Pathway-based signatures predict patient outcome, chemotherapy benefit and synthetic lethal dependencies in invasive lobular breast cancer.
Date
2024-05-27Author
Alexander, J
Schipper, K
Nash, S
Brough, R
Kemp, H
Iacovacci, J
Isacke, C
Natrajan, R
Sawyer, E
Lord, CJ
Haider, S
Type
Journal Article
Metadata
Show full item recordAbstract
BACKGROUND: 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. METHODS: Pathway-centric multivariable models using statistical machine learning were developed and tested 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). RESULTS AND CONCLUSIONS: 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.
Collections
Subject
Humans
Female
Breast Neoplasms
Carcinoma, Lobular
Prognosis
Retrospective Studies
Biomarkers, Tumor
Machine Learning
Middle Aged
Gene Expression Regulation, Neoplastic
Neoplasm Invasiveness
Research team
Molecular Cell Biology
Functional Genomics
Gene Function
Language
eng
Date accepted
2024-04-03
License start date
2024-05-27
Citation
British Journal of Cancer, 2024, 130 (11), pp. 1828 - 1840
Publisher
SPRINGERNATURE