Abstract 1412: Utilizing cell surface markers to define osteosarcoma and the stages of osteoblast differentiation
Notice bibliographique
Résumé
Abstract Background Osteosarcoma (OS) is the most common primary malignant bone tumor in children. Despite advances in OS treatment, survival rates have remained stagnant over the past three decades. This may in part be reflective of the complex nature of OS—although all OS’ are pleomorphic, spindle shaped cells that are capable of producing osteoid, this is often where the similarities between tumors ends. Tumor phenotypes can be used to classify OS into multiple groups including the most common form, conventional, which can be further subdivided into osteoblastic, chondroblastic, or fibroblastic OS. Although these tumors display unique phenotypes, they have similar response rates to standard treatments such as chemotherapy and surgery suggesting that they may have a common progenitor, which we seek to identify. Methods In order to determine the OS cell of origin, we first performed flow cytometry analysis using cell surface markers that are differentially expressed on mesenchymal stem cells (MSC) and osteoblasts (OB)—CD44, CD105, CD54, CD49b, CD325, and GD2. MSCs were differentiated into OBs using induction media and cells were collected for analysis at multiple time points in differentiation; day (d)0, d3, d5, d10, d15, and d20. MSCs became fully differentiated OBs by d20, as determined by alizarin red staining. Similar flow cytometry analysis was performed on four OS standard cell lines, SaOS, U2OS, HOS, and HOS-143B, and marker profiles were compared to MSC to OB differentiation. Results CD44 and CD105 expression, both of which are known MSC markers, was high in MSCs and immediately dropped off during differentiation, while decrease in CD325 expression was more gradual. CD49b expression increased over time and GD2 expression varied greatly. CD54 expression peaked at d10 and decreased as differentiation continued. Expression of these markers was varied in the OS standard cell lines. These markers can be used to sort out specific populations of cells at different time points during differentiation. Conclusions and Future Directions In this study we have identified potential markers of OB progenitor populations and compared their expression to OS standard cell lines. We will use these prospective makers to sort progenitor populations and drive their differentiation into OBs, chondroblasts, and fibroblasts in order to determine branch points in MSC differentiation. Finally, in order to assess the potential of these cells to form OS, we plan to transform candidate progenitor cells with human telomerase reverse transcriptase, simian virus 40 large T antigen, and lentivirus containing oncogenic H-Ras serially. Citation Format: Pratistha Koirala, Vincent Poon, Sajida Piperdi, Amy Park, Michael Fremed, Michael Roth, Jonathan Gill, Richard Gorlick. Utilizing cell surface markers to define osteosarcoma and the stages of osteoblast differentiation. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1412. doi:10.1158/1538-7445.AM2015-1412
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 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,003 | 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 ».