Pathway-based dissection of the genomic heterogeneity of cancer hallmarks’ acquisition with SLAPenrich

ABSTRACTCancer hallmarks are evolutionary traits required by a tumour to develop. While extensively characterised, the way these traits are achieved through the accumulation of somatic mutations in key biological pathways is not fully understood. To shed light on this subject, we characterised the landscape of pathway alterations associated with somatic mutations observed in 4,415 patients across ten cancer types, using 374 orthogonal pathway gene-sets mapped onto canonical cancer hallmarks. Towards this end, we developed SLAPenrich: a computational method based on population-level statistics, freely available as an open source R package. Assembling the identified pathway alterations into sets of hallmark signatures allowed us to connect somatic mutations to clinically interpretable cancer mechanisms. Further, we explored the heterogeneity of these signatures, in terms of ratio of altered pathways associated with each individual hallmark, assuming that this is reflective of the extent of selective advantage provided to the cancer type under consideration. Our analysis revealed the predominance of certain hallmarks in specific cancer types, thus suggesting different evolutionary trajectories across cancer lineages.Finally, although many pathway alteration enrichments are guided by somatic mutations in frequently altered high-confidence cancer genes, excluding these driver mutations preserves the hallmark heterogeneity signatures, thus the detected hallmarks’ predominance across cancer types. As a consequence, we propose the hallmark signatures as a ground truth to characterise tails of infrequent genomic alterations and identify potential novel cancer driver genes and networks.

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CHASMplus reveals the scope of somatic missense mutations driving human cancers

10.1101/313296 ◽

2018 ◽

Cited By ~ 2

Author(s):

Collin Tokheim ◽

Rachel Karchin

Keyword(s):

Missense Mutation ◽

Large Scale ◽

Computational Method ◽

The Cancer Genome Atlas ◽

Superior Performance ◽

Driver Mutations ◽

Cancer Type ◽

Missense Mutations ◽

Driver Genes ◽

Cancer Types

SummaryLarge-scale cancer sequencing studies of patient cohorts have statistically implicated many genes driving cancer growth and progression, and their identification has yielded substantial translational impact. However, a remaining challenge is to increase the resolution of driver prediction from the gene level to the mutation level, because mutation-level predictions are more closely aligned with the goal of precision cancer medicine. Here we present CHASMplus, a computational method, that is uniquely capable of identifying driver missense mutations, including those specific to a cancer type, as evidenced by significantly superior performance on diverse benchmarks. Applied to 8,657 tumor samples across 32 cancer types in The Cancer Genome Atlas, CHASMplus identifies over 4,000 unique driver missense mutations in 240 genes, supporting a prominent role for rare driver mutations. We show which TCGA cancer types are likely to yield discovery of new driver missense mutations by additional sequencing, which has important implications for public policy.SignificanceMissense mutations are the most frequent mutation type in cancers and the most difficult to interpret. While many computational methods have been developed to predict whether genes are cancer drivers or whether missense mutations are generally deleterious or pathogenic, there has not previously been a method to score the oncogenic impact of a missense mutation specifically by cancer type, limiting adoption of computational missense mutation predictors in the clinic. Cancer patients are routinely sequenced with targeted panels of cancer driver genes, but such genes contain a mixture of driver and passenger missense mutations which differ by cancer type. A patient’s therapeutic response to drugs and optimal assignment to a clinical trial depends on both the specific mutation in the gene of interest and cancer type. We present a new machine learning method honed for each TCGA cancer type, and a resource for fast lookup of the cancer-specific driver propensity of every possible missense mutation in the human exome.

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Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

10.1101/2022.01.13.476163 ◽

2022 ◽

Author(s):

Malvika Sudhakar ◽

Raghunathan Rengaswamy ◽

Karthik Raman

Keyword(s):

Tumour Progression ◽

Suppressor Gene ◽

Tumour Suppressor Gene ◽

Driver Mutations ◽

Cancer Type ◽

Expression Data ◽

Multiple Cancer ◽

Driver Genes ◽

Cancer Types ◽

Omic Data

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require a large number of samples to produce reliable results. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify the perturbation in the network. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy >0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at https://github.com/RamanLab/PIVOT.

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OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers

Nucleic Acids Research ◽

10.1093/nar/gkaa1033 ◽

2020 ◽

Vol 49 (D1) ◽

pp. D1289-D1301 ◽

Cited By ~ 2

Author(s):

Tao Wang ◽

Shasha Ruan ◽

Xiaolu Zhao ◽

Xiaohui Shi ◽

Huajing Teng ◽

...

Keyword(s):

Cancer Genome ◽

The Cancer Genome Atlas ◽

Driver Mutations ◽

Cancer Genes ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Cell Population ◽

Cancer Types ◽

Neutral Mutations ◽

Analysis Platform

Abstract The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, ‘Mutation’, ‘Gene’, ‘Pathway’ and ‘Cancer’, to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine.

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Identifying cancer type specific oncogenes and tumor suppressors using limited size data

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720016500311 ◽

2016 ◽

Vol 14 (06) ◽

pp. 1650031 ◽

Cited By ~ 4

Author(s):

Ana B. Pavel ◽

Cristian I. Vasile

Keyword(s):

Tumor Suppressors ◽

Molecular Mechanisms ◽

Lung Squamous Cell Carcinoma ◽

The Cancer Genome Atlas ◽

Driver Mutations ◽

Cancer Type ◽

Multiple Cancer ◽

Driver Genes ◽

Cancer Subtypes ◽

Cancer Types

Cancer is a complex and heterogeneous genetic disease. Different mutations and dysregulated molecular mechanisms alter the pathways that lead to cell proliferation. In this paper, we explore a method which classifies genes into oncogenes (ONGs) and tumor suppressors. We optimize this method to identify specific (ONGs) and tumor suppressors for breast cancer, lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and colon adenocarcinoma (COAD), using data from the cancer genome atlas (TCGA). A set of genes were previously classified as ONGs and tumor suppressors across multiple cancer types (Science 2013). Each gene was assigned an ONG score and a tumor suppressor score based on the frequency of its driver mutations across all variants from the catalogue of somatic mutations in cancer (COSMIC). We evaluate and optimize this approach within different cancer types from TCGA. We are able to determine known driver genes for each of the four cancer types. After establishing the baseline parameters for each cancer type, we identify new driver genes for each cancer type, and the molecular pathways that are highly affected by them. Our methodology is general and can be applied to different cancer subtypes to identify specific driver genes and improve personalized therapy.

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Contextual Classifications of Cancer Driver Genes

10.1101/715508 ◽

2019 ◽

Author(s):

Pramod Chandrashekar ◽

Navid Ahmadinejad ◽

Junwen Wang ◽

Aleksandar Sekulic ◽

Jan B. Egan ◽

...

Keyword(s):

Computational Method ◽

Cancer Type ◽

Sequencing Data ◽

Multiple Cancer ◽

Driver Genes ◽

Cancer Driver ◽

Link Type ◽

Mutational Hotspots ◽

Cancer Types ◽

Cancer Driver Genes

ABSTRACTFunctions of cancer driver genes depend on cellular contexts that vary substantially across tissues and organs. Distinguishing oncogenes (OGs) and tumor suppressor genes (TSGs) for each cancer type is critical to identifying clinically actionable targets. However, current resources for context-aware classifications of cancer drivers are limited. In this study, we show that the direction and magnitude of somatic selection of missense and truncating mutations of a gene are suggestive of its contextual activities. By integrating these features with ratiometric and conservation measures, we developed a computational method to categorize OGs and TSGs using exome sequencing data. This new method, named genes under selection in tumors (GUST) shows an overall accuracy of 0.94 when tested on manually curated benchmarks. Application of GUST to 10,172 tumor exomes of 33 cancer types identified 98 OGs and 179 TSGs, >70% of which promote tumorigenesis in only one cancer type. In broad-spectrum drivers shared across multiple cancer types, we found heterogeneous mutational hotspots modifying distinct functional domains, implicating the synchrony of convergent and divergent disease mechanisms. We further discovered two novel OGs and 28 novel TSGs with high confidence. The GUST program is available at https://github.com/liliulab/gust. A database with pre-computed classifications is available at https://liliulab.shinyapps.io/gust

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Deep learning for cancer type classification

10.1101/612762 ◽

2019 ◽

Cited By ~ 1

Author(s):

Zexian Zeng ◽

Chengsheng Mao ◽

Andy Vo ◽

Janna Ore Nugent ◽

Seema A Khan ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Somatic Mutations ◽

Disease Classification ◽

Cancer Type ◽

Cancer Genes ◽

Sequencing Data ◽

Germline Variants ◽

Cancer Types ◽

Independent Features

ABSTRACTGenetic information is becoming more readily available and is increasingly being used to predict patient cancer types as well as their subtypes. Most classification methods thus far utilize somatic mutations as independent features for classification and are limited by study power. To address these limitations, we propose DeepCues, a deep learning model that utilizes convolutional neural networks to derive features from DNA sequencing data for disease classification and relevant gene discovery. Using whole-exome sequencing, germline variants and somatic mutations, including insertions and deletions, are interactively amalgamated as features. In this study, we applied DeepCues to a dataset from TCGA to classify seven different types of major cancers and obtained an overall accuracy of 77.6%. We compared DeepCues to conventional methods and demonstrated a significant overall improvement (p=8.8E-25). Using DeepCues, we found that the top 20 genes associated with breast cancer have a 40% overlap with the top 20 breast cancer genes in the COSMIC database. These data support DeepCues as a novel method to improve the representational resolution of both germline variants and somatic mutations interactively and their power in predicting cancer types, as well the genes involved in each cancer.

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Mutational landscape of primary, metastatic, and recurrent ovarian cancer reveals c-MYC gains as potential target for BET inhibitors

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1814027116 ◽

2018 ◽

Vol 116 (2) ◽

pp. 619-624 ◽

Cited By ~ 11

Author(s):

Charles Li ◽

Elena Bonazzoli ◽

Stefania Bellone ◽

Jungmin Choi ◽

Weilai Dong ◽

...

Keyword(s):

Ovarian Cancer ◽

Somatic Mutations ◽

Driver Mutations ◽

Cancer Evolution ◽

Metastatic Tumors ◽

Driver Genes ◽

Primary Tumors ◽

Mutational Landscape ◽

Recurrent Tumors ◽

Bet Inhibitors

Ovarian cancer remains the most lethal gynecologic malignancy. We analyzed the mutational landscape of 64 primary, 41 metastatic, and 17 recurrent fresh-frozen tumors from 77 patients along with matched normal DNA, by whole-exome sequencing (WES). We also sequenced 13 pairs of synchronous bilateral ovarian cancer (SBOC) to evaluate the evolutionary history. Lastly, to search for therapeutic targets, we evaluated the activity of the Bromodomain and Extra-Terminal motif (BET) inhibitor GS-626510 on primary tumors and xenografts harboring c-MYC amplifications. In line with previous studies, the large majority of germline and somatic mutations were found in BRCA1/2 (21%) and TP53 (86%) genes, respectively. Among mutations in known cancer driver genes, 77% were transmitted from primary tumors to metastatic tumors, and 80% from primary to recurrent tumors, indicating that driver mutations are commonly retained during ovarian cancer evolution. Importantly, the number, mutation spectra, and signatures in matched primary–metastatic tumors were extremely similar, suggesting transcoelomic metastases as an early dissemination process using preexisting metastatic ability rather than an evolution model. Similarly, comparison of SBOC showed extensive sharing of somatic mutations, unequivocally indicating a common ancestry in all cases. Among the 17 patients with matched tumors, four patients gained PIK3CA amplifications and two patients gained c-MYC amplifications in the recurrent tumors, with no loss of amplification or gain of deletions. Primary cell lines and xenografts derived from chemotherapy-resistant tumors demonstrated sensitivity to JQ1 and GS-626510 (P = 0.01), suggesting that oral BET inhibitors represent a class of personalized therapeutics in patients harboring recurrent/chemotherapy-resistant disease.

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Discovering the drivers of clonal hematopoiesis

10.1101/2020.10.22.350140 ◽

2020 ◽

Cited By ~ 1

Author(s):

Oriol Pich ◽

Iker Reyes-Salazar ◽

Abel Gonzalez-Perez ◽

Nuria Lopez-Bigas

Keyword(s):

Positive Selection ◽

Molecular Mechanisms ◽

Somatic Mutations ◽

Cancer Genomics ◽

Variant Calling ◽

Selective Advantage ◽

Cancer Genes ◽

Driver Genes ◽

Hematopoietic Stem ◽

Clonal Hematopoiesis

AbstractMutations in genes that confer a selective advantage to hematopoietic stem cells (HSCs) in certain conditions drive clonal hematopoiesis (CH). While some CH drivers have been identified experimentally or through epidemiological studies, the compendium of all genes able to drive CH upon mutations in HSCs is far from complete. We propose that identifying signals of positive selection in blood somatic mutations may be an effective way to identify CH driver genes, similarly as done to identify cancer genes. Using a reverse somatic variant calling approach, we repurposed whole-genome and whole-exome blood/tumor paired samples of more than 12,000 donors from two large cancer genomics cohorts to identify blood somatic mutations. The application of IntOGen, a robust driver discovery pipeline, to blood somatic mutations across both cohorts, and more than 24,000 targeted sequenced samples yielded a list of close to 70 genes with signals of positive selection in CH, available at http://www.intogen.org/ch. This approach recovers all known CH genes, and discovers novel candidates. Generating this compendium is an essential step to understand the molecular mechanisms of CH and to accurately detect individuals with CH to ascertain their risk to develop related diseases.

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Additive effects of variants of unknown significance in replication repair-associated DNA polymerase genes on mutational burden and prognosis across diverse cancers

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2021-002336 ◽

2021 ◽

Vol 9 (9) ◽

pp. e002336

Author(s):

Jieer Ying ◽

Lin Yang ◽

Jiani C Yin ◽

Guojie Xia ◽

Minyan Xing ◽

...

Keyword(s):

Hot Spot ◽

The Cancer Genome Atlas ◽

Driver Mutations ◽

Cancer Type ◽

Additive Effects ◽

Variants Of Unknown Significance ◽

Mutational Burden ◽

Pathway Gene ◽

Exonuclease Domain ◽

Unknown Significance

BackgroundDefects in replication repair-associated DNA polymerases often manifest an ultra-high tumor mutational burden (TMB), which is associated with higher probabilities of response to immunotherapies. The functional and clinical implications of different polymerase variants remain unclear.MethodsTargeted next-generation sequencing using a 425-cancer gene panel, which covers all exonic regions of three polymerase genes (POLE, POLD1, and POLH), was conducted in a cohort of 12,266 patients across 16 different tumor types from January 2017 to January 2019. Prognostication of POL variant-positive patients was performed using a cohort of 4679 patients from the The Cancer Genome Atlas (TCGA) datasets.ResultsThe overall prevalence of somatic and germline polymerase variants was 4.2% (95% CI 3.8% to 4.5%) and 0.7% (95% CI 0.5% to 0.8%), respectively, with highest frequencies in endometrial, urinary, prostate, and colorectal cancers (CRCs). While most germline polymerase variants showed no clear functional consequences, we identified a candidate p.T466A affecting the exonuclease domain of POLE, which might be underlying the early onset in a case with childhood CRC. Low frequencies of known hot-spot somatic mutations in POLE were detected and were associated with younger age, the male sex, and microsatellite stability. In both the panel and TCGA cohorts, POLE drivers exhibited high frequencies of alterations in genes in the DNA damage and repair (DDR) pathways, including BRCA2, ATM, MSH6, and ATR. Variants of unknown significance (VUS) of different polymerase domains showed variable penetrance with those in the exonuclease domain of POLE and POLD1 displaying high TMB. VUS in POL genes exhibited an additive effect as carriers of multiple VUS had exponentially increased TMB and prolonged overall survival. Similar to cases with driver mutations, the TMB-high POL VUS samples showed DDR pathway involvement and polymerase hypermutation signatures. Combinatorial analysis of POL and DDR pathway status further supported the potential additive effects of POL VUS and DDR pathway genes and revealed distinct prognostic subclasses that were independent of cancer type and TMB.ConclusionsOur results demonstrate the pathogenicity and additive prognostic value of POL VUS and DDR pathway gene alterations and suggest that genetic testing may be warranted in patients with diverse solid tumors.

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Clonal haematopoiesis in chronic ischaemic heart failure: prognostic role of clone size for DNMT3A- and TET2-driver gene mutations

European Heart Journal ◽

10.1093/eurheartj/ehaa845 ◽

2020 ◽

Cited By ~ 1

Author(s):

Birgit Assmus ◽

Sebastian Cremer ◽

Klara Kirschbaum ◽

David Culmann ◽

Katharina Kiefer ◽

...

Keyword(s):

Heart Failure ◽

Somatic Mutations ◽

Prognostic Significance ◽

Gene Mutations ◽

Amplicon Sequencing ◽

Driver Mutations ◽

Driver Gene ◽

Roc Curve Analysis ◽

Driver Genes ◽

Clone Size

Abstract Aims Somatic mutations of the epigenetic regulators DNMT3A and TET2 causing clonal expansion of haematopoietic cells (clonal haematopoiesis; CH) were shown to be associated with poor prognosis in chronic ischaemic heart failure (CHF). The aim of our analysis was to define a threshold of variant allele frequency (VAF) for the prognostic significance of CH in CHF. Methods and results We analysed bone marrow and peripheral blood-derived cells from 419 patients with CHF by error-corrected amplicon sequencing. Cut-off VAFs were optimized by maximizing sensitivity plus specificity from a time-dependent receiver operating characteristic (ROC) curve analysis from censored data. 56.2% of patients were carriers of a DNMT3A- (N = 173) or a TET2- (N = 113) mutation with a VAF >0.5%, with 59 patients harbouring mutations in both genes. Survival ROC analyses revealed an optimized cut-off value of 0.73% for TET2- and 1.15% for DNMT3A-CH-driver mutations. Five-year-mortality was 18% in patients without any detected DNMT3A- or TET2 mutation (VAF < 0.5%), 29% with only one DNMT3A- or TET2-CH-driver mutations above the respective cut-off level and 42% in patients harbouring both DNMT3A- and TET2-CH-driver mutations above the respective cut-off levels. In carriers of a DNMT3A mutation with VAF ≥ 1.15%, 5-year mortality was 31%, compared with 18% mortality in those with VAF < 1.15% (P = 0.048). Likewise, in patients with TET2 mutations, 5-year mortality was 32% with VAF ≥ 0.73%, compared with 19% mortality with VAF < 0.73% (P = 0.029). Conclusion The present study defines novel threshold levels for clone size caused by acquired somatic mutations in the CH-driver genes DNMT3A and TET2 that are associated with worse outcome in patients with CHF.

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