Molecular characterization of long-term and short-term survivors of advanced pancreatic ductal adenocarcinoma.
Notice bibliographique
Résumé
4024 Background: Median overall survival (mOS) for advanced pancreatic ductal adenocarcinoma (PDAC) is ≤1 year. However, there are patients (pts) who live for >2 years on chemotherapy and those who rapidly progress prior to a first scan, underscoring biological heterogeneity in PDAC subgroups. Understanding these differences is important in clinical trial design and provides prognostic information for pts. Methods: Clinical and molecular data (WGS and RNAseq) from pts with a diagnosis of locally advanced or metastatic PDAC enrolled in the COMPASS (NCT02750657) and POG/PanGen (NCT02155621, NCT02869802) studies were available for analysis as part of the TFRI’s EPPIC program and Marathon of Hope Cancer Centres Network, a multi-institutional collaborative network aiming to implement precision medicine in Canada. Clinical data were collected prospectively from both cohorts. Pts had an ECOG PS 0-1 and chemotherapy regimen was based on clinician’s preference. Profiling was performed on fresh biopsies and homologous recombination deficiency was identified with the HRDetect assay. We compared pts with very short OS (STS; ≤3 months, mo) vs long OS (LTS; ≥24 mo). Results: 341 pts were included in the analysis of which 47 were STS (mOS 1.6 mo) and 42 LTS (mOS 29.7 mo). There was no difference in median age, BMI or sex (p>0.1) between cohorts. STS were more likely to have an ECOG PS 1 vs 0 (p=0.03), higher tumor burden (RECIST) (p<0.001) and higher CA19.9 (p=0.04) at diagnosis. In STS, 71% had non evaluable responses as a result of clinical decline or death before first scan, and 28% PD as best response; in LTS, ORR to chemotherapy was 69%. Sixty-four % and 33% of LTS and 32% and 36% of STS had received mFFX and GA, respectively. There was no difference in the prevalence of KRAS mutations (90% vs 98%, ns) or specific mutant alleles, however amplification of mutant KRAS was more common in STS (35% vs 65%, p=0.01). The prevalence of inactivating driver mutations in TP53, CDKN2A, and SMAD4 (90% vs 98%, ns), and in genes involved in chromatin modification ( ARID1A, SMARCA4, PBRM1, KDM6A) (17% vs 30%, ns) was similar in both groups. However, STS had a higher prevalence of mutations resulting in activation of the PI3K/AKT/mTOR pathway ( PIK3CA, PTEN loss, STK11) (10% vs 26%, p=0.049). Genotypes consistent with HRD were present (17% vs 11%); however, in LTS 6/7 were a result of germline pathogenic variants, compared with only 1/5 in STS. Median structural variant loads, ploidy, TMB and substitution base signatures were similar in both cohorts. More basal-like PDAC were present in STS vs LTS (3% vs 30%, p=0.006). Conclusions: Pts who survive ≤3 mo with advanced PDAC are characterized by a higher tumor burden and molecular profiles consistent with enhanced RAS signaling, a deregulation of the PI3K/AKT/mTOR pathway, and a basal-like transcriptomic subtype. HRD genotypes are heterogeneous with germline carriers accounting for some of the LTS.
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 | 0,001 |
| 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 | 0,000 | 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) | 0,001 | 0,000 |
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 ».