Leman, R. | Search bioRxiv

bioRxiv2025

Long-term patient-derived ovarian cancer organoids closely recapitulate tumor of origin and clinical response

There is an urgent need of precision medicine for ovarian cancer patients to identify patients who respond to chemotherapy and PARP inhibitors, a therapy targeting homologous recombination deficiency (HRD). Here we established a panel of 37 long-term patient-derived tumor organoids (PDTO) models of various histological subtypes from 224 patients and demonstrated that they mimic the histological and molecular characteristics of original tumors. Screening of chemotherapeutic drugs showed that PDTO exhibit heterogeneous responses, and that response of PDTO from high-grade serous ovarian carcinoma to carboplatin recapitulated patient response to first-line treatment. Additionally, the detection of HRD phenotype of PDTO using functional assay was associated with the results of the HRD test Genomic Instability Scar (GIScar). Although larger-scale investigations are needed to confirm the predictive potential of PDTO, these results provide further evidence of the potential interest of ovarian PDTO for functional precision medicine even if many challenges remain to be addressed.

cancer biology↗

bioRxiv2023

Predicting the impact of rare variants on RNA splicing in CAGI6

BackgroundVariants which disrupt splicing are a frequent cause of rare disease that have been under-ascertained clinically. Accurate and efficient methods to predict a variants impact on splicing are needed to interpret the growing number of variants of unknown significance (VUS) identified by exome and genome sequencing. Here we present the results of the CAGI6 Splicing VUS challenge, which invited predictions of the splicing impact of 56 variants ascertained clinically and functionally validated to determine splicing impact. ResultsThe performance of 12 prediction methods, along with SpliceAI and CADD, was compared on the 56 functionally validated variants. The maximum overall accuracy achieved was 82% from two different approaches, one weighting SpliceAI scores by minor allele frequency, and one applying the recently published Splicing Prediction Pipeline (SPiP). SPiP performed optimally in terms of sensitivity, while an ensemble method combining multiple prediction tools and information from databases exceeded all others for specificity. ConclusionsSeveral challenge methods equalled or exceeded the performance of SpliceAI, with ultimate choice of prediction method likely to depend on experimental or clinical aims. One quarter of the variants were incorrectly predicted by at least 50% of the methods, highlighting the need for further improvements to splicing prediction methods for successful clinical application.

bioinformatics↗