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Home » Biology Articles » Methods & Techniques » Mass Spectrometry as a Diagnostic and a Cancer Biomarker Discovery Tool » Discrepancies between research groups

Discrepancies between research groups
- Mass Spectrometry as a Diagnostic and a Cancer Biomarker Discovery Tool


As it currently stands, SELDI-TOF technology requires pretreatment of a small amount of serum with SELDI protein chips. These protein chips have either 8 or 16 spots containing a specific chromatographic surface. Currently available surfaces are based on either hydrophobic, ion-exchange, metal affinity, or normal phase chromatography. It is also possible, but not widely utilized at the moment, to immobilize more specific reagents such as antibodies, receptors, DNA, etc. For diagnostics, one would expect that the discriminatory peaks for one cancer type can be identified by using preferentially one of these surfaces. After surveying the literature for prostate and ovarian cancer diagnostics, I identified five papers that used SELDI-TOF technology for prostate cancer (3943) and two papers for ovarian cancer (30, 44). The different groups found that different proteomic chips may be optimal for disease diagnosis. Metal affinity (IMAC-Cu), hydrophobic (C16 or H4), or weak cation exchange (WCX2) chips were used for prostate cancer. In two of the studies (40, 41), the same mass spectroscopic data were used by the same group but different bioinformatic tools were employed to analyze them. A summary of the prostate and ovarian cancer studies are presented in Tables II and III. The following points are relevant. The distinguishing peaks between cancer and non-cancer patients are very different between the various groups. In fact, none of the distinguishing peaks between the four different research groups for prostate cancer agree with each other. The only agreements were two peaks for distinguishing non-cancer versus cancer from the same group of investigators and the same datasets (40, 41). A different bioinformatic analysis revealed other discriminatory peaks between the two studies from the same group (41). Similar discrepancies are seen with ovarian cancer (Table III). How could these discrepancies be explained? One hypothesis is that serum may indeed contain a huge number of discriminatory molecules between cancer and non-cancer patients and that the chance of two groups finding the same discriminatory peaks is very small. Another explanation may be methodological differences in which different chips were used to immobilize the candidate discriminatory peaks. This is not a likely hypothesis because Banez et al., Adam et al., and Qu et al. (4042) used the same protein chip (IMAC-Cu), yet they came up with different discriminatory peaks (Table II). In my opinion, it will be highly unlikely that a small, localized tumor and its microenvironment will generate such diverse populations of informative peptides/proteins in the circulation. Another important difference, displayed in Table II, refers to the diagnostic sensitivity and specificity. Contrast to Qu et al., Petricoin et al., and Adam et al., Banez et al. reported that at least with the IMAC-Cu proteomic chips, their sensitivity and specificity was only 66 and 38%, significantly inferior than the other three studies.

What needs to be done to investigate these discrepancies further? First, the experiments should be independently repeated by other laboratories. Second, these validation studies should be done with the older (Ciphergen) and with higher resolution instruments, various batches of proteomic chips, and by using different bioinformatic tools. Also, internal controls (such as already validated classical discriminatory cancer biomarkers) should be incorporated to validate the actual analytical sensitivity of the technology (see below).

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