Dataset Information

Evaluation of a genomic classifier in radical prostatectomy patients with lymph node metastasis.

ABSTRACT: OBJECTIVE:To evaluate the performance of the Decipher test in predicting lymph node invasion (LNI) on radical prostatectomy (RP) specimens. METHODS:We identified 1,987 consecutive patients with RP who received the Decipher test between February and August 2015 (contemporary cohort). In the contemporary cohort, only the Decipher score from RP specimens was available for analysis. In addition, we identified a consecutive cohort of patients treated with RP between 2006 and 2012 at the University of California, San Diego, with LNI upon pathologic examination (retrospective cohort). The retrospective cohort yielded seven, 22, and 18 tissue specimens from prostate biopsy, RP, and lymph nodes (LNs) for individual patients, respectively. Univariable and multivariable logistic regression analyses were used to evaluate the performance of Decipher in the contemporary cohort with LNI as the endpoint. In the retrospective cohort, concordance of risk groups was assessed using validated cut-points for low (<0.45), intermediate (0.45-0.60), and high (>0.60) Decipher scores. RESULTS:In the contemporary cohort, 51 (2.6%) patients had LNI. Decipher had an odds ratio of 1.73 (95% confidence interval, 1.46-2.05) and 1.42 (95% confidence interval, 1.19-1.7) per 10% increase in score on univariable and multivariable (adjusting for pathologic Gleason score, extraprostatic extension, and seminal vesicle invasion), respectively. No significant difference in the clinical and pathologic characteristics between the LN positive patients of contemporary and retrospective cohorts was observed (all P>0.05). Accordingly, among LN-positive patients in the contemporary cohort and retrospective cohort, 80% and 77% had Decipher high risk scores (P=1). In the retrospective cohort, prostate biopsy cores with the highest Gleason grade and percentage of tumor involvement had 86% Decipher risk concordance with both RP and LN specimens. CONCLUSION:Decipher scores were highly concordant between pre- and post-surgical specimens. Further, Decipher scores from RP tissue were predictive of LNI at RP. If validated in a larger cohort of prostate biopsy specimens for prediction of adverse pathology at RP, Decipher may be useful for improved pre-operative staging.

SUBMITTER: Lee HJ

PROVIDER: S-EPMC4935177 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Evaluation of a genomic classifier in radical prostatectomy patients with lymph node metastasis.

Lee Hak J HJ Yousefi Kasra K Haddad Zaid Z Abdollah Firas F Lam Lucia Lc LL Shin Heesun H Alshalalfa Mohammed M Godebu Elana E Wang Song S Shabaik Ahmed A Davicioni Elai E Kane Christopher J CJ

Research and reports in urology 20160628

<h4>Objective</h4>To evaluate the performance of the Decipher test in predicting lymph node invasion (LNI) on radical prostatectomy (RP) specimens.<h4>Methods</h4>We identified 1,987 consecutive patients with RP who received the Decipher test between February and August 2015 (contemporary cohort). In the contemporary cohort, only the Decipher score from RP specimens was available for analysis. In addition, we identified a consecutive cohort of patients treated with RP between 2006 and 2012 at th ...[more]

PMID: 27419104

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Project description:Purpose: Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis. Methods: A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry that underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 that experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set. Results: Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67 - 0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression. Conclusion: A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.

Project description:PurposeClinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis.MethodsA nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry who underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 who experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases--men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set.ResultsExpression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67-0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression.ConclusionA genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.

Project description: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. Method: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 235 patients with available genomic data. ROC and decision curves, competing risk and weighted regression models were used to assess genomic classifier performance. Patients treated with RP between 2000 and 2006 were identified from the Mayo Clinic tumor registry for a case-cohort study design. This involved identification of all patients with metastasis and a representative of the full cohort .Thus, men at high risk of recurrence post-RP (open/robotic) with no prior neoadjuvant/prostate cancer treatment were selected based on any of preoperative PSA >20 ng/mL, pathological Gleason score (GS) â¥8, SVI, or GPSM (GS; preoperative PSA; SVI; margins) score â¥10.The cohort of 1,010 men included 73 cases with metastasis as evidenced by CT or bone scan. A 20% random sample (n=202) was drawn from the cohort. This included 19 of 73 metastatic cases. To increase sampling of metastasis, the remaining 54 metastatic cases were added, resulting in a final study set of 256 patients. 21 samples did not pass QC and were eliminated from this study. Patients not experiencing metastasis regardless of BCR (defined as follow-up PSA â¥0.4 ng/mL >30 days post-RP) were censored at last follow-up. The study was approved by Mayo Clinic Institutional Review Board.

Project description:BackgroundLymph-node (LN) metastasis in prostate cancer (PC) is a main risk factor for tumor recurrence after radical prostatectomy (RP). Molecular analysis facilitates detection of small-volume LN metastases with higher sensitivity than histopathology. We aimed to prospectively evaluate six candidate gene markers for detection of pelvic LN metastases and to determine their ability to predict biochemical recurrence-free survival (bRFS) in patients treated with RP.MethodsThe expression of kallikrein 2, 3, and 4 (KLK2, KLK3, and KLK4), prostate-specific membrane antigen (PSMA), transmembrane serine protease 2 (TMPRSS2) and transient receptor potential cation channel subfamily M member 8 (TRPM8) was assessed using qPCR. We analyzed LNs from 111 patients (intermediate PC, n = 32 (29%); high-risk PC, n = 79 (71%)) who underwent RP and extended pelvic lymph-node dissection without neoadjuvant treatment.ResultsOverall, 2411 LNs were examined by molecular and histopathologic examination. Histopathology detected 69 LN metastases in 28 (25%) patients. KLK2 and KLK3 diagnostically performed best and classified all pN1-patients correctly as molecular node-positive (molN1/pN1). The concordance on LN level was best for KLK3 (96%). KLK2, KLK3, KLK4, PSMA, TMPRSS2, and TRPM8 reclassified 27 (24%), 32 (29%), 29 (26%), 8 (7%), 13 (12%), and 23 (21%) pN0-patients, respectively, as node-positive (pN0/molN1). On multivariable cox regression analysis molecular LN status (molN1 vs. molN0) using KLK3 (HR 4.0, p = 0.04) and TMPRSS2 (HR 5.1, p = 0.02) were independent predictors of bRFS. Median bRFS was shorter in patients with only molecular positive LNs (molN1/pN0) for KLK3 (24 months, p = 0.001) and for TMPRSS2 (12 months, p < 0.001) compared to patients with negative nodes (molN0/pN0) (median bRFS not reached).ConclusionsFor diagnostic purposes, KLK3 showed highest concordance with histopathology for detection of LN metastases in PC patients undergoing RP. For prognostic purposes, KLK3 and TMPRSS2 expression were superior to histopathologic LN status and other transcripts tested for molecular LN status. We suggest a combined KLK3/TMPRSS2 panel as a valuable diagnostic and prognostic tool for molecular LN analysis.

Dataset Information

Evaluation of a genomic classifier in radical prostatectomy patients with lymph node metastasis.

Publications

Evaluation of a genomic classifier in radical prostatectomy patients with lymph node metastasis.

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