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Review
. 2015 May 21;58(4):690-8.
doi: 10.1016/j.molcel.2015.05.008.

The cancer cell map initiative: defining the hallmark networks of cancer

Nevan J Krogan  1 , Scott Lippman  2 , David A Agard  3 , Alan Ashworth  4 , Trey Ideker  5
Affiliations
Review

The cancer cell map initiative: defining the hallmark networks of cancer

Nevan J Krogan et al. Mol Cell. .

Abstract

Progress in DNA sequencing has revealed the startling complexity of cancer genomes, which typically carry thousands of somatic mutations. However, it remains unclear which are the key driver mutations or dependencies in a given cancer and how these influence pathogenesis and response to therapy. Although tumors of similar types and clinical outcomes can have patterns of mutations that are strikingly different, it is becoming apparent that these mutations recurrently hijack the same hallmark molecular pathways and networks. For this reason, it is likely that successful interpretation of cancer genomes will require comprehensive knowledge of the molecular networks under selective pressure in oncogenesis. Here we announce the creation of a new effort, The Cancer Cell Map Initiative (CCMI), aimed at systematically detailing these complex interactions among cancer genes and how they differ between diseased and healthy states. We discuss recent progress that enables creation of these cancer cell maps across a range of tumor types and how they can be used to target networks disrupted in individual patients, significantly accelerating the development of precision medicine.

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Figures

Figure 1
Figure 1
(A) Current paradigm for genome wide association studies (GWAS) seeking to associate genetic variation in single genes with the incidence of diseases or disease outcomes. With all genes tested individually for association, often none are able to pass a genome-wide level of significance. (B) Translation of variation at the nucleotide or gene level to variation in protein networks. Multiple genetic variants, each of which is observed rarely in a disease population, are found to impact a common region of a protein network due to different means of altering the activity of a protein complex, metabolic pathway, or signaling cascade. Such common events can be realized with an integrated physical and genetic interaction map where directionality of pathways can be inferred (Battle et al., 2010; Fischer et al., 2015; St Onge et al., 2007). (C) A pipeline for understanding mutants involved in disease using network biology, which initially requires a reference map to help interrupt the underlying biology behind the mutations.
Figure 2
Figure 2. Network based stratification of somatic mutations in ovarian cancer
(A) Network of genes for which mutation leads to an aggressive ‘subtype 1’ of ovarian tumor. Node size (importance) shows the network proximity of that gene to somatic mutations within tumors of that subtype. Node color corresponds to functional classes of interest. Edge width shows the interaction confidence score from the HumanNet resource (http://www.functionalnet.org/) representing the confidence that the connected genes function in the same process given the available experimental evidence for interaction. Thick node borders indicate genes that are included in the COSMIC cancer gene census. (B) Kaplan-Meier survival plot for NBS ovarian cancer subtypes (k = 4). Subtype 1 has the lowest survival and highest platinum resistance rates amongst the four subtypes. (C) Scatterplot of mutations arising in ovarian tumors in The Cancer Genome Atlas (http://cancergenome.nih.gov/), focused on genes in the subtype 1 subnetwork. Each gray bar represents a gene (x-axis) that is mutated in a particular tumor (y-axis).
Figure 3
Figure 3. Pipeline for cancer cell network mapping and network interpretation of patient data
Growing databases of tumor genomes are mined to identify genes and cell types in which alterations drive or predispose to development of cancer. Seeded by this information, systematic network mapping efforts lead to assembly of the ‘Cancer Cell Map’, providing a working scaffold of molecular interactions and the cell types and conditions under which they are active. New patient data are assessed by query against this resource, which translates alterations at the genetic and molecular level to reveal the impact these alterations have on the hallmark networks of cancer. Key interactions and network structures are explored for fine mapping of their basis in molecular structure or implications to biomolecular mechanism.
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