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Pharmacogenomics

Exploiting combinatorial patterns in cancer genomic data for personalized therapy and new target discovery

“…it is now established that particular configurations underlying large cancer genomic datasets, and their observed divergence from random expectation, might reflect the evolutionary adaptation that enables a cancer to obtain its hallmarks or to escape a given therapeutic intervention.”

Keywords:  cancer genomics • co-occurrent mutations • data-driven analyses • data mining • mutual exclusivity • synthetic lethality Michael Schubert

New views on the genomic landscape of cancer Recent progresses in large-scale sequencing projects are providing the scientific community with unprecedented views of the mutational landscape of human cancers [1,2] . These advances have been lately complemented by comprehensive molecular characterizations of large panels of cancer cell lines and their response to anticancer compounds [3,4] . At the same time, novel mathematical approaches have been applied to data generated within these projects to deconvolute patterns of cancer somatic mutations, and shed light on the different evolutionary processes inducing them [5] . Through multiomic data integration, oncogenic signature classes containing patterns of evolutionary selected genomic events have been identified, hier­ archically classified and associated with tumor samples from multiple tissue types [6] . Finally, efforts have recently been devoted to investigating how patterns of genomic lesions relate to each other, with the aim of identifying functional links between genes and pathways involved in the onset of cancer, and novel therapeutic targets [7] . To summarize, it is now established that particular configurations underlying large

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cancer genomic datasets, and their observed divergence from random expectation, might reflect the evolutionary adaptation that enables a cancer to obtain its hallmarks [8] or to escape a given therapeutic intervention [9] . The quest for singularities in large cancer genomics datasets The divergence from expectation principle has so far been exploited at the individual gene level; for example, to identify cancer driver mutations, and to discriminate them from passenger genomic lesions, whose frequency generally follows a background distribution [10] . Combined with the oncogenic addiction principle (i.e., the tendency of a transformed cell to become dependent on the sustained impact of a given genomic lesion), the identification of driver mutations in different cancer types forms the basis of many targeted therapies. Despite this, therapeutic interventions are rarely completely successful due to the emergence of drug resistance or the pre-existence of subpopulations of intrinsically resistant cells within a given tumor. Thus, while targeting mutations in driver genes is a good foundation for novel therapies, this approach needs to be augmented by the analysis of the mutational status of

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European Molecular Biology Laboratory – European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK

Francesco Iorio Author for correspondence: European Molecular Biology Laboratory – European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK Tel.: +44 223 494 576 iorio@ ebi.ac.uk

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ISSN 1462-2416

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Editorial  Schubert & Iorio such genes in the context of the pathway where they are operative. Therefore the search for genomic singularities (i.e., peculiar configurations, unlikely to occur by random chance) is now focused also on alterations involving multiple genes, to detect unexpected patterns of interactions. These have so far been quantified through the statistical evaluation of trends of mutual exclusivity [11,12] and co-occurrence of mutations [13] . Selective pressure leads to combinatorial patterns Co-occurrence is where mutations in multiple genes tend to co-exist in the same tumor more frequently than expected by random chance. This might indicate that an individual mutation is not sufficient to confer a certain selective trait, for which additional mutations are required. An alternative scenario is that a single mutation could be disadvantageous for the cell but the occurrence of a second one provides a permissive survival environment.



…therapeutic interventions are rarely completely successful due to the emergence of drug resistance or the pre-existence of subpopulations of intrinsically resistant cells within a given tumor.



On the other hand, mutual exclusivity is the tendency for two or more genes to not be mutated in the same tumor. While the mutational landscape of cancer is large and heterogeneous, cancer driver mutations tend to be enriched within a limited set of pathways. Hence, mutual exclusivity occurs in cancer if a crucial node is altered in an oncogenic pathway and a secondary mutation in the same pathway is unlikely to confer further selective growth advantages. This tendency may be due to evolutionary parsimony or a fitness defect. In the first case, a second mutation that would inactivate, for example, an already inactivated tumor suppressor pathway is unlikely to be observed on the population-level by the pure fact that it is not selected for, as the required trait has already been acquired by the cell. In the second case, the growth advantage conferred by one mutation might be reduced or counteracted by the presence of a second mutation, even to the point of cell death. This last phenomenon is termed synthetic lethality and has been used to study genetic interactions in model organisms for a long time [14] . More recently, these interactions have been used to infer drug sensitivity in cancer by identifying si/sh-RNA probes that are purified from a starting population in large-scale gene silencing screens on predefined genotypes (characterized by a certain set of mutations) [15] . In this approach, the success-

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fully identified targets (composing a synthetic lethal pair where one gene has lost its functionality in the selected genotype) are then considered as the basis for the development of a new drug. The underlying idea is that targeting a gene that is involved in a synthetic lethal pair together with a cancer driver gene should kill only cancer cells harboring mutations in the second gene of the pair. For example, in [16] the authors show that silencing CDK6, MET or MAP2K strongly impairs the viability of kidney cancer cell lines harboring loss of function mutations in VHL. Another wellknown example is the specific sensitivity of cancers with defective homologous recombination, through loss of function mutations in BRCA1 and BRCA2, to PARP inhibitors [17] . Exploiting combinatorial patterns for pharmacogenomics & new target discovery By adopting a data-driven approach, recent studies have intriguingly shown how the analysis of mutual exclusivity trends in the genomic lesion patterns of a given gene pair can predict their level of synthetic lethality, and hence predict novel therapeutic options for selected cancer populations [18] . Additionally, a network of synthetic lethal gene pairs can be built and mined to predict sensitivity to certain drug treatment and then this can be experimentally validated. On the other hand, a group of genes whose mutation patterns tend to mutual exclusivity could compose a network that drives cancer progression and determines its response to therapy. The aggregate mutational status of such a network could be then used as a robust marker of drug response, whose predictive ability is significantly higher than that of the individual composing genes. For example, it is now well established that the RASMAPK signaling cascade is constitutively activated in human melanoma due to oncogenic mutations in BRAF or NRAS, making this pathway one of the most attractive and successful therapeutic targets for human melanoma [19] . Accordingly, mutations in BRAF and NRAS tend to be mutually exclusive and they individually enhance the response to MEK inhibition [3] . From this, the mutual exclusive network composed of BRAF and NRAS could have been inferred in a datadriven way, by mining publically available genomic datasets. Furthermore, by virtue of its larger sample size, and increased accuracy, the mutational status of this network (accounting for mutations in both BRAF and NRAS) is likely to be a stronger predictor of response to MEK inhibition, compared with that of each individual gene taken separately. The question is: How many other cases like this are out there, ready to be mined?

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Exploiting combinatorial patterns in cancer genomic data for personalized therapy & new target discovery 

Open challenges The computational approaches described previously present us with unique challenges. First, the statistical significance of the observed genetic inter­actions needs to be properly quantified. Unfortunately, there is no easy analytical solution for this but there has been some amount of work in defining proper null models [20] , that preserve interdependencies both across samples and genes, and against which empirical p-values can be derived. However, there is as yet no single standard approach to the statistical definition of mutual exclusivity, and a number of groups have each published differing solutions to this ­problem [11,12] .



By adopting a data-driven approach, recent studies have intriguingly shown how the analysis of mutual exclusivity trends in the genomic lesion patterns of a given gene pair can predict their level of synthetic lethality, and hence predict novel therapeutic options for selected cancer populations.



Second, it is not trivial to design a selection strategy for the gene sets to be tested that is comprehensive enough but also avoids the problem of massive multiple hypothesis testing; how many and which gene sets should be tested for mutual exclusivity and/or mutation co-occurrence, within a given genomic dataset? Third, novel computational strategies integrating prior knowledge about pathways and signaling maps are needed to discriminate those mutual exclusivity cases due to the lack of comutations between genes belonging to the same cancer driver network (potentially usable as prognostic and therapeutic markers) References 1

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International Cancer Genome Consortium, Hudson TJ, Anderson W et al. International network of cancer genome projects. Nature 464(7291), 993–998 (2010). Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455(7216), 1061–1068 (2008).

from those due to synthetic lethality (potentially usable to predict cancer-specific drug targets). Finally, combinatorial patterns of mutations could be highly context-specific: for example, lethal gene pairs may be conditional on a third factor (the kind of mutation, a co-occurring germline variant, certain environmental conditions, etc). Summary An emergent trend in computational cancer research is a focus on the analysis of combinatorial properties among patterns of mutations in large genomics datasets, with the aim of identifying novel cancer-driver networks. One of these properties is, for example, the tendency of cancer driver genes to be mutated in a mutual exclusive or co-occurrent manner. Whether results from these recent analyses might significantly advance the pharmacogenomics field (through the identification of robust network markers of drug response), and/or whether these properties can be exploited for the prediction of synthetic lethal gene pairs (thus novel drug targets), through analyses of already available genomic data only, represent open and very intriguing questions. Financial & competing interests disclosure M Schubert is funded by a Medical Research Council CASE studentship. F Iorio is funded by the Centre for Therapeutic Target Validation (project CTTV015). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.

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Ciriello G, Miller ML, Aksoy BA, Senbabaoglu Y, Schultz N, Sander C. Emerging landscape of oncogenic signatures across human cancers. Nature Genet. 45(10), 1127–1133 (2013).

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Greenman C, Stephens P, Smith R et al. Patterns of somatic mutation in human cancer genomes. Nature 446(7132), 153–158 (2007).

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Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 144(5), 646–674 (2011).

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Garnett MJ, Edelman EJ, Heidorn SJ et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483(7391), 570–575 (2012).

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Barretina J, Caponigro G, Stransky N et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483(7391), 603–607 (2012).

McDermott U, Sharma S, Dowell L et al. Identification of genotype-correlated sensitivity to selective kinase inhibitors by using high-throughput tumor cell line profiling. Proc. Natl Acad. Sci. USA 104(50), 19936–19941 (2007).

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Alexandrov LB, Nik-Zainal S, Wedge DC et al. Signatures of mutational processes in human cancer. Nature 500(7463), 415–421 (2013).

Bignell GR, Greenman CD, Davies H et al. Signatures of mutation and selection in the cancer genome. Nature 463(7283), 893–898 (2010).

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Ciriello G, Cerami E, Sander C, Schultz N. Mutual exclusivity analysis identifies oncogenic network modules. Genome Res. 22(2), 398–406 (2012).

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Vandin F, Upfal E, Raphael BJ. De novo discovery of mutated driver pathways in cancer. Genome Res. 22(2), 375–385 (2012).

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Uren AG, Kool J, Matentzoglu K et al. Large-Scale Mutagenesis in p19ARF- and p53-Deficient Mice Identifies Cancer Genes and Their Collaborative Networks. Cell 133(4), 727–741 (2008).

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Nijman SMB. Synthetic lethality: General principles, utility and detection using genetic screens in human cells. FEBS Lett. 585(1), 1–6 (2011).

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Cheung HW, Cowley GS, Weir BA et al. Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer. Proc. Natl Acad. Sci. USA 108(30), 12372–12377 (2011).

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Bommi-Reddy A, Almeciga I, Sawyer J et al. Kinase requirements in human cells: III. Altered kinase

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requirements in VHL-/- cancer cells detected in a pilot synthetic lethal screen. Proc. Natl Acad. Sci. USA 105(43), 16484–16489 (2008). 17

Farmer H, McCabe N, Lord CJ et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434(7035), 917–921 (2005).

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Jerby-Arnon L, Pfetzer N, Waldman YY et al. Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality. Cell 158(5), 1199–1209 (2014).

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Chapman PB, Hauschild A, Robert C et al. Improved survival with vemurafenib in melanoma with BR AF V600E mutation. N. Engl. J. Med. 364(26), 2507–2516 (2011).

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Gobbi A, Iorio F, Dawson KJ et al. Fast randomization of large genomic datasets while preserving alteration counts. Bioinformatics 30(17), i617–i623 (2014).

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Exploiting combinatorial patterns in cancer genomic data for personalized therapy and new target discovery.

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