Integrative Modeling Identifies Genetic Ancestry-associated Molecular Correlates in Human Cancer

Overview

Journal STAR Protoc

Publisher Cell Press

Specialties Biology
Biomedical Engineering
Science

Date 2021 May 13

PMID 33982016

Citations 2

Authors

A Gordon Robertson

Christina Yau

Jian Carrot-Zhang

Jeffrey S Damrauer

Theo A Knijnenburg

Nyasha Chambwe

Katherine A Hoadley

Anab Kemal

Jean C Zenklusen

Andrew D Cherniack

Rameen Beroukhim

Wanding Zhou

Affiliations

Soon will be listed here.

Abstract

Cellular and molecular aberrations contribute to the disparity of human cancer incidence and etiology between ancestry groups. Multiomics profiling in The Cancer Genome Atlas (TCGA) allows for querying of the molecular underpinnings of ancestry-specific discrepancies in human cancer. Here, we provide a protocol for integrative associative analysis of ancestry with molecular correlates, including somatic mutations, DNA methylation, mRNA transcription, miRNA transcription, and pathway activity, using TCGA data. This protocol can be generalized to analyze other cancer cohorts and human diseases. For complete details on the use and execution of this protocol, please refer to Carrot-Zhang et al. (2020).

Citing Articles

Analysis of germline-driven ancestry-associated gene expression in cancers.

Chambwe N, Sayaman R, Hu D, Huntsman S, Kemal A, Caesar-Johnson S STAR Protoc. 2022; 3(3):101586.

PMID: 35942349 PMC: 9356164. DOI: 10.1016/j.xpro.2022.101586.

DNA methylation dynamics and dysregulation delineated by high-throughput profiling in the mouse.

Zhou W, Hinoue T, Barnes B, Mitchell O, Iqbal W, Lee S Cell Genom. 2022; 2(7).

PMID: 35873672 PMC: 9306256. DOI: 10.1016/j.xgen.2022.100144.

References

Gao G, Parker J, Reynolds S, C Silva T, Wang L, Zhou W . Before and After: Comparison of Legacy and Harmonized TCGA Genomic Data Commons' Data. Cell Syst. 2019; 9(1):24-34.e10. PMC: 6707074. DOI: 10.1016/j.cels.2019.06.006. View

Huo D, Hu H, Rhie S, Gamazon E, Cherniack A, Liu J . Comparison of Breast Cancer Molecular Features and Survival by African and European Ancestry in The Cancer Genome Atlas. JAMA Oncol. 2017; 3(12):1654-1662. PMC: 5671371. DOI: 10.1001/jamaoncol.2017.0595. View

Zhou W, Triche Jr T, Laird P, Shen H . SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions. Nucleic Acids Res. 2018; 46(20):e123. PMC: 6237738. DOI: 10.1093/nar/gky691. View

Campbell J, Yau C, Bowlby R, Liu Y, Brennan K, Fan H . Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas. Cell Rep. 2018; 23(1):194-212.e6. PMC: 6002769. DOI: 10.1016/j.celrep.2018.03.063. View

Chou C, Shrestha S, Yang C, Chang N, Lin Y, Liao K . miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 2017; 46(D1):D296-D302. PMC: 5753222. DOI: 10.1093/nar/gkx1067. View

Bosson A, Zamudio J, Sharp P . Endogenous miRNA and target concentrations determine susceptibility to potential ceRNA competition. Mol Cell. 2014; 56(3):347-359. PMC: 5048918. DOI: 10.1016/j.molcel.2014.09.018. View

Moore J, Purcaro M, Pratt H, Epstein C, Shoresh N, Adrian J . Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature. 2020; 583(7818):699-710. PMC: 7410828. DOI: 10.1038/s41586-020-2493-4. View

Vejnar C, Zdobnov E . MiRmap: comprehensive prediction of microRNA target repression strength. Nucleic Acids Res. 2012; 40(22):11673-83. PMC: 3526310. DOI: 10.1093/nar/gks901. View

Chu A, Robertson G, Brooks D, Mungall A, Birol I, Coope R . Large-scale profiling of microRNAs for The Cancer Genome Atlas. Nucleic Acids Res. 2015; 44(1):e3. PMC: 4705681. DOI: 10.1093/nar/gkv808. View

10.

Alexander D, Novembre J, Lange K . Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009; 19(9):1655-64. PMC: 2752134. DOI: 10.1101/gr.094052.109. View

11.

McCarroll S, Kuruvilla F, Korn J, Cawley S, Nemesh J, Wysoker A . Integrated detection and population-genetic analysis of SNPs and copy number variation. Nat Genet. 2008; 40(10):1166-74. DOI: 10.1038/ng.238. View

12.

Kozomara A, Birgaoanu M, Griffiths-Jones S . miRBase: from microRNA sequences to function. Nucleic Acids Res. 2018; 47(D1):D155-D162. PMC: 6323917. DOI: 10.1093/nar/gky1141. View

13.

Ellrott K, Bailey M, Saksena G, Covington K, Kandoth C, Stewart C . Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines. Cell Syst. 2018; 6(3):271-281.e7. PMC: 6075717. DOI: 10.1016/j.cels.2018.03.002. View

14.

Lawrence M, Huber W, Pages H, Aboyoun P, Carlson M, Gentleman R . Software for computing and annotating genomic ranges. PLoS Comput Biol. 2013; 9(8):e1003118. PMC: 3738458. DOI: 10.1371/journal.pcbi.1003118. View

15.

Gurtan A, Sharp P . The role of miRNAs in regulating gene expression networks. J Mol Biol. 2013; 425(19):3582-600. PMC: 3757117. DOI: 10.1016/j.jmb.2013.03.007. View

16.

Bailey M, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A . Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell. 2018; 173(2):371-385.e18. PMC: 6029450. DOI: 10.1016/j.cell.2018.02.060. View

17.

de Rie D, Abugessaisa I, Alam T, Arner E, Arner P, Ashoor H . An integrated expression atlas of miRNAs and their promoters in human and mouse. Nat Biotechnol. 2017; 35(9):872-878. PMC: 5767576. DOI: 10.1038/nbt.3947. View

18.

Carter S, Cibulskis K, Helman E, McKenna A, Shen H, Zack T . Absolute quantification of somatic DNA alterations in human cancer. Nat Biotechnol. 2012; 30(5):413-21. PMC: 4383288. DOI: 10.1038/nbt.2203. View

19.

Taylor A, Shih J, Ha G, Gao G, Zhang X, Berger A . Genomic and Functional Approaches to Understanding Cancer Aneuploidy. Cancer Cell. 2018; 33(4):676-689.e3. PMC: 6028190. DOI: 10.1016/j.ccell.2018.03.007. View

20.

Carrot-Zhang J, Chambwe N, Damrauer J, Knijnenburg T, Robertson A, Yau C . Comprehensive Analysis of Genetic Ancestry and Its Molecular Correlates in Cancer. Cancer Cell. 2020; 37(5):639-654.e6. PMC: 7328015. DOI: 10.1016/j.ccell.2020.04.012. View