Reconstructing and characterizing focal amplifications in cancer using AmpliconArchitect

AbstractFocal oncogene amplification and rearrangements drive tumor growth and evolution in multiple cancer types. We developed a tool, AmpliconArchitect (AA), which can robustly reconstruct the fine structure of focally amplified regions using whole genome sequencing. AA-reconstructed amplicons in pan-cancer data and in virus-driven cervical cancer samples revealed many novel insights about focal amplifications. Specifically, the findings lend support to extrachromosomally mediated mechanisms for copy number expansion, and oncoviral pathogenesis.

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Using low-coverage whole genome sequencing technique to analyze the chromosomal copy number alterations in the exfoliative cells of cervical cancer

Journal of Gynecologic Oncology ◽

10.3802/jgo.2018.29.e78 ◽

2018 ◽

Vol 29 (5) ◽

Author(s):

Tong Ren ◽

Jing Suo ◽

Shikai Liu ◽

Shu Wang ◽

Shan Shu ◽

...

Keyword(s):

Cervical Cancer ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Copy Number ◽

Whole Genome ◽

Copy Number Alterations ◽

Sequencing Technique ◽

Chromosomal Copy Number ◽

Chromosomal Copy ◽

Low Coverage

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Pan-cancer driver copy number alterations identified by joint expression/CNA data analysis

Scientific Reports ◽

10.1038/s41598-020-74276-6 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Gaojianyong Wang ◽

Dimitris Anastassiou

Keyword(s):

Gene Expression ◽

Copy Number ◽

The Cancer Genome Atlas ◽

Copy Number Alterations ◽

Multiple Cancer ◽

Cancer Driver ◽

Large Gene ◽

Cancer Genome Atlas ◽

Cancer Types ◽

Pan Cancer

Abstract Analysis of large gene expression datasets from biopsies of cancer patients can identify co-expression signatures representing particular biomolecular events in cancer. Some of these signatures involve genomically co-localized genes resulting from the presence of copy number alterations (CNAs), for which analysis of the expression of the underlying genes provides valuable information about their combined role as oncogenes or tumor suppressor genes. Here we focus on the discovery and interpretation of such signatures that are present in multiple cancer types due to driver amplifications and deletions in particular regions of the genome after doing a comprehensive analysis combining both gene expression and CNA data from The Cancer Genome Atlas.

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Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing

10.1101/333617 ◽

2018 ◽

Cited By ~ 11

Author(s):

Isidro Cortés-Ciriano ◽

June-Koo Lee ◽

Ruibin Xi ◽

Dhawal Jain ◽

Youngsook L. Jung ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Copy Number ◽

Human Cancer ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

End Joining ◽

Cancer Types ◽

Non Homologous End Joining

SummaryChromothripsis is a newly discovered mutational phenomenon involving massive, clustered genomic rearrangements that occurs in cancer and other diseases. Recent studies in cancer suggest that chromothripsis may be far more common than initially inferred from low resolution DNA copy number data. Here, we analyze the patterns of chromothripsis across 2,658 tumors spanning 39 cancer types using whole-genome sequencing data. We find that chromothripsis events are pervasive across cancers, with a frequency of >50% in several cancer types. Whereas canonical chromothripsis profiles display oscillations between two copy number states, a considerable fraction of the events involves multiple chromosomes as well as additional structural alterations. In addition to non-homologous end-joining, we detect signatures of replicative processes and templated insertions. Chromothripsis contributes to oncogene amplification as well as to inactivation of genes such as mismatch-repair related genes. These findings show that chromothripsis is a major process driving genome evolution in human cancer.

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Investigating Multi-cancer Biomarkers and Their Cross-predictability in the Expression Profiles of Multiple Cancer Types

Biomarker Insights ◽

10.4137/bmi.s930 ◽

2009 ◽

Vol 4 ◽

pp. BMI.S930 ◽

Cited By ~ 8

Author(s):

George C. Tseng ◽

Chunrong Cheng ◽

Yan Ping Yu ◽

Joel Nelson ◽

George Michalopoulos ◽

...

Keyword(s):

Expression Profiles ◽

Meta Analysis ◽

Organ Donor ◽

Cancer Biomarkers ◽

Data Sets ◽

Cancer Type ◽

Whole Genome ◽

Multiple Cancer ◽

Cancer Data ◽

Cancer Types

Microarray technology has been widely applied to the analysis of many malignancies, however, integrative analyses across multiple studies are rarely investigated. In this study we performed a meta-analysis on the expression profiles of four published studies analyzing organ donor, benign tissues adjacent to tumor and tumor tissues from liver, prostate, lung and bladder samples. We identified 99 distinct multi-cancer biomarkers in the comparison of all three tissues in liver and prostate and 44 in the comparison of normal versus tumor in liver, prostate and lung. The bladder samples appeared to have a different list of biomarkers from the other three cancer types. The identified multi-cancer biomarkers achieved high accuracy similar to using whole genome in the within-cancer-type prediction. They also performed superior than the one using whole genome in inter-cancer-type prediction. To test the validity of the multi-cancer biomarkers, 23 independent prostate cancer samples were evaluated and 96% accuracy was achieved in inter-study prediction from the original prostate, liver and lung cancer data sets respectively. The result suggests that the compact lists of multi-cancer biomarkers are important in cancer development and represent the common signatures of malignancies of multiple cancer types. Pathway analysis revealed important tumorogenesis functional categories.

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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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MEXCOWalk: Mutual Exclusion and Coverage Based Random Walk to Identify Cancer Modules

10.1101/547653 ◽

2019 ◽

Author(s):

Rafsan Ahmed ◽

Ilyes Baali ◽

Cesim Erten ◽

Evis Hoxha ◽

Hilal Kazan

Keyword(s):

Random Walk ◽

Mutual Exclusion ◽

Risk Scores ◽

Cancer Genes ◽

Multiple Cancer ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Data ◽

Cancer Types ◽

Pan Cancer

AbstractMotivationGenomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein-protein interaction networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules.ResultsWe present a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein-protein interactions, mutual exclusion, and coverage to identify cancer driver modules. MEXCOWalk outperforms several state-of-the-art computational methods on TCGA pan-cancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples, and that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data. The data, the source code, and useful scripts are available at:https://github.com/abu-compbio/[email protected]

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Computing MicroRNA-Gene Interaction Networks in Pan-cancer Using MiRDriver

10.21203/rs.3.rs-1101651/v1 ◽

2021 ◽

Author(s):

Banabithi Bose ◽

Matthew Moravec ◽

Serdar Bozdag

Keyword(s):

Copy Number ◽

Target Genes ◽

The Cancer Genome Atlas ◽

Gene Interactions ◽

Microrna Target ◽

Multiple Cancer ◽

Driver Genes ◽

Microrna Gene ◽

Cancer Types ◽

Pan Cancer

Abstract DNA copy number aberrated regions in cancer are known to harbor cancer driver genes and the short non-coding RNA molecules, i.e., microRNAs. In this study, we integrated the multi-omics datasets such as copy number aberration, DNA methylation, gene and microRNA expression to identify the signature microRNA-gene associations from frequently aberrated DNA regions across pan-cancer utilizing a LASSO-based regression approach. We studied 7,294 patient samples associated with eighteen different cancer types from The Cancer Genome Atlas (TCGA) database and identified several cancer-specific microRNA-gene interactions enriched in experimentally validated microRNA-target databases. We highlighted several oncogenic and tumor suppressor microRNAs and genes that were common in several cancer types. Our method substantially outperformed the five state-of-art methods in selecting significantly known microRNA-gene interactions in multiple cancer types. Several microRNAs and genes were found to be associated with tumor survival and progression. Selected target genes were found to be significantly enriched in cancer-related pathways, cancer Hallmark and Gene Ontology (GO) terms. Furthermore, subtype-specific potential gene signatures were discovered in multiple cancer types.

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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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