Gene expression profiling predicts clinical outcome of prostate cancer

Abstract

One of the major problems in management of prostate cancer is the lack of reliable genetic markers predicting the clinical course of the disease. We analyzed expression profiles of 12,625 transcripts in prostate tumors from patients with distinct clinical outcomes after therapy as well as metastatic human prostate cancer xenografts in nude mice. We identified small clusters of genes discriminating recurrent versus nonrecurrent disease with 90% and 75% accuracy in two independent cohorts of patients. We examined one group of samples (21 tumors) to discover the recurrence predictor genes and then validated the predictive power of these genes in a different set (79 tumors). Kaplan-Meier analysis demonstrated that recurrence predictor signatures are highly informative (P < 0.0001) in stratification of patients into subgroups with distinct relapse-free survival after therapy. A gene expression-based recurrence predictor algorithm was informative in predicting the outcome in patients with early-stage disease, with either high or low preoperative prostate-specific antigen levels and provided additional value to the outcome prediction based on Gleason sum or multiparameter nomogram. Overall, 88% of patients with recurrence of prostate cancer within 1 year after therapy were correctly classified into the poor-prognosis group. The identified algorithm provides additional predictive value over conventional markers of outcome and appears suitable for stratification of prostate cancer patients at the time of diagnosis into subgroups with distinct survival probability after therapy.

Figure 1

PAIs defined by the expression profile of the prostate cancer recurrence predictor signature 1 for 21 prostate carcinoma samples constituting a signature discovery (training) data set. Prostate tumor samples were taken from the patients at the time of surgery and subjected to a microarray gene expression analysis as described in Methods. Note that all samples derived from tumors of patients who subsequently manifested a biochemical relapse of disease have positive PAI values, whereas 12 of 13 samples obtained from patients who remained disease-free have negative PAI values. See text for details.

Figure 2

Kaplan-Meier analysis of the probability that patients would remain disease-free among 21 prostate cancer patients constituting a signature discovery group according to whether they had good-prognosis or poor-prognosis signatures defined by the recurrence predictor signature 1 (A), recurrence predictor signature 2 (B), recurrence predictor signature 3 (C), and the recurrence predictor algorithm, which takes into account calls from all three signatures (D). The cut-off values for each marker were identified through the detailed analysis of behavior of log-rank test P values across the range of the measurements for each marker. We selected the prognosis discrimination cut-off value for each signature based on highest level of statistical significance in patients’ stratification into poor- and good-prognosis groups as determined by the log-rank test (lowest P value and highest hazard ratio; see Supplemental Table 6S). CI, confidence interval.

Figure 3

Kaplan-Meier analysis of the probability that patients would remain disease-free among 79 prostate cancer patients constituting a signature validation group for all patients (A), patients with high (B) or low (C) preoperative PSA levels in blood according to whether they had good-prognosis or poor-prognosis signatures defined by the recurrence predictor algorithm, or whether they had high or low preoperative PSA level in the blood (D). Preoperative PSA level of 7.8 ng/ml was used as a cut-off discrimination level for patients’ stratification into poor- and good-prognosis subgroups. The cut-off values for each marker were identified through the detailed analysis of behavior of log-rank test P values across the range of the measurements for each marker. We selected the prognosis discrimination cut-off value for each marker based on highest level of statistical significance in patients’ stratification into poor- and good-prognosis groups as determined by the log-rank test (lowest P value and highest hazard ratio).

Figure 4

Kaplan-Meier analysis of the probability that patients would remain disease-free among prostate cancer patients with Gleason sum 6 and 7 tumors (A) and patients with Gleason sum 8 and 9 tumors (B) according to whether they had good-prognosis or poor-prognosis signatures defined by the recurrence predictor algorithm or whether they had Gleason sum 8 and 9 or Gleason sum 6 and 7 prostate tumors (C).

Figure 5

Kaplan-Meier analysis of the probability that patients would remain disease-free among 79 prostate cancer patients constituting a signature validation group for all patients (A), patients with poor prognosis (B), or good prognosis (C), defined by the Kattan nomogram according to whether they had a good-prognosis or poor-prognosis signatures defined by the recurrence predictor algorithm (B and C) or whether they had poor or good prognosis defined by the Kattan nomogram (A).

Figure 6

Kaplan-Meier analysis of the probability that patients would remain disease-free among prostate cancer patients with stage 1C tumors (A) and patients with stage 2A tumors (B) according to whether they had a good-prognosis or poor-prognosis signatures defined by the recurrence predictor algorithm.