Spatial characterization of tertiary lymphoid structures as predictive biomarkers for immune checkpoint blockade in head and neck squamous cell carcinoma
Immune checkpoint blockade (ICB) is the standard of care for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), yet efficacy remains low. The combined positive score (CPS) for PD-L1 is the only biomarker approved to predict response to ICB and has limited performance. Tertiary Lymph...
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Taylor & Francis Group
2025-12-01
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| Series: | OncoImmunology |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/2162402X.2025.2466308 |
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| author | Daniel A. Ruiz-Torres Michael E. Bryan Shun Hirayama Ross D. Merkin Evelyn Luciani Thomas J. Roberts Manisha Patel Jong C. Park Lori J. Wirth Peter M. Sadow Moshe Sade-Feldman Shannon L. Stott Daniel L. Faden |
| author_facet | Daniel A. Ruiz-Torres Michael E. Bryan Shun Hirayama Ross D. Merkin Evelyn Luciani Thomas J. Roberts Manisha Patel Jong C. Park Lori J. Wirth Peter M. Sadow Moshe Sade-Feldman Shannon L. Stott Daniel L. Faden |
| author_sort | Daniel A. Ruiz-Torres |
| collection | DOAJ |
| description | Immune checkpoint blockade (ICB) is the standard of care for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), yet efficacy remains low. The combined positive score (CPS) for PD-L1 is the only biomarker approved to predict response to ICB and has limited performance. Tertiary Lymphoid Structures (TLS) have shown promising potential for predicting response to ICB. However, their exact composition, size, and spatial biology in HNSCC remain understudied. To elucidate the impact of TLS spatial biology in response to ICB, we utilized pre-ICB tumor tissue sections from 9 responders (complete response, partial response, or stable disease) and 11 non-responders (progressive disease) classified via RECISTv1.1. A custom multi-immunofluorescence (mIF) staining assay was applied to characterize tumor cells (pan-cytokeratin), T cells (CD4, CD8), B cells (CD19, CD20), myeloid cells (CD16, CD56, CD163), dendritic cells (LAMP3), fibroblasts (α Smooth Muscle Actin), proliferative status (Ki67) and immunoregulatory molecules (PD1). A machine learning model was employed to measure the effect of spatial metrics on achieving a response to ICB. A higher density of B cells (CD20+) was found in responders compared to non-responders to ICB (p = 0.022). The presence of TLS within 100 µm of the tumor was associated with improved overall (p = 0.04) and progression-free survival (p = 0.03). A multivariate machine learning model identified TLS density as a leading predictor of response to ICB with 80% accuracy. Immune cell densities and TLS spatial location play a critical role in the response to ICB in HNSCC and may potentially outperform CPS as a predictor of response. |
| format | Article |
| id | doaj-art-32af956872c8428ea194801ace4e6b3d |
| institution | OA Journals |
| issn | 2162-402X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | OncoImmunology |
| spelling | doaj-art-32af956872c8428ea194801ace4e6b3d2025-08-20T01:56:56ZengTaylor & Francis GroupOncoImmunology2162-402X2025-12-0114110.1080/2162402X.2025.2466308Spatial characterization of tertiary lymphoid structures as predictive biomarkers for immune checkpoint blockade in head and neck squamous cell carcinomaDaniel A. Ruiz-Torres0Michael E. Bryan1Shun Hirayama2Ross D. Merkin3Evelyn Luciani4Thomas J. Roberts5Manisha Patel6Jong C. Park7Lori J. Wirth8Peter M. Sadow9Moshe Sade-Feldman10Shannon L. Stott11Daniel L. Faden12Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, MA, USADepartment of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, MA, USADepartment of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, MA, USADepartment of Medicine, Harvard Medical School, Boston MA, USAKrantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Boston, MA, USADepartment of Medicine, Harvard Medical School, Boston MA, USADepartment of Medicine, Harvard Medical School, Boston MA, USADepartment of Medicine, Harvard Medical School, Boston MA, USADepartment of Medicine, Harvard Medical School, Boston MA, USADepartment of Medicine, Harvard Medical School, Boston MA, USAKrantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Boston, MA, USAKrantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Boston, MA, USADepartment of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, MA, USAImmune checkpoint blockade (ICB) is the standard of care for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), yet efficacy remains low. The combined positive score (CPS) for PD-L1 is the only biomarker approved to predict response to ICB and has limited performance. Tertiary Lymphoid Structures (TLS) have shown promising potential for predicting response to ICB. However, their exact composition, size, and spatial biology in HNSCC remain understudied. To elucidate the impact of TLS spatial biology in response to ICB, we utilized pre-ICB tumor tissue sections from 9 responders (complete response, partial response, or stable disease) and 11 non-responders (progressive disease) classified via RECISTv1.1. A custom multi-immunofluorescence (mIF) staining assay was applied to characterize tumor cells (pan-cytokeratin), T cells (CD4, CD8), B cells (CD19, CD20), myeloid cells (CD16, CD56, CD163), dendritic cells (LAMP3), fibroblasts (α Smooth Muscle Actin), proliferative status (Ki67) and immunoregulatory molecules (PD1). A machine learning model was employed to measure the effect of spatial metrics on achieving a response to ICB. A higher density of B cells (CD20+) was found in responders compared to non-responders to ICB (p = 0.022). The presence of TLS within 100 µm of the tumor was associated with improved overall (p = 0.04) and progression-free survival (p = 0.03). A multivariate machine learning model identified TLS density as a leading predictor of response to ICB with 80% accuracy. Immune cell densities and TLS spatial location play a critical role in the response to ICB in HNSCC and may potentially outperform CPS as a predictor of response.https://www.tandfonline.com/doi/10.1080/2162402X.2025.2466308Head and neck cancerimmune checkpoint blockadespatial biologytertiary lymphoid structures |
| spellingShingle | Daniel A. Ruiz-Torres Michael E. Bryan Shun Hirayama Ross D. Merkin Evelyn Luciani Thomas J. Roberts Manisha Patel Jong C. Park Lori J. Wirth Peter M. Sadow Moshe Sade-Feldman Shannon L. Stott Daniel L. Faden Spatial characterization of tertiary lymphoid structures as predictive biomarkers for immune checkpoint blockade in head and neck squamous cell carcinoma OncoImmunology Head and neck cancer immune checkpoint blockade spatial biology tertiary lymphoid structures |
| title | Spatial characterization of tertiary lymphoid structures as predictive biomarkers for immune checkpoint blockade in head and neck squamous cell carcinoma |
| title_full | Spatial characterization of tertiary lymphoid structures as predictive biomarkers for immune checkpoint blockade in head and neck squamous cell carcinoma |
| title_fullStr | Spatial characterization of tertiary lymphoid structures as predictive biomarkers for immune checkpoint blockade in head and neck squamous cell carcinoma |
| title_full_unstemmed | Spatial characterization of tertiary lymphoid structures as predictive biomarkers for immune checkpoint blockade in head and neck squamous cell carcinoma |
| title_short | Spatial characterization of tertiary lymphoid structures as predictive biomarkers for immune checkpoint blockade in head and neck squamous cell carcinoma |
| title_sort | spatial characterization of tertiary lymphoid structures as predictive biomarkers for immune checkpoint blockade in head and neck squamous cell carcinoma |
| topic | Head and neck cancer immune checkpoint blockade spatial biology tertiary lymphoid structures |
| url | https://www.tandfonline.com/doi/10.1080/2162402X.2025.2466308 |
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