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Updated: May 8, 2026

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
Formalizing an integrative, multidisciplinary cancer therapy discovery workflow
Mary F McGuire1, Heiko Enderling, Dorothy I Wallace
1Authors' Affiliations: University of Texas Medical School at Houston, Houston, Texas; Center of Cancer Systems Biology, Steward Research & Specialty Projects Corp., St. Elizabeth's Medical Center, Tufts University School of Medicine; Division of Cell and Molecular Biology, Department of Biology, Boston University, Boston, Massachusetts; Department of Mathematics, Dartmouth College, Hanover, New Hampshire; St. Jude Children's Research Hospital, Memphis, Tennessee; Hospital for Sick Children, Toronto, Ontario, Canada; Children's Cancer Institute Australia, Lowy Cancer Research Centre, UNSW, Randwick, NSW, Australia; and Metronomics Global Health Initiative, Marseille, France.
Abstract:
Although many clinicians and researchers work to understand cancer, there has been limited success to effectively combine forces and collaborate over time, distance, data, and budget constraints. Here we present a workflow template for multidisciplinary cancer therapy that was developed during the 2nd Annual Workshop on Cancer Systems Biology sponsored by Tufts University, Boston, Massachusetts, in July 2012. The template was applied to the development of a metronomic therapy backbone for neuroblastoma. Three primary groups were identified: clinicians, biologists, and quantitative scientists (mathematicians, computer scientists, and engineers). The workflow described their integrative interactions; parallel or sequential processes; data sources and computational tools at different stages as well as the iterative nature of therapeutic development from clinical observations to in vitro, in vivo, and clinical trials. We found that theoreticians in dialog with experimentalists could develop calibrated and parameterized predictive models that inform and formalize sets of testable hypotheses, thus speeding up discovery and validation while reducing laboratory resources and costs. The developed template outlines an interdisciplinary collaboration workflow designed to systematically investigate the mechanistic underpinnings of a new therapy and validate that therapy to advance development and clinical acceptance.
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