Abstract 5338: Inferring evolutionary models of tumor progression from single-cell heterogeneity data

Abstract

Abstract We describe computational methods to compute likely evolutionary histories from tumor single-cell copy number data and next generation sequencing data and apply the methods to data collected from diverse types of tumors. Experimental techniques for assessing heterogeneity in tumor cell populations have undergone great advances, but these improvements have created a great need for more sophisticated computer algorithms capable of making sense of these data sources in terms of coherent models of tumor evolution. We have addressed this problem by developing computer algorithms for building phylogenetic trees describing evolution of individual tumors based on copy numbers of fluorescence in situ hybridization (FISH) probes from single cells in these tumors. These algorithms reconstruct evolutionary trees for observed cell populations so as to heuristically minimize the number of mutational events needed to explain the observed combinations of probe counts by evolution from a common diploid ancestral cell. We have extended this work from initial simple evolutionary models of evolution by single copy number changes to account for distinct mechanisms of evolution at the gene, chromosome, or whole-genome scale, with potentially different rates of evolution by mutation type. We have applied these algorithms to several FISH data sets, including cervical cancers probed for four genes (LAMP3, PROX1, PRKAA1 and CCND1) measured for up to 250 cells of paired primary and metastatic samples from 16 patients, head-and-neck cancers probed for four genes (TERC, CCND1, EGFR and TP53) measured on up to 250 cells per patient for 65 patients at four tumor stages, prostate cancers probed for six genes (TBL1XR1, CTTNBP2, MYC, PTEN, MEN1 and PDGFB) measured for up to 407 cells in 6 non-progressive and 7 progressive carcinomas, and breast cancers probed for eight genes (COX-2, MYC, CCND1, HER-2, ZNF217, DBC2, CDH1 and TP53) measured on up to 220 cells of paired of ductal carcinoma in situ and invasive ductal carcinoma samples from 13 patients. We have then applied statistical and machine learning analysis to examine the ability of these trees to classify tumors by stage or potential for progression. The evolutionary tree models reveal robust features of evolutionary processes distinguishing progression stages and predicting future progression that lead to improved classification accuracy relative to predictions from cellular heterogeneity data alone. Our software is freely available at ftp://ftp.ncbi.nlm.nih.gov/pub/FISHtrees. In continuing work, we are exploring extension of these approaches to better modeling and analysis of tumor evolution using single-cell sequencing data and to more detailed models of tumor evolution. Citation Format: Salim A. Chowdhury, Ayshwarya Subramanian, Alejandro A. Schäffer, Stanley E. Shackney, Darawalee Wangsa, Kerstin Heselmeyer-Haddad, Thomas Ried, Russell Schwartz. Inferring evolutionary models of tumor progression from single-cell heterogeneity data. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5338. doi:10.1158/1538-7445.AM2014-5338