Zimmermann, Benedikt

4565 Background: The efficient delivery of adjuvant therapy after radical prostatectomy (RP) in patients with prostate cancer is limited by the lack of biomarkers, beyond clinicopathologic factors, that are able to assess the risk of clinically significant disease progression. Previously, routine FFPE patient specimens from the Mayo Clinic Radical Prostatectomy Registry with long term follow-up were selected to develop a genomic classifier (GC) to predict clinical progression. Here, we present the validation of a GC in a cohort of patients at high risk of disease progression. Methods: A case-cohort study of high-risk RP patients from the Mayo Clinic (N=219) was used to validate the genomic classifier (GC) for predicting clinical progression (defined by positive bone or CT scan post-RP). Its performance was compared to a multivariable clinical classifier (CC) and a genomic-clinical classifier (GCC) which combines GC with established clinicopathologic variables. Concordance index, Cox modeling and decision curve analysis were used to compare the different models. Results: GC and GCC were predictive of clinical progression in the high-risk cohort with c-indices of 0.79 and 0.82, respectively, compared to the clinical classifier (0.70). Multivariable survival analysis showed that the majority of prognostic information of GCC came from the GC with a minor contribution from Gleason score. Decision curve analysis showed that GCC had a higher overall net benefit compared to CC over a wide range of ‘decision-to-treat’ thresholds for the risk of progression. Conclusions: In this high-risk cohort, GC and GCC classifiers showed improved performance over CC in prediction of clinical progression. GC is an independent prognostic factor in this cohort and captures the majority of prognostic information. GC and GCC’s prognostic performance and their usefulness in guiding decision-making in the adjuvant setting after RP need further testing in studies of additional prostate cancer risk groups.

90 Background: Biochemical recurrence (BCR) may be an indicator of metastatic prostate cancer; however, it isn't specific enough to differentiate patients who will experience rapid onset of metastases from those who do not. Such lack of specificity leads to overtreatment of men who will likely never experience metastatic disease in their lifetime. We hypothesize that genomic biomarkers, detectable in primary tumor tissue, may better identify rapidly progressing metastatic prostate cancer. Methods: FFPE prostatectomy specimens (n = 309) from the Mayo Clinic tumor registry were profiled using a high-density expression array. Patients were retrospectively classified into three outcome groups: NED (no evidence of disease recurrence); BCR-only (patients that experienced BCR but no metastatic progression); and MET (metastatic disease confirmed by positive CT or bone scans) and were matched on several criteria for analysis . Uni- and multi-variable analyses between outcome groups were used to analyze clinical and genomic variables (adjusting for adjuvant hormone therapy as a confounding factor). Results: Median follow-up was 16.8 years and MET patients were diagnosed a mean of 3.1 years following BCR. Univariable and multivariable analysis of clinical variables failed to show statistically significant differences between NED and BCR-only outcome groups. Differential expression analysis between NED and BCR-only groups showed no significant differences after false discovery correction. Conversely, 6,277 and 52,020 biomarkers were differentially expressed between MET and the BCR-only and NED groups, respectively. Conclusions: Considering the clinical and genomic results together, patients who experienced rapid metastatic disease were significantly different from both the NED and BCR-only groups. However, no significant differences were identified between the BCR-only and NED outcome groups. These results imply that BCR is not a good surrogate for men who are likely to experience rapid onset of metastatic disease and lethal prostate cancer.

40 Background: Gleason score (GS) is the most widely used grading system of cell differentiation in prostate cancer and one of the best pathological predictors of disease progression. Patients with high GS (> 7) are the most likely to experience metastasis, whereas patients with low GS (< 7) are expected to have favorable long-term outcomes. In addition to the subjective nature of GS assessment, patients with GS 7 represent a heterogeneous group in terms of patient outcomes. In this study a biomarker signature is developed and validated which could improve the prediction of high risk disease among GS 7 radical prostatectomy (RP) patients. Methods: Patient specimens from the Mayo Clinic RP Registry (n = 764) were used for feature selection and training of a 392-feature K-nearest neighbor (KNN, k = 11) classifier. These 392 genomic features were identified by assessing differential expression between patients with GS < 7 and those with GS > 7, using a Bonferroni adjusted T-Test with a p-value threshold of 0.05. The classifier was subsequently validated in two independent patient cohorts (Memorial Sloan Kettering [MSKCC] and German Cancer Research Center [DKFZ]) and compared to a state of the art biomarker signature (Penney et al. 2011). Results: In the MSKCC dataset (GSE21034) our model segregated GS < 7 from GS > 7 patients (n = 56) with an area under the receiver operating characteristic curve (AUC) of 0.97, comparable to the AUC of 0.94 obtained by Penney et al. in their independent validation set (n = 45). Strong performance was observed by our model when discriminating between primary Gleason grade (pGG) 3 and pGG 4 & 5 patients in the MSKCC (n = 130) and DKFZ prostate cancer (GSE29079, n = 47) datasets, achieving AUCs of 0.75 and 0.87, respectively. In the challenging GS 7 subset, our model segregated GS 3 + 4 and 4 + 3 patients in the DKFZ dataset (n = 64) with an AUC of 0.81 outperforming the Penney et al. signature (AUC = 0.60). Conclusions: A biomarker signature was developed which discriminates between low and high GS patients, outperforming a previously reported signature. Further validation of this biomarker signature in additional post RP patients, as well as, pretreatment biopsy specimens is warranted.

36 Background: Following radical prostatectomy (RP), 30-40% of patients have adverse pathology and are deemed at high risk for metastatic progression. The objective of this study was to validate the ability of Decipher, a genomic classifier (GC), to improve prediction of metastatic disease progression compared with clinical variables in order to better identify candidates for therapy intensification. Methods: A previously developed 22-feature GC model was validated in a prospectively designed case-cohort study of a clinically high-risk population (i.e., with one or more adverse pathological features) of 1,010 RP patients treated at Mayo Clinic between 2000-2006. A random sample of 20% of the cohort was subjected to microarray analysis and GC scores were generated for 219 patients. The primary endpoint, the c-index for predicting metastatic disease progression (i.e., positive bone or CT scans) was evaluated in a blinded analysis. Cox modeling and decision curve analyses were used to compare the performance of GC to individual clinical variables and prediction models. Results: GC had a c-index 0.79 (95% CI 0.71-0.86) that was significantly better than any single clinical variable. Cumulative incidence curves in the cohort showed that 72% of patients had low GC scores with only 3% and 6% incidence of metastatic disease at 5 and 10 years post RP. In contrast, for the 28% of patients with high GC scores, the cumulative incidence was 17% and 25% at 5 and 10 years post RP. Decision curve analysis showed that the GC model had higher overall net benefit compared to clinical variables over a wide range of ‘decision-to-treat’ thresholds for risk of metastasis. In multivariable modeling with clinicopathologic variables, GC remained the only significant independent predictor of metastasis (HR=1.51, for each 0.1 unit increment, p<0.001). Conclusions: GC can better predict metastatic disease progression compared with clinical variables and may select among patients with adverse pathology a majority that is in fact at low risk for metastasis.

175 Background: The efficient delivery of adjuvant and salvage therapy after radical prostatectomy in patients with prostate cancer is hampered by a lack of biomarkers to assess the risk of clinically significant recurrence and progression. Methods: Mayo Clinic Radical Prostatectomy Registry (RP) patient specimens were selected from a case-control cohort with 14 years median follow-up for training and initial validation of an expression biomarker genomic classifier (GC). An independent, blinded case-cohort study of high-risk RP subjects was used to validate GC, comparing the performance of GC to a multivariate logistic regression clinical model (CM) and GC combined with clinical variables (genomic-clinical classifier, GCC) for predicting clinical recurrence (defined as positive bone or CT scan within 5 years after biochemical recurrence). The concordance index (c-index) and Cox model were used to evaluate discrimination and estimate the risk of clinical recurrence. Results: In the training subset (n=359), both GC and GCC had a c-index of 0.90 whereas CM had a c-index of 0.76. In the internal validation set (n=186), GC and GCC had a c-index of 0.76 and 0.75, while CM had a c-index of 0.69. In an independent high-risk study (n=219), GC and GCC had a c-index of 0.77 and 0.76, while CM had a c-index of 0.68. In subset analysis of Gleason score 7 patients within the high-risk group, GC and GCC showed improved discrimination with c-index of 0.78 and 0.76, respectively compared to 0.70 for CM. In the high-risk group, the risk of recurrence by GC model score quartiles at 5 years after RP was estimated at 1%, 5%, 5% and 18%. Conclusions: The GC model shows improved performance over CM in the prediction of clinical recurrence in a high-risk cohort and in subset analysis of Gleason score 7 patients. The addition of clinical variables to the GC model did not significantly contribute to classifier performance in patients with high-risk features. We are further testing the performance of the GC and GCC models and their usefulness in guiding decision-making (e.g., for the adjuvant therapy setting) in additional studies of prostate cancer clinical risk groups.

Abstract Motivations: Gleason score is the most prognostic variable in prostate cancer pathology. Patients with high Gleason (8+) are the most likely to experience biochemical recurrence (BCR), clinical progression (CP) and prostate-cancer specific mortality (PCSM), whereas patients with Gleason ≤6 are expected to have excellent long-term outcomes. However, the majority of prostate cancer patients are diagnosed with Gleason score 7, a heterogeneous group in terms of patient outcomes. In the absence of adverse pathological factors (APF, such as preoperative PSA, SM+, ECE and SVI) most Gleason 7 patients will have excellent outcomes. In contrast, Gleason 7 patients with APF are considered at high-risk for recurrence and therefore are candidates for adjuvant therapy. In this study, we have investigated whether a classifier based on differentially expressed genomic features could be used to improve prognostication of Gleason 7 patients, independent of APF. Methods: Two whole-transcriptome (1.4 million feature) oligonucleotide microarray expression datasets from radical prostatectomy specimens were analyzed. The first one (GEO accession: GSE21032) consisted of 125 fresh frozen (FF) samples from the MSKCC Oncogenome Project and the second dataset had 545 formalin-fixed-paraffin- embedded (FFPE) samples from the Mayo Clinic tumor registry. For genomic feature selection, differential expression in the FF dataset between Gleason ≤6 (n=40) and ≥8 (n=15) was assessed with false discovery rate adjusted t-tests (p&lt;0.05). Next, a k-nearest neighbor (KNN) classifier was trained and tuned using the selected features to predict clinical progression (i.e., positive bone or CT scans after BCR) in the FFPE dataset, again using samples with Gleason score ≤6 (n=63) and ≥8 (n=211). The KNN classifier was evaluated on Gleason score 7 (n=209) FFPE samples (not used for training) using multivariable logistic regression. Results: The KNN classifier segregated the Gleason 7 samples into high and low risk CP groups. The KNN odds ratios were 2.9 (p&lt;0.005), 2.1 (p&lt;0.05) and 2.8 (p&lt;0.05) for BCR, CP and PCSM end-points, respectively when comparing high and low risk groups. Multivariable analysis revealed that the KNN classifier was independently prognostic. Conclusions: A KNN classifier developed using differentially expressed genomic features significantly stratified Gleason score 7 patients for several clinically relevant endpoints. Future validation studies will address the utility of this classifier in predicting pathological Gleason score from biopsy specimens, which may be particularly useful for evaluation of biopsy Gleason 6 cases. In these patients, deferring prostatectomy in favour of active surveillance is an appealing treatment option unless the ‘true’ Gleason of the index lesion is in fact a Gleason 7 or higher, where immediate surgery is likely a life-saving procedure. Citation Format: Nicholas Erho, Thomas Sierocinski, Elai Davicioni, Robert B. Jenkins, Mercedeh Gadhessi, Christine Buerki, Ismael A. Vergara, Anamaria Crisan, Zaid Haddad, Benedikt Zimmermann, Anirban Mitra, Timothy J. Triche. Identification of biomarkers for stratification of Gleason score 7 patients using whole-transcriptome expression profiling [abstract]. In: Proceedings of the AACR Special Conference on Advances in Prostate Cancer Research; 2012 Feb 6-9; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2012;72(4 Suppl):Abstract nr B46.