scholarly journals Biallelic mutations in cancer genomes reveal local mutational determinants

2021 ◽  
Author(s):  
Jonas Demeulemeester ◽  
Stefan C Dentro ◽  
Moritz Gerstung ◽  
Peter Van Loo
Keyword(s):  
Binding Sites ◽  
Variant Calling ◽  
Mutation Rates ◽  
Uv Damage ◽  
Sequencing Data ◽  
Whole Genomes ◽  
Cancer Genomes ◽  
Infinite Sites Model ◽  
Biallelic Mutations ◽  
Pan Cancer

The infinite sites model of molecular evolution requires that every position in the genome is mutated at most once. It is a cornerstone of tumour phylogenetic analysis, and is often implied when calling, phasing and interpreting variants or studying the mutational landscape as a whole. Here we identify 20,555 biallelic mutations, where the same base is mutated independently on both parental copies, in 722 (26.0%) bulk sequencing samples from the Pan-Cancer Analysis of Whole Genomes study (PCAWG). Biallelic mutations reveal UV damage hotspots at ETS and NFAT binding sites, and hypermutable motifs in POLE-mutant and other cancers. We formulate recommendations for variant calling and provide frameworks to model and detect biallelic mutations. These results highlight the need for accurate models of mutation rates and tumour evolution, as well as their inference from sequencing data.

scholarly journals SvABA: Genome-wide detection of structural variants and indels by local assembly

2017 ◽  
Author(s):  
Jeremiah Wala ◽  
Pratiti Bandopadhayay ◽  
Noah Greenwald ◽  
Ryan O’Rourke ◽  
Ted Sharpe ◽  
...  
Keyword(s):  
Variant Calling ◽  
Accurate Method ◽  
Structural Variants ◽  
Sequencing Data ◽  
Cancer Driver ◽  
Insertion And Deletion ◽  
Genome Wide ◽  
Cancer Genomes ◽  
Local Assembly ◽  
Genomic Regions

AbstractStructural variants (SVs), including small insertion and deletion variants (indels), are challenging to detect through standard alignment-based variant calling methods. Sequence assembly offers a powerful approach to identifying SVs, but is difficult to apply at-scale genome-wide for SV detection due to its computational complexity and the difficulty of extracting SVs from assembly contigs. We describe SvABA, an efficient and accurate method for detecting SVs from short-read sequencing data using genome-wide local assembly with low memory and computing requirements. We evaluated SvABA’s performance on the NA12878 human genome and in simulated and real cancer genomes. SvABA demonstrates superior sensitivity and specificity across a large spectrum of SVs, and substantially improved detection performance for variants in the 20-300 bp range, compared with existing methods. SvABA also identifies complex somatic rearrangements with chains of short (< 1,000 bp) templated-sequence insertions copied from distant genomic regions. We applied SvABA to 344 cancer genomes from 11 cancer types, and found that templated-sequence insertions occur in ~4% of all somatic rearrangements. Finally, we demonstrate that SvABA can identify sites of viral integration and cancer driver alterations containing medium-sized SVs.

scholarly journals DriverPower: Combined burden and functional impact tests for cancer driver discovery

2017 ◽  
Author(s):  
Shimin Shuai ◽  
Steven Gallinger ◽  
Lincoln Stein ◽  
Keyword(s):  
Driver Mutations ◽  
Functional Interpretation ◽  
Functional Impact ◽  
Genomic Features ◽  
Cancer Driver ◽  
Mutational Burden ◽  
Mutation Model ◽  
Whole Genomes ◽  
Cancer Genomes ◽  
Pan Cancer

AbstractWe describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify cancer driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1,373 genomic features derived from public sources, DriverPower’s background mutation model explains up to 93% of the regional variance in the mutation rate across a variety of tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2,583 cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Group, DriverPower has the highest F1-score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery.

scholarly journals Pan-cancer analysis of whole genomes

2017 ◽  
Author(s):  
Peter J Campbell ◽  
Gaddy Getz ◽  
Joshua M Stuart ◽  
Jan O Korbel ◽  
Lincoln D Stein
Keyword(s):  
Structural Variation ◽  
Integrative Analysis ◽  
Background Information ◽  
Cancer Driver ◽  
Normal Tissues ◽  
Whole Genomes ◽  
Cancer Genomes ◽  
Pan Cancer ◽  
Events Study

We report the integrative analysis of more than 2,600 whole cancer genomes and their matching normal tissues across 39 distinct tumour types. By studying whole genomes we have been able to catalogue non-coding cancer driver events, study patterns of structural variation, infer tumour evolution, probe the interactions among variants in the germline genome, the tumour genome and the transcriptome, and derive an understanding of how coding and non-coding variations together contribute to driving individual patient's tumours. This work represents the most comprehensive look at cancer whole genomes to date. NOTE TO READERS: This is an incomplete draft of the marker paper for the Pan-Cancer Analysis of Whole Genomes Project, and is intended to provide the background information for a series of in-depth papers that will be posted to BioRixv during the summer of 2017.

scholarly journals Parallelized Latent Dirichlet Allocation Provides a Novel Interpretability of Mutation Signatures in Cancer Genomes

Genes ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1127
Author(s):  
Taro Matsutani ◽  
Michiaki Hamada
Keyword(s):  
Latent Dirichlet Allocation ◽  
Extended Model ◽  
Tumor Type ◽  
Whole Genomes ◽  
Cancer Genomes ◽  
The One ◽  
Tumor Types ◽  
Apobec Family ◽  
Pan Cancer ◽  
Dirichlet Allocation

Mutation signatures are defined as the distribution of specific mutations such as activity of AID/APOBEC family proteins. Previous studies have reported numerous signatures, using matrix factorization methods for mutation catalogs. Different mutation signatures are active in different tumor types; hence, signature activity varies greatly among tumor types and becomes sparse. Because of this, many previous methods require dividing mutation catalogs for each tumor type. Here, we propose parallelized latent Dirichlet allocation (PLDA), a novel Bayesian model to simultaneously predict mutation signatures with all mutation catalogs. PLDA is an extended model of latent Dirichlet allocation (LDA), which is one of the methods used for signature prediction. It has parallelized hyperparameters of Dirichlet distributions for LDA, and they represent the sparsity of signature activities for each tumor type, thus facilitating simultaneous analyses. First, we conducted a simulation experiment to compare PLDA with previous methods (including SigProfiler and SignatureAnalyzer) using artificial data and confirmed that PLDA could predict signature structures as accurately as previous methods without searching for the optimal hyperparameters. Next, we applied PLDA to PCAWG (Pan-Cancer Analysis of Whole Genomes) mutation catalogs and obtained a signature set different from the one predicted by SigProfiler. Further, we have shown that the mutation spectrum represented by the predicted signature with PLDA provides a novel interpretability through post-analyses.

scholarly journals Framework for quality assessment of whole genome cancer sequences

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Justin P. Whalley ◽  
Ivo Buchhalter ◽  
Esther Rheinbay ◽  
Keiran M. Raine ◽  
Miranda D. Stobbe ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Quality Measures ◽  
Poor Quality ◽  
Rating System ◽  
Test Case ◽  
Whole Genome ◽  
Whole Genomes ◽  
Cancer Genomes ◽  
Pan Cancer

Abstract Bringing together cancer genomes from different projects increases power and allows the investigation of pan-cancer, molecular mechanisms. However, working with whole genomes sequenced over several years in different sequencing centres requires a framework to compare the quality of these sequences. We used the Pan-Cancer Analysis of Whole Genomes cohort as a test case to construct such a framework. This cohort contains whole cancer genomes of 2832 donors from 18 sequencing centres. We developed a non-redundant set of five quality control (QC) measurements to establish a star rating system. These QC measures reflect known differences in sequencing protocol and provide a guide to downstream analyses and allow for exclusion of samples of poor quality. We have found that this is an effective framework of quality measures. The implementation of the framework is available at: https://dockstore.org/containers/quay.io/jwerner_dkfz/pancanqc:1.2.2.

scholarly journals Pan-cancer analysis of whole genomes reveals driver rearrangements promoted by LINE-1 retrotransposition in human tumours

2017 ◽  
Author(s):  
Bernardo Rodriguez-Martin ◽  
Eva G. Alvarez ◽  
Adrian Baez-Ortega ◽  
Jorge Zamora ◽  
Fran Supek ◽  
...  
Keyword(s):  
Large Scale ◽  
Tumour Suppressor Genes ◽  
Cancer Subtypes ◽  
Human Tumours ◽  
Whole Genomes ◽  
Cancer Genomes ◽  
Relevant Role ◽  
High Level ◽  
Pan Cancer

AbstractAbout half of all cancers have somatic integrations of retrotransposons. To characterize their role in oncogenesis, we analyzed the patterns and mechanisms of somatic retrotransposition in 2,954 cancer genomes from 37 histological cancer subtypes. We identified 19,166 somatically acquired retrotransposition events, affecting 35% of samples, and spanning a range of event types. L1 insertions emerged as the first most frequent type of somatic structural variation in esophageal adenocarcinoma, and the second most frequent in head-and-neck and colorectal cancers. Aberrant L1 integrations can delete megabase-scale regions of a chromosome, sometimes removing tumour suppressor genes, as well as inducing complex translocations and large-scale duplications. Somatic retrotranspositions can also initiate breakage-fusion-bridge cycles, leading to high-level amplification of oncogenes. These observations illuminate a relevant role of L1 retrotransposition in remodeling the cancer genome, with potential implications in the development of human tumours.

SAMSVM: A tool for misalignment filtration of SAM-format sequences with support vector machine

2015 ◽  
Vol 13 (06) ◽  
pp. 1550025 ◽  
Author(s):  
Jianfeng Yang ◽  
Xiaofan Ding ◽  
Xing Sun ◽  
Shui-Ying Tsang ◽  
Hong Xue
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Variant Calling ◽  
Mutation Rates ◽  
Support Vector ◽  
Main Role ◽  
Sequencing Data ◽  
Multiple Features ◽  
Simple Interface ◽  
Dimension Space

Sequence alignment/map (SAM) formatted sequences [Li H, Handsaker B, Wysoker A et al., Bioinformatics 25(16):2078–2079, 2009.] have taken on a main role in bioinformatics since the development of massive parallel sequencing. However, because misalignment of sequences poses a significant problem in analysis of sequencing data that could lead to false positives in variant calling, the exclusion of misaligned reads is a necessity in analysis. In this regard, the multiple features of SAM-formatted sequences can be treated as vectors in a multi-dimension space to allow the application of a support vector machine (SVM). Applying the LIBSVM tools developed by Chang and Lin [Chang C-C, Lin C-J, ACM Trans Intell Syst Technol 2:1–27, 2011.] as a simple interface for support vector classification, the SAMSVM package has been developed in this study to enable misalignment filtration of SAM-formatted sequences. Cross-validation between two simulated datasets processed with SAMSVM yielded accuracies that ranged from 0.89 to 0.97 with F-scores ranging from 0.77 to 0.94 in 14 groups characterized by different mutation rates from 0.001 to 0.1, indicating that the model built using SAMSVM was accurate in misalignment detection. Application of SAMSVM to actual sequencing data resulted in filtration of misaligned reads and correction of variant calling.

scholarly journals Large-Scale Uniform Analysis of Cancer Whole Genomes in Multiple Computing Environments

2017 ◽  
Author(s):  
Christina K. Yung ◽  
Brian D. O’Connor ◽  
Sergei Yakneen ◽  
Junjun Zhang ◽  
Kyle Ellrott ◽  
...  
Keyword(s):  
Large Scale ◽  
Variant Calling ◽  
Sequencing Data ◽  
Coding Regions ◽  
Whole Genomes ◽  
Portable Software ◽  
Working Groups ◽  
Genome Consortium ◽  
Data Portal ◽  
Downstream Analysis

AbstractThe International Cancer Genome Consortium (ICGC)’s Pan-Cancer Analysis of Whole Genomes (PCAWG) project aimed to categorize somatic and germline variations in both coding and non-coding regions in over 2,800 cancer patients. To provide this dataset to the research working groups for downstream analysis, the PCAWG Technical Working Group marshalled ~800TB of sequencing data from distributed geographical locations; developed portable software for uniform alignment, variant calling, artifact filtering and variant merging; performed the analysis in a geographically and technologically disparate collection of compute environments; and disseminated high-quality validated consensus variants to the working groups. The PCAWG dataset has been mirrored to multiple repositories and can be located using the ICGC Data Portal. The PCAWG workflows are also available as Docker images through Dockstore enabling researchers to replicate our analysis on their own data.

scholarly journals Functional and genetic determinants of mutation rate variability in regulatory elements of cancer genomes

2020 ◽  
Author(s):  
Christian A. Lee ◽  
Diala Abd-Rabbo ◽  
Jüri Reimand
Keyword(s):  
Binding Sites ◽  
Scale Effects ◽  
Regulatory Elements ◽  
Ctcf Binding ◽  
Mutation Rates ◽  
Driver Gene ◽  
Open Chromatin ◽  
Cancer Driver ◽  
Cancer Genomes ◽  
Mutational Processes

Localised variation of somatic mutation rates affects diverse functional sequence elements in cancer genomes through poorly understood mutational processes. Here, we characterise the mutational landscape of 640,000 gene regulatory and chromatin architectural elements in 2,421 whole cancer genomes using our new statistical model RM2. This method quantifies differential mutation rates and signatures in classes of genomic elements via genetic, trinucleotide and megabase-scale effects. We report a detailed map of localised mutational processes affecting CTCF binding sites, transcription start sites (TSS) and cancer-specific open-chromatin regions. This includes a pan-cancer indel depletion in open-chromatin sites, a TSS-specific mutational process correlated with mRNA abundance in core cellular and cancer-associated processes, a subset of hypermutated, constitutively active CTCF binding sites involved in chromatin architectural interactions, and an enrichment of signature SBS17b in CTCF sites in gastrointestinal cancers. We also detect genetic driver alterations potentially underlying localised mutation rates, including RAD21 amplifications and BRAF mutations associating with mutagenesis of CTCF binding sites, and SDHA amplifications indicative of frequent lung cancer mutations in open-chromatin sites. Our framework and the catalogue of localised mutational processes provide novel perspectives to cancer genome evolution and its implications for oncogenesis, tumor heterogeneity and cancer driver gene discovery.

scholarly journals Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marleen M. Nieboer ◽  
Luan Nguyen ◽  
Jeroen de Ridder
Keyword(s):  
Multiple Instance Learning ◽  
Cancer Diagnostics ◽  
Common Mechanism ◽  
Open Chromatin ◽  
Driver Genes ◽  
3D Genome ◽  
Whole Genomes ◽  
Cancer Genomes ◽  
Cancer Types ◽  
The Impact

AbstractOver the past years, large consortia have been established to fuel the sequencing of whole genomes of many cancer patients. Despite the increased abundance in tools to study the impact of SNVs, non-coding SVs have been largely ignored in these data. Here, we introduce svMIL2, an improved version of our Multiple Instance Learning-based method to study the effect of somatic non-coding SVs disrupting boundaries of TADs and CTCF loops in 1646 cancer genomes. We demonstrate that svMIL2 predicts pathogenic non-coding SVs with an average AUC of 0.86 across 12 cancer types, and identifies non-coding SVs affecting well-known driver genes. The disruption of active (super) enhancers in open chromatin regions appears to be a common mechanism by which non-coding SVs exert their pathogenicity. Finally, our results reveal that the contribution of pathogenic non-coding SVs as opposed to driver SNVs may highly vary between cancers, with notably high numbers of genes being disrupted by pathogenic non-coding SVs in ovarian and pancreatic cancer. Taken together, our machine learning method offers a potent way to prioritize putatively pathogenic non-coding SVs and leverage non-coding SVs to identify driver genes. Moreover, our analysis of 1646 cancer genomes demonstrates the importance of including non-coding SVs in cancer diagnostics.

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