Cell Reports
(Feb 2014)
Inference of Tumor Evolution during Chemotherapy by Computational Modeling and In Situ Analysis of Genetic and Phenotypic Cellular Diversity
Vanessa Almendro,
Yu-Kang Cheng,
Amanda Randles,
Shalev Itzkovitz,
Andriy Marusyk,
Elisabet Ametller,
Xavier Gonzalez-Farre,
Montse Muñoz,
Hege G. Russnes,
Åslaug Helland,
Inga H. Rye,
Anne-Lise Borresen-Dale,
Reo Maruyama,
Alexander van Oudenaarden,
Mitchell Dowsett,
Robin L. Jones,
Jorge Reis-Filho,
Pere Gascon,
Mithat Gönen,
Franziska Michor,
Kornelia Polyak
Affiliations
Vanessa Almendro
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
Yu-Kang Cheng
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
Amanda Randles
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
Shalev Itzkovitz
Departments of Physics and Biology and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Andriy Marusyk
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
Elisabet Ametller
Department of Medical Oncology, Hospital Clinic, Institut d’Investigacions Biomediques August Pi i Sunyer, Barcelona 08036, Spain
Xavier Gonzalez-Farre
Department of Medical Oncology, Hospital Clinic, Institut d’Investigacions Biomediques August Pi i Sunyer, Barcelona 08036, Spain
Montse Muñoz
Department of Medical Oncology, Hospital Clinic, Institut d’Investigacions Biomediques August Pi i Sunyer, Barcelona 08036, Spain
Hege G. Russnes
Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo 0424, Norway
Åslaug Helland
Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo 0424, Norway
Inga H. Rye
Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo 0424, Norway
Anne-Lise Borresen-Dale
Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo 0424, Norway
Reo Maruyama
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
Alexander van Oudenaarden
Departments of Physics and Biology and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Mitchell Dowsett
The Royal Marsden Hospital, The Breakthrough Breast Cancer Research Centre, Institute of Cancer Research, London SW3 6JJ, UK
Robin L. Jones
The Royal Marsden Hospital, The Breakthrough Breast Cancer Research Centre, Institute of Cancer Research, London SW3 6JJ, UK
Jorge Reis-Filho
The Royal Marsden Hospital, The Breakthrough Breast Cancer Research Centre, Institute of Cancer Research, London SW3 6JJ, UK
Pere Gascon
Department of Medical Oncology, Hospital Clinic, Institut d’Investigacions Biomediques August Pi i Sunyer, Barcelona 08036, Spain
Mithat Gönen
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
Franziska Michor
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
Kornelia Polyak
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
DOI
https://doi.org/10.1016/j.celrep.2013.12.041
Journal volume & issue
Vol. 6,
no. 3
pp.
514
– 527
Abstract
Read online
Cancer therapy exerts a strong selection pressure that shapes tumor evolution, yet our knowledge of how tumors change during treatment is limited. Here, we report the analysis of cellular heterogeneity for genetic and phenotypic features and their spatial distribution in breast tumors pre- and post-neoadjuvant chemotherapy. We found that intratumor genetic diversity was tumor-subtype specific, and it did not change during treatment in tumors with partial or no response. However, lower pretreatment genetic diversity was significantly associated with pathologic complete response. In contrast, phenotypic diversity was different between pre- and posttreatment samples. We also observed significant changes in the spatial distribution of cells with distinct genetic and phenotypic features. We used these experimental data to develop a stochastic computational model to infer tumor growth patterns and evolutionary dynamics. Our results highlight the importance of integrated analysis of genotypes and phenotypes of single cells in intact tissues to predict tumor evolution.
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