Abstract 5112: Investigating the use of circulating tumor DNA for sarcoma management
Notice bibliographique
Résumé
Abstract Circulating tumor DNA (ctDNA) has the potential to detect sarcoma recurrence and metastasis but requires highly sensitive methods to detect and quantify genetic variants present in very low quantities. Plasma was isolated from 20mL peripheral blood samples collected from over 400 pre-operative sarcoma patients, and matched tumor samples from surgical resection were frozen and stored. Cell-free DNA (cfDNA) extracted from plasma was quantified using qPCR, and the quality was assessed using capillary electrophoresis. A subset of these cases were selected for whole exome sequencing (WES). WES of bulk tumor and whole blood samples identified tumor-specific genetic alterations, which then serve as personalized biomarkers of tumor DNA in patient plasma. We previously showed that droplet digital PCR (ddPCR) can detect and quantify ctDNA, by targeting patient-specific variants. However, ddPCR is limited in that it can only investigate one tumor variant sequence at a time. The purpose of the present study is to investigate methods of targeting multiple tumor variants simultaneously, increasing the chances of detecting ctDNA in patient blood. To this end, four cases were selected for multiplex PCR (mPCR) followed by targeted amplicon sequencing. For each case, six to eight of the tumor variants identified by WES were selected as targets, and primers were designed to amplify these sequences concurrently by mPCR. The amplicons will then be sequenced to detect the tumor variants. Additionally, two of the four cases have plasma collected at two different time points. To assess the viability of this method as a way to monitor disease surveillance, these cfDNA samples will be compared to determine how the abundance and nature of ctDNA changes over time. To date, cfDNA has been extracted from over 100 cases, the majority of which were positive for cfDNA. For each of the cases whole exome sequenced, a variety of tumor-specific variations were identified. The variants chosen as targets were selected based on having the highest variant allele frequency (VAF), with priority being given to mutations that alter the protein coding sequence. Thus far, mPCR primers have been designed and optimized for four separate cases. Across all cases analyzed by amplicon sequencing, the variant sequences could be detected in the amplicons generated by mPCR of tumor DNA. Furthermore, amplicon sequencing was able to recapitulate the variant allele frequency observed in WES. This indicates that the mPCR successfully amplified the sequences of interest in the tumor DNA, and that the sequencing results are accurate. Furthermore, no tumor variants were detected in the amplicons generated from blood DNA, which is to be expected. The cfDNA amplicons for these cases will be sequenced in this manner to investigate the presence of ctDNA. If successful, the ability to detect ctDNA in plasma will be an important first step in developing a testing protocol for clinical use. Citation Format: Paige Darville-O'Quinn, Nalan Gokgoz, Kim M. Tsoi, Jay S. Wunder, Irene L. Andrulis. Investigating the use of circulating tumor DNA for sarcoma management [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5112.
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 | 0,002 | 0,004 |
| 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) | 0,002 | 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 ».