Peptide Profiling Techniques to Detect Thyroid Carcinoma

检测甲状腺癌的肽分析技术

• 批准号: 6859786
• 负责人: PAUL TEMPST
• 金额: $ 14.4万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-02-09 至 2006-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6859786
• 关键词: automated data processing; biomarker; biotechnology; blood proteins; carcinoma; clinical research; computer system design /evaluation; diagnosis design /evaluation; diagnosis quality /standard; high throughput technology; human tissue; matrix assisted laser desorption ionization; neoplasm /cancer diagnosis; protein sequence; proteomics; robotics; thyroid neoplasm

DESCRIPTION (provided by applicant): The information required for adequate diagnosis, treatment and monitoring of cancers is so complex that a panel of measurements, used in sum, may provide the best answers. The concept is embodied in SELDI-TOF mass spectrometric (MS) peptide profiling, an emerging technique for serum based cancer detection. Even though SELDI has thus far only produced low complexity spectra, the patterns, when analyzed as groups, have the potential to create learning algorithms with diagnostic accuracies as good as or better than conventional biomarkers. We have developed a system to capture peptides on magnetic reversed-phase beads, followed by MALDI-TOF MS, to yield increasingly complex, yet very reproducible patterns. This has clear advantages, as more displayed peptides provide more opportunity to select unique patterns ('barcodes') for cancer subtypes and stages, and to predict and monitor clinical outcome. Extreme care has also been taken to standardize specimen collection, handling and storage to avoid the introduction of artifact. Pilot projects at MSKCC with a variety of malignancies suggest that peptide patterns thus obtained appear to hold information that may have direct clinical utility. The goals of this project are to (i) automate our prototype serum peptide profiling platform and implement machine learning methods that use the resulting peptide patterns ('barcodes') for sample classification [R21]; and (ii) to test the 'barcode diagnostic' model in a high-throughput setting, using well defined and carefully observed groups of thyroid carcinoma patients [R33]. R21 aim one is to automate serum sample processing and analysis; aim two is to automate all data processing, to examine pattern selection and sample class prediction methods, and to integrate all software platforms; aim three is to develop routine MALDI-TOF/TOF tandem MS sequencing of 'barcode' peptides. R33 aim one is to define reproducibility of serum patterns in patients with thyroid disease; aim two is to determine barcodes that can distinguish patients with thyroid cancer from those with benign thyroid nodules; aim three is to assess if serum peptidome barcodes can identify occult metastasis in a large group of thyroid cancer survivors.

描述（由申请人提供）：对癌症进行适当诊断，治疗和监测所需的信息是如此复杂，以至于总结使用的一组测量值可以提供最佳答案。该概念体现在Seldi-TOF质谱（MS）肽分析中，这是一种基于血清的癌症检测的新兴技术。尽管迄今为止，Seldi仅产生了较低的复杂性光谱，但在分析成组时，这些模式具有与常规生物标志物一样好或更好的诊断精度创建学习算法的潜力。我们已经开发了一个系统，以捕获磁反相珠的肽，其次是MALDI-TOF MS，以产生越来越复杂但非常可重现的模式。这具有明显的优势，因为更多显示的肽为癌症亚型和阶段选择独特的模式（“条形码”）提供了更多机会，并预测和监测临床结果。还要格外小心以标准化标本收集，处理和存储，以避免引入工件。 MSKCC的试点项目具有多种恶性肿瘤，表明因此获得的肽模式似乎拥有可能具有直接临床实用性的信息。该项目的目标是（i）自动化我们的原型血清肽分析平台，并实现使用所得肽模式（'Barcodes'）进行样本分类[R21]的机器学习方法； （ii）使用明确和精心观察的甲状腺癌患者组测试“条形码诊断”模型，以在高通量设置中测试[R33]。 R21目标是自动化血清样品处理和分析。目标二是自动化所有数据处理，检查模式选择和示例类预测方法，并集成所有软件平台；目的三是开发“条形码”肽的常规MALDI-TOF/TOF TANDEM MS测序。 R33目的是定义甲状腺疾病患者血清模式的可重复性。目标二是确定可以将甲状腺癌患者与甲状腺良性结节患者区分开的条形码；目的三是评估血清肽组条形码是否可以鉴定一大批甲状腺癌幸存者中的隐匿转移。