Genetic interactions: the missing links for a better understanding of cancer susceptibility, progression and treatment

Abstract

It is increasingly clear that complex networks of relationships between genes and/or proteins govern neoplastic processes. Our understanding of these networks is expanded by the use of functional genomic and proteomic approaches in addition to computational modeling. Concurrently, whole-genome association scans and mutational screens of cancer genomes identify novel cancer genes. Together, these analyses have vastly increased our knowledge of cancer, in terms of both "part lists" and their functional associations. However, genetic interactions have hitherto only been studied in depth in model organisms and remain largely unknown for human systems. Here, we discuss the importance and potential benefits of identifying genetic interactions at the human genome level for creating a better understanding of cancer susceptibility and progression and developing novel effective anticancer therapies. We examine gene expression profiles in the presence and absence of co-amplification of the 8q24 and 20q13 chromosomal regions in breast tumors to illustrate the molecular consequences and complexity of genetic interactions and their role in tumorigenesis. Finally, we highlight current strategies for targeting tumor dependencies and outline potential matrix screening designs for uncovering molecular vulnerabilities in cancer cells.

Figure 1

Correlation of genetic and genomic alterations and transcript profiles across hundreds of tumors may reveal gene/protein functional dependencies that are useful for predicting genetic interactions of interest for cancer progression and anticancer therapies.

Figure 2

Empirical analysis of genetic interactions acting on tumorigenesis. Pre-processed and normalized data were taken from Chin et al. [60]. White squares represent missing data. Log expression ratios are relative to tumors that do not show amplification at 8q24 or 20q13. Average expression values are shown for each gene probe (not detailed) in each tumor set (8q24/20q13, 8q24 or 20q13 amplification). Transcript correlations were calculated using the Pearson correlation coefficient and adjusted P values with a false discovery rate of 5%; only significant correlations with Q values < 0.05 are shown, except for the 8q24/20q13 co-amplification set (dashed line; Q ~0.07).

Figure 3

Examples of matrix screens of genetic interactions of interest in studying cancer susceptibility, progression and treatment: (A) Interactions are identified in standard cancer cell lines by depleting one gene at a time and through quantitation of emerging phenotypes; (B) Interactions are identified between genes involved in the DNA repair processes, with quantitation of emerging phenotypes highlighting repair subcategories; and (C) Interactions are revealed between known tumor suppressor genes and oncogenes, with quantitation of emerging phenotypes highlighting different properties of the neoplastic process.