Profiling of functional intercellular interactions in a model of the leukemia microenvironment
Notice bibliographique
Résumé
During leukemia development malignant cells occupy hematopoietic stem cell niches suppressing normal hematopoiesis and infiltrate other sanctuary niches such as the central nervous system or the testis. Interactions with the microenvironment are critical for leukemia cell survival, but the mechanisms involved in these processes are largely unknown. A better understanding of the patterns of intercellular dependence between leukemia and its microenvironment will possibly provide new options to improve leukemia treatment. To identify new pathways that contribute to the leukemia niche function, I established a large scale high-content (automated image-based) screening platform using co-cultures of primary acute lymphoblastic leukemia cells (ALL) on human mesenchymal stromal cells (MSC). Patient samples were expanded by xenotransplantation in immunodeficient mice to generate a renewable source of leukemic cells for systematic functional investigation. The methodology and analytic pipeline was developed in our laboratory in collaboration with the Light Microscopy and Screening Centre of ETH Zurich. We established a robust workflow to discriminate viable ALL and stromal cells using a fluorescent dye. Detailed protocols were optimized to use this platform for in vitro drug testing and for functional genomic projects. Based on gene expression and cell surface proteomic data that we had obtained from both cellular compartments, I generated a customized siRNA library for 110 candidate genes with a potential function in stromal support. Primary ALL cells were seeded on reversely transfected MSC cells, and ALL cell viability was assessed after 6 days using the established high-content screening platform. From a first screen with three cases with highly resistant disease, 20 candidate genes were identified that reproducibly reduced ALL survival in this assay. These were validated in 10 different patient samples. Importantly, specific and distinct contributions of stromal genes for the survival of individual ALL samples were detected. The strongest effects were observed after RNA interference of the vascular endothelial growth factor C (VEGFC) or of Basigin (BSG, alias CD147) in a subset of patient samples. Dependence from stromal VEGFC predicted sensitivity to two different VEGF receptor kinase inhibitors, confirming the patient specific support pattern of stromal VEGFC. Furthermore, the Notch and Wnt pathways were identified to play an important role for the support of ALL cells, extending experimental data obtained in other model systems of the tumour microenvironment or HSC niches. The largest subset of ALL samples was most dependent on the expression of BSG on stromal cells. I could show that this multifunctional cell surface protein was required to provide metabolic support to a subset of ALL in association with the solute carrier family 3 protein, SLC3A2. This protein forms heterodimeric amino acid transporters (HAT) on MSCs indicating that amino acid transport of stromal cells is important for survival of ALL cells. Leukemic cells are deficient to import cystine, and a subset of leukemic cases requires continuous supply of cysteine for de novo glutathione synthesis. We could show that the metabolic transport function of amino acids by stromal cells maintains glutathione levels and reduces oxidative stress specifically in ALL cells that were dependent on stromal BSG/SLC3A2. Indeed, addition of cysteine but not cystine rescued the effect of RNA interference with BSG/SLC3A2 in stromal cells. Taken together, I describe the development of a new platform for systematic investigation of interactions between primary leukemia and stromal cells. The identification of relevant and leukemia-specific pro-survival cues from stromal cells validates this approach. Strong interaction patterns such as the one reported here suggest new possibilities for targeted therapy provided appropriate biomarkers are developed to select patient cohorts efficiently. The platform can also be used for the analysis of anti-leukemic activity of small molecules as single agents and in combinations. This work will also constitute the basis for a more comprehensive functional genomic screen and will stimulate the development of in vivo models.
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 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 | 0,000 | 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) | 0,000 | 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 ».