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Identification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFβ as key predictors

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dc.contributor.authorDomingo, Enric
dc.contributor.authorRathee, Sanjay
dc.contributor.authorBlake, Andrew
dc.contributor.authorSamuel, Leslie
dc.contributor.authorMurray, Graeme
dc.contributor.authorSebag-Montefiore, David
dc.contributor.authorGollins, Simon
dc.contributor.authorWest, Nicholas
dc.contributor.authorBegum, Rubina
dc.contributor.authorRichman, Susan
dc.contributor.authorQuirke, Phil
dc.contributor.authorRedmond, Keara
dc.contributor.authorChatzipli, Aikaterini
dc.contributor.authorBarberis, Alessandro
dc.contributor.authorHassanieh, Sylvana
dc.contributor.authorMahmood, Umair
dc.contributor.authorYoudell, Michael
dc.contributor.authorMcDermott, Ultan
dc.contributor.authorKoelzer, Viktor
dc.contributor.authorLeedham, Simon
dc.contributor.authorTomlinson, Ian
dc.contributor.authorDunne, Philip
dc.contributor.authorBuffa, Francesca M
dc.contributor.authorMaughan, Timothy S
dc.contributor.authorS:CORT consortium
dc.contributor.institutionUniversity of Aberdeen.Administation Applied Medicineen
dc.contributor.institutionUniversity of Aberdeen.Aberdeen Cancer Centreen
dc.contributor.institutionUniversity of Aberdeen.Applied Medicineen
dc.date.accessioned2024-07-22T13:15:01Z
dc.date.available2024-07-22T13:15:01Z
dc.date.issued2024-08-01
dc.descriptionAcknowledgements F.M.B., A.B. and S.R. received funding from CRUK grant 23969 and ERC Consolidator Grant 772970 to F.M.B. The ARISTOTLE trial was funded by Cancer Research UK (CRUK/08/032). V.H.K. gratefully acknowledges funding by the Swiss National Science Foundation (P2SKP3_168322/1 and P2SKP3_168322/2) and the Promedica Foundation (F-87701-41-01). N.P.W acknowledges payment to institution from Yorkshire Cancer Research and Cancer Research UK (CRUK). P.D. acknowledges funding by CRUKearly detection project grant (grant no. A29834). I.T and TSM acknowledge funding from CRUK and MRC. This research was funded in whole, or in part, by the UKRI [MR/M016587/1]. Patients and/or the public were involved in the design and conduct of this work through the S:CORT consortium.en
dc.description.statusPeer revieweden
dc.format.extent13
dc.format.extent3620185
dc.identifier291854744
dc.identifier11d07152-ce24-4cf1-80fb-d7950444250f
dc.identifier39013324
dc.identifier85198587361
dc.identifier.citationDomingo, E, Rathee, S, Blake, A, Samuel, L, Murray, G, Sebag-Montefiore, D, Gollins, S, West, N, Begum, R, Richman, S, Quirke, P, Redmond, K, Chatzipli, A, Barberis, A, Hassanieh, S, Mahmood, U, Youdell, M, McDermott, U, Koelzer, V, Leedham, S, Tomlinson, I, Dunne, P, Buffa, F M, Maughan, T S & S:CORT consortium 2024, 'Identification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFβ as key predictors', EBioMedicine, vol. 106, 105228. https://doi.org/10.1016/j.ebiom.2024.105228en
dc.identifier.doi10.1016/j.ebiom.2024.105228
dc.identifier.issn2352-3964
dc.identifier.otherORCID: /0000-0002-8402-8670/work/164096063
dc.identifier.urihttps://hdl.handle.net/2164/23878
dc.identifier.vol106en
dc.language.isoeng
dc.relation.ispartofEBioMedicineen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subjectRectal neoplasmsen
dc.subjectRadiotherapyen
dc.subjectPrecision medicineen
dc.subjectPredictionen
dc.subjectTGFβen
dc.subjectImmune responseen
dc.subjectGenesen
dc.subjectR Medicineen
dc.subjectUK Research and Innovation (UKRI)en
dc.subjectMR/M016587/1en
dc.subjectCancer Research UKen
dc.subject23969en
dc.subjectCRUK/08/032en
dc.subjectA29834en
dc.subjectEuropean Research Councilen
dc.subject772970en
dc.subjectSupplementary Dataen
dc.subject.lccRen
dc.titleIdentification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFβ as key predictorsen
dc.typeJournal articleen

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