Abstract A002: Understanding mechanisms of resistance to WRN small molecule inhibitors
Notice bibliographique
Résumé
Abstract The Werner syndrome helicase (WRN) has emerged as a promising synthetic lethal target in cancers with microsatellite instability (MSI). This discovery has spurred numerous efforts developing therapeutics targeting WRN and the advancement of at least two WRN programs into Phase I clinical trials. The development of acquired resistance remains a significant obstacle for the durable efficacy of targeted therapies in oncology and this challenge may be particularly pronounced in the setting of mismatch repair deficient (dMMR) tumors, which inherently possess large reservoirs of mutational burden. Despite the potential of WRN-targeted treatments, there remains a notable gap in the literature concerning the mechanisms of resistance to these drugs. To explore potential acquired resistance mechanisms of WRN-directed therapies, we examined three helicase inhibitors—HRO761 and two proprietary compounds which all possess a similar mechanism of action (MOA). Continuous treatment of HCT116 and SW48 cell lines with these inhibitors rapidly lead to the emergence of phenotypically resistant cell populations. Subsequent sequencing of several independently derived resistant cultures identified 5 emergent point mutations within the helicase domain of WRN that are potentially responsible for the observed inhibitor resistance. Parallel in vivo studies with HRO761 in SW48 xenograft tumors also demonstrated the acquisition of resistance after an initial period of deep response. Whole exome sequencing of these tumors revealed 2 of the previously observed, as well as 4 novel WRN helicase mutations. Structural modeling of WRN with putative resistance mutations suggested their capacity to either directly impede inhibitor binding or disfavor the adoption of the compound binding confirmation. Surprisingly, despite the general similarity in MOA and ligand binding sites, cross-resistance analyses demonstrated that some mutations conferred preferential resistance to HRO761 and were only minimally detrimental to the activity of our proprietary compounds. We conclude that WRN inhibitor monotherapy leads to drug resistance by acquisition of on-target mutations that disrupt inhibitor binding through direct or indirect mechanisms. This process may be extremely rapid in the dMMR background that represents the intent to treat population for WRN-directed therapeutics and is potentially further exacerbated by the complex allosteric MOA of WRN inhibitors currently in the clinic. Notably, some WRN inhibitor-resistant cell lines were not resistant to all WRN inhibitors tested, suggesting the potential for employing different WRN inhibitors at different stages of treatment to overcome resistance. This study underscores the importance of understanding and addressing resistance mechanisms to enhance the effectiveness of WRN-targeted therapies in MSI cancers. Citation Format: Faith C. Fowler, Aileen Kelly, Cindy Jeffries, Jessica Gajda, Jonathan Hickson, Fei Han, Nate Elsen, Mariam George, Danli Towne, Xiangdong Xu, Nate Gesmundo, Wei Qiu, Henry Tang, Charlie Hutchins, Yunsong Tong, Mick Dart, Ari Firestone. Understanding mechanisms of resistance to WRN small molecule inhibitors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr A002.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,001 |
| 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) | 0,000 | 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 tête enseignante, 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 ».