BMC Cancer 2019 Jun 17; 19(1):593
BACKGROUND: 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.
Berlow, Noah E; Rikhi, Rishi; Geltzeiler, Mathew; Abraham, Jinu; Svalina, Matthew N; Davis, Lara E; Wise, Erin; Mancini, Maria; Noujaim, Jonathan; Mansoor, Atiya; Quist, Michael J; Matlock, Kevin L; Goros, Martin W; Hernandez, Brian S; Doung, Yee C; Thway, Khin; Tsukahara, Tomohide; Nishio, Jun; Huang, Elaine T; Airhart, Susan; Bult, Carol J; Gandour-Edwards, Regina; Maki, Robert G; Jones, Robin L; Michalek, Joel E; Milovancev, Milan; Ghosh, Souparno; Pal, Ranadip; and Keller, Charles, "Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma." (2019). Faculty Research 2019. 137.