Abstract 124: Cellular context in epigenetics: Per-cell quantitation of miR-let-7d and its putative target in caspase-3 in reovirus-infected cancer cells
Notice bibliographique
Résumé
Abstract An oncolytic virus, due to its ability to replicate and lyse cancer cells while leaving normal cells intact, is an example of targeted cancer therapy. Reovirus is intrinsically oncolytic without the need for any genetic manipulation due to its ability to target cells with an activated ras pathway. micro RNAs (miRNA) play critical role in both viral infection and oncogene activation. While significant advances in the role of miRNA in a variety of diseases including cancer have been made, the majority of the studies done on miRNA signatures have been done using homogenized tissue, typically via RT PCR. While useful, one drawback of this methodology is that it is, in essence, a population study, sampled from a heterogeneous collection of cells and tissue types. The miRNA signatures found via these methods are not able to determine whether the miR and target protein were found in the same tissue type (tumor, stroma, etc). Microscopy-based multi-analyte methods offer the benefit of visualizing miRNAs and their putative targets within the context of disease-specific molecular anatomy and on a per-cell basis.The development of simple spectral imaging systems capable of both brightfield and fluorescence multispectral imaging and morphologic image analysis packages that can be trained to recognize specific morphometric regions of interest have greatly facilitated the imaging, visualization and quantitative analysis of multicolor tissue samples. This study describes the means by which tissue sections labeled for multiple markers (proteins and microRNAs) can be analyzed and then be displayed as scatter plots, in a manner analogous to flow cytometry data, and multimarker phenotypes can be determined from threshold-based quadrant analysis.We have shown that reovirus infection of a variety of cancers, such as melanomas, head and neck squamous cell cancers, and ovarian serous carcinomas, induces increased cell death that is accentuated with taxol therapy. In situ based co-expression analysis showed that reovirus induced caspase-3 protein expression which, in turn, increased cancer cell death via apoptosis, as documented by the TUNEL assay. Reovirus plus taxol accentuated the caspase-3 expression compared to reovirus infection alone. microRNA nanostring analysis showed that miRNA-let-7-d was the most down-regulated in the cancer cells after reovirus infection. Analysis by the InForm system after co-expression analysis showed that reovirus was directly downregulating miR-let-7-d. Target Scan analysis documented that caspase-3 is directly targeted by miR-let-7d. Hence, the direct in situ co-expression testing via the InForm system is a powerful adjunct to microRNA analysis as it allows documentation of physiologic modulation of protein expression by the microRNA of interest. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 124. doi:10.1158/1538-7445.AM2011-124
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 | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».