Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma

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dc.creatorBerlow, Noah E. (TTU)
dc.creatorRikhi, Rishi
dc.creatorGeltzeiler, Mathew
dc.creatorAbraham, Jinu
dc.creatorSvalina, Matthew N.
dc.creatorDavis, Lara E.
dc.creatorWise, Erin
dc.creatorMancini, Maria
dc.creatorNoujaim, Jonathan
dc.creatorMansoor, Atiya
dc.creatorQuist, Michael J.
dc.creatorMatlock, Kevin L. (TTU)
dc.creatorGoros, Martin W.
dc.creatorHernandez, Brian S.
dc.creatorDoung, Yee C.
dc.creatorThway, Khin
dc.creatorTsukahara, Tomohide
dc.creatorNishio, Jun
dc.creatorHuang, Elaine T.
dc.creatorAirhart, Susan
dc.creatorBult, Carol J.
dc.creatorGandour-Edwards, Regina
dc.creatorMaki, Robert G.
dc.creatorJones, Robin L.
dc.creatorMichalek, Joel E.
dc.creatorMilovancev, Milan
dc.creatorGhosh, Souparno (TTU)
dc.creatorPal, Ranadip (TTU)
dc.creatorKeller, Charles
dc.date.accessioned2023-07-14T17:17:50Z
dc.date.available2023-07-14T17:17:50Z
dc.date.issued2019
dc.description© 2019 The Author(s). cc-by
dc.description.abstractBackground: 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.
dc.identifier.citationBerlow, 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/s12885-019-5681-6
dc.identifier.urihttps://doi.org/10.1186/s12885-019-5681-6
dc.identifier.urihttps://hdl.handle.net/2346/94965
dc.language.isoeng
dc.subjectArtificial intelligence and machine learning
dc.subjectCombination therapy
dc.subjectComputational modeling
dc.subjectDrug screening
dc.subjectHigh-throughput sequencing
dc.subjectPediatric cancer
dc.subjectPersonalized therapy
dc.subjectSarcoma
dc.titleProbabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma
dc.typeArticle

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