Project 3: Single Cell Measures of Intratumor Diversity for Optimal Breast Cancer Therapy

项目3：肿瘤内多样性的单细胞测量用于最佳乳腺癌治疗 项目总结/摘要 尽管治疗方法有所改进，但转移性乳腺癌仍然不可避免地是致命的，并且是癌症的主要原因- 相关死亡。三阴性和炎性乳腺癌是缺乏靶向治疗的乳腺肿瘤亚型， 治疗，与远处转移扩散的高倾向相结合，导致不良结局; 70-80%的TNBC或IBC患者在诊断后5年内死亡。因此，新的治疗 迫切需要战略。 我们先前分析了乳腺癌中肿瘤内异质性的临床和功能相关性。 我们分析了术前化疗前后和化疗后不同阶段的乳腺肿瘤样本， 肿瘤内细胞遗传和表型特征异质性的疾病进展。我们发现 较低的治疗前遗传异质性预示着较好的反应，远处转移具有 多样性指数最高。我们还建立了一种乳腺癌肿瘤内克隆异质性的异种移植模型， 并利用该模型来评估转移性肿瘤中克隆相互作用的功能相关性。 进展我们发现，多克隆肿瘤更容易转移， 推动这一进程的合作机制。最后，我们在此基础上建立了数学模型。 可以推断治疗期间肿瘤演变和疾病进展的实验数据， 临床样品和异种移植物中。根据我们的初步数据，我们假设（1）肿瘤内 异质性是疾病进展的驱动因素，（2）肿瘤内异质性的单细胞测量及其 拓扑分布可用于建立肿瘤演变和治疗反应的数学模型，（3） 这些模型的使用将有助于设计个性化的治疗策略， 乳腺肿瘤我们提出了三个具体目标来测试这些假设：目标1。乳腺单细胞分析 肿瘤样本目标二。乳腺癌异种移植模型中治疗反应的表征。目标3. 预测最佳治疗策略，以防止转移性生长和治疗耐药性，并验证 异种移植模型中的这些策略。我们的目标是将我们的发现转化为未来的临床试验。

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.