Detecting ctDNA using personalized structural variants to forecast recurrence in localized soft tissue sarcoma (STS).
Notice bibliographique
Résumé
11511 Background: The current standard for definitive management of localized STS involves surgery and (neo)adjuvant radiation (RT). Unfortunately, up to 50% of these patients (pts) recur but the role of adjuvant systemic therapy remains controversial. Circulating tumor DNA (ctDNA) is a promising biomarker for molecular residual disease (MRD) in STS but its clinical validity and utility remains unclear. Given that structural variants (SVs) are prevalent in the tumor genome of STS pts, this longitudinal study aims to utilize an ultra-sensitive, tumor-informed MRD assay that tracks somatic SVs for the detection of ctDNA. Methods: Pts with newly diagnosed, localized, high-risk (≥ 5cm, grade ≥2) STS planned for curative-intent (neo)adjuvant RT and surgery were recruited from Feb 2019 to Aug 2023. Blood samples for ctDNA analyses were collected at diagnosis, post RT, post-surgery and every 3 months for up to two years in tandem with radiologic surveillance. The MRD window was defined as the first 8 weeks after surgery. Whole genome sequencing (WGS) was performed on archival tumor samples to detect all genomic SVs. A personalized multiplex digital PCR assay was then designed based on WGS data to track up to 16 somatic SVs in cell-free DNA from serial plasma samples for ctDNA detection and quantification. ctDNA data was then correlated to clinical outcomes (last updated on Jan 2025). Results: A total of 228 plasma samples from 32 pts were analyzed with a median follow-up of 20.1 months. STS subtypes included myxofibrosarcoma (12), undifferentiated pleomorphic sarcoma (10), dedifferentiated liposarcoma (6), pleomorphic liposarcoma (2), myxoid liposarcoma (1) and leiomyosarcoma (1). The ctDNA detection rate at diagnosis was 97% (31/32 pts). Of the cohort, 22 pts received preoperative RT and had blood collected within the MRD window. ctDNA was detectable at baseline and in the MRD window in 4/22 pts (18%). All 4 (100%) developed metastatic disease with a median lead time of 136 days (range: 28-210 days) in ctDNA detection prior to radiologic relapse. Of the 18 pts who were ctDNA-negative in the MRD window, 3 (17%) developed metastatic recurrence, all of which was preceded by detectable ctDNA with a median lead time of 87 days (range: 80-147 days). The median time from surgery to recurrence was 153 days (range: 57-224 days) vs 521 days (range: 406-631 days) for pts with detectable vs undetectable ctDNA within the MRD window, respectively. Conclusions: Detection of ctDNA using personalized tumor-informed assays for somatic SV tracking was feasible and highly sensitive in localized high-risk STS pts prior to surgery. Positive ctDNA within the MRD window was predictive of subsequent and earlier radiologic relapse. Based on this data, an interception trial of adjuvant systemic therapy for MRD-positive STS pts is planned. Future analysis, including the measurement of circulating extrachromosomal DNA (ecDNA) is planned. Clinical trial information: NCT03818412 .
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 | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».