Merolla, F. | Search bioRxiv

bioRxiv2025

Using CCDC6 immunostaining in conjunction with the RAD51 HRD assay as a novel approach to expand PARPi treatment eligibility in HGSOC patients

PurposeHGSOC patients with BRCA1/2 mutations show HRD and PARPi sensitivity. Notably, HRD and PARPi response can occur without BRCA mutations, suggesting other factors are involved. Loss of CCDC6 function can lead to HRD and PARPi sensitivity in HGSOC cells, making CCDC6 a potential therapeutic target and biomarker. Three CCDC6 missense mutations in HGSOC prompted investigation into their impact on HRD. Analyzing CCDC6 expression, localization and HRD data in the MITO16A trial aims to clarify the CCDC6-HRD relationship in a large cohort. Experimental DesignThe biochemical and morphological effects of CCDC6 mutants on the native protein were examined using pull-down assays and immunofluorescence. HR-reporter and cell viability assays determined the impact of these mutants on HRD and PARPi sensitivity. CCDC6 histochemical score and intracellular-localization were assessed in MITO16A samples after immunostaining and digitalization. ResultsCCDC6-mutated isoforms act as dominant-negative, preventing native CCDC6 nuclear translocation, disrupting RAD51 foci and HR-repair, and increasing PARPi sensitivity. In the MITO16A patient sample set, 66 of 185 (35%) showed barely detectable CCDC6 or nuclear exclusion ("CCDC6-inactive"). CCDC6 impairment in these "CCDC6-inactive" samples was associated with HRD in 75% (30/40) of suitable samples analyzed by the RAD51 test and in 52% (34/65) of suitable samples analyzed by genomic HRD testing, even in the presence of wild-type BRCA1/BRCA2 genes. ConclusionThe association between CCDC6 inactivity and HRD, both at genomic and functional level, occurred even in presence of wild-type BRCA1/BRCA2 genes, suggesting that CCDC6 may play a crucial role in DNA repair pathways independent of these well-known genes.

cancer biology↗

bioRxiv2024

Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning algorithms into the laboratory information system

Digital pathology (DP) has revolutionized cancer diagnostics, allowing the development of deep-learning (DL) models supporting pathologists in their daily work and contributing to the improvement of patient care. However, the clinical adoption of such models remains challenging. Here we describe a proof-of-concept framework that, leveraging open-source DP software and Health Level 7 (HL7) standards, allows the integration of DL models in the clinical workflow. Development and testing of the workflow were carried out in a fully digitized Italian pathology department. A Python-based server-client architecture was implemented to interconnect the anatomic pathology laboratory information system (AP-LIS) with an external artificial intelligence decision support system (AI-DSS) containing 16 pre-trained DL models through HL7 messaging. Open-source toolboxes for DL model deployment, including WSInfer and WSInfer-MIL, were used to run DL model inference. Visualization of model predictions as colored heatmaps was performed in QuPath. As soon as a new slide is scanned, DL model inference is automatically run on the basis of the slides tissue type and staining. In addition, pathologists can initiate the analysis on-demand by selecting a specific DL model from the virtual slides tray. In both cases the AP-LIS transmits an HL7 message to the AI-DSS, which processes the message, runs DL model inference, and creates the appropriate type of colored heatmap on the basis of the employed classification model. The AI-DSS transmits model inference results to the AP-LIS, where pathologists can visualize the output in QuPath and/or directly from the virtual slides tray. The developed framework supports multiple DL toolboxes and it is thus suitable for a broad range of applications. In addition, this integration workflow is a key step to enable the future widespread adoption of DL models in pathology diagnostics.

pathology↗