Neoadjuvant degarelix with or without apalutamide followed by radical prostatectomy for intermediate and high-risk prostate cancer: ARNEO, a randomized, double blind, placebo-controlled trial

BACKGROUND

Recent retrospective data suggest that neoadjuvant androgen deprivation therapy can improve the prognosis of high-risk prostate cancer (PCa) patients. Novel androgen receptor pathway inhibitors are nowadays available for treatment of metastatic PCa and these compounds are promising for early stage disease. Apalutamide is a pure androgen antagonist with a very high affinity with the androgen receptor. The combination of apalutamide with degarelix, an LHRH antagonist, could increase the efficacy compared to degarelix alone.

OBJECTIVE

The primary objective is to assess the difference in proportions of minimal residual disease at prostatectomy specimen between apalutamide + degarelix vs placebo + degarelix. Various secondary endpoints are assessed: variations of different biomarkers at the tumour level (tissue microarrays to evaluate DNA-PKs, PARP, AR and splice variants, PSMA, etc.), whole transcriptome sequencing, exome sequencing and clinical (PSA and testosterone kinetics, early biochemical recurrence free survival, quality of life, safety, etc.) and radiological endpoints.

METHODS

ARNEO is a single centre, phase II, randomized, double blind, placebo-controlled trial. The plan is to include at least 42 patients per each of the two study arms. Patients with intermediate/high-risk PCa and who are amenable for radical prostatectomy with pelvic lymph node dissection can be included. After signing an informed consent, every patient will undergo a pelvic 68Ga -PSMA-11 PSMA PET/MR and receive degarelix at standard dosage and start assuming apalutamide/placebo (60 mg 4 tablets/day) for 12 weeks. Within thirty days from the last study medication intake the same imaging will be repeated. Every patient will undergo PSA and testosterone testing the day of randomization, before the first drug intake, and after the last dose. Formalin fixed paraffin embedded tumour samples will be collected and used for transcriptome analysis, exome sequencing and immunohistochemistry.

DISCUSSION

ARNEO will allow us to answer, first, whether the combined treatment can result in an increased proportion of patients with minimal residual disease. Secondly, It will enable the study of the molecular consequences at the level of the tumour. Thirdly, what the consequences are of new generation androgen receptor pathway inhibitors on 68Ga -PSMA-11 PET/MR. Finally, various clinical, safety and quality of life data will be collected.

TRIAL REGISTRATION

EUDRaCT number: 2016-002854-19 (authorization date 3rd August 2017). clinicalTrial.gov: NCT03080116 .

The diverse genomic landscape of low−risk prostate cancer

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Background: Among men with clinically low-risk prostate cancer, we have previously documented heterogeneity in terms of clinical characteristics and genomic risk scores. In this study, we aimed to study the underlying tumor biology of this patient population, by interrogating patterns of gene expression among men with clinically low-risk tumors. Methods: Prostate biopsies from 427 patients considered potentially suitable for active surveillance underwent central pathology review and genome-wide expression profiling. These cases were compared to 1290 higher-risk biopsy cases with diverse clinical features from a prospective genomic registry. Average genomic risk (AGR) was determined from 18 published prognostic signatures, and MSigDB Hallmark gene sets were analyzed using bootstrapped clustering methods. These sets were examined in relation to clinical variables and pathologic and biochemical outcomes using multivariable regression analysis. Results: 408 (96%) of biopsies passed RNA quality control. Based on average genomic risk quartiles defined by the high-risk multicenter cases, the UCSF low-risk patients were distributed across the quartiles as 219 (54%), 107 (26%), 61 (15%), and 21 (5%). Unsupervised clustering analysis of the Hallmark gene set scores revealed 3 clusters, which were enriched for the previously described PAM50 luminal A, luminal B and basal subtypes. These three clusters did not associate with existing clinical or known genomic risk characteristics, suggesting a novel and independent classification for low-risk prostate cancer. AGR was associated with both pathological (OR: 1.3, p < 0.001) and biochemical outcomes (OR: 1.5, p = 0.001 ) but the clusters were not. Conclusions: Prostate cancers that are largely homogeneously low-risk by traditional characteristics demonstrate substantial diversity at the level of genomic expression. Molecular sub-stratification of low-risk prostate cancer may facilitate better decision-making with respect to both timing and intensity of cancer surveillance and treatment.

Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies

Standard clinicopathological variables are inadequate for optimal management of prostate cancer patients. While genomic classifiers have improved patient risk classification, the multifocality and heterogeneity of prostate cancer can confound pre-treatment assessment. The objective was to investigate the association of multiparametric (mp)MRI quantitative features with prostate cancer risk gene expression profiles in mpMRI-guided biopsies tissues.Global gene expression profiles were generated from 17 mpMRI-directed diagnostic prostate biopsies using an Affimetrix platform. Spatially distinct imaging areas ('habitats') were identified on MRI/3D-Ultrasound fusion. Radiomic features were extracted from biopsy regions and normal appearing tissues. We correlated 49 radiomic features with three clinically available gene signatures associated with adverse outcome. The signatures contain genes that are over-expressed in aggressive prostate cancers and genes that are under-expressed in aggressive prostate cancers. There were significant correlations between these genes and quantitative imaging features, indicating the presence of prostate cancer prognostic signal in the radiomic features. Strong associations were also found between the radiomic features and significantly expressed genes. Gene ontology analysis identified specific radiomic features associated with immune/inflammatory response, metabolism, cell and biological adhesion. To our knowledge, this is the first study to correlate radiogenomic parameters with prostate cancer in men with MRI-guided biopsy.

Development and Validation of a Novel Integrated Clinical-Genomic Risk Group Classification for Localized Prostate Cancer

Purpose It is clinically challenging to integrate genomic-classifier results that report a numeric risk of recurrence into treatment recommendations for localized prostate cancer, which are founded in the framework of risk groups. We aimed to develop a novel clinical-genomic risk grouping system that can readily be incorporated into treatment guidelines for localized prostate cancer. Materials and Methods Two multicenter cohorts (n = 991) were used for training and validation of the clinical-genomic risk groups, and two additional cohorts (n = 5,937) were used for reclassification analyses. Competing risks analysis was used to estimate the risk of distant metastasis. Time-dependent c-indices were constructed to compare clinicopathologic risk models with the clinical-genomic risk groups. Results With a median follow-up of 8 years for patients in the training cohort, 10-year distant metastasis rates for National Comprehensive Cancer Network (NCCN) low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.3%, 9.2%, 38.0%, and 39.5%, respectively. In contrast, the three-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.5%, 29.4%, and 54.6%, for low-, intermediate-, and high-risk, respectively, which were consistent in the validation cohort (0%, 25.9%, and 55.2%, respectively). C-indices for the clinical-genomic risk grouping system (0.84; 95% CI, 0.61 to 0.93) were improved over NCCN (0.73; 95% CI, 0.60 to 0.86) and Cancer of the Prostate Risk Assessment (0.74; 95% CI, 0.65 to 0.84), and 30% of patients using NCCN low/intermediate/high would be reclassified by the new three-tier system and 67% of patients would be reclassified from NCCN six-tier (very-low- to very-high-risk) by the new six-tier system. Conclusion A commercially available genomic classifier in combination with standard clinicopathologic variables can generate a simple-to-use clinical-genomic risk grouping that more accurately identifies patients at low, intermediate, and high risk for metastasis and can be easily incorporated into current guidelines to better risk-stratify patients.

Gene Expression Correlates of Site-specific Metastasis Among Men With Lymph Node Positive Prostate Cancer Treated With Radical Prostatectomy: A Case Series

Predictors of site-specific metastasis after radical prostatectomy (RP) are unknown despite prognostic differences between metastatic sites. We performed RNA expression analysis for 19 genes known to be correlated with aggressive prostate cancer in primary tumors of 63 men pN+ at RP (N = 35 developing metastases after RP vs N = 28 without metastases after RP). Of the men developing metastases, 22 (62.9%) had bone metastases, 8 (22.9%) had nonregional nodal metastases, and 5(14.3%) had visceral metastases. Patients with nodal metastases had higher androgen receptor expression relative to other metastatic sites and nonmetastatic controls (P = .001). This may explain the favorable prognosis of nodal metastases as it may be more androgen dependent.

Impact of Molecular Subtypes in Muscle-invasive Bladder Cancer on Predicting Response and Survival after Neoadjuvant Chemotherapy

BACKGROUND

An early report on the molecular subtyping of muscle-invasive bladder cancer (MIBC) by gene expression suggested that response to neoadjuvant chemotherapy (NAC) varies by subtype.

OBJECTIVE

To investigate the ability of molecular subtypes to predict pathological downstaging and survival after NAC.

DESIGN, SETTING, AND PARTICIPANTS

Whole transcriptome profiling was performed on pre-NAC transurethral resection specimens from 343 patients with MIBC. Samples were classified according to four published molecular subtyping methods. We developed a single-sample genomic subtyping classifier (GSC) to predict consensus subtypes (claudin-low, basal, luminal-infiltrated and luminal) with highest clinical impact in the context of NAC. Overall survival (OS) according to subtype was analyzed and compared with OS in 476 non-NAC cases (published datasets).

INTERVENTION

Gene expression analysis was used to assign subtypes.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Receiver-operating characteristics were used to determine the accuracy of GSC. The effect of GSC on survival was estimated by Cox proportional hazard regression models.

RESULTS AND LIMITATIONS

The models generated subtype calls in expected ratios with high concordance across subtyping methods. GSC was able to predict four consensus molecular subtypes with high accuracy (73%), and clinical significance of the predicted consensus subtypes could be validated in independent NAC and non-NAC datasets. Luminal tumors had the best OS with and without NAC. Claudin-low tumors were associated with poor OS irrespective of treatment regimen. Basal tumors showed the most improvement in OS with NAC compared with surgery alone. The main limitations of our study are its retrospective design and comparison across datasets.

CONCLUSIONS

Molecular subtyping may have an impact on patient benefit to NAC. If validated in additional studies, our results suggest that patients with basal tumors should be prioritized for NAC. We discovered the first single-sample classifier to subtype MIBC, which may be suitable for integration into routine clinical practice.

PATIENT SUMMARY

Different molecular subtypes can be identified in muscle-invasive bladder cancer. Although cisplatin-based neoadjuvant chemotherapy improves patient outcomes, we identified that the benefit is highest in patients with basal tumors. Our newly discovered classifier can identify these molecular subtypes in a single patient and could be integrated into routine clinical practice after further validation.

Individual Patient-Level Meta-Analysis of the Performance of the Decipher Genomic Classifier in High-Risk Men After Prostatectomy to Predict Development of Metastatic Disease

Purpose To perform the first meta-analysis of the performance of the genomic classifier test, Decipher, in men with prostate cancer postprostatectomy. Methods MEDLINE, EMBASE, and the Decipher genomic resource information database were searched for published reports between 2011 and 2016 of men treated by prostatectomy that assessed the benefit of the Decipher test. Multivariable Cox proportional hazards models fit to individual patient data were performed; meta-analyses were conducted by pooling the study-specific hazard ratios (HRs) using random-effects modeling. Extent of heterogeneity between studies was determined with the I² test. Results Five studies (975 total patients, and 855 patients with individual patient-level data) were eligible for analysis, with a median follow-up of 8 years. Of the total cohort, 60.9%, 22.6%, and 16.5% of patients were classified by Decipher as low, intermediate, and high risk, respectively. The 10-year cumulative incidence metastases rates were 5.5%, 15.0%, and 26.7% ( P < .001), respectively, for the three risk classifications. Pooling the study-specific Decipher HRs across the five studies resulted in an HR of 1.52 (95% CI, 1.39 to 1.67; I² = 0%) per 0.1 unit. In multivariable analysis of individual patient data, adjusting for clinicopathologic variables, Decipher remained a statistically significant predictor of metastasis (HR, 1.30; 95% CI, 1.14 to 1.47; P < .001) per 0.1 unit. The C-index for 10-year distant metastasis of the clinical model alone was 0.76; this increased to 0.81 with inclusion of Decipher. Conclusion The genomic classifier test, Decipher, can independently improve prognostication of patients postprostatectomy, as well as within nearly all clinicopathologic, demographic, and treatment subgroups. Future study of how to best incorporate genomic testing in clinical decision-making and subsequent treatment recommendations is warranted.

Individual patient level meta-analysis of the performance of the Decipher genomic classifier in high-risk men post-prostatectomy to predict development of metastatic disease

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Background: The genomic classifier, Decipher, has been validated to predict risk of metastasis after radical prostatectomy (RP). However, the cohort size and event rate in the previous studies did not allow for a thorough investigation into performance within individual clinicopathologic or treatment subgroups. In this study, we present the first meta-analysis of the performance of the 22-marker genomic classifier in men with prostate cancer (PCa) post-RP. Methods: MEDLINE, EMBASE, and the Decipher genomic resource information database were searched for published reports of men with PCa treated by RP between 2010 and 2016 where the benefit of the Decipher genomic classifier test was assessed. The primary end point was the ability of Decipher to independently improve prognostication of regional or distant metastasis over routine clinicopathologic factors. Meta-analysis was performed with random-effects modeling, and extent of heterogeneity between studies was determined with the I2 test. Results: Five studies (975 total patients, and 855 with individual patient genomic and clinicopathologic data) were eligible for analysis. The median follow-up was 8 years. All patients had clinical high-risk disease, yet 60.9%, 22.6%, and 16.5% of patients were classified as low, intermediate, and high-risk, respectively by Decipher and had 10-year cumulative incidence rates of metastases of 5.5%, 15.0% and 26.7% (p < 0.001), respectively. Adjusting for standard clinicopathologic variables, on multivariable analysis Decipher remained a statistically significant predictor of metastasis (hazard ratio [HR] 1.30 per 0.1 unit, 95% confidence interval [CI] 1.14-1.47, p < 0.001), and the summary HR for metastasis of Decipher across the 5 studies was 1.52 (95% CI 1.39-1.67) per 0.1 unit. Conclusions: The genomic classifier test, Decipher, has the ability to independently improve prognostication of men post-RP, as well as within nearly all clinicopathologic and treatment subgroups. Strong consideration should be given to incorporating the use of genomic testing in clinical decision making and clinical trials to better individualize treatment.

Evaluation of the Decipher prostate cancer classifier to predict metastasis and disease-specific mortality from genomic analysis of diagnostic prostate needle biopsy specimens

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Background: Accurate risk stratification after diagnosis of prostate cancer (PCa) is key to optimal treatment decision-making. Decipher RP is an extensively validated genomic classifier used to determine biological potential for metastasis. Here, in a multi-institutional cohort, we aimed to evaluate its ability to predict metastasis and prostate cancer-specific mortality from analysis of PCa needle biopsy tumor tissue specimens. Methods: We identified 175 patients treated with either first-line RP or first-line radiation therapy (RT) + androgen deprivation therapy (ADT) with available genomic expression profiles generated from diagnostic biopsy specimens obtained from three tertiary referral centers: Cleveland Clinic, Brigham and Women’s Hospital and Johns Hopkins. The core with the highest grade was sampled and Decipher was calculated based on a locked random forest model. Cox univariable and multivariable (MVA) proportional hazards model and survival c-index were used to evaluate the performance of Decipher. Results: Overall, 85% of patients had NCCN intermediate and high-risk disease. Of the 175 patients, 43% and 57% were treated with first-line RP and RT+ADT, respectively. With a median follow-up of 6 years, 32 patients developed metastases and 11 of these patients died of PCa. For prediction of metastasis 5 years post biopsy, Decipher had a c-index of 0.74 (95% confidence interval [CI] 0.63-0.84) compared to 0.66 (95% CI 0.53-0.77) for CAPRA and 0.66 (95% CI 0.55-0.77) for NCCN risk group. On MVA, when modeled with CAPRA, Bx Decipher remained a significant predictor of metastasis (Decipher Bx hazard ratio [HR] 1.33 per 10% increase in score, 95% CI 1.06–1.69, P = 0.01). Decipher Bx was also a significant predictor of PCSM (Decipher Bx HR 1.57 per 10%, 95% CI 1.07–2.40, P = 0.02) Conclusions: Decipher Bx was able to predict metastasis and PCSM from diagnostic biopsy specimens in a cohort of primarily intermediate and high-risk men regardless of first line treatment. This additional genomic information provides important risk stratification to help guide therapy for men with intermediate- and high-risk disease.

The diverse genomic landscape of low-risk prostate cancer

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Background: Active surveillance (AS) is becoming standard of care for men with low-risk prostate cancer; however a need exists for better tools to assess which men are optimal candidates for AS. In this study we compare genomic expression profiles of AS candidates against higher-risk radical prostatectomy (RP) patients to characterize the genomics of clinically low-risk prostate cancer. Methods: Biopsies from 473 UCSF patients potentially suitable for AS (stage ≤ cT2N0M0, PSA ≤ 10 ng/ml, Gleason 3+3 or low-volume 3+4 ) were profiled using the Affymetrix HuEx microarray to generate RNA expression data. These cases were compared to 2043 RP cases previously profiled on the same microarray platform. Scores for 21 published prognostic signatures were calculated and pathway associated genes were summarized to provide levels of patient risk and pathway activity. Results: Of the 473 AS biopsies profiled, 408 (86%) passed quality control and were used for analysis. Based on the quartiles of average scores for 21 prognostic signature risk models, 49%, 36%, 11%, and 4%, respectively, were classified into the 1st, 2nd, 3rd, or 4th score quartiles. Considering only the clinically low-risk patients at diagnosis, 356 (87%) were low, 45 (11%) were intermediate and 7 (2%) were high risk. Genomic risk was positively associated with cell cycle related pathways (p < 0.001) and negatively associated with apical junction (p < 0.001), epithelial−mesenchymal transition (p < 0.001), and androgen receptor (p < 0.05) pathways. Clustering of patients based on the expression of 36 pathways revealed two biologic groups corresponding to putative basal and luminal subtypes. Compared to higher risk RP patients, the low risk prostate cancer tumors at diagnosis were enriched for basal-like tumors (20% vs 33%, p < 0.001). Conclusions: Although only 2% of low risk AS candidates have high risk genomic characteristics, very substantial genomic heterogeneity exists in this population, and pathway activation overlaps significantly with higher-risk RP patients. These results suggest that even in potential AS candidates, genomic profiling could eventually be used to better guide management.

Her2 alterations in muscle-invasive bladder cancer: Patient selection beyond protein expression for targeted therapy

Although the introduction of novel targeted agents has improved patient outcomes in several human cancers, no such advance has been achieved in muscle-invasive bladder cancer (MIBC). However, recent sequencing efforts have begun to dissect the complex genomic landscape of MIBC, revealing distinct molecular subtypes and offering hope for implementation of targeted therapies. Her2 (ERBB2) is one of the most established therapeutic targets in breast and gastric cancer but agents targeting Her2 have not yet demonstrated anti-tumor activity in MIBC. Through an integrated analysis of 127 patients from three centers, we identified alterations of Her2 at the DNA, RNA and protein level, and demonstrate that Her2 relevance as a tumor driver likely may vary even within ERBB2 amplified cases. Importantly, tumors with a luminal molecular subtype have a significantly higher rate of Her2 alterations than those of the basal subtype, suggesting that Her2 activity is also associated with subtype status. Although some of our findings present rare events in bladder cancer, our study suggests that comprehensively assessing Her2 status in the context of tumor molecular subtype may help select MIBC patients most likely to respond to Her2 targeted therapy.

Prediction of Lymph Node Metastasis in Patients with Bladder Cancer Using Whole Transcriptome Gene Expression Signatures

PURPOSE

Clinical staging in patients with muscle invasive bladder cancer misses up to 25% of lymph node metastasis. These patients are at high risk for disease recurrence and improved clinical staging is critical to guide management.

MATERIALS AND METHODS

Whole transcriptome expression profiles were generated in 199 patients who underwent radical cystectomy and extended pelvic lymph node dissection. The cohort was divided randomly into a discovery set of 133 patients and a validation set of 66. In the discovery set features were identified and modeled in a KNN51 (K-nearest neighbor classifier 51) to predict pathological lymph node metastases. Two previously described bladder cancer gene signatures, including RF15 (15-gene cancer recurrence signature) and LN20 (20-gene lymph node signature), were also modeled in the discovery set for comparison. The AUC and the OR were used to compare the performance of these signatures.

RESULTS

In the validation set KNN51 achieved an AUC of 0.82 (range 0.71-0.93) to predict lymph node positive cases. It significantly outperformed RF15 and LN20, which had an AUC of 0.62 (range 0.47-0.76) and 0.46 (range 0.32-0.60), respectively. Only KNN51 showed significant odds of predicting LN metastasis with an OR of 2.65 (range 1.68-4.67) for every 10% increase in score (p <0.001). RF15 and LN20 had a nonsignificant OR of 1.21 (range 0.97-1.54) and 1.39 (range 0.52-3.77), respectively.

CONCLUSIONS

The new KNN51 signature was superior to previously described gene signatures for predicting lymph node metastasis. If validated prospectively in transurethral resection of bladder tumor samples, KNN51 could be used to guide patients at high risk to early multimodal therapy.

Validation of the Decipher prostate cancer classifier for predicting 10-year postoperative metastasis from analysis of diagnostic needle biopsy specimens

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Background: Accurate riskstratification after diagnosis of prostate cancer (PCa) is key to optimal treatment decision-making. Decipher is an extensively validated genomic classifier of metastasis after radical prostatectomy (RP). Here, we evaluate its ability to predict metastasis from analysis of prostate needle biopsy diagnostic tumor tissue specimens in a cohort of intermediate risk PCa patients treated with RP. Methods: Fifty-seven patients with available diagnostic biopsy specimens were identified from a previously reported post-RP validation study of Decipher in a cohort of 169 patients treated at Cleveland Clinic. The core with at least 1mm tumor of the highest Gleason grade was sampled and subjected to whole transcriptome analysis. Decipher was calculated based on a locked random forest model. Cox multivariable (MVA) proportional hazards model and survival c-index were used to evaluate the performance of Decipher. Results: 61% of patients had biopsy Gleason score 6 and 67% of patients had NCCN intermediate risk disease. With a median 8 years follow up, 8 patients metastasized and 3 of these patients died of PCa. Decipher had a c-index of 0.80 (95% confidence interval [CI], 0.58-0.95) compared to 0.58 (95% CI, 0.18-0.91) for biopsy Gleason score and 0.57 (0.57; 95% CI, 0.23-0.89) for preoperative PSA at 10 years post-RP for prediction of metastasis. A combined model consisting of Decipher, preoperative PSA, and Gleason score had a c-index of 0.84 (95% CI, 0.68-0.96). On MVA, Decipher was the only significant predictor of metastasis when adjusting for age, preoperative PSA and biopsy Gleason score (Decipher hazard ratio per 10% increase: 1.72; 95% CI, 1.04–2.83; P = 0.02). Conclusions: Decipher was able to predict metastatic outcome from diagnostic biopsy specimens in a cohort of primarily intermediate risk men treated with RP. This additional genomic information may help identify patients who may not be optimal candidates for active surveillance and better identify appropriate first line therapy for men with intermediate risk disease.

Validation of a genomic classifier for prediction of metastasis following postoperative salvage radiation therapy

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Background: Management of patients with a postoperative rising prostate-specific antigen (PSA) level is complex. Additional local treatment such as salvage radiation therapy (SRT) may be sufficient for many patients but some may require concurrent systemic therapy in order to delay or prevent metastatic disease. As PSA recurrence on its own is a poor surrogate for metastatic disease we hypothesized that the Decipher genomic classifier (GC), a validated predictor of metastasis may be able to better distinguish those patients where additional therapy is beneficial from those where SRT on its own is likely sufficient. Methods: Genomic classifier (GC) scores were calculated from 170 prostate cancer patients, who received SRT at the Veteran Affairs Medical Center Durham, Thomas Jefferson University and Mayo Clinic, between 1990 and 2010. SRT was defined as administration of RT with Pre-RT PSA levels > 0.2 ng/ml. GC and CAPRA-S scores were compared using survival c-index, competing-risks and Cox regression analysis for the prediction of metastasis. Results: Survival c-index for predicting metastasis 5 years post SRT was 0.85 (95% CI: 0.73-0.88) for GC and 0.63 (95% CI: 0.49-0.77) for CAPRA-S. The cumulative incidence of metastasis at 5 years post-SRT was 2.7%, 8.4%, and 33.1% for low, average, and high GC scores (p < 0.001) and 16.9%, 2.3% and 17.2% for low, average and high CAPRA-S scores (p = 0.113). In univariable analysis only GC, extraprostatic extension, path GS and Pre-RT PSA were significant predictors of metastasis. In multivariable analyses with clinical risk factors or the CAPRA-S risk model, GC was the only independent predictor of metastasis with a HR of 1.63 (1.22-2.18, p < 0.001) for a 10% unit increase in risk score. Conclusions: In patients treated with postoperative SRT for PSA recurrence, GC is a powerful predictor of metastasis. Patients with low Decipher have excellent prognosis with SRT and may avoid concurrent hormonal therapy. Patients with high Decipher risk are at highest risk for metastatic disease and SRT failure and may benefit from intensified systemic therapy.

Validation of a Genomic Classifier for Predicting Post-Prostatectomy Recurrence in a Community Based Health Care Setting

PURPOSE

We determined the value of Decipher®, a genomic classifier, to predict prostate cancer outcomes among patients after prostatectomy in a community health care setting.

MATERIALS AND METHODS

We examined the experience of 224 men treated with radical prostatectomy from 1997 to 2009 at Kaiser Permanente Northwest, a large prepaid health plan in Portland, Oregon. Study subjects had aggressive prostate cancer with at least 1 of several criteria such as preoperative prostate specific antigen 20 ng/ml or greater, pathological Gleason score 8 or greater, stage pT3 disease or positive surgical margins at prostatectomy. The primary end point was clinical recurrence or metastasis after surgery evaluated using a time dependent c-index. Secondary end points were biochemical recurrence and salvage treatment failure. We compared the performance of Decipher alone to the widely used CAPRA-S (Cancer of the Prostate Risk Assessment Post-Surgical) score, and assessed the independent contributions of Decipher, CAPRA-S and their combination for the prediction of recurrence and treatment failure.

RESULTS

Of the 224 patients treated 12 experienced clinical recurrence, 68 had biochemical recurrence and 34 experienced salvage treatment failure. At 10 years after prostatectomy the recurrence rate was 2.6% among patients with low Decipher scores but 13.6% among those with high Decipher scores (p=0.02). When CAPRA-S and Decipher scores were considered together, the discrimination accuracy of the ROC curve was increased by 0.11 compared to the CAPRA-S score alone (combined c-index 0.84 at 10 years after radical prostatectomy) for clinical recurrence.

CONCLUSIONS

Decipher improves our ability to predict clinical recurrence in prostate cancer and adds precision to conventional pathological prognostic measures.

Tissue-based Genomics Augments Post-prostatectomy Risk Stratification in a Natural History Cohort of Intermediate- and High-Risk Men

BACKGROUND

Radical prostatectomy (RP) is a primary treatment option for men with intermediate- and high-risk prostate cancer. Although many are effectively cured with local therapy alone, these men are by definition at higher risk of adverse pathologic features and clinical disease recurrence. It has been shown that the Decipher test predicts metastatic progression in cohorts that received adjuvant and salvage therapy following RP.

OBJECTIVE

To evaluate the Decipher genomic classifier in a natural history cohort of men at risk who received no additional treatment until the time of metastatic progression.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective case-cohort design for 356 men who underwent RP between 1992 and 2010 at intermediate or high risk and received no additional treatment until the time of metastasis. Participants met the following criteria: (1) Cancer of the Prostate Risk Assessment postsurgical (CAPRA-S) score ≥3; (2) pathologic Gleason score ≥7; and (3) post-RP prostate-specific antigen nadir <0.2 ng/ml.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary endpoint was defined as regional or distant metastases. Time-dependent receiver operating characteristic (ROC) curves, extension of decision curve analysis to survival data, and univariable and multivariable Cox proportional-hazards models were used to measure the discrimination, net benefit, and prognostic potential of genomic and pathologic risk factors. Cumulative incidence curves were constructed using Fine-Gray competing-risks analysis with appropriate weighting of the controls to account for the case-cohort study design.

RESULTS AND LIMITATIONS

Ninety six patients had unavailable tumor blocks or failed microarray quality control. Decipher scores were then obtained for 260 patients, of whom 99 experienced metastasis. Decipher correlated with increased cumulative incidence of biochemical recurrence, metastasis, and prostate cancer-specific mortality (p<0.01). The cumulative incidence of metastasis was 12% and 47% for patients with low and high Decipher scores, respectively, at 10 yr after RP. Decipher was independently prognostic of metastasis in multivariable analysis (hazard ratio 1.26 per 10% increase; p<0.01). Decipher had a c-index of 0.76 and increased the c-index of Eggener and CAPRA-S risk models from 0.76 and 0.77 to 0.86 and 0.87, respectively, at 10 yr after RP. Although the cohort was large, the single-center retrospective design is an important limitation.

CONCLUSIONS

In a patient population that received no adjuvant or salvage therapy after prostatectomy until metastatic progression, higher Decipher scores correlated with clinical events, and inclusion of Decipher scores improved the prognostic performance of validated clinicopathologic risk models. These results confirm the utility already reported for Decipher.

PATIENT SUMMARY

The Decipher test improves identification of patients most at risk of metastatic progression and death from prostate cancer after radical prostatectomy.

Evaluating the clinical impact of a genomic classifier in prostate cancer using individualized decision analysis

BACKGROUND

Currently there is controversy surrounding the optimal way to treat patients with prostate cancer in the post-prostatectomy setting. Adjuvant therapies carry possible benefits of improved curative results, but there is uncertainty in which patients should receive adjuvant therapy. There are concerns about giving toxicity to a whole population for the benefit of only a subset. We hypothesized that making post-prostatectomy treatment decisions using genomics-based risk prediction estimates would improve cancer and quality of life outcomes.

METHODS

We developed a state-transition model to simulate outcomes over a 10 year horizon for a cohort of post-prostatectomy patients. Outcomes included cancer progression rates at 5 and 10 years, overall survival, and quality-adjusted survival with reductions for treatment, side effects, and cancer stage. We compared outcomes using population-level versus individual-level risk of cancer progression, and for genomics-based care versus usual care treatment recommendations.

RESULTS

Cancer progression outcomes, expected life-years (LYs), and expected quality-adjusted life-years (QALYs) were significantly different when individual genomics-based cancer progression risk estimates were used in place of population-level risk estimates. Use of the genomic classifier to guide treatment decisions provided small, but statistically significant, improvements in model outcomes. We observed an additional 0.03 LYs and 0.07 QALYs, a 12% relative increase in the 5-year recurrence-free survival probability, and a 4% relative reduction in the 5-year probability of metastatic disease or death.

CONCLUSIONS

The use of genomics-based risk prediction to guide treatment decisions may improve outcomes for prostate cancer patients. This study offers a framework for individualized decision analysis, and can be extended to incorporate a wide range of personal attributes to enable delivery of patient-centered tools for informed decision-making.

Clinical and genomic analysis of metastatic prostate cancer progression with a background of postoperative biochemical recurrence

OBJECTIVE

To better characterize the genomics of patients with biochemical recurrence (BCR) who have metastatic disease progression in order to improve treatment decisions for prostate cancer.

METHODS

The expression profiles of three clinical outcome groups after radical prostatectomy (RP) were compared: those with no evidence of disease (NED; n = 108); those with BCR (rise in prostate-specific antigen [PSA] level without metastasis; n = 163); and those with metastasis (n = 192). The patients were profiled using Human Exon 1.0 ST microarrays, and outcomes were supported by a median 18 years of follow-up. A metastasis signature was defined and verified in an independent RP cohort to ensure the robustness of the signature. Furthermore, bioinformatics characterization of the signature was conducted to decipher its biology.

RESULTS

Minimal gene expression differences were observed between adjuvant treatment-naïve patients in the NED group and patients without metastasis in the BCR group. More than 95% of the differentially expressed genes (metastasis signature) were found in comparisons between primary tumours of metastasis patients and the two other outcome groups. The metastasis signature was validated in an independent cohort and was significantly associated with cell cycle genes, ubiquitin-mediated proteolysis, DNA repair, androgen, G-protein coupled and NOTCH signal transduction pathways.

CONCLUSION

This study shows that metastasis development after BCR is associated with a distinct transcriptional programme that can be detected in the primary tumour. Patients with NED and BCR have highly similar transcriptional profiles, suggesting that measurement of PSA on its own is a poor surrogate for lethal disease. Use of genomic testing in patients undergoing RP with an initial rise in PSA level may be useful to improve secondary therapy decision-making.

A genomic classifier to improve prediction of metastatic disease within 5 years after surgery in node-negative high-risk prostate cancer patients managed by radical prostatectomy without adjuvant therapy

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Background: Surgery is a standard first line therapy for most intermediate-high risk men diagnosed with prostate cancer. While clinical factors such as tumor grade, stage and prostate specific antigen (PSA) are currently used to identify patients at risk of cancer recurrence, novel biomarkers that can improve risk stratification and distinguish local from systemic recurrence are needed. The Decipher Genomic Classifier (GC) is a validated model for predicting men at risk of metastasis. We evaluated its performance in predicting metastatic disease within 5 years after surgery (rapid metastasis, RM) in an independent cohort. Methods: Tumors and clinicopathologic data were obtained from a cohort of 2,641 RP patients treated between 1987-2008 at Cleveland Clinic. The final study cohort consisted of 15 RM patients and 154 patients as non-RM controls who met the following criteria: 1) preoperative PSA>20 ng/mL, stage pT3 or margin positive, or Gleason score ≥8; 2) pathologic node negative; 3) undetectable post-RP PSA; 4) no neoadjuvant or adjuvant therapy; and 5) minimum 5 year follow-up for the controls. Results: RM patients developed metastasis with a median of 2.3 (IQR: 0.8-5) years. In multivariable analysis, GC was a significant predictor of RM (OR=1.48, p=0.018) after adjusting for clinical risk factors. GC had the highest c-index, 0.77, compared to the Stephenson model (c-index 0.75) and CAPRA-S (c-index 0.72) as well as a panel of previously reported prostate cancer biomarkers unrelated to GC. Integration of GC into the Stephenson nomogram increased the c-index from 0.75 (95% CI: 0.65-0.85) to 0.79 (95% CI: 0.68-0.89). Conclusions: GC was independently validated as a genomic metastasis signature for predicting RM in a cohort of high-risk men treated with RP and managed conservatively without any adjuvant therapy. Integration of GC into clinical nomograms led to improvement in prediction of RM. GC may further allow identification of men most at risk for metastatic progression who should be considered for multimodal therapy or inclusion in clinical trials.

A biomarker panel associated with distant metastasis in prostate cancer patients treated with radiotherapy as prognostic for DM in a large cohort of prostatectomy patients

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Background: A number of biomarkers related to cell cycle, angiogenesis or apoptosis have been found to be associated with patient outcome in tissue samples from men treated with first line radiation therapy in RTOG clinical trials using immunohistochemical staining and analysis. In a prior study, four biomarkers (Ki-67, MDM2, p16 and Cox-2) and clinical covariates were included in a model of distant metastasis (DM; Pollack et al, Clin Cancer Res, 2014 [epub ahead of print]) risk. The current study tested the hypothesis that these genes are prognostic for DM in men treated primarily with total prostatectomy using RNA expression profiling. Methods: RNA fromprostatectomy samples from Cleveland Clinic (CC, n=182); Mayo Clinic (MC)-I (n = 545) and II (n=235); Memorial Sloan Kettering Cancer Center (MSKCC, n=131); Erasmus Medical Center (EMC, n=48) and Thomas Jefferson University (TJU, n=130) were profiled using 1.4 million RNA features. A Cox proportional hazards model was built on the MC-I training set to combine the 4 biomarkers into a prognostic risk score (4BMSig). 4BMSig was subsequently evaluated for its prognostic significance separately and in combination with clinical risk factors (biopsy Gleason Score, cT-category and Preop-PSA) for DM. Results: 4BMSig was found to discriminate DM patients significantly for the MC-II (AUC = 0.66, p < 0.001), CCF (AUC = 0.68, p < 0.001), and MSKCC (AUC = 0.71, p = 0.04) datasets, and achieved borderline significance for EMC (AUC = 0.70, p = 0.06). 4BMSig did not discriminate DM in the TJU dataset (only 10 DM events). Pooled multivariable analysis (n = 726) with clinical covariates revealed that 4BMSig is a strong independent prognostic covariate for DM (p < 0.001) and prostate cancer specific mortality (p = 0.005). Conclusions: The four genes identified previously as being associated with DM in radiotherapy patients were incorporated herein into 4BMSig, which was found to have potential as a pretreatment prognostic DM risk assessment tool for men treated with prostatectomy. Further validation would consist of testing 4BMSig from RNA in diagnostic tissue from prostate cancer patients prior to prostatectomy.

Validation of the Decipher prostate cancer classifier in intermediate to high-risk men treated with radical prostatectomy but without additional therapy upon PSA rise

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Background: Radical prostatectomy (RP) is a primary treatment option for men with intermediate and high risk prostate cancer. Though many will be effectively cured with local therapy alone, these men are by definition at higher risk of adverse pathologic findings and clinical disease recurrence. The Decipher test has been previously shown to predict metastatic progression in cohorts that included adjuvant and salvage therapy after RP. Here we evaluate Decipher in a natural history cohort of at risk men who received no additional treatment until the time of metastatic progression. Methods: Men with NCCN intermediate or high risk localized prostate cancer treated with RP at the Johns Hopkins Medical Institute (1992-2010) with at least 5 years of post-operative follow up were identified. Only men with initial undetectable PSA after surgery and who received no therapy prior to metastasis detection were included (n=765). A case-cohort design was used to randomly sample the cohort. The highest Gleason grade cancer tissue was used for RNA extraction and Decipher genomic classifier (GC) scores were calculated with a locked 22-biomarker signature and algorithm. Results: GC results were obtained for 260 patients, 28% had positive margins, 77% had EPE, 28% had SVI, 20% had lymph node invasion and 36% had Gleason ≥8 disease. Median follow up was 9 (IQR 6-12) years and at 15 years post RP the cumulative incidence of BCR, metastasis and prostate cancer specific death was 38%, 21% and 9%. Median GC score was 0.34 (IQR: 0.22-0.52) and was significantly higher among men experiencing metastatic progression during follow up (0.47 vs 0.28 respectively p<0.001). In UVA and MVA (adjusting for clinical covariates), GC had an HR of 1.48 (95% CI: 1.30-1.69, p<0.001) and 1.37 (95% CI: 1.21-1.55, p<0.001) per 10% increase, respectively. Conclusions: The majority of the men in this study had excellent long-term outcomes with surgery alone. Elevated Decipher scores correlated with metastatic events, independent of clinical risk factors. Use of Decipher may allow for selection of candidates for immediate vs. delayed adjuvant or salvage therapy following prostatectomy.

Using radiogenomics to characterize MRI-guided prostate cancer biopsy heterogenity

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Background: Current methods for prostate cancer risk stratification are often insufficient to accurately predict outcome after definitive therapy. As tumor multi-focality and genetic heterogeneity can lead to diagnostic prostate biopsy sampling bias, we hypothesize that quantitative imaging with multiparametric (MP)-MRI will more accurately direct targeted biopsies to index lesions associated with highest risk clinical and genomic features, and improve accuracy of current risk classification systems. Methods: Regionally distinct prostate habitats were delineated on MP-MRI (T2w, perfusion and diffusion imaging). Directed biopsies were performed on 17 habitats from 6 patients using MRI-ultrasound fusion. Biopsy location was characterized with 51 radiographic features (including intensity, volume, perfusion, and diffusion paramters). Transcriptome-wide analysis of 1.4 million RNA probes was performed on RNA from each habitat. Genomics features with insignificant expression values (<0.25) and interquartile range <0.5 were filtered, leaving ~2K features. Results: High quality genomic data was derived from 17 (100%) biopsies and clustered by patient origin. Using only prostate cancer related genomic features for hierarchical clustering, samples clustered by Gleason score (GS), indicating these biopsies contain prognostic signal. Similarly, when principal component analysis was performed on 51 imaging features, the primary source of variance segregated the samples into high (≥7) and low (6) GS. Pearson’s correlation analysis identified 152 genomic features that were highly associated with the imaging features (|r| > 0.7). Furthermore, genomic features were found to be significantly enriched for prostate cancer related pathways (p < 0.05), representing a potential biologically meaningful link between imaging and genomic data. Conclusions: MP-MRI-targeted diagnostic biopsies can potentially improve risk classification by directing pathological and genomic analysis to highest risk index lesions. This is the first demonstration of a link between quantitative imaging features (radiomics) with genomic features in MRI-directed prostate biopsies.

A genomic classifier improves prediction of metastatic disease within 5 years after surgery in node-negative high-risk prostate cancer patients managed by radical prostatectomy without adjuvant therapy

BACKGROUND

Surgery is a standard first-line therapy for men with intermediate- or high-risk prostate cancer. Clinical factors such as tumor grade, stage, and prostate-specific antigen (PSA) are currently used to identify those who are at risk of recurrence and who may benefit from adjuvant therapy, but novel biomarkers that improve risk stratification and that distinguish local from systemic recurrence are needed.

OBJECTIVE

To determine whether adding the Decipher genomic classifier, a validated metastasis risk-prediction model, to standard risk-stratification tools (CAPRA-S and Stephenson nomogram) improves accuracy in predicting metastatic disease within 5 yr after surgery (rapid metastasis [RM]) in an independent cohort of men with adverse pathologic features after radical prostatectomy (RP).

DESIGN, SETTING, AND PARTICIPANTS

The study population consisted of 169 patients selected from 2641 men who underwent RP at the Cleveland Clinic between 1987 and 2008 who met the following criteria: (1) preoperative PSA>20 ng/ml, stage pT3 or margin positive, or Gleason score≥8; (2) pathologic node negative; (3) undetectable post-RP PSA; (4) no neoadjuvant or adjuvant therapy; and (5) minimum of 5-yr follow-up for controls. The final study cohort consisted of 15 RM patients and 154 patients as non-RM controls.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The performance of Decipher was evaluated individually and in combination with clinical risk factors using concordance index (c-index), decision curve analysis, and logistic regression for prediction of RM.

RESULTS AND LIMITATIONS

RM patients developed metastasis at a median of 2.3 yr (interquartile range: 1.7-3.3). In multivariable analysis, Decipher was a significant predictor of RM (odds ratio: 1.48; p=0.018) after adjusting for clinical risk factors. Decipher had the highest c-index, 0.77, compared with the Stephenson model (c-index: 0.75) and CAPRA-S (c-index: 0.72) as well as with a panel of previously reported prostate cancer biomarkers unrelated to Decipher. Integration of Decipher into the Stephenson nomogram increased the c-index from 0.75 (95% confidence interval [CI], 0.65-0.85) to 0.79 (95% CI, 0.68-0.89).

CONCLUSIONS

Decipher was independently validated as a genomic metastasis signature for predicting metastatic disease within 5 yr after surgery in a cohort of high-risk men treated with RP and managed conservatively without any adjuvant therapy. Integration of Decipher into clinical nomograms increased prediction of RM. Decipher may allow identification of men most at risk for metastatic progression who should be considered for multimodal therapy or inclusion in clinical trials.

PATIENT SUMMARY

Use of Decipher in addition to standard clinical information more accurately identified men who developed metastatic disease within 5 yr after surgery. The results suggest that Decipher allows improved identification of the men who should consider secondary therapy from among the majority that may be managed conservatively after surgery.

Discovery and validation of novel expression signature for postcystectomy recurrence in high-risk bladder cancer

Background

Nearly half of muscle-invasive bladder cancer patients succumb to their disease following cystectomy. Selecting candidates for adjuvant therapy is currently based on clinical parameters with limited predictive power. This study aimed to develop and validate genomic-based signatures that can better identify patients at risk for recurrence than clinical models alone.

Methods

Transcriptome-wide expression profiles were generated using 1.4 million feature-arrays on archival tumors from 225 patients who underwent radical cystectomy and had muscle-invasive and/or node-positive bladder cancer. Genomic (GC) and clinical (CC) classifiers for predicting recurrence were developed on a discovery set (n = 133). Performances of GC, CC, an independent clinical nomogram (IBCNC), and genomic-clinicopathologic classifiers (G-CC, G-IBCNC) were assessed in the discovery and independent validation (n = 66) sets. GC was further validated on four external datasets (n = 341). Discrimination and prognostic abilities of classifiers were compared using area under receiver-operating characteristic curves (AUCs). All statistical tests were two-sided.

Results

A 15-feature GC was developed on the discovery set with area under curve (AUC) of 0.77 in the validation set. This was higher than individual clinical variables, IBCNC (AUC = 0.73), and comparable to CC (AUC = 0.78). Performance was improved upon combining GC with clinical nomograms (G-IBCNC, AUC = 0.82; G-CC, AUC = 0.86). G-CC high-risk patients had elevated recurrence probabilities (P < .001), with GC being the best predictor by multivariable analysis (P = .005). Genomic-clinicopathologic classifiers outperformed clinical nomograms by decision curve and reclassification analyses. GC performed the best in validation compared with seven prior signatures. GC markers remained prognostic across four independent datasets.

Conclusions

The validated genomic-based classifiers outperform clinical models for predicting postcystectomy bladder cancer recurrence. This may be used to better identify patients who need more aggressive management.

Effect of a genomic classifier test on clinical practice decisions for patients with high-risk prostate cancer after surgery

Objectives

To evaluate the impact of a genomic classifier (GC) test for predicting metastasis risk after radical prostatectomy (RP) on urologists' decision-making about adjuvant treatment of patients with high-risk prostate cancer.

Subjects and Methods

Patient case history was extracted from the medical records of each of the 145 patients with pT3 disease or positive surgical margins (PSMs) after RP treated by six high-volume urologists, from five community practices. GC results were available for 122 (84%) of these patients. US board-certified urologists (n = 107) were invited to provide adjuvant treatment recommendations for 10 cases randomly drawn from the pool of patient case histories. For each case, the study participants were asked to make an adjuvant therapy recommendation without (clinical variables only) and with knowledge of the GC test results. Recommendations were made without knowledge of other participants' responses and the presentation of case histories was randomised to minimise recall bias.

Results

A total of 110 patient case histories were available for review by the study participants. The median patient age was 62 years, 71% of patients had pT3 disease and 63% had PSMs. The median (range) 5-year predicted probability of metastasis by the GC test for the cohort was 3.9 (1–33)% and the GC test classified 72% of patients as having low risk for metastasis. A total of 51 urologists consented to the study and provided 530 adjuvant treatment recommendations without, and 530 with knowledge of the GC test results. Study participants performed a mean of 130 RPs/year and 55% were from community-based practices. Without GC test result knowledge, observation was recommended for 57% (n = 303), adjuvant radiation therapy (ART) for 36% (n = 193) and other treatments for 7% (n = 34) of patients. Overall, 31% (95% CI: 27–35%) of treatment recommendations changed with knowledge of the GC test results. Of the ART recommendations without GC test result knowledge, 40% (n = 77) changed to observation (95% CI: 33–47%) with this knowledge. Of patients recommended for observation, 13% (n = 38 [95% CI: 9–17%]) were changed to ART with knowledge of the GC test result. Patients with low risk disease according to the GC test were recommended for observation 81% of the time (n = 276), while of those with high risk, 65% were recommended for treatment (n = 118; P < 0.001). Treatment intensity was strongly correlated with the GC-predicted probability of metastasis (P < 0.001) and the GC test was the dominant risk factor driving decisions in multivariable analysis (odds ratio 8.6, 95% CI: 5.3–14.3%; P < 0.001).

Conclusions

Knowledge of GC test results had a direct effect on treatment strategies after surgery. Recommendations for observation increased by 20% for patients assessed by the GC test to be at low risk of metastasis, whereas recommendations for treatment increased by 16% for patients at high risk of metastasis. These results suggest that the implementation of genomic testing in clinical practice may lead to significant changes in adjuvant therapy decision-making for high-risk prostate cancer.

Effect of a genomic classifier on adjuvant radiation recommendations after prostate cancer surgery

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Background: Clinical guidelines recommend adjuvant radiation therapy (ART) after radical prostatectomy in men with adverse pathological features. Practice patterns vary on use of ART. This prospective, multi-center study examines the effect of a genomic classifier (GC) on ART recommendations post-prostatectomy. Methods: A prospective, pre-post tumor-board–like survey was conducted to assess urologists’ treatment recommendations for ART as part of a clinical utility study; results are from a pre-specified interim analysis of 11 unique de-identified cases with adverse pathology. All case histories were based on patients treated by at least one of the urologists participating in the study. Patient age, pathological features, and preoperative prostate-specific antigen were presented to the respondents. Presentation of cases was randomized to minimize recall bias. For each case history, physician respondents first were asked to render an ART recommendation without knowledge of the GC findings (pre-GC); they were then asked to render an ART recommendation after GC findings were provided for the same cases (post-GC). Recommendations were made without knowledge of others’ responses. Results: Twelve urologists at 11 US institutions provided 132 adjuvant therapy recommendations. Pre-GC, ART was recommended in 56 (42%) cases. Thirty three percent (95% CI: 25-41%) of recommendations changed following review of GC results. Among pre-GC recommendations for ART, 39% (95% CI: 27-53%; n=22) changed to observation and among pre-GC recommendations for observation, 8% (95% CI: 3%-17%; n=5) changed to ART. Compared to observation, ART was 11.8 times (odds ratio 95% CI: 2.9 - 46.3) more likely to be recommended for cases with high risk GC scores. Adjuvant therapy recommendations were more strongly influenced by GC score (p=0.0006) than any clinical variable (all p>0.33) when both informed recommendations. Conclusions: Additional knowledge of the tumor’s genomic characteristics, as assessed by GC, results in a statistically significant and clinically meaningful change in treatment recommendations in patients indicated for adjuvant radiation therapy by current clinical guidelines.

Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population

PURPOSE

Patients with locally advanced prostate cancer after radical prostatectomy are candidates for secondary therapy. However, this higher risk population is heterogeneous. Many cases do not metastasize even when conservatively managed. Given the limited specificity of pathological features to predict metastasis, newer risk prediction models are needed. We report a validation study of a genomic classifier that predicts metastasis after radical prostatectomy in a high risk population.

MATERIALS AND METHODS

A case-cohort design was used to sample 1,010 patients after radical prostatectomy at high risk for recurrence who were treated from 2000 to 2006. Patients had preoperative prostate specific antigen greater than 20 ng/ml, Gleason 8 or greater, pT3b or a Mayo Clinic nomogram score of 10 or greater. Patients with metastasis at diagnosis or any prior treatment for prostate cancer were excluded from analysis. A 20% random sampling created a subcohort that included all patients with metastasis. We generated 22-marker genomic classifier scores for 219 patients with available genomic data. ROC and decision curves, competing risk and weighted regression models were used to assess genomic classifier performance.

RESULTS

The genomic classifier AUC was 0.79 for predicting 5-year metastasis after radical prostatectomy. Decision curves showed that the genomic classifier net benefit exceeded that of clinical only models. The genomic classifier was the predominant predictor of metastasis on multivariable analysis. The cumulative incidence of metastasis 5 years after radical prostatectomy was 2.4%, 6.0% and 22.5% in patients with low (60%), intermediate (21%) and high (19%) genomic classifier scores, respectively (p<0.001).

CONCLUSIONS

Results indicate that genomic information from the primary tumor can identify patients with adverse pathological features who are most at risk for metastasis and potentially lethal prostate cancer.

Validation of a genomic classifier that predicts metastatic disease progression in men with biochemical recurrence post radical prostatectomy

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Background: Almost 50,000 men per year will present with biochemical recurrence (BCR) following local treatment for prostate cancer. These men with rising PSAs as the lone indicator of recurrence present a management dilemma due to their varied outcomes with only a proportion developing subsequent metastatic disease. Thus, there is a clear need to improve patient risk stratification in this context. Here, we evaluate Decipher, a genomic classifier (GC) in men with BCR following radical prostatectomy (RP) for its ability to predict metastasis. Methods: The 22-marker GC was validated in a prospectively designed case-cohort study of 1,010 clinically high-risk RP patients. 219 patients, including 85 who developed BCR at least 6 months post-RP were subjected to microarray analysis and GC scores were generated. Survival ROC curves, weighted Cox proportional hazards, and decision curves were used to compare the performance of the GC to Gleason score (GS), PSA doubling time (PSAdT) and time to BCR (ttBCR). Results: GC scores significantly stratified these men into those who would or would not develop metastasis after BCR (8% versus 40% of patients developed metastasis at 3 years following BCR depending on GC score category, p<0.001). The AUC for GC was 0.82 (95% CI, 0.76-0.86), compared to that of GS 0.64 (0.58-0.70), PSAdT 0.69 (0.61-0.77) and ttBCR 0.52 (0.46-0.59). In decision curve analysis, the GC had the highest overall ‘net benefit’ and in multivariable modeling with clinicopathologic variables, only GC (p=0.006) and GS (p=0.046) scores were significant predictors of metastasis. Conclusions: When compared to clinicopathologic variables, the GC better predicted metastatic progression among men with BCR following RP. While confirmatory studies in additional patient populations are required, these results suggest that use of the GC can allow for better selection of men requiring additional treatment at the time of BCR.

Development of a genomic-clinical classifier for predicting progression after radical cystectomy in patients with muscle invasive bladder cancer

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Background: The mainstay of muscle-invasive bladder cancer treatment is surgical resection with/without multi-agent chemotherapy. Management decisions are based on a small number of clinical and pathologic parameters with poor prognostic and predictive power. There is an urgent need for enhanced biomarkers to guide therapy of this lethal disease. Here we have developed a genomic signature of bladder cancer progression using whole transcriptome profiling technology. Methods: 251 FFPE bladder cancer specimens were obtained from patients undergoing radical cystectomy at the University of Southern California (1998-2004). All patients had pT2-T4a,N0 urothelial carcinoma in the absence of pre-operative chemotherapy. Median follow-up was 5 years. RNA expression levels were measured with 1.4 million feature oligonucleotide microarrays. Patients were divided into a training set (2/3 of cohort) to develop a genomic classifier for risk of progression (defined as any type of bladder cancer recurrence), and a validation set (1/3 of cohort). In parallel, multivariable analysis was used to develop a clinical classifier using typical clinical and pathologic variables. Finally, a genomic-clinical classifier was built combining the genomic classifier with clinical variables using logistic regression. The receiver-operator characteristic (ROC) area under the curve (AUC) metric was used to evaluate each classifier in the validation set. Results: The genomic classifier consisted of 89 features corresponding to 80 genes that were combined in a k-nearest neighbor model (KNN89). KNN89 showed an AUC of 0.77 in ROC analysis on the validation set. The best clinical classifier showed an AUC of 0.72. The genomic-clinical classifier demonstrated an AUC of 0.81. Multivariable analysis incorporating all clinical parameters and KNN89 further revealed that KNN89 was the only significant predictor of bladder cancer progression (p=0.0077). Conclusions: We have developed a combined genomic-clinical classifier that shows improved performance over clinical models alone for prediction of progression after radical cystectomy. External validation of this classifier is ongoing.

Impact of a genomic classifier of metastatic risk on treatment recommendations post-radical prostatectomy: Report from the DECIDE study group

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Background: Only a minority of prostate cancer patients with adverse pathology and biochemical recurrence (BCR) post radical prostatectomy (RP) experience metastasis and die from prostate cancer. Improved risk prediction models using genomic information may enable clinicians to better weigh the risk of metastasis and the morbidity and costs of treatment in a clinically heterogeneous population. We present a clinical utility study that evaluates the influence on urologist treatment recommendations for patients at risk of metastasis using a genomic-based prediction model (Decipher). Methods: A prospective, pre-post design was used to assess urologist treatment recommendations following RP in both the adjuvant (without any evidence of PSA rise) and salvage (BCR) settings. Urologists were presented de-identified pathology reports and genomic classifier (GC) test results for 24 patients from a previously conducted GC validation study in high-risk post RP men. Participants were fellowship trained, high-volume urologic oncologists (n=21) from 18 US institutions. Treatment recommendations for secondary therapy were made based solely on clinical information (pre-GC) and then with genomic biomarker information (post-GC). This study was approved by an independent IRB. Results: Treatment recommendations changed from pre-GC to post- GC in 43% of adjuvant, and in 53% of salvage setting case evaluations. In the adjuvant setting, urologists changed their treatment recommendations from treatment (i.e. radiation and/or hormones) to close observation post-GC in 27% of cases. However, for cases with low GC risk (<3% risk of metastasis), 79% of these men were recommended for observation post-GC. Consistent trends were observed in the salvage setting including 24% patients with low GC risk recommended for observation even after BCR. Conclusions: These results indicate that urologists across a range of practice settings are likely to change many treatment decisions when presented with genomic biomarker information following RP. Implementation of the GC test into routine clinical practice may better direct treatment decision-making post-RP.