Predictive interpretable models of cancer vulnerabilities via network based integration of multi-omic data

Predictive interpretable models of cancer vulnerabilities via network based integration of multi-omic data

The development of new oncology drugs is at a critically high attrition level, and the number of molecular entities that are successfully developed into new therapeutic targets is constantly decreasing [1]. This is most often due to a lack of robust genetic linkage between the trialled therapies and the disease they are designed for [2]. To tackle this problem, large-scale pharmacological screenings have been performed recently across panels of immortalised human cancer cell lines [3, 4]. The drug-response datasets resulting from these studies have been integrated with multi-omic characterisations of the screened in vitro models, unveiling established and novel associations between cell molecular features and drug sensitivities [5–7]. The advent of genome editing by CRISPR-Cas9 has allowed extending these studies beyond the domain of currently druggable targets with precision and scale [8, 9]. Pooled CRISPR-Cas9 screenings employing genome-scale libraries of single guide RNAs are being performed on growing numbers of cancer in-vitro models [10–17]. This makes possible testing the extent to which inactivating each gene in turn impacts cancer viability in the context of a defined genomic/molecular make-up.

Datasets and results from these efforts are being increasingly released on the public domain, laying a rich preclinical foundation for the development of novel selective cancer targeted therapies.

By making use of these data, we will investigate how aggregating the multi-omic characterisation of the screened in vitro models might allow predicting their genetic-dependencies and responses to drug treatment. This will be achieved by leveraging deep-learning computational methods and prior-known large-scale pathway/signaling maps, and it will be oriented at producing mechanistically interpretable models, which might be translated into future therapeutic biomarkers and rules for potential combinatorial anti-cancer therapies.

Results from these efforts will be released through dedicated web-portals and beside software infrastructures allowing programmatic access. Most promising hits will be put forward for experimental validation with clinical/experimental collaborators.

References

1. Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J. Clinical development success rates for investigational drugs. Nat Biotechnol. 2014;32:40–51.

2. Cook D, Brown D, Alexander R, March R, Morgan P, Satterthwaite G, et al. Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat Rev Drug Discov. 2014;13:419–31.

3. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603–7.

4. Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483:570–5.

5. Seashore-Ludlow B, Rees MG, Cheah JH, Cokol M, Price EV, Coletti ME, et al. Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset. Cancer Discov. 2015;5:1210–23.

6. Basu A, Bodycombe NE, Cheah JH, Price EV, Liu K, Schaefer GI, et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell. 2013;154:1151–61.

7. Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, et al. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. 2016;166:740–54.

8. Evers B, Jastrzebski K, Heijmans JPM, Grernrum W, Beijersbergen RL, Bernards R. CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes. Nat Biotechnol. 2016;34:631–3.

9. Morgens DW, Deans RM, Li A, Bassik MC. Systematic comparison of CRISPR/Cas9 and RNAi screens for essential genes. Nat Biotechnol. 2016;34:634–6.

10. Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA, Mikkelson T, et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science. 2014;343:84–7.

11. Koike-Yusa H, Li Y, Tan E-P, Velasco-Herrera MDC, Yusa K. Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat Biotechnol. 2014;32:267–73.