Project 3: Single cell measures of intratumor diversity for optimal breast cancer therapy Project Summary / Abstract Despite improved treatment, metastatic breast cancer is still inevitably fatal and is a major cause of cancer- related deaths. Triple negative and inflammatory breast cancer are breast tumor subtypes that lack targeted therapy, which, in combination with the high propensity to distant metastatic spread, leads to poor outcome; 70-80% of patients diagnosed with TNBC or IBC die within 5 years of diagnosis. Thus, new treatment strategies are urgently needed. We have previously analyzed the clinical and functional relevance of intratumor heterogeneity in breast cancer. We analyzed breast tumor samples before and after pre-operative chemotherapy and at different stages of disease progression for intratumor cellular heterogeneity for genetic and phenotypic features. We found that lower pretreatment genetic heterogeneity predicts better response and that distant metastases have the highest diversity index. We have also developed a xenograft model of intratumor clonal heterogeneity in breast cancer and utilized this model to assess the functional relevance of clonal interactions in metastatic progression. We found that polyclonal tumors are more likely to metastasize and identified underlying clonal cooperative mechanisms driving this process. Lastly, we have developed mathematical models based on these experimental data that can infer the evolution of tumors during treatment and disease progression both in clinical samples and in xenografts. Based on our preliminary data, we hypothesize that (1) intratumor heterogeneity is a driver of disease progression, (2) single cell measures of intratumor heterogeneity and their topologic distribution can be used to build mathematical models of tumor evolution and treatment response, (3) the use of these models will aid the design of individualized treatment strategies that more effectively eliminate breast tumors. We propose three specific aims to test these hypotheses:
Aim 1. Single cell analyses of breast tumor samples.
Aim 2. Characterization of therapeutic responses in xenograft models of breast cancer.
Aim 3. Predict optimal therapeutic strategies to prevent metastatic outgrowth and treatment resistance and validate these strategies in xenograft models. Our goal is to translate our findings into future clinical trials.
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