A two-drug combination simulation study for metastatic castrate resistant prostate cancer Journal Article


Authors: Root, A.; Ebhardt, H. A.
Article Title: A two-drug combination simulation study for metastatic castrate resistant prostate cancer
Abstract: Background: Prostate cancer often evolves resistance to androgen deprivation therapy leading to a lethal metastatic castrate-resistant form. Besides androgen independence, subpopulations of the tumor are genetically heterogeneous. With the advent of tumor genome sequencing we asked which has the greater influence on reducing tumor size: genetic background, heterogeneity, or drug potency?. Methods: A previously developed theoretical evolutionary dynamics model of stochastic branching processes is applied to compute the probability of tumor eradication with two targeted drugs. Publicly available data sets were surveyed to parameterize the model. Results: Our calculations reveal that the greatest influence on successful treatment is the genetic background including the number of mutations overcoming resistance. Another important criteria is the tumor size at which it is still possible to achieve tumor eradication, for example, 2-4 cm large tumors have at best a 10% probability to be eradicated when 50 mutations can confer resistance to each drug. Conclusion: Overall, this study finds that genetic background and tumor heterogeneity are more important than drug potency in treating mCRPC. It also points toward identifying metastatic sites early using biochemical assays and/or dPET. © 2018 The Authors. The Prostate Published by Wiley Periodicals, Inc.
Keywords: drug combinations; pet; evolutionary dynamics; tumor heterogeneity; mcrpc; dpet; overcoming resistance
Journal Title: Prostate
Volume: 78
Issue: 15
ISSN: 0270-4137
Publisher: John Wiley & Sons  
Date Published: 2018-11-01
Start Page: 1196
End Page: 1200
Language: English
DOI: 10.1002/pros.23694
PROVIDER: scopus
PUBMED: 30027544
PMCID: PMC6519289
DOI/URL:
Notes: Article -- Export Date: 1 October 2018 -- Source: Scopus
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  1. Alex Root
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