Presence of Viral DNA in Whole-Genome Sequencing of Brain Tumor Tissues from The Cancer Genome Atlas

AbstractThe Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

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Decoding human cancer with whole genome sequencing: a review of PCAWG Project studies published in February 2020

Cancer and Metastasis Reviews ◽

10.1007/s10555-021-09969-z ◽

2021 ◽

Author(s):

Simona Giunta

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Cancer Genetics ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Driver Mutations ◽

Causative Mutation ◽

Whole Genome ◽

Genetic Changes ◽

Pan Cancer

AbstractCancer is underlined by genetic changes. In an unprecedented international effort, the Pan-Cancer Analysis of Whole Genomes (PCAWG) of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) sequenced the tumors of over two thousand five hundred patients across 38 different cancer types, as well as the corresponding healthy tissue, with the aim of identifying genome-wide mutations exclusively found in cancer and uncovering new genetic changes that drive tumor formation. What set this project apart from earlier efforts is the use of whole genome sequencing (WGS) that enabled to explore alterations beyond the coding DNA, into cancer’s non-coding genome. WGS of the entire cohort allowed to tease apart driving mutations that initiate and support carcinogenesis from passenger mutations that do not play an overt role in the disease. At least one causative mutation was found in 95% of all cancers, with many tumors showing an average of 5 driver mutations. The PCAWG Project also assessed the transcriptional output altered in cancer and rebuilt the evolutionary history of each tumor showing that initial driver mutations can occur years if not decades prior to a diagnosis. Here, I provide a concise review of the Pan-Cancer Project papers published on February 2020, along with key computational tools and the digital framework generated as part of the project. This represents an historic effort by hundreds of international collaborators, which provides a comprehensive understanding of cancer genetics, with publicly available data and resources representing a treasure trove of information to advance cancer research for years to come.

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Chromoanagenesis Landscape in 10,000 TCGA Patients

Cancers ◽

10.3390/cancers13164197 ◽

2021 ◽

Vol 13 (16) ◽

pp. 4197

Author(s):

Roni Rasnic ◽

Michal Linial

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Learning Algorithm ◽

The Cancer Genome Atlas ◽

Cancer Type ◽

Whole Genome ◽

Cancer Genome Atlas ◽

Cancer Types ◽

Tumor Biopsies ◽

Pan Cancer

During the past decade, whole-genome sequencing of tumor biopsies and individuals with congenital disorders highlighted the phenomenon of chromoanagenesis, a single chaotic event of chromosomal rearrangement. Chromoanagenesis was shown to be frequent in many types of cancers, to occur in early stages of cancer development, and significantly impact the tumor’s nature. However, an in-depth, cancer-type dependent analysis has been somewhat incomplete due to the shortage in whole genome sequencing of cancerous samples. In this study, we extracted data from The Pan-Cancer Analysis of Whole Genome (PCAWG) and The Cancer Genome Atlas (TCGA) to construct and test a machine learning algorithm that can detect chromoanagenesis with high accuracy (86%). The algorithm was applied to ~10,000 unlabeled TCGA cancer patients. We utilize the chromoanagenesis assignment results, to analyze cancer-type specific chromoanagenesis characteristics in 20 TCGA cancer types. Our results unveil prominent genes affected in either chromoanagenesis or non-chromoanagenesis tumorigenesis. The analysis reveals a mutual exclusivity relationship between the genes impaired in chromoanagenesis versus non-chromoanagenesis cases. We offer the discovered characteristics as possible targets for cancer diagnostic and therapeutic purposes.

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Harvestman: A framework for hierarchical feature learning and selection from whole genome sequencing data

10.1101/2020.03.24.005603 ◽

2020 ◽

Author(s):

Trevor S. Frisby ◽

Shawn James Baker ◽

Guillaume Marçais ◽

Quang Minh Hoang ◽

Carl Kingsford ◽

...

Keyword(s):

Feature Selection ◽

Feature Learning ◽

Data Access ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Whole Genome ◽

1000 Genomes Project ◽

1000 Genomes ◽

Cancer Genome Atlas ◽

Genome Atlas

AbstractWe present Harvestman, a method that takes advantage of hierarchical relationships among the possible biological interpretations and representations of genomic variants to perform automatic feature learning, feature selection, and model building. We demonstrate that Harvestman scales to thousands of genomes comprising more than 84 million variants by processing phase 3 data from the 1000 Genomes Project, the largest publicly available collection of whole genome sequences. Next, using breast cancer data from The Cancer Genome Atlas, we show that Harvestman selects a rich combination of representations that are adapted to the learning task, and performs better than a binary representation of SNPs alone. Finally, we compare Harvestman to existing feature selection methods and demonstrate that our method selects smaller and less redundant feature subsets, while maintaining accuracy of the resulting classifier. The data used is available through either the 1000 Genomes Project or The Cancer Genome Atlas. Access to TCGA data requires the completion of a Data Access Request through the Database of Genotypes and Phenotypes (dbGaP). Binary releases of Harvestman compatible with Linux, Windows, and Mac are available for download at https://github.com/cmlh-gp/Harvestman-public/releases

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Chromoanagenesis landscape in 10,000 TCGA patients

10.1101/2021.04.29.441937 ◽

2021 ◽

Author(s):

Roni Rasnic ◽

Michal Linial

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Learning Algorithm ◽

The Cancer Genome Atlas ◽

Cancer Type ◽

Whole Genome ◽

Cancer Genome Atlas ◽

Cancer Types ◽

Tumor Biopsies ◽

Pan Cancer

During the past decade, whole-genome sequencing of tumor biopsies and individuals with congenital disorders highlighted the phenomenon of chromoanagenesis, a single chaotic event of chromosomal rearrangement. Chromoanagenesis was shown to be frequent in many types of cancers, to occur in early stages of cancer development, and significantly impact the tumors nature. However, an in-depth, cancer-type dependent analysis has been somewhat incomplete due to the shortage in whole genome sequencing of cancerous samples. In this study, we extracted data from The Pan-Cancer Analysis of Whole Genome (PCAWG) and The Cancer Genome Atlas (TCGA) to construct a machine learning algorithm that can detect chromoanagenesis with high accuracy (86%). The algorithm was applied to ~10,000 TCGA cancer patients. We utilize the chromoanagenesis assignment results, to analyze cancer-type specific chromoanagenesis characteristics in 20 TCGA cancer types. Our results unveil prominent genes affected in either chromoanagenesis or non-chromoanagenesis tumorigenesis. The analysis reveals a mutual exclusivity relationship between the genes impaired in chromoanagenesis versus non-chromoanagenesis cases. We offer the discovered characteristics as possible targets for cancer diagnostic and therapeutic purposes.

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The Landscape of Whole-genome Alterations and Pathologic Features in Genitourinary Malignancies: An Analysis of the Cancer Genome Atlas

European Urology Focus ◽

10.1016/j.euf.2017.01.007 ◽

2017 ◽

Vol 3 (6) ◽

pp. 584-589 ◽

Cited By ~ 6

Author(s):

Mark W. Ball ◽

Michael A. Gorin ◽

Charles G. Drake ◽

Hans J. Hammers ◽

Mohamad E. Allaf

Keyword(s):

Cancer Genome ◽

The Cancer Genome Atlas ◽

Whole Genome ◽

Pathologic Features ◽

Cancer Genome Atlas ◽

Genome Atlas

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Special Report: The Cancer Genome Atlas Begins with 3-Year, $100 Million Pilot

PsycEXTRA Dataset ◽

10.1037/e481292006-009 ◽

2005 ◽

Author(s):

Keyword(s):

Cancer Genome ◽

The Cancer Genome Atlas ◽

Special Report ◽

Cancer Genome Atlas ◽

Genome Atlas

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Differences in endometrial cancer molecular portraits based on ethnicity in The Cancer Genome Atlas

10.26226/morressier.59ba7298d462b80296ca20c6 ◽

2017 ◽

Author(s):

David Guttery

Keyword(s):

Endometrial Cancer ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Cancer Genome Atlas ◽

Genome Atlas

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Exceptional Chemotherapy Response in Metastatic Colorectal Cancer Associated With Hyper-Indel–Hypermutated Cancer Genome and Comutation of POLD1 and MLH1

JCO Precision Oncology ◽

10.1200/po.16.00015 ◽

2017 ◽

pp. 1-12

Author(s):

Manish R. Sharma ◽

James T. Auman ◽

Nirali M. Patel ◽

Juneko E. Grilley-Olson ◽

Xiaobei Zhao ◽

...

Keyword(s):

Colorectal Cancer ◽

Colon Cancer ◽

Somatic Mutation ◽

Mutation Rate ◽

Germ Line ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Response To Chemotherapy ◽

Cancer Genome Atlas ◽

Genome Atlas

Purpose A 73-year-old woman with metastatic colon cancer experienced a complete response to chemotherapy with dose-intensified irinotecan that has been durable for 5 years. We sequenced her tumor and germ line DNA and looked for similar patterns in publicly available genomic data from patients with colorectal cancer. Patients and Methods Tumor DNA was obtained from a biopsy before therapy, and germ line DNA was obtained from blood. Tumor and germline DNA were sequenced using a commercial panel with approximately 250 genes. Whole-genome amplification and exome sequencing were performed for POLE and POLD1. A POLD1 mutation was confirmed by Sanger sequencing. The somatic mutation and clinical annotation data files from the colon (n = 461) and rectal (n = 171) adenocarcinoma data sets were downloaded from The Cancer Genome Atlas data portal and analyzed for patterns of mutations and clinical outcomes in patients with POLE- and/or POLD1-mutated tumors. Results The pattern of alterations included APC biallelic inactivation and microsatellite instability high (MSI-H) phenotype, with somatic inactivation of MLH1 and hypermutation (estimated mutation rate > 200 per megabase). The extremely high mutation rate led us to investigate additional mechanisms for hypermutation, including loss of function of POLE. POLE was unaltered, but a related gene not typically associated with somatic mutation in colon cancer, POLD1, had a somatic mutation c.2171G>A [p.Gly724Glu]. Additionally, we noted that the high mutation rate was largely composed of dinucleotide deletions. A similar pattern of hypermutation (dinucleotide deletions, POLD1 mutations, MSI-H) was found in tumors from The Cancer Genome Atlas. Conclusion POLD1 mutation with associated MSI-H and hyper-indel–hypermutated cancer genome characterizes a previously unrecognized variant of colon cancer that was found in this patient with an exceptional response to chemotherapy.

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