Abstract B046: Analyzing heterogeneity of tumor microenvironment of TNBC patients by meta-analysis of 7 breast cancer scRNAseq studies
Notice bibliographique
Résumé
Abstract Breast cancer can be classified into several types. Among them, triple-negative breast cancer (TNBC) is breast cancer that does not express estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Since targeted therapy for breast cancer relies on the three receptors, treatment outcomes have been worst in TNBCs. However, the clinical benefit of immune-checkpoint inhibitors (ICIs) with chemotherapy over chemotherapy alone was demonstrated in several trials, opening a new avenue for patients with metastatic PD-L1+ TNBC. After several studies with ICIs, researchers found that the tumor microenvironment affects the response rate of immunotherapies. Therefore, to unravel the heterogeneity of the tumor microenvironment of TNBC patients, we collected 7 public single-cell RNA sequencing breast cancer studies. From 141,068 cells populating tumors and tumor-microenvironment (TMEs), we first identified epithelial cells separately. Using whole cells including epithelial and immune-stromal cells, we identified the Baylor-proposed TNBC molecular subtype of each patient. This approach with epithelial cells revealed that 19 basal-like immune-activated (BLIA), 5 basal-like immune-suppressed (BLIS), 2 luminal-androgen receptor (LAR), 3 mesenchymal (MES), and 3 unclassified subtypes. By comparing cell number and cell-cell interactions, the heterogeneity of TME-consisting cell populations between the TNBC subtypes was analyzed. Analysis of tumor-infiltrating lymphocytes revealed their difference in activation, expansion, and exhaustion programs across patients. Among the subtypes, the BLIA subtype had a greater amount of exhausted CD8+ T cells and regulatory T cells. This finding was consistent with cell-cell interaction analysis. In the case of the BLIA subtype, the interaction between regulatory T cells and CD8 T cells was more active than in other subtypes. Also, the interaction between cytotoxic cells (T cells and NK cells) and regulatory T cells were more active in the BLIA subtype. Additionally, tumor cells in the BLIA subtype seem to inhibit cytotoxic cells to a greater extent. Among the interactions between epithelial cells and exhausted CD8+ T cells, MDK interaction was higher in the BLIA subtype. Additionally, the expression of the MDK gene in endothelial cells and macrophages leads to upregulation in immune infiltration which causes more T cells in TME. Also, we found that the progression-free survival of patients differs due to the expression of the MDK gene. In conclusion, the MDK gene causes a T cell-enriched environment in TME, which leads to different progression-free survival. Citation Format: Kyungsoo Kim, Jee Hung Kim, Soong June Bae, Sung Gwe Ahn, Joon Jeong. Analyzing heterogeneity of tumor microenvironment of TNBC patients by meta-analysis of 7 breast cancer scRNAseq studies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr B046.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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 tête enseignante, 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 ».