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.

项目摘要/摘要 看似相同胰腺神经内分泌肿瘤（Pannets）的患者的临床结局通常有所不同 （转移，治疗反应，生存等的出现），表明这些损害实际上可能不同 标准诊断（例如组织学或基因组学）目前未知且无法区分的亚型。 pannets 仅通过传统的基因组或转录组方法，很难进一步对或风险分层进行分类。现在， 手术切除后的后续监测主要基于射线照相成像。没有治疗疗法 一旦发生转移，就不会有早期检测和转移风险的蛋白质标记物，也没有建立的分子平均值 监视。我们假设可以根据蛋白质组特征更好地对Pannet进行分类。我们建议 通过深度蛋白质组学分析发现新的基于蛋白质组的亚型，从而更好地定义了这些肿瘤。 在初步研究中，我们表明蛋白质组学分析可以区分和亚分类，并确定各种肿瘤 原发性或转移性病变的蛋白质特征。在此提案中，我们将首先阐明深蛋白质组 从G1到G3不等的组织学等级的分化良好。我们将研究两个临床结果的组织 队列，一个“低风险”组（肿瘤在手术后至少5年没有显示转移）和“高风险”组 （肿瘤随后发生了转移，但以前的诊断手段是无法区分的）。通过检查 原发性病变和转移性病变，我们将确定区分这两个结果和特征的蛋白质标志物 这将原发性与转移性病变区分开。肿瘤内和之间的空间异质性，肿瘤/新蛋白质蛋白 也将检查标记和磷光蛋白质组学信号通路。广泛的蛋白质组学 将进行蛋白质分析，以定义pannets内以及原发性和初级和之间的基于蛋白质组的亚型 转移性病变。我们将通过免疫组织化学验证独立队列中的标记面板，并与 临床结果。基于蛋白质面板的特征，我们将能够开发出预测的风险分层模型 Pannets的转移倾向。 我们的研究将对了解胰腺神经内分泌肿瘤有重大影响。成功的可能性 该建议很高，因为我们在初步研究中已经发现了新的癌症亚型。该项目将受益 从项目团队的强大科学和临床专业知识以及所处理的稀有胰腺肿瘤的大量 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者 目前看似相似的Pannets暴露了明显不同的转移性倾向，治疗反应和长度的长度 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来为未来的治疗开发提供信息 特定的蛋白质靶标。该项目中最有希望的候选蛋白标记物可能会迅速转化为 临床诊断和未来疗法，可直接受益。