Abstract 2631: Restrictions on access to systemic therapy limit the application of whole genome sequencing in clinical care
Notice bibliographique
Résumé
Abstract Background: Whole genome analyses have the potential to identify the full landscape of activating and inactivating genomic abnormalities at work within cancers, and can thus be used to provide rationales for selection of treatment agents or clinical trials in a broad range of patients. Patients & Methods: Eligible patients (pts) with metastatic cancers were recruited within a general oncology practice across the province of B.C., Canada. Each pt underwent a fresh tumor biopsy and a blood sample and had comprehensive DNA (80X) and RNA sequencing. In-depth bioinformatic analyses were preformed to identify genomic changes that may be cancer “drivers” or therapeutically actionable targets. Aberrant pathways were matched to drug databases and manual literature reviews undertaken to identify drugs or clinical trials of potential utility for the individual pt. Results: Between July 2012 - Oct 2015: 380 pts (358 adult + 22 peds) consented; 227 have completed whole DNA and RNA sequencing and analysis to date (remainder ongoing). For this analysis, data is available on 160 pts. Genome bioinformaticians assessed the genomic data to be potentially druggable in all cases. Medical Oncologists assessed this data to be directly clinically actionable in 135 (84%); the difference being that clinicians did not agree that some putative “druggable” drivers (such as p53) or pathways with no current drugs available (MMP, AURA, WNT) were “actionable”. Of the 135 cases, 58 (43%) pts received therapy based directly on this genomic information; 6 on a phase 1 clinical trial. The most common reasons for 77 pts defined as actionable but who have not received genomically-informed therapy were: drug only available on a clinical trial but trial not available to pt - 22 (26%); drug approved but not available off label - 18 (23%); pt presently on first-line therapy that is working - 14 (18%); or death/too unwell 13 (17%). The limited availability of clinical trials was primarily because of highly restrictive trials entry criteria, primarily limiting patients to one primary tumour type or narrowly defined biomarker entry criteria. The most commonly mutated cancer genes identified by the genome analysts were: p53 as the predominant driver in 42%; APC in 16%; KRAS and PI3KCA mutations in 14%. Going forward it is essential to distinguish what driver mutations might be clinically actionable from those that are still only theoretically druggable; and similarly learn to distinguish which targets are actionable but not truly drivers. Conclusions: Genomic DNA and RNA sequencing data were found to be clinically actionable in 84% of pts with advanced cancers in a population cancer care setting. However, the ability to act on this information is limited by the restrictive nature of clinical trials and the lack of accessibility of off-label drugs despite an identified biomarker. As genome sequencing becomes integrated into cancer management these drug access issues need to be addressed. Citation Format: Janessa J. Laskin, Yaoqing Shen, Daniel Renouf, Martin Jones, Howard Lim, Alexandra Fok, Cheryl Ho, Balvir Deol, Karen A. Gelmon, Stephen Chia, Richard Moore, Andrew Mungall, Stephen Yip, Steven Jones, Marco Marra. Restrictions on access to systemic therapy limit the application of whole genome sequencing in clinical care. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2631.
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 | 0,015 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,102 | 0,042 |
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 ».