Spatial features of tumor-infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasis
Abstract 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 relations...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
|
| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-025-06860-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849234784443695104 |
|---|---|
| author | Huibo Zhang Ming Luo Junwei Feng Juan Tan Yan Jiang Dmitrij Frishman Yang Liu |
| author_facet | Huibo Zhang Ming Luo Junwei Feng Juan Tan Yan Jiang Dmitrij Frishman Yang Liu |
| author_sort | Huibo Zhang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-9829d6102afb4cc18aa3969eefcda554 |
| institution | Kabale University |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-9829d6102afb4cc18aa3969eefcda5542025-08-20T04:03:01ZengBMCJournal of Translational Medicine1479-58762025-07-0123111410.1186/s12967-025-06860-1Spatial features of tumor-infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasisHuibo Zhang0Ming Luo1Junwei Feng2Juan Tan3Yan Jiang4Dmitrij Frishman5Yang Liu6Department of Bioinformatics, TUM School of Life Sciences, Technical University of MunichDepartment of Oncology, Taihe Hospital, Hubei University of MedicineDepartment of Interventional Radiology, Renmin Hospital of Wuhan UniversityDepartment of Pathology, The Third Xiangya Hospital, Central South UniversityDepartment of Pathology, The Third Xiangya Hospital, Central South UniversityDepartment of Bioinformatics, TUM School of Life Sciences, Technical University of MunichDepartment of Pathology, The Third Xiangya Hospital, Central South UniversityAbstract 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.https://doi.org/10.1186/s12967-025-06860-1Tumor-infiltrating lymphocytesTILScoutWhole slide imagesLung adenocarcinomaLymph node metastasis |
| spellingShingle | Huibo Zhang Ming Luo Junwei Feng Juan Tan Yan Jiang Dmitrij Frishman Yang Liu Spatial features of tumor-infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasis Journal of Translational Medicine Tumor-infiltrating lymphocytes TILScout Whole slide images Lung adenocarcinoma Lymph node metastasis |
| title | Spatial features of tumor-infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasis |
| title_full | Spatial features of tumor-infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasis |
| title_fullStr | Spatial features of tumor-infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasis |
| title_full_unstemmed | Spatial features of tumor-infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasis |
| title_short | Spatial features of tumor-infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasis |
| title_sort | spatial features of tumor infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasis |
| topic | Tumor-infiltrating lymphocytes TILScout Whole slide images Lung adenocarcinoma Lymph node metastasis |
| url | https://doi.org/10.1186/s12967-025-06860-1 |
| work_keys_str_mv | AT huibozhang spatialfeaturesoftumorinfiltratinglymphocytesinprimarylesionsoflungadenocarcinomapredictlymphnodemetastasis AT mingluo spatialfeaturesoftumorinfiltratinglymphocytesinprimarylesionsoflungadenocarcinomapredictlymphnodemetastasis AT junweifeng spatialfeaturesoftumorinfiltratinglymphocytesinprimarylesionsoflungadenocarcinomapredictlymphnodemetastasis AT juantan spatialfeaturesoftumorinfiltratinglymphocytesinprimarylesionsoflungadenocarcinomapredictlymphnodemetastasis AT yanjiang spatialfeaturesoftumorinfiltratinglymphocytesinprimarylesionsoflungadenocarcinomapredictlymphnodemetastasis AT dmitrijfrishman spatialfeaturesoftumorinfiltratinglymphocytesinprimarylesionsoflungadenocarcinomapredictlymphnodemetastasis AT yangliu spatialfeaturesoftumorinfiltratinglymphocytesinprimarylesionsoflungadenocarcinomapredictlymphnodemetastasis |