Validation of a 10-gene molecular signature for predicting biochemical recurrence and clinical metastasis in localized prostate cancer

Abstract

Purpose: To validate a previously characterized 10-gene signature in prostate cancer with implication to distinguish aggressive and indolent disease within low and intermediate patients' risk groups.

Methods: A case-control study design used to select 545 patients from the Mayo clinic tumor registry who underwent radical prostatectomy. A training set from this cohort (n = 359) was used to build a 10-gene model, based on high-dimensional discriminant analysis (HDDA10) to predict several endpoints of clinical patients' outcome. An independent set (n = 219) from the same institution was used as validation set.

Results: HDDA10 showed significant performance for predicting metastasis (Mets) (AUC 0.68, p = 6.4E - 6) and biochemical recurrence (BCR) (AUC = 0.65, p = 0.003) in the validation set outperforming Gleason grade grouping (GG) for BCR (AUC 0.57, p = 0.03) and with comparable performance for Mets endpoint (GG AUC 0.66, p = 8.1E - 5). HDDA10 prognostic significance was superior to any clinical-pathological parameter within GG2 + 3 (GS7) patients achieving an AUC of 0.74 (p = 0.0037) for BCR compared to Gleason pattern 4 (AUC 0.64) (p = 0.015) and AUC for Mets of 0.68 versus AUC of 0.65 for Gleason pattern 4 (p = 0.01). HDDA10 remained significant for both BCR and Mets in multivariate analysis, suggesting that it can be used to increase accuracy in stratifying patients eligible for active surveillance.

Conclusion: HDDA10 is of added value to GG and other clinical-pathological parameters in predicting BCR and Mets endpoint, especially in the low to intermediate patients' risk groups. HDDA10 prognostic value should be further validated prospectively in stratifying patients specifically in low to intermediate GS (GG1-2), such as active surveillance programs.

Keywords: Biochemical recurrence; Biomarkers; Clinical metastasis; Genetic classifier; Gleason score; Grade grouping; Prognosis; Prostate cancer.

Fig. 1

Survival AUC curves to assess the HDDA10 classifier and other clinico-patholgical variables to predict biochemical recurrence (BCR) and metastasis (Mets) post radical prostatectomy. a Survival AUC to predict BCR in all the samples. b Survival AUC to predict Mets in all samples. c Survival AUC to predict BCR in GG2–3 samples. d Survival AUC to predict Mets in GG2–3 samples. HDDA10 had the highest AUC compared to the single clinico-pathological variables in all samples and the GG2–3 subsets to predict both endpoints

Fig. 2

Survival AUC curves to assess the HDDA10 classifier and other clinico-patholgical variables to predict biochemical recurrence (BCR) and metastasis (Mets) post radical prostatectomy. a Survival AUC to predict BCR in GG2. b Survival AUC to predict Met in GG2. c Survival AUC to predict BCR in GG3. d Survival AUC to predict Mets in GG3. HDDA10 had the highest AUC compared to the single clinico-pathological variables in the GG2 and PSA had the highest AUC in the GG3 subset

Fig. 3

Kaplan–Meier curves of the HDDA10 and pathological Gleason group in all samples to predict time to BCR and Mets. KM curves of HDDA10 and PathGG to predict time to BCR (a, b) and Mets (c, d) in all samples. KM shows that HDDA10 can better predict BCR- and Mets-free survival. High HDDA10 are defined as patients with HDDA10 score greater than 0.5 and low otherwise

Fig. 4

Kaplan–Meier curves of the HDDA10 and primary pathological Gleason score in all samples to predict time to BCR and Mets. KM curves of HDDA10 and Primary PathGG to predict time to BCR (a, b) and Mets (c, d) in GG2-3 samples. KM shows that HDDA10 can better predict BCR- and Mets-free survival in the background of GG2–3. High HDDA10 are defined as patients with HDDA10 score greater than 0.5 and low otherwise