Abstract 1183: Establishment,characterization and utilization of models of central nervous system metastasis
Notice bibliographique
Résumé
Abstract Metastases to the central nervous system (CNS) are the most common intracranial malignancy and are typically associated with survival times of only a few months. Lung and breast are the most common type of CNS metastasis, representing over half of such tumors. The molecular characteristics of these tumors as well as the factors driving cancers to metastasize to the CNS are poorly understood. This is due in part to inaccessibility of clinical samples and a lack of established, well-characterized models. In order to shed light on the molecular biology of these highly heterogeneous tumors and establish resources for preclinical testing, we are working to create both in vivo and in vitro models of CNS metastases originating from lung or breast cancer. We have thus far received surgical specimens from 7 lung and two breast CNS metastases. Of these, one was a spine metastasis and 6 were brain metastases. Each surgical specimen was used for the generation of patient-derived xenografts (PDX) and cell lines. Of these specimens, we have successfully established continuous cell lines of CNS metastases originating from three lung adenocarcinomas, one squamous lung carcinoma and one ductal breast carcinoma. To verify the presence of metastatic carcinoma cells in culture, cell lines were characterized by immunofluorescence based on staining profiles obtained from each tumor's pathology report. Adenocarcinomas of the lung expressed cytokeratins typical of lung cancer (CK7, 8 and 18), as well as Napsin-A, a highly specific pulmonary marker. The squamous cell carcinoma of the lung expressed these as well as CK5/6 and p63-α, pathological markers of squamous differentiation. A metastatic ductal carcinoma of the breast expressed ER, Her2, GATA-3, and Vimentin. For the development of PDX models, tumors were initially implanted into flank of NOG mice and then passaged orthotopically for preclinical testing. Of nine implants, tumor growth was evident for 3 animals harboring adenocarcinoma of the lung, 2 with squamous cell carcinoma of the lung, and 1 with breast carcinoma. Samples from metastatic tissue, xenograft tissue and primary tumors (when available) were flow sorted to identify different clonal subpopulations and sequenced for exome and RNA level changes. Genomic characterization of the different clones allowed reconstruction of the evolution of metastasis and identified therapeutically targetable genes. Studies are ongoing to develop personalized preclinical mouse studies based on the integration of multi-omics data derived from the models and patient tumors. Citation Format: Kyle N. Johnson, Paul M. Gonzalez, Mario Sepulveda, Loren Gorgol, Jennifer Glen, Danielle M. DiPerna, Mark Bernstein, Steven A. Toms, Bodour Salhia. Establishment,characterization and utilization of models of central nervous system metastasis. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1183. doi:10.1158/1538-7445.AM2014-1183
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 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».