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Metabolic characterization of tumor-immune interactions by multiplexed immunofluorescence reveals spatial mechanisms of immunotherapy response in non-small cell lung carcinoma (NSCLC).

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Nature communications 📖 저널 OA 92.8% 2021: 2/2 OA 2022: 3/3 OA 2023: 3/3 OA 2024: 21/21 OA 2025: 202/202 OA 2026: 178/210 OA 2021~2026 2026 Vol.17(1) p. 837
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유사 논문
P · Population 대상 환자/모집단
환자: advanced NSCLC, however a substantial proportion of patients remain treatment resistant
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
The selected features in the model imply a role for cell-cell proximities within discrete metabolic contexts. These tissue insights may supplement our understanding of the current paradigms around classical immunology in the NSCLC TME and its influence on immunotherapy outcomes.

Monkman J, Kilgallon A, Lawler C, Tubelleza R, Aung TN, Warrell JH, Vathiotis I, Trontzas IP, Gavrielatou N, Nyein Chan NN, Czertok R, Bookstein S, O'Byrne K, Markovits E, Rimm DL, Kulasinghe A

📝 환자 설명용 한 줄

Immune checkpoint inhibitors (ICI) have improved clinical outcomes for some patients with advanced NSCLC, however a substantial proportion of patients remain treatment resistant.

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APA Monkman J, Kilgallon A, et al. (2026). Metabolic characterization of tumor-immune interactions by multiplexed immunofluorescence reveals spatial mechanisms of immunotherapy response in non-small cell lung carcinoma (NSCLC).. Nature communications, 17(1), 837. https://doi.org/10.1038/s41467-026-68633-8
MLA Monkman J, et al.. "Metabolic characterization of tumor-immune interactions by multiplexed immunofluorescence reveals spatial mechanisms of immunotherapy response in non-small cell lung carcinoma (NSCLC).." Nature communications, vol. 17, no. 1, 2026, pp. 837.
PMID 41634004 ↗

Abstract

Immune checkpoint inhibitors (ICI) have improved clinical outcomes for some patients with advanced NSCLC, however a substantial proportion of patients remain treatment resistant. Here we analyze the NSCLC tumor microenvironment (TME) using multiplexed immunofluorescence (mIF) of biopsies taken from patients prior to ICI treatment. We apply a deep-learning model to classify the cellular phenotypes and probe functional and metabolic states of both tumor and immune cells, aiming to reveal predictive features of response to ICI. Tissue neighborhoods are generated to allow geometric profiling of spatial densities and interactions at a range of scales. Multivariate modelling of ICI response yields a model that predicts progression-free survival (PFS) over 24 months (AUC = 0.8). The selected features in the model imply a role for cell-cell proximities within discrete metabolic contexts. These tissue insights may supplement our understanding of the current paradigms around classical immunology in the NSCLC TME and its influence on immunotherapy outcomes.

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