Abstract 2909: High-precision quantification of clonal evolution during comparative treatments of triple negative breast cancer
Authors: Hyunsoo Kim, Pooja Kumar, Francesca Menghi, Eliza Cerveira, Mallory Romanovitch, Guru Ananda, Joshy George, Henry Chen, Susan Mockus, Chengsheng Zhang, Yan Yang, James Keck, R. Krishna Murthy Karuturi, Carol Bult, Charles Lee, Edison Liu, Jeffrey H. Chuang
Journal: Cancer Research (2017)

Abstract Population heterogeneity within tumors is essential to the development of drug resistance. However, precise quantification of cellularity levels of subpopulations, and in particular how they evolve in response to treatment, has been challenging. Here we describe the high precision characterization of subclonal evolution within triple-negative breast cancer patient-derived xenografts (PDXs) generated from three patients. For each patient model, we established multiple PDXs and treated them in cohorts of 5-10 mice each for the therapies cyclophosphamide, doxorubicin, cisplatin, or docetaxel, with treatments lasting one month. In all three patient models, the average behavior across mice was a reduction in size in response to docetaxel, but growth under doxorubicin and cyclophosphamide. For cisplatin treatment, one of the three models showed tumor shrinkage while the other two models showed continued growth but at a rate lower than the doxorubicin or cyclophosphamide cohorts. To determine the evolutionary behaviors underlying these observations, we initially performed exome panel sequencing of 34 residual tumor samples from these and untreated xenografts. Computational mutation and copy number analysis indicated sample-specific differences in tumor populations both in response to treatment and due to genetic drift. However, they also revealed measurement uncertainties related to exome capture efficiency, locus-specific read counts, and computational copy number estimation that limited quantitative inference of evolutionary behaviors. To solve this problem we used droplet digital PCR (ddPCR) to measure variant allele frequencies and local copy number at selected loci from the prior residual tumors as well as additional samples from replicate cohort treatments and cultured conditionally reprogrammed progenitor cells. In total we performed 1665 ddPCR measurements across 150 cancer samples. These ddPCR measurements reduced sample-specific uncertainty in variant allele frequency to ~2% and copy number to ~0.2, allowing for precise identification of subclones and their cellularities in each sample. We observed several common modes of evolution within these tumors including selective sweeps, spatial diffusion, and stable coexistence between distinct subpopulations. In the samples from one patient model, we observed frequent symbiotic growth of two distinct subpopulations having differential cisplatin sensitivity, such that the degree of clonal selection during cisplatin treatment was proportional to the tumor volume change on a mouse-to-mouse basis. This study demonstrates how high precision genomic characterization across comparatively treated samples can reveal treatment-relevant subclonal ecology, as well as the mutations that distinguish populations with different behaviors. Citation Format: Hyunsoo Kim, Pooja Kumar, Francesca Menghi, Eliza Cerveira, Mallory Romanovitch, Guru Ananda, Joshy George, Henry Chen, Susan Mockus, Chengsheng Zhang, Yan Yang, James Keck, R. Krishna Murthy Karuturi, Carol Bult, Charles Lee, Edison Liu, Jeffrey H. Chuang. High-precision quantification of clonal evolution during comparative treatments of triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2909. doi:10.1158/1538-7445.AM2017-2909