Abstract SY03-01: Genotypic and phenotypic features of de novo and acquired resistance to cancer therapies.
Notice bibliographique
Résumé
Abstract Drug resistance is the single cause of cancer treatment failure resulting in cancer related death. Despite its paramount importance, our mechanistic understanding and means of circumventing cancer drug resistance is limited. In the current era, the evolutionary processes in cancer development and progression have been elucidated and provide insights into the biology of cancer and in particular cancer heterogeneity. However, our understanding of the contribution of evolutionary processes and tumor heterogeneity to the development of drug resistance is limited. As a result, our ability to translate knowledge into effective clinical strategies is similarly limited and remains largely on the use combination chemotherapy based on Goldie and Coldman's principles first described in 1984. Pragmatically, resistance may be de novo (i.e. treatment has no effect) or acquired (i.e. treatment had initial effect but tumors eventually progress due to repopulation by resistant cells). De novo resistance is thus equivalent to primary refractoriness and acquired resistance is emergent in the presence and presumably as a direct response to the selective pressures imposed by therapy. Mechanisms of resistance may be divided into various categories; however, a simple construct is to consider resistance as a manifestation of failure of one or more of the following: drug delivery, drug uptake, drug-target interaction and cellular response and cellular adaptation. Genetic, epigenetic, physiochemical and spatial properties of cancers may contribute to drug resistance and it is likely that different mechanisms of drug resistance develop concomitantly. The most important challenge is how to tackle the interrelated problems of tumor genetic and epigenetic heterogeneity and drug resistance in cancer using rational drug selections. The identification of tumor dependencies driven by dominant oncogenes, hormones, or metabolites may prove vulnerable to regimens designed to intercept them assuming dependencies are identifiable, targets or target pathways are “druggable” and such agents and combinations are tolerable and effective for cancer patients. Although the issues are complex, they can and should be addressed. Serial and comprehensive sampling to identify genetic and epigenetic changes of tumor to guide drug therapy will be required as will identifying susceptibilities such as oncogene addiction, synthetic lethality and genomic instability. Rational selection of drug dose, schedule and combination partners can follow. Treatment advances will occur to the extent that mechanisms of resistance can be identified or predicted and circumvented. Citation Format: Janet E. Dancey. Genotypic and phenotypic features of de novo and acquired resistance to cancer therapies. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr SY03-01. doi:10.1158/1538-7445.AM2013-SY03-01
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 | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,003 |
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 ».