176 Spatial and seromic profiling of tumor infiltrating B-cell subtypes and autoantibody production in oral cancers
Notice bibliographique
Résumé
Background Immune Checkpoint Inhibitors (ICIs) are approved for the treatment of recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) showing long-term clinical benefit in a small proportion of patients. Excitingly, ICI are now moving to first line therapy. To predict response and improve efficacy of ICIs, detailed insights into the HNSCC tumor immune microenvironment (TiME) is essential. Although cytotoxic CD8 T-cells are considered to be the effectors of immunotherapy approaches, a critical role of tumor-infiltrating B-cells (TIL-B) is gaining attention. Previous research in our group showed that high numbers of TIL-B were associated with improved patient survival in oral HNSCC (OSCC), independent of CD8 T-cell infiltration. 1 A recent meta-analysis identified TIL-B as key prognostic marker in HNSCC outcome and ICI efficacy, outperforming established biomarkers like tumor mutational burden and PD-L1 combined positivity score.2 However, TIL-B phenotypes and their function within the TiME remain unclear. The aim of this study was to characterize B-cell subsets and antibody production spatially within OSCC and to identify OSCC-specific antibody responses in patient plasma samples.Methods Seven- and nine marker panels were designed to perform multiplexed fluorescent immunohistochemistry (mfIHC) on a cohort of OSCC FFPE samples (n=120) to spatially characterize TIL-B phenotypes and IgG/IgA production. To investigate autoantibody production and identify OSCC-specific autoantibody targets, we screened plasma samples of 50 OSCC patients and 25 healthy controls on the HuProt high-density protein array platform.Results Our spatial analyses revealed increased infiltration of naïve and memory B cells, plasmablasts and plasma cells in TIL-B high OSCC. In tumors enriched for plasmablasts, heterogeneous expression of IgG and IgA was observed. Plasmablasts were mostly present at the invasive tumor front, sometimes near tertiary lymphoid structures, but did not infiltrate the tumor fields. Seromic analysis revealed the presence of autoantibodies against tumor-associated antigens such as p53 and the MAGEA cancer/testis antigens, among others. Additionally, we observed an enrichment of autoantibodies directed against squamous cell-specific proteins, indicative of a humoral response in the TiME of OSCC. Top autoantibody hits not previously linked to OSCC, were validated at the protein level in patient tumor tissue and HNSCC-cell lines.Conclusions We identified a subset of OSCC tumors enriched in plasmablast populations, and identified squamous cell-associated autoantibodies. Our data support the apparent important role of B-cells in the TiME of HNSCC. These TIL-B populations and associated autoantibody profiles could serve as biomarkers of immunotherapy response and reveal novel targets for immunotherapy.Ethics Approval This study was conducted in agreement with the Declaration of Helsinki and the medical ethical guidelines in the Code of Conduct for Proper Secondary Use of Human Tissue of the Dutch Federation of Biomedical Scientific Societies. For plasma sample collection written informed consent was obtained. The Institutional Review Board of VUmc approved the use of the material under protocols 2008.071 (A2016.035) and 2021-0511.References Nauta IH, Nijenhuis DNLM, Ganzevles SH, Raaff PI, Kloosterman J, Bloemena E, Brakenhoff RH, Leemans CR, van de Ven R. Richness for tumor-infiltrating B-Cells in the oral cancer tumor microenvironment is a prognostic factor in early-stage disease and improves outcome in advanced-stage disease. Cancers. 2025;17:113 (p1–19).Chang TG, Spathis A, Schäffer AA, Gavrielatou N, Kuo F, Jia D, Mukherjee S, Sievers C, Economopoulou P, Anastasiou M, Moutafi M, Pal LR, Vos J, Lee AS, Lam S, Zhao K, Jiang P, Allen CT, Foukas P, Gomatou G, Altan-Bonnet G, Morris LTG, Psyrri A, Ruppin E. Tumor and blood B-cell abundance ourperforms established immune checkpoint response predisction signatures in head and neck cancer. Ann Oncol. 2024;36:309–320.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».