DOI: 10.3390/cancers18132089 ISSN: 2072-6694

Tumour–Stroma Ratio as a Predictive Biomarker for Neoadjuvant Therapy Efficacy in Rectal Cancer

Jonathan P. Callaghan, Caroline R. Cartlidge, Kenal Patel, Nicholas P. West

Background: The treatment of rectal cancer frequently involves a multimodal approach, including neoadjuvant therapy prior to surgery in patients with locally advanced disease. However, the response to such treatment is variable. Robust biomarkers to predict neoadjuvant therapy response represent an unmet clinical need; they could help to stratify patients for organ preservation strategies or treatment intensification. The tumour–stroma ratio (TSR) is an established prognostic marker that has recently gained attention for its potential predictive value when assessed in pre-treatment biopsies. Objective: This narrative review critically evaluates the existing evidence regarding TSR as a predictive biomarker for neoadjuvant therapy response in rectal cancer. Results: Emerging evidence from retrospective studies of large cohorts suggests that stroma-high tumours often demonstrate resistance to standard neoadjuvant chemoradiotherapy, resulting in lower major pathological response rates. Conversely, some smaller studies report no significant association between biopsy TSR and treatment efficacy. This conflicting evidence could be attributable to methodological heterogeneity, including inconsistent definitions, varying measurement techniques (manual versus automated), and mixed patient cohorts. The predictive value of TSR appears to be neoadjuvant regimen-specific, with stroma-high phenotypes interacting differently with treatments like short-course radiotherapy or intensified chemotherapy. Conclusions: TSR is a simple, biologically plausible, and readily assessable promising biomarker with apparently predictive as well as prognostic potential. It is likely to represent a regimen-specific predictor rather than a universal marker of resistance to neoadjuvant therapy in rectal cancer. Future clinical translation will require standardised, AI-driven quantification and robust prospective clinical validation.

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