Abstract 2177: Sensitive single cell copy number profiling using a novel microfluidic droplet based platform
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".