Abstract A036: Plasma extracellular RNA for the detection of pancreatic cancer
Notice bibliographique
Résumé
Abstract Pancreatic ductal adenocarcinoma (PDA) is expected to become the second leading cause of cancer-related death by 2030. Patients are often diagnosed at the metastatic stage, at which point treatments are limited and have non-uniform efficacy in patients. Advances in transcriptomic subtyping of tumor tissue can segregate patients into two broad subtypes, Classical and Basal-like, which are predictive of survival and response to first-line chemotherapy. However, tumor tissue is often difficult or impossible to obtain, particularly in patients with advanced disease, hindering the clinical implementation of transcriptomic subtyping. Profiling the extracellular RNA (exRNA) content in plasma provides an opportunity for a non-invasive readout of the tumor transcriptome. Here, we aim to establish the feasibility of liquid biopsy—particularly, plasma exRNA-profiling—for the diagnosis and subtyping of PDA. Extracellular vesicle (EV) encapsulated exRNA was isolated from 1mL of frozen plasma collected between 2017-2020 from a pilot cohort of 8 patients with stage III-IV PDA and processed alongside plasma from 6 healthy volunteers. UMI-tagged libraries were constructed from low-input exRNA to generate sequencing data. Gene body coverage analysis indicated that exRNAs consisted of intact full-length transcripts. Of these transcripts, we found that there was a median of 34,975 genes encoded by exRNAs, deriving mostly from protein-coding regions. Most genes captured by exRNA sequencing had low expression. Comparing between groups, we detected a significantly greater number of genes and novel splicing junctions in PDA patients. Of the cancer-specific transcripts, differential expression analysis revealed significant upregulation of membrane organelle and RNA-processing related pathways. PDA exRNA profiles did not completely recapitulate the bulk tumor transcriptome, suggesting specific mechanisms for EV encapsulation of RNAs and exRNA release. To classify tumor subtypes by plasma exRNA sequencing, we identified an exRNA-specific gene panel from bulk tumor transcriptome signatures. Using this gene panel, tumor subtype was correctly inferred from 6/7 PDA patients. A combined gene panel derived from all tumor-associated exRNA signatures distinguished 7/8 PDA patients from healthy controls. In conclusion, plasma exRNA recapitulated elements of the tumor transcriptome and has utility in PDA diagnosis. The establishment of diagnostic markers will be further investigated in the expanded study cohort comprised of 91 unique stage III-IV PDA donors with plasma collected at baseline, 150 stage III-IV PDA donors with plasma banked at timepoints throughout chemotherapy treatment, and 100 age- and sex-matched non-PDA controls donors. Leveraging the study cohort, future work will also investigate plasma exRNA signatures as prognostic markers to predict treatment outcomes and monitor treatment responses in patients with advanced PDA. Citation Format: Linsey Gong, Ling Xi, Yuanchang Fang, Karen Ng, Gun Ho Jang, Amy X. Zhang, Julie M. Wilson, Stephanie Ramotar, Jennifer J. Knox, Grainne M. O'Kane, Steven Gallinger, Hansen H. He, Faiyaz Notta. Plasma extracellular RNA for the detection of pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A036.
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 | 0,000 |
| 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) | 0,002 | 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 ».