Abstract 3423: Immunohistochemical expression and cluster analysis of mesenchymal and neural stem cell-associated proteins in pediatric undifferentiated soft tissue sarcomas
Notice bibliographique
Résumé
Abstract Pediatric undifferentiated soft tissue sarcomas (USTS) are a challenging group of tumors to diagnose and accurately categorize. By definition, they do not exhibit consistent immunohistochemical profiles or harbor recurrent chromosomal rearrangements to aid in their recognition. Traditionally, they have been classified with poorly differentiated forms of embryonal rhabdomyosarcoma. More recently, however, this notion has been challenged as such tumors share characteristics with the Ewing family of tumors. Thus, the histogenesis of pediatric USTS remains unclear and optimum therapy remains a challenging decision. The aims were twofold: firstly, to determine whether differential expression of stem cell-associated proteins could be used to aid in determining the histogenesis of pediatric USTSs; and secondly, to determine whether pediatric USTS expressed a unique panel of stem cell-associated proteins and form a distinct sarcoma subcategory. Tumors included 32 Ewing sarcoma/PNETs (EWS), 22 embryonal rhabdomyosarcomas (ERMSs), 8 alveolar rhabdomyosarcomas (ARMSs), 5 synovial sarcomas (SSs), 5 malignant peripheral nerve sheath tumours (MPNSTs) and 16 USTSs. Stem cell antibodies used included a series of three mesenchymal stem cell markers (CD44, CD105 and CD166) and five neural stem cell markers (CD15, CD29, CD56, CD133 and nestin). Antibodies were applied to the sections using standard immunohistochemical techniques and the sections were scored on a six-tiered scoring system using both intensity and distribution components. The scores were then imported into Partek Genomic Suite Software, for statistical analyses, cluster analysis, and visualizations. The datasets were analyzed by comparing the expression scores from the groups using the 1-way ANOVA. Each group was compared to each individual group, as well as the remaining groups combined. The resultant Euclidean clustering divided the tumors into two major groups. EWSs and USTSs formed the majority of the first group, whereas ERMSs, ARMSs, MPNSTs and SSs formed the second group. Negativity for CD56 was strongly associated with the EWS/USTS cluster (p < 0.0001). EWSs and USTSs were further separated by CD166 staining, wherein positivity was associated with EWS and negativity with USTS (p < 0.0001). The second group included some EWS (n=5), but the vast majority (n=27) were included in the first cluster. No consistent separation of the different subgroups was seen in the second cluster of tumors. The current study demonstrates the usefulness of applying stem cell markers to pediatric sarcomas, and suggests that pediatric USTSs and EWSs are closely related and may share a common cell of origin. Furthermore, the majority of USTSs have a unique stem-cell expression profile. The findings have both biologic and diagnostic significance. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3423.
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 | 0,000 | 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) | 0,001 | 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 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 ».