Drug Sensitivity and Allele‐specificity of First‐line Osimertinib Resistance <i>EGFR</i> Mutations
Notice bibliographique
Résumé
Osimertinib, a mutant‐specific third generation EGFR TKI, is emerging as the preferred first‐line therapy for EGFR mutant lung cancer. Despite initial responses in patients, however, resistance inevitably develops over time. In order to investigate mechanisms of resistance to first‐line osimertinib, we modeled acquired resistance to this drug in transgenic mouse models of EGFR L858R ‐induced lung adenocarcinoma and found that it is mediated largely through secondary mutations in EGFR – either C797S or L718V/Q (Figure and 1B). Analysis of circulating free DNA data from patients with EGFR mutant lung cancer revealed that L718Q/V mutations almost always arise in the context of an L858R driver mutation. Therapeutic testing in mice revealed that both erlotinib and afatinib caused regression of osimertinib‐resistant C797S‐containing tumors, whereas only afatinib was effective in L718Q mutant tumors (Figure ). Combination first‐line osimertinib plus erlotinib treatment prevented the emergence of secondary mutations in EGFR . Our data identify specific secondary EGFR mutations as a major mechanism of acquired resistance to first‐line osimertinib treatment and highlight potential strategies to overcome or prevent osimertinib resistance in vivo . Furthermore, these findings emphasize how knowledge of the specific characteristics of resistance mutations are important for determining potential subsequent treatment approaches. Support or Funding Information This work was supported by ‐‐‐‐‐Yale’s Specialized Program of Research Excellence in Lung Cancer grant (to K. Politi, S.B. Goldberg and M.A. Lemmon) and funding from AstraZeneca (to K. Politi). Additional support came from the NIH/NCI‐funded Yale Cancer Biology Training Program T32 CA193200‐01A1 and F31 CA228268‐01A1 (to J.H. Starrett), R01 CA198164 (M.A. Lemmon), the Ginny and Kenneth Grunley Fund for Lung Cancer Research, and the Canadian Institutes of Health Research Project Grant PJT‐148725 (to W.W. Lockwood). W.W. Lockwood is supported by a Michael Smith Foundation for Health Research Scholar and NIHR New Investigator Awards, A. Guernet is a fellow funded by the IMED AstraZeneca postdoc program, A. Nagelberg is supported by a scholarship from the CIHR, and K.D. Ashtekar is an Arnold and Mabel Beckman Foundation Postdoctoral Fellow. Yale Cancer Center Shared Resources used for this work were in part supported by NIH/NCI Cancer Center Support Grant P30 CA016359. Acquired resistance to first‐line osimertinib arises partially due to the emergence of secondary mutations in EGFR , which are differentially sensitive to other EGFR TKIs. A . Schema of the experiment. CCSP‐rtTA;TetO‐EGFR L858R mice were administered doxycycline (dox) for the duration of the experiment and developed tumors after ~6 weeks on dox. When tumors were detected by MRI ( see pre‐treatment image ), osimertinib treatment was initiated (25 mg/kg QD M‐F) which elicited a response ( see representative response MRI ) and treated until the emergence of resistant tumors by MRI. Coronal MR images are shown, in which ‘H’ indicates heart and red arrows indicate tumor. The osimertinib‐resistant tumors were then collected and analyzed to determine the resistance mechanisms present. B . Pie‐chart illustrating the resistance mechanisms found in osimertinib‐resistant tumors. C and D . Average tumor volume changes for the osimertinib‐resistant tumors switched to 25 mg/kg erlotinib for 3 weeks ( C ) or 25 mg/kg afatinib for 10 days ( D ), as determined by MRI tumor volume measurements. Tumor volume is normalized to the point of TKI switch. Error bars represent SEM. For C , curves are the average of n=11 total tumors (C797S n=5; L718V n=3; L718Q n=3). For D , curves are the average of n=12 total tumors (C797S n=7; L718Q n=5). Figure 1
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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 | 0,000 |
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 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 ».