Abstract 763: Implications of resistance patterns with NSCLC targeted agents
Notice bibliographique
Résumé
Abstract Introduction: Tyrosine kinase inhibitors (TKIs) can give regression of NSCLCs with mutations of EGFR, HER2 & BRAF and fusion genes for ALK, ROS1 & RET, but resistance develops in most patients. Methods: We reviewed available clinical data for insights on potential approaches to delay resistance. Results: Target absence is a major cause of intrinsic resistance. Most NSCLCs with target undergo at least some regression although the degree varies between patients due to largely unexplored factors. Many mechanisms of acquired resistance have been defined, including outgrowth of subclones with secondary mutations or alternative pathways, and pharmacological effects & sanctuaries. Available data suggest that resistant cells are present in small numbers in the initial tumor but that their growth is suppressed by the more rapidly growing parent tumor cells until this parent population is itself suppressed by TKI initiation. If TKI is stopped due to progression, there can be tumor flare as the initial parent cell population regrows rapidly. Exponential decay nonlinear regression analysis of patient survival curves suggests that TKI discontinuation at time of progression may partially “synchronize” patient deaths. Initial tumor progression may occur in a single site. Available data indicate that focal treatment (eg, radiation) to that site combined with continuation of initial TKI may give optimal control and reduce tumor flare. Of interest, progression may occur in many sites concurrently. Since there are many potential resistance mechanisms one might expect substantial discordance of resistance mechanisms between these sites, but preliminary data with 2nd line agents (eg, anti-T790M agents in EGFR-mutant patients and 2nd generation anti-ALK agents for acquired resistance to crizotinib) indicate response in most sites if any are sensitive, suggesting that all or most sites have a common resistance mechanism. This raises the possibility that an initial resistant site is seeding other distant tumor sites with resistant cells, in keeping with animal data. The probability of a resistant cell being present in an individual tumor deposit is proportional to the total number of tumor cells in that deposit. If 1 area of resistant tumor can seed other tumor areas with resistant cells, then (particularly in oligometastatic disease) we should consider clinical trials exploring: 1) at initiation of TKI therapy treating as many tumor sites as possible (particularly large tumors) with a modality (eg radiation) that is “indifferent” to the factors giving TKI resistance; 2) treating any progressing lesions as early as possible with intensive focal therapy, if feasible, rather than waiting until symptomatic or rather than treating only with low palliative doses. Conclusion: Extrapolation from available data suggests we should explore trials using multifocal radiation at initiation of TKIs for advanced NSCLC and early, intense treatment of areas of isolated progression. Citation Format: David J. Stewart, Paul Wheatley-Price, Rob MacRae, Jason Pantarotto. Implications of resistance patterns with NSCLC targeted agents. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 763. doi:10.1158/1538-7445.AM2015-763
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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».