Decipher Genomic Classifier Measured on Prostate Biopsy Predicts Metastasis Risk

Klein, Eric A.; Haddad, Zaid; Yousefi, Kasra; Lam, Lucia L.C.; Wang, Qiqi; Choeurng, Voleak; Palmer-Aronsten, Beatrix; Buerki, Christine; Davicioni, Elai; Li, Jianbo; Kattan, Michael W.; Stephenson, Andrew J.; Magi‐Galluzzi, Cristina

doi:10.1016/j.urology.2016.01.012

Cited by 140 publications

(109 citation statements)

References 15 publications

(19 reference statements)

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“…Studies on needle biopsy cohort would be required for its implementation in the presurgical setting. The spatial resolution afforded by a histology-based assay such as FISH would facilitate sensitive assessment of individual cancer foci (43,44) in a context of tumor heterogeneity and bypass the need for nucleic acid extraction from bulk tumor tissue required by several recently developed commercial assays based on gene expression signatures, such as Prolaris and Decipher (45)(46)(47)(48). Of course, head to head comparison with these emerging assays would have to be done to select the best test for a given clinical context.…”

Section: Discussionmentioning

confidence: 99%

Genomic Gain of 16p13.3 in Prostate Cancer Predicts Poor Clinical Outcome after Surgical Intervention

Bramhecha

Guérard

Rouzbeh

et al. 2018

Molecular Cancer Research

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Identifying tumors with high metastatic potential is key to improving the clinical management of prostate cancer. Recently, we characterized a chromosome 16p13.3 gain frequently observed in prostate cancer metastases and now demonstrate the prognostic value of this genomic alteration in surgically treated prostate cancer. Dual-color FISH was used to detect 16p13.3 gain on a human tissue microarray representing 304 primary radical prostatectomy (RP) cases with clinical followup data. The results were validated in an external dataset. The 16p13.3 gain was detected in 42% (113/267) of the specimens scorable by FISH and was significantly associated with clinicopathologic features of aggressive prostate cancer, including high preoperative PSA (P ¼ 0.03) levels, high Gleason score (GS, P < 0.0001), advanced pathologic tumor stage (P < 0.0001), and positive surgical margins (P ¼ 0.009). The 16p13.3 gain predicted biochemical recurrence (BCR) in the overall cohort (log-rank P ¼ 0.0005), and in subsets of patients with PSA 10 or GS 7 (log-rank P ¼ 0.02 and P ¼ 0.006, respectively). Moreover, combining the 16p13.3 gain status with standard prognostic markers improved BCR risk stratification and identified a subgroup of patients with high probability of recurrence. The 16p13.3 gain status was also associated with an increased risk of developing distant metastases (log-rank P ¼ 0.03) further substantiating its role in prostate cancer progression.Implications: This study demonstrates the prognostic significance of the 16p13.3 genomic gain in primary prostate tumors, suggesting potential utility in the clinical management of the disease by identifying patients at high risk of recurrence who may benefit from adjuvant therapies.

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Section: Discussionmentioning

confidence: 99%

Genomic Gain of 16p13.3 in Prostate Cancer Predicts Poor Clinical Outcome after Surgical Intervention

Bramhecha

Guérard

Rouzbeh

et al. 2018

Molecular Cancer Research

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“…Both the 17‐gene Oncotype DX and the 31‐gene Prolaris improve risk stratification of patients with high risk of PC recurrence at time of diagnosis (Albala et al ., 2016; Cuzick et al ., 2011; Klein et al ., 2014; Knezevic et al ., 2013; Oderda et al ., 2017) and after radical prostatectomy (RP) (Cooperberg et al ., 2013; Cullen et al ., 2015). The 22‐gene Decipher predicts metastasis following RP (Erho et al ., 2013; Karnes et al ., 2013; Klein et al ., 2016). While these and other biomarkers assist decision making and thus improve patient management, their clinical application requires further validation (Lamy et al ., 2017; Martin, 2016; McGrath et al ., 2016; Patel and Gnanapragasam, 2016; Ross et al ., 2016; Zhuang and Johnson, 2016).…”

Section: Introductionmentioning

confidence: 99%

Construction of a set of novel and robust gene expression signatures predicting prostate cancer recurrence

Jiang

Mei

et al. 2018

Molecular Oncology

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We report here numerous novel genes and multiple new signatures which robustly predict prostate cancer (PC) recurrence. We extracted 696 differentially expressed genes relative to a reported PC signature from the TCGA dataset (n = 492) and built a 15‐gene signature (SigMuc1NW) using Elastic‐net with 10‐fold cross‐validation through analyzing their expressions at 1.5 standard deviation/SD below and 2 SD above a population mean. SigMuc1NW predicts biochemical recurrence (BCR) following surgery with 56.4% sensitivity, 72.6% specificity, and 63.24 median months disease free (MMDF) (P = 1.12e‐12). The prediction accuracy is improved with the use of SigMuc1NW's cutpoint (P = 3e‐15) and is further enhanced (sensitivity 67%, specificity 75.7%, MMDF 45.2, P = 0) when all 15 genes were analyzed through their cutpoints instead of their SDs. These genes individually associate with BCR using either SD or cutpoint as the cutoff points. Eight of 15 genes are individual risk factors after adjusting for age at diagnosis, Gleason score, surgical margin, and tumor stage. Eleven of 15 genes are novel to PC. SigMuc1NW discriminates BCR with time‐dependent AUC (tAUC) values of 76.6% at 11.5 months (76.6%–11.5 m), 73.8%‐22.3 m, 78.5%‐32.1 m, and 76.4%–48.4 m. SigMuc1NW is correlated with adverse features of PC, high Gleason scores (odds ratio/OR 1.48, P < 2e‐16), and advanced tumor stages (OR 1.33, P = 4.37e‐13). SigMuc1NW remains an independent risk factor of BCR (HR 2.44, 95% CI 1.53–3.87, P = 1.62e‐4) after adjusting for age at diagnosis, Gleason score, surgical margin, and tumor stage. In an independent PC (MSKCC) cohort (n = 140), these 15 genes were altered in PC vs normal tissue, metastatic PCs vs primary PCs, and recurrent PCs vs nonrecurrent PCs. Importantly, a 10‐gene subsignature SigMuc1NW1 predicts BCR in MSKCC (P = 3.11e‐15) and TCGA (P = 3.13e‐12); SigMuc1NW1 discriminates BCR at 18.4 m with tAUC as 82.5%. Collectively, our analyses support SigMuc1NW as a novel and robust signature in predicting BCR of PC.

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“…On multivariate analysis, GC score from biopsy was the only significant predictor of metastasis within 10 years after RP with an AUC of 0.80 (95% CI 0.58-0.95). [47] A second biopsy study was run on biopsy specimens with National Comprehensive Cancer Network (NCCN) intermediate or high-risk prostate cancer who received definitive radiation with at least 6 months of androgen deprivation therapy. From the 100 patients evaluated, 18 developed metastases and on multivariate analysis each 0.1 unit increase in GC score was significantly associated with time to distant metastasis (HR 1.36, p = 0.024).…”

Section: Currently Available Testsmentioning

confidence: 99%

Tissue-based biomarkers in prostate cancer

Clinton

Bagrodia²,

Lotan³

et al. 2017

Expert Review of Precision Medicine and Drug Development

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Introduction Prostate cancer is a heterogeneous disease. Existing risk stratification tools based on standard clinlicopathologic variables (prostate specific antigen [PSA], Gleason score, and tumor stage) provide a modest degree of predictive ability. Advances in high-throughput sequencing has led to the development of several novel tissue-based biomarkers that can improve prognostication in prostate cancer management. Areas Covered The authors review commercially-available, tissue-based biomarker assays that improve upon existing risk-stratification tools in several areas of prostate cancer management, including the appropriateness of active surveillance and aiding in decision making regarding the use of adjuvant therapy. Additionally, some of the obstacles to the widespread adoption of these biomarkers and discuss several investigational sources of new biomarkers are discussed. Expert Commentary Work is ongoing to answer pertinent clinical questions in prostate cancer management including which patients should undergo biopsy, active surveillance, receive adjuvant therapy, and what systemic therapy is best in the first-line. Incorporation into novel biomarkers may allow for the incorporation of a ‘personalized’ approach to management. Further validation will be required and questions of cost must be considered before wide scale adoption of these biomarkers. Tumor heterogeneity may impose a ceiling on the prognostic ability of biomarkers using currently available techniques.

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Decipher Genomic Classifier Measured on Prostate Biopsy Predicts Metastasis Risk

Cited by 140 publications

References 15 publications

Genomic Gain of 16p13.3 in Prostate Cancer Predicts Poor Clinical Outcome after Surgical Intervention

Genomic Gain of 16p13.3 in Prostate Cancer Predicts Poor Clinical Outcome after Surgical Intervention

Construction of a set of novel and robust gene expression signatures predicting prostate cancer recurrence

Tissue-based biomarkers in prostate cancer

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