Purpose: Latent grade group ≥2 prostate cancer can impact the performance of active surveillance protocols. To date, molecular biomarkers for active surveillance have relied solely on RNA or protein. We trained and independently validated multimodal (mRNA abundance, DNA methylation, and/or DNA copy number) biomarkers that more accurately separate grade group 1 from grade group ≥2 cancers. Materials and Methods: Low- and intermediate-risk prostate cancer patients were assigned to training (n=333) and validation (n=202) cohorts. We profiled the abundance of 342 mRNAs, 100 DNA copy number alteration loci, and 14 hypermethylation sites at 2 locations per tumor. Using the training cohort with cross-validation, we evaluated methods for training classifiers of pathological grade group ≥2 in centrally reviewed radical prostatectomies. We trained 2 distinct classifiers, PRONTO-e and PRONTO-m, and validated them in an independent radical prostatectomy cohort. Results: PRONTO-e comprises 353 mRNA and copy number alteration features. PRONTO-m includes 94 clinical, mRNAs, copy number alterations, and methylation features at 14 and 12 loci, respectively. In independent validation, PRONTO-e and PRONTO-m predicted grade group ≥2 with respective true-positive rates of 0.81 and 0.76, and false-positive rates of 0.43 and 0.26. Both classifiers were resistant to sampling error and identified more upgrading cases than a well-validated presurgical risk calculator, CAPRA (Cancer of the Prostate Risk Assessment; P < .001). Conclusions: Two grade group classifiers with superior accuracy were developed by incorporating RNA and DNA features and validated in an independent cohort. Upon further validation in biopsy samples, classifiers with these performance characteristics could refine selection of men for active surveillance, extending their treatment-free survival and intervals between surveillance.

Purpose: Latent Grade Group (GG) ≥2 prostate cancer can impact the performance of active surveillance (AS) protocols. To date, molecular biomarkers for AS have relied solely on RNA or protein. We trained and independently validated multimodal (mRNA abundance, DNA methylation, and DNA copy number) biomarkers that more accurately separate GG1 from GG≥2 cancers. Materials and Methods: Low- and intermediate-risk prostate cancer patients were assigned to training (n=333) and validation (n=202) cohorts. We profiled the abundance of 342 mRNAs, 100 DNA copy number aberration (CNA) loci and 14 hypermethylation sites at two locations per tumor. Using the training cohort with cross- validation, we evaluated methods for training classifiers of pathologic GG≥2 in centrally reviewed radical prostectomies (RPs). We trained two distinct classifiers, PRONTO-e and PRONTO-m, and validated them in an independent RP cohort. Results: PRONTO-e comprises 353 mRNA and CNA features. PRONTO-m includes 94 clinical, mRNAs, CNAs and methylation features at 14 and 12 loci, respectively. In independent validation, PRONTO-e and PRONTO-m predicted GG≥2 with respective true positive rates of 0.81 and 0.76, false positive rates of 0.43 and 0.26. Both classifiers were resistant to sampling error and identified more upgraded men than a well-validated pre-surgical risk calculator, CAPRA (p <0.001). Conclusions: Two GG classifiers with superior accuracy were developed by incorporating RNA and DNA features and validated in an independent cohort. Upon further validation in biopsy samples, classifiers with these performance characteristics could refine selection of men for AS, extending their treatment-free survival and intervals between surveillance. Citation Format: Anna Y. Lee, David M. Berman, Robert Lesurf, Palak G. Patel, Walead Ebrahimizadeh, Jane Bayani, Laura A. Lee, Nadia Boufaied, Shamini Selvarajah, Tamara Jamaspishvili, Karl-Philippe Guérard, Dan Dion, Atsunari Kawashima, Gina M. Clarke, Nathan How, Chelsea L. Jackson, Eleonora Scarlata, Khurram Siddiqui, John B.A. Okello, Armen G. Aprikian, Madeleine Moussa, Antonio Finelli, Joseph Chin, Fadi Brimo, Glenn Bauman, Andrew Loblaw, Vasundara Venkateswaran, Ralph Buttyan, Simone Chevalier, Axel Thomson, Paul C. Park, D. Robert Siemens, Jacques Lapointe, Paul C. Boutros, John M.S. Bartlett. Multimodal biomarkers that predict the presence of Gleason pattern 4: Potential impact for active surveillance [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr B046.

Background: Phosphatase and tensin homolog (PTEN) loss has long been associated with adverse findings in early prostate cancer. Studies to date have yet to employ quantitative methods (qPTEN) for measuring of prognostically relevant amounts of PTEN loss in postsurgical settings and demonstrate its clinical application. Methods: PTEN protein levels were measured by immunohistochemistry in radical prostatectomy samples from training (n = 410) and validation (n = 272) cohorts. PTEN loss was quantified per cancer cell and per tissue microarray core. Thresholds for identifying clinically relevant PTEN loss were determined using log-rank statistics in the training cohort. Univariate (Kaplan-Meier) and multivariate (Cox proportional hazards) analyses on various subpopulations were performed to assess biochemical recurrence-free survival (BRFS) and were independently validated. All statistical tests were two-sided. Results: PTEN loss in more than 65% cancer cells was most clinically relevant and had statistically significant association with reduced BRFS in training (hazard ratio [HR] = 2.48, 95% confidence interval [CI] = 1.59 to 3.87; P < .001) and validation cohorts (HR = 4.22, 95% CI = 2.01 to 8.83; P < .001). The qPTEN scoring method identified patients who recurred within 5.4 years after surgery (P < .001). In men with favorable risk of biochemical recurrence (Cancer of the Prostate Risk Assessment - Postsurgical scores <5 and no adverse pathological features), qPTEN identified a subset of patients with shorter BRFS (HR = 5.52, 95% CI = 2.36 to 12.90; P < .001) who may be considered for intensified monitoring and/or adjuvant therapy. Conclusions: Compared with previous qualitative approaches, qPTEN improves risk stratification of postradical prostatectomy patients and may be considered as a complementary tool to guide disease management after surgery.

We conducted a systematic review with meta-analysis and qualitative synthesis. This study aims to characterize pseudarthrosis after long-segment fusion in spinal deformity by identifying incidence rates by etiology, risk factors for its development, and common features. Pseudarthrosis can be a painful and debilitating complication of spinal fusion that may require reoperation. It is poorly characterized in the setting of spinal deformity. The MEDLINE, EMBASE, and Cochrane databases were searched for clinical research including spinal deformity patients treated with long-segment fusions reporting pseudarthrosis as a complication. Meta-analysis was performed on etiologic subsets of the studies to calculate incidence rates for pseudarthrosis. Qualitative synthesis was performed to identify characteristics of and risk factors for pseudarthrosis. The review found 162 articles reporting outcomes for 16,938 patients which met inclusion criteria. In general, the included studies were of medium to low quality according to recommended reporting standards and study design. Meta-analysis calculated an incidence of 1.4% (95% CI 0.9–1.8%) for pseudarthrosis in adolescent idiopathic scoliosis, 2.2% (95% CI 1.3–3.2%) in neuromuscular scoliosis, and 6.3% (95% CI 4.3–8.2%) in adult spinal deformity. Risk factors for pseudarthrosis include age over 55, construct length greater than 12 segments, smoking, thoracolumbar kyphosis greater than 20°, and fusion to the sacrum. Choice of graft material, pre-operative coronal alignment, post-operative analgesics, and sex have no significant impact on fusion rates. Older patients with greater deformity requiring more extensive instrumentation are at higher risk for pseudarthrosis. Overall incidence of pseudarthrosis requiring reoperation is low in adult populations and very low in adolescent populations.

Background We identify and validate accurate diagnostic biomarkers for prostate cancer through a systematic evaluation of DNA methylation alterations. Materials and methods We assembled three early prostate cancer cohorts (total patients = 699) from which we collected and processed over 1300 prostatectomy tissue samples for DNA extraction. Using real‐time methylation‐specific PCR, we measured normalized methylation levels at 15 frequently methylated loci. After partitioning sample sets into independent training and validation cohorts, classifiers were developed using logistic regression, analyzed, and validated. Results In the training dataset, DNA methylation levels at 7 of 15 genomic loci (glutathione S‐transferase Pi 1 [ GSTP1 ], CCDC181, hyaluronan, and proteoglycan link protein 3 [ HAPLN3 ], GSTM2, growth arrest‐specific 6 [ GAS6 ], RASSF1, and APC ) showed large differences between cancer and benign samples. The best binary classifier was the GAS6/GSTP1/HAPLN3 logistic regression model, with an area under these curves of 0.97, which showed a sensitivity of 94%, and a specificity of 93% after external validation. Conclusion We created and validated a multigene model for the classification of benign and malignant prostate tissue. With false positive and negative rates below 7%, this three‐gene biomarker represents a promising basis for more accurate prostate cancer diagnosis.

Work by T.J., P.P. and D.M.B. was awarded by Prostate Cancer Canada (PCC) and is proudly funded by the Movember Foundation-Grant #T2014-01. T.J. was supported by a Transformative Pathology Fellowship funded by the Ontario Institute for Cancer Research (OICR) through funding provided by the Government of Ontario. P.P was supported by Terry Fox Transdisciplinary Fellowship. V.O., A.-M.M.-M and F.S. are researchers of the Centre de recherche du Centre hospitalier de l'Universitéde Montréal which receives support from the FRQS. Biobanking was done in collaboration with the Réseau de Recherche sur le cancer of the Fonds de Recherche Québec - Santé (FRQS) that is affiliated with the Canadian Tumor Repository Network (CTRNet). TMA construction was supported by the Terry Fox Research Institute. F. Saad holds the Montreal University Research Chair in Prostate Cancer. J.A.S. and T.V. are supported by FAPESP grant no. 2015/09111-5. J.S. by CNPq Bolsa Produtividade em Pesquisa - Nàvel: PQ-1B grant no. 306864/2014-2. M.K. is supported by funding from Prostate Cancer Cancer, Terry Fox Research Institute-Canadian Prostate Cancer Biomarker Network and Canadian Institutes for Health Research.

Formalin-fixed paraffin embedded tissue (FFPET) represents a valuable, well-annotated substrate for molecular investigations. The utility of FFPET in molecular analysis is complicated both by heterogeneous tissue composition and low yields when extracting nucleic acids. A literature search revealed a paucity of protocols addressing these issues, and none that showed a validated method for simultaneous extraction of RNA and DNA from regions of interest in FFPET. This method addresses both issues. Tissue specificity was achieved by mapping cancer areas of interest on microscope slides and transferring annotations onto FFPET blocks. Tissue cores were harvested from areas of interest using 0.6 mm microarray punches. Nucleic acid extraction was performed using a commercial FFPET extraction system, with modifications to homogenization, deparaffinization, and Proteinase K digestion steps to improve tissue digestion and increase nucleic acid yields. The modified protocol yields sufficient quantity and quality of nucleic acids for use in a number of downstream analyses, including a multi-analyte gene expression platform, as well as reverse transcriptase coupled real time PCR analysis of mRNA expression, and methylation-specific PCR (MSP) analysis of DNA methylation.