Abstract A27: An apoptosis-based screen for targeted agents in rhabdomyosarcoma reveals potential combination therapies
Notice bibliographique
Résumé
Abstract Intensification of therapy for rhabdomyosarcoma (RMS) by combining conventional chemotherapies has not proven effective at improving long-term survival. More selective small molecules may improve outcomes when added to chemotherapeutic backbones, but the optimal actionable targets in RMS are not well defined. An alternative strategy to develop combination therapies is to identify agents with complementary methods of apoptotic induction that can be paired with synergistic results. We first set out to identify classes of drugs that induce apoptosis in representative fusion-positive (FP) and fusion-negative (FN) cell lines. We built a screening assay using high-throughput microscopy to quantify both cell number (Hoechst nuclear staining) and apoptosis (NucView 488 Caspase-3 Enzyme Substrate [Biotium]). Technical controls included DMSO and staurosporine. The assay had a SSMD β score of 6.4, demonstrating excellent quality for screening. We represented FP-RMS with RMS13 cells and represented FN-RMS with RD cells. We screened ~2,000 bioactive compounds spanning a wide variety of biochemical mechanisms and targets. We tested compounds at 5 μM to identify even weakly active agents, since they might synergize with other drugs. We deemed “active” compounds to be those that reduced cell number 4 SD below DMSO-treated wells. Specific drug targets were enriched among the set of active compounds. In RD cells, for example, 16 of 25 (64%) HDAC inhibitors reduced cell number and activated caspase 3, which is greater than the library average of 264/1,921 compounds (14%) (p<10-4 by Fisher exact test). Similarly, 11 of 15 (73%) mTOR inhibitors were active (p<10-4). By contrast, only a few neurotransmitter antagonists were active compounds, suggesting that these are random or noncanonical effects. Enriched targets included Aurora kinase, bromodomains, CDK, CHK, HDAC, HSP, kinesin/KSP, mTOR, PLK1, the proteasome, ROCK, tyrosine kinases, Wee1, and XPO1. We separately considered whether any classes of compounds showed differential activity against the two cell lines, which represent two distinct biologic types of RMS. We grouped compounds by their nominal target and compared the mean effects against RD vs. RMS13 cells for each group. MEK inhibitors were more effective against RD cells than RMS13 cells, consistent with the importance of an NRAS mutation in RD cells. Conversely, inhibitors of ROCK or CHK were more active against the FP RMS13 cells. This analysis supports investigation of MEK inhibition in FN-RMS and suggests new areas for biologic discovery in FP-RMS. In summary, the screen identified multiple druggable pathways that suppress apoptosis in RMS. The added depth of a functional readout (caspase activity) suggests that this screen has identified agents with greater than cytostatic effects. Ongoing work will identify the proximate biochemical mediators of cell death in relevant conditions, and test whether synergistic drug combinations can be explained by complementary modes of BH3 family member regulation. Citation Format: Amit J. Sabnis, Steven Chen, Carolina Morales, Benjamin S. Braun. An apoptosis-based screen for targeted agents in rhabdomyosarcoma reveals potential combination therapies [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A27.
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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| 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) | 0,003 | 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 ».