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

  • Author(s): 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
  • Keller, Charles
  • et al.
Abstract

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.

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