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Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution

Jonsson, Vanessa D. and Blakely, Collin M. and Lin, Luping and Asthana, Saurabh and Matni, Nikolai and Olivas, Victor and Pazarentzos, Evangelos and Gubens, Matthew A. and Bastian, Boris C. and Taylor, Barry S. and Doyle, John C. and Bivona, Trever G. (2017) Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution. Scientific Reports, 7 . Art. No. 44206. ISSN 2045-2322. PMCID PMC5347024.

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The success of targeted cancer therapy is limited by drug resistance that can result from tumor genetic heterogeneity. The current approach to address resistance typically involves initiating a new treatment after clinical/radiographic disease progression, ultimately resulting in futility in most patients. Towards a potential alternative solution, we developed a novel computational framework that uses human cancer profiling data to systematically identify dynamic, pre-emptive, and sometimes non-intuitive treatment strategies that can better control tumors in real-time. By studying lung adenocarcinoma clinical specimens and preclinical models, our computational analyses revealed that the best anti-cancer strategies addressed existing resistant subpopulations as they emerged dynamically during treatment. In some cases, the best computed treatment strategy used unconventional therapy switching while the bulk tumor was responding, a prediction we confirmed in vitro. The new framework presented here could guide the principled implementation of dynamic molecular monitoring and treatment strategies to improve cancer control.

Item Type:Article
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URLURL TypeDescription CentralArticle Paper
Matni, Nikolai0000-0003-4936-3921
Doyle, John C.0000-0002-1828-2486
Additional Information:© 2017 the Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit Received: 23 November 2016; Accepted: 06 February 2017; Published online: 13 March 2017. The authors acknowledge funding support from NIH, the Pew-Stewart Charitable Trust, the Kinship-Searle Foundation, and the Van Auken Foundation (to TGB). C.M.B. was supported by grants from The Lung Cancer Research Foundation (P0060520/A123243) and AACR (14-40-18-BLAK). J.C.D. provided funding for V.D.J. and N.M. Author Contributions: T.G.B., V.D.J. and C.M.B. conceived and designed the study. V.D.J. conceived and developed the math model and control theoretic algorithm. J.C.D. advised on control theoretic problem approach. V.D.J. and N.M. implemented the algorithm. C.M.B. and V.D.J. designed experiments, performed experiments and analyzed data. V.O., L.L. and E.P. performed experiments. L.L., and B.C.B. performed sequencing. S.A., B.C.B., and B.S.T. analyzed sequencing data. M.A.G. provided tumors for analysis. V.D.J., C.M.B. and T.G.B. wrote the manuscript, with input from all authors. We thank Nikoletta Sidiropoulos for pathology assessment and independent confirmation of the BRAF V600E mutation. We thank Tyrrell Nelson for assistance in sequencing library preparation. We thank Swapna Vemula for assistance with FISH analysis. We thank Russell Johnson for assistance with figure formatting. Competing interests: T.G.B. is a consultant to Astrazeneca, Novartis, Array, Revolution Medicines and has received research funding from Ignyta. C.M.B. has received funding from Clovis Oncology, Ignyta, and MedImmune.
Funding AgencyGrant Number
Pew-Stewart Charitable TrustUNSPECIFIED
Kinship-Searle FoundationUNSPECIFIED
Van Auken FoundationUNSPECIFIED
Lung Cancer Research FoundationP0060520/A123243
American Association for Cancer Research14-40-18-BLAK
PubMed Central ID:PMC5347024
Record Number:CaltechAUTHORS:20170206-090159752
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:74066
Deposited By: Tony Diaz
Deposited On:06 Feb 2017 17:38
Last Modified:03 Oct 2019 16:34

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