Proteomic Characterization of Pancreatic Neuroendocrine Tumors and Metastatic Progression

胰腺神经内分泌肿瘤的蛋白质组学特征和转移进展

• 批准号: 10290014
• 负责人: Michael H. A. Roehrl
• 金额: $ 20.69万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-20 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10290014
• 关键词: Behavior; Biology; Cancer Patient; Carcinoma; Caring; Characteristics; Classification; Clinical; Development; Diagnosis; Diagnostic; Diagnostic radiologic examination; Differentiation Antigens; Disease Outcome; Evolution; Excision; Exhibits; Future; Genomics; Goals; Grant; Heterogeneity; Histology; Histopathologic Grade; Image; Immunohistochemistry; Islet Cell Tumor; Knowledge; Length; Malignant Neoplasms; Medical; Memorial Sloan-Kettering Cancer Center; Metastatic Neoplasm to the Liver; Metastatic to; Methods; Modeling; Molecular; Monitor; Neoplasm Metastasis; Neuroendocrine Tumors; Neurosecretory Systems; Nonmetastatic; Operative Surgical Procedures; Outcome; Pathway interactions; Patients; Primary Lesion; Primary Neoplasm; Proteins; Proteome; Proteomics; Recurrence; Resistance; Risk; Signal Pathway; Therapeutic; Tissues; Translating; Treatment outcome; Tumor Subtype; Validation; Work; base; biomarker panel; cancer diagnosis; cancer subtypes; clinical diagnostics; clinically relevant; cohort; curative treatments; differential expression; drug development; early detection biomarkers; follow-up; high risk; high risk population; innovation; insight; metastatic process; novel; pancreatic neoplasm; personalized management; personalized medicine; phosphoproteomics; predictive marker; predictive modeling; protein biomarkers; proteogenomics; risk stratification; sarcoma; success; therapeutic target; therapy development; transcriptomics; treatment response; tumor

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.

项目概要/摘要 看似相同的胰腺神经内分泌肿瘤 (PanNET) 患者的临床结果往往不同 （转移的出现、治疗反应、生存等），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（例如组织学或基因组学）区分的亚型。 目前，仅通过传统的基因组或转录组方法很难进一步分类或风险分层。 手术切除后的后续监测主要基于放射线成像，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质标记，也没有既定的分子手段 我们致力于根据蛋白质组特征对 PanNET 进行更好的分类。 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，从而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识别 原发性或转移性病变的蛋白质特征在本提案中，我们将首先阐明深层蛋白质组。 我们将研究两种临床结果的组织。 队列，“低风险”组（肿瘤在手术后至少 5 年内没有出现转移）和“高风险”组 （肿瘤随后发生转移，但通过当前的诊断手段无法区分）。 无论是原发性病变还是转移性病变，我们将识别区分这两种结果和特征的蛋白质标记 区分肿瘤内和肿瘤之间的空间异质性、新肽/新蛋白。 还将检查广泛的蛋白质组和整合的标记物和磷酸化蛋白质组信号通路。 将进行蛋白质组学分析，以定义 PanNET 内以及初级和二级之间基于蛋白质组的亚型。 我们将通过免疫组织化学验证独立队列中的标记物组并与相关联。 根据蛋白质组特征，我们将能够开发预测的风险分层模型。 PanNET 的转移倾向。 我们的研究将对了解胰腺神经内分泌肿瘤的成功可能性产生重大影响。 这个提议很高，因为我们已经在初步研究中发现了新的癌症亚型，该项目将从中受益。 得益于项目团队强大的科学和临床专业知识以及大量治疗的罕见胰腺肿瘤 在 MSKCC，我们设想的基于蛋白质组的风险分层可以解释为什么患者患有以下临床难题： 目前看似相似的 PanNET 表现出截然不同的转移倾向、治疗反应和持续时间 新的蛋白质组亚型可以通过提供亚型特异性和转移来指导未来的治疗开发。 该项目中最有希望的候选蛋白质标记物可以快速转化为特定的蛋白质靶标。 临床诊断和未来治疗，使癌症患者直接受益。