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

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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.