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

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

Annals of Oncology（2023）

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摘要

ML-based immune phenotyping by spatial distribution of T cells in resected NSCLC is able to identify patients at greater risk of disease recurrence after surgical resection. Lung adenocarcinomas with concurrent KEAP1 and STK11 mutations are enriched for altered and desert immune phenotypes.

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关键词

KEAP1,NF-kappa-B,NSCLC,STK11,immune phenotypes,machine learning

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