Spectral cytometry-based immune profiling coupled with machine learning identifies circulating Th1-like cells as a...

Colorectal cancer is the third most common malignancy worldwide. Although current screening relies on faecal occult blood testing (FOBT), its sensitivity for advanced adenomas (key precursor lesions) remains limited. Indeed, colonoscopy remains the gold standard for definitive diagnosis. Hence, development of novel blood-based screening tools in FOBT-positive patients is needed to optimize colonoscopy referral.

We prospectively recruited 104 FOBT-positive patients undergoing colonoscopy. Based on endoscopic and histopathological assessment, patients were classified into no polyps (NP, n = 46), non-advanced polyps (NA, n = 33) and advanced polyps (AP, n = 25). Seventy-five immune cell subsets and their homing, activation and exhaustion profiles were characterized by spectral cytometry, yielding 900 variables. Differentially expressed variables were used to train five supervised machine learning models including random forest, decision tree, multinomial regression, polynomial kernel support vector machine (SVM) and Kernel K-nearest neighbours.

Among 91 differentially expressed immune variables, Boruta-based feature selection identified seven key cell populations. Th1-like cells emerged as the dominant predictive variable. The decision tree model achieved the best overall performance, with total classification of AP patients (AUC=1.0, 100% sensitivity and specificity). Global model accuracy reached 75% (p < 0.01 vs. no-information rate), with a macro-AUC of 0.85.

This study demonstrates that spectral-cytometry immunotyping of the circulating immunome, combined with machine learning, can identify patients harbouring advanced colorectal adenomas from a blood sample. Hence, Th1-like cells emerge not just as key cells, but also as a promising biomarker that could complement current FOBT screening to prioritize colonoscopy in patients at highest risk of pre-malignant lesions.