A machine learning model integrating circulating Temra cell transcriptional profiles to predict immunotherapy...

While immunotherapy has transformed treatment paradigms for cancer, durable response rates remain below 30%. Currently available biomarkers are insufficient for precise patient stratification. Blood-based biomarkers, as minimally invasive and accessible tools, have attracted attention for their potential to dynamically monitor immune checkpoint inhibitor response.

Paired peripheral blood and tumor samples from 15 patients were analyzed using single-cell RNA sequencing and T cell receptor sequencing. To construct a comprehensive CD8⁺ T cell transcriptomic atlas, we integrated 13 publicly available single-cell datasets. A logistic regression-based model was trained on the transcriptional profiles of terminally differentiated effector memory CD8⁺ T (Temra) cells and then validated in a prospective, multi-center non-small cell lung cancer (NSCLC) cohort undergoing first-line immunotherapy.

Circulating Temra cells were the most clonally expanded CD8⁺ subset identified in peripheral blood. These cells exhibited distinct differentiation trajectories between responders and non-responders, with transcriptional profiles correlated with treatment efficacy. In the aggregated dataset comprising 748,082 CD8⁺ T cells, Temra transcriptional signatures were consistently associated with therapeutic outcomes. The predictive model, built upon the transcriptional profiles of Temra cells, achieved high accuracy (87%-95%) across two external validation cohorts. Validation in a prospective cohort of 131 patients with advanced NSCLC (ClinicalTrials.gov: NCT06054152) confirmed strong predictive performance (area under the curve [AUC] = 0.834).

Circulating Temra cells serve as robust predictors of immunotherapy efficacy. This study establishes a machine learning-based, pan-cancer, blood-derived transcriptomic biomarker that was prospectively validated in an NSCLC cohort and may substantially improve clinical decision-making in immuno-oncology.

This study was funded by the National Natural Science Foundation of China (no. 92259205).