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）的定义是缺乏雌激素受体（ER）、孕激素受体（PR）和人表皮生长因子受体2 （HER-2）的表达，是一种典型的侵袭性癌症，特别是在转移性环境中。大约15-20%的乳腺癌是三阴癌。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，顺铂化疗重新引起了人们的兴趣，越来越多的证据表明，临床前和临床数据取得了更好的结果。然而，许多TNBC患者对治疗没有反应；目前还没有临床可行的方法来确定哪些个体的顺铂化疗是有效的，以避免不必要的毒性和医疗成本。

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.