Novel Machine Learning Methods for Analysis of MALDI-TOF Mass Spectrometry Data

描述(由申请人提供)： 肝细胞癌是一种世界范围内常见的癌症，每年新增病例多达50万例。从1981年到1998年，肝细胞癌患者的5年生存率仅从2%上升到5%。这种低存活率部分与晚期肝癌的诊断有关，在晚期，缺乏有效的治疗方法。及早发现肝细胞癌可提高患者存活率。肝硬变患者通常是患上肝细胞癌的人。因此，监测肝硬变患者可能会降低癌症相关死亡率。目前可用的工具灵敏度和特异度差，阻碍了肝细胞癌监测的广泛实施。因此，需要额外的血清标志物来提供更高的敏感性和特异性，以提高早期肝细胞癌的检出率。该合作项目的目标是确定一组用于肝细胞癌早期诊断的血清生物标志物。长期目标是寻找和验证有助于在肝硬变高危人群中识别处于可治疗阶段的肝细胞癌的标志物。该项目将通过基质辅助激光解吸/电离飞行时间(MALDI-TOF)质谱仪(MS)对低分子(LMW)浓缩血清进行鉴定，从而开发创新的质谱学数据预处理和生物标记物选择方法，用于识别特定于肝癌的候选生物标记物。该项目的具体目标如下：目标1：开发改进的MALDI-TOF质谱图数据预处理算法，包括孤立点筛选、入库、平滑、基线校正、归一化、峰检测和峰校正。所提出的算法将使我们能够减少标准血清复制光谱中的Run-to-Run变异性，并提高区分肝细胞癌患者和肝硬变患者或健康人的预测准确性。目的：将粒子群优化(PSO)和支持向量机(SVMs)这两种流行的机器学习方法相结合，提出一种优于现有生物标志物选择方法的新算法。所提出的算法将被用于从经过预处理的MALDI-TOF光谱中识别出肝细胞癌特异性标志物。为了避免混淆的影响，如果峰值与病毒感染或协变量相关，如年龄、性别、吸烟状况、饮酒状况和居住地(城市或农村)，则在选择生物标记物之前将其移除。从剩余的峰值中，将识别出一小组候选生物标记物，它们可以准确地区分肝细胞癌患者和肝硬变患者。该算法识别具有高灵敏度和高特异度的一小部分标记物的能力对于建立临床试验至关重要。此外，该算法将识别区分不同对(正常与肝硬变，正常与肝细胞癌，肝硬变与早期肝细胞癌，以及肝硬变与晚期肝细胞癌)的标志物。这将使我们能够分离出肝细胞癌特异性标志物，并确定疾病进展标志物。此外，将识别由所选择的候选生物标记物代表的多肽。最后，将该算法的性能与现有方法进行了比较。

DESCRIPTION (provided by applicant): Hepatocellular carcinoma (HCC) is a common cancer worldwide with as many as 500,000 new cases each year. Between 1981 to 1998, the 5-year patient survival rate with HCC only rose from 2% to 5%. This poor survival rate is in part related to the diagnosis of HCC at advanced stages, where effective therapies are lacking. Early detection of HCC improves patient survival. Patients with cirrhosis are typically the ones to develop HCC. Hence, monitoring cirrhotic patients can potentially decrease the cancer-related mortality rate. The poor sensitivity and specificity of currently available tools has prevented widespread implementation of HCC surveillance. Therefore, additional serum markers that provide higher sensitivity and specificity are needed to improve the detection rate of early HCC. The goal of this collaborative project is to identify a panel of serum biomarkers for early diagnosis of HCC. The long-term goal is to find and validate markers that would help identify HCC at a treatable stage in high-risk population of cirrhotic patients. This project will lead to the development of innovative mass spectral data preprocessing and biomarker selection methods that for the identification of candidate biomarkers specific to HCC by using matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry (MS) of low-molecular-weight (LMW) enriched sera. The specific aims of the project are the following: Aim 1: To develop algorithms for improved MALDI-TOF mass spectral data preprocessing including outlier screening, binning, smoothing, baseline correction, normalization, peak detection, and peak calibration. The proposed algorithms will enable us to reduce run-to-run variability in replicate spectra of a standard serum and to enhance the prediction accuracy in distinguishing HCC patients from cirrhotic patients or healthy individuals. Aim 2: To develop a novel algorithm that is superior to currently used biomarker selection methods by combining two popular machine learning methods, particle swarm optimization (PSO) and support vector machines (SVMs). The proposed algorithm will be used to identify HCC-specific markers from the preprocessed MALDI-TOF spectra. To avoid confounding effects, peaks will be removed prior to biomarker selection if they are associated with viral infection or covariates such as age, gender, smoking status, drinking status, and residency (urban or rural). From the remaining peaks, a small set of candidate biomarkers that accurately distinguishes HCC patients from cirrhotic patients will be identified. The capability of the algorithm to identify a small set of markers with high sensitivity and specificity is critical for establishment of clinical tests. Additionally, the algorithm will identify markers that distinguish various pairs (normal vs. cirrhosis, normal vs. HCC, cirrhosis vs. early-stage HCC, and cirrhosis vs. late-stage HCC). This will enable us to isolate HCC- specific markers and identify disease progression markers. Furthermore, the peptides represented by the selected candidate biomarkers will be identified. Finally, the performance of the algorithm will be compared with existing methods.