Diagnostic Proteomics: Back to Basics?

The recent emergence of methods for rapidly profiling large numbers of protein markers by use of mass spectrometry (MS) has raised hopes for the rapid identification of novel cancer biomarkers (1 ). In particular, MSbased assays using surface-enhanced laser desorption/ ionization (SELDI) have been used to compare serum proteomic patterns from healthy and diseased individuals in hopes of finding diagnostic signatures that can be adapted for use in the clinical laboratory. Early investigations in ovarian cancer patients suggested that panels of anonymous markers might substantially advance our current diagnostic capabilities (2, 3), although subsequent criticism has been directed at the primary data from these studies (4, 5) as well as at the SELDI time-of-flight (TOF) profiling approach itself (6, 7) [for summaries of this ongoing controversy, see Refs. (8, 9)]. Specific questions have been raised regarding the reproducibility of SELDITOF spectra, possible changes in protocols or inadequate calibration, and the ability of SELDI to detect low-abundance tumor markers. Additionally, observations that spectra can vary based on analytical factors such as the time of processing have been noted during large profiling experiments (10 ). Several of these issues have recently been addressed through the combined efforts of investigators in the National Cancer Institute–sponsored Early Detection Research Network (EDRN). This group has undertaken a systematic assessment of the interlaboratory reproducibility of SELDI-TOF measurements along with their potential applicability to prostate cancer diagnosis. Their initial results demonstrated that relevant portions of SELDI-TOF profiles can be measured reproducibly and used to distinguish a reference set of prostate cancer cases from controls (11 ). This study was an encouraging step toward defining the analytical reproducibility of serum proteome profiling, although it highlighted the need for rigorous calibration of instruments and adherence to standardized technical procedures. Although promising, the efforts to date have focused on parallel profiling of serum aliquots without directly addressing the importance of variation attributable to differences in sample collection and other sources of preanalytical bias that can be expected in routine clinical practice. A more comprehensive list of issues that must be addressed to understand the effects of preanalytical, analytical, and postanalytical factors on SELDI-TOF and matrix-assisted laser desorption/ionization (MALDI)-TOF profiles has recently been proposed (12 ). A valuable example of the effects of preanalytical and analytical variability in SELDI-TOF profiling is reported in this issue of Clinical Chemistry. Karsan et al. (13 ) describe serum profiles obtained from patients undergoing core needle biopsies of breast lumps. The primary question they wished to address is whether a set of mass spectral peaks can distinguish women with breast cancer from those with benign lesions. Unfortunately, although such a diagnostic signature would have represented a valuable step forward, they were unable to reliably separate these patient groups by serum profiling. In contrast, however, they were able to identify robust SELDI-TOF patterns that grouped samples based on the clinic site from which the samples were obtained and on the date of processing. The ease with which these preanalytical and analytical biases could be detected, coupled with a reported inability to replicate previously published results, led the authors to speculate that similar effects may have tainted previous SELDI-TOF–based marker studies. Their data provide evidence that preanalytical and analytical variation can affect profiled markers, and this result must, at a minimum, raise awareness of the strong potential for bias in serum profiling experiments that are not carefully controlled. It is not entirely surprising—or necessarily problematic—that such bias can be detected within the serum proteome. Given the large number of proteins surveyed, it is reasonable to suspect that a subset of these markers might be susceptible to effects of the method of collection, storage conditions, or time of processing. The data from Karsan et al. (13 ) demonstrate not only that proteomic profiles are susceptible to these types of influences but also that the effects are sufficiently reproducible to form the basis for a reliable classification scheme. These results confirm—if it was not already apparent—that substantial investigation is warranted to understand the susceptibility of serum proteomic profiling methods to preanalytical and analytical variation. Questions surrounding the nature of this susceptibility are particularly pressing because at least 2 models (with very different implications) can account for the reported results. In the first model, a relatively small number of proteins or posttranslational modifications may be susceptible to preanalytical factors such as those associated in this study (13 ) with different clinic sites. As long as differences in these “badly behaved” peaks are of sufficient magnitude and reproducibility, they will reliably distinguish samples on the basis of, for example, their clinic of origin. In fact, Karsan et al. (13 ) identify 2 peaks (m/z 2992 and 5643) with just this property. Additionally, such sets of peaks may well change more as a result of preanalytical factors than other, comparable groups of peaks change as a result of disease state; global clustering approaches would therefore group samples on the basis of this preanalytical variation, and any inadvertent, systematic correlation between preanalytic factors and disease state would likely lead to the spurious incorporation of bias-related peaks into the resulting diagnostic signature. As long as these effects are primarily limited to a specified set of peaks, however, eliminating this set from consideration when searching for diagnostic patterns might reduce or even eliminate the problem. We can envision a systematic study, similar to that proposed by Hortin (12 ), that would examine a range of typical clinical