Abstract PR010: Development of a pre-clinical metastatic model of human sarcoma to identify therapeutic targets
Notice bibliographique
Résumé
Abstract Targeted therapies have led to significant advances in the treatment of multiple tumor types resulting in more effective and often, less toxic therapeutic options. In sarcomas, the development of targeted therapies has been met limited success. There are more than 70 sarcoma subtypes that vary in histology, clinical course and patient demographics. Despite these clear differences, clinically, sarcomas are treated similarly with variable efficacy. Patients with localized disease are treated with surgery, radiation, and often chemotherapy. Even with this aggressive multimodality treatment, 35% of patients will develop incurable metastatic disease. This highlights the need for additional therapies targeting micrometastatic disease or cells within the primary tumor with a high propensity for metastasis. Understanding the pathways driving the formation of sarcoma metastasis would allow for the development of new therapies. Targeted treatments are limited due to the heterogeneity of the disease and the paucity of pre-clinical models that accurately reflect the human disease. We have focused on creating in vivo models of sarcoma development and metastasis that can be used as the basis of further studies and to test potential therapeutic targets. Mesenchymal stem cells (MSCs) are the presumed cell of origin for sarcomas and therefore, the starting cell for our investigations. We hypothesized that by recreating key genetic events in human MSCs, we could generate sarcomas in vivo that are reflective of the human disease. RB1 and P53, tumor suppressors that are often mutated or functionally inactive in sarcomas were first targeted using CRISPR-Cas9 technology in MSCs that have been immortalized by human telomerase (hTERT). Genes that are overexpressed or amplified in The Cancer Genome Atlas (TCGA) data were identified and a library of potential oncogenes was generated. This library was then added to RB1-/-P53+/- cells through lentiviral transduction. Targeting of key tumor suppressors and adding oncogenic drivers resulted in the formation of high-grade human sarcomas subcutaneously. We then sought to investigate the ability of these cells to metastasize. Injection of cells intramuscularly (into the thigh) in immunocompromised mice resulted in the formation of spontaneous lung metastasis without clear evidence of disease in other organs. This pattern clinically reflects that of human disease. Comparing oncogenic genes from metastatic outgrowths to primary tumors identified KLF4, DDIT3, JUN, and KRAS as being enriched in metastatic cells. This system allows for reproducible and robust genetic manipulation of tumor cells to characterize key drivers of metastatic growth. This will result in the identification and validation of new therapeutic targets to treat or prevent metastatic disease in our patients. Citation Format: Janai R. Carr-Ascher. Development of a pre-clinical metastatic model of human sarcoma to identify therapeutic targets [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr PR010.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».