Untangling a complex web: Computational analyses of tumor molecular profiles to decode driver mechanisms

Sirvan Khalighi; Salendra Singh; Vinay Varadan

doi:10.1016/j.jgg.2020.11.001

Volume 47 Issue 10

Oct. 2020

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Article Navigation > Journal of Genetics and Genomics > 2020 > 47(10): 595-609

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Untangling a complex web: Computational analyses of tumor molecular profiles to decode driver mechanisms

doi: 10.1016/j.jgg.2020.11.001

Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA

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Corresponding author: E-mail address: vxv89@case.edu (Vinay Varadan)
Publish Date: 2020-10-25

Abstract

Abstract

Genome-scale studies focusing on molecular profiling of cancers across tissue types have revealed a plethora of aberrations across the genomic, transcriptomic, and epigenomic scales. The significant molecular heterogeneity across individual tumors even within the same tissue context complicates decoding the key etiologic mechanisms of this disease. Furthermore, it is increasingly likely that biologic mechanisms underlying the pathobiology of cancer involve multiple molecular entities interacting across functional scales. This has motivated the development of computational approaches that integrate molecular measurements with prior biological knowledge in increasingly intricate ways to enable the discovery of driver genomic aberrations across cancers. Here, we review diverse methodological approaches that have powered significant advances in our understanding of the genomic underpinnings of cancer at the cohort and at the individual tumor scales. We outline the key advances and challenges in the computational discovery of cancer mechanisms while motivating the development of systems biology approaches to comprehensively decode the biologic drivers of this complex disease.
- Mutations,
- Systems biology,
- Mutational significance,
- Functional impact,
- Pan-cancer analysis,
- Multiomics integration

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References(100)

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