Abstract
Identification of actionable genomic vulnerabilities is key to precision oncology. Utilizing a large-scale drug screening in patient-derived xenografts, we uncover driver gene alteration connections, derive driver co-occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug-response predictors attains an average balanced accuracy of 58% in a cross-validation setting, rising to 66% for a subset of high-confidence predictions. We experimentally validated 12 out of 14 predictions in mice and adapted our strategy to obtain drug-response models from patients’ progression-free survival data. Our strategy reveals links between oncogenic alterations, increasing the clinical impact of genomic profiling.
Highlights
In light of the complexity and molecular heterogeneity of tumors, clinical and histopathological evaluation of cancer patients is nowadays complemented with genomic information
In order to increase the clinical translatability, we subsampled both datasets to consider those oncogenic alterations covered by MSK-IMPACT [34] or by Foundation Medicine [35] targeted gene panels to obtain driver co-occurrence (DCO) networks and TCT4U models that could be directly used with those kind of molecular profiles, which are becoming widely used in the clinical setting
In the same way we did for the LOOCV, we described the molecular profile of each patient-derived mouse xenograft (PDX) according to the DiffD_DiP feature vectors associated to each DCO network and used them to predict the response to the 53 treatments in the TCT4U collection
Summary
In light of the complexity and molecular heterogeneity of tumors, clinical and histopathological evaluation of cancer patients is nowadays complemented with genomic information. Genome-guided therapy has been shown to improve patient outcome [1, 2] and clinical trial success rate [3], and despite some controversy [4], prospective molecular profiling of personal cancer genomes has enabled the identification of an increasing number of actionable vulnerabilities [5]. Cancer genome sequencing initiatives have found that any given tumor contains from tens to thousands of mutations. On top of identifying key alterations in tumor development, it is fundamental to pinpoint those that can shed light on the most appropriate therapy to treat each tumor (i.e., biomarkers). Patients with similar clinicopathological characteristics might be molecularly different [6]; this inter-patient heterogeneity is one of the reasons why only a subset of them will
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