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Lupat, R.

Publications and source records attributed to Lupat, R..

3 recordsLinked to original sources

Comprehensive drug efficacy data for mucinous ovarian carcinoma using a novel and extensive biobank of patient-derived organoid models

Mucinous Ovarian Carcinoma (MOC) is a rare ovarian cancer histological subtype with distinct pathology, genomics and clinical outcomes compared to other epithelial ovarian cancers. Accordingly, there is little evidence to guide clinical care, particularly in the use of systemic therapies, and the field has lacked informative and diverse pre-clinical models. We developed MOC-specific methods for generating tumour organoids with a success rate of 70% for long-term cultured lines (n=19). Organoid lines were developed from localised, advanced and recurrent tumours, including from biopsy tissue, and represent diverse genomic features not previously captured by existing cell lines. The organoid lines were highly similar to the tumours of origin for genomic and immunohistochemical markers. Screening using a panel of 11 chemotherapy agents highlighted resistance to standard-of-care agents such as carboplatin. Gastrointestinal cancer chemotherapy agents and their combination regimens lacked activity. Paclitaxel was often highly potent at low doses but failed to kill all cells. However, less frequently used drugs such as gemcitabine, topotecan and doxorubicin inhibited many of the lines more effectively than paclitaxel. Available, but non-standard of care, chemotherapy agents should be considered for clinical management of MOC. This is the largest (by [~]10 fold) cohort of fully characterised patient-derived MOC organoid lines described and the first with extensive drug screening data affording an opportunity for drug discovery and screening for personalised treatment.

cancer biology↗

macpie: scalable workflow for high-throughput transcriptomic profiling

High-throughput transcriptomic profiling (HTTr) enables scalable characterisation of transcriptional responses to chemical and genetic perturbations. While plate-based technologies such as MAC-Seq, TempO-seq and PLATE-seq have made HTTr more accessible, they pose unique computational challenges in modelling data and integration across modalities. We present macpie, an R package designed to streamline the analysis of HTTr data from plate-based screens. Built on the tidySeurat framework, macpie streamlines the entire analytical pipeline from preprocessing and quality control to pathway enrichment, chemical feature extraction, and multimodal data integration. The package incorporates multiple statistical frameworks and leverages parallelisation for scalability. By leveraging Docker and Nextflow, macpie ensures reproducibility and ease of use for transcriptome-wide screening. AvailabilityThe R package macpie is freely available at https://github.com/PMCC-BioinformaticsCore/macpie, with images of the working environment hosted at Docker Hub: xliu81/macpie. A companion Nextflow pipeline for preprocessing from FASTQ files is available at https://github.com/PMCC-BioinformaticsCore/dinoflow. Contactnenad.bartonicek@petermac.org Supplementary informationPackage vignettes with the full analytical workflow available at https://pmcc-bioinformaticscore.github.io/macpie/articles/macpie.html

bioinformatics↗

Predictive biomarkers of breast ductal carcinoma in situ may underestimate the risk of recurrence due to de novo ipsilateral breast carcinoma development

Development of ipsilateral breast carcinoma following diagnosis of breast ductal carcinoma in situ (DCIS) has been assumed to represent recurrence of the primary tumour. However, this may not be the case and it is important to know how often recurrences are new tumours. Ipsilateral primary-recurrence pairs (n=78) were sequenced to test their clonal relatedness. Shared genetic events were identified from whole exome sequencing (n=54 pairs) using haplotype-specific copy number and phylogenetic analysis. The remaining pairs were sequenced by a targeted panel or low-coverage whole genome sequencing. We included 32 non-recurrent DCIS to compare recurrent and non-recurrent disease. We found that 7% of DCIS recurrences were non-clonal by whole exome sequencing, indicative of a new breast carcinoma. Lower resolution methods detected a higher non-clonality rate (29%). Comparing primary DCIS with their recurrences found that evolution of DCIS to invasive disease was associated with increased ploidy and copy number events. TP53 mutations were enriched in DCIS with clonal recurrence compared with non-recurrent DCIS. Our results verify that de novo "recurrent tumours" of independent origin occur in patients who may be at high risk.

cancer biology↗