Abstract P4-06-03: Unravelling triple-negative breast cancer tumor microenvironment heterogeneity using an integrative multiomic analysis
Notice bibliographique
Résumé
Abstract Introduction: Triple negative breast cancer (TNBC) constitute 10-20% of all breast cancers and is associated with a worse prognosis and limited treatment options. Recent trials evaluating immune checkpoint blockade in TNBC demonstrated encouraging results for a subset of patients. TNBC is highly heterogeneous and its tumour microenvironment (TME) has been recognized as a critical determinant of its behavior and clinical outcome. Genome-wide gene expression profiling analyses have already improved our understanding of the complexity of this disease and have defined 6 different molecular subtypes namely Basal-like 1 (BL1), basal-like 2 (BL2), immunomodulatory (IM), mesenchymal (M), mesenchymal stem-like (MSL) and luminal androgen receptor (LAR), exhibiting distinct biological and clinical characteristic. In this study, we aim to dissect the molecular diversity of the TME and more specifically to assess the immune landscape according to TNBC molecular subtypes. Methods: A cohort of 485 TNBC patient with publicly available data (RNA-Seq and Illumina HT-12 v3) from the METABRIC and the TCGA consortia were used in the gene expression analysis. Gene signatures reflecting different features or cellular components (immune, stromal, angiogenesis, lymphangiogenesis, hypoxia, metabolism) of the TME were used to evaluate multiple biological processes known to contribute to tumorogenesis. A compendium of 17 immune specific gene signatures and T cell localisation classification were used to evaluate the immune composition and spatial pattern of immune infiltrates. All parameters were compared using a logistic regression model to evaluate their relative contribution according to each molecular subtype. Results: Our analyses demonstrated that each molecular subtype exhibits different TME profiles, as well as specific immune composition and localisation. IM tumors were associated with the highest expression of immune-related gene signatures, enriched with adaptive immune cells and with a fully inflamed spatial pattern. MSL tumors were mostly associated with the expression of Lymphangiogenesis and Stromal TME signatures. They also exhibited some immune activity through the expression of immune gene signatures capturing innate immune and adaptive immunosuppressive cells. This subtype was mainly associated with margin restricted and to some extent with fully inflamed spatial pattern. BL1 tumors were associated with the expression of Metabolism TME signatures, along with fully inflamed and stroma restricted spatial pattern. To a lesser extent, this subtype was also associated with activated DC and CD4 Tem cells. LAR and M tumors exhibited an immune cold phenotype. They were associated with Stromal and Metabolism TME signatures, enriched in margin restricted spatial pattern and negatively associated with every immune cells. Conclusions: Our results demonstrate for the first time the huge heterogeneity that characterizes the TME of TNBCs. Identification of specific TME profiles could help to design more rationale and appropriate synergistic therapeutic combinations targeting TME elements in this high-risk disease. Citation Format: Bareche Y, Buisseret L, Gruosso T, Girard E, Venet D, Dupont F, Desmedt C, Park M, Rothé F, Stagg J, Sotiriou C. Unravelling triple-negative breast cancer tumor microenvironment heterogeneity using an integrative multiomic analysis [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-06-03.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».