Statistics, Department of



Noah E. Berlow, Children's Cancer Therapy Development Institut
Rishi Rikhi, Children's Cancer Therapy Development Institut
Mathew Geltzeiler, Oregon Health & Science University
Jinu Abraham, Oregon Health & Science University
Matthew N. Svalina, Children's Cancer Therapy Development Institut
Lara E. Davis, Oregon Health & Science University
Erin Wise, Champions Oncology
Maria Mancini, Champions Oncology
Jonathan Noujaim, Royal Marsden Hospital and Institute of Cancer Research
Atiya Mansoor, Oregon Health & Science University
Michael J. Quist, Oregon Health & Science University
Kevin L. Matlock, Texas Tech University
Martin W. Goros, University of Texas Health Science Center San Antonio
Brian S. Hernandez, University of Texas Health Science Center San Antonio
Yee C. Doung, Oregon Health & Science University
Khin Thway, Royal Marsden Hospital and Institute of Cancer Research
Tomohide Tsukahara, Sapporo Medical University School of Medicine
Jun Nishio, Fukuoka University
Elaine T. Huang, Oregon Health & Science University
Susan Airhart, The Jackson Laboratory, Bar Harbor
Carol J. Bult, The Jackson Laboratory, Bar Harbor
Regina Gandour-Edwards, UC Davis Health System
Robert G. Maki, 1Zucker School of Medicine at Hofstra/Northwell & Cold Spring Harbor Laboratory
Robin L. Jones, Royal Marsden Hospital and Institute of Cancer Research
Joel E. Michalek, University of Texas Health Science Center San Antonio
Milan Milovancev, Oregon State University
Souparno Ghosh, , Texas Tech University
Ranadip Pal, Texas Tech University
Charles Keller, Children's Cancer Therapy Development Institute


Noah E. Berlow

Date of this Version



Berlow et al. BMC Cancer (2019) 19:593




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.