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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.

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