Association between Incidental Pelvic Inflammation and Aggressive Prostate Cancer

The impact of pelvic inflammation on prostate cancer (PCa) biology and aggressive phenotype has never been studied. Our study objective was to evaluate the role of pelvic inflammation on PCa aggressiveness and its association with clinical outcomes in patients following radical prostatectomy (RP). This study has been conducted on a retrospective single-institutional consecutive cohort of 2278 patients who underwent robot-assisted laparoscopic prostatectomy (RALP) between 01/2013 and 10/2019. Data from 2085 patients were analyzed to study the association between pelvic inflammation and adverse pathology (AP), defined as Gleason Grade Group (GGG) > 2 and ≥ pT3 stage, at resection. In a subset of 1997 patients, the association between pelvic inflammation and biochemical recurrence (BCR) was studied. Alteration in tumor transcriptome and inflammatory markers in patients with and without pelvic inflammation were studied using microarray analysis, immunohistochemistry, and culture supernatants derived from inflamed sites used in functional assays. Changes in blood inflammatory markers in the study cohort were analyzed by O-link. In univariate analyses, pelvic inflammation emerged as a significant predictor of AP. Multivariate cox proportional-hazards regression analyses showed that high pelvic inflammation with pT3 stage and positive surgical margins significantly affected the time to BCR (p ≤ 0.05). PCa patients with high inflammation had elevated levels of pro-inflammatory cytokines in their tissues and in blood. Genes involved in epithelial-to-mesenchymal transition (EMT) and DNA damage response were upregulated in patients with pelvic inflammation. Attenuation of STAT and IL-6 signaling decreased tumor driving properties of conditioned medium from inflamed sites. Pelvic inflammation exacerbates the progression of prostate cancer and drives an aggressive phenotype.</p

The Mount Sinai Prebiopsy Risk Calculator for Predicting any Prostate Cancer and Clinically Significant Prostate Cancer: Development of a Risk Predictive Tool and Validation with Advanced Neural Networking, Prostate Magnetic Resonance Imaging Outcome Database, and European Randomized Study of Screening for Prostate Cancer Risk Calculator

Background: The European Association of Urology guidelines recommend the use of imaging, biomarkers, and risk calculators in men at risk of prostate cancer. Risk predictive calculators that combine multiparametric magnetic resonance imaging with prebiopsy variables aid as an individualized decision-making tool for patients at risk of prostate cancer, and advanced neural networking increases reliability of these tools.Objective: To develop a comprehensive risk predictive online web-based tool using magnetic resonance imaging (MRI) and clinical data, to predict the risk of any prostate cancer (PCa) and clinically significant PCa (csPCa) applicable to biopsy-naive men, men with a prior negative biopsy, men with prior positive low-grade cancer, and men with negative MRI.Design, setting, and participants: Institutional review board-approved prospective data of 1902 men undergoing biopsy from October 2013 to September 2021 at Mount Sinai were collected.Outcome measurements and statistical analysis: Univariable and multivariable analyses were used to evaluate clinical variables such as age, race, digital rectal examination, family history, prostate-specific antigen (PSA), biopsy status, Prostate Imaging Reporting and Data System score, and prostate volume, which emerged as predictors for any PCa and csPCa. Binary logistic regression was performed to study the probability. Validation was performed with advanced neural networking (ANN), multi-institutional European cohort (Prostate MRI Outcome Database [PROMOD]), and European Randomized Study of Screening for Prostate Cancer Risk Calculator (ERSPC RC) 3/4.Results and limitations: Overall, 2363 biopsies had complete clinical information, with 57.98% any cancer and 31.40% csPCa. The prediction model was significantly associated with both any PCa and csPCa having an area under the curve (AUC) of 81.9% including clinical data. The AUC for external validation was calculated in PROMOD, ERSPC RC, and ANN for any PCa (0.82 vs 0.70 vs 0.90) and csPCa (0.82 vs 0.78 vs 0.92), respectively. This study is limited by its retrospective design and over-estimation of csPCa in the PROMOD cohort.Conclusions: The Mount Sinai Prebiopsy Risk Calculator combines PSA, imaging and clinical data to predict the risk of any PCa and csPCa for all patient settings. With accurate validation results in a large European cohort, ERSPC RC, and ANN, it exhibits its efficiency and applicability in a more generalized population. This calculator is available online in the form of a free web-based tool that can aid clinicians in better patients counseling and treatment decision-making.Patient summary: We developed the Mount Sinai Prebiopsy Risk Calculator (MSP-RC) to assess the likelihood of any prostate cancer and clinically significant disease based on a combination of clinical and imaging characteristics. MSP-RC is applicable to all patient settings and accessible online. Crown Copyright (C) 2022 Published by Elsevier B.V. on behalf of European Association of Urology.</p

Increased Hospitalization and Mortality from COVID-19 in Prostate Cancer Patients

Background: Cancer patients with COVID-19 have a poor disease course. Among tumor types, prostate cancer and COVID-19 share several risk factors, and the interaction of prostate cancer and COVID-19 is purported to have an adverse outcome. Methods: This was a single-institution retrospective study on 286,609 patients who underwent the COVID-19 test at Mount Sinai Hospital system from March 2020 to December 2020. Chi-square/Fisher’s exact tests were used to summarize baseline characteristics of categorical data, and Mann–Whitney U test was used for continuous variables. Univariable logistic regression analysis to compare the hospitalization and mortality rates and the strength of association was obtained by the odds ratio and confidence interval. Results: This study aimed to compare hospitalization and mortality rates between men with COVID-19 and prostate cancer and those who were COVID-19-positive with non-prostate genitourinary malignancy or any solid cancer, and with breast cancer patients. We also compared our studies to others that reported the incidence and severity of COVID-19 in prostate cancer patients. Our studies highlight that patients with prostate cancer had higher susceptibility to COVID-19-related pathogenesis, resulting in higher mortality and hospitalization rates. Hospitalization and mortality rates were higher in prostate cancer patients with COVID-19 when compared with COVID-19 patients with non-prostate genitourinary (GU) malignancies

Inflammatory marker levels in children with tobacco smoke exposure

BackgroundTobacco smoke exposure (TSE) has inflammatory and immunosuppressive effects which may be associated with altered levels of inflammatory markers and pediatric illnesses.ObjectiveThe primary objective was to examine the associations of cotinine-confirmed and parent-reported child TSE patterns and discharge diagnoses with C-reactive protein (CRP), IL-8, and IL-10 in 0-11-year-old pediatric emergency department (PED) patients who lived with&nbsp;≥&nbsp;1 smoker.MethodsSaliva samples were obtained from 115 children with a mean (SD) age of 3.5 (3.1) years during the PED visit (T0). Saliva was analyzed for cotinine, CRP, IL-8, and IL-10. Parents self-reported their children's TSE patterns; children's medical records were reviewed to identify and categorize discharge diagnoses. Linear regression models were utilized to find T0 associations of cotinine-confirmed and parent-reported child TSE patterns, and PED diagnoses with each inflammatory marker. All models were adjusted for child race/ethnicity, child sex, annual household income, and housing type. The TSE models also adjusted for child discharge diagnosis.ResultsAt T0, the geometric mean (GeoM) of cotinine was 4.1&nbsp;ng/ml [95&nbsp;%CI&nbsp;=&nbsp;3.2-5.2]; the GeoMs of CRP, IL-8, and IL-10 were 3,326&nbsp;pg/ml [95&nbsp;%CI&nbsp;=&nbsp;2,696-4,105], 474&nbsp;pg/ml [95&nbsp;%CI&nbsp;=&nbsp;386-583], and 1.1&nbsp;pg/ml [95&nbsp;%CI&nbsp;=&nbsp;0.9-1.3], respectively. Parent-reported child TSE patterns were positively associated with ln-transformed CRP levels, while adjusting for the covariates (β^&nbsp;=&nbsp;0.012 [95&nbsp;%CI:0.004-0.020], p&nbsp;=&nbsp;0.037). In the parent-reported child TSE pattern model, there were significant positive associations between the covariate of child age with CRP and IL-8 levels (p&nbsp;=&nbsp;0.028 and p&nbsp;&lt;&nbsp;0.001, respectively). Children with a bacterial diagnosis had higher IL-8 levels (p&nbsp;=&nbsp;0.002) compared to the other diagnosis groups.ConclusionsResults indicate that parent-reported child TSE increases the expression of CRP in ill children and supports prior work demonstrating that IL-8 is higher in children with TSE who have bacterial infections. These findings should be examined in future research with ill children with and without TSE

Association between Expression of Connective Tissue Genes and Prostate Cancer Growth and Progression

To find an association between genomic features of connective tissue and pejorative clinical outcomes on radical prostatectomy specimens. We performed a retrospective analysis of patients who underwent radical prostatectomy and underwent a Decipher transcriptomic test for localized prostate cancer in our institution (n = 695). The expression results of selected connective tissue genes were analyzed after multiple t tests, revealing significant differences in the transcriptomic expression (over- or under-expression). We investigated the association between transcript results and clinical features such as extra-capsular extension (ECE), clinically significant cancer, lymph node (LN) invasion and early biochemical recurrence (eBCR), defined as earlier than 3 years after surgery). The Cancer Genome Atlas (TCGA) was used to evaluate the prognostic role of genes on progression-free survival (PFS) and overall survival (OS). Out of 528 patients, we found that 189 had ECE and 27 had LN invasion. The Decipher score was higher in patients with ECE, LN invasion, and eBCR. Our gene selection microarray analysis showed an overexpression in both ECE and LN invasion, and in clinically significant cancer for COL1A1, COL1A2, COL3A1, LUM, VCAN, FN1, AEBP1, ASPN, TIMP1, TIMP3, BGN, and underexpression in FMOD and FLNA. In the TCGA population, overexpression of these genes was correlated with worse PFS. Significant co-occurrence of these genes was observed. When presenting overexpression of our gene selection, the 5-year PFS rate was 53% vs. 68% (p = 0.0315). Transcriptomic overexpression of connective tissue genes correlated to worse clinical features, such as ECE, clinically significant cancer and BCR, identifying the potential prognostic value of the gene signature of the connective tissue in prostate cancer. TCGAp cohort analysis showed a worse PFS in case of overexpression of the connective tissue genes

A 4K score/ MRI ‐based nomogram for predicting prostate cancer, clinically significant prostate cancer, and unfavorable prostate cancer

BACKGROUND: The detection of prostate cancer requires histological confirmation in biopsy core. Currently, number of unnecessary prostate biopsies are being performed in the United States. This is due to the absence of appropriate biopsy decision‐making protocol. AIM: To develop and validate a 4K score/multiparametric magnetic resonance imaging (mpMRI)‐based nomogram to predict prostate cancer (PCa), clinically significant prostate cancer (csPCa), and unfavorable prostate cancer (uPCa). METHODS AND RESULTS: Retrospective, single‐center study evaluating a cohort of 574 men with 4K score test >7% or suspicious digital rectal examination (DRE) or Prostate Imaging Reporting and Data System (PI‐RADS) scores 3, 4, or 5 on mpMRI that underwent systematic and/or mpMRI/ultrasound fusion–targeted prostate biopsy between 2016 and 2020. External cohort included 622 men. csPCa and uPCa were defined as Gleason score ≥3 + 4 and ≥4 + 3 on biopsy, respectively. Multivariable logistic regression analysis was performed to build nomogram for predicting PCa, csPCa, and uPCa. Validation was performed by plotting the area under the curve (AUC) and comparing nomogram‐predicted probabilities with actual rates of PCa, csPCa, and uPCa probabilities in the external cohort. 4K score, a PI‐RADS ≥4, prostate volume and prior negative biopsy were significant predictors of PCa, csPCa, and uPCa. AUCs were 0.84, 0.88, and 0.86 for the prediction of PCa, csPCa, and uPCa, respectively. The predicted and actual rates of PCa, csPCa, and uPCa showed agreement across all percentage probability ranges in the validation cohort. Using the prediction model at threshold of 30, 30% of overall biopsies, 41% of benign biopsies, and 19% of diagnosed indolent PCa could be avoided, while missing 9% of csPCa. CONCLUSION: This novel nomogram would reduce unnecessary prostate biopsies and decrease detection of clinically insignificant PCa

Clinical Utility of Negative Multiparametric Magnetic Resonance Imaging in the Diagnosis of Prostate Cancer and Clinically Significant Prostate Cancer

Multiparametric magnetic resonance imaging (MRI) is increasingly used to diagnose prostate cancer (PCa). It is not yet established whether all men with negative MRI (Prostate Imaging-Reporting and Data System version 2 score <3) should undergo prostate biopsy or not. To develop and validate a prediction model that uses clinical parameters to reduce unnecessary prostate biopsies by predicting PCa and clinically significant PCa (csPCa) for men with negative MRI findings who are at risk of harboring PCa. This was a retrospective analysis of 200 men with negative MRI at risk of PCa who underwent prostate biopsy (2014–2020) with prostate-specific antigen (PSA) >4 ng/ml, 4Kscore of >7%, PSA density ≥0.15 ng/ml/cm3, and/or suspicious digital rectal examination. The validation cohort included 182 men from another centre (University of Miami) with negative MRI who underwent systematic prostate biopsy with the same criteria. csPCa was defined as Gleason grade group ≥2 on biopsy. Multivariable logistic regression analysis was performed using coefficients of logit function for predicting PCa and csPCa. Nomogram validation was performed by calculating the area under receiver operating characteristic curves (AUC) and comparing nomogram-predicted probabilities with actual rates of PCa and csPCa. Of 200 men in the development cohort, 18% showed PCa and 8% showed csPCa on biopsy. Of 182 men in the validation cohort, 21% showed PCa and 6% showed csPCa on biopsy. PSA density, 4Kscore, and family history of PCa were significant predictors for PCa and csPCa. The AUC was 0.80 and 0.87 for prediction of PCa and csPCa, respectively. There was agreement between predicted and actual rates of PCa in the validation cohort. Using the prediction model at threshold of 40, 47% of benign biopsies and 15% of indolent PCa cases diagnosed could be avoided, while missing 10% of csPCa cases. The small sample size and number of events are limitations of the study. Our prediction model can reduce the number of prostate biopsies among men with negative MRI without compromising the detection of csPCa. We developed a tool for selection of men with negative MRI (magnetic resonance imaging) findings for prostate cancer who should undergo prostate biopsy. This risk prediction tool safely reduces the number of men who need to undergo the procedure. Men with negative magnetic resonance imaging (MRI) findings pose a diagnostic challenge to physicians as they may have prostate cancer in the absence of accepted clinical predictors. Our model for selection of men with negative MRI findings for biopsy safely reduces the number of men who need to undergo a biopsy procedure

Association between Incidental Pelvic Inflammation and Aggressive Prostate Cancer

The impact of pelvic inflammation on prostate cancer (PCa) biology and aggressive phenotype has never been studied. Our study objective was to evaluate the role of pelvic inflammation on PCa aggressiveness and its association with clinical outcomes in patients following radical prostatectomy (RP). This study has been conducted on a retrospective single-institutional consecutive cohort of 2278 patients who underwent robot-assisted laparoscopic prostatectomy (RALP) between 01/2013 and 10/2019. Data from 2085 patients were analyzed to study the association between pelvic inflammation and adverse pathology (AP), defined as Gleason Grade Group (GGG) &gt; 2 and &ge; pT3 stage, at resection. In a subset of 1997 patients, the association between pelvic inflammation and biochemical recurrence (BCR) was studied. Alteration in tumor transcriptome and inflammatory markers in patients with and without pelvic inflammation were studied using microarray analysis, immunohistochemistry, and culture supernatants derived from inflamed sites used in functional assays. Changes in blood inflammatory markers in the study cohort were analyzed by O-link. In univariate analyses, pelvic inflammation emerged as a significant predictor of AP. Multivariate cox proportional-hazards regression analyses showed that high pelvic inflammation with pT3 stage and positive surgical margins significantly affected the time to BCR (p &le; 0.05). PCa patients with high inflammation had elevated levels of pro-inflammatory cytokines in their tissues and in blood. Genes involved in epithelial-to-mesenchymal transition (EMT) and DNA damage response were upregulated in patients with pelvic inflammation. Attenuation of STAT and IL-6 signaling decreased tumor driving properties of conditioned medium from inflamed sites. Pelvic inflammation exacerbates the progression of prostate cancer and drives an aggressive phenotype