Literature DB >> 30992299

Multidrug Cancer Therapy in Metastatic Castrate-Resistant Prostate Cancer: An Evolution-Based Strategy.

Jeffrey B West1, Mina N Dinh1,2, Joel S Brown1, Jingsong Zhang3, Alexander R Anderson1, Robert A Gatenby4.   

Abstract

PURPOSE: Integration of evolutionary dynamics into systemic therapy for metastatic cancers can prolong tumor control compared with standard maximum tolerated dose (MTD) strategies. Prior investigations have focused on monotherapy, but many clinical cancer treatments combine two or more drugs. Optimizing the evolutionary dynamics in multidrug therapy is challenging because of the complex cellular interactions and the large parameter space of potential variations in drugs, doses, and treatment schedules. However, multidrug therapy also represents an opportunity to further improve outcomes using evolution-based strategies. EXPERIMENTAL
DESIGN: We examine evolution-based strategies for two-drug therapy and identify an approach that divides the treatment drugs into primary and secondary roles. The primary drug has the greatest efficacy and/or lowest toxicity. The secondary drug is applied solely to reduce the resistant population to the primary drug.
RESULTS: Simulations from the mathematical model demonstrate that the primary-secondary approach increases time to progression (TTP) compared with conventional strategies in which drugs are administered without regard to evolutionary dynamics. We apply our model to an ongoing adaptive therapy clinical trial of evolution-based administration of abiraterone to treat metastatic castrate-resistant prostate cancer. Model simulations, parameterized with data from individual patients who progressed, demonstrate that strategic application of docetaxel during abiraterone therapy would have significantly increased their TTP.
CONCLUSIONS: Mathematical models can integrate evolutionary dynamics into multidrug cancer clinical trials. This has the potential to improve outcomes and to develop clinical trials in which these mathematical models are also used to estimate the mechanism(s) of treatment failure and explore alternative strategies to improve outcomes in future trials. ©2019 American Association for Cancer Research.

Entities:  

Year:  2019        PMID: 30992299      PMCID: PMC6665681          DOI: 10.1158/1078-0432.CCR-19-0006

Source DB:  PubMed          Journal:  Clin Cancer Res        ISSN: 1078-0432            Impact factor:   12.531


  42 in total

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3.  A change of strategy in the war on cancer.

Authors:  Robert A Gatenby
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4.  Class III beta-tubulin expression predicts prostate tumor aggressiveness and patient response to docetaxel-based chemotherapy.

Authors:  Guillaume Ploussard; Stéphane Terry; Pascale Maillé; Yves Allory; Nanor Sirab; Laurence Kheuang; Pascale Soyeux; Nathalie Nicolaiew; Estelle Coppolani; Bernard Paule; Laurent Salomon; Stéphane Culine; Ralph Buttyan; Francis Vacherot; Alexandre de la Taille
Journal:  Cancer Res       Date:  2010-11-02       Impact factor: 12.701

5.  Prednisone plus cabazitaxel or mitoxantrone for metastatic castration-resistant prostate cancer progressing after docetaxel treatment: a randomised open-label trial.

Authors:  Johann Sebastian de Bono; Stephane Oudard; Mustafa Ozguroglu; Steinbjørn Hansen; Jean-Pascal Machiels; Ivo Kocak; Gwenaëlle Gravis; Istvan Bodrogi; Mary J Mackenzie; Liji Shen; Martin Roessner; Sunil Gupta; A Oliver Sartor
Journal:  Lancet       Date:  2010-10-02       Impact factor: 79.321

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Review 7.  Molecular mechanisms of drug resistance.

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  27 in total

1.  Modeling Resistance and Recurrence Patterns of Combined Targeted-Chemoradiotherapy Predicts Benefit of Shorter Induction Period.

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Review 4.  Treating Cancer as an Invasive Species.

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Review 5.  Resistance to second-generation androgen receptor antagonists in prostate cancer.

Authors:  Keith T Schmidt; Alwin D R Huitema; Cindy H Chau; William D Figg
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Review 6.  Executable cancer models: successes and challenges.

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7.  Reverting to single-cell biology: The predictions of the atavism theory of cancer.

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8.  The somatic molecular evolution of cancer: Mutation, selection, and epistasis.

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9.  An evolutionary framework for treating pediatric sarcomas.

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10.  Turnover Modulates the Need for a Cost of Resistance in Adaptive Therapy.

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Journal:  Cancer Res       Date:  2020-11-10       Impact factor: 12.701

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