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dc.contributor.authorSammut, Stephen
dc.contributor.authorCrispin-Ortuzar, Mireia
dc.contributor.authorChin, Suet-Feung
dc.contributor.authorProvenzano, Elena
dc.contributor.authorBardwell, Helen A
dc.contributor.authorMa, Wenxin
dc.contributor.authorCope, Wei
dc.contributor.authorDariush, Ali
dc.contributor.authorDawson, Sarah-Jane
dc.contributor.authorAbraham, Jean E
dc.contributor.authorDunn, Janet
dc.contributor.authorHiller, Louise
dc.contributor.authorThomas, Jeremy
dc.contributor.authorCameron, David A
dc.contributor.authorBartlett, John MS
dc.contributor.authorHayward, Larry
dc.contributor.authorPharoah, Paul
dc.contributor.authorMarkowetz, Florian
dc.contributor.authorRueda Palacio, Oscar
dc.contributor.authorEarl, Helena
dc.contributor.authorCaldas, Carlos
dc.date.accessioned2022-01-28T16:49:27Z
dc.date.available2022-01-28T16:49:27Z
dc.date.issued2022-01
dc.date.submitted2021-07-30
dc.identifier.issn0028-0836
dc.identifier.others41586-021-04278-5
dc.identifier.other4278
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/333351
dc.description.abstractBreast cancers are complex ecosystems of malignant cells and the tumour microenvironment1. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy2. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery3 were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.subjectArticle
dc.subject/631/67/1347
dc.subject/631/67/1059/99
dc.subject/631/67/1857
dc.subject/631/114/2401
dc.subject/692/308/575
dc.subject/45/23
dc.subject/45/91
dc.subject/38/39
dc.subjectarticle
dc.titleMulti-omic machine learning predictor of breast cancer therapy response.
dc.typeArticle
dc.date.updated2022-01-28T16:49:26Z
prism.endingPage629
prism.issueIdentifier7894
prism.publicationNameNature
prism.startingPage623
prism.volume601
dc.identifier.doi10.17863/CAM.80774
dcterms.dateAccepted2021-11-23
rioxxterms.versionofrecord10.1038/s41586-021-04278-5
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidSammut, Stephen [0000-0003-4472-904X]
dc.contributor.orcidChin, Suet-Feung [0000-0001-5697-1082]
dc.contributor.orcidProvenzano, Elena [0000-0003-3345-3965]
dc.contributor.orcidDawson, Sarah-Jane [0000-0002-8276-0374]
dc.contributor.orcidThomas, Jeremy [0000-0002-4652-610X]
dc.contributor.orcidPharoah, Paul [0000-0001-8494-732X]
dc.contributor.orcidMarkowetz, Florian [0000-0002-2784-5308]
dc.contributor.orcidRueda Palacio, Oscar [0000-0003-0008-4884]
dc.contributor.orcidEarl, Helena [0000-0003-1549-8094]
dc.contributor.orcidCaldas, Carlos [0000-0003-3547-1489]
dc.identifier.eissn1476-4687
pubs.funder-project-idWellcome Trust (106566/Z/14/Z)
pubs.funder-project-idEuropean Research Council (694620)
pubs.funder-project-idCancer Research UK (A27657)
pubs.funder-project-idCancer Research UK (CRUK-A19274)
pubs.funder-project-idNational Institute for Health Research (NIHRDH-IS-BRC-1215-20014)
cam.issuedOnline2021-12-07


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