Abstract B020: Multimodal single-cell analyses reveal identification of unique transcriptional subgroups in ewing sarcoma
Bibliographic record
Abstract
Abstract Ewing sarcoma (EwS) tumors are driven by pathognomonic fusions between FET proteins and ETS family transcription factors (most frequently EWS::FLI1). EWS::FLI1 drives tumorigenesis through massive transcriptomic and epigenomic rewiring. Despite mutational homogeneity, EwS are highly transcriptionally heterogeneous suggesting that cell context is a key determinant of EWS::FLI1-driven gene signatures. Fusion protein activity has been revealed as a core source of EwS heterogeneity. However, deep investigation into the transcriptomic and epigenomic heterogeneity of EwS cells has yet to be described. Molecular subtyping of EwS has thus far proven to be difficult to achieve with bulk RNA-sequencing approaches. We hypothesized that single-cell characterization of EwS cells would provide the necessary resolution to allow identification and characterization of transcriptionally distinct tumor subgroups. We subjected eight established EwS cell lines, one PDX-derived EwS line, and five non-EwS cell lines to single-cell multiomic (ATAC + RNA) sequencing. A range of 1300-3800 live cells were captured per sample, with an average of 25,000 transcript and accessible reads per cell. Initial analysis of transcriptomes confirmed the EwS-specific, EWS::FLI1-dependent signature gene sets in EwS samples. Integrated analysis of the multiome data from the 9 EwS samples identified inter-tumor heterogeneity and unsupervised k-means clustering revealed three molecular subgroups that were not evident from expression data alone. Subgroup 1 comprised only the A673 cell line, while the remaining 8 EwS samples were split between subgroups 2 and 3. Gene ontology analysis defined enrichment of distinct gene sets across the 3 subgroups. Interestingly, expression of subgroup 2-defining genes was reduced following knockdown of EWSR1::FLI1, while expression of subgroup 3-defining genes increased. Thus, these data show that transcriptionally distinct subgroups of EwS exist and these are defined, in part, by whether EWS::FLI1-dependent gene activation or gene repression dominates transcriptional rewiring. Our ongoing studies suggest that these molecular subgroups are defined by cooperation between the fusion and cell context-dependent transcription factors. Future analyses will determine if these distinct multiomic programs exist in primary patients tumors and test the pathobiologic and clinical significance of these novel molecular subgroups. Citation Format: April A. Apfelbaum, Olivia Waltner, Shruti S. Bhise, Sami Kanaan, Jay F. Sarthy, Scott N. Furlan, Elizabeth R. Lawlor. Multimodal single-cell analyses reveal identification of unique transcriptional subgroups in ewing sarcoma [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr B020.
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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".