Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation.

BACKGROUND: Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Pathologists are in critical need of accurate biomarkers to characterize PC, particularly to confirm the presence of intraductal carcinoma of the prostate (IDC-P), an aggressive histopathological variant for which therapeutic options are now available. Our aim was to identify IDC-P with Raman micro-spectroscopy (RµS) and machine learning technology following a protocol suitable for routine clinical histopathology laboratories. METHODS AND FINDINGS: We used RµS to differentiate IDC-P from PC, as well as PC and IDC-P from benign tissue on formalin-fixed paraffin-embedded first-line radical prostatectomy specimens (embedded in tissue microarrays [TMAs]) from 483 patients treated in 3 Canadian institutions between 1993 and 2013. The main measures were the presence or absence of IDC-P and of PC, regardless of the clinical outcomes. The median age at radical prostatectomy was 62 years. Most of the specimens from the first cohort (Centre hospitalier de l'Université de Montréal) were of Gleason score 3 + 3 = 6 (51%) while most of the specimens from the 2 other cohorts (University Health Network and Centre hospitalier universitaire de Québec-Université Laval) were of Gleason score 3 + 4 = 7 (51% and 52%, respectively). Most of the 483 patients were pT2 stage (44%-69%), and pT3a (22%-49%) was more frequent than pT3b (9%-12%). To investigate the prostate tissue of each patient, 2 consecutive sections of each TMA block were cut. The first section was transferred onto a glass slide to perform immunohistochemistry with H&E counterstaining for cell identification. The second section was placed on an aluminum slide, dewaxed, and then used to acquire an average of 7 Raman spectra per specimen (between 4 and 24 Raman spectra, 4 acquisitions/TMA core). Raman spectra of each cell type were then analyzed to retrieve tissue-specific molecular information and to generate classification models using machine learning technology. Models were trained and cross-validated using data from 1 institution. Accuracy, sensitivity, and specificity were 87% ± 5%, 86% ± 6%, and 89% ± 8%, respectively, to differentiate PC from benign tissue, and 95% ± 2%, 96% ± 4%, and 94% ± 2%, respectively, to differentiate IDC-P from PC. The trained models were then tested on Raman spectra from 2 independent institutions, reaching accuracies, sensitivities, and specificities of 84% and 86%, 84% and 87%, and 81% and 82%, respectively, to diagnose PC, and of 85% and 91%, 85% and 88%, and 86% and 93%, respectively, for the identification of IDC-P. IDC-P could further be differentiated from high-grade prostatic intraepithelial neoplasia (HGPIN), a pre-malignant intraductal proliferation that can be mistaken as IDC-P, with accuracies, sensitivities, and specificities > 95% in both training and testing cohorts. As we used stringent criteria to diagnose IDC-P, the main limitation of our study is the exclusion of borderline, difficult-to-classify lesions from our datasets. CONCLUSIONS: In this study, we developed classification models for the analysis of RµS data to differentiate IDC-P, PC, and benign tissue, including HGPIN. RµS could be a next-generation histopathological technique used to reinforce the identification of high-risk PC patients and lead to more precise diagnosis of IDC-P.

Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction.

Colorectal liver metastases (CLM) affect almost half of all colon cancer patients and the response to systemic chemotherapy plays a crucial role in patient survival. While oncologists typically use tumor grading scores, such as tumor regression grade (TRG), to establish an accurate prognosis on patient outcomes, including overall survival (OS) and time-to-recurrence (TTR), these traditional methods have several limitations. They are subjective, time-consuming, and require extensive expertise, which limits their scalability and reliability. Additionally, existing approaches for prognosis prediction using machine learning mostly rely on radiological imaging data, but recently histological images have been shown to be relevant for survival predictions by allowing to fully capture the complex microenvironmental and cellular characteristics of the tumor. To address these limitations, we propose an end-to-end approach for automated prognosis prediction using histology slides stained with Hematoxylin and Eosin (H&E) and Hematoxylin Phloxine Saffron (HPS). We first employ a Generative Adversarial Network (GAN) for slide normalization to reduce staining variations and improve the overall quality of the images that are used as input to our prediction pipeline. We propose a semi-supervised model to perform tissue classification from sparse annotations, producing segmentation and feature maps. Specifically, we use an attention-based approach that weighs the importance of different slide regions in producing the final classification results. Finally, we exploit the extracted features for the metastatic nodules and surrounding tissue to train a prognosis model. In parallel, we train a vision Transformer model in a knowledge distillation framework to replicate and enhance the performance of the prognosis prediction. We evaluate our approach on an in-house clinical dataset of 258 CLM patients, achieving superior performance compared to other comparative models with a c-index of 0.804 (0.014) for OS and 0.735 (0.016) for TTR, as well as on two public datasets. The proposed approach achieves an accuracy of 86.9% to 90.3% in predicting TRG dichotomization. For the 3-class TRG classification task, the proposed approach yields an accuracy of 78.5% to 82.1%, outperforming the comparative methods. Our proposed pipeline can provide automated prognosis for pathologists and oncologists, and can greatly promote precision medicine progress in managing CLM patients.

A senescence restriction point acting on chromatin integrates oncogenic signals.

We identify a senescence restriction point (SeRP) as a critical event for cells to commit to senescence. The SeRP integrates the intensity and duration of oncogenic stress, keeps a memory of previous stresses, and combines oncogenic signals acting on different pathways by modulating chromatin accessibility. Chromatin regions opened upon commitment to senescence are enriched in nucleolar-associated domains, which are gene-poor regions enriched in repeated sequences. Once committed to senescence, cells no longer depend on the initial stress signal and exhibit a characteristic transcriptome regulated by a transcription factor network that includes ETV4, RUNX1, OCT1, and MAFB. Consistent with a tumor suppressor role for this network, the levels of ETV4 and RUNX1 are very high in benign lesions of the pancreas but decrease dramatically in pancreatic ductal adenocarcinomas. The discovery of senescence commitment and its chromatin-linked regulation suggests potential strategies for reinstating tumor suppression in human cancers.

In situ Raman spectroscopy and machine learning unveil biomolecular alterations in invasive breast cancer.

Significance: As many as 60% of patients with early stage breast cancer undergo breast-conserving surgery. Of those, 20% to 35% need a second surgery because of incomplete resection of the lesions. A technology allowing in situ detection of cancer could reduce re-excision procedure rates and improve patient survival. Aim: Raman spectroscopy was used to measure the spectral fingerprint of normal breast and cancer tissue ex-vivo. The aim was to build a machine learning model and to identify the biomolecular bands that allow one to detect invasive breast cancer. Approach: The system was used to interrogate specimens from 20 patients undergoing lumpectomy, mastectomy, or breast reduction surgery. This resulted in 238 ex-vivo measurements spatially registered with standard histology classifying tissue as cancer, normal, or fat. A technique based on support vector machines led to the development of predictive models, and their performance was quantified using a receiver-operating-characteristic analysis. Results: Raman spectroscopy combined with machine learning detected normal breast from ductal or lobular invasive cancer with a sensitivity of 93% and a specificity of 95%. This was achieved using a model based on only two spectral bands, including the peaks associated with C-C stretching of proteins around 940 cm - 1 and the symmetric ring breathing at 1004 cm - 1 associated with phenylalanine. Conclusions: Detection of cancer on the margins of surgically resected breast specimen is feasible with Raman spectroscopy.

T cell-loaded injectable chitosan scaffold shows short-term efficacy in localised cancer immunotherapy in mice.

Adoptive cell therapy (ACT) shows success against treatment-resistant cancers, but is limited by the large number of intravenously delivered T cells required and toxicity related to systemic administration. In this work, we hypothesized that localized T cell delivery in an in situ gelling chitosan hydrogel will allow similar treatment efficacy despite delivering fewer cells than systemic intravenous delivery. A rapidly gelling chitosan gel with good mechanical properties was used for this study. Gel biocompatibility and biodegradability were tested over 8 weeks in mice. No adverse effects were observed. The gel elicited a local granulomatous reaction (foreign body reaction), degrading by about 75% volume at 8 weeks. The survival, escape and bioactivity against the tumour cells of encapsulated murine lymphocytes (OT-I) and human Jurkat cells were confirmed in vitro by live/dead assay and flow cytometry. Efficacy was studied using a mouse tumour model where the injected OT-I can specifically recognize and attack ovalbumin (OVA) protein-expressing tumours. The OT-I cell delivery scaffold was compared to untreated controls, OT-I in saline and intravenous systemic treatment with 3-fold more OT-I, observing tumour growth and localization by intravital microscopy and histology. Gel-encapsulated OT-I limited tumour growth significantly up to 11 days after treatment compared to that of untreated mice and mice with longer PBS-suspended OT-I treatment (9 days), but slightly less than that of mice with IV-delivered OT-I treatment (14 days). No significant difference was observed when directly comparing the gel and IV treatments. Although further optimization of the treatment is required, this work shows the feasibility and potential of the chitosan gel for localised OT-I delivery in cancer immunotherapy.

High Levels of MFG-E8 Confer a Good Prognosis in Prostate and Renal Cancer Patients.

Milk fat globule-epidermal growth factor-8 (MFG-E8) is a glycoprotein secreted by different cell types, including apoptotic cells and activated macrophages. MFG-E8 is highly expressed in a variety of cancers and is classically associated with tumor growth and poor patient prognosis through reprogramming of macrophages into the pro-tumoral/pro-angiogenic M2 phenotype. To date, correlations between levels of MFG-E8 and patient survival in prostate and renal cancers remain unclear. Here, we quantified MFG-E8 and CD68/CD206 expression by immunofluorescence staining in tissue microarrays constructed from renal (n = 190) and prostate (n = 274) cancer patient specimens. Percentages of MFG-E8-positive surface area were assessed in each patient core and Kaplan-Meier analyses were performed accordingly. We found that MFG-E8 was expressed more abundantly in malignant regions of prostate tissue and papillary renal cell carcinoma but was also increased in the normal adjacent regions in clear cell renal carcinoma. In addition, M2 tumor-associated macrophage staining was increased in the normal adjacent tissues compared to the malignant areas in renal cancer patients. Overall, high tissue expression of MFG-E8 was associated with less disease progression and better survival in prostate and renal cancer patients. Our observations provide new insights into tumoral MFG-E8 content and macrophage reprogramming in cancer.

High Keratin-7 Expression in Benign Peri-Tumoral Prostatic Glands Is Predictive of Bone Metastasis Onset and Prostate Cancer-Specific Mortality.

BACKGROUND: New predictive biomarkers are needed to accurately predict metastasis-free survival (MFS) and cancer-specific survival (CSS) in localized prostate cancer (PC). Keratin-7 (KRT7) overexpression has been associated with poor prognosis in several cancers and is described as a novel prostate progenitor marker in the mouse prostate. METHODS: KRT7 expression was evaluated in prostatic cell lines and in human tissue by immunohistochemistry (IHC, on advanced PC, n = 91) and immunofluorescence (IF, on localized PC, n = 285). The KRT7 mean fluorescence intensity (MFI) was quantified in different compartments by digital analysis and correlated to clinical endpoints in the localized PC cohort. RESULTS: KRT7 is expressed in prostatic cell lines and found in the basal and supra-basal compartment from healthy prostatic glands and benign peri-tumoral glands from localized PC. The KRT7 staining is lost in luminal cells from localized tumors and found as an aberrant sporadic staining (2.2%) in advanced PC. In the localized PC cohort, high KRT7 MFI above the 80th percentile in the basal compartment was significantly and independently correlated with MFS and CSS, and with hypertrophic basal cell phenotype. CONCLUSION: High KRT7 expression in benign glands is an independent biomarker of MFS and CSS, and its expression is lost in tumoral cells. These results require further validation on larger cohorts.

Acute Kidney Injury, Microvascular Rarefaction, and Estimated Glomerular Filtration Rate in Kidney Transplant Recipients.

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.

Pharmacological Preconditioning Improves the Viability and Proangiogenic Paracrine Function of Hydrogel-Encapsulated Mesenchymal Stromal Cells.

The efficacy of cell therapy is limited by low retention and survival of transplanted cells in the target tissues. In this work, we hypothesize that pharmacological preconditioning with celastrol, a natural potent antioxidant, could improve the viability and functions of mesenchymal stromal cells (MSC) encapsulated within an injectable scaffold. Bone marrow MSCs from rat (rMSC) and human (hMSC) origin were preconditioned for 1 hour with celastrol 1 µM or vehicle (DMSO 0.1% v/v), then encapsulated within a chitosan-based thermosensitive hydrogel. Cell viability was compared by alamarBlue and live/dead assay. Paracrine function was studied first by quantifying the proangiogenic growth factors released, followed by assessing scratched HUVEC culture wound closure velocity and proliferation of HUVEC when cocultured with encapsulated hMSC. In vivo, the proangiogenic activity was studied by evaluating the neovessel density around the subcutaneously injected hydrogel after one week in rats. Preconditioning strongly enhanced the viability of rMSC and hMSC compared to vehicle-treated cells, with 90% and 75% survival versus 36% and 58% survival, respectively, after 7 days in complete media and 80% versus 64% survival for hMSC after 4 days in low serum media (p < 0.05). Celastrol-treated cells increased quantities of proangiogenic cytokines compared to vehicle-pretreated cells, with a significant 3.0-fold and 1.8-fold increase of VEGFa and SDF-1α, respectively (p < 0.05). The enhanced paracrine function of preconditioned MSC was demonstrated by accelerated growth and wound closure velocity of injured HUVEC monolayer (p < 0.05) in vitro. Moreover, celastrol-treated cells, but not vehicle-treated cells, led to a significant increase of neovessel density in the peri-implant region after one week in vivo compared to the control (blank hydrogel). These results suggest that combining cell pretreatment with celastrol and encapsulation in hydrogel could potentiate MSC therapy for many diseases, benefiting particularly ischemic diseases.

Dimensional reduction based on peak fitting of Raman micro spectroscopy data improves detection of prostate cancer in tissue specimens.

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'Université de Montréal, CHUM) and tested on two independent cohorts of 76 patients [University Health Network (UHN)] and 135 patients (Centre hospitalier universitaire de Québec-Université 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.

Metformin Abrogates Age-Associated Ovarian Fibrosis.

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.

WISP1 is associated to advanced disease, EMT and an inflamed tumor microenvironment in multiple solid tumors.

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.