Long noncoding RNAs (lncRNAs) represent a vast and largely unexplored layer of cancer biology, harbouring the majority of somatic mutations in cancer. Here, we present an integrated computational and functional genomics framework to systematically discover, characterise and prioritise lncRNAs for therapeutic targeting in breast and ovarian cancers.

Over the past four years, we developed a framework powered by a range of computational tools. We started developing in silico metatranscriptome assembly strategies to uncover thousands of previously unannotated lncRNAs from relevant cell and tissue samples, initially using short-read data (ShROOM) and now extended to hybrid long- and short-read integration (HyDRA). These tools enabled the redefinition of normal breast epithelial cell populations and the construction of pseudo-longitudinal models of ovarian cancer progression and chemoresistance, comprehensively mapping lncRNA involvement in disease.

To prioritise candidates from this expanded gene set, we computationally integrate lncRNA discovery with genetic association signals, including genome-wide association studies. Functional interrogation is achieved through a suite of CRISPR-based technologies. We pioneered RNA-targeting CRISPR–Cas13 screens, establishing the first platform for transcript perturbation at scale, and are extending this approach to single-cell readouts (CROP-seq). We are also developing high-throughput CRISPR prime editing to assess the functional impact of thousands of lncRNA mutations.

This framework enables the identification of clinically actionable lncRNAs, including functional drivers like BRRIAR, a strong candidate for oestrogen receptor–positive breast cancer therapy, the highlight of this talk. Our work establishes a scalable path from discovery to therapeutic targeting, positioning lncRNAs as central figures in precision oncology.

Co-authors: Stacey Edwards, Juliet French, Haran Sivakumaran