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Title: Phenotype heterogeneity in cancer cell populations

Abstract

Phenotype heterogeneity in cancer cell populations, be it of genetic, epigenetic or stochastic origin, has been identified as a main source of resistance to drug treatments and a major source of therapeutic failures in cancers. The molecular mechanisms of drug resistance are partly understood at the single cell level (e.g., overexpression of ABC transporters or of detoxication enzymes), but poorly predictable in tumours, where they are hypothesised to rely on heterogeneity at the cell population scale, which is thus the right level to describe cancer growth and optimise its control by therapeutic strategies in the clinic. We review a few results from the biological literature on the subject, and from mathematical models that have been published to predict and control evolution towards drug resistance in cancer cell populations. We propose, based on the latter, optimisation strategies of combined treatments to limit emergence of drug resistance to cytotoxic drugs in cancer cell populations, in the monoclonal situation, which limited as it is still retains consistent features of cell population heterogeneity. The polyclonal situation, that may be understood as “bet hedging” of the tumour, thus protecting itself from different sources of drug insults, may lie beyond such strategies and will need furthermore » developments. In the monoclonal situation, we have designed an optimised therapeutic strategy relying on a scheduled combination of cytotoxic and cytostatic treatments that can be adapted to different situations of cancer treatments. Finally, we review arguments for biological theoretical frameworks proposed at different time and development scales, the so-called atavistic model (diachronic view relying on Darwinian genotype selection in the coursof billions of years) and the Waddington-like epigenetic landscape endowed with evolutionary quasi-potential (synchronic view relying on Lamarckian phenotype instruction of a given genome by reversible mechanisms), to represent evolution towards heterogeneity, possibly polyclonal, in cancer cell populations and propose innovative directions for therapeutic strategies based on such frameworks.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [7]
  1. CNRS UMR 7598, LJLL, & INRIA MAMBA team, Sorbonne Universités, UPMC Univ Paris 06, Boîte courrier 187, 4 Pl. Jussieu, 75252 Paris cedex 05, France, luis@ann.jussieu.fr (France)
  2. School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia, rebecca.chisholm@gmail.com (Australia)
  3. INRIA MAMBA team & LJLL, UMR 7598, Sorbonne Universités, UPMC Univ Paris 06, Boîte courrier 187, 4 Pl. Jussieu, 75252 Paris cedex 05, France, jean.clairambault@inria.fr, Corresponding author (France)
  4. INSERM “Cancer Biology and Therapeutics”, Sorbonne Universités, UPMC Univ Paris 06, UMR-S 938, CDR St Antoine, Hôpital St Antoine, 184 Fbg. St Antoine, 75571 Paris cedex 12, France, alexandre.escargueil@upmc.fr (France)
  5. CMLA, ENS Cachan, 61, Av. du Président Wilson, 94230 Cachan cedex & INRIA MAMBA team, & LJLL, UMR 7598, UPMC Univ Paris 06, Boîte courrier 187, 4 Pl. Jussieu, 75252 Paris cedex 05, France, tommaso.lorenzi@gmail.com (France)
  6. Sorbonne Universités, UPMC Univ Paris 06, LJLL, UMR 7598 & INRIA Boîte courrier 187, 4 Pl. Jussieu, 75252 Paris cedex 05, France, alex.lorz@ann.jussieu.fr (France)
  7. Institut Universitaire de France, Sorbonne Universités, UPMC Univ Paris 06, LJLL, UMR 7598, Boîte courrier 187, UPMC Univ Paris 06, 4 Pl. Jussieu, 75252 Paris cedex 05, France, emmanuel.trelat@upmc.fr (France)
Publication Date:
OSTI Identifier:
22608981
Resource Type:
Journal Article
Journal Name:
AIP Conference Proceedings
Additional Journal Information:
Journal Volume: 1738; Journal Issue: 1; Conference: ICNAAM 2015: International conference of numerical analysis and applied mathematics 2015, Rhodes (Greece), 22-28 Sep 2015; Other Information: (c) 2016 Author(s); Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-243X
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ANTIMITOTIC DRUGS; BIOPHYSICS; CONTROL; DESIGN; ENZYMES; EVOLUTION; GENOTYPE; HYPOTHESIS; MATHEMATICAL MODELS; NEOPLASMS; OPTIMIZATION; PHENOTYPE; REVIEWS; SCHEDULES; STOCHASTIC PROCESSES

Citation Formats

Almeida, Luis, Chisholm, Rebecca, Clairambault, Jean, Escargueil, Alexandre, Lorenzi, Tommaso, Lorz, Alexander, and Trélat, Emmanuel. Phenotype heterogeneity in cancer cell populations. United States: N. p., 2016. Web. doi:10.1063/1.4952107.
Almeida, Luis, Chisholm, Rebecca, Clairambault, Jean, Escargueil, Alexandre, Lorenzi, Tommaso, Lorz, Alexander, & Trélat, Emmanuel. Phenotype heterogeneity in cancer cell populations. United States. doi:10.1063/1.4952107.
Almeida, Luis, Chisholm, Rebecca, Clairambault, Jean, Escargueil, Alexandre, Lorenzi, Tommaso, Lorz, Alexander, and Trélat, Emmanuel. Wed . "Phenotype heterogeneity in cancer cell populations". United States. doi:10.1063/1.4952107.
@article{osti_22608981,
title = {Phenotype heterogeneity in cancer cell populations},
author = {Almeida, Luis and Chisholm, Rebecca and Clairambault, Jean and Escargueil, Alexandre and Lorenzi, Tommaso and Lorz, Alexander and Trélat, Emmanuel},
abstractNote = {Phenotype heterogeneity in cancer cell populations, be it of genetic, epigenetic or stochastic origin, has been identified as a main source of resistance to drug treatments and a major source of therapeutic failures in cancers. The molecular mechanisms of drug resistance are partly understood at the single cell level (e.g., overexpression of ABC transporters or of detoxication enzymes), but poorly predictable in tumours, where they are hypothesised to rely on heterogeneity at the cell population scale, which is thus the right level to describe cancer growth and optimise its control by therapeutic strategies in the clinic. We review a few results from the biological literature on the subject, and from mathematical models that have been published to predict and control evolution towards drug resistance in cancer cell populations. We propose, based on the latter, optimisation strategies of combined treatments to limit emergence of drug resistance to cytotoxic drugs in cancer cell populations, in the monoclonal situation, which limited as it is still retains consistent features of cell population heterogeneity. The polyclonal situation, that may be understood as “bet hedging” of the tumour, thus protecting itself from different sources of drug insults, may lie beyond such strategies and will need further developments. In the monoclonal situation, we have designed an optimised therapeutic strategy relying on a scheduled combination of cytotoxic and cytostatic treatments that can be adapted to different situations of cancer treatments. Finally, we review arguments for biological theoretical frameworks proposed at different time and development scales, the so-called atavistic model (diachronic view relying on Darwinian genotype selection in the coursof billions of years) and the Waddington-like epigenetic landscape endowed with evolutionary quasi-potential (synchronic view relying on Lamarckian phenotype instruction of a given genome by reversible mechanisms), to represent evolution towards heterogeneity, possibly polyclonal, in cancer cell populations and propose innovative directions for therapeutic strategies based on such frameworks.},
doi = {10.1063/1.4952107},
journal = {AIP Conference Proceedings},
issn = {0094-243X},
number = 1,
volume = 1738,
place = {United States},
year = {2016},
month = {6}
}