WEHI Wednesday Seminar hosted by Dr Nadia Davidson
 

Ashley Weir
PhD Student – Davidson Laboratory, Blood Cells and Blood Cancer division & Bioinformatics and Computational Biology divisions, WEHI
Molecular Oncology Group, School of Clinical Medicine, UNSW Medicine and Health

 

Characterising genomic damage in high grade serous ovarian carcinoma using transcriptomics

 

Davis Auditorium

Join via SLIDO enter code #WEHIWednesday

Including Q&A session
 

(this is a PhD Completion seminar) 

High-grade serous ovarian carcinomas (HGSCs) are aggressive cancers marked by extensive genomic damage due to faulty DNA repair. About 50% of HGSCs are deficient in homologous recombination (HRD), an error-free DNA repair pathway. HRD cases respond better to treatment and are eligible for targeted therapies. While the genomic features of HRD are well-studied and used clinically, the transcriptomic consequences of HRD remain poorly understood and therefore underutilised.

Ashley Weir, nearing the end of her computational PhD in Dr Nadia Davidson’s lab at WEHI (co-supervised by Prof Susan Ramus and Dr Chin Wee Tan), has investigated the transcriptomic signature of HRD in HGSC. She developed IdentifiHR, a machine learning model that predicts HR status using gene expression data. IdentifiHR outperforms other expression-based models in both bulk and single-cell RNA sequencing datasets. Ashley has also compared the transcriptomic signature of HRD HGSCs with HRD breast and prostate cancers, highlighting common patterns of altered expression across large regions of the genome that can be linked back to recurrent copy number changes.

In parallel, she explored how HR status influences prognosis in HGSC. Using the Ovarian Tumour Tissue Analysis (OTTA) consortium dataset (>6000 cases), she analysed known prognostic signatures, disease subtypes, and biomarkers, revealing HR-specific survival indicators. This led to a focused study on FOLR1, now being validated as a marker of HGSC differentiation. Ashley is further characterising FOLR1-negative cases using genomic and transcriptomic data.

All welcome!