Petricoin et al. have pioneered the use of mass spectrometry as a diagnostic tool (30). They suggested that this approach represents a paradigm shift in cancer diagnostics, based on complex mass spectrometric differences between proteomic patterns in serum between patients with or without cancer identified by bioinformatics. Their premise is that no matter what the nature of these molecules are, their potential to discriminate between these two conditions should be further exploited. The central hypothesis of this approach is as follows: protein or protein fragments produced by cancer cells or their microenvironment may eventually enter the general circulation. Then, the concentration (abundance) of these proteins/fragments could be analyzed by mass spectrometry and used for diagnostic purposes, in combination with a mathematical algorithm (30).
The vast majority of the currently available data have been produced by using the SELDI-TOF technology, marketed by Ciphergen Biosystems (Fremont, CA). Ciphergen claims that over 200 papers have already been published with this technology. The types of cancers that have been examined include ovarian, prostate, breast, bladder, renal, and others, and the biological fluids analyzed include serum, urine, cerebrospinal fluid, nipple aspirate fluid, etc. The apparent successes with this technology have been recently reviewed by many investigators (2–12). In general, it has been suggested that this technology can achieve much higher diagnostic sensitivity and specificity (approaching 100%) in comparison to the classical cancer biomarkers (31). The technology’s potential has been expanded to other diseases such as Alzheimer’s disease, Creutzfeldt-Jakob disease, renal allograft rejection, etc. (32–34).
The analytical procedure with this technology involves a few common steps. The biological fluid of interest is first interacted with a protein chip that incorporates some kind of an affinity separation between "noninformative" and "informative" proteins. After washing, the immobilized (and fortunately mostly informative) proteins can be studied by using SELDI-TOF mass spectrometry. Two types of data have been reported in the literature: 1) discriminating peaks of unknown identity that are different in amplitude (increased or decreased) between normal individuals and patients with cancer; and 2) data in which at least some of these peaks have been positively identified (see below). Computer algorithms have been used to analyze these multidimensional data to demonstrate that a pattern consisting of several peaks (from tens to thousands) is sufficiently different between the two groups of subjects. In this review, I will not comment much on peaks that have not been positively identified, because nothing is known about them, except that their heights go up or down in the disease state. I will use the few positively identified molecules to draw comparisons between them and the classical cancer biomarkers.
The extraordinary data presented in the literature with this new approach were welcomed by scientists, the press, the public, and even by politicians (31, 35). This technology is now seen as the most promising way of diagnosing early cancer (35). Clinical trials are now underway and will reveal, in a blinded fashion, if these data can be reproduced and if they are robust enough for clinical use. In the following paragraphs, I will concentrate on issues that have not been adequately addressed and raise concerns that at least some of this data may not be accurate or expected on theoretical grounds.
The use of SELDI-TOF technology as a cancer biomarker discovery tool (as opposed to a cancer diagnostic tool) is straightforward. The discriminatory peaks, if positively identified, may represent molecules that could be measured with simpler and cheaper techniques for the purpose of diagnosing cancer. For example, some investigators postulate that such molecules may be routinely quantified by using enzyme-linked immunosorbent assay (ELISA) technologies. In practice, very few, if any, of the SELDI-TOF identified novel candidate biomarkers have been validated by using alternative technologies.