Abstract 5853: Identification of <i>TSC1</i> and <i>TSC2</i> as potential determinants of sensitivity to trastuzumab emtansine
Notice bibliographique
Résumé
Abstract Trastuzumab emtansine (T-DM1, Kadcyla) is an antibody-drug conjugate used in the treatment of HER2-positive breast cancer. However, its use is limited by acquired and intrinsic resistance, the mechanisms of which are not well understood. Further knowledge of T-DM1 resistance may provide new combination strategies or therapeutic targets to overcome resistance or new predictive biomarkers to identify the patients most likely to benefit from T-DM1 therapy. To discover genes responsible for T-DM1 sensitivity and resistance in an unbiased manner, we have conducted CRISPR/Cas9 functional genomics screens by a two-stage process. Firstly, we performed whole genome screens in MDA-MB-361 and MDA-MB-453 cells transduced with Cas9 and the GeCKOv2 lentiviral library that were exposed to T-DM1 and its effector DM1 for 8-13 weeks. Gene knockouts enriched or depleted in response to T-DM1 or DM1 treatment in either cell line were identified by sequencing of genomic DNA and differentially expressed genes by RNA sequencing, revealing 599 candidate genes of T-DM1 sensitivity. For high-throughput validation of the 599 genes, we developed a custom library of 2539 guide RNAs (gRNAs) to target these 599 genes, plus non-targeting controls. Cas9-expressing MDA-MB-361 cells were transduced with the custom library and exposed to T-DM1 for 28 days. MAGeCK analysis of gRNA sequencing revealed 11 genes that were significantly enriched and one gene that was significantly depleted at a false discovery rate (FDR) of <0.1. Two of the top hits in the secondary screen were two genes whose loss is known to promote T-DM1 resistance: ERBB2 (HER2) and SLC46A3 (P<8 × 10−5; FDR <0.007). Other top hits were TSC1 and TSC2 (P<3 × 10−6; FDR= 0.0004); which are both tumor suppressor genes and negative regulators of mTOR complex 1 (mTORC1). For subsequent validation, we have generated TSC2 knockout cell pools, which were more resistant to T-DM1 than wildtype cells in a competition growth assay. Knockout clones have been isolated and are being tested for T-DM1 resistance. Since mTOR inhibitors can phenocopy TSC1 and TSC2 by inhibiting mTORC1 activity, we have also evaluated T-DM1 in combination with the mTOR inhibitor KU-0063794 in a sulforhodamine B assay in MDA-MB-361 and MDA-MB-453 cells. Each agent potently inhibited cell proliferation and demonstrated synergistic anti-proliferative activity in combination. Together, our results suggest that TSC1 and TSC2 knockout may promote T-DM1 resistance and that targeting mTOR may be an effective strategy to overcome T-DM1 resistance. Citation Format: Francis W. Hunter, Barbara A. Lipert, Kyla N. Siemens, Aziza Khan, Hilary R. Barker, Troy W. Ketela, William R. Wilson, Tet-Woo Lee, Stephen M. Jamieson. Identification of TSC1 and TSC2 as potential determinants of sensitivity to trastuzumab emtansine [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5853.
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 | 0,000 |
| 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 ».