Abstract 2177: Sensitive single cell copy number profiling using a novel microfluidic droplet based platform
Notice bibliographique
Résumé
Abstract Intratumoral genetic heterogeneity may relate to aggressiveness and treatment-resistance. However, the genomic abnormalities underlie this heterogeneity remain poorly characterized. To better describe the picture of tumour heterogeneity, single cell approaches to accommodate larger number of cells are highly in demand. Here, we present results of single cell DNA sequencing by using a novel microfluidic droplet system. We applied this approach to discover copy number profiles in two common and therapeutically challenging cancers, isocitrate dehydrogenase wild-type glioblastoma (GBM) and breast cancer. The GBM cells were extracted from patients operated on site and cultured for 2 months in restricted media to isolate glioma stem cells. The breast cancer cells were extracted from patient-derived xenografts (PDXs). A novel microfluidic droplet based platform (10x GenomicsTM) was used to perform sensitive copy number profiling of thousands of single cells simultaneously. Briefly, the platform integrates single cell encapsulation, cell lysis, genomic amplification, and barcoding with oligonucleotides. The barcoded genomic DNA fragments from individual cells were pooled and converted into libraries that are compatible with Illumina sequencers. Thus far, we sequenced one glioma stem cell sample and two breast cancer PDXs. Uniquely mapped reads were counted in variable-bins with GC bias corrected by lowess fit. The Circular Binary Segmentation was performed and the integer copy number was estimated by least square fitting. The single cell copy number profiles were clustered by hierarchical clustering. We achieved approximately 2% and 6% coverage of the genome per cell for the glioblastoma and the breast tumors, respectively. The uniformity of coverage was comparable to other whole genome amplification and single cell multiplex approaches, while requiring 700k-2.1mi reads/single cell with the median absolute deviation of pair-wise differences in read counts between neighboring bins between 0.17-0.21 vs 0.16)1. We identified two major clonal subpopulations in the glioblastoma sample. One subclone had chr1q amplification and the other subclone had a 9.8Mb deletion on chr4. The clonal CNVs observed were validated using single-cell RNA-seq-based CNV analysis, and the frequent CNVs using fluorescent in situ hybridization. We also identified the HER2 copy number profile within a basal like triple-negative breast cancer as 40% of 2.5N and 25% of 3N. This was previously validated by fluorescence in situ hybridization2. By applying this new microfluidic system, our work will provide more insights into genomic heterogeneity for malignant properties of cancers. 1. Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90-94 (2011). 2. Savage P. et al. A Targetable EGFR-Dependent Tumor-Initiating Program in Breast Cancer. Cell Rep. 21, 1140-1149 (2017). Citation Format: Rui Li, Charles Couturier, Paul Savage, Jean Monlong, Guillaume Bourque, Kevin Petrecca, Morag Park, Jiannis Ragoussis. Sensitive single cell copy number profiling using a novel microfluidic droplet based platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2177.
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 | 0,000 |
| 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 | 0,001 |
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 ».