Abstract B81: Gene expression analysis demonstrates prognostic subtypes in metastatic pancreatic ductal adenocarcinoma (PDAC)
Notice bibliographique
Résumé
Abstract Background: In the absence of defined tumor molecular subtypes and validated predictive markers, PDAC has been largely treated as a single disease. Recent studies of molecular subtyping in PDAC [1-4] reveal a complex mutational landscape with data suggesting the presence of genomic and gene expression signatures that may have prognostic and therapeutic significance. However, most of these PDAC datasets consisted of resected tumors, cell lines or xenografts and lack data on metastatic tumors. The aim of this study is to evaluate gene signatures using whole genome sequencing (WGS) and transcriptome (RNA-Seq) data from metastatic biopsy samples in patients with advanced PDAC. Methods: Patients with incurable advanced cancers undergo fresh tumor biopsies for indepth WGS and RNA sequencing as part of an ongoing prospective study (NCT02155621). DNA and RNA extraction and library construction were performed according to standard protocols. Paired-end reads were generated on an Illumina HiSeq2500 sequencer. RNA-Seq expression values were converted into centile expression ranks against the TCGA PDAC dataset. Centile distributions for genes in published signatures were compared by pairwise Wilcoxon-Mann-Whitney tests using one-sided p=0.1 as the significance cutoff. Survival analysis was performed using the Kaplan-Meier method. Results: Molecular data is available for 12 patients with metastatic PDAC; median age 63 years, 6 males, 8 with de novo metastatic disease (67%). 10 tumor samples (83%) were obtained from liver biopsies; average tumor content was 41% (range 24-51%). The average number of structural variants per sample was 125 (range 40-271). Rearrangement-based subtypes [3] were distributed as follows: stable (n=3), locally rearranged (n=1), scattered (n=7) and unstable (n=1). 1 patient harbored a germline BRCA1 185delAG founder mutation but had a stable genotype. Gene expression analysis for classical and basal subtypes similar to those recently described [4] identified 3 and 7 patients with classical and basal expression patterns respectively. Gene signatures were undetermined for 2 patients, where no significant difference in expression of classical or basal signature genes was noted. At median follow-up of 16.7 months, 8 patients had died. Median overall survival was 19.1 vs 7 months in patients with classical and basal subtypes respectively (p=0.078). Conclusion: Despite small patient numbers, gene expression analysis demonstrated the presence of distinct signatures in metastatic PDAC, with a trend towards worse outcomes for patients with a basal expression subtype. Future challenges include prospective validation in larger cohorts, standardization of RNA data acquisition and analysis, and better definition of prognostic and predictive signatures that may be of clinical utility in metastatic PDAC. References 1. Collison E, et al. Nat Med. 2011 [PMID: 21460848] 2. Biankin A, et al. Nature. 2012 [PMID: 23103869] 3. Waddell N, et al. Nature. 2015 [PMID: 25719666] 4. Moffitt R, et al. Nat Genet. 2015 [PMID: 26343385] Citation Format: Hui-li Wong, Joanna M. Karasinska, Martin Jones, Peter Eirew, Kasmintan Schrader, Howard Lim, Yaoqing Shen, Steven Jones, Stephen Yip, Janessa Laskin, Marco Marra, David F. Schaeffer, Daniel J. Renouf.{Authors}. Gene expression analysis demonstrates prognostic subtypes in metastatic pancreatic ductal adenocarcinoma (PDAC). [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2016 May 12-15; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(24 Suppl):Abstract nr B81.
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 | 0,001 |
| 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 | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».