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Biology subjects

Canzonieri, V.

Publications and source records attributed to Canzonieri, V..

2 recordsLinked to original sources

Longitudinal Prediction of Drug Response in High-Grade Serous Ovarian Cancer Organoid Cultures Aligning with Clinical Responses

High-grade serous ovarian cancer (HGSOC) ranks among the most aggressive gynecological malignancies. Its high mortality stems from frequent recurrence post-primary treatments and development of platinum resistance. Since, traditional drug development via animal models is time-consuming and lacks reproducibility, in precision cancer medicine, primary patient-derived organoids (PDOs) offer a solution by replicating disease pathophysiology and expediting drug screening. We developed an expandable HGSOC organoid platform for rapid drug screening and resistance testing. Our study aimed to validate the stability of seven PDO pairs in predicting drug responses over time. Organoids underwent low- and high-passage drug screenings over nine months, involving 21 conventional and FDA-approved drugs and proteomic analyses. Comparison of in vitro outcomes with clinical data confirmed the platforms predictive capacity. Notably, a PDO with BRCA1 mutation exhibited resistance to Carboplatin and PARP inhibitors, highlighting organoid models clinical relevance for novel targeted therapies such as the Peptidylprolyl Cis/Trans Isomerase, NIMA-Interacting 1 (Pin1), a valuable target for HGSOC patients.

cancer biology↗

Comparative Analysis of Gene Expression Analysis Methods for RNA In Situ Hybridization Images

Gene expression analysis is pivotal in cancer research and clinical practice. While traditional methods lack spatial context, RNA in situ hybridization (RNA-ISH) is a powerful technique that retains spatial tissue information. Here, we investigated RNAscope score, RT-droplet digital PCR (RT-ddPCR), and automated QuantISH and QuPath in quantifying RNA-ISH expression values from formalin-fixed paraffin-embedded samples. We compared the methods using high-grade serous ovarian carcinoma samples, focusing on CCNE1, WFDC2, and PPIB genes. Our findings demonstrate good concordance between automated methods and RNAscope, with RT-ddPCR showing less concordance. We conclude that QuantISH exhibits robust performance, even for low-expressed genes like CCNE1, showcasing its modular design and enhancing accessibility as a viable alternative for gene expression analysis.

bioinformatics↗