Pathway-based signatures predict patient outcome, chemotherapy benefit and synthetic lethal dependencies in invasive lobular breast cancer
Invasive Lobular Carcinoma (ILC) is a morphologically distinct breast cancer subtype that represents up to 15% of all breast cancers. Compared to Invasive Breast Carcinoma of No Special Type (IBC-NST), ILCs exhibit poorer long-term outcome and a unique pattern of metastasis. Despite these differences, the systematic discovery of robust prognostic biomarkers and therapeutically actionable molecular pathways in ILC remains limited. In this study, we developed and tested a series of pathway-centric multivariable models using statistical machine learning in seven retrospective clinico-genomic cohorts (n=996). Further external validation was performed using a new RNA-Seq clinical cohort of aggressive ILCs (n=48). By integrating these models with CRISPR-Cas9 screening data from breast cancer cell lines, we identified novel therapeutic targets in high-risk ILCs, including 16 high-confidence candidate synthetic lethal genes. This study provides interpretable prognostic and predictive biomarkers of ILC which could serve as the starting points for targeted drug discovery.