Abstract 4283: Whole genome sequencing is superior to cancer panels to aid in decision-making in patients with advanced malignancies
Notice bibliographique
Résumé
Abstract Background: It is increasingly common to use targeted cancer panels to assess for informative or actionable targets to guide cancer treatment. We compared the utility of information obtained from such a panel compared to whole genome and transcriptome sequencing. Methods: Eligible subjects with incurable cancers for whom there were limited or no standard chemotherapy options, have several samples analyzed: a fresh tumour biopsy; a blood sample for normal comparison; and archival tumours when available. Samples underwent both the Ion Torrent AmpliSeq cancer panel analysis and comprehensive DNA (80X) and RNA sequencing followed by in-depth bioinformatic analysis to identify somatic mutations, copy number alterations, structural rearrangements, and corresponding gene expression changes that may be cancer “drivers” or provide informative (diagnostic) or actionable/druggable targets. Aberrant pathways were matched to drug databases and manual literature reviews undertaken to identify drugs that may be useful or potentially contraindicated; a report is generated and discussed in a multidisciplinary team. Results: Between July 2012 - November 2013, 51 subjects have consented and 45 have been sequenced: 12 breast, 7 lung; 4 colorectal, 3 squamous, 3 adrenal; 2 pancreas; 2 sarcomas, and 1 of each of nasopharynx, primary unknown, CLL-peripheral mantle cell, parotid, anal, appendix, peripheral T-cell, prostate, ovary, endometrial, glioma, and mesothelioma. The median number of lines of chemo prior to sequencing was 3. Of the first 40 cases, the panel did not yield informative or actionable results in 60% of cases: 36% of the cases the panel did not detect any somatic variants and in a further 24% TP53 mutations were the only variants identified. In the other 40% of cases, the panel might have identified an informative abnormality but when put into context of the pathways that can be drawn with the whole genome and transcriptome data the detected panel abnormalities were not comprehensive enough to guide treatment decisions. Examples include: in two colon cancer patients the panel detected a KRAS mutation in one and a RET mutation in the other, however both were actually inactivating mutations and therefore unlikely to be good targets. In contrast, the full genomic data was informative in 70% of cases and treatments were delivered based on the results in 60%, in this heavily pretreated population. Conclusions: The future of cancer medicine lies in genomic profiling to assist therapeutic decision-making. The cancer panel has advantages in terms of speed and cost; however it has not been as informative for identifying candidate druggable driver events and it has missed critical genomic abnormalities that have changed diagnoses. Given the complexity of the cancer genome it is our observation that the panels do not provide the depth and context required to make treatment decisions for the majority of patients with cancer. Citation Format: Janessa J. Laskin, Yaoqing Shen, Howard Lim, Karen A. Gelmon, Daniel Renouf, Stephen Yip, David Huntsman, Anna Tinker, Cheryl Ho, Stephen Chia, Yvonne Li, Katayoon Kasaian, Peter Eirew, Sreeja Leelakumari, Richard Moore, Samuel Aparicio, Yusanne Ma, Steven Jones, Marco Marra. Whole genome sequencing is superior to cancer panels to aid in decision-making in patients with advanced malignancies. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4283. doi:10.1158/1538-7445.AM2014-4283
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 | 0,005 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
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 ».