Validation of the Decipher Test for Predicting Distant Metastatic Recurrence in Men with High-risk Nonmetastatic Prostate Cancer 10 Years After Surgery.

BACKGROUND: Decipher is a genomic classifier designed to predict the development of distant metastases after surgical treatment of prostate cancer (PC). Its long-term prognostic role in a high-risk PC population has not been investigated previously. OBJECTIVE: To determine the prognostic role of the Decipher genomic classifier in two high-risk PC case-control studies. DESIGN, SETTING, AND PARTICIPANTS: Patients who developed distant metastases after surgery for high-risk, nonmetastatic PC in a European tertiary referral center from 1991 to 2011 were matched to patients not developing distant metastases (n=54). A validation study (n=298) was performed using a similar US case-control cohort. Formalin-fixed, paraffin-embedded tissue blocks from the index PC lesion were used for RNA extraction and gene expression analysis. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The outcome investigated was the development of distant metastasis within 10-yr follow-up. Multivariable logistic regression analysis was performed, with statistical significance considered at p<0.05. RESULTS AND LIMITATIONS: In both the European and US case-control studies, the median Decipher scores were higher in the population that developed metastases. In the multivariable analysis, each 10% increase in Decipher score translated to an increase in the risk of distant metastases within 10-yr follow-up, with an odds ratio of 1.53 (95% confidence interval [CI] 1.06-2.22; p=0.025) and 1.58 (95% CI 1.31-1.92; p<0.001) for the European and US cohorts, respectively. Median follow-up for the European cohort was 12yr (interquartile range 8-12). The study limitation is the small size of the European cohort. CONCLUSIONS: Our study validates Decipher as a predictor for metastatic recurrence even in patients with high-risk, nonmetastatic PC within 10-yr follow-up. PATIENT SUMMARY: Decipher is a test based on gene expression profiles in primary tumors in prostate cancer. It has already been proven to predict cancer recurrence after surgery, but this has not yet been shown for patients with high-risk prostate cancer. This is the first study confirming that Decipher predicts a patient's risk of developing cancer recurrence after surgery for high-risk prostate cancer.

Correlation between MRI phenotypes and a genomic classifier of prostate cancer: preliminary findings.

OBJECTIVES: We sought to evaluate the correlation between MRI phenotypes of prostate cancer as defined by PI-RADS v2 and the Decipher Genomic Classifier (used to estimate the risk of early metastases). METHODS: This single-center, retrospective study included 72 nonconsecutive men with prostate cancer who underwent MRI before radical prostatectomy performed between April 2014 and August 2017 and whose MRI registered lesions were microdissected from radical prostatectomy specimens and then profiled using Decipher (89 lesions; 23 MRI invisible [PI-RADS v2 scores ≤ 2] and 66 MRI visible [PI-RADS v2 scores ≥ 3]). Linear regression analysis was used to assess clinicopathologic and MRI predictors of Decipher results; correlation coefficients (r) were used to quantify these associations. AUC was used to determine whether PI-RADS v2 could accurately distinguish between low-risk (Decipher score < 0.45) and intermediate-/high-risk (Decipher score ≥ 0.45) lesions. RESULTS: MRI-visible lesions had higher Decipher scores than MRI-invisible lesions (mean difference 0.22; 95% CI 0.13, 0.32; p < 0.0001); most MRI-invisible lesions (82.6%) were low risk. PI-RADS v2 had moderate correlation with Decipher (r = 0.54) and had higher accuracy (AUC 0.863) than prostate cancer grade groups (AUC 0.780) in peripheral zone lesions (95% CI for difference 0.01, 0.15; p = 0.018). CONCLUSIONS: MRI phenotypes of prostate cancer are positively correlated with Decipher risk groups. Although PI-RADS v2 can accurately distinguish between lesions classified by Decipher as low or intermediate/high risk, some lesions classified as intermediate/high risk by Decipher are invisible on MRI. KEY POINTS: • MRI phenotypes of prostate cancer as defined by PI-RADS v2 positively correlated with a genomic classifier that estimates the risk of early metastases. • Most but not all MRI-invisible lesions had a low risk for early metastases according to the genomic classifier. • MRI could be used in conjunction with genomic assays to identify lesions that may carry biological potential for early metastases.

The Diverse Genomic Landscape of Clinically Low-risk Prostate Cancer.

BACKGROUND: Among men with clinically low-risk prostate cancer, we have previously documented heterogeneity in terms of clinical characteristics and genomic risk scores. OBJECTIVE: To further study the underlying tumor biology of this patient population, by interrogating broader patterns of gene expression among men with clinically low-risk tumors. DESIGN, SETTING, AND PARTICIPANTS: Prostate biopsies from 427 patients considered potentially suitable for active surveillance underwent central pathology review and genome-wide expression profiling. These cases were compared with 1290 higher-risk biopsy cases with diverse clinical features from a prospective genomic registry. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: 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 pathological and biochemical outcomes using multivariable regression analysis. RESULTS AND LIMITATIONS: A total of 408 (96%) biopsies passed RNA quality control. Based on AGR quartiles defined by the high-risk multicenter cases, the University of California, San Francisco (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 three clusters, which were enriched for the previously described PAM50 luminal A, luminal B, and basal subtypes. AGR, but not the clusters, was associated with both pathological (odds ratio 1.34, 95% confidence interval [CI] 1.14-1.58) and biochemical outcomes (hazard ratio 1.53, 95% CI 1.19-1.93). These results may underestimate within-prostate genomic heterogeneity. CONCLUSIONS: Prostate cancers that are homogeneously low risk by traditional characteristics demonstrate substantial diversity at the level of genomic expression. Molecular substratification of low-risk prostate cancer will yield a better understanding of its divergent biology and, in the future may help personalize treatment recommendations. PATIENT SUMMARY: We studied the genomic characteristics of tumors from men diagnosed with low-risk prostate cancer. We found three main subtypes of prostate cancer with divergent tumor biology, similar to what has previously been found in women with breast cancer. In addition, we found that genomic risk scores were associated with worse pathology findings and prostate-specific antigen recurrence after surgery. These results suggest even greater genomic diversity among low-risk patients than has previously been documented with more limited signatures.

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 .

Transcriptome Wide Analysis of Magnetic Resonance Imaging-targeted Biopsy and Matching Surgical Specimens from High-risk Prostate Cancer Patients Treated with Radical Prostatectomy: The Target Must Be Hit.

BACKGROUND: The most suspicious lesions on multiparametric magnetic resonance imaging (MRI) may be representative of final pathology. OBJECTIVE: We connect imaging with high-precision spatial annotation of biopsies and genomic cancer signatures to compare the genomic signals of the index lesion and biopsy cores of adjacent and far away locations. DESIGN, SETTING, AND PARTICIPANTS: Eleven patients diagnosed with high-risk prostate cancer on MRI/transrectal ultrasound-fusion biopsy (Bx) and treated with radical prostatectomy (RP). Five tissue specimens were collected from each patient. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Whole transcriptome RNA-expression was profiled for each sample. Genomic signatures were used to compare signals in MRI invisible versus visible foci using Pearson's correlation and to assess intratumoral heterogeneity using hierarchical clustering. RESULTS AND LIMITATIONS: Ten RP and 27 Bx-samples passed quality control. Gene expression between RP and index Bx, but not adjacent benign samples, was highly correlated. Genomic Gleason grade classifier features measured across the different samples showed concordant expression across Bx and RP tumor samples, while an inverse expression pattern was observed between tumor and benign samples indicating the lack of a strong field-effect. The distribution of low and high Prostate Imaging Reporting and Data System (PI-RADS) samples was 10 and 11, respectively. Genomics of all low PI-RADS samples resembled benign tissue and most high PI-RADS samples resembled cancer tissue. A strong association was observed between PI-RADS version 2 and Decipher as well as the genomic Gleason grade classifier score. Clustering analysis showed that most samples cluster tightly by patient. One patient showed unique tumor biology in index versus secondary lesion suggesting the presence of intrapatient heterogeneity and the utility in profiling multiple foci identified by MRI. CONCLUSIONS: MRI-targeted Bx-genomics show excellent correlation with RP-genomics and confirm the information captured by PI-RADS. Sampling of the target lesion must be precise as correlation between index and benign lesions was not seen. PATIENT SUMMARY: In this report, we tested if targeted prostate sampling using magnetic resonance imaging-fusion biopsy allows to genetically describe index tumors of prostate cancer. We found that imaging genomics correlated well with final prostatectomy provided that the target is hit precisely.

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.

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.

Ability of a Genomic Classifier to Predict Metastasis and Prostate Cancer-specific Mortality after Radiation or Surgery based on Needle Biopsy Specimens.

BACKGROUND: Decipher is a validated genomic classifier developed to determine the biological potential for metastasis after radical prostatectomy (RP). OBJECTIVE: To evaluate the ability of biopsy Decipher to predict metastasis and Prostate cancer-specific mortality (PCSM) in primarily intermediate- to high-risk patients treated with RP or radiation therapy (RT). DESIGN, SETTING, AND PARTICIPANTS: Two hundred and thirty-five patients treated with either RP (n=105) or RT±androgen deprivation therapy (n=130) with available genomic expression profiles generated from diagnostic biopsy specimens from seven tertiary referral centers. The highest-grade core was sampled and Decipher was calculated based on a locked random forest model. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Metastasis and PCSM were the primary and secondary outcomes of the study, respectively. Cox analysis and c-index were used to evaluate the performance of Decipher. RESULTS AND LIMITATIONS: With a median follow-up of 6 yr among censored patients, 34 patients developed metastases and 11 died of prostate cancer. On multivariable analysis, biopsy Decipher remained a significant predictor of metastasis (hazard ratio: 1.37 per 10% increase in score, 95% confidence interval [CI]: 1.06-1.78, p=0.018) after adjusting for clinical variables. For predicting metastasis 5-yr post-biopsy, Cancer of the Prostate Risk Assessment score had a c-index of 0.60 (95% CI: 0.50-0.69), while Cancer of the Prostate Risk Assessment plus biopsy Decipher had a c-index of 0.71 (95% CI: 0.60-0.82). National Comprehensive Cancer Network risk group had a c-index of 0.66 (95% CI: 0.53-0.77), while National Comprehensive Cancer Network plus biopsy Decipher had a c-index of 0.74 (95% CI: 0.66-0.82). Biopsy Decipher was a significant predictor of PCSM (hazard ratio: 1.57 per 10% increase in score, 95% CI: 1.03-2.48, p=0.037), with a 5-yr PCSM rate of 0%, 0%, and 9.4% for Decipher low, intermediate, and high, respectively. CONCLUSIONS: Biopsy Decipher predicted metastasis and PCSM from diagnostic biopsy specimens of primarily intermediate- and high-risk men treated with first-line RT or RP. PATIENT SUMMARY: Biopsy Decipher predicted metastasis and prostate cancer-specific mortality risk from diagnostic biopsy specimens.

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.

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.

Cost-effectiveness of the Decipher Genomic Classifier to Guide Individualized Decisions for Early Radiation Therapy After Prostatectomy for Prostate Cancer.

BACKGROUND: Controversy exists regarding the effectiveness of early adjuvant versus salvage radiation therapy after prostatectomy for prostate cancer. Estimates of prostate cancer progression from the Decipher genomic classifier (GC) could guide informed decision-making and improve the outcomes for patients. MATERIALS AND METHODS: We developed a Markov model to compare the costs and quality-adjusted life years (QALYs) associated with GC-based treatment decisions regarding adjuvant therapy after prostatectomy with those of 2 control strategies: usual care (determined from patterns of care studies) and the alternative of 100% adjuvant radiation therapy. Using the bootstrapping method of sampling with replacement, the cases of 10,000 patients were simulated during a 10-year time horizon, with each subject having individual estimates for cancer progression (according to GC findings) and noncancer mortality (according to age). RESULTS: GC-based care was more effective and less costly than 100% adjuvant radiation therapy and resulted in cost savings up to an assay cost of $11,402. Compared with usual care, GC-based care resulted in more QALYs. Assuming a $4000 assay cost, the incremental cost-effectiveness ratio was $90,833 per QALY, assuming a 7% usage rate of adjuvant radiation therapy. GC-based care was also associated with a 16% reduction in the percentage of patients with distant metastasis at 5 years compared with usual care. CONCLUSION: The Decipher GC could be a cost-effective approach for genomics-driven cancer treatment decisions after prostatectomy, with improvements in estimated clinical outcomes compared with usual care. The individualized decision analytic framework applied in the present study offers a flexible approach to estimate the potential utility of genomic assays for personalized cancer medicine.

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.

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.

Utilization of a Genomic Classifier for Prediction of Metastasis Following Salvage Radiation Therapy after Radical Prostatectomy.

BACKGROUND: Despite salvage radiation therapy (SRT) for recurrent prostate cancer (PCa) after radical prostatectomy (RP), some patients still progress to metastases. Identifying these men would allow them to undergo systemic therapy including testing novel therapies to reduce metastases risk. OBJECTIVE: To test whether the genomic classifier (GC) predicts development of metastatic disease. DESIGN, SETTING, AND PARTICIPANTS: Retrospective multi-center and multi-ethnic cohort study from two academic centers and one Veterans Affairs Medical Center in the United States involving 170 men receiving SRT for recurrent PCa post-RP. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Time from SRT to development of metastatic disease tested using Cox regression, survival c-index, and decision curve analysis. Performance of GC was compared to the Cancer of the Prostate Risk Assessment Score and Briganti risk models based on these metrics. RESULTS AND LIMITATIONS: With a median 5.7 yr follow-up after SRT, 20 patients (12%) developed metastases. On multivariable analysis, for each 0.1 unit increase in GC (scaled from 0 to 1), the hazard ratio for metastasis was 1.58 (95% confidence interval 1.16-2.17; p=0.002). Adjusting for androgen deprivation therapy did not materially change the results. The c-index for GC was 0.85 (95% confidence interval 0.73-0.88) versus 0.63-0.65 for published clinico-pathologic risk models. The 5-yr cumulative incidence of metastasis post-SRT in patients with low, intermediate, and high GC scores was 2.7%, 8.4%, and 33.1%, respectively (p<0.001). CONCLUSIONS: While validation in larger, prospectively collected cohorts is required, these data suggest GC is a strong predictor of metastases among men receiving SRT for recurrent PCa post-RP, accurately identifying men who are excellent candidates for systemic therapy due to their very high-risk of metastases. PATIENT SUMMARY: Genomic classifier and two clinico-pathologic risk models were evaluated on their ability to predict metastases among men receiving salvage radiation therapy for recurrent prostate cancer. Genomic classifier was able to identify candidates for further therapies due to their very high-risk of metastases.

Decipher Genomic Classifier Measured on Prostate Biopsy Predicts Metastasis Risk.

OBJECTIVES: To evaluate the ability of the Decipher genomic classifier in predicting metastasis from analysis of prostate needle biopsy diagnostic tumor tissue specimens. MATERIALS AND METHODS: Fifty-seven patients with available biopsy specimens were identified from a cohort of 169 men treated with radical prostatectomy in a previously reported Decipher validation study at Cleveland Clinic. A Cox multivariable proportional hazards model and survival C-index were used to evaluate the performance of Decipher. RESULTS: With a median follow up of 8 years, 8 patients metastasized and 3 died of prostate cancer. The Decipher plus National Comprehensive Cancer Network (NCCN) model had an improved C-index of 0.88 (95% confidence interval [CI] 0.77-0.96) compared to NCCN alone (C-index 0.75, 95% CI 0.64-0.87). On multivariable analysis, Decipher was the only significant predictor of metastasis when adjusting for age, preoperative prostate-specific antigen and biopsy Gleason score (Decipher hazard ratio per 10% increase 1.72, 95% CI 1.07-2.81, P = .02). CONCLUSION: Biopsy Decipher predicted the risk of metastasis at 10 years post radical prostatectomy. While further validation is required on larger cohorts, preoperative knowledge of Decipher risk derived from biopsy could indicate the need for multimodality therapy and help set patient expectations of therapeutic burden.

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.

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.

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.