A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection

Abstract

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of 'signature' protein profiles specific to each pathologic state (e.g. normal vs. cancer) or differential profiles between experimental conditions (e.g. treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data-analytic strategy for discovering protein biomarkers based on such high-dimensional mass spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data-analytic strategy takes properties of the SELDI mass spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After this pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.

Discovery of Protein Biomarkers for Renal Diseases

“…Third, it may be difficult to obtain appropriate clinical tissue samples that match the clinical question, especially for early disease biomarkers. Newer proteomic techniques (serum purification followed by two-dimensional gels [2-D], SELDI fingerprinting) bypass the tissue step and allow for a direct search for serum biomarkers (Figure 3, c and d) (26,30). However, it is still difficult to identify an individual serum biomarkers.…”

HingeBoost: ROC-Based Boost for Classification and Variable Selection

“…For a binary classification, early and late stage of prostate cancer are grouped in one cancer class (CAN), and we investigate whether the MS data can distinguish noncancer (HM or BPH) from cancer (NONCAN-CAN), healthy men from cancer, BPH from cancer, and healthy men from BPH. These are the primary issues investigated in the literature Qu et al, 2002;Yasui et al, 2003). After the filtering step, the remaining peaks are between 200 to 300 for the above binary classification problems.…”

“…One of the big challenges for analyzing the MS data is the high-dimension of m/z measurements and small number of subjects, which typically requires selecting a small number of m/z measurements to predict disease status. Discovering proteomics prostate cancer biomarker is an active research topic, and advances are summarized in a recent review in Goo and Goodlett (2010), with bioinformatics methods applications Qu et al, 2002;Yasui et al, 2003). Prostate diagnostic test can generate two types of error: false positive (classifying a healthy subject to disease) and false negative (classifying a diseased subject to health).…”

Analysis of Mass Spectrometry Data Using Bayesian Wavelet-Based Functional Mixed Models

“…It is common to use a two-step approach to analyze mass spectrometry data , Yasui, et al 2003, Morris, et al 2005c. First, some type of feature detection algorithm is applied to identify peaks in the spectra, then a quantification for each peak is obtained for each spectrum, e.g.…”