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Functional variomics and network perturbation: connecting genotype to phenotype in cancer

Key Points

  • Current cancer therapies targeted to particular genetic lesions are primarily hampered by the extreme genetic heterogeneity observed across patient populations.

  • Cancer genomic variants and regulatory molecules interact with each other in cellular networks. Network biology has recently emerged as a systems-level approach to stratifying mutations that give rise to markedly different phenotypes.

  • Different cancer mutations often lead to distinct perturbations in signal transduction networks.

  • Several computational tools have been developed to analyse functional effects of cancer mutations and to prioritize drivers of oncogenesis.

  • Various experimental strategies have emerged to study mutation-specific functional effects. Such functional variomics approaches can dissect cancer variants at high resolution.

  • Integration of computational predictions with systems biology experimental approaches will be crucial for interpreting complex genotype-to-phenotype relationships in human disease including cancer. Together this effort represents a critical step towards precision medicine.

Abstract

Proteins interact with other macromolecules in complex cellular networks for signal transduction and biological function. In cancer, genetic aberrations have been traditionally thought to disrupt the entire gene function. It has been increasingly appreciated that each mutation of a gene could have a subtle but unique effect on protein function or network rewiring, contributing to diverse phenotypic consequences across cancer patient populations. In this Review, we discuss the current understanding of cancer genetic variants, including the broad spectrum of mutation classes and the wide range of mechanistic effects on gene function in the context of signalling networks. We highlight recent advances in computational and experimental strategies to study the diverse functional and phenotypic consequences of mutations at the base-pair resolution. Such information is crucial to understanding the complex pleiotropic effect of cancer genes and provides a possible link between genotype and phenotype in cancer.

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Figure 1: Complex genetic heterogeneity in human cancer.
Figure 2: Mutational landscape across cancer types.
Figure 3: Effects of cancer variants on molecular interaction networks in cells.
Figure 4: Computational tools that prioritize cancer genes and mutations.
Figure 5: Experimental platforms to characterize cancer mutations.

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Acknowledgements

N.S. would like to acknowledge the following grants: the Cancer Prevention and Research Institute of Texas (CPRIT) New Investigator Grant RR160021, the University of Texas System Rising STARs award, the US National Institutes of Health (NIH)–National Cancer Institute (NCI) grants P30CA016672, U54HG008100 and U01CA168394, and the University Center Foundation via the Institutional Research Grant program at the University of Texas MD Anderson Cancer Center.

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Correspondence to Song Yi or Nidhi Sahni.

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Node centered computational methods to characterize the function of cancer mutations. (DOC 94 kb)

Glossary

Missense mutations

(Also known as non-synonymous mutations). Nucleotide mutations in exons of protein-coding genes that cause amino acid substitutions in the protein.

Frame-shift mutations

Nucleotide mutations in exons of protein-coding genes that cause an alteration to the reading frame of translation and usually result in a premature stop codon and a truncated or non-expressed protein. They typically involve small insertions or deletions of a number of nucleotides that is not divisible by three.

Silent mutations

(Also known as synonymous mutations). Nucleotide mutations in exons of protein-coding genes that do not alter the coded amino acid (due to degeneracy in the genetic code).

Nonsense mutations

Nucleotide mutations in exons of protein-coding genes that change amino acid-encoding codons into stop codons.

ChIP-seq

(Chromatin immunoprecipitation followed by sequencing). Antibody-based immunoprecipitation of a chromatin-associated protein, such as a transcription factor (often epitope tagged) and its potentially interacting crosslinked DNA fragments, followed by sequencing to reveal the identity of these DNA fragments. Overall, this approach reveals the genomic sites of occupancy of the protein of interest.

DNase-seq

Genome-wide sequencing of open chromatin regions that are sensitive to cleavage by DNase I. Open chromatin is enriched for regulatory sequences.

Chromosome conformation capture

A method that analyses the spatial organization of chromatin in a cell by quantifying the interactions between genomic loci that are in proximity in three-dimensional space.

Gene ontology

A unified representation of attributes for genes and gene products across species, which helps functional interpretation of experimental data.

Topological centrality

In molecular interaction networks, topological centrality is an intrinsic network property that measures the overall position and 'connectedness' of a node in the networks.

Nicking

Creating a single-strand DNA break.

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Yi, S., Lin, S., Li, Y. et al. Functional variomics and network perturbation: connecting genotype to phenotype in cancer. Nat Rev Genet 18, 395–410 (2017). https://doi.org/10.1038/nrg.2017.8

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