Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial.

doi:10.1016/j.annonc.2023.04.005

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Article

Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial.

Annals of oncology : official journal of the European Society for Medical Oncology - 1 Jul 2023

Rakaee M, Andersen S, Giannikou K, Paulsen E-E, Kilvaer T K, Busund L-T R, Berg T, Richardsen E, Lombardi A P, Adib E, Pedersen M I, Tafavvoghi M, Wahl S G F, Petersen R H, Bondgaard A L, Yde C W, Baudet C, Licht P, Lund-Iversen M, Grønberg B H, Fjellbirkeland L, Helland Å, Pøhl M, Kwiatkowski D J, Donnem T

Abstract excerpt

BACKGROUND: We aim to implement an immune cell score model in routine clinical practice for resected non-small-cell lung cancer (NSCLC) patients (NCT03299478). Molecular and genomic features associated with immune phenotypes in NSCLC have not been explored in detail. PATIENTS AND METHODS: We developed a machine learning (ML)-based model to classify tumors into one of three categories: inflamed, altered, and...

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PMID: 37100205
DOI: 10.1016/j.annonc.2023.04.005

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