Development and validation of a machine learning model for predicting immune checkpoint inhibitor efficacy in advanced gastric cancer using dynamic changes in peripheral blood clinlabomics data: a retrospective multicenter cohort study.

BACKGROUND

Immune checkpoint inhibitors (ICIs) play a pivotal role in the treatment of advanced gastric cancer (GC). However, the biomarkers used to predict ICI efficacy are limited due to their reliance on single or static tumor characteristics. This study aims to develop a machine learning (ML) model that incorporates dynamic changes in clinlabomics data to optimize the predictive accuracy of ICI efficacy.

METHODS

This multicenter, retrospective study utilized nine ML to construct the model. Participants were further stratified into low-risk and high-risk groups based on the predicted efficacy of ICI. Kaplan-Meier survival curves and RNA-sequencing were used for differential analysis.

RESULTS

This study enrolled 377 patients with advanced GC who underwent first-line ICI treatment across eleven hospitals between January 2018 and May 2023. Among them, 220 patients from Qilu Hospital of Shandong University were selected for the development model. The remaining ten hospitals contributed to two external test cohorts. Ten dynamic clinlabomics features were identified. The XGBoost demonstrated optimal performance in predicting ICI response, achieving an AUC of 0.863 in the training cohort, and 0.790-0.842 in the validation and two external cohorts. Notably, the model exhibited strong predictive capabilities compared to single point-in-time and previously proposed model. In the subgroup analysis, the low-risk subtype demonstrated a significantly improved prognosis and exhibited characteristics of "hot tumors". A web tool was generated: https://ici-therapeutic-efficacy-predictor-ztwwfwek2uckbmhxlnsayq.streamlit.app/ .

CONCLUSIONS

The dynamic clinlabomics model can effectively predict the ICI efficacy in advanced GC. The model was validated using multicenter data and provides new evidence to optimize treatment decisions.