Metabolic alterations in advanced Breast Cancer and response to systemic therapy.

乳腺癌全身化疗的使用有助于最近乳腺癌的下降 死亡率;然而，不可接受的患者数量无法接受全身治疗，死于播散性 疾病确定耐药的重要因素并指导患者进行更有效的治疗， 项目3的翻译目标。使用定量PET成像测量葡萄糖代谢，以及 最近，动态对比增强（DCE）MRI测量血流，我们已经确定了一个体内 新辅助化疗耐药的局部晚期乳腺癌（LABC）的代谢特征（1） 治疗前代谢和灌注不匹配，（2）持续或甚至增加的肿瘤灌注 尽管治疗，和（3）葡萄糖代谢的改变模式相对于葡萄糖输送后， 治疗这种模式预测不完全反应，早期复发和死亡，独立于既定的 预后因素，包括病理原发肿瘤和淋巴结病理反应。我们还发现 这种模式与三阴性（TN、ER/PR/HER 2阴性）肿瘤的相关性更深， 表达ER/PR和/或过表达HER 2的那些。 我们现在建议将我们在患者中的临床和体内发现转化回实验室，以确定 这些发现背后的肿瘤生物学特征。在接受新辅助治疗的LABC患者队列中， 化疗后，我们将（1）将影像学结果与通过IHC和表达确定的肿瘤表型进行比较， 微阵列来确定哪些分子途径最参与耐药成像表型， (2)确定肿瘤微环境的作用，特别是通过FMISO PET测量的肿瘤缺氧 用免疫组化法检测缺氧组织标志物的表达;（3）与宏观代谢相关 通过对活检标本和一组细胞系中的细胞代谢成像测量的性质， ToF-SIMS，目标是将细胞系中测量的代谢途径的发现与耐药 在患者中观察到的表型。成功完成研究将确定乳腺癌患者可能 失败的全身化疗和直接治疗对那些生物靶点最有可能克服 阻力

The use of systemic chemotherapy for breast cancer has contributed to the recent decline in breast cancer mortality; however, an unacceptable number of patients fail systemic therapy and die of disseminated disease. Identifying factors important in resistance and directing patients towards more effective treatment is the translational goal of Project 3. Using quantitative PET imaging to measure glucose metabolism, and more recently dynamic contrast-enhanced (DCE) MRI to measure blood flow, we have identified an in vivo metabolic signature for locally advanced breast cancer (LABC) resistant to neoadjuvant chemotherapy as (1) a pre-therapy mismatch between metabolism and perfusion, (2) persistent or even increased tumor perfusion despite treatment, and (3) an altered pattern of glucose metabolism relative to glucose delivery after treatment. This pattern predicts incomplete response, early relapse and death independent of established prognostic factors, including pathologic primary tumor and nodal pathologic response. We have also found this pattern is more profoundly associated with triple-negative (TN, ER/PR/HER2 negative) tumors versus those that express ER/PR and/or over-express HER2. We now propose to translate our clinical, in vivo findings in patients back into the laboratory to identify the biologic features of tumors underlying these findings. In a cohort of LABC patients undergoing neoadjuvant chemotherapy, we will (1) compare imaging findings to tumor phenotype determined by IHC and expression microarrays to determine which molecular pathways are most involved In the resistant imaging phenotype, (2) determine the role ofthe tumor microenvironment, specifically tumor hypoxia measured by FMISO PET and the expression ofthe hypoxic tissue markers measured by IHC, and (3) relate macroscopic metabolic properties measured by imaging to cellular metabolism in biopsy specimens and a panel of cell lines using ToF-SIMS, with the goal of relating findings on metabolic pathways measured in cell lines to the resistant phenotype seen in patients. Successful completion of the studies will identify breast cancer patients likely to fail systemic chemotherapy and direct therapy towards those biologic targets most likely to overcome resistance.