Integrative statistical models for TNBC biomarker discovery

项目摘要/摘要 三重阴性乳腺癌(TNBC)，指的是一组异质性的肿瘤 雌激素受体(ER)、孕激素受体(PR)的表达和HER2的扩增。不像，呃- 阳性和HER2扩增的乳腺癌；TNBC中缺乏高频致癌驱动突变 对患有这种疾病的女性的治疗选择有限。然而，TNBCs的临床发病率较高。 对术前(新辅助)化疗的反应，尽管缺乏有针对性的治疗。尽管有更好的 化疗反应，TNBC患者仍有较高的远处复发率和较差的 与其他乳腺癌亚型相比，女性预后更差。 对新辅助化疗经历病理完全应答(PCR)的TNBC患者 与残存患者相比，无病存活率和总存活率均有显著改善 侵袭性疾病。相比之下，那些有残留疾病的患者预后更差，只有6岁。 复发的可能性增加12倍，死亡的可能性增加12倍。而30%的TNBC患者受益 从新辅助化疗中，目前还没有有效的方法来识别那些将 受益最大的是。 TNBC对化疗的异质性反应提示可能存在不同的TNBC亚型 是相关的药物反应。我们最近开发了一种具有2188个基因的新的基因表达特征 基于一种新的算法将TNBCs分类为六个亚型，并在软件中实现了该算法 “TNBCtype”。我们的研究表明，每个TNBC亚型都表现出独特的生物学特性。此外，我们还确定了 针对这些亚型的具有代表性的TNBC细胞系模型，对靶向和 化疗。 因此，为了转化我们的临床前成果，迫切需要开发新的策略来开发 改进的、可重复的、强大的、临床有用的亚型工具，以确定最有可能受益的TNBC患者 从新辅助化疗中，发现新的生物标志物用于靶向治疗的患者 对化疗有抗药性。我们提出以下具体目标来应对这些挑战：(1)发展 并验证了一个稳健的TNBC亚型模型；(2)确定了TNBC亚型特异性化疗反应基因 (3)利用整合基因组学方法发现TNBC化疗耐药的生物标志物。

PROJECT SUMMARY/ABSTRACT The “triple negative breast cancer” (TNBC), refers to a heterogeneous collection of the tumors that lack expression of the estrogen receptor (ER), progesterone receptor (PR), and HER2 amplification. Unlike, ER- positive and HER2-amplified breast cancers; the lack of high frequency oncogenic driver mutations in TNBC has limited treatment options for women with the disease. However, TNBCs have higher rates of clinical response to pre-surgical (neo-adjuvant) chemotherapy, despite the lack of targeted therapy. Despite better responses to chemotherapy, TNBC patients still have a higher rate of distant recurrence and a poorer prognosis than women with other breast cancer subtypes. TNBC patients who experience a pathologic complete response (pCR) to neoadjuvant chemotherapy have significant improvements in both disease-free and overall survival compared with patients with residual invasive disease. In contrast, those patients with residual disease have a much poorer prognosis and are 6 times more likely to have recurrence and 12 times more likely to die. While 30% of patients with TNBC benefit from neoadjuvant chemotherapy, currently there is no effective way to identify those TNBC patients that would benefit most. TNBC's heterogeneous response to chemotherapy suggests that different TNBC subtypes may exist and are associated drug responses. We recently developed a novel gene expression signature with 2188 genes based on a new algorithm to classify TNBCs into six subtypes and implemented the algorithm in the software “TNBCtype”. Our study showed that each TNBC subtype displays a unique biology. Furthermore, we identified representative TNBC cell line models for these subtypes that display differential sensitivity to targeted and chemotherapy. Therefore, to translate our pre-clinical results, there is a critical need to develop new strategies to develop a refined, reproducible, robust and clinically useful subtyping tool to identify TNBC patients most likely to benefit from neoadjuvant chemotherapy, and discover the new biomarkers for targeted treatments in patients that are resistant to chemotherapy. We propose the following specific aims to address these challenges: (1) develop and validate a robust TNBC subtyping model; (2) identify TNBC subtype specific chemotherapy response gene signatures; (3) discover TNBC chemotherapy resistant biomarkers by integrative genomic approach.