Mapping genetic interactions in cancer: a road to rational combination therapies.

The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies

The next frontier of systems biology: higher-order and interspecies interactions

Systems biology is set to go beyond single species to the study of interspecies interactions

Towards accurate imputation of quantitative genetic interactions

Recent technological breakthroughs have enabled high-throughput quantitative measurements of hundreds of thousands of genetic interactions among hundreds of genes in Saccharomyces cerevisiae. However, these assays often fail to measure the genetic interactions among up to 40% of the studied gene pairs. Here we present a novel method, which combines genetic interaction data together with diverse genomic data, to quantitatively impute these missing interactions. We also present data on almost 190,000 novel interactions.Tel Aviv University. Edmond J, Safra Bioinformatics CenterIsrael Science Foundation (grant no. 802/08)Raymond and Beverley Sackler Foundatio

Differential network biology

Protein and genetic interaction maps have typically been generated under a single condition, providing a static view of the interactome. Recent studies employing differential analysis, however, have revealed that widespread re-wiring of the interactome underlies key biological responses

Backup without redundancy: genetic interactions reveal the cost of duplicate gene loss.

Many genes can be deleted with little phenotypic consequences. By what mechanism and to what extent the presence of duplicate genes in the genome contributes to this robustness against deletions has been the subject of considerable interest. Here, we exploit the availability of high-density genetic interaction maps to provide direct support for the role of backup compensation, where functionally overlapping duplicates cover for the loss of their paralog. However, we find that the overall contribution of duplicates to robustness against null mutations is low ( approximately 25%). The ability to directly identify buffering paralogs allowed us to further study their properties, and how they differ from non-buffering duplicates. Using environmental sensitivity profiles as well as quantitative genetic interaction spectra as high-resolution phenotypes, we establish that even duplicate pairs with compensation capacity exhibit rich and typically non-overlapping deletion phenotypes, and are thus unable to comprehensively cover against loss of their paralog. Our findings reconcile the fact that duplicates can compensate for each other's loss under a limited number of conditions with the evolutionary instability of genes whose loss is not associated with a phenotypic penalty

Integrative structure determination of histones H3 and H4 using genetic interactions

Integrative structure modeling is increasingly used for determining the architectures of biological assemblies, especially those that are structurally heterogeneous. Recently, we reported on how to convert in vivo genetic interaction measurements into spatial restraints for structural modeling: first, phenotypic profiles are generated for each point mutation and thousands of gene deletions or environmental perturbations. Following, the phenotypic profile similarities are converted into distance restraints on the pairs of mutated residues. We illustrate the approach by determining the structure of the histone H3-H4 complex. The method is implemented in our open-source IMP program, expanding the structural biology toolbox by allowing structural characterization based on in vivo data without the need to purify the target system. We compare genetic interaction measurements to other sources of structural information, such as residue coevolution and deep-learning structure prediction of complex subunits. We also suggest that determining genetic interactions could benefit from new technologies, such as CRISPR-Cas9 approaches to gene editing, especially for mammalian cells. Finally, we highlight the opportunity for using genetic interactions to determine recalcitrant biomolecular structures, such as those of disordered proteins, transient protein assemblies, and host-pathogen protein complexes

A strategy for extracting and analyzing large-scale quantitative epistatic interaction data

Recently, approaches have been developed for high-throughput identification of synthetic sick/lethal gene pairs. However, these are only a specific example of the broader phenomenon of epistasis, wherein the presence of one mutation modulates the phenotype of another. We present analysis techniques for generating high-confidence quantitative epistasis scores from measurements made using synthetic genetic array and epistatic miniarray profile (E-MAP) technology, as well as several tools for higher-level analysis of the resulting data that are greatly enhanced by the quantitative score and detection of alleviating interactions