Germline and somatic genetic variants in the p53 pathway interact to affect cancer risk, progression and drug response

AbstractInsights into oncogenesis derived from cancer susceptibility loci could facilitate better cancer management and treatment through precision oncology. However, therapeutic applications have thus far been limited by our current lack of understanding regarding both their interactions with somatic cancer driver mutations and their influence on tumorigenesis. Here, by integrating germline datasets relating to cancer susceptibility with tumour data capturing somatically-acquired genetic variation, we provide evidence that single nucleotide polymorphism (SNPs) and somatic mutations in the p53 tumor suppressor pathway can interact to influence cancer development, progression and treatment response. We go on to provide human genetic evidence of a tumor-promoting role for the pro-survival activities of p53, which supports the development of more effective therapy combinations through their inhibition in cancers retaining wild-type p53.SignificanceWe describe significant interactions between heritable and somatic genetic variants in the p53 pathway that affect cancer susceptibility, progression and treatment response. Our results offer evidence of how cancer susceptibility SNPs can interact with cancer driver genes to affect cancer progression and identify novel therapeutic targets.

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Mutational likeliness and entropy help to identify driver mutations and their functional role in cancer

10.1101/354324 ◽

2018 ◽

Author(s):

Giorgio Mattiuz ◽

Salvatore Di Giorgio ◽

Lorenzo Tofani ◽

Antonio Frandi ◽

Francesco Donati ◽

...

Keyword(s):

Cancer Progression ◽

Somatic Mutations ◽

Driver Mutations ◽

Cancer Evolution ◽

Loss Of Function ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Genomes ◽

Passenger Mutations ◽

Mutational Processes

AbstractAlterations in cancer genomes originate from mutational processes taking place throughout oncogenesis and cancer progression. We show that likeliness and entropy are two properties of somatic mutations crucial in cancer evolution, as cancer-driver mutations stand out, with respect to both of these properties, as being distinct from the bulk of passenger mutations. Our analysis can identify novel cancer driver genes and differentiate between gain and loss of function mutations.

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Current cancer driver variant predictors learn to recognize driver genes instead of functional variants

BMC Biology ◽

10.1186/s12915-020-00930-0 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Daniele Raimondi ◽

Antoine Passemiers ◽

Piero Fariselli ◽

Yves Moreau

Keyword(s):

Data Sets ◽

Precision Oncology ◽

Complex Task ◽

Excellent Performance ◽

Driver Genes ◽

Significant Drop ◽

Data Set ◽

Gene Effects ◽

Cancer Driver ◽

Functional Variants

Abstract Background Identifying variants that drive tumor progression (driver variants) and distinguishing these from variants that are a byproduct of the uncontrolled cell growth in cancer (passenger variants) is a crucial step for understanding tumorigenesis and precision oncology. Various bioinformatics methods have attempted to solve this complex task. Results In this study, we investigate the assumptions on which these methods are based, showing that the different definitions of driver and passenger variants influence the difficulty of the prediction task. More importantly, we prove that the data sets have a construction bias which prevents the machine learning (ML) methods to actually learn variant-level functional effects, despite their excellent performance. This effect results from the fact that in these data sets, the driver variants map to a few driver genes, while the passenger variants spread across thousands of genes, and thus just learning to recognize driver genes provides almost perfect predictions. Conclusions To mitigate this issue, we propose a novel data set that minimizes this bias by ensuring that all genes covered by the data contain both driver and passenger variants. As a result, we show that the tested predictors experience a significant drop in performance, which should not be considered as poorer modeling, but rather as correcting unwarranted optimism. Finally, we propose a weighting procedure to completely eliminate the gene effects on such predictions, thus precisely evaluating the ability of predictors to model the functional effects of single variants, and we show that indeed this task is still open.

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Mutational analysis of driver genes with tumor suppressive and oncogenic roles in gastric cancer

PeerJ ◽

10.7717/peerj.3585 ◽

2017 ◽

Vol 5 ◽

pp. e3585 ◽

Cited By ~ 2

Author(s):

Tianfang Wang ◽

Yining Liu ◽

Min Zhao

Keyword(s):

Gastric Cancer ◽

Cancer Progression ◽

Complex Disease ◽

Mutational Analysis ◽

Driver Mutations ◽

Driver Genes ◽

Protein Coding ◽

Mirna Genes ◽

Genetic Mechanisms ◽

New Treatment

Gastric cancer (GC) is a complex disease with heterogeneous genetic mechanisms. Genomic mutational profiling of gastric cancer not only expands our knowledge about cancer progression at a fundamental genetic level, but also could provide guidance on new treatment decisions, currently based on tumor histology. The fact that precise medicine-based treatment is successful in a subset of tumors indicates the need for better identification of clinically related molecular tumor phenotypes, especially with regard to those driver mutations on tumor suppressor genes (TSGs) and oncogenes (ONGs). We surveyed 313 TSGs and 160 ONGs associated with 48 protein coding and 19 miRNA genes with both TSG and ONG roles. Using public cancer mutational profiles, we confirmed the dual roles of CDKN1A and CDKN1B. In addition to the widely recognized alterations, we identified another 82 frequently mutated genes in public gastric cancer cohort. In summary, these driver mutation profiles of individual GC will form the basis of personalized treatment of gastric cancer, leading to substantial therapeutic improvements.

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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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Diversity spectrum analysis identifies mutation-specific effects of cancer driver genes

Communications Biology ◽

10.1038/s42003-019-0736-4 ◽

2020 ◽

Vol 3 (1) ◽

Cited By ~ 2

Author(s):

Xiaobao Dong ◽

Dandan Huang ◽

Xianfu Yi ◽

Shijie Zhang ◽

Zhao Wang ◽

...

Keyword(s):

Clinical Trials ◽

Spectrum Analysis ◽

Driver Mutations ◽

Driver Gene ◽

Cancer Type ◽

Driver Genes ◽

Drug Responses ◽

Cancer Driver ◽

Specific Effects ◽

Cancer Driver Genes

AbstractMutation-specific effects of cancer driver genes influence drug responses and the success of clinical trials. We reasoned that these effects could unbalance the distribution of each mutation across different cancer types, as a result, the cancer preference can be used to distinguish the effects of the causal mutation. Here, we developed a network-based framework to systematically measure cancer diversity for each driver mutation. We found that half of the driver genes harbor cancer type-specific and pancancer mutations simultaneously, suggesting that the pervasive functional heterogeneity of the mutations from even the same driver gene. We further demonstrated that the specificity of the mutations could influence patient drug responses. Moreover, we observed that diversity was generally increased in advanced tumors. Finally, we scanned potentially novel cancer driver genes based on the diversity spectrum. Diversity spectrum analysis provides a new approach to define driver mutations and optimize off-label clinical trials.

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Whole exome precision oncology targeting synthetic lethal vulnerabilities across the tumor transcriptome

10.1101/2020.02.16.951699 ◽

2020 ◽

Cited By ~ 1

Author(s):

Joo Sang Lee ◽

Nishanth Ulhas Nair ◽

Lesley Chapman ◽

Sanju Sinha ◽

Kun Wang ◽

...

Keyword(s):

Predictive Performance ◽

Patient Treatment ◽

Precision Oncology ◽

Driver Genes ◽

Patient Response ◽

Synthetic Lethal ◽

Cancer Driver ◽

Whole Exome ◽

Transcriptomics Data ◽

Cancer Types

AbstractPrecision oncology has made significant advances in the last few years, mainly by targeting actionable mutations in cancer driver genes. However, the proportion of patients whose tumors can be targeted therapeutically remains limited. Recent studies have begun to explore the benefit of analyzing tumor transcriptomics data to guide patient treatment, raising the need for new approaches for systematically accomplishing that. Here we show that computationally derived genetic interactions can successfully predict patient response. Assembling a broad repertoire of 32 datasets spanning more than 1,500 patients and including both tumor transcriptomics and response data, we predicted the response in 17 out of 21 targeted and 8 out of 11 checkpoint therapy datasets across 8 different cancer types with considerable accuracy, without ever training on these datasets. Analyzing the recently published multi-arm WINTHER trial, we show that the fraction of patients benefitting from transcriptomic-based treatments could potentially be markedly increased from 15% to about 85% by targeting synthetic lethal vulnerabilities in their tumors. In summary, this is the first computational approach to obtain considerable predictive performance across many different targeted and immunotherapy datasets, providing a promising new way for guiding cancer treatment based on the tumor transcriptomics of cancer patients.

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Papillary Thyroid Cancer Prognosis: An Evolving Field

Cancers ◽

10.3390/cancers13215567 ◽

2021 ◽

Vol 13 (21) ◽

pp. 5567

Author(s):

Salvatore Ulisse ◽

Enke Baldini ◽

Augusto Lauro ◽

Daniele Pironi ◽

Domenico Tripodi ◽

...

Keyword(s):

Thyroid Cancer ◽

Papillary Thyroid Cancer ◽

Cancer Progression ◽

Clinical Management ◽

Cancer Prognosis ◽

Driver Mutations ◽

Clinical Approach ◽

Papillary Thyroid ◽

Cancer Driver ◽

Staging Systems

Over the last few years, a great advance has been made in the comprehension of the molecular pathogenesis underlying thyroid cancer progression, particularly for the papillary thyroid cancer (PTC), which represents the most common thyroid malignancy. Putative cancer driver mutations have been identified in more than 98% of PTC, and a new PTC classification into molecular subtypes has been proposed in order to resolve clinical uncertainties still present in the clinical management of patients. Additionally, the prognostic stratification systems have been profoundly modified over the last decade, with a view to refine patients’ staging and being able to choose a clinical approach tailored on single patient’s needs. Here, we will briefly discuss the recent changes in the clinical management of thyroid nodules, and review the current staging systems of thyroid cancer patients by analyzing promising clinicopathological features (i.e., gender, thyroid auto-immunity, multifocality, PTC histological variants, and vascular invasion) as well as new molecular markers (i.e., BRAF/TERT promoter mutations, miRNAs, and components of the plasminogen activating system) potentially capable of ameliorating the prognosis of PTC patients.

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WITER: A powerful method for the estimation of cancer-driver genes using a weighted iterative regression accurately modelling background mutation rate

10.1101/437061 ◽

2018 ◽

Author(s):

Lin Jiang ◽

Jingjing Zheng ◽

Johnny Sheung Him Kwan ◽

Sheng Dai ◽

Cong Li ◽

...

Keyword(s):

Negative Binomial ◽

Negative Binomial Regression ◽

Alternative Methods ◽

Driver Mutations ◽

Driver Genes ◽

Cancer Driver ◽

Binomial Regression ◽

Background Mutation Rate ◽

Technical Advances ◽

Cancer Driver Genes

AbstractGenomic identification of driver mutations and genes in cancer cells are critical for precision medicine. Due to difficulty in modeling distribution of background mutations, existing statistical methods are often underpowered to discriminate driver genes from passenger genes. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER), to detect cancer-driver genes showing an excess of somatic mutations. By solving the problem of inaccurately modeling background mutations, this approach works even in small or moderate samples. Compared to alternative methods, it detected more significant and cancer-consensus genes in all tested cancers. Applying this approach, we estimated 178 driver genes in 26 different cancers types. In silico validation confirmed 90.5% of predicted genes as likely known drivers and 7 genes unique for individual cancers as likely new drivers. The technical advances of WITER enable the detection of driver genes in TCGA datasets as small as 30 subjects, rescuing more genes missed by alternative tools.

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A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations

10.1101/399014 ◽

2018 ◽

Author(s):

Paul Ashford ◽

Camilla S.M. Pang ◽

Aurelio A. Moya-García ◽

Tolulope Adeyelu ◽

Christine A. Orengo

Keyword(s):

Zinc Finger Protein ◽

Point Mutations ◽

Driver Mutations ◽

Cancer Genes ◽

Driver Genes ◽

Recurrent Point ◽

Cancer Driver ◽

Functional Sites ◽

Cancer Driver Genes ◽

Family Based

Tumour sequencing identifies highly recurrent point mutations in cancer driver genes, but rare functional mutations are hard to distinguish from large numbers of passengers. We developed a novel computational platform applying a multi-modal approach to filter out passengers and more robustly identify putative driver genes. The primary filter identifies enrichment of cancer mutations in CATH functional families (CATH-FunFams) – structurally and functionally coherent sets of evolutionary related domains. Using structural representatives from CATH-FunFams, we subsequently seek enrichment of mutations in 3D and show that these mutation clusters have a very significant tendency to lie close to known functional sites or conserved sites predicted using CATH-FunFams. Our third filter identifies enrichment of putative driver genes in functionally coherent protein network modules confirmed by literature analysis to be cancer associated.Our approach is complementary to other domain enrichment approaches exploiting Pfam families, but benefits from more functionally coherent groupings of domains. Using a set of mutations from 22 cancers we detect 151 putative cancer drivers, of which 79 are not listed in cancer resources and include recently validated cancer genes EPHA7, DCC netrin-1 receptor and zinc-finger protein ZNF479.

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