Systems Modeling of Tumor Heterogeneity and Therapy Response in Colorectal Cancer

项目摘要/摘要 结直肠癌(CRC)是全球发病率和死亡率最高的癌症之一(新增病例130万例)。 (每年约610,000人死亡)。III期和III期的基因毒性化疗带来的治疗益处微乎其微。 证实II期患者存活率为3-4%，III期患者存活率为15-20%)，以及治疗的预测标志 缺乏回应。结直肠癌患者将从新的预后和预测性生物标志物中受益匪浅 确定不会从以5-FU为基础的化疗中受益的患者，将他们重新定向到靶向或 新的干预措施和加强的疾病监测。实施这样的生物标志物面临的挑战之一 治疗方法是，结直肠癌是一种高度异质性的疾病，出现了多种亚型的证据。普尔-- 化疗耐药的门脉原因是复杂的和多因素的，包括功能失调的细胞凋亡 免疫细胞的细胞毒作用(反过来，对细胞凋亡产生负面影响并降低免疫能力) 肿瘤干细胞的存在(以及多药耐药蛋白的表达)。数字，空间分布- 这些细胞在肿瘤和间质区域的分布和途径状态(以及它们的异质性)是重要的。 蚂蚁的考虑，但其意义并不被理解。在这项研究中的中心假设是 POSAL是在细胞水平上对细胞凋亡途径进行建模，并与肿瘤异质性标志- 与标准相比，II和III期结直肠癌患者样本中的ERS将更好地预测化疗反应 生物标志物和治疗算法。虽然立即应用于II期患者，但重新- SULTS将为第三阶段的结果和诊断机会提供亟需的新的机械性见解 寻找新的治疗方法。我们将使用一种新的单细胞成像技术在单个组织中分析多达50个蛋白质- Sue微阵列(TMA)切片，并量化肿瘤和间质区域的细胞和空间分布。在……里面 &gt；1500名II和III期结直肠癌患者。这些蛋白质将代表细胞凋亡途径、肿瘤微环境- 干细胞、间质和上皮细胞，并在单细胞水平上进行量化。已建立的模式将是 用于将单细胞凋亡数据转换为凋亡能力分数。细胞凋亡中的异质性 将与治疗和未治疗患者的复发风险相关。肿瘤微环境措施 (间质、免疫、内皮细胞)、干细胞和可用的分子亚型数据将与细胞凋亡相结合 评分以进一步阐明治疗反应。我们将在一项随机试验中验证重要的预测生物标志物 化疗患者和未治疗患者的对照试验。最后，在可用患者的子集中 细胞株，我们将通过实验研究耐药机制，并测试新的细胞凋亡- 诱导疗法可能会提供化疗的另一种选择。结直肠癌中的预测性生物标志物 每年有可能挽救数十万名患者，使他们免于接受有限的益处和有利于 让肿瘤学家有更大的能力指导患者使用其他治疗方法。

PROJECT SUMMARY/ABSTRACT Colorectal cancer (CRC) has one of the highest worldwide incidence (>1.3 million new cases) and mortality rates (~610,000 deaths annually). Genotoxic chemotherapy in stage II and III confers minimal treatment benefit (im- proved survival in 3-4% stage II patients and 15-20% patients in stage III), and predictive markers for therapy response are lacking. CRC patients would strongly benefit from novel prognostic and predictive biomarkers that identify patients who will not benefit from 5-FU-based chemotherapy, redirecting them towards targeted or novel interventions and intensified disease monitoring. One of the challenges in implementing such a biomarker approach is that CRC is a highly heterogeneous disease, with evidence for multiple subtypes emerging. The pur- ported causes of chemotherapy resistance are complex and multi-factorial, including dysfunctional apoptosis pathways, immune cell cytotoxicity (in turn, negatively affecting apoptosis and reducing immune-competency) and presence of cancer stem cells (and expression of multi-drug resistance proteins). The numbers, spatial dis- tribution and pathway status of these cells in the tumor and stromal area (and their heterogeneity) is an import- ant consideration, but the significance is not understood. The central hypothesis under investigation in this pro- posal is that modeling of apoptosis pathways at a cellular level and integration with tumor heterogeneity mark- ers in stage II and III CRC patient samples will better predict chemotherapy response, compared to standard biomarkers and treatment algorithms. While the immediate application would be for stage II patients, the re- sults will provide much needed new mechanistic insights into stage III outcomes and diagnostic opportunities for new therapies. We will use a novel single cell imaging technology to profile up to 50 proteins in a single tis- sue micro array (TMA) sections and quantify cellular and spatial distribution in tumor and stromal regions. in >1500 stage II and III CRC patients. These proteins will represent the apoptosis pathway, tumor microenviron- ment, stem cells, stroma and epithelial cells and be quantified at single cell level. Established models will be used to convert single cell apoptosis data into apoptosis competency scores. Heterogeneity in cellular apoptosis will be correlated with recurrence risk in treated and untreated patients. Tumor microenviroment measures (stroma, immune, endothelial), stem cells and available molecular subtype data will be combined with apoptosis scores to further elucidate therapy response. We will validate significant predictive biomarkers in a randomized controlled trial with chemotherapy treated and untreated patients. Finally, in a subset of patients with available cell lines, we will experimentally investigate mechanisms of drug resistance and test whether novel apoptosis- inducing therapies could potentially provide an alternative to chemotherapy. Predictive biomarkers in CRC could potentially save hundreds of thousands of patients per year from treatments with limited benefit and pro- vide oncologists with greater ability to direct patients towards other therapies.