Abstract 7130: Spatially resolved single-cell analysis reveals cGAS-STING-mediated tumor-immune interactions via IFNα as determinants of pembrolizumab sensitivity in triple-negative breast cancer
Notice bibliographique
Résumé
Abstract Purpose: While an addition of immune checkpoint inhibitor (ICI), pembrolizumab, has improved outcomes of patients with TNBC, early-stage TNBC patients with residual cancer burden (RCB) II/III after neoadjuvant chemotherapy in combination with pembrolizumab (NAP) still have poor outcomes. Previously, we demonstrated that intratumoral HLA-DRA expression and interferon-alpha (IFNα) responses were associated with immune-inflamed phenotype and better outcomes in TNBC patients receiving chemotherapy. This study utilized the CosMx™ SMI to investigate in early-stage TNBC treated with NAP. Methods: Of 151 samples from TNBC patients treated with NAP at Mayo Clinic, 8 pre-treatment biopsy samples (5 pCR and 3 RCB II/III were analyzed using the CosMx™ SMI platform. Linear mixed models to assess differential expression (DE), adjusting for hierarchical data structures. DE analyses were expressed as log2-fold changes (logFC), with the Benjamini-Yekutieli method. GSEA and IPA for signaling pathway exploration. Results: 1.7 million cells were identified across 8 samples after stringent quality control. Immune cell profiling revealed higher B cells, plasma cells, plasmablasts, and T cells (CD4 and CD8) in responders, whereas fibroblasts and myeloid cells were enriched in non-responders. DE analysis showed significantly elevated expression of HLA-DRA (logFC 1.955, p<0.001), other MHC class I and II components, and IFNα in responders. Tumor cells in responders exhibited the highest IFNα expression across cell types. GSEA and IPA analysis revealed enrichment in interferon signaling, antigen presentation, and the cGAS-STING pathway, which drives IFNα production. Spatial analysis categorized cells by their proximity to tumor cells: within 50 µm, 50-100 µm, and over 100 µm. T cells, particularly CD8 T cells, showed a distance-dependent distribution, with a higher abundance near tumor cells in responders. CD8 T cell activation scores declined with increasing distance from IFNα-positive tumor cells but remained consistently low near IFNα-negative tumor cells. IFNγ-positive CD8 T cells exhibited higher activation compared to IFNγ-negative cells, with activation levels influenced by proximity to tumor cells. Lastly, MHC expression in tumor cells was examined relative to CD8 T cell proximity. HLA-DRA and CIITA expression decreased as tumor cells were farther from CD8 T cells. Conclusions: This spatially resolved single-cell analysis highlights the pivotal role of tumor-immune interactions in response to pembrolizumab in TNBC. Our data suggest that tumor cells secrete IFNα through the activation of the cGAS-STING pathway, which activates T cells within close proximity, highlighting the potential role of cGAS-STING agonist as well as IFNα signaling and antigen presentation in augmenting TNBC responses to ICIs. Citation Format: Yi Liu, Saranya Chumsri, Yaohua Ma, Jodi M. Carter, Aziza Nassar, Edith Perez, Roberto A. Leon-Ferre, David Zahrieh, David W. Hillman, Judy C. Boughey, James Ingle, Krishna R. Kalari, Fergus J. Couch, Matthew P. Goetz, Keith L. Knutson, E. Aubrey Thompson. Spatially resolved single-cell analysis reveals cGAS-STING-mediated tumor-immune interactions via IFNα as determinants of pembrolizumab sensitivity in triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7130.
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 | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,001 | 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 ».