Author
Listed:
- Jennifer L. Caswell-Jin
(Stanford University School of Medicine)
- Katherine McNamara
(Stanford University School of Medicine
Stanford University School of Medicine
Stanford University School of Medicine)
- Johannes G. Reiter
(Stanford University School of Medicine)
- Ruping Sun
(Stanford University School of Medicine
Stanford University School of Medicine
Stanford University School of Medicine)
- Zheng Hu
(Stanford University School of Medicine
Stanford University School of Medicine
Stanford University School of Medicine)
- Zhicheng Ma
(Stanford University School of Medicine
Stanford University School of Medicine
Stanford University School of Medicine)
- Jie Ding
(Stanford University School of Medicine
Stanford University School of Medicine
Stanford University School of Medicine)
- Carlos J. Suarez
(Stanford University School of Medicine)
- Susanne Tilk
(Stanford University)
- Akshara Raghavendra
(The University of Texas MD Anderson Cancer Center)
- Victoria Forte
(Maimonides Medical Center
Norris Comprehensive Cancer Center)
- Suet-Feung Chin
(University of Cambridge)
- Helen Bardwell
(University of Cambridge)
- Elena Provenzano
(Cambridge University Hospitals NHS Foundation Trust)
- Carlos Caldas
(University of Cambridge)
- Julie Lang
(Norris Comprehensive Cancer Center
University of Southern California)
- Robert West
(Stanford University School of Medicine)
- Debu Tripathy
(The University of Texas MD Anderson Cancer Center)
- Michael F. Press
(Norris Comprehensive Cancer Center
University of Southern California)
- Christina Curtis
(Stanford University School of Medicine
Stanford University School of Medicine
Stanford University School of Medicine)
Abstract
Genomic changes observed across treatment may result from either clonal evolution or geographically disparate sampling of heterogeneous tumors. Here we use computational modeling based on analysis of fifteen primary breast tumors and find that apparent clonal change between two tumor samples can frequently be explained by pre-treatment heterogeneity, such that at least two regions are necessary to detect treatment-induced clonal shifts. To assess for clonal replacement, we devise a summary statistic based on whole-exome sequencing of a pre-treatment biopsy and multi-region sampling of the post-treatment surgical specimen and apply this measure to five breast tumors treated with neoadjuvant HER2-targeted therapy. Two tumors underwent clonal replacement with treatment, and mathematical modeling indicates these two tumors had resistant subclones prior to treatment and rates of resistance-related genomic changes that were substantially larger than previous estimates. Our results provide a needed framework to incorporate primary tumor heterogeneity in investigating the evolution of resistance.
Suggested Citation
Jennifer L. Caswell-Jin & Katherine McNamara & Johannes G. Reiter & Ruping Sun & Zheng Hu & Zhicheng Ma & Jie Ding & Carlos J. Suarez & Susanne Tilk & Akshara Raghavendra & Victoria Forte & Suet-Feung, 2019.
"Clonal replacement and heterogeneity in breast tumors treated with neoadjuvant HER2-targeted therapy,"
Nature Communications, Nature, vol. 10(1), pages 1-12, December.
Handle:
RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-08593-4
DOI: 10.1038/s41467-019-08593-4
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