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Phenotype-based probabilistic analysis of heterogeneous responses to cancer drugs and their combination efficacy

Cell-to-cell variability generates subpopulations of drug-tolerant cells that diminish the efficacy of cancer drugs. Efficacious combination therapies are thus needed to block drug-tolerant cells via minimizing the impact of heterogeneity. Probabilistic models such as Bliss independence have been de...

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Autores principales: Comandante-Lou, Natacha, Khaliq, Mehwish, Venkat, Divya, Manikkam, Mohan, Fallahi-Sichani, Mohammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055924/
https://www.ncbi.nlm.nih.gov/pubmed/32084135
http://dx.doi.org/10.1371/journal.pcbi.1007688
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author Comandante-Lou, Natacha
Khaliq, Mehwish
Venkat, Divya
Manikkam, Mohan
Fallahi-Sichani, Mohammad
author_facet Comandante-Lou, Natacha
Khaliq, Mehwish
Venkat, Divya
Manikkam, Mohan
Fallahi-Sichani, Mohammad
author_sort Comandante-Lou, Natacha
collection PubMed
description Cell-to-cell variability generates subpopulations of drug-tolerant cells that diminish the efficacy of cancer drugs. Efficacious combination therapies are thus needed to block drug-tolerant cells via minimizing the impact of heterogeneity. Probabilistic models such as Bliss independence have been developed to evaluate drug interactions and their combination efficacy based on probabilities of specific actions mediated by drugs individually and in combination. In practice, however, these models are often applied to conventional dose-response curves in which a normalized parameter with a value between zero and one, generally referred to as fraction of cells affected (f(a)), is used to evaluate the efficacy of drugs and their combined interactions. We use basic probability theory, computer simulations, time-lapse live cell microscopy, and single-cell analysis to show that f(a) metrics may bias our assessment of drug efficacy and combination effectiveness. This bias may be corrected when dynamic probabilities of drug-induced phenotypic events, i.e. induction of cell death and inhibition of division, at a single-cell level are used as metrics to assess drug efficacy. Probabilistic phenotype metrics offer the following three benefits. First, in contrast to the commonly used f(a) metrics, they directly represent probabilities of drug action in a cell population. Therefore, they deconvolve differential degrees of drug effect on tumor cell killing versus inhibition of cell division, which may not be correlated for many drugs. Second, they increase the sensitivity of short-term drug response assays to cell-to-cell heterogeneities and the presence of drug-tolerant subpopulations. Third, their probabilistic nature allows them to be used directly in unbiased evaluation of synergistic efficacy in drug combinations using probabilistic models such as Bliss independence. Altogether, we envision that probabilistic analysis of single-cell phenotypes complements currently available assays via improving our understanding of heterogeneity in drug response, thereby facilitating the discovery of more efficacious combination therapies to block drug-tolerant cells.
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spelling pubmed-70559242020-03-13 Phenotype-based probabilistic analysis of heterogeneous responses to cancer drugs and their combination efficacy Comandante-Lou, Natacha Khaliq, Mehwish Venkat, Divya Manikkam, Mohan Fallahi-Sichani, Mohammad PLoS Comput Biol Research Article Cell-to-cell variability generates subpopulations of drug-tolerant cells that diminish the efficacy of cancer drugs. Efficacious combination therapies are thus needed to block drug-tolerant cells via minimizing the impact of heterogeneity. Probabilistic models such as Bliss independence have been developed to evaluate drug interactions and their combination efficacy based on probabilities of specific actions mediated by drugs individually and in combination. In practice, however, these models are often applied to conventional dose-response curves in which a normalized parameter with a value between zero and one, generally referred to as fraction of cells affected (f(a)), is used to evaluate the efficacy of drugs and their combined interactions. We use basic probability theory, computer simulations, time-lapse live cell microscopy, and single-cell analysis to show that f(a) metrics may bias our assessment of drug efficacy and combination effectiveness. This bias may be corrected when dynamic probabilities of drug-induced phenotypic events, i.e. induction of cell death and inhibition of division, at a single-cell level are used as metrics to assess drug efficacy. Probabilistic phenotype metrics offer the following three benefits. First, in contrast to the commonly used f(a) metrics, they directly represent probabilities of drug action in a cell population. Therefore, they deconvolve differential degrees of drug effect on tumor cell killing versus inhibition of cell division, which may not be correlated for many drugs. Second, they increase the sensitivity of short-term drug response assays to cell-to-cell heterogeneities and the presence of drug-tolerant subpopulations. Third, their probabilistic nature allows them to be used directly in unbiased evaluation of synergistic efficacy in drug combinations using probabilistic models such as Bliss independence. Altogether, we envision that probabilistic analysis of single-cell phenotypes complements currently available assays via improving our understanding of heterogeneity in drug response, thereby facilitating the discovery of more efficacious combination therapies to block drug-tolerant cells. Public Library of Science 2020-02-21 /pmc/articles/PMC7055924/ /pubmed/32084135 http://dx.doi.org/10.1371/journal.pcbi.1007688 Text en © 2020 Comandante-Lou et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Comandante-Lou, Natacha
Khaliq, Mehwish
Venkat, Divya
Manikkam, Mohan
Fallahi-Sichani, Mohammad
Phenotype-based probabilistic analysis of heterogeneous responses to cancer drugs and their combination efficacy
title Phenotype-based probabilistic analysis of heterogeneous responses to cancer drugs and their combination efficacy
title_full Phenotype-based probabilistic analysis of heterogeneous responses to cancer drugs and their combination efficacy
title_fullStr Phenotype-based probabilistic analysis of heterogeneous responses to cancer drugs and their combination efficacy
title_full_unstemmed Phenotype-based probabilistic analysis of heterogeneous responses to cancer drugs and their combination efficacy
title_short Phenotype-based probabilistic analysis of heterogeneous responses to cancer drugs and their combination efficacy
title_sort phenotype-based probabilistic analysis of heterogeneous responses to cancer drugs and their combination efficacy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055924/
https://www.ncbi.nlm.nih.gov/pubmed/32084135
http://dx.doi.org/10.1371/journal.pcbi.1007688
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