Received on May 7, 2007
Accepted on October 17, 2007

Cancer Diagnostics

SELDI-TOF MS Whole Serum Proteomic Profiling with IMAC Surface Does Not Reliably Detect Prostate Cancer

¹ Fred Hutchinson Cancer Research Center, Seattle, WA
² University of Alabama at Birmingham, Birmingham, AL
³ University of Texas Health Science Center at San Antonio, San Antonio, TX
⁴ University of Pittsburgh Cancer Institute/Hillman Cancer Center, Pittsburgh, PA
⁵ Virginia Prostate Center, Eastern Virginia Medical School, Norfolk, VA
⁶ Johns Hopkins Medical Institutions, Baltimore
⁷ National Cancer Institute, Rockville
⁸ University of Washington, Seattle, WA
⁹ Uniformed Services University of Health Sciences, Rockville

^* To whom correspondence should be addressed. E-mail: semmesoj{at}evms.edu.

BACKGROUND: The analysis of bodily fluids using SELDI-TOF MS has been reported to identify signatures of spectral peaks that can be used to differentiate patients with a specific disease from normal or control patients. This report is the 2nd of 2 companion articles describing a validation study of a SELDI-TOF MS approach with IMAC surface sample processing to identify prostatic adenocarcinoma.

METHODS: We sought to derive a decision algorithm for classification of prostate cancer from SELDI-TOF MS spectral data from a new retrospective sample cohort of 400 specimens. This new cohort was selected to minimize possible confounders identified in the previous study described in the companion paper.

RESULTS: The resulting new classifier failed to separate patients with prostate cancer from biopsy-negative controls; nor did it separate patients with prostate cancer with Gleason scores <7 from those with Gleason scores 7.

CONCLUSIONS: In this, the 2nd stage of our planned validation process, the SELDI-TOF MS–based protein expression profiling approach did not perform well enough to advance to the 3rd (prospective study) stage. We conclude that the results from our previous studies—in which differentiation between prostate cancer and noncancer was demonstrated—are not generalizable. Earlier study samples likely had biases in sample selection that upon removal, as in the present study, resulted in inability of the technique to discriminate cancer from noncancer cases.