Abstract IA13: Molecular targeted therapies and precision medicine for children with neuroblastoma and other refractory malignancies
Notice bibliographique
Résumé
Abstract Despite the increase in overall child cancer survival rates, pediatric malignancies such as high-risk neuroblastoma, high-risk leukemias (including MLL-translocated infant ALL), and aggressive brain tumors (including DIPG) remain refractory to current multimodal therapies. We have been developing new treatment approaches for these aggressive childhood cancers by (i) utilizing novel targeted therapies either alone or combined with other new agents or established chemotherapeutic drugs, and (ii) by developing new drugs that target key pathways in these child cancers. In neuroblastoma, we have targeted polyamines, showing that combined inhibition of polyamine synthesis by the ODC1 inhibitor DFMO, and of polyamine uptake using the small-molecule drug AMXT 1501, is highly effective at inhibiting tumor growth in Th-MYCN transgenic mice. This combination also shows great efficacy in preclinical models of DIPG, and clinical trials for these diseases are now being planned. We are also targeting metabolism of arginine, the precursor of ornithine, using the pegylated-recombinant arginase BCT-100, which significantly delays tumor development and prolongs survival of neuroblastoma-prone Th-MYCN mice. We have further shown that combining BCT-100 with either DFMO or conventional chemotherapy results in increased survival benefit. CBL0137 is a nontoxic novel anticancer drug currently in phase I trial for adult refractory and relapsed cancers. CBL0137 destabilizes nucleosomes and traps histone chaperone FACT into chromatin, thereby modulating several anticancer mechanisms. We have shown that CBL0137 is effective in mouse models of neuroblastoma, MLL-rearranged leukemia, and DIPG, and that its action is potentiated by the HDAC inhibitor, panobinostat. Moreover, we have developed OT-82, a novel nontoxic NAMPT inhibitor with impressive anticancer activity against mouse models of high-risk childhood ALL, potentiating standard-of-care drugs, and showing similar efficacy as the three-drug induction-type treatment used for pediatric ALL. In addition, for all Australian children with high-risk malignancies, we have developed the Zero Childhood Cancer national precision medicine program. ZERO utilizes whole-genome and whole-transcriptome sequencing, methylation profiling, and where possible, in vitro and in vivo drug testing. To date (July 2019), 74% of 207 patients on the national clinical trial have received a Multidisciplinary Tumor Board recommendation (therapy, germline referral, or change of diagnosis), and of 25 patients with evaluable response data thus far who have received the ZERO recommended therapy, a significant proportion have had a complete response, partial response, or maintained stable disease. Moreover, early experience with drug efficacy studies suggests these data may corroborate genomic therapeutic recommendations and may also identify unanticipated active therapeutics. Citation Format: Michelle Haber, Laura Gamble, Lin Xiao, Ruby Pandher, Klaartje Somers, Jayne Murray, Aaminah Khan, Denise Yu, Laura Franshaw, Mark R. Burns, Maria Tsoli, Anahid Ehteda, Anthony Cesare, Aisling O’Connor, Francis Mussai, Carmela de Santo, Paul Cheng, Lioubov Korotchkina, Katerina Gurova, Vanessa Tyrrell, Emily Mould, Loretta Lau, Dong Anh Khuong Quang, Chelsea Mayoh, Greg Arndt, Paulette Barahona, Tim Failes, Jamie Fletcher, Noemi Fuentes- Bolanos, Marie-Emilie Gauthier, Andrew Gifford, Dylan Grebert-Wade, Alvin Kamili, Amit Kumar, Sumanth Nagabushan, Tracey O’Brien, Patrick Strong, Alexandra Sherstyuk, David Thomas, Toby Trahair, Katherine Tucker, Meera Warby, Marie Wong, Jinhan Xie, Kathryn Evans, Richard Lock, Olga B. Chernova, Michelle Henderson, Andrei V Gudkov, Paul Ekert, Mark J. Cowley, Glenn M. Marshall, David S. Ziegler, Murray D. Norris. Molecular targeted therapies and precision medicine for children with neuroblastoma and other refractory malignancies [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 IA13.
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 | 0,000 | 0,000 |
| 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 | 0,001 |
| 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 ».