Biology subjects

Trudel, D.

Publications and source records attributed to Trudel, D..

3 recordsLinked to original sources

bioRxiv2025

BRCA mutation alters the stromal landscape in normal ovaries

For BRCA mutation (BRCA+) carriers, the risk of ovarian cancer can be as high as 59% compared with 1.4% for the general population. While the impact of BRCA mutations on epithelial cell transformation has been extensively studied, we hypothesize that these mutations cause structural changes that prematurely transform the ovary into a rich metastatic niche that supports the early onset of ovarian cancer. Analysis of collagen content and organization in human ovaries revealed increased coherence associated with fibrosis in premenopausal BRCA+ ovaries relative to those without a BRCA mutation. Brca1 deficiency and haploinsufficiency in murine ovarian fibroblasts triggered the expression of hallmarks of senescence, including Cdkn2a (p16) and acidic {beta}-galactosidase activity. Brca1-deficient fibroblasts also acquired an antigen-presenting myofibroblastic phenotype, characterized by expression of MHC-II molecules, -SMA and extracellular matrix components, suggesting the capacity to modulate immune activity and drive structural changes resembling fibrosis. These results provide insight into the mechanisms contributing to accelerated ovarian aging in BRCA+ carriers. TeaserBRCA mutation promotes fibroblast hyperactivity and senescence that changes the stromal architecture of the ovarian niche.

molecular biology↗

bioRxiv2024

Texture-Analysis Classification of Intraductal Carcinoma of the Prostate Using Label-Free Multimodal Nonlinear Optical Imaging

Intraductal carcinoma of the prostate (IDC-P) is a very aggressive histopathological subtype of prostate cancer (PCa) for which no accurate biomarkers exist. In our work, we apply a multimodal nonlinear optical imaging approach that uses second-harmonic generation (SHG) and stimulated Raman scattering (SRS) imaging to distinguish IDC-P from regular PCa and benign prostate tissue. Images from each tissue type were classified using support vector machine (SVM). The technique classified the images from each region based on first-order statistics and texture-based second-order statistics derived from the gray-level co-occurrence matrix (GLCM) of the images. Our results demonstrate that SVM models trained on either SHG or SRS images accurately classify IDC-P as well as high-grade PCa, low-grade PCa, and benign tissue with a mean classification accuracy of over 89%. Furthermore, a classification model combining both SHG and SRS imaging modalities can accurately classify all tissue types with a mean classification accuracy of 98%.

pathology↗

bioRxiv2023

Artificial Intelligence Helps to Predict Recurrence and Mortality for Prostate Cancer using Histology Images

Besides grading, deep learning could improve expert consensus to predict prostate cancer (PCa) recurrence. We developed a novel PCa recurrence prediction system based on artificial intelligence (AI). We validated it using multi-institutional and international datasets comprising 2,647 PCa patients with at least a 10-year follow-up. Survival analyses were performed and goodness-of-fit of multivariate models was evaluated using partial likelihood ratio tests, Akaikes test, or Bayesian information criteria to determine the superiority of our system over existing grading systems. Comprehensive survival analyses demonstrated the effectiveness of our AI- system in categorizing PCa into four distinct risk groups. The system was independent and superior to the existing five grade groups for malignancies. A high consensus level was observed among five blinded genitourinary pathology experts in ranking images according to our prediction system. Therefore, AI may help develop an accurate and clinically interpretable PCa recurrence prediction system, facilitating informed decision-making for PCa patients.

pathology↗