Integrative modeling identifies genetic ancestry-associated molecular correlates in human cancer

• Main 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
• Format: Article
• Language: English
• Published: Elsevier 2021-06-01
• Series: STAR Protocols
• Subjects: Bioinformatics; Cancer; Genomics
• Online Access: http://www.sciencedirect.com/science/article/pii/S2666166721001908
• ISSN: 2666-1667

Summary: 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).