Pathway-based signatures predict patient outcome, chemotherapy benefit and synthetic lethal dependencies in invasive lobular breast cancer.
John AlexanderKoen SchipperSarah NashRachel BroughHarriet KempJacopo IacovacciClare M IsackeRachael NatrajanElinor J SawyerChristopher J LordSyed HaiderPublished in: British journal of cancer (2024)
mRNA dysregulation scores of 25 pathways were strongly prognostic in ILC (FDR-adjusted P < 0.05). Of these, three pathways including Cell-cell communication, Innate immune system and Smooth muscle contraction were also independent predictors of chemotherapy response. To aggregate these findings, a multivariable machine learning predictor called PSILC was developed and successfully validated for predicting overall and metastasis-free survival in ILC. Integration of PSILC with CRISPR-Cas9 screening data from breast cancer cell lines revealed 16 candidate therapeutic targets that were synthetic lethal with high-risk ILCs. This study provides interpretable prognostic and predictive biomarkers of ILC which could serve as the starting points for targeted drug discovery for this disease.
Keyphrases
- smooth muscle
- single cell
- drug discovery
- free survival
- crispr cas
- machine learning
- immune response
- cell therapy
- locally advanced
- genome editing
- nk cells
- mesenchymal stem cells
- electronic health record
- cancer therapy
- drug delivery
- squamous cell carcinoma
- gene expression
- dna methylation
- binding protein
- data analysis
- chemotherapy induced
- rectal cancer