Neoadjuvant chemoradiotherapy is commonly used to treat rectal cancer but patients have different levels of response and/or toxic effects.

As part of the Stratification in COloRecTal cancer (S:CORT) programme, we collected 257 rectal biopsies from two cohorts: Grampian (single hospital) and Aristotle (clinical trial). All patients had been subsequently treated with identical regimen of neoadjuvant radiotherapy and capecitabine. We performed trancriptomic, mutation and copy number profiling and aimed to identify biomarkers associated with the robust pathological endpoint of complete response (CR). Key biological determinants were identified by linear regression of different pre-defined, hypothesis-driven biomarkers for radiotherapy response, adjusted by the known confounders T and N stage. A novel RNA signature was derived using a personalised bioinformatical pipeline using a wide range of machine learning approaches. Results were validated in a publicly available transcriptomic cohort of 107 patients treated with similar dose of radiotherapy and 5-fluorouracil infusion. Further comparision of the biological determinants and the novel RNA signature were performed in the same cohorts and also TCGA by linear regression. Previously published transcriptomic signatures were retrieved and assessed in the validation, unseen cohort.

Grampian and Aristotle cohorts had similar statistical power and showed similar associations of CR with biological candidates, 10 of them being significant or borderline (p<0.1). Accordingly, both cohorts were merged into a single discovery set to better assess which ones would show additive, independent association. Following multivariable stepwise regression the final model was composed of the immune biomarkers cytotoxic lymphocytes and CMS1 for radiosensitivity while the stromal TGFb Fibroblasts and epithelial APC mutations were for radioresistance. The first three variables were validated in the transcriptomic validation set (Cyt lymph OR 7.09, p=0.01; CMS1 OR 5.39, p=0.02; TGFb Fib OR 0.27, p=0.04). In parallel, a 33-gene signature, trained in the discovery cohort by a comprehensive machine learning pipeline, showed excellent predictive ability in the validation cohort (0.9 AUC; 88% accuracy, 90% sensitivity, 86% specificity). Most genes were associated with at least one of the four biological features identified in the discovery set, validation set and a third cohort of colorectal cancer resections. Our novel signature showed much better predictive ability than other previously published transcriptomic signatures in the validation, unseen cohort.

The immune, stromal and epithelial components of rectal tumours are important players for prediction of CR to radiotherapy in rectal cancer. A 33-gene transcriptomic biomarker can be used to effectively select patients that are highly likely to achieve CR allowing organ preservation while modulation of the relevant biological features in the other patients may be tested to improve their poor outcome with current treatment strategies.

Citation Format: Enric Domingo, Sanjay Rathee, Andrew Blake, Leslie M. Samuel, Graeme I. Murray, David Sebag-Montefiore, Simon Gollins, Nicholas West, Rubina Begum, Marian Duggan, Laura White, Susan Richman, Philip Quirke, James Robineau, Keara Redmond, Aikaterini Chatzipli, Ultan McDermott, Ian Tomlinson, Philip Dunne, Francesca Buffa, Tim Maughan. Stratification of radiotherapy and fluoropyrimidine-based chemotherapy from multi-omic profiling in rectal cancer biopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR …