5.9
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5.9
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Volume 47 Issue 10
Oct.  2020
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Article Contents

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

doi: 10.1016/j.jgg.2020.11.001
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  • Corresponding author: E-mail address: vxv89@case.edu (Vinay Varadan)
  • Publish Date: 2020-10-25
  • 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.
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