Pharmacological prevention of type 2 diabetes.

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A Machine Learning Model of Complete Response to Radiation in Rectal Cancer Reveals Immune Infiltrate and TGFβ Signalling as Key Predictors

Background: It is uncertain which biological features underpin the response of rectal cancer (RC) to radiotherapy. No biomarker is currently in clinical use to select patients for treatment modifications. Methods: We identified two cohorts of patients (total N=249) with RC treated with neoadjuvant radiotherapy (45Gy/25) plus fluoropyrimidine. This discovery set included 57 cases with pathological complete response (pCR) to chemoradiotherapy (23%). Pre-treatment cancer biopsies were assessed using transcriptome-wide mRNA expression and targeted DNA sequencing for copy number and driver mutations. Biological candidate and machine learning (ML) approaches were used to identify predictors of pCR to radiotherapy independent of tumour stage. Findings were assessed in 107 cases from an independent validation set (GSE87211). Findings: Three gene expression sets showed significant independent associations with pCR: Fibroblast-TGFβ Response Signature (F-TBRS) with radioresistance; and cytotoxic lymphocyte (CL) expression signature and consensus molecular subtype CMS1 with radiosensitivity. These associations were replicated in the validation cohort. In parallel, a gradient boosting machine model comprising the expression of 33 genes was generated in the discovery cohort, and showed high performance in GSE87211 with 90% sensitivity, 86% specificity. Biological and ML signatures indicated similar mechanisms underlying radiation response, and showed better AUC and p-values than published transcriptomic signatures of radiation response in RC. Interpretation: RCs responding completely to chemoradiotherapy (CRT) have biological characteristics of immune response and absence of immune inhibitory TGFb signalling. These tumours can be identified by measurement of expression of a 33 gene signature. This could help select patients likely to respond to treatment with a primary radiotherapy approach as for anal cancer. Conversely, those with predicted radioresistance may be candidates for clinical trials evaluating addition of immune-oncology agents and stromal TGFb signalling inhibition. Funding Information: The Stratification in Colorectal Cancer Consortium (S:CORT) was funded by the Medical Research Council and Cancer Research UK (MR/M016587/1). Declaration of Interests: 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 CRU. P.D. acknowledges funding by Cancer Research UK (CRUK) early detection project grant (grant no. A29834. I.T and TSM acknowledge funding from CRUK and MRC. Other authors declare no conflict of interests. Ethics Approval Statement: The S:CORT consortium including this specific analysis was reviewed and approved by the South Cambs Research Ethics committee (REC ref 15/EE/0241; IRAS reference 169363)