A data review and re-assessment of ovarian cancer serum proteomic profiling

By James Sorace, M. Zhan. BMC Bioinformatics, 2003

351 citations recorded by the source corpus.

BackgroundThe early detection of ovarian cancer has the potential to dramatically reduce mortality. Recently, the use of mass spectrometry to develop profiles of patient serum proteins, combined with advanced data mining algorithms has been reported as a promising method to achieve this goal. In this report, we analyze the Ovarian Dataset 8-7-02 downloaded from the Clinical Proteomics Program Databank website, using nonparametric statistics and stepwise discriminant analysis to develop rules to diagnose patients, as well as to understand general patterns in the data that may guide future research.ResultsThe mass spectrometry serum profiles derived from cancer and controls exhibited numerous statistical differences. For example, use of the Wilcoxon test in comparing the intensity at each of the 15,154 mass to charge (M/Z) values between the cancer and controls, resulted in the detection of 3,591 M/Z values whose intensities differed by a p-value of 10-6 or less. The region containing the M/Z values of greatest statistical difference between cancer and controls occurred at M/Z values less than 500. For example the M/Z values of 2.7921478 and 245.53704 could be used to significantly separate the cancer from control groups. Three other sets of M/Z values were developed using a training set that could distinguish between cancer and control subjects in a test set with 100% sensitivity and specificity.ConclusionThe ability to discriminate between cancer and control subjects based on the M/Z values of 2.7921478 and 245.53704 reveals the existence of a significant non-biologic experimental bias between these two groups. This bias may invalidate attempts to use this dataset to find patterns of reproducible diagnostic value. To minimize false discovery, results using mass spectrometry and data mining algorithms should be carefully reviewed and benchmarked with routine statistical methods.

Subfields: Molecular Biology, Oncology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/90c9beb2db1b8884198d044f83648057ce1e7319

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Citations in the corpus

Multiplexed protein measurement: technologies and applications of protein and antibody arrays (2006, neutral): “Rigorous attention to experimental technique and bioinformatic analysis is crucial for succes”
Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry (2005, background): “There is speculation that improper experimental methodology is to blame, and that the patterns observed are not in fact related to disease state, but instead to extraneous factors such as sample collection, preparation, or storage (9, 11, 22, 36, 53).”
Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments (2004, background): “The fact that there is structure in the noise has recently been discussed in depth by Sorace and Zhan (2003).”
Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum (2005, methodology): “It has been shown that the use of inadequate or ineffective methods in the first step may make it difficult to extract meaningful biological information from these data (Sorace and Zhan, 2003; Baggerly et al., 2003, 2004).”
Application of the GA/KNN method to SELDI proteomics data (2004, background): “Sorace and Zhan (2003) drew attention to the same issue when analyzing a newer ovarian cancer dataset (dated July 8, 2002) from the same source as ours.”
Peak selection from MALDI-TOF mass spectra using ant colony optimization (2007, neutral): “Sorace and Zhan (2003) reported the possibility of experimental bias in their assessment of surface enhanced laser desorption/ionization time-of-flight (SELDI-TOF) analysis of ovarian cancer.”