Novel integrative method to detect biomakers of breast cancer resistance

描述(申请人提供)：三阴性乳腺癌(TNBC)的定义是雌激素受体(ER)、孕激素受体(PR)和人表皮生长因子受体2(HER-2)的表达缺失，是一种侵袭性癌症，尤其是在转移性环境中。大约15%-20%的乳腺癌是TNBC。尽管最近在TNBC治疗方面有所改进，但由于缺乏已知的特异性治疗靶点和对化疗的异质性反应，因此很难攻击TNBC并获得一致的结果和有意义的好处。最近，基于越来越多的证据表明，从临床前和临床数据中获得了更好的结果，顺铂化疗重新引起了人们的兴趣。然而，许多TNBC患者对治疗没有反应；而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，以避免不必要的毒性和医疗费用。 这项研究的目的是开发一个基于信号处理和机器学习技术的计算框架，以便从下一代测序(NGS)数据中更准确和有效地识别TNBC中新的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修复状态对TNBC患者顺铂敏感性的影响表明，顺铂应答预测因素的存在，需要进一步研究。在这项研究中，我们将利用信号处理技术开发一种新的基于序列的拷贝数变异(CNV)检测工具，以及一种新的基于贝叶斯网络分析的监督综合分析工具，该工具集成了CNV、点突变和基因表达数据。我们将在公开的数据上磨练和验证创新的方法和工具，例如癌症基因组图谱(TCGA)数据。然后，通过与贝丝以色列女执事医疗中心(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，我们将从现有的临床试验中对反应和无反应的TNBC肿瘤样本生成新的DNA序列和RNA-SEQ数据集，该试验旨在研究早期乳腺癌的术前顺铂。通过应用所提出的计算框架，我们将前所未有地阐明TNBC对顺铂治疗反应的潜在预测因素，这有助于指导生物标记物的选择。我们将通过基因本体论和通径分析来验证候选生物标记物。此外，我们将分析TCGA数据以确定这些候选生物标记物在TNBC中的流行率。

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.