Abstract A035 Characterization of circulating tumor DNA in a pediatric oncology cohort and implementation into the SickKids Cancer Sequencing (KiCS) precision oncology program
Notice bibliographique
Résumé
Abstract Introduction Sensitive methods to monitor disease and identify novel therapeutic targets are key to improving survival for children with hard-to-cure cancers. Circulating tumor DNA (ctDNA) analysis is a promising, non-invasive technique that may improve outcomes. SickKids Cancer Sequencing (KiCS) is a precision oncology program that performs next-generation sequencing on samples from pediatric patients (pts) with rare or hard-to-cure tumors. To date, KiCS has enrolled >800 pts, collecting paired tumor/blood samples and clinical data. Here we describe the implementation of a plasma biobank initiative and preliminary results from our first cohorts. Methods Serial blood samples were collected from pts at scheduled timepoints, at time of treatment change, or if recurrence was suspected. Plasma and matched cell pellets were separated and banked. Extracted cell free DNA (cfDNA) that met adequate QC metrics by Qubit and Bioanalyzer was subjected to low-pass whole genome sequencing (LP-WGS) (1-2X coverage) and/or deep WGS (60-80X) and/or custom-designed target exome panel (cancer panel, CP, 1000X) sequencing. The bioinformatics analysis pipeline included single nucleotide variant (SNV) calling with Mutect2/Sage and filtering using a custom script; copy number variant (CNV) calling with ichorCNA/ PURPLE (for WGS) and CNVkit (for CP). ctDNA data were compared to available tumor and germline data from the same patient. Results Over 400 plasma samples from 167 pts have been banked. In our initial pilot, cfDNA was successfully extracted from 8 plasma samples (pts with neuroblastoma (n=4), rhabdomyosarcoma (n=3), GIST (n=1)). The concentration of cfDNA was comparable to published adult cohorts (average 28.4 ng/mL, median 7.53 ng/mL) though total yield was decreased due to lower collection volumes. cfDNA fragment size was within expected ranges for all samples. From deep WGS, tumor informed SNVs were called in 3/8 samples (estimated ctDNA fraction: 1.83% - 83%). Comparable CNV profiles were identified in 6/8 samples. Three additional samples, with known tumor CNV profiles, were submitted for LP-WGS and CP, yielding comparable profiles and tumour fractions, confirming that LP-WGS is an appropriate and sufficient tool to screen for tumor fraction. We next focused on baseline samples from pts with solid tumors and active disease (n=24). With an optimized extraction protocol, we obtained higher cfDNA concentrations (average=67.9 ng/mL; median=17.7 ng/mL). Samples with >35 ng cfDNA available were selected for LP-WGS (n=15). LP-WGS will be used to screen for tumour fraction of cfDNA samples. Samples with relevant tumour fraction will then be subjected to deep WGS and CP. Conclusion We implemented a robust blood collection and cfDNA sequencing workflow within the KiCS program. We successfully isolated cfDNA from minimal plasma volumes and identified tumor-informed SNVs and CNVs in ctDNA. Ongoing work focuses on optimizing our workflow and sequencing pipelines, with the aim to provide reliable clinically useful results with limited amounts of ctDNA. Citation Format: Sarah Cohen-Gogo, Taegi Choi, Ryan Ripsman, Matt Hudson, Sandy Fong, Reem Khan, Rosemarie E. Venier, Tristan Charlinski, Anita Villani, David Malkin, Adam Shlien. Characterization of circulating tumor DNA in a pediatric oncology cohort and implementation into the SickKids Cancer Sequencing (KiCS) precision oncology program [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A035.
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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 | 0,003 |
| 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 | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».