scholarly journals Chromoanagenesis landscape in 10,000 TCGA patients

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.

scholarly journals Chromoanagenesis Landscape in 10,000 TCGA Patients

Cancers ◽  
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.

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

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.

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

2013 ◽  
Vol 88 (1) ◽  
pp. 774-774 ◽  
Author(s):  
E. S. Amirian ◽  
M. L. Bondy ◽  
Q. Mo ◽  
M. N. Bainbridge ◽  
M. E. Scheurer
Keyword(s):  
Brain Tumor ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Whole Genome ◽  
Viral Dna ◽  
Tumor Tissues ◽  
Cancer Genome Atlas ◽  
Genome Atlas

scholarly journals Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Matthew H. Bailey ◽  
◽  
William U. Meyerson ◽  
Lewis Jonathan Dursi ◽  
Liang-Bo Wang ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Gc Content ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Whole Exome ◽  
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.

scholarly journals Integrated Dissection of lncRNA-Perturbated Triplets Reveals Novel Prognostic Signatures Across Cancer Types

2020 ◽  
Vol 21 (17) ◽  
pp. 6087
Author(s):  
Yunzhen Wei ◽  
Limeng Zhou ◽  
Yingzhang Huang ◽  
Dianjing Guo
Keyword(s):  
Cancer Biology ◽  
Noncoding Rna ◽  
The Cancer Genome Atlas ◽  
Dynamic Changes ◽  
Computational Framework ◽  
Potential Biomarker ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Tcga Dataset ◽  
Pan Cancer

Long noncoding RNA (lncRNA)/microRNA(miRNA)/mRNA triplets contribute to cancer biology. However, identifying significative triplets remains a major challenge for cancer research. The dynamic changes among factors of the triplets have been less understood. Here, by integrating target information and expression datasets, we proposed a novel computational framework to identify the triplets termed as “lncRNA-perturbated triplets”. We applied the framework to five cancer datasets in The Cancer Genome Atlas (TCGA) project and identified 109 triplets. We showed that the paired miRNAs and mRNAs were widely perturbated by lncRNAs in different cancer types. LncRNA perturbators and lncRNA-perturbated mRNAs showed significantly higher evolutionary conservation than other lncRNAs and mRNAs. Importantly, the lncRNA-perturbated triplets exhibited high cancer specificity. The pan-cancer perturbator OIP5-AS1 had higher expression level than that of the cancer-specific perturbators. These lncRNA perturbators were significantly enriched in known cancer-related pathways. Furthermore, among the 25 lncRNA in the 109 triplets, lncRNA SNHG7 was identified as a stable potential biomarker in lung adenocarcinoma (LUAD) by combining the TCGA dataset and two independent GEO datasets. Results from cell transfection also indicated that overexpression of lncRNA SNHG7 and TUG1 enhanced the expression of the corresponding mRNA PNMA2 and CDC7 in LUAD. Our study provides a systematic dissection of lncRNA-perturbated triplets and facilitates our understanding of the molecular roles of lncRNAs in cancers.

scholarly journals Mutational landscape of gastric adenocarcinoma in Chinese: Implications for prognosis and therapy

2015 ◽  
Vol 112 (4) ◽  
pp. 1107-1112 ◽  
Author(s):  
Kexin Chen ◽  
Da Yang ◽  
Xiangchun Li ◽  
Baocun Sun ◽  
Fengju Song ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Clonal Evolution ◽  
Treatment Strategies ◽  
The Cancer Genome Atlas ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Phylogenetic Tree Analysis ◽  
Primary Tumors

Gastric cancer (GC) is a highly heterogeneous disease. To identify potential clinically actionable therapeutic targets that may inform individualized treatment strategies, we performed whole-exome sequencing on 78 GCs of differing histologies and anatomic locations, as well as whole-genome sequencing on two GC cases, each with three primary tumors and two matching lymph node metastases. The data showed two distinct GC subtypes with either high-clonality (HiC) or low-clonality (LoC). The HiC subtype of intratumoral heterogeneity was associated with older age, TP53 (tumor protein P53) mutation, enriched C > G transition, and significantly shorter survival, whereas the LoC subtype was associated with younger age, ARID1A (AT rich interactive domain 1A) mutation, and significantly longer survival. Phylogenetic tree analysis of whole-genome sequencing data from multiple samples of two patients supported the clonal evolution of GC metastasis and revealed the accumulation of genetic defects that necessitate combination therapeutics. The most recurrently mutated genes, which were validated in a separate cohort of 216 cases by targeted sequencing, were members of the homologous recombination DNA repair, Wnt, and PI3K-ERBB pathways. Notably, the drugable NRG1 (neuregulin-1) and ERBB4 (V-Erb-B2 avian erythroblastic leukemia viral oncogene homolog 4) ligand-receptor pair were mutated in 10% of GC cases. Mutations of the BRCA2 (breast cancer 2, early onset) gene, found in 8% of our cohort and validated in The Cancer Genome Atlas GC cohort, were associated with significantly longer survivals. These data define distinct clinicogenetic forms of GC in the Chinese population that are characterized by specific mutation sets that can be investigated for efficacy of single and combination therapies.

scholarly journals Cancer Type-Dependent Correlations betweenTP53Mutations and Antitumor Immunity

2019 ◽  
Author(s):  
Lin Li ◽  
Mengyuan Li ◽  
Xiaosheng Wang
Keyword(s):  
Antitumor Immunity ◽  
Colon Adenocarcinoma ◽  
Joint Effect ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Mutation Burden ◽  
Cancer Genome Atlas ◽  
Negative Role ◽  
Stomach Adenocarcinoma ◽  
Cancer Types

AbstractMany studies have shown thatTP53mutations play a negative role in antitumor immunity. However, a few studies reported thatTP53mutations could promote antitumor immunity. To explain these contradictory findings, we analyzed five cancer cohorts from The Cancer Genome Atlas (TCGA) project. We found thatTP53-mutated cancers had significantly higher levels of antitumor immune signatures thanTP53-wildtype cancers in breast invasive carcinoma (BRCA) and lung adenocarcinoma (LUAD). In contrast,TP53-mutated cancers had significantly lower antitumor immune signature levels thanTP53-wildtype cancers in stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and head and neck squamous cell carcinoma (HNSC). Moreover,TP53-mutated cancers likely had higher tumor mutation burden (TMB) and tumor aneuploidy level (TAL) thanTP53-wildtype cancers. However, the TMB differences were more marked betweenTP53-mutated andTP53-wildtype cancers than the TAL differences in BRCA and LUAD, and the TAL differences were more significant in STAD and COAD. Furthermore, we showed that TMB and TAL had a positive and a negative correlation with antitumor immunity and that TMB affected antitumor immunity more greatly than TAL did in BRCA and LUAD while TAL affected antitumor immunity more strongly than TMB in STAD and HNSC. These findings indicate that the distinct correlations betweenTP53mutations and antitumor immunity in different cancer types are a consequence of the joint effect of the altered TMB and TAL caused byTP53mutations on tumor immunity. Our data suggest that theTP53mutation status could be a useful biomarker for cancer immunotherapy response depending on cancer types.

scholarly journals Pan-cancer analysis reveals complex tumor-specific alternative polyadenylation

2017 ◽  
Author(s):  
Zhuyi Xue ◽  
René L Warren ◽  
Ewan A Gibb ◽  
Daniel MacMillan ◽  
Johnathan Wong ◽  
...  
Keyword(s):  
Alternative Polyadenylation ◽  
Length Change ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Rna Seq ◽  
Multiple Cancer ◽  
Tissue Samples ◽  
Cancer Genome Atlas ◽  
Specific Alternative ◽  
Cancer Types

AbstractAlternative polyadenylation (APA) of 3’ untranslated regions (3’ UTRs) has been implicated in cancer development. Earlier reports on APA in cancer primarily focused on 3’ UTR length modifications, and the conventional wisdom is that tumor cells preferentially express transcripts with shorter 3’ UTRs. Here, we analyzed the APA patterns of 114 genes, a select list of oncogenes and tumor suppressors, in 9,939 tumor and 729 normal tissue samples across 33 cancer types using RNA-Seq data from The Cancer Genome Atlas, and we found that the APA regulation machinery is much more complicated than what was previously thought. We report 77 cases (gene-cancer type pairs) of differential 3’ UTR cleavage patterns between normal and tumor tissues, involving 33 genes in 13 cancer types. For 15 genes, the tumor-specific cleavage patterns are recurrent across multiple cancer types. While the cleavage patterns in certain genes indicate apparent trends of 3’ UTR shortening in tumor samples, over half of the 77 cases imply 3’ UTR length change trends in cancer that are more complex than simple shortening or lengthening. This work extends the current understanding of APA regulation in cancer, and demonstrates how large volumes of RNA-seq data generated for characterizing cancer cohorts can be mined to investigate this process.

scholarly journals A pan-cancer analysis of prognostic genes

PeerJ ◽  
2016 ◽  
Vol 3 ◽  
pp. e1499 ◽  
Author(s):  
Jordan Anaya ◽  
Brian Reon ◽  
Wei-Min Chen ◽  
Stefan Bekiranov ◽  
Anindya Dutta
Keyword(s):  
Microarray Data ◽  
The Cancer Genome Atlas ◽  
Rna Seq ◽  
Cancer Pathogenesis ◽  
Gene Sets ◽  
Protective Genes ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Measure Of Association ◽  
Pan Cancer

Numerous studies have identified prognostic genes in individual cancers, but a thorough pan-cancer analysis has not been performed. In addition, previous studies have mostly used microarray data instead of RNA-SEQ, and have not published comprehensive lists of associations with survival. Using recently available RNA-SEQ and clinical data from The Cancer Genome Atlas for 6,495 patients, we have investigated every annotated and expressed gene’s association with survival across 16 cancer types. The most statistically significant harmful and protective genes were not shared across cancers, but were enriched in distinct gene sets which were shared across certain groups of cancers. These groups of cancers were independently recapitulated by both unsupervised clustering of Cox coefficients (a measure of association with survival) for individual genes, and for gene programs. This analysis has revealed unappreciated commonalities among cancers which may provide insights into cancer pathogenesis and rationales for co-opting treatments between cancers.

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