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

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.

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

2018 ◽  
Vol 116 (2) ◽  
pp. 619-624 ◽  
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.

scholarly journals Sequence Neighborhoods Enable Reliable Prediction of Pathogenic Mutations in Cancer Genomes

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2366
Author(s):  
Shayantan Banerjee ◽  
Karthik Raman ◽  
Balaraman Ravindran
Keyword(s):  
Cancer Progression ◽  
Nucleotide Sequences ◽  
Feature Representation ◽  
Estimation Methods ◽  
Driver Mutations ◽  
Cancer Mutation ◽  
Evolutionary Features ◽  
Cancer Genomes ◽  
Passenger Mutations ◽  
Pan Cancer

Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. “Driver” mutations are primarily responsible for cancer progression, while “passengers” are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on using the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5′ and 3′ from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments provided comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural, and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with three other commonly used driver prediction tools (FATHMM (cancer), CONDEL, and MutationTaster) significantly outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of using raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.

scholarly journals Sequence neighborhoods enable reliable prediction of pathogenic mutations in cancer genomes

2021 ◽  
Author(s):  
Shayantan Banerjee ◽  
Karthik Raman ◽  
Balaraman Ravindran
Keyword(s):  
Cancer Progression ◽  
Nucleotide Sequences ◽  
Feature Representation ◽  
Estimation Methods ◽  
Driver Mutations ◽  
Cancer Mutation ◽  
Evolutionary Features ◽  
Cancer Genomes ◽  
Passenger Mutations ◽  
Pan Cancer

AbstractIdentifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. “Driver” mutations are primarily responsible for cancer progression, while “passengers” are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on utilizing the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5’ and 3’ from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments gave comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with two other commonly used driver prediction tools (CONDEL and Mutation Taster) outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of utilizing raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.

scholarly journals Early and multiple origins of metastatic lineages within primary tumors

2016 ◽  
Vol 113 (8) ◽  
pp. 2140-2145 ◽  
Author(s):  
Zi-Ming Zhao ◽  
Bixiao Zhao ◽  
Yalai Bai ◽  
Atila Iamarino ◽  
Stephen G. Gaffney ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Cancer Biology ◽  
Tumor Development ◽  
Evolutionary Process ◽  
Driver Mutations ◽  
Cancer Evolution ◽  
Driver Genes ◽  
Genetic Changes ◽  
Primary Tumors ◽  
Multiple Origins

Many aspects of the evolutionary process of tumorigenesis that are fundamental to cancer biology and targeted treatment have been challenging to reveal, such as the divergence times and genetic clonality of metastatic lineages. To address these challenges, we performed tumor phylogenetics using molecular evolutionary models, reconstructed ancestral states of somatic mutations, and inferred cancer chronograms to yield three conclusions. First, in contrast to a linear model of cancer progression, metastases can originate from divergent lineages within primary tumors. Evolved genetic changes in cancer lineages likely affect only the proclivity toward metastasis. Single genetic changes are unlikely to be necessary or sufficient for metastasis. Second, metastatic lineages can arise early in tumor development, sometimes long before diagnosis. The early genetic divergence of some metastatic lineages directs attention toward research on driver genes that are mutated early in cancer evolution. Last, the temporal order of occurrence of driver mutations can be inferred from phylogenetic analysis of cancer chronograms, guiding development of targeted therapeutics effective against primary tumors and metastases.

scholarly journals Passenger mutations in 2500 cancer genomes: Overall molecular functional impact and consequences

2018 ◽  
Author(s):  
Sushant Kumar ◽  
Jonathan Warrell ◽  
Shantao Li ◽  
Patrick D. McGillivray ◽  
William Meyerson ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Complex Trait ◽  
Driver Mutations ◽  
Additive Variance ◽  
Functional Impact ◽  
Cancer Genomes ◽  
Classical Models ◽  
Cancer Phenotypes ◽  
Passenger Mutations ◽  
The Impact

AbstractThe Pan-cancer Analysis of Whole Genomes (PCAWG) project provides an unprecedented opportunity to comprehensively characterize a vast set of uniformly annotated coding and non-coding mutations present in thousands of cancer genomes. Classical models of cancer progression posit that only a small number of these mutations strongly drive tumor progression and that the remaining ones (termed “putative passengers”) are inconsequential for tumorigenesis. In this study, we leveraged the comprehensive variant data from PCAWG to ascertain the molecular functional impact of each variant. The impact distribution of PCAWG mutations shows that, in addition to high- and low-impact mutations, there is a group of medium-impact putative passengers predicted to influence gene activity. Moreover, the predicted impact relates to the underlying mutational signature: different signatures confer divergent impact, differentially affecting distinct regulatory subsystems and gene categories. We also find that impact varies based on subclonal architecture (i.e., early vs. late mutations) and can be related to patient survival. Finally, we note that insufficient power due to limited cohort sizes precludes identification of weak drivers using standard recurrence-based approaches. To address this, we adapted an additive effects model derived from complex trait studies to show that aggregating the impact of putative passenger variants (i.e. including yet undetected weak drivers) provides significant predictability for cancer phenotypes beyond the PCAWG identified driver mutations (12.5% additive variance). Furthermore, this framework allowed us to estimate the frequency of potential weak driver mutations in the subset of PCAWG samples lacking well-characterized driver alterations.

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

2019 ◽  
Author(s):  
Ping Zhang ◽  
Isaac Kitchen-Smith ◽  
Lingyun Xiong ◽  
Giovanni Stracquadanio ◽  
Katherine Brown ◽  
...  
Keyword(s):  
Treatment Response ◽  
Cancer Progression ◽  
Genetic Variants ◽  
Cancer Susceptibility ◽  
Driver Mutations ◽  
Precision Oncology ◽  
Driver Genes ◽  
P53 Pathway ◽  
Cancer Driver ◽  
Cancer Management

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.

scholarly journals The Genomic Landscape of Serrated Lesion of the Colorectum: Similarities and Differences With Tubular and Tubulovillous Adenomas

2021 ◽  
Vol 11 ◽  
Author(s):  
Luigi Tornillo ◽  
Frank Serge Lehmann ◽  
Andrea Garofoli ◽  
Viola Paradiso ◽  
Charlotte K. Y. Ng ◽  
...  
Keyword(s):  
Somatic Mutations ◽  
Bioinformatic Analysis ◽  
Genetic Alterations ◽  
University Hospital ◽  
Library Preparation ◽  
Loss Of Function ◽  
Driver Genes ◽  
Cancer Driver ◽  
Genomic Landscape ◽  
Serrated Lesions

Serrated lesions of the colorectum are the precursors of 15–30% of colorectal cancers (CRCs). These lesions have a peculiar morphological appearance, and they are more difficult to detect than conventional adenomatous polyps. In this study, we sought to define the genomic landscape of these lesions using high-depth targeted sequencing. Eight sessile serrated lesions without dysplasia (SSL), three sessile serrated lesions with dysplasia (SSL/D), two traditional serrated adenomas (TSA), and three tubular adenomas (TA) were retrieved from the files of the Institute of Pathology of the University Hospital Basel and from the GILAB AG, Allschwil, Switzerland. Samples were microdissected together with the matched normal counterpart, and DNA was extracted for library preparation. Library preparation was performed using the Oncomine Comprehensive Assay targeting 161 common cancer driver genes. Somatic genetic alterations were defined using state-of-the-art bioinformatic analysis. Most SSLs, as well as all SSL/Ds and TSAs, showed the classical BRAF p.V600E mutation. The BRAF-mutant TSAs showed additional alterations in CTNNB1, NF1, TP53, NRAS, PIK3CA, while TA showed a consistently different profile, with mutations in ARID1A (two cases), SMAD4, CDK12, ERBB3, and KRAS. In conclusion, our results provide evidence that SSL/D and TSA are similar in somatic mutations with the BRAF hotspot somatic mutation as a major driver of the disease. On the other hand, TAs show a different constellation of somatic mutations such as ARID1A loss of function.

scholarly journals Chromatin accessibility of primary human cancers ties regional mutational processes with tissues of origin

2021 ◽  
Author(s):  
Oliver Ocsenas ◽  
Jüri Reimand
Keyword(s):  
Human Cancer ◽  
Replication Timing ◽  
Chromatin Accessibility ◽  
Cancer Genes ◽  
Cancer Evolution ◽  
Normal Cells ◽  
Cancer Genomes ◽  
Localized Mutagenesis ◽  
Passenger Mutations ◽  
Mutational Processes

Regional mutagenesis in cancer genomes associates with DNA replication timing (RT) and chromatin accessibility (CA) of normal cells, however human cancer epigenomes remain uncharacterized in this context. Here we model megabase-scale mutation frequencies in 2517 cancer genomes with 773 CA and RT profiles of cancers and normal cells. We find that CA profiles of matching cancers, rather than normal cells, predict regional mutagenesis and mutational signatures, indicating that most passenger mutations follow the epigenetic landscapes of transformed cells. Carcinogen-induced and unannotated signatures show the strongest associations with epigenomes. Associations with normal cells in melanomas, lymphomas and SBS1 signatures suggest earlier occurrence of mutations in cancer evolution. Frequently mutated regions unexplained by CA and RT are enriched in cancer genes and developmental pathways, reflecting contributions of localized mutagenesis and positive selection. These results underline the complex interplay of mutational processes, genome function and evolution in cancer and tissues of origin.

scholarly journals Discovery of cancer driver genes based on nucleotide context

2018 ◽  
Author(s):  
Felix Dietlein ◽  
Donate Weghorn ◽  
Amaro Taylor-Weiner ◽  
André Richters ◽  
Brendan Reardon ◽  
...  
Keyword(s):  
Mutation Rates ◽  
Driver Mutations ◽  
Supporting Evidence ◽  
Cancer Genes ◽  
Driver Genes ◽  
High Background ◽  
Cancer Driver ◽  
Nucleotide Context ◽  
Cancer Driver Genes ◽  
Passenger Mutations

Many cancer genomes contain large numbers of somatic mutations, but few of these mutations drive tumor development. Current approaches to identify cancer driver genes are largely based on mutational recurrence, i.e. they search for genes with an increased number of nonsynonymous mutations relative to the local background mutation rate. Multiple studies have noted that the sensitivity of recurrence-based methods is limited in tumors with high background mutation rates, because passenger mutations dilute their statistical power. Here, we observe that passenger mutations tend to occur in characteristic nucleotide sequence contexts, while driver mutations follow a different distribution pattern determined by the location of functionally relevant genomic positions along the protein-coding sequence. To discover new cancer genes, we searched for genes with an excess of mutations in unusual nucleotide contexts that deviate from the characteristic context around passenger mutations. By applying this statistical framework to whole-exome sequencing data from 12,004 tumors, we discovered a long tail of novel candidate cancer genes with mutation frequencies as low as 1% and functional supporting evidence. Our results show that considering both the number and the nucleotide context around mutations helps identify novel cancer driver genes, particularly in tumors with high background mutation rates.

scholarly journals driveR: a novel method for prioritizing cancer driver genes using somatic genomics data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ege Ülgen ◽  
O. Uğur Sezerman
Keyword(s):  
Biological Knowledge ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Prior Biological Knowledge ◽  
Wilcoxon Rank Sum Test ◽  
Cancer Genomes ◽  
Novel Method ◽  
Cancer Driver Genes ◽  
Batch Analysis

Abstract Background Cancer develops due to “driver” alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomics data. However, methods for personalized analysis of driver genes are underdeveloped. In this study, we developed a novel personalized/batch analysis approach for driver gene prioritization utilizing somatic genomics data, called driveR. Results Combining genomics information and prior biological knowledge, driveR accurately prioritizes cancer driver genes via a multi-task learning model. Testing on 28 different datasets, this study demonstrates that driveR performs adequately, achieving a median AUC of 0.684 (range 0.651–0.861) on the 28 batch analysis test datasets, and a median AUC of 0.773 (range 0–1) on the 5157 personalized analysis test samples. Moreover, it outperforms existing approaches, achieving a significantly higher median AUC than all of MutSigCV (Wilcoxon rank-sum test p < 0.001), DriverNet (p < 0.001), OncodriveFML (p < 0.001) and MutPanning (p < 0.001) on batch analysis test datasets, and a significantly higher median AUC than DawnRank (p < 0.001) and PRODIGY (p < 0.001) on personalized analysis datasets. Conclusions This study demonstrates that the proposed method is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes in personalized or batch analyses. driveR is available on CRAN: https://cran.r-project.org/package=driveR.

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