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

Domingo, EnricRathee, SanjayBlake, AndrewSamuel, LeslieMurray, GraemeSebag-Montefiore, DavidGollins, SimonWest, NicholasBegum, RubinaRichman, SusanQuirke, PhilRedmond, KearaChatzipli, AikateriniBarberis, AlessandroHassanieh, SylvanaMahmood, UmairYoudell, MichaelMcDermott, UltanKoelzer, ViktorLeedham, SimonTomlinson, IanDunne, PhilipBuffa, Francesca MMaughan, Timothy SS:CORT consortium2024-07-222024-07-222024-08-01Domingo, 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.2024.1052282352-3964ORCID: /0000-0002-8402-8670/work/164096063https://hdl.handle.net/2164/23878Acknowledgements 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.133620185engSDG 3 - Good Health and Well-beingRectal neoplasmsRadiotherapyPrecision medicinePredictionTGFβImmune responseGenesR MedicineUK Research and Innovation (UKRI)MR/M016587/1Cancer Research UK23969CRUK/08/032A29834European Research Council772970Supplementary DataRIdentification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFβ as key predictorsJournal article10.1016/j.ebiom.2024.105228106