Mechanisms of tumor immune modulation and stemness in breast cancer
Notice bibliographique
Résumé
English AbstractBreast cancer is a heterogenous set of diseases that arise in the mammary gland, affecting 1 in 8 Canadian women over their lifetimes. Breast cancer classification is based on expression of the hormone receptors and human epidermal growth factor receptor 2 (HER2). The HER2+ subtype represents an aggressive subtype with multiple targeted therapy options. One major advance in the study of breast cancer stems from using genetically engineered mouse models (GEMMs) that represent the various subtypes of human breast cancer. The HER2+ subtype GEMMs are probably the best characterized and much of the understanding of this disease subtype stems from these mouse models. Modelling HER2 allowed for the discovery of a spliced isoform, known here as HER2Δ16, which is highly oncogenic in multiple models of cancers including breast and lung. HER2Δ16 has been demonstrated to promote multiple hallmarks of cancer including upregulating of stem cell capacity, increased metastasis and promoting therapeutic resistance. However, the immune microenvironment of HER2Δ16 expressing tumors has not been studied. Our work focused on understanding the different immune microenvironments driven by either proto-oncogenic HER2 or HER2Δ16. Our interest in the tumor immune microenvironment is due to clinical and pre-clinical observations that the immune microenvironment is essential for therapeutic response or resistance. Our studies demonstrated that HER2Δ16 tumors are characteristically immune cold (low immune cell infiltration) as well as exhibited an anti-inflammatory cytokine profile. Using cell surface proteome profiling we identified that HER2Δ16 induced upregulation of multiple cell surface proteins that regulate immune suppression, cancer stem cell function and metastasis. Our third most upregulated cell surface protein was Ectonucleotide pyrophosphatase/phosphodiesterase 1 (Enpp1) which has been tightly correlated with immune suppression and cancer stem cell properties. Knockdown and inhibition of Enpp1 lead to increased expression of inflammatory cytokines and increased T-cell infiltration in the tumors.We further studied the role of Enpp1 in breast cancer stem cells using our polyoma virus middle T antigen (PyMT)-driven mouse model. We identified that Enpp1 expression is tightly correlated with expression of CD44 a prominent stem cell marker. Additionally, we identified that the highest expression of Enpp1 was in a putative mammary stem cell population. In vitro modeling demonstrated that Enpp1+ cancer cells exhibited an increased in tumor initiating capacity. Additionally, we observe in models of breast cancer recurrence following de-induction an enrichment for Enpp1 expression in recurrent mammary tumors, supporting its role as an immunosuppressive molecule as well as its enrichment on cancer stem cells. We finally demonstrate that Enpp1 is potentially a hypoxia regulated protein, with protein, but not mRNA expression restricted to areas of hypoxia
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 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
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 ».