Journal of translational medicine · 2025
Spatial features of tumor-infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasis.
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Zhang Huibo, Luo Ming, Feng Junwei, Tan Juan, Jiang Yan, Frishman Dmitrij, Liu Yang
Аннотация
BACKGROUND: Lymph node metastasis (LNM) is critical for staging, prognosis, and treatment decisions in lung adenocarcinoma (LUAD). While tumor-infiltrating lymphocytes (TILs) have demonstrated prognostic value, their role in LNM risk remains uninvestigated. This study evaluates the relationship between TIL features from primary tumor whole slide images (WSIs) and LNM in LUAD.
METHODS: TILScout was utilized to derive patch-level TIL scores and generate global TIL maps from primary tumor WSIs. Hot spot analysis and deep learning-based feature extraction followed by K-means clustering were applied to identify and characterize spatial TIL clusters (sTILCs) from the global TIL maps. Random forest models incorporating clinical/pathological data with (M1) and without (M2) TIL features (TIL scores and sTILCs) were developed on a training cohort (N = 312) to predict LNM, and performance was compared across validation (N = 78) and independent test cohorts (N = 148).
RESULTS: Two sTILC types ("TIL-cold" cluster [sTILC1] and "TIL-hot" cluster [sTILC2]) were identified. Model M1 significantly improved LNM prediction over M2, with AUCs increasing from 0.63 to 0.78 (Z = 5.366, P < 0.001) and from 0.61 to 0.72 (Z = 1.999, P = 0.046) in the training and validation cohorts, and from 0.69 to 0.80 (Z = 3.030, P = 0.002) in the test cohort. Decision curve analysis indicated that M1 provided greater net benefit across a broad spectrum of threshold probabilities. Importantly, patients with lower TIL scores and/or classified as sTILC1 consistently had an increased risk of LNM.
CONCLUSIONS: Spatial TIL features in primary tumors are linked to LNM in LUAD, thereby enabling the identification of high-risk patients and guiding personalized treatment strategies.
METHODS: TILScout was utilized to derive patch-level TIL scores and generate global TIL maps from primary tumor WSIs. Hot spot analysis and deep learning-based feature extraction followed by K-means clustering were applied to identify and characterize spatial TIL clusters (sTILCs) from the global TIL maps. Random forest models incorporating clinical/pathological data with (M1) and without (M2) TIL features (TIL scores and sTILCs) were developed on a training cohort (N = 312) to predict LNM, and performance was compared across validation (N = 78) and independent test cohorts (N = 148).
RESULTS: Two sTILC types ("TIL-cold" cluster [sTILC1] and "TIL-hot" cluster [sTILC2]) were identified. Model M1 significantly improved LNM prediction over M2, with AUCs increasing from 0.63 to 0.78 (Z = 5.366, P < 0.001) and from 0.61 to 0.72 (Z = 1.999, P = 0.046) in the training and validation cohorts, and from 0.69 to 0.80 (Z = 3.030, P = 0.002) in the test cohort. Decision curve analysis indicated that M1 provided greater net benefit across a broad spectrum of threshold probabilities. Importantly, patients with lower TIL scores and/or classified as sTILC1 consistently had an increased risk of LNM.
CONCLUSIONS: Spatial TIL features in primary tumors are linked to LNM in LUAD, thereby enabling the identification of high-risk patients and guiding personalized treatment strategies.