Abstract ES5-3: Investigating Breast Cancer with Single-Cell Sequencing

Abstract

Abstract Tumors evolve from single cells. As they evolve, they acquire complex somatic mutations and diversify, forming distinct subpopulations of cells. This intratumor heterogeneity confounds basic research and clinical practice because tools do not exist to resolve it. Standard genomic methods are limited to reporting an average signal from a complex population of cells, because they require a large amount of input material. These bulk methods may mask population diversity and rare cells in the tumor - that may be the most malignant. To address this limitation my laboratory has developed single-cell sequencing tools to delineate intratumor heterogeneity and investigate mutational evolution in human cancers. We developed a method to profile genomic copy number in single cells and used this method to study genome evolution in triple-negative (ER−/PR−/Her2-) breast tumors (Navin et al., 2011 Nature) which revealed a punctuated model for copy number evolution. Recently, we have extended this method to obtain whole-genome, high-coverage sequencing data from single human tumor cells. From this data we can detect the full spectrum of genomic mutations, including point mutations, indels and microdeletions in single human cells. We applied this method to study clonal diversity in an estrogen-receptor-positive breast carcinoma and sequenced four single cells in addition to a population sample. In the population we detected only 36 coding mutations. However, our whole-genome single-cell sequencing data revealed hundreds of additional coding mutations, suggesting extensive clonal diversity in the tumor. Our data from this tumor support a mutator phenotype model for tumor progression, in which cancer is driven by elevating the rate of random mutations. Our studies show that a wealth of information can be obtained from studying single cancer cells, data that could not be obtained by analyzing the tumor en masse. We expect single-cell sequencing to have major clinical applications in early detection, analyzing scarce tissue samples and monitoring residual disease in patients with cancer. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr ES5-3.