Predicting neoadjuvant chemoradiotherapy response in rectal cancer: Insights from biomarkers to clinical practice

Table 1 Tumor regression grading systems and prognostic value

TRG system	Description	Prognostic value	Limitations
mDworak	Evaluates primary tumor + regional LNs; TRG 4 = ypT0N0, TRG 3 = near-complete regression	Best predictor of RFS and OS (Kim et al[21]); C-statistic: 0.6492 (RFS), 0.6783 (OS)	Requires LN assessment, less commonly used
AJCC	TRG 0-3 scale (complete response to poor response)	Predictive of survival (Kim et al[21]); C-statistic: 0.6359 (RFS), 0.6718 (OS)	Moderate reproducibility
Dworak	TRG 4 = complete regression, TRG 1 = minimal regression	Associated with survival, but lower reproducibility (Chetty et al[27])	Interobserver variability (kappa 0.28-0.35)
Ryan	TRG 0-3 scale; used in multiple cancers	Predictive of recurrence and survival (Kim et al[21])	Similar performance to AJCC and Dworak
Mandard	TRG 1-5; based on fibrosis and residual tumor	Predicts DFS in esophageal cancer (Mandard et al[23])	Lower reproducibility compared to Becker (kappa 0.28)
Becker	TRG 1a-b (< 10% residual tumor) predictive of survival	High reproducibility	Less widely used outside gastric cancer
Rödel	TRG 4 = complete regression	Favorable outcomes in rectal cancer (Rödel et al[26])	Requires histopathological expertise

mDworak: Modified Dworak; LNs: Lymph nodes; TRG: Tumor regression grading; RFS: Recurrence-free survival; OS: Overall survival; AJCC: American Joint Committee on Cancer; DFS: Disease-free survival.

Table 2 Biomarkers for predicting neoadjuvant chemoradiotherapy response

Category	Predictors	Key findings
Clinical markers	Tumor size (< 4 cm), clinical node negativity (cN0), well-differentiated histology	Associated with higher pCR rates (Turri et al[17])
	Distance to anal verge (< 5 cm), low pretreatment CEA (< 5 µg/L)	Favorable response predictors (Peng et al[33]; Shao et al[32])
Histopathological markers	Low tumor budding, absence of LVI and PNI	Correlates with better nCRT response (Agarwal et al[36]; Rogers et al[35])
	Mucinous histology	Linked to treatment resistance (Simha et al[37])
Molecular markers	KRAS mutations (resistance), TP53 status (variable), low RAD18, high Beclin 1	Genetic alterations impact therapy sensitivity (Chow et al[42]; Zaanan et al[41])
	Epigenetics: CpG island methylation, microRNAs (e.g., miR-21-5p overexpression)	Influence gene expression and therapy resistance (Jo et al[44]; Lopes-Ramos et al[46])
Tumor microenvironment	High CD8+ TILs, low FOXP3+ Tregs, low M2 macrophage density	Favorable immune landscape (McCoy et al[48]; Shabo et al[49])
	Gut microbiome diversity	Emerging predictor of response (Yi et al[50])
Radiological biomarkers	MRI-based radiomics (T2WI, DWI), CRM, EMVI	MRI features enhance response prediction (Jiang et al[52]; Taylor et al[6])
	PET-based metabolic response	Functional assessment of tumor viability post-nCRT

CEA: Carcinoembryonic antigen; pCR: Pathological complete response; LVI: Lymphovascular; PNI: Perineural invasion; nCRT: Neoadjuvant chemoradiotherapy; TILs: Tumor-infiltrating lymphocytes; MRI: Magnetic resonance imaging; CRM: Circumferential resection margin; EMVI: Extramural vascular invasion; PET: Positron emission tomography; Tregs: Regulatory T cells; T2WI: T2-weighted imaging; DWI: Diffusion-weighted imaging.