2023-07-142023-07-142019Berlow, N.E., Rikhi, R., Geltzeiler, M., Abraham, J., Svalina, M.N., Davis, L.E., Wise, E., Mancini, M., Noujaim, J., Mansoor, A., Quist, M.J., Matlock, K.L., Goros, M.W., Hernandez, B.S., Doung, Y.C., Thway, K., Tsukahara, T., Nishio, J., Huang, E.T., . . . Keller, C.. 2019. Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma. BMC Cancer, 19(1). https://doi.org/10.1186/s12885-019-5681-6https://doi.org/10.1186/s12885-019-5681-6https://hdl.handle.net/2346/94965© 2019 The Author(s). cc-byBackground: 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. Methods: Tissue 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. Results: Our 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). Conclusions: 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.engArtificial intelligence and machine learningCombination therapyComputational modelingDrug screeningHigh-throughput sequencingPediatric cancerPersonalized therapySarcomaArticle