Leveraging personalized circulating tumor DNA (ctDNA) for detection and monitoring of molecular residual disease in high-risk melanoma.
Notice bibliographique
Résumé
9579 Background: High-risk melanoma has variable prognosis. Adjuvant immuno- (IO) and targeted therapy (TT) are approved for stage III-IV resected disease. However, a significant proportion of patients (pts) are cured by local treatment alone or relapse despite adjuvant therapy. Liquid biopsy with ctDNA assays have been used to predict response to treatment and identify pts at higher risk of progression/death. Personalized ctDNA assays are a highly sensitive approach that may enhance upfront risk stratification and early detection of relapse. Methods: Serial ctDNA Monitoring as a predictive Biomarker in advanced neoplAsms (SAMBA) is a Princess Margaret prospective ctDNA kinetics study (NCT03702309) in high-risk melanoma pts. Plasma is collected pre-op (pre-local treatment, if feasible), post-op (after surgery), and every 3-6 months (m) until radiological progressive disease (rPD). Personalized amplicon based NGS assays by Inivata (RaDaR) were used to detect somatic variants in ctDNA identified through whole-exome sequencing of matched tumor tissue. Progression free survival (PFS) and overall survival (OS) from the time of surgery were estimated with the Kaplan Meier and compared with the log-rank test. Results: As of December 2021, 82 of 100 planned pts have been enrolled. A total of 191 samples from 47 pts have been analyzed. Median age was 66 years (27-87), 33 were male (70%). Seven (15%), 30 (64%) and 10 (21%) were stage II/III/IV respectively. All pts had surgery and 8 (17%) adjuvant radiation. No systemic therapy was given to 11 pts (23%); 30 (64%) had IO and 6 (13%) TT. rPD occurred in 13 pts (28%). Median follow up was 24 months. A median of 48 variants were included in the personalized ctDNA panel design (35-52). ctDNA was detected (ctDNA+) at any time point in 12/47 pts (26%), of which 5/12 (42%) were BRAF and NRAS wt on tissue. Median PFS was 4.9 months (m) for ctDNA+ pts and not reached (NR) for ctDNA- pts at post-op (HR = 2.71 CI 0.60-12.31, p = 0.179). Median OS was 23.1 m vs NR in ctDNA+ vs ctDNA- pts (HR = 8.9, CI 1.45-54.77, p = 0.004). Two ctDNA+ pts had neoadjuvant IO and became ctDNA- before surgery. One, free of disease after 12 m, had ctDNA- in 4 follow up samples. The other pt was ctDNA+ in the post-op sample and relapsed within 3 m. Four of 45 (9%) pts had ctDNA+ at post-op. Two of them, including a pt who had neoadjuvant IO, did not receive adjuvant therapy and had rPD within 3 m. The other 2 pts received adjuvant IO; ctDNA cleared and pts remain free of disease at 12 and 34 m. Three pts with rising ctDNA over time experienced rPD after a median of 4 m (2-7). Conclusions: Personalized ctDNA analysis with RaDaR may improve risk of death stratification and selection of pts who could benefit from adjuvant treatment. Detection of ctDNA may precede rPD. Follow-up will continue in pts with rising ctDNA who have not yet had rPD. Pts accrual and sample collection are ongoing, and additional data will be presented. Clinical trial information: NCT03702309.
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 | 0,000 | 0,000 |
| 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 ».