Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity

Almendro, Vanessa; Cheng, Yu -Kang; Randles, Amanda; Itzkovitz, Shalev; Marusyk, Andriy; Ametller, Elisabet; Gonzalez-Farre, Xavier; Muñoz, Montse; Russnes, Hege  G.; Helland, Åslaug; Rye, Inga  H.; Borresen-Dale, Anne -Lise; Maruyama, Reo; van Oudenaarden, Alexander; Dowsett, Mitchell; Jones, Robin  L.; Reis-Filho, Jorge; Gascon, Pere; Gönen, Mithat; Michor, Franziska; Polyak, Kornelia

doi:10.1016/j.celrep.2013.12.041

Title: Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity

Journal Article · Sat Feb 01 00:00:00 EST 2014 · Cell Reports

DOI:https://doi.org/10.1016/j.celrep.2013.12.041· OSTI ID:1201672

Almendro, Vanessa ^[1]; Cheng, Yu -Kang ^[2]; Randles, Amanda ^[3]; Itzkovitz, Shalev ^[4]; Marusyk, Andriy ^[5]; Ametller, Elisabet ^[6]; Gonzalez-Farre, Xavier ^[6]; Muñoz, Montse ^[6]; Russnes, Hege G. ^[7]; Helland, Åslaug ^[8]; Rye, Inga H. ^[9]; Borresen-Dale, Anne -Lise ^[9]; Maruyama, Reo ^[5]; van Oudenaarden, Alexander ^[10]; Dowsett, Mitchell ^[11]; Jones, Robin L. ^[12]; Reis-Filho, Jorge ^[13]; Gascon, Pere ^[6]; Gönen, Mithat ^[14]; Michor, Franziska ^[2] more »

Dana-Farber Cancer Institute, Boston, MA (United States); Brigham and Women's Hospital, Boston, MA (United States); Harvard Medical School, Boston, MA (United States); Institut d'Investigacions Biomediques, Barcelona (Spain)
Dana-Farber Cancer Institute, Boston, MA (United States); Harvard School of Public Health, Boston, MA (United States)
Dana-Farber Cancer Institute, Boston, MA (United States); Harvard School of Public Health, Boston, MA (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
MIT (Massachusetts Inst. of Technology), Cambridge, MA (United States); Weizmann Institute of Science, Rehovot (Israel)
Dana-Farber Cancer Institute, Boston, MA (United States); Brigham and Women's Hospital, Boston, MA (United States); Harvard Medical School, Boston, MA (United States)
Institut d'Investigacions Biomediques, Barcelona (Spain)
Oslo Univ. Hospital Radiumhospitalet, Oslo (Norway); Univ. of Oslo, Oslo (Norway); Oslo Univ. Hospital, Oslo (Norway)
Oslo Univ. Hospital Radiumhospitalet, Oslo (Norway); Oslo Univ. Hospital, Oslo (Norway); Univ. of Oslo, Oslo (Norway)
Oslo Univ. Hospital Radiumhospitalet, Oslo (Norway); Univ. of Oslo, Oslo (Norway)
MIT (Massachusetts Inst. of Technology), Cambridge, MA (United States); Royal Netherlands Academy of Arts and Sciences and Univ. Medical Center Utrecht, Utrecht (The Netherlands)
The Royal Marsden Hospital, London (United Kingdom)
The Royal Marsden Hospital, London (United Kingdom); Seattle Cancer Care Alliance, Seattle, WA (United States)
The Royal Marsden Hospital, London (United Kingdom); Memorial Sloan-Kettering Cancer Center, New York, NY (United States)
Memorial Sloan-Kettering Cancer Center, New York, NY (United States)
Dana-Farber Cancer Institute, Boston, MA (United States); Brigham and Women's Hospital, Boston, MA (United States); Harvard Medical School, Boston, MA (United States); Harvard Stem Cell Institute, Cambridge, MA (United States); Broad Institute, Cambridge, MA (United States)

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 post-treatment 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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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE

OSTI ID:: 1201672

Journal Information:: Cell Reports, Vol. 6, Issue 3; ISSN 2211-1247

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 192 works

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