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

项目3：用于最佳乳腺癌治疗的肿瘤内多样性的单细胞测量 项目摘要/摘要 尽管治疗方法有所改进，转移性乳腺癌仍然不可避免地是致命的，是癌症的主要原因。 相关死亡。三重阴性和炎症性乳腺癌是缺乏针对性的乳腺癌亚型。 治疗，再加上远处转移扩散的高倾向，导致结果不佳； 70%-80%被诊断为TNBC或IBC的患者在确诊后5年内死亡。因此，新的治疗方法 战略是迫切需要的。 我们先前已经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术后不同时期的乳腺肿瘤标本。 肿瘤内细胞遗传和表型特征的异质性导致疾病进展。我们发现 较低的治疗前遗传异质性预示着较好的反应，并且远处转移具有 多样性指数最高。我们还建立了乳腺瘤内克隆异质性的异种移植模型。 并利用该模型评估克隆相互作用在转移中的功能相关性 进步。我们发现多克隆肿瘤更容易转移，并确定了潜在的克隆。 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 可以推断治疗过程中肿瘤的演变和疾病进展的实验数据 临床标本和异种移植物中。根据我们的初步数据，我们假设(1)瘤内 异质性是疾病进展的驱动力，(2)肿瘤内单细胞异质性的测量及其 拓扑分布可用于建立肿瘤演化和治疗反应的数学模型，(3) 这些模型的使用将有助于个性化治疗策略的设计，从而更有效地消除 乳房肿瘤。我们提出了三个具体的目标来检验这些假设：目标1.乳房的单细胞分析 肿瘤样本。目的2.乳腺癌异种移植模型的治疗反应特征。目标3. 预测最佳治疗策略以防止转移和治疗耐药性，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.