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

  • Noah Berlow (Creator)
  • Rishi Rikhi (Contributor)
  • Mathew Geltzeiler (Creator)
  • Jinu Abraham (Contributor)
  • Matthew N. Svalina (Creator)
  • Lara E. Davis (Creator)
  • Erin Wise (Creator)
  • Maria Mancini (Creator)
  • Jonathan Noujaim (Creator)
  • Atiya Mansoor (Contributor)
  • Michael J. Quist (Creator)
  • Kevin L. Matlock (Creator)
  • Martin W. Goros (Creator)
  • Brian S. Hernandez (Creator)
  • Yee C. Doung (Creator)
  • Khin Thway (Contributor)
  • Tomohide Tsukahara (Contributor)
  • Jun Nishio (Creator)
  • Elaine T. Huang (Creator)
  • Susan Airhart (Creator)
  • Carol J. Bult (Creator)
  • Regina Gandour-Edwards (Creator)
  • Robert G. Maki (Creator)
  • Robin L. Jones (Creator)
  • Joel E Michalek (Creator)
  • Milan Milovancev (Creator)
  • Souparno Ghosh (Contributor)
  • Ranadip Pal (Contributor)
  • Charles Keller (Creator)
  • Robin L. Jones (Creator)

Dataset

Description

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 patientâ 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 patientâ 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.
Date made available2019
PublisherFigshare

Cite this