Novel integrative method to detect biomakers of breast cancer resistance

描述（由申请人提供）：三阴性乳腺癌（TNBC）定义为缺乏雌激素受体（ER）、孕酮受体（PR）和人表皮生长因子受体2（HER-2）的表达，其特征是侵袭性癌症，尤其是在转移性环境中。大约15-20%的乳腺癌是TNBC。尽管TNBC治疗最近有所改善，但缺乏已知的特异性治疗靶点和对化疗的异质性反应使得难以攻击TNBC并获得一致的结果和有意义的益处。最近，顺铂化疗已重新获得兴趣的基础上，越来越多的证据，实现更好的结果，从临床前和临床数据。然而，许多TNBC患者对治疗没有反应;并且没有临床实用的方法来确定哪些个体的顺铂化疗将有效避免不必要的毒性和医疗保健费用。 本研究的目的是开发一种基于信号处理和机器学习技术的计算框架，用于从下一代测序（NGS）数据中更准确有效地识别TNBC中的新型顺铂反应候选生物标志物。最近发现的p63/p73表达，p53突变和DNA修复状态的测量对TNBC患者对顺铂的敏感性的影响表明顺铂反应预测因子的存在和进一步研究的必要性。在这项研究中，我们将开发一种新的基于序列的拷贝数变异（CNV）检测工具，使用信号处理技术;和一种新的监督综合分析工具，基于贝叶斯网络分析，整合CNV，点突变和基因表达数据。我们将磨练和验证创新的方法和工具，如癌症基因组图谱（TCGA）数据。然后通过与Beth Israel Deaconess Medical Center（BIDMC）的肿瘤学家和病理学家合作，并使用Dana- Farber/哈佛癌症中心DNA资源核心服务，我们将生成来自现有临床试验的反应性和非反应性TNBC肿瘤样本的新型DNA序列和RNA-seq数据集，该临床试验旨在研究早期乳腺癌中的术前顺铂。通过应用所提出的计算框架，我们将前所未有地揭示TNBC对顺铂治疗的反应的潜在预测因子，这可以帮助指导生物标志物的选择。我们将通过基因本体和途径分析来验证候选生物标志物。此外，我们将分析TCGA数据，以确定这些候选生物标志物在TNBC中的患病率。

DESCRIPTION (provided by applicant): Triple-negative breast cancer (TNBC) is defined by lack of expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) and is characteristically an aggressive cancer, especially in a metastatic setting. Approximately 15-20% of all breast cancers are TNBC. In spite of recent improvements in TNBC treatment, the lack of known specific therapeutic targets and the heterogeneous response to chemotherapy make it difficult to attack TNBC and obtain a consistent outcome and meaningful benefit. Recently, cisplatin chemotherapy has regained interest based on growing evidence on achieving better outcome from preclinical and clinical data. However, many TNBC patients are not responding to the treatment; and there is no clinical practical way to identify in which individuals' cisplatin chemotherapy will be effective t avoid unnecessary toxicity and cost of healthcare. The objective of this study is to develop a computational framework, based on signal processing and machine learning techniques, for identifying novel cisplatin response candidate biomarkers in TNBC more accurately and efficiently from next-generation sequencing (NGS) data. The recent discovery of the p63/p73 expression, p53 mutation and measurements of DNA repair status effects on the sensitivity to cisplatin in TNBC patients has indicated the existence of cisplatin response predictors and the need for further investigation. In this study, we will develo a novel sequence-based copy number variation (CNV) detection tool, using signal processing techniques; and a novel supervised integrative analysis tool, based on Bayesian network analysis which integrates CNV, point mutation and gene expression data. We will hone and validate the innovative methods and tools on publically available data such as The Cancer Genome Atlas (TCGA) data. Then by collaborating with oncologists and pathologists from Beth Israel Deaconess Medical Center (BIDMC) and using the Dana- Farber/Harvard Cancer Center DNA Resource Core services, we will generate novel DNA sequence and RNA- seq datasets on responsive and non-responsive TNBC tumor samples from an existing clinical trial, which was designed to study preoperative cisplatin in early-stage breast cancer. By applying the proposed computational framework we will shed unprecedented light on potential predictors of TNBC response to cisplatin therapy that can help guide biomarker selection. We will verify the candidate biomarkers through gene ontology and pathway analyses. In addition, we will analyze TCGA data to determine the prevalence of these candidate biomarkers in TNBC.