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Clone Wars: Quantitatively Understanding Cancer Drug Resistance
Author(s) -
James Yates,
Hitesh Mistry
Publication year - 2020
Publication title -
jco clinical cancer informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.188
H-Index - 12
ISSN - 2473-4276
DOI - 10.1200/cci.20.00089
Subject(s) - clone (java method) , selection (genetic algorithm) , drug resistance , cancer , medicine , intensive care medicine , drug development , oncology , drug , computer science , biology , pharmacology , artificial intelligence , gene , genetics
A key aim of early clinical development for new cancer treatments is to detect the potential for efficacy early and to identify a safe therapeutic dose to take forward to phase II. Because of this need, researchers have sought to build mathematical models linking initial radiologic tumor response, often assessed after 6 to 8 weeks of treatment, with overall survival. However, there has been mixed success of this approach in the literature. We argue that evolutionary selection pressure should be considered to interpret these early efficacy signals and so optimize cancer therapy.

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