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）：充分诊断、治疗和监测癌症所需的信息非常复杂，综合使用一组测量结果可以提供最佳答案。这一概念体现在 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 SELDI 迄今为止仅产生了低复杂性光谱，但当将这些模式作为组进行分析时，有可能创建诊断精度与传统生物标志物一样好或更好的学习算法。我们开发了一种系统，用于在反相磁珠上捕获肽，然后进行 MALDI-TOF MS，以产生日益复杂但重现性非常好的模式。这具有明显的优势，因为更多展示的肽提供了更多机会来选择癌症亚型和阶段的独特模式（“条形码”），并预测和监测临床结果。我们还非常谨慎地标准化标本采集、处理和储存，以避免引入人工制品。 MSKCC 针对多种恶性肿瘤的试点项目表明，由此获得的肽模式似乎包含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施机器学习方法，使用生成的肽模式（“条形码”）进行样本分类 [R21]； (ii) 使用明确且仔细观察的甲状腺癌患者组在高通量环境中测试“条形码诊断”模型[R33]。 R21的目标一是自动化血清样本处理和分析；目标二是自动化所有数据处理，检查模式选择和样本类预测方法，并集成所有软件平台；目标三是开发“条形码”肽的常规 MALDI-TOF/TOF 串联 MS 测序。 R33 的目标一是确定甲状腺疾病患者血清模式的可重复性；目标二是确定能够区分甲状腺癌患者和良性甲状腺结节患者的条形码；目标三是评估血清肽组条形码是否可以识别大量甲状腺癌幸存者的隐匿性转移。