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Background and objectives Animal studies suggest that microvascular rarefaction is a key factor in the acute kidney disease to CKD transition. Hence, delayed graft function appears as a unique human model of AKI to further explore the role of microvascular rarefaction in kidney transplant recipients. Here, we assessed whether delayed graft function is associated with peritubular capillary loss and evaluated the association between this loss and long-term kidney graft function. Design, setting, participants, & measurements This observational, retrospective cohort study included 61 participants who experienced delayed graft function and 130 who had immediate graft function. We used linear regression models to evaluate associations between delayed graft function and peritubular capillary density expressed as the percentage of efficient cortical area occupied by peritubular capillaries in pre- and post-transplant graft biopsies. eGFRs 1 and 3 years post-transplant were secondary outcomes. Results Post-transplant biopsies were performed at a median of 113 days (interquartile range, 101-128) after transplantation. Peritubular capillary density went from 15.4% to 11.5% in patients with delayed graft function (median change, -3.7%; interquartile range, -6.6% to -0.8%) and from 19.7% to 15.1% in those with immediate graft function (median change, -4.5%; interquartile range, -8.0% to -0.8%). Although the unadjusted change in peritubular capillary density was similar between patients with and without delayed graft function, delayed graft function was associated with more peritubular capillary loss in the multivariable analysis (adjusted difference in change, -2.9%; 95% confidence interval, -4.0 to -1.8). Pretransplant peritubular capillary density and change in peritubular capillary density were associated with eGFR 1 and 3 years post-transplantation. Conclusions Perioperative AKI is associated with lower density in peritubular capillaries before transplantation and with loss of peritubular capillaries following transplantation. Lower peritubular capillary density is linked to lower long-term eGFR.

Significance Prostate cancer is the most common cancer among men. An accurate diagnosis of its severity at detection plays a major role in improving their survival. Recently, machine learning models using biomarkers identified from Raman micro-spectroscopy discriminated intraductal carcinoma of the prostate (IDC-P) from cancer tissue with a ≥85 % detection accuracy and differentiated high-grade prostatic intraepithelial neoplasia (HGPIN) from IDC-P with a ≥97.8 % accuracy. Aim To improve the classification performance of machine learning models identifying different types of prostate cancer tissue using a new dimensional reduction technique. Approach A radial basis function (RBF) kernel support vector machine (SVM) model was trained on Raman spectra of prostate tissue from a 272-patient cohort (Centre hospitalier de l'Universite de Montreal, CHUM) and tested on two independent cohorts of 76 patients [University Health Network (UHN)] and 135 patients (Centre hospitalier universitaire de Quebec-Universite Laval, CHUQc-UL). Two types of engineered features were used. Individual intensity features, i.e., Raman signal intensity measured at particular wavelengths and novel Raman spectra fitted peak features consisting of peak heights and widths. Results Combining engineered features improved classification performance for the three aforementioned classification tasks. The improvements for IDC-P/cancer classification for the UHN and CHUQc-UL testing sets in accuracy, sensitivity, specificity, and area under the curve (AUC) are (numbers in parenthesis are associated with the CHUQc-UL testing set): +4 % (+8 % ), +7 % (+9 % ), +2 % (6%), +9 (+9) with respect to the current best models. Discrimination between HGPIN and IDC-P was also improved in both testing cohorts: +2.2 % (+1.7 % ), +4.5 % (+3.6 % ), +0 % (+0 % ), +2.3 (+0). While no global improvements were obtained for the normal versus cancer classification task [+0 % (-2 % ), +0 % (-3 % ), +2 % (-2 % ), +4 (+3)], the AUC was improved in both testing sets. Conclusions Combining individual intensity features and novel Raman fitted peak features, improved the classification performance on two independent and multicenter testing sets in comparison to using only individual intensity features.

Purpose: The ovarian cancer risk factors of age and ovulation are curious because ovarian cancer incidence increases in postmenopausal women, long after ovulations have ceased. To determine how age and ovulation underlie ovarian cancer risk, we assessed the effects of these risk factors on the ovarian microenvironment. Experimental Design: Aged C57/lcrfa mice (0–33 months old) were generated to assess the aged ovarian microenvironment. To expand our findings into human aging, we assembled a cohort of normal human ovaries (n = 18, 21–71 years old). To validate our findings, an independent cohort of normal human ovaries was assembled (n = 9, 41–82 years old). Results: We first validated the presence of age-associated murine ovarian fibrosis. Using interdisciplinary methodologies, we provide novel evidence that ovarian fibrosis also develops in human postmenopausal ovaries across two independent cohorts (n = 27). Fibrotic ovaries have an increased CD206+:CD68+ cell ratio, CD8+ T-cell infiltration, and profibrotic DPP4+αSMA+ fibroblasts. Metformin use was associated with attenuated CD8+ T-cell infiltration and reduced CD206+:CD68+ cell ratio. Conclusions: These data support a novel hypothesis that unifies the primary nonhereditary ovarian cancer risk factors through the development of ovarian fibrosis and the formation of a premetastatic niche, and suggests a potential use for metformin in ovarian cancer prophylaxis. See related commentary by Madariaga et al., p. 523

INTRODUCTION AND OBJECTIVE:A huge barrier to implement computational pathology grading constitutes great effort of manual patch annotation within digitized tissue images for training and validation...

Background Prostate cancer (PC), initially diagnosed on biopsies by pathologists, is the most frequent cancer in North American men. However, better tools are needed for pathologists to diagnose intraductal carcinoma of the prostate (IDC), an aggressive histopathological variant of PC for which therapeutic options are now available. Indeed, no technique or biomarker is clinically available to support the diagnosis of IDC. Raman spectroscopy (RS) provides a global molecular characterisation of the tissue by analysing how photons interact with the molecules present in the tissue. Indeed, we and other groups previously used RS to detect cancer from multiple organ types, machine learning classification models being employed to process the complex Raman data. Methods We used Raman micro-spectroscopy (RµS) to detect IDC on tissues from 483 first-line radical prostatectomies from three Canadian institutions. Following a rapid, standardized and low-cost protocol, we acquired an average of 7 Raman spectra per patient and generated classification models using machine learning technology. Importantly, models were trained with data from one institution before independent testing on the data from the other two institutions. Results The three institutions included 272, 76 and 135 patients. Median age at diagnosis ranged from 61-62 years-old, with median pre-operative PSA ranging from 6.6-7.4 µg/L. Most patient had ≤3 + 4 Gleason score (60-80% of the specimens) and pT3-stage incidence was 31-55%. IDC was identified in 6-18% of the patients of each cohort. Overall, we acquired an average of 7 Raman spectra per patient. In the training cohort (N = 272), RµS identified IDC with a sensitivity of 95%, a specificity of 94% and an accuracy of 94%. Results from the testing cohort were in a similar range, with sensitivities of 88 and 92%, specificities of 83 and 91% and accuracies of 85 and 91%. Conclusions As clinically available biomarkers of IDC have reported sensitivities/specificities of ∼80%, we here identified IDC with accuracies ≥85%. Since our classification model was trained on a cohort and independently tested on the other two, these are likely to be close to real life experience making clinical implementation a realistic outcome. Legal entity responsible for the study The authors. Funding Centre de Recherche du Centre Hospitalier de l’Universite de Montreal (CRCHUM, continuum program) IVADO, Institut de valorisation des donnees. Disclosure All authors have declared no conflicts of interest.

Background: WNT1-Inducible Signaling Pathway Protein 1 (WISP1) is implicated in prostate cancer growth and metastasis and the regulation of inflammation in diverse benign diseases. The objectives of this study were to assess the prognostic value of WISP1, its association to inflammation and its relevance as a biomarker for immune checkpoint blockade (ICB) response. Methods: Publicly available RNA-seq datasets were used to evaluate the prognostic value of WISP1 gene expression and its association with tumor-infiltrating lymphocytes, inflamed tumor microenvironment, and anti-PD-1 ICB response. A tissue microarray (TMA) including 285 radical prostatectomy specimens was used to confirm these associations in prostate cancer. The effect of recombinant WISP1 (rWISP1) on inflammatory cytokines was assessed in vitro. Results: High levels of WISP1 correlated with BCR-free survival in prostate adenocarcinoma and overall survival in primary melanoma, low-grade glioma, and kidney papillary cell carcinoma. Some effects could be accounted for by higher WISP1 expression in advanced disease. High WISP1 expression in prostate adenocarcinoma was correlated with CD8+ cells density. In vitro, rWISP1 increased inflammatory cytokine production. High WISP1 gene expression in RNA-seq datasets was correlated with gene signatures of multiple immune cell types as well as an inflammatory cytokine, immune checkpoint, and epithelial-mesenchymal transition (EMT) gene expression. WISP1 mRNA expression was associated with primary resistance to ICB in datasets showing EMT. Conclusions: Our results support an association between WISP1 expression and advanced disease, EMT and an inflamed tumor microenvironment in multiple solid tumors. The consequences of WISP1 expression on cancer immunotherapy remains to be addressed.