Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error

Abstract

Background: Key challenges of biopsy-based determination of prostate cancer aggressiveness include tumour heterogeneity, biopsy-sampling error, and variations in biopsy interpretation. The resulting uncertainty in risk assessment leads to significant overtreatment, with associated costs and morbidity. We developed a performance-based strategy to identify protein biomarkers predictive of prostate cancer aggressiveness and lethality regardless of biopsy-sampling variation.

Methods: Prostatectomy samples from a large patient cohort with long follow-up were blindly assessed by expert pathologists who identified the tissue regions with the highest and lowest Gleason grade from each patient. To simulate biopsy-sampling error, a core from a high- and a low-Gleason area from each patient sample was used to generate a 'high' and a 'low' tumour microarray, respectively.

Results: Using a quantitative proteomics approach, we identified from 160 candidates 12 biomarkers that predicted prostate cancer aggressiveness (surgical Gleason and TNM stage) and lethal outcome robustly in both high- and low-Gleason areas. Conversely, a previously reported lethal outcome-predictive marker signature for prostatectomy tissue was unable to perform under circumstances of maximal sampling error.

Conclusions: Our results have important implications for cancer biomarker discovery in general and development of a sampling error-resistant clinical biopsy test for prediction of prostate cancer aggressiveness.

Figure 1

Creation of biopsy simulation tissue microarrays (TMAs). A tissue block from a prostatectomy sample was annotated with all visible Gleason patterns (top). The example shown is from a patient with an overall Gleason score (GS) of 4+3=7. As shown in a higher-magnification view (middle), patterns within the same block can be highly diverse. Two 1-mm cores were taken from each tissue block. One was taken from an area with the highest GS (4+4=8) and embedded into agarose/paraffin along with high-scoring cores from other blocks to create the H TMA (bottom left). The other was taken from an area with the lowest GS (3+3=6) and embedded into agarose/paraffin along with low-scoring cores from other blocks to create the L TMA (bottom right).

Figure 2

Biomarker selection strategy. Three types of criteria were used to select 12 final biomarkers. (DAB: Ab specificity assessed based on chromogenic tissue staining with diamino benzidine (DAB); IF: Ab specificity and performance based on immunofluorescent tissue staining).

Figure 3

Univariate performance of 39 biomarkers measured in both low- (L TMA; black bars) and high-(H TMA; brown bars) Gleason areas for disease aggressiveness and disease-specific mortality. (A) The odds ratio (OR) for predicting severe disease pathology (aggressiveness) was calculated for each marker. Markers with an OR to the left of the vertical line are negatively correlated with the severity of the disease as assessed by pathology. Those to the right of the line are positively correlated. The markers were ranked based on OR when measured in L TMA. (B) The hazard ratio for death from disease (lethality) was calculated for each marker and plotted as described for A. Biomarkers in red indicate statistical significance at the 0.1 level in both L and H TMAs. Biomarkers in blue indicate statistical significance in only H TMA, but not L TMA. Note the large overlap of biomarkers with statistically significant univariate performance for both aggressive disease and death from disease.

Figure 4

Performance-based biomarker selection process for disease aggressiveness. (A) The bioinformatics workflow selected the most frequently utilised biomarkers from all combinations of up to five markers from a set of 31. (B) Example of performance of top-ranked five-marker models, including comparison with training on L TMA and then testing on independent samples from L TMA and H TMA. Note that the test performances on L TMA and H TMA are consistent, with substantial overlap in confidence intervals. (C) Combinations were generated allowing a maximum of three, four, or five biomarkers. The figure shows the proteins most frequently included when five-biomarker models were used to predict aggressive disease, ranked by test.

Figure 5

Final biomarker set and selection criteria. (A) Twelve biomarkers were selected based on univariate performance for aggressiveness (shown as OR on left) and lethality as well as frequency of appearance in multivariate models for disease aggressiveness or lethal outcome (table on right) (B) The biomarker set comprises proteins known to function in the regulation of cell proliferation, cell survival, and metabolism. (C) A multivariate 12-marker model for disease aggressiveness was developed based on logistic regression. The resulting AUC and OR are shown. Subsequently, the risk scores generated by the aggressiveness model for all patients were correlated with lethal outcome. The resulting AUC and HR are shown.