scholarly journals Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
John K. L. Wong ◽  
Christian Aichmüller ◽  
Markus Schulze ◽  
Mario Hlevnjak ◽  
Shaymaa Elgaafary ◽  
...  
Keyword(s):  
Somatic Hypermutation ◽  
Regulatory Elements ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Base Substitution ◽  
Single Nucleotide Variants ◽  
Mutational Signatures ◽  
Coding Regions ◽  
Mutational Hotspots

AbstractCancer driving mutations are difficult to identify especially in the non-coding part of the genome. Here, we present sigDriver, an algorithm dedicated to call driver mutations. Using 3813 whole-genome sequenced tumors from International Cancer Genome Consortium, The Cancer Genome Atlas Program, and a childhood pan-cancer cohort, we employ mutational signatures based on single-base substitution in the context of tri- and penta-nucleotide motifs for hotspot discovery. Knowledge-based annotations on mutational hotspots reveal enrichment in coding regions and regulatory elements for 6 mutational signatures, including APOBEC and somatic hypermutation signatures. APOBEC activity is associated with 32 hotspots of which 11 are known and 11 are putative regulatory drivers. Somatic single nucleotide variants clusters detected at hypermutation-associated hotspots are distinct from translocation or gene amplifications. Patients carrying APOBEC induced PIK3CA driver mutations show lower occurrence of signature SBS39. In summary, sigDriver uncovers mutational processes associated with known and putative tumor drivers and hotspots particularly in the non-coding regions of the genome.

scholarly journals Non-Coding Variants in Cancer: Mechanistic Insights and Clinical Potential for Personalized Medicine

2021 ◽  
Vol 7 (3) ◽  
pp. 47
Author(s):  
Marios Lange ◽  
Rodiola Begolli ◽  
Antonis Giakountis
Keyword(s):  
Disease Onset ◽  
Cancer Genome ◽  
Driver Mutations ◽  
Nucleotide Polymorphisms ◽  
Structural Variations ◽  
Coding Regions ◽  
Functional Variants ◽  
Coding Variants ◽  
Clinical Potential

The cancer genome is characterized by extensive variability, in the form of Single Nucleotide Polymorphisms (SNPs) or structural variations such as Copy Number Alterations (CNAs) across wider genomic areas. At the molecular level, most SNPs and/or CNAs reside in non-coding sequences, ultimately affecting the regulation of oncogenes and/or tumor-suppressors in a cancer-specific manner. Notably, inherited non-coding variants can predispose for cancer decades prior to disease onset. Furthermore, accumulation of additional non-coding driver mutations during progression of the disease, gives rise to genomic instability, acting as the driving force of neoplastic development and malignant evolution. Therefore, detection and characterization of such mutations can improve risk assessment for healthy carriers and expand the diagnostic and therapeutic toolbox for the patient. This review focuses on functional variants that reside in transcribed or not transcribed non-coding regions of the cancer genome and presents a collection of appropriate state-of-the-art methodologies to study them.

scholarly journals FIREVAT: finding reliable variants without artifacts in human cancer samples using etiologically relevant mutational signatures

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyunbin Kim ◽  
Andy Jinseok Lee ◽  
Jongkeun Lee ◽  
Hyonho Chun ◽  
Young Seok Ju ◽  
...  
Keyword(s):  
Data Storage ◽  
Human Cancer ◽  
Point Mutations ◽  
Variant Calling ◽  
Human Tumor ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Precision Oncology ◽  
Mutational Signatures ◽  
Accurate Identification

Abstract Background Accurate identification of real somatic variants is a primary part of cancer genome studies and precision oncology. However, artifacts introduced in various steps of sequencing obfuscate confidence in variant calling. Current computational approaches to variant filtering involve intensive interrogation of Binary Alignment Map (BAM) files and require massive computing power, data storage, and manual labor. Recently, mutational signatures associated with sequencing artifacts have been extracted by the Pan-cancer Analysis of Whole Genomes (PCAWG) study. These spectrums can be used to evaluate refinement quality of a given set of somatic mutations. Results Here we introduce a novel variant refinement software, FIREVAT (FInding REliable Variants without ArTifacts), which uses known spectrums of sequencing artifacts extracted from one of the largest publicly available catalogs of human tumor samples. FIREVAT performs a quick and efficient variant refinement that accurately removes artifacts and greatly improves the precision and specificity of somatic calls. We validated FIREVAT refinement performance using orthogonal sequencing datasets totaling 384 tumor samples with respect to ground truth. Our novel method achieved the highest level of performance compared to existing filtering approaches. Application of FIREVAT on additional 308 The Cancer Genome Atlas (TCGA) samples demonstrated that FIREVAT refinement leads to identification of more biologically and clinically relevant mutational signatures as well as enrichment of sequence contexts associated with experimental errors. FIREVAT only requires a Variant Call Format file (VCF) and generates a comprehensive report of the variant refinement processes and outcomes for the user. Conclusions In summary, FIREVAT facilitates a novel refinement strategy using mutational signatures to distinguish artifactual point mutations called in human cancer samples. We anticipate that FIREVAT results will further contribute to precision oncology efforts that rely on accurate identification of variants, especially in the context of analyzing mutational signatures that bear prognostic and therapeutic significance. FIREVAT is freely available at https://github.com/cgab-ncc/FIREVAT

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

2020 ◽  
Vol 49 (D1) ◽  
pp. D1289-D1301 ◽  
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.

scholarly journals Pan-cancer pharmacogenetics: targeted sequencing panels or exome sequencing?

2020 ◽  
Vol 21 (15) ◽  
pp. 1073-1084
Author(s):  
Laurentijn Tilleman ◽  
Björn Heindryckx ◽  
Dieter Deforce ◽  
Filip Van Nieuwerburgh
Keyword(s):  
Drug Interactions ◽  
Exome Sequencing ◽  
Informed Choice ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Targeted Sequencing ◽  
Driver Mutations ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

Aim: This study provides clinicians and researchers with an informed choice between current commercially available targeted sequencing panels and exome sequencing panels in the context of pan-cancer pharmacogenetics. Materials & methods: Nine contemporary commercially available targeted pan-cancer panels and the xGen Exome Research Panel v2 were investigated to determine to what extent they cover the pharmacogenetic variant–drug interactions in five available cancer knowledgebases, and the driver mutations and fusion genes in the Cancer Genome Atlas. Results: xGen Exome Research Panel v2 and TrueSight Oncology 500 target 71.0 and 68.9% of the pharmacogenetic interactions in the available knowledgebases; and 93.7 and 86.0% of the driver mutations in the Cancer Genome Atlas, respectively. All other studied panels target lower percentages. Conclusion: Exome sequencing outperforms pan-cancer targeted sequencing panels in terms of covered cancer pharmacogenetic variant–drug interactions and pharmacogenetic cancer variants.

scholarly journals Identification of recurrent noncoding mutations in B-cell lymphoma using capture Hi-C

2019 ◽  
Vol 3 (1) ◽  
pp. 21-32 ◽  
Author(s):  
Alex J. Cornish ◽  
Phuc H. Hoang ◽  
Sara E. Dobbins ◽  
Philip J. Law ◽  
Daniel Chubb ◽  
...  
Keyword(s):  
B Cell ◽  
Cancer Biology ◽  
Cell Lymphoma ◽  
Regulatory Element ◽  
B Cell Lymphoma ◽  
Regulatory Elements ◽  
Driver Mutations ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
Single Nucleotide Variants

Abstract The identification of driver mutations is fundamental to understanding oncogenesis. Although genes frequently mutated in B-cell lymphoma have been identified, the search for driver mutations has largely focused on the coding genome. Here we report an analysis of the noncoding genome using whole-genome sequencing data from 117 patients with B-cell lymphoma. Using promoter capture Hi-C data in naive B cells, we define cis-regulatory elements, which represent an enriched subset of the noncoding genome in which to search for driver mutations. Regulatory regions were identified whose mutation significantly alters gene expression, including copy number variation at cis-regulatory elements targeting CD69, IGLL5, and MMP14, and single nucleotide variants in a cis-regulatory element for TPRG1. We also show the commonality of pathways targeted by coding and noncoding mutations, exemplified by MMP14, which regulates Notch signaling, a pathway important in lymphomagenesis and whose expression is associated with patient survival. This study provides an enhanced understanding of lymphomagenesis and describes the advantages of using chromosome conformation capture to decipher noncoding mutations relevant to cancer biology.

scholarly journals Reproducibility of SNV-calling in multiple sequencing runs from single tumors

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1508 ◽  
Author(s):  
Dakota Z. Derryberry ◽  
Matthew C. Cowperthwaite ◽  
Claus O. Wilke
Keyword(s):  
Glioblastoma Multiforme ◽  
Large Fraction ◽  
Point Mutations ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Single Nucleotide Variants ◽  
Specific Point ◽  
Single Nucleotide ◽  
Cancer Genome Atlas ◽  
Genome Atlas

We examined 55 technical sequencing replicates of Glioblastoma multiforme (GBM) tumors from The Cancer Genome Atlas (TCGA) to ascertain the degree of repeatability in calling single-nucleotide variants (SNVs). We used the same mutation-calling pipeline on all pairs of samples, and we measured the extent of the overlap between two replicates; that is, how many specific point mutations were found in both replicates. We further tested whether additional filtering increased or decreased the size of the overlap. We found that about half of the putative mutations identified in one sequencing run of a given sample were also identified in the second, and that this percentage remained steady throughout orders of magnitude of variation in the total number of mutations identified (from 23 to 10,966). We further found that using filtering after SNV-calling removed the overlap completely. We concluded that there is variation in the frequency of mutations in GBMs, and that while some filtering approaches preferentially removed putative mutations found in only one replicate, others removed a large fraction of putative mutations found in both.

scholarly journals Tumour mutations in long noncoding RNAs that enhance cell fitness

2021 ◽  
Author(s):  
Roberta Esposito ◽  
Andres Lanzos ◽  
Taisia Polidori ◽  
Hugo Guillen-Ramirez ◽  
Bernard Merlin ◽  
...  
Keyword(s):  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Protein Coding ◽  
Genomic Features ◽  
Coding Regions ◽  
Lncrna Neat1

Tumour DNA contains thousands of single nucleotide variants (SNVs) in non-protein-coding regions, yet it remains unclear which are driver mutations that promote cell fitness. Amongst the most highly mutated non-coding elements are long noncoding RNAs (lncRNAs), which can promote cancer and may be targeted therapeutically. We here searched for evidence that driver mutations may act through alteration of lncRNA function. Using an integrative driver discovery algorithm, we analysed single nucleotide variants (SNVs) from 2583 primary tumours and 3527 metastases to reveal 54 candidate driver lncRNAs (FDR<0.1). Their relevance is supported by enrichment for previously-reported cancer genes and by clinical and genomic features. Using knockdown and transgene overexpression, we show that tumour SNVs in two novel lncRNAs can boost cell fitness. Researchers have noted particularly high yet unexplained mutation rates in the iconic cancer lncRNA, NEAT1. We apply in cellulo mutagenesis by CRISPR-Cas9 to identify vulnerable regions of NEAT1 where SNVs reproducibly increase cell fitness in both transformed and normal backgrounds. In particular, mutations in the 5-prime region of NEAT1 alter ribonucleoprotein assembly and boost the population of subnuclear paraspeckles. Together, this work reveals function-altering somatic lncRNA mutations as a new route to enhanced cell fitness during transformation and metastasis.

scholarly journals Frequent POLE-Driven Hypermutation in Ovarian Endometrioid Cancer Revealed by Mutational Signatures in RNA Sequencing

2021 ◽  
Author(s):  
Jaime Davila ◽  
Pritha Chanana ◽  
Vivekananda Sarangi ◽  
Zach Fogarty ◽  
John Weroha ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Mayo Clinic ◽  
Somatic Mutations ◽  
Age At Onset ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Rna Seq ◽  
Mutational Signatures ◽  
Mutational Signature ◽  
Hotspot Mutations

Abstract Background: DNA polymerase epsilon (POLE) is encoded by the POLE gene, and POLE-driven tumors are characterized by high mutational rates. POLE-driven tumors are relatively common in endometrial and colorectal cancer, and their presence is increasingly recognized in ovarian cancer (OC) of endometrioid type. POLE-driven cases possess an abundance of TCT>TAT and TCG>TTG somatic mutations characterized by mutational signature 10 from the Catalog of Somatic Mutations in Cancer (COSMIC). By quantifying the contribution of COSMIC mutational signature 10 in RNA sequencing (RNA-seq) we set out to identify POLE-driven tumors in a set of unselected Mayo Clinic OC. Methods: Mutational profiles were calculated using expressed single-nucleotide variants (eSNV) in the Mayo Clinic OC tumors (n=195), The Cancer Genome Atlas (TCGA) OC tumors (n=419), and the Genotype-Tissue Expression (GTEx) normal ovarian tissues (n=84). Non-negative Matrix Factorization (NMF) of the mutational profiles inferred the contribution per sample of four distinct mutational signatures, one of which corresponds to COSMIC mutational signature 10. Results: In the Mayo Clinic OC cohort we identified six tumors with a predicted contribution from COSMIC mutational signature 10 of over five mutations per megabase. These six cases harbored known POLE hotspot mutations (P286R, S297F, V411L, and A456P) and were of endometrioid histotype (P=5e-04). These six tumors were hypermutated with a higher tumor mutation load (mean, 54.02 mutations per megabase) compared to non-POLE endometrioid OC cases (mean, 7.69 mutations per megabase; P=5e-04), and had an early onset (average age of patients at onset, 48.33 years) when compared to non-POLE endometrioid OC cohort (average age at onset, 60.13 years; P=.008). Samples from TCGA and GTEx had a low COSMIC signature 10 contribution (median 0.16 mutations per megabase; maximum 1.78 mutations per megabase) and carried no POLE hotspot mutations.Conclusions: From the largest cohort of RNA-seq from endometrioid OC to date (n=53), we identified six hypermutated samples likely driven by POLE (frequency, 11%). Our result suggests the clinical need to screen for POLE driver mutations in endometrioid OC, which can guide enrollment in immunotherapy clinical trials.

scholarly journals Mutational signatures among young-onset testicular cancers

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Nicole E. Mealey ◽  
Dylan E. O’Sullivan ◽  
Cheryl E. Peters ◽  
Daniel Y. C. Heng ◽  
Darren R. Brenner
Keyword(s):  
Testicular Cancer ◽  
Age Of Onset ◽  
Treatment Strategies ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Targeted Prevention ◽  
Mutational Load ◽  
Mutational Signatures ◽  
Chewing Tobacco ◽  
Young Onset

Abstract Background Incidence of testicular cancer is highest among young adults and has been increasing dramatically for men born since 1945. This study aimed to elucidate the factors driving this trend by investigating differences in mutational signatures by age of onset. Methods We retrieved somatic variant and clinical data pertaining to 135 testicular tumors from The Cancer Genome Atlas. We compared mutational load, prevalence of specific mutated genes, mutation types, and mutational signatures between age of onset groups (< 30 years, 30–39 years, ≥ 40 years) after adjusting for subtype. A recursively partitioned mixture model was utilized to characterize combinations of signatures among the young-onset cases. Results Mutational load was significantly higher among older-onset tumors (p < 0.05). There were no highly prevalent driver mutations among young-onset tumors. Mutated genes and types of nucleotide mutations were not significantly different by age group (p > 0.05). Signatures 1, 8 and 29 were more common among young-onset tumors, while signatures 11 and 16 had higher prevalence among older-onset tumors (p < 0.05). Among young-onset tumors, clustering of signatures resulted in four distinct tumor classes. Conclusions Signature contributions differ by age with signatures 1, 8 and 29 were more common among younger-onset tumors. While these signatures are connected with endogenous deamination of 5-methylcytosine, late replication errors and chewing tobacco, respectively, additional research is needed to further elucidate the etiology of young-onset testicular cancer. Large studies of mutational signatures among young-onset patients are required to understand epidemiologic trends as well as inform targeted prevention and treatment strategies.

scholarly journals Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Madeleine Darbyshire ◽  
Zachary du Toit ◽  
Mark F. Rogers ◽  
Tom R. Gaunt ◽  
Colin Campbell
Keyword(s):  
Machine Learning ◽  
Human Cancer ◽  
Single Point ◽  
Cancer Genome ◽  
Solid Tumours ◽  
Driver Mutations ◽  
Cancer Type ◽  
Single Nucleotide Variants ◽  
Driver Genes ◽  
Single Nucleotide

Abstract For cancers, such as common solid tumours, variants in the genome give a selective growth advantage to certain cells. It has recently been argued that the mean count of coding single nucleotide variants acting as disease-drivers in common solid tumours is frequently small in size, but significantly variable by cancer type (hypermutation is excluded from this study). In this paper we investigate this proposal through the use of integrative machine-learning-based classifiers we have proposed recently for predicting the disease-driver status of single nucleotide variants (SNVs) in the human cancer genome. We find that predicted driver counts are compatible with this proposal, have similar variabilities by cancer type and, to a certain extent, the drivers are identifiable by these machine learning methods. We further discuss predicted driver counts stratified by stage of disease and driver counts in non-coding regions of the cancer genome, in addition to driver-genes.

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