sequencing.

Accurately classifying patients with non-small cell lung cancer (NSCLC) from the perspective of tumor evolution has not been systematically studied to date. Here, we reconstructed phylogenetic relationships of somatic mutations in 100 early NSCLC patients (327 lesions) through reanalyzing the TRACERx data. Based on the genomic evolutionary patterns presented on the phylogenetic trees, we grouped NSCLC patients into three evolutionary subtypes. The phylogenetic trees among three subtypes exhibited distinct branching structures, with one subtype representing branched evolution and another reflecting the early accumulation of genomic variation. However, in the evolutionary pattern of the third subtype, some mutations experienced selective sweeps and were gradually replaced by multiple newly formed subclonal populations. The subtype patients with poor prognosis had higher intra-tumor heterogeneity and subclonal diversity. We combined genomic heterogeneity with clinical phenotypes analysis and found that subclonal expansion results in the progression and deterioration of the tumor. The molecular mechanisms of subtype-specific Early Driver Feature (EDF) genes differed across the evolutionary subtypes, reflecting the characteristics of the subtype itself. In summary, our study provided new insights on the stratification of NSCLC patients based on genomic evolution that can be valuable for us to understand the development of pulmonary tumor profoundly.

Identifying bifurcated paths with differential function impact in glioblastomas evolution.

The evolutionary dynamics of human cancers has been investigated popularly and several bifurcated paths in cancer evolutionary trajectories are revealed to be with differential outcomes and phenotypes. However, whether such bifurcated paths exist in glioblastoma (GBM) remains unclear. In 385 GBM samples, through determining the clonal status of cancer driver events and inferring their temporal order, we constructed a temporal map of evolutionary trajectories at the patient population level. By investigating the differential impact on clinical outcome, we identified four key bifurcated paths, namely, "chromosome 10 copy number loss (ie, 10 loss) → chromosome 19 copy number gain (ie, 19 gain): 10 loss → 13q loss"; "10 loss → 19 gain: 10 loss → 15q loss"; "10 loss → 19 gain: 10 loss → 6q loss" and "10 loss → 19 gain: 10 loss → 16q loss". They formed a core multibranches path, with 10 loss being regarded as the common earliest event followed by 19 gain and four other departure events (13q loss, 15q loss, 6q loss and 16q loss), which may account for their difference in genome instability and patient survival time. Compared to "10 loss → 19 gain", the patients with "10 loss → 13q loss" had higher telomerase activity. Notably, there were obvious discrepancies in immune activity and immune cell infiltration level between patients with "10 loss → 13q/16q loss" and "10 loss → 19 gain", highlighting the bifurcated paths' effect on tumor immune microenvironment. In summary, our study identifies four key bifurcated paths in GBM for the first time, suggesting the feasibility of patient stratification and prognosis prediction based on key bifurcated paths.

Sex difference of mutation clonality in diffuse glioma evolution.

BACKGROUND: Sex differences in glioma incidence and outcome have been previously reported but remain poorly understood. Many sex differences that affect the cancer risk were thought to be associated with cancer evolution. METHODS: In this study, we used an integrated framework to infer the timing and clonal status of mutations in ~600 diffuse gliomas from The Cancer Genome Atlas (TCGA) including glioblastomas (GBMs) and low-grade gliomas (LGGs), and investigated the sex difference of mutation clonality. RESULTS: We observed higher overall and subclonal mutation burden in female patients with different grades of gliomas, which could be largely explained by the mutations of the X chromosome. Some well-established drivers were identified showing sex-biased clonality, such as CDH18 and ATRX. Focusing on glioma subtypes, we further found a higher subclonal mutation burden in females than males in the majority of glioma subtypes, and observed opposite clonal tendency of several drivers between male and female patients in a specific subtype. Moreover, analysis of clinically actionable genes revealed that mutations in genes of the mitogen-activated protein kinase (MAPK) signaling pathway were more likely to be clonal in female patients with GBM, whereas mutations in genes involved in the receptor tyrosine kinase signaling pathway were more likely to be clonal in male patients with LGG. CONCLUSIONS: The patients with diffuse glioma showed sex-biased mutation clonality (eg, different subclonal mutation number and different clonal tendency of cancer genes), highlighting the need to consider sex as an important variable for improving glioma therapy and clinical care.

SEECancer: a resource for somatic events in evolution of cancer genome.

Cancer cells progressively evolve from a premalignant to a malignant state, which is driven by accumulating somatic alterations that confer normal cells a fitness advantage. Improvements in high-throughput sequencing techniques have led to an increase in construction of tumor phylogenetics and identification of somatic driver events that specifically occurred in different tumor progression stages. Here, we developed the SEECancer database (http://biocc.hrbmu.edu.cn/SEECancer), which aims to present the comprehensive cancer evolutionary stage-specific somatic events (including early-specific, late-specific, relapse-specific, metastasis-specific, drug-resistant and drug-induced genomic events) and their temporal orders. By manually curating over 10 000 published articles, 1231 evolutionary stage-specific genomic events and 5772 temporal orders involving 82 human cancers and 23 tissue origins were collected and deposited in the SEECancer database. Each entry contains the somatic event, evolutionary stage, cancer type, detection approach and relevant evidence. SEECancer provides a user-friendly interface for browsing, searching and downloading evolutionary stage-specific somatic events and temporal relationships in various cancers. With increasing attention on cancer genome evolution, the necessary information in SEECancer will facilitate understanding of cancer etiology and development of evolutionary therapeutics, and help clinicians to discover biomarkers for monitoring tumor progression.