Abstract
BackgroundCancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments.MethodsTissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the primary murine tumor were allografted into mouse models and used to validate the personalized two-drug combination.Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient’s epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient’s primary tumor.Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay.ResultsOur computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model).ConclusionsThese proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.
Highlights
Cancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses
Berlow et al BMC Cancer (2019) 19:593 (Continued from previous page). These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy
1) integrative genomic models using Elastic Net regression techniques have been developed from large datasets such as the Cancer Cell Line Encyclopedia (CCLE) [8] database; 2) integrative models using Random Forests with Stacking [9, 10] to integrate multiple genetic data sets for sensitivity prediction; and 3) a team science based sensitivity prediction challenge produced independent models integrating multiple data types for sensitivity prediction [11]; despite 44 individual models and a “wisdom of crowds” approach merging the top-ranked predictive models together, none of the approaches surpassed 70% predictive accuracy [11] falling short of a reasonable accuracy threshold for clinical utility
Summary
Cancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments. Numerous approaches to computational modeling of drug sensitivity and therapy assignment exist, in part to address ambiguity in DNA sequencing results [2, 5]. These approaches are primarily based on gene expression [6], or a combination of genomic and epigenomic data [7]. Integration of functional data may improve overall predictive accuracy over solely molecular data-based predictive models, especially for individual patient samples, emphasizing the need for improved drug sensitivity prediction to enable patient-specific therapy design
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