Abstract A044 Enhanced disease detection using single cell RNAseq in children with brain cancer
Notice bibliographique
Résumé
Abstract Introduction: Serial liquid biopsy sampling is used to risk-stratify and assign therapy in childhood acute lymphoblastic leukemia. While circulating tumor DNA (ctDNA) from cerebrospinal fluid (CSF) has been evaluated, experience with single cell RNA sequencing (scRNAseq) and circulating tumor cells (CTCs) in childhood brain cancer has not been well described. Furthermore, a large scale analysis of the cellular ecosystem using scRNAseq in CSF has not been performed in this population. Here we assessed the value of high throughput scRNAseq on CSF samples from children with brain cancer, to test the hypothesis that CTCs from CSF could provide insight into diagnosis and disease progression. Method: We profiled cells in the CSF of both high and standard risk brain cancer patients collected at Sydney Children’s Hospital, Randwick, Australia. By using advanced informatic tools, we assessed cell counts, gene expression, copy number variation, single nucleotide variation, cell-cell interactions and performed pathway analysis. These data were evaluated alongside clinical disease parameters including CSF cytology and matched tumour bulk sequencing data (RNA and DNA) obtained through the ZERO Childhood Cancer Precision Medicine Program. Results: scRNAseq was performed on 92,965 CSF cells from 22 samples across 14 patients (including 4 patients with sequential samples). The cohort includes ATRT, DLGNT, DMG, medulloblastoma, infant-type hemispheric glioma, astrocytoma, pineoblastoma, and ependymoma. Cells identified were multiple immune subsets, fibroblasts, microglial and cancer cells. Analysis of 10 timepoints where scRNAseq was matched to cytology showed both approaches found positive disease (>10 putative cancer cells) in 2 timepoints and negative disease in 5 timepoints. Our scRNAseq approach found disease in 2 timepoints that were negative and 1 which was uncertain by cytology. Matching results to bulk RNA sequencing from the primary tumour showed that, in samples containing high CTC numbers (n= 3), we were able to confidently identify patient specific biomarkers of disease (including MYC, PVT1, and OTX2) and have identified evidence of CNS disease heterogeneity. In addition, we identified cell-cell interactions in a patient with DMG. These interactions were via IGTB3, which has previously been associated with metastasis. Finally, serial samples have been collected to enable minimally invasive disease monitoring, including in one patient with ATRT who is currently disease free. Conclusion: We show that scRNAseq of CTCs is feasible in pediatric brain tumors. This provides a novel method to understand the biology of pediatric brain cancer. By coupling high sensitivity detection with a detailed characterization of individual cells, we have shown potential utility in disease monitoring including at the level of minimal residual disease. In future, this technology could be applied to assess treatment response kinetics, aid treatment selection, enable detection of early relapse and assist in disease prognostication. Citation Format: Robert Salomon, Wenyan Li, Mojgan Toumari, Aileen Lowe, Chelsea Mayoh, Paulettte Barahona, Loretta MS Lau, Jordan Staunton, Erica Jacobson, Sumanth Nagabushan, Neevika Manoharan, Ruth Mitchell, Faustine Ong, Michelle Haber, David S Ziegler, Mark J. Cowley, Marion K. Mateos. Enhanced disease detection using single cell RNAseq in children with brain cancer [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 A044.
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 | 0,002 |
| 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 | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».