High-resolution serum proteomic features for ovarian cancer detection.

Abstract

Serum proteomic pattern diagnostics is an emerging paradigm employing low-resolution mass spectrometry (MS) to generate a set of biomarker classifiers. In the present study, we utilized a wellcontrolled ovarian cancer serum study set to compare the sensitivity and specificity of serum proteomic diagnostic patterns acquired using a high-resolution versus a low-resolution MS platform. In blinded testing sets, the high-resolution mass spectral data contained multiple diagnostic signatures that were superior to the low-resolution spectra in terms of sensitivity and specificity ðP < 0:00001Þ throughout the range of modeling conditions. Four mass spectral feature set patterns acquired from data obtained exclusively with the high-resolution mass spectrometer were 100% specific and sensitive in their diagnosis of serum samples as being acquired from either unaffected patients or those suffering from ovarian cancer. Important to the future of proteomic pattern diagnostics is the ability to recognize inferior spectra statistically, so that those resulting from a specific process error are recognized prior to their potentially incorrect (and damaging) diagnosis. To meet this need, we have developed a series of quality-assurance and in-process control procedures to (a) globally evaluate sources of sample variability, (b) identify outlying mass spectra, and (c) develop quality-control release specifications. From these quality-assurance and control (QA/QC) specifications, we identified 32 mass spectra out of the total 248 that showed statistically significant differences from the norm. Hence, 216 of the initial 248 high-resolution mass spectra were determined to be of high quality and were remodeled by pattern-recognition analysis. Again, we obtained four mass spectral feature set patterns that also exhibited 100% sensitivity and specificity in blinded validation tests (68/68 cancer: including 18/18 stage I, and 43/43 healthy). We conclude that (a) the use of high-resolution MS yields superior classification patterns as compared with those obtained with lower resolution instrumentation; (b) although the process error that we discovered did not have a deleterious impact on the present results obtained from proteomic pattern analysis, the major source of spectral variability emanated from mass spectral acquisition, and not bias at the clinical collection site; (c) this variability can be reduced and monitored through the use of QA/QC statistical procedures; (d) multiple and distinct proteomic patterns, comprising low molecular weight biomarkers, detected by high-resolution MS achieve accuracies surpassing individual biomarkers, warranting validation in a large clinical study.

A serum proteomic approach to gauging the state of remission in Wegener's granulomatosis

“…Such strategies have detected significant differences between the serum proteomic profiles of cancer patients and healthy subjects in the low molecular weight range (i.e., Ͻ20,000 daltons). The evolution of high-resolution mass spectrometers now permits even greater precision in analyte measurement (9,10). Because systemic vasculitis is a condition in which the target organ (the blood vessel) is in intimate contact with serum, this group of inflammatory vascular disorders is a logical set of diseases in which to test this emerging diagnostic approach.…”

Mining whole-sample mass spectrometry proteomics data for biomarkers – An overview

“…However one of the most striking aspects of the MALDI plot is the apparent variation from day to day in the course of the experiment, despite the efforts that were made to maintain a consistent protocol and calibration. This was also borne out by an analysis of the twelve quality control spectra This kind of daily drift has been observed before in [Conrads et al, 2004]. Evidently we risk introducing bias into our study if we run our samples in a non-random order (by group), which may in turn lead to spurious results.…”

“…Frequently this will be an indicator of the presence or absence of a disease or infection (eg. much of the literature to date has focused on different types of cancer, see [Li et al, 2005] or [Conrads et al, 2004]). In principle, however, the difference might also be hereditary, dietary, or deliberately induced (an example of an application based on the latter might be a 'doping' test to identify athletes using artificial supplements).…”

Novel technologies for the discovery and quantitation of biomarkers of toxicity

“…SELDI-TOF-MS combines data on time of flight (TOF) in a constant electric field with mass spectrometry to sequentially determine the charge and mass of proteins in a single sample (Issaq et al 2003). An algorithm can then be used to identify trends in mass/charge ratio peaks across a spectrum of proteins, and use the resultant expression profiles as biomarkers for specific disease types, as has been done for ovarian (Conrads et al 2004), prostate (Li et al 2004), breast (Vlahou et al 2003), lung (Xiao et al 2003;Zhukov et al 2003), pancreas (Koopman et al 1999;Rosty et al 2002), and liver cancers (Melle et al 2004;Poon et al 2003). SELDI-TOF-MS is especially attractive for disease biomarker identification because of its quick assay time and low required sample volumes.…”