Characterizing <i>KRAS</i> allele variants within biliary tract cancers.
Notice bibliographique
Résumé
4088 Background: Biliary tract cancers (BTC) are aggressive malignancies with a poor 5-year survival rate and growing incidence globally. KRAS mutations (mut) in BTC are associated with a poor prognosis; however, PD-L1 inhibition with Durvalumab may lead to an improved survival with KRAS mut (TOPAZ-1 trial). It is important to understand the genomic landscape and immunophenotype of KRAS mut in BTC given the advent of immunotherapeutics and small molecular inhibitors targeting KRAS mut. Methods: A retrospective pooled analysis was performed from the following patient databases: Princess Margaret Cancer Centre, MD Anderson Cancer Center, Foundation Medicine, along with the publicly accessible cBioPortal for Cancer Genomics that includes the American Association for Cancer Research Project Genie cancer registry of real-world data assembled between 19 leading international cancer centers. Any overlapping cases were excluded. Patients included had a diagnosis of a BTC and completed molecular testing from January 2017 to December 2022. Cohort demographics, KRAS allelic variants, concurrent genetic aberrations, and immune biomarkers (PD-L1, TMB, MSI and gLOH) were summarized. Log-rank, Wilcoxon, and Kaplan-Meier tests were conducted for survival analysis. Results: 5,813 BTC patients were included, and 1000 patients (17.2%) had a KRAS mutation. The prevalence of KRAS mut was higher in extra-hepatic cholangiocarcinoma (EH-CCA) (36.1%) and perihilar (PH)-CCA (28.6%) than in intra-hepatic (IH)-CCA (11.82%) and gallbladder cancer (GBC) (7.6 %). The most common KRAS allelic variant was G12D, and the most common co-mutation was TP53, except in PH-CCA, which was G12V and SMAD4, respectively. In this cohort, race was primarily White (73%). The most prevalent variant in North America was G12D, while G12V and Q61H were more prevalent with genomic African American and genomic East Asian descent, respectively. For patients with KRAS mut, GBC had the most PD-L1 high positivity (17%) compared to IH-CCA (7%) and EH-CCA (3%), along with the most MSI-H phenotypes and the highest mean and median TMB compared to other sites, especially in G12V and Q61H variant patients. Genomic loss of heterozygosity was low among all groups. In the survival analysis, patients with the G12V allele subtype had the lowest OS at 17.8 months, followed by Q61H (22.8 months) and G12D (25.1 months) (p = 0.022). Survival analysis with KRAS co-mut ( TP53, SMAD4, CDK2NA, or additional KRAS mut) was not significant (p = 0.7). In a co-variance analysis of KRAS variants and tumour site, there was no difference in IH-CCA and GBC but lower OS in Q61H variants in PH-CCA and G12V variants in EH-CCA (p = 0.0081). Conclusions: This large series adds to the growing body of comprehensive genomic and immune landscape data of KRAS mut in BTC and will be of value in planning specific therapies in this heterogeneous group. Immune profiling studies are ongoing to further describe the immunophenotype of this subset.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| 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) | 0,002 | 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 ».