Background

Improved clinical predictors for disease progression are needed for localised prostate cancer, since only a subset of patients develop recurrent or refractory disease after first-line treatment. Therefore, we undertook an unbiased analysis to identify RNA biomarkers associated with metastatic progression after prostatectomy.

Methods

Prostate cancer samples from patients treated with radical prostatectomy at three academic institutions were analysed for gene expression by a high-density Affymetrix GeneChip platform, encompassing more than 1 million genomic loci. In a discovery cohort, all protein-coding genes and known long non-coding RNAs were ranked by fold change in expression between tumours that subsequently metastasised versus those that did not. The top ranked gene was then validated for its prognostic value for metastatic progression in three additional independent cohorts. 95% of the gene expression assays were done in a Clinical Laboratory Improvements Amendments certified laboratory facility. All genes were assessed for their ability to predict metastatic progression by receiver-operating-curve area-under-the-curve analyses. Multivariate analyses were done for the primary endpoint of metastatic progression, with variables including Gleason score, preoperative prostate-specific antigen concentration, seminal vesicle invasion, surgical margin status, extracapsular extension, lymph node invasion, and expression of the highest ranked gene.

Findings

1008 patients were included in the study: 545 in the discovery cohort and 463 in the validation cohorts. The long non-coding RNA SChLAP1 was identified as the highest-ranked overexpressed gene in cancers with metastatic progression. Validation in three independent cohorts confirmed the prognostic value of SChLAP1 for metastatic progression. On multivariate modelling, SChLAP1 expression (high vs low) independently predicted metastasis within 10 years (odds ratio [OR] 2·45, 95% CI 1·70-3·53; p<0·0001). The only other variable that independently predicted metastasis within 10 years was Gleason score (8-10 vs 5-7; OR 2·14, 95% CI 1·77-2·58; p<0·0001).

Interpretation

We identified and validated high SChLAP1 expression as significantly prognostic for metastatic disease progression of prostate cancer. Our findings suggest that further development of SChLAP1 as a potential biomarker, for treatment intensification in aggressive prostate cancer, warrants future study.

Funding

Prostate Cancer Foundation, National Institutes of Health, Department of Defense, Early Detection Research Network, Doris Duke Charitable Foundation, and Howard Hughes Medical Institute.

Background

We developed and validated an integrated radiomic-clinicopathologic nomogram (RadClip) for post-surgical biochemical recurrence free survival (bRFS) and adverse pathology (AP) prediction in men with prostate cancer (PCa). RadClip was further compared against extant prognostics tools like CAPRA and Decipher.

Methods

A retrospective study of 198 patients with PCa from four institutions who underwent pre-operative 3 Tesla MRI followed by radical prostatectomy, between 2009 and 2017 with a median 35-month follow-up was performed. Radiomic features were extracted from prostate cancer regions on bi-parametric magnetic resonance imaging (bpMRI). Cox Proportional-Hazards (CPH) model warped with minimum redundancy maximum relevance (MRMR) feature selection was employed to select bpMRI radiomic features for bRFS prediction in the training set (D₁, N = 71). In addition, a bpMRI radiomic risk score (RadS) and associated nomogram, RadClip, were constructed in D₁ and then compared against the Decipher, pre-operative (CAPRA), and post-operative (CAPRA-S) nomograms for bRFS and AP prediction in the testing set (D₂, N = 127).

Findings

"RadClip yielded a higher C-index (0.77, 95% CI 0.65-0.88) compared to CAPRA (0.68, 95% CI 0.57-0.8) and Decipher (0.51, 95% CI 0.33-0.69) and was found to be comparable to CAPRA-S (0.75, 95% CI 0.65-0.85). RadClip resulted in a higher AUC (0.71, 95% CI 0.62-0.81) for predicting AP compared to Decipher (0.66, 95% CI 0.56-0.77) and CAPRA (0.69, 95% CI 0.59-0.79)."

Interpretation

RadClip was more prognostic of bRFS and AP compared to Decipher and CAPRA. It could help pre-operatively identify PCa patients at low risk of biochemical recurrence and AP and who therefore might defer additional therapy.

Funding

The National Institutes of Health, the U.S. Department of Veterans Affairs, and the Department of Defense.

Background

Prostate tumor heterogeneity and biopsy undersampling pose challenges to accurate, individualized risk assessment for men with localized disease.

Objective

To identify and validate a biopsy-based gene expression signature that predicts clinical recurrence, prostate cancer (PCa) death, and adverse pathology.

Design, setting, and participants

Gene expression was quantified by reverse transcription-polymerase chain reaction for three studies-a discovery prostatectomy study (n=441), a biopsy study (n=167), and a prospectively designed, independent clinical validation study (n=395)-testing retrospectively collected needle biopsies from contemporary (1997-2011) patients with low to intermediate clinical risk who were candidates for active surveillance (AS).

Outcome measures and statistical analysis

The main outcome measures defining aggressive PCa were clinical recurrence, PCa death, and adverse pathology at prostatectomy. Cox proportional hazards regression models were used to evaluate the association between gene expression and time to event end points. Results from the prostatectomy and biopsy studies were used to develop and lock a multigene-expression-based signature, called the Genomic Prostate Score (GPS); in the validation study, logistic regression was used to test the association between the GPS and pathologic stage and grade at prostatectomy. Decision-curve analysis and risk profiles were used together with clinical and pathologic characteristics to evaluate clinical utility.

Results and limitations

Of the 732 candidate genes analyzed, 288 (39%) were found to predict clinical recurrence despite heterogeneity and multifocality, and 198 (27%) were predictive of aggressive disease after adjustment for prostate-specific antigen, Gleason score, and clinical stage. Further analysis identified 17 genes representing multiple biological pathways that were combined into the GPS algorithm. In the validation study, GPS predicted high-grade (odds ratio [OR] per 20 GPS units: 2.3; 95% confidence interval [CI], 1.5-3.7; p<0.001) and high-stage (OR per 20 GPS units: 1.9; 95% CI, 1.3-3.0; p=0.003) at surgical pathology. GPS predicted high-grade and/or high-stage disease after controlling for established clinical factors (p<0.005) such as an OR of 2.1 (95% CI, 1.4-3.2) when adjusting for Cancer of the Prostate Risk Assessment score. A limitation of the validation study was the inclusion of men with low-volume intermediate-risk PCa (Gleason score 3+4), for whom some providers would not consider AS.

Conclusions

Genes representing multiple biological pathways discriminate PCa aggressiveness in biopsy tissue despite tumor heterogeneity, multifocality, and limited sampling at time of biopsy. The biopsy-based 17-gene GPS improves prediction of the presence or absence of adverse pathology and may help men with PCa make more informed decisions between AS and immediate treatment.

Patient summary

Prostate cancer (PCa) is often present in multiple locations within the prostate and has variable characteristics. We identified genes with expression associated with aggressive PCa to develop a biopsy-based, multigene signature, the Genomic Prostate Score (GPS). GPS was validated for its ability to predict men who have high-grade or high-stage PCa at diagnosis and may help men diagnosed with PCa decide between active surveillance and immediate definitive treatment.

AIMS: Despite efforts to standardise grading of prostate cancer, even among experts there is still a considerable variation in grading practices. In this study we describe the use of Pathology Imagebase, a novel reference image library, for setting an international standard in prostate cancer grading. METHODS AND RESULTS: The International Society of Urological Pathology (ISUP) recently launched a reference image database supervised by experts. A panel of 24 international experts in prostate pathology reviewed independently microphotographs of 90 cases of prostate needle biopsies with cancer. A linear weighted kappa of 0.67 (95% confidence interval = 0.62-0.72) and consensus was reached in 50 cases. The interobserver weighted kappa varied from 0.48 to 0.89. The highest level of agreement was seen for Gleason score (GS) 3 + 3 = 6 (ISUP grade 1), while higher grades and particularly GS 4 + 3 = 7 (ISUP grade 3) showed considerable disagreement. Once a two-thirds majority was reached, images were moved automatically into a public database available for all ISUP members at www.isupweb.org. Non-members are able to access a limited number of cases. CONCLUSIONS: It is anticipated that the database will assist pathologists to calibrate their grading and, hence, decrease interobserver variability. It will also help to identify instances where definitions of grades need to be clarified.

Background

In the Prostate Cancer Prevention Trial, finasteride selectively suppressed low-grade prostate cancer and significantly reduced the incidence of prostate cancer in men treated with finasteride compared with placebo. However, an apparent increase in high-grade disease was also observed among men randomized to finasteride. We aimed to determine why and hypothesized that there is a grade-dependent response to finasteride.

Methods

From 2007 to 2012, we randomized dynamically by intranet-accessible software 183 men with localized prostate cancer to receive 5mg finasteride or placebo daily in a double-blind study during the 4-6weeks preceding prostatectomy. As the primary end point, the expression of a predefined molecular signature (ERβ, UBE2C, SRD5A2, and VEGF) differentiating high- and low-grade tumors in Gleason grade (GG) 3 areas of finasteride-exposed tumors from those in GG3 areas of placebo-exposed tumors, adjusted for Gleason score (GS) at prostatectomy, was compared. We also determined androgen receptor (AR) levels, Ki-67, and cleaved caspase 3 to evaluate the effects of finasteride on the expression of its downstream target, cell proliferation, and apoptosis, respectively. The expression of these markers was also compared across grades between and within treatment groups. Logistic regression was used to assess the expression of markers.

Findings

We found that the predetermined molecular signature did not distinguish GG3 from GG4 areas in the placebo group. However, AR expression was significantly lower in the GG4 areas of the finasteride group than in those of the placebo group. Within the finasteride group, AR expression was also lower in GG4 than in GG3 areas, but not significantly. Expression of cleaved caspase 3 was significantly increased in both GG3 and GG4 areas in the finasteride group compared to the placebo group, although it was lower in GG4 than in GG3 areas in both groups.

Interpretation

We showed that finasteride's effect on apoptosis and AR expression is tumor grade dependent after short-term intervention. This may explain finasteride's selective suppression of low-grade tumors observed in the PCPT.

Aims

Formal depiction of granulomatous inflammation associated with renal neoplasms has mainly consisted of case reports. Herein, we investigate the clinicopathological features and potential significance of granulomas associated with renal tumours from a large multi-institutional cohort.

Methods and results

One hundred and eleven study cases were collected from 22 institutions, including 57 partial nephrectomies and 54 radical nephrectomies. Patient ages ranged from 27 to 85 years (average = 60.1 years; male = 61%). Renal neoplasms included clear cell renal cell carcinoma (RCC; 86%), papillary RCC (8%), chromophobe RCC (3%), clear cell papillary RCC (1%), mixed epithelial stromal tumour (1%) and oncocytoma (1%). Granulomas were peritumoral in 36%, intratumoral in 24% and both in 40% of cases. Total granuloma count per case ranged from one to 300 (median = 15) with sizes ranging from 0.15 to 15 mm (mean = 1.9 mm). Necrotising granulomas were seen in 14% of cases. Histochemical stains for organisms were performed on 45% of cases (all negative). Sixteen cases (14%) had a prior biopsy/procedure performed, and eight patients had neoadjuvant immunotherapy or chemotherapy. Eleven patients (10%) had a confirmed diagnosis of sarcoidosis, including five in whom sarcoidosis was diagnosed after nephrectomy.

Conclusion

Based on this largest case-series to date, peri-/intratumoral granulomas associated with renal neoplasms may be more common than initially perceived. The extent of granulomatous inflammation can vary widely and may or may not have necrosis with possible aetiologies, including prior procedure or immunotherapy/chemotherapy. Although a clinical association with sarcoidosis is infrequent it can still occur, and the presence of granulomas warrants mention in pathology reports.

Existing tools for post-radical prostatectomy (RP) prostate cancer biochemical recurrence (BCR) prognosis rely on human pathologist-derived parameters such as tumor grade, with the resulting inter-reviewer variability. Genomic companion diagnostic tests such as Decipher tend to be tissue destructive, expensive, and not routinely available in most centers. We present a tissue non-destructive method for automated BCR prognosis, termed "Histotyping", that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Patients from two institutions (n = 214) were used to train Histotyping for identifying high-risk patients based on six features of glandular morphology extracted from RP specimens. Histotyping was validated for post-RP BCR prognosis on a separate set of n = 675 patients from five institutions and compared against Decipher on n = 167 patients. Histotyping was prognostic of BCR in the validation set (p < 0.001, univariable hazard ratio [HR] = 2.83, 95% confidence interval [CI]: 2.03-3.93, concordance index [c-index] = 0.68, median years-to-BCR: 1.7). Histotyping was also prognostic in clinically stratified subsets, such as patients with Gleason grade group 3 (HR = 4.09) and negative surgical margins (HR = 3.26). Histotyping was prognostic independent of grade group, margin status, pathological stage, and preoperative prostate-specific antigen (PSA) (multivariable p < 0.001, HR = 2.09, 95% CI: 1.40-3.10, n = 648). The combination of Histotyping, grade group, and preoperative PSA outperformed Decipher (c-index = 0.75 vs. 0.70, n = 167). These results suggest that a prognostic classifier for prostate cancer based on digital images could serve as an alternative or complement to molecular-based companion diagnostic tests.