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.
Highlights
Early-stage HER2-positive breast cancers afford the optimal setting to study genomic changes in breast tumors treated with targeted combination therapy
We performed whole-exome sequencing on a separate, nonoverlapping cohort of a single pre-treatment diagnostic core biopsy and multiple regions of the post-treatment surgical specimen from five archival HER2-positive breast tumors that were treated with neoadjuvant HER2-targeted therapy combined with chemotherapy and did not achieve a pCR
We analyzed whole-exome sequencing data from four multi-region sampled primary breast tumors generated for this study, as well as data from 11 tumors from four previous studies that performed genome or exome sequencing of high-quality multi-region samples from primary breast tumors[2,14,15,16] (Supplementary Data)
Summary
Early-stage HER2-positive breast cancers afford the optimal setting to study genomic changes in breast tumors treated with targeted combination therapy. We performed whole-exome sequencing on a separate, nonoverlapping cohort of a single pre-treatment diagnostic core biopsy and multiple regions of the post-treatment surgical specimen from five archival HER2-positive breast tumors that were treated with neoadjuvant HER2-targeted therapy combined with chemotherapy and did not achieve a pCR. We use these data to determine evolutionary trajectories and mathematical modeling to define evolutionary parameters that could lead to these trajectories. We infer wide variability in evolutionary parameters across breast tumors, including the rate of mutation and copy number change as well as the number of available paths to resistance, that dictate in large part the outcomes of neoadjuvant therapy
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