Proteomic Characterization of Pancreatic Neuroendocrine Tumors and Metastatic Progression

项目总结/摘要 患有看似相同的胰腺神经内分泌肿瘤（PanNETs）的患者的临床结局往往不同 （转移的出现、治疗反应、生存期等），这表明这些恶性肿瘤实际上可能是不同的 目前未知且无法通过标准诊断区分的亚型（例如，组织学或基因组学）。PanNETs 已经难以单独通过传统的基因组学或转录组学方法进一步分类或风险分层。目前， 手术切除后的跟踪监测主要仅基于放射成像。没有治愈性疗法 一旦发生转移，没有早期检测和转移风险的蛋白标志物，也没有既定的分子手段 监视我们假设PanNET可以更好地根据蛋白质组签名进行分类。我们建议 通过深入的蛋白质组学分析发现新的基于蛋白质组的亚型，更好地定义这些肿瘤。 在初步研究中，我们已经表明，蛋白质组学分析可以区分和细分各种肿瘤，并确定 原发性或转移性病变的蛋白质特征。在这个建议中，我们将首先阐明深层蛋白质组 组织学分级为G1至G3的分化良好的PanNET。我们将研究两种临床结果的组织 队列，“低风险”组（手术后至少5年肿瘤未显示转移）和“高风险”组 （肿瘤发展为随后的转移，但通过目前的诊断手段无法区分）。通过检查 无论是原发性还是转移性病灶，我们都将确定区分这两种结果和特征的蛋白质标记物， 区分原发性和转移性病变肿瘤内和肿瘤间的空间异质性，新肽/新蛋白 标记物和磷酸蛋白质组信号通路也将被检查。广泛的蛋白质组学和整合 将进行蛋白质基因组学分析，以确定PanNET内以及原发和原发之间基于蛋白质组的亚型。 转移性病变我们将通过免疫组化在独立队列中验证标志物组，并与 临床结果。基于蛋白质组特征，我们将能够开发风险分层模型， PanNET的转移倾向。 我们的研究将对认识胰腺神经内分泌肿瘤产生重要影响。的成功的可能性 这个建议是很高的，因为我们在初步研究中已经发现了新的癌症亚型。该项目将有利于 由于项目团队强大的科学和临床专业知识以及治疗的大量罕见胰腺肿瘤， 在MSKCC。我们设想的基于蛋白质组的风险分层可能解释了为什么患有糖尿病的患者 目前看起来相似的PanNET表现出显著不同的转移倾向、治疗反应和 生存新的蛋白质组学亚型可以通过提供亚型特异性和转移- 特定的蛋白质目标。来自该项目的最有希望的候选蛋白质标记物可以被快速翻译成 临床诊断和未来的治疗方法，直接造福癌症患者。

Project Summary/Abstract Patients with seemingly identical pancreatic neuroendocrine tumors (PanNETs) often differ in their clinical outcomes (emergence of metastases, treatment response, survival, etc.), suggesting that these malignancies may in fact be of different subtypes that are currently unknown and indistinguishable by standard diagnostics (e.g., histology or genomics). PanNETs have been difficult to further classify or risk-stratify by traditional genomic or transcriptomic methods alone. Currently, follow-up monitoring after surgical resection is mostly based on radiographic imaging alone. There are no curative therapies once metastases occur, no protein markers of early detection and metastatic risk, and no established molecular means of surveillance. We hypothesize that PanNETs can be better classified according to proteome signatures. We propose to uncover new proteome-based subtypes by deep proteomic analysis that better define these tumors. In preliminary studies, we have shown that proteomic profiling can distinguish and sub-classify various tumors and identify protein signatures characteristic of primary or metastatic lesions. In this proposal, we will first elucidate the deep proteomes of well differentiated PanNETs of histologic grades ranging from G1 to G3. We will study tissues from two clinical outcome cohorts, a “low risk” group (tumors did not show metastases for at least 5 years after surgery) and a “high risk” group (tumors developed subsequent metastases, but are otherwise indistinguishable by current diagnostic means). By examining both primary and metastatic lesions, we will identify protein markers that differentiate these two outcomes and signatures that distinguish primary from metastatic lesions. Spatial heterogeneity within and between tumors, neopeptide/neoprotein markers, and phosphoproteomic signaling pathways will also be examined. Extensive proteomic and integrated proteogenomic analyses will be performed to define proteome-based subtypes within PanNETs and between primary and metastatic lesions. We will validate marker panels in independent cohorts by immunohistochemistry and correlate with clinical outcomes. Based on the protein-panel signatures, we will be able to develop risk stratification models that predict metastatic propensity of PanNETs. Our study will have significant impact on understanding pancreatic neuroendocrine tumors. The likelihood of success of this proposal is high, as we have already discovered new cancer subtypes in our preliminary studies. The project will benefit from the strong scientific and clinical expertise of the project team and the high volume of rare pancreatic neoplasms treated at MSKCC. Our envisioned proteome-based risk stratification may explain the clinical conundrum of why patients with currently seemingly similar PanNETs exhibit strikingly different metastatic propensity, treatment response, and length of survival. New proteomic subtyping may inform future therapy development by providing subtype-specific and metastasis- specific protein targets. The most promising candidate protein markers from this project may be rapidly translated into clinical diagnostics and future therapies for the direct benefit of cancer patients.