Peptide Profiling Techniques to Detect Thyroid Carcinoma

描述（由申请人提供）：癌症的充分诊断、治疗和监测所需的信息非常复杂，因此一组测量结果（汇总使用）可能提供最佳答案。这一概念体现在SELDI-TOF质谱（MS）肽谱分析中，这是一种用于基于血清的癌症检测的新兴技术。尽管SELDI到目前为止只产生了低复杂度的光谱，但当作为组进行分析时，这些模式有可能创建诊断准确性与传统生物标志物一样好或更好的学习算法。我们已经开发了一个系统，以捕获磁性反相珠上的肽，然后通过MALDI-TOF MS，产生越来越复杂，但非常可重复的模式。这具有明显的优势，因为展示的肽越多，为选择癌症亚型和阶段的独特模式（“条形码”）以及预测和监测临床结果提供了更多的机会。还特别注意使标本采集、处理和储存标准化，以避免引入伪影。MSKCC的各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）使我们的原型血清肽分析平台自动化，并实施机器学习方法，使用所得肽模式（“条形码”）进行样本分类[R21];以及（ii）在高通量环境中测试“条形码诊断”模型，使用明确定义和仔细观察的甲状腺癌患者组[R33]。R21的目标一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，检查模式选择和样品类别预测方法，并集成所有软件平台;目标三是开发常规MALDI-TOF/TOF串联MS测序的“条形码”肽。R33目的一是确定甲状腺疾病患者血清模式的可重复性;目的二是确定可以区分甲状腺癌患者和良性甲状腺结节患者的条形码;目的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。

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.