Abstract

Cancer therapy exerts a strong selection pressure that shapes tumor evolution, yet our knowledge of how tumors change during treatment is limited. To understand the effects of treatment on tumor heterogeneity we devise a strategy to quantify the extent of genetic and phenotypic cellular diversity in breast tumors pre- and post-neoadjuvant chemotherapy. Cellular genetic diversity is distinct from clonal diversity, as it combines inputs from both clonal architecture and lower-scale differences arising from genomic instability that are not amplified by selection. We found that intratumor genetic diversity was tumor subtype-specific and it did not change during treatment in tumors with partial or no response. However, lower pre-treatment genetic diversity was significantly associated with complete pathologic response. In contrast, phenotypic diversity was different between pre- and post-treatment samples. We also observed significant changes in the spatial distribution of cells with distinct genetic and phenotypic features. We used these experimental data to develop a stochastic computational model to infer tumor growth patterns and evolutionary dynamics. We also used the same strategy to interrogate the genetic and phenotypic heterogeneity at the single cell level in distant metastatic lesions from rapid autopsy cases and in matched primary tumors and lymph node metastases of breast cancer collected prior to systemic therapy. We observed that genetic diversity was highest in distant metastases and was generally concordant across lesions within the same patient, whereas treatment-naïve primary tumors and matched lymph node metastases were frequently genetically more divergent. In contrast, cellular phenotypes were more discordant between distant metastases than primary tumors and matched lymph node metastases. These results highlight the importance of integrated analysis of genotypes and phenotypes of single cells in intact tissues to predict tumor evolution. In addition, quantitative measures of intratumor heterogeneity might aid in the clinical management of cancer patients including identifying those at a high risk of progression and recurrence.