Detection of pre-neoplastic and neoplastic prostate disease by MALDI profiling of urine

Abstract

The heterogeneous progression to the development of prostate cancer (PCa) has precluded effective early detection screens. Existing prostate cancer screening paradigms have relatively poor specificity for cancer relative to other prostate diseases, commonly benign prostatic hyperplasia (BPH). A method for discrimination of BPH, HGPIN, and PCa urine proteome was developed through testing 407 patient samples using matrix assisted laser desorption-mass spectrometry time of flight (MALDI-TOF). Urine samples were adsorbed to reverse phase resin, washed, and the eluant spotted directly for MALDI-TOF analysis of peptides. The processing resolved over 130 verifiable signals of a mass range of 1000-5000 m/z to suggest 71.2% specificity and 67.4% sensitivity in discriminating PCa vs. BPH. Comparing BPH and HGPIN resulted in 73.6% specificity and 69.2% sensitivity. Comparing PCa and HGPIN resulted in 80.8% specificity and 81.0% sensitivity. The high throughput, low-cost assay method developed is amenable for large patient numbers required for supporting biomarker identification.

Figure 1

Analyses of the reproducibility from MALDI-TOF profiles have the urine proteome and supervised classification of PCa, HGPIN and control subjects. Urine from 3 normal subjects was processed 16 times each as described in the text. Each sample was spotted 3 times each and the spectra from the spotted samples analyzed by MALDI-TOF were assigned bins. The individual signals from each profile were compared and the variance for signals in a particular bin based on indicated parameters was plotted.

Figure 2

The MADLI-MS data collected compare urine proteome of PCa, HGPIN, and BPH patients. A pair-wise comparisons of PCa and BPH subjects (A), as well as HGPIN patients with BPH (B) and PCa (C) patients were plotted to show the sensitivity and specificity as well as the overall probability of correct assignment (combined) based on increasing number of bins used in the analysis. The top 150 bins were chosen using leave one out cross-validation, the graph shows the approximate number of bins necessary to achieve optimal probability of correct assignment. The positive classification rate was determined to indicate correct classification rate (y axis) in relation to the number of bins required to achieve the score (x axis). The number of subjects in each cohort is indicated in parenthesis.

Figure 3

Distinguishing MALDI-TOF signals that differentiate between PCa and BPH patients. Data collected in the reflectron mode enables isotopic resolution of the following m/z values (A) 1373.1, (B) 1433.5, (C) 2236.3, and (D) 2484.6. The BPH (black) and PCa (gray) mean spectra in each panel were overlaid. The arrowhead indicates the monoisotopic peak. In each panel the offset of the overlaid spectra is set at 0%, however the differing level of baseline noise in the particular regions of the spectra cause the offset to appear greater in some cases.

Figure 4

HPLC profiles of the BPH and PCa urine peptides were collected on a diode array detector (Agilent) at 280 nm. All the fractions were analyzed from the (A) BPH and (B) PCa patient sample pools by MALDI-TOF for the presence of the distinguishing peaks determined from the profiling studies. Fractions 68 (1373.1 m/z), 70 (2236.3 m/z and 1433.5 m/z), and 74 (2484.6 m/z) were found to contain the MALDI peaks of interest, thus subjected to subsequent sequencing by LC-MS/MS.