Differential sensitivity analysis for resistant malignancies (DISARM) identifies common candidate therapies across platinum-resistant cancers

Purpose: Despite a growing arsenal of approved drugs, therapeutic resistance remains a formidable and, often, insurmountable challenge in cancer treatment. The mechanisms underlying therapeutic resistance remain largely unresolved and, thus, examples of effective combinatorial or sequential strategies to combat resistance are rare. Here, we present Differential Sensitivity Analysis for Resistant Malignancies (DISARM), a novel, integrated drug screen analysis tool designed to address this dilemma. Experimental Design: DISARM, a software package and web-based application, analyzes drug response data to prioritize candidate therapies for models with resistance to a reference drug and to assess whether response to a reference drug can be utilized to predict future response to other agents. Using cisplatin as our reference drug, we applied DISARM to models from nine cancers commonly treated with first-line platinum chemotherapy including recalcitrant malignancies such as small cell lung cancer (SCLC) and pancreatic adenocarcinoma (PAAD). Results: In cisplatin-resistant models, DISARM identified novel candidates including multiple inhibitors of PI3K, MEK, and BCL-2, among other classes, across unrelated malignancies. Additionally, DISARM facilitated the selection of predictive biomarkers of response and identification of unique molecular subtypes, such as contrasting ASCL1-low/cMYC-high SCLC targetable by AURKA inhibitors and ASCL1-high/cMYC-low SCLC targetable by BCL-2 inhibitors. Utilizing these predictions, we assessed several of DISARM's top candidates, including inhibitors of AURKA, BCL-2, and HSP90, to confirm their activity in cisplatin-resistant SCLC models. Conclusions: DISARM represents the first validated tool to analyze large-scale in vitro drug response data to statistically optimize candidate drug and biomarker selection aimed at overcoming candidate drug resistance.