The Diverse Genomic Landscape of Clinically Low-risk Prostate Cancer

Cooperberg, Matthew R.; Erho, Nicholas; Chan, June M.; Feng, Felix Y.; Fishbane, Nick; Zhao, Shuang G.; Simko, Jeffry P.; Cowan, Janet E.; Lehrer, Jonathan; Alshalalfa, Mohammed; Kolisnik, Tyler; Chelliserry, Jijumon; Margrave, Jennifer; Aranes, Maria; Plessis, Marguerite du; Buerki, Christine; Tenggara, Imelda; Davicioni, Elai; Carroll, Peter R.

doi:10.1016/j.eururo.2018.05.014

Cited by 61 publications

(39 citation statements)

References 30 publications

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“…As these cohorts were orientated mainly at particular signatures or products and were not intended to use as translational cohorts for further biomarker research, they do not adhere to our selection criteria. However, an excellent overview and recent validation of these cohorts in a large group of patients with clinically low‐risk prostate cancer ( n = 408) was conducted by Cooperberg et al …”

Section: Review Methodologymentioning

confidence: 99%

An overview of translational prostate cancer cohorts for prognostic and predictive studies

Tolkach

Kristiansen

2018

Histopathology

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The aim of this review is to describe the characteristics of patient cohorts commonly used for translational biomarker research in prostate cancer and to outline the most prominent contemporary cohorts which serve as a source of prognostic and predictive biomarkers. A non‐systematic review of the literature was performed to identify and summarise well‐characterized translational prostate cancer cohorts that provide state‐of‐the‐art characterization of (i) primary and (ii) metastatic and castration‐resistant prostate cancer. The main advantages and features of these cohorts are a substantial number of patients, unique patient groups, comprehensive genetic characterisation of tumours using multi‐omics/next‐generation sequencing approaches, high‐quality control standards and fully or partially open data for the research community. This overview includes the contemporary cohorts which serve as a rich source of new targets for prognostic and predictive biomarkers as well as a reference database for validation of known biomarkers, therefore representing the cohorts whose impact extends over the current state of biomarker research into the near future (5–10 years).

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Section: Review Methodologymentioning

confidence: 99%

An overview of translational prostate cancer cohorts for prognostic and predictive studies

Tolkach

Kristiansen

2018

Histopathology

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“…The initial list of candidate markers included 151 genes that were selected based on the following criteria [17]: (1) have significant differential gene expression in prostate tumor versus normal comparison; (2) are regulated by androgen; (3) are associated with prognosis of prostate cancer; (4) are associated with the ETS family of transcription factors; (5) are commonly rearranged in prostate cancer; (6) are involved in prostate cancer cell invasion; (7) are associated with multiple malignancies; (8) or can distinguish prostate epithelial from stromal cells (Table S1). These 151 genes were incorporated into a NanoString Code Set, which was used to identify differential mRNA expression in a series of PCa samples across clinical outcomes.…”

Section: Selection Of Biomarker Candidatesmentioning

confidence: 99%

“…Prostate cancer (PCa) is the most commonly diagnosed cancer in men, except for superficial skin cancer, and the second leading cause of death due to cancer among American men [1,2]. Prostate cancer has a complex disease spectrum, ranging from clinically indolent to aggressive subtypes with a high degree of molecular and cellular heterogeneity [3][4][5][6][7]. In order to provide optimal personalized management of the disease, both the physician and the patient need to consider if the disease is likely or unlikely to progress based on biomarkers and imaging tests and to then select the best course of action, either active surveillance for benign and low risk tumor or treatment for a tumor that is likely to progress.…”

Section: Introductionmentioning

confidence: 99%

“…There is a critical unmet need to distinguish early between indolent and aggressive PCa, so that the proper treatment can be selected while overtreatment of indolent disease can be avoided. Although recent decades have seen the development of several RNA-based panels directed at prognosis for PCa [5,13,14], none of these has yet received endorsement as the early marker of choice for future progression, and the genes/proteins represented in the various assays do not overlap. In addition to mRNA-based biomarker panels, recent efforts have also explored the possibility of using protein-based biomarkers in PCa, most focused on the identification and verification of differentially abundant proteins in case-control studies, including the development of urinary protein signatures for extracapsular PCa [15].…”

Section: Introductionmentioning

confidence: 99%

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Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression

Gao

Wang

Chen

et al. 2020

Cancers

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Although ~40% of screen-detected prostate cancers (PCa) are indolent, advanced-stage PCa is a lethal disease with 5-year survival rates around 29%. Identification of biomarkers for early detection of aggressive disease is a key challenge. Starting with 52 candidate biomarkers, selected from existing PCa genomics datasets and known PCa driver genes, we used targeted mass spectrometry to quantify proteins that significantly differed in primary tumors from PCa patients treated with radical prostatectomy (RP) across three study outcomes: (i) metastasis ≥1-year post-RP, (ii) biochemical recurrence ≥1-year post-RP, and (iii) no progression after ≥10 years post-RP. Sixteen proteins that differed significantly in an initial set of 105 samples were evaluated in the entire cohort (n = 338). A five-protein classifier which combined FOLH1, KLK3, TGFB1, SPARC, and CAMKK2 with existing clinical and pathological standard of care variables demonstrated significant improvement in predicting distant metastasis, achieving an area under the receiver-operating characteristic curve of 0.92 (0.86, 0.99, p = 0.001) and a negative predictive value of 92% in the training/testing analysis. This classifier has the potential to stratify patients based on risk of aggressive, metastatic PCa that will require early intervention compared to low risk patients who could be managed through active surveillance.

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“…Specimen collection and sample processing were conducted as described previously [13,14]. The prostate needle biopsy core with highest grade and percentage of core involved with tumor was sampled for the Decipher assay, a CAP/CLIA clinical-grade whole-transcriptome assay.…”

Section: Study Cohortmentioning

confidence: 99%

Decipher identifies men with otherwise clinically favorable-intermediate risk disease who may not be good candidates for active surveillance

Herlemann

Huang

Alam

et al. 2019

Prostate Cancer Prostatic Dis

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Background We aimed to validate Decipher to predict adverse pathology (AP) at radical prostatectomy (RP) in men with National Comprehensive Cancer Network (NCCN) favorable-intermediate risk (F-IR) prostate cancer (PCa), and to better select FIR candidates for active surveillance (AS). Methods In all, 647 patients diagnosed with NCCN very low/low risk (VL/LR) or FIR prostate cancer were identified from a multi-institutional PCa biopsy database; all underwent RP with complete postoperative clinicopathological information and Decipher genomic risk scores. The performance of all risk assessment tools was evaluated using logistic regression model for the endpoint of AP, defined as grade group 3−5, pT3b or higher, or lymph node invasion. Results The median age was 61 years (interquartile range 56-66) for 220 patients with NCCN FIR disease, 53% classified as low-risk by Cancer of the Prostate Risk Assessment (CAPRA 0−2) and 47% as intermediate-risk (CAPRA 3−5). Decipher classified 79%, 13% and 8% of men as low-, intermediate-and high-risk with 13%, 10%, and 41% rate of AP, respectively. Decipher was an independent predictor of AP with an odds ratio of 1.34 per 0.1 unit increased (p value = 0.002) and remained significant when adjusting by CAPRA. Notably, FIR with Decipher low or intermediate score did not associate with significantly higher odds of AP compared to VL/LR. Conclusions NCCN risk groups, including FIR , are highly heterogeneous and should be replaced with multivariable riskstratification. In particular, incorporating Decipher may be useful for safely expanding the use of AS in this patient population.

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The Diverse Genomic Landscape of Clinically Low-risk Prostate Cancer

Cited by 61 publications

References 30 publications

An overview of translational prostate cancer cohorts for prognostic and predictive studies

An overview of translational prostate cancer cohorts for prognostic and predictive studies

Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression

Decipher identifies men with otherwise clinically favorable-intermediate risk disease who may not be good candidates for active surveillance

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