Systemic nutritional status and its dynamic changes as predictors of response to neoadjuvant immunotherapy in locally advanced MSS/pMMR colorectal cancer

Background Patients with locally advanced microsatellite-stable/mismatch repair–proficient (MSS/pMMR) colorectal cancer show heterogeneous responses to neoadjuvant immunotherapy, and effective predictive biomarkers are lacking. Systemic nutritional status correlates with host immune function, but its predictive value for immunotherapy response in this population is unclear. This study aimed to explore the value of systemic nutritional indicators and their dynamic changes in predicting complete response (pCR) after neoadjuvant immunotherapy in these patients. Methods Clinical data from 255 patients with locally advanced MSS/pMMR colorectal cancer who received neoadjuvant immunotherapy were retrospectively analyzed. Patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. In the training cohort, univariate analysis was first performed to screen potential predictive variables, followed by multivariate logistic regression analysis to identify independent predictors of pCR and to construct a predictive model. The discriminative ability, calibration, and clinical utility of the model were evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. A nomogram was subsequently developed for visualized individualized prediction. Results Multivariate analysis identified younger age (OR = 0.96, 95% CI: 0.93–0.99), lower post–neoadjuvant treatment carcinoembryonic antigen (CEA) levels (OR = 0.19, 95% CI: 0.05–0.70), and an increase in the Geriatric Nutritional Risk Index (GNRI) during treatment (OR = 1.07, 95% CI: 1.02–1.12) as independent predictors of achieving pCR. The logistic regression model incorporating these three variables demonstrated good predictive performance, with AUCs of 0.76 in the training cohort and 0.80 in the validation cohort. The model showed good calibration, and decision curve analysis indicated favorable net clinical benefit. The resulting nomogram provided a practical reference tool for individualized prediction of pCR probability. Conclusion Age, post–neoadjuvant treatment CEA levels, and changes in the GNRI were independent predictors of pathological complete response following neoadjuvant immunotherapy in patients with locally advanced MSS/pMMR colorectal cancer. The predictive model and nomogram presented in this study provide a reference for clinical practice and provide a novel perspective for future studies combining nutritional interventions with immunotherapy.

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Journal
Frontiers in Medicine
Published
2026-03-27
DOI
https://doi.org/10.3389/fmed.2026.1803929
Primary Topic
Inflammatory Biomarkers in Disease Prognosis
Type
article
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article

Systemic nutritional status and its dynamic changes as predictors of response to neoadjuvant immunotherapy in locally advanced MSS/pMMR colorectal cancer

Ming Qiu, H R Chen, Shen Pang, Minghui Lin et al.
Frontiers in Medicine
Inflammatory Biomarkers in Disease Prognosis
article

Systemic nutritional status and its dynamic changes as predictors of response to neoadjuvant immunotherapy in locally advanced MSS/pMMR colorectal cancer

Ming Qiu, H R Chen, Shen Pang, Minghui Lin, Xihua Xie, Yefu Shen, Zhiyuan Xia, Sen Zhang, Dongchao Liu, Wei Huang
article en

Abstract

Background Patients with locally advanced microsatellite-stable/mismatch repair–proficient (MSS/pMMR) colorectal cancer show heterogeneous responses to neoadjuvant immunotherapy, and effective predictive biomarkers are lacking. Systemic nutritional status correlates with host immune function, but its predictive value for immunotherapy response in this population is unclear. This study aimed to explore the value of systemic nutritional indicators and their dynamic changes in predicting complete response (pCR) after neoadjuvant immunotherapy in these patients. Methods Clinical data from 255 patients with locally advanced MSS/pMMR colorectal cancer who received neoadjuvant immunotherapy were retrospectively analyzed. Patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. In the training cohort, univariate analysis was first performed to screen potential predictive variables, followed by multivariate logistic regression analysis to identify independent predictors of pCR and to construct a predictive model. The discriminative ability, calibration, and clinical utility of the model were evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. A nomogram was subsequently developed for visualized individualized prediction. Results Multivariate analysis identified younger age (OR = 0.96, 95% CI: 0.93–0.99), lower post–neoadjuvant treatment carcinoembryonic antigen (CEA) levels (OR = 0.19, 95% CI: 0.05–0.70), and an increase in the Geriatric Nutritional Risk Index (GNRI) during treatment (OR = 1.07, 95% CI: 1.02–1.12) as independent predictors of achieving pCR. The logistic regression model incorporating these three variables demonstrated good predictive performance, with AUCs of 0.76 in the training cohort and 0.80 in the validation cohort. The model showed good calibration, and decision curve analysis indicated favorable net clinical benefit. The resulting nomogram provided a practical reference tool for individualized prediction of pCR probability. Conclusion Age, post–neoadjuvant treatment CEA levels, and changes in the GNRI were independent predictors of pathological complete response following neoadjuvant immunotherapy in patients with locally advanced MSS/pMMR colorectal cancer. The predictive model and nomogram presented in this study provide a reference for clinical practice and provide a novel perspective for future studies combining nutritional interventions with immunotherapy.

Frontiers in MedicineVol. 13
First Affiliated Hospital of GuangXi Medical University (CN)
Openalex Percentile: Top 6%
Inflammatory Biomarkers in Disease Prognosis
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