dc.contributor.author | Alexander, J | |
dc.contributor.author | Schipper, K | |
dc.contributor.author | Nash, S | |
dc.contributor.author | Brough, R | |
dc.contributor.author | Kemp, H | |
dc.contributor.author | Iacovacci, J | |
dc.contributor.author | Isacke, C | |
dc.contributor.author | Natrajan, R | |
dc.contributor.author | Sawyer, E | |
dc.contributor.author | Lord, CJ | |
dc.contributor.author | Haider, S | |
dc.coverage.spatial | England | |
dc.date.accessioned | 2024-07-03T12:34:22Z | |
dc.date.available | 2024-07-03T12:34:22Z | |
dc.date.issued | 2024-05-27 | |
dc.identifier | 10.1038/s41416-024-02679-7 | |
dc.identifier.citation | British Journal of Cancer, 2024, 130 (11), pp. 1828 - 1840 | en_US |
dc.identifier.issn | 0007-0920 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/6280 | |
dc.identifier.eissn | 1532-1827 | |
dc.identifier.eissn | 1532-1827 | |
dc.identifier.doi | 10.1038/s41416-024-02679-7 | |
dc.identifier.doi | 10.1038/s41416-024-02679-7 | |
dc.description.abstract | 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. | |
dc.format | Print-Electronic | |
dc.format.extent | 1828 - 1840 | |
dc.language | eng | |
dc.language.iso | eng | en_US |
dc.publisher | SPRINGERNATURE | en_US |
dc.relation.ispartof | British Journal of Cancer | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_US |
dc.subject | Humans | |
dc.subject | Female | |
dc.subject | Breast Neoplasms | |
dc.subject | Carcinoma, Lobular | |
dc.subject | Prognosis | |
dc.subject | Retrospective Studies | |
dc.subject | Biomarkers, Tumor | |
dc.subject | Machine Learning | |
dc.subject | Middle Aged | |
dc.subject | Gene Expression Regulation, Neoplastic | |
dc.subject | Neoplasm Invasiveness | |
dc.title | Pathway-based signatures predict patient outcome, chemotherapy benefit and synthetic lethal dependencies in invasive lobular breast cancer. | en_US |
dc.type | Journal Article | |
dcterms.dateAccepted | 2024-04-03 | |
dc.date.updated | 2024-07-03T12:33:35Z | |
rioxxterms.version | VoR | en_US |
rioxxterms.versionofrecord | 10.1038/s41416-024-02679-7 | en_US |
rioxxterms.licenseref.startdate | 2024-05-27 | |
rioxxterms.type | Journal Article/Review | en_US |
pubs.author-url | https://www.ncbi.nlm.nih.gov/pubmed/38600325 | |
pubs.issue | 11 | |
pubs.organisational-group | ICR | |
pubs.organisational-group | ICR/Primary Group | |
pubs.organisational-group | ICR/Primary Group/ICR Divisions | |
pubs.organisational-group | ICR/Primary Group/ICR Divisions/Breast Cancer Research | |
pubs.organisational-group | ICR/Primary Group/ICR Divisions/Breast Cancer Research/Functional Genomics | |
pubs.organisational-group | ICR/Primary Group/ICR Divisions/Breast Cancer Research/Gene Function | |
pubs.organisational-group | ICR/Primary Group/ICR Divisions/Breast Cancer Research/Molecular Cell Biology | |
pubs.organisational-group | ICR/Primary Group/ICR Divisions/Molecular Pathology | |
pubs.organisational-group | ICR/Primary Group/ICR Divisions/Molecular Pathology/Functional Genomics | |
pubs.organisational-group | ICR/Primary Group/ICR Divisions/Molecular Pathology/Gene Function | |
pubs.organisational-group | ICR/ImmNet | |
pubs.publication-status | Published | |
pubs.publisher-url | http://dx.doi.org/10.1038/s41416-024-02679-7 | |
pubs.volume | 130 | |
icr.researchteam | Molecular Cell Biology | en_US |
icr.researchteam | Functional Genomics | en_US |
icr.researchteam | Gene Function | en_US |
dc.contributor.icrauthor | Isacke, Clare | |
dc.contributor.icrauthor | Natrajan, Rachael | |
dc.contributor.icrauthor | Lord, Christopher | |
icr.provenance | Deposited by Mr Arek Surman on 2024-07-03. Deposit type is initial. No. of files: 1. Files: Pathway-based signatures predict patient outcome, chemotherapy benefit and synthetic lethal dependencies in invasive lobular.pdf | |