Abstract A002: Dynamic single cell imaging of cancer stem cells and clonality in fusion-negative rhabdomyosarcoma
Bibliographic record
Abstract
Abstract Rhabdomyosarcoma (RMS) accounts for 50% of all soft-tissue childhood sarcomas and is characterized by tumor cells that molecularly and morphologically resemble undifferentiated skeletal muscle. Treatment involves an aggressive regimen of chemotherapy followed by surgical resection and/or radiation therapy. Although survival rates can be as high as 70-90% in low-risk or localized disease, patients with oligoclonal or relapsed disease have extremely poor prognoses. Thus, there is a clinical imperative to identify novel therapeutic targets, particularly ones that could reduce clonality and suppress cancer stem cell self-renewal. Our lab has recently shown that fusion-negative (FN-)RMS contain four dominant tumor cell states: proliferative, ground, mesenchymal cancer stem cells (mCSCs) and differentiated muscle. Importantly, the mCSCs are largely quiescent under normal growth conditions but re-enter the cell cycle to promote tumor growth following stress. This suggests that mCSCs are likely responsible for driving therapy resistance and relapse. Building on our previous successes in live imaging cancer stem cells using transgenic zebrafish models that express fluorophores under control of developmentally restricted muscle promoters, we are now developing similar approaches to drive fluorescent protein expression in human FN-RMS cell lines to trace lineage fate and to witness the division history of mCSCs in real time. Using a combination of CRISPR/Cas9 gene inactivation and these newly developed tools, we are poised to identify new self-renewal pathways by the direct, live-cell imaging of FN-RMS engrafted into optically clear, immune-deficient zebrafish. In addition, we are also using tumor clonality as a surrogate of increased tumor aggression and cancer stem cell potential in the zebrafish RAS-induced RMS model. We have adapted the transgenic zebrafish model of kRASG12D-induced RMS to analyze tumor clonality using multispectral Zebrabow and GESTALT, a CRISPR barcoding technique that tracks cell fate. Using RMS-specific gene expression datasets that are associated with cancer stem cells, we are now screening for genes that elevate tumor clonality, increase tumor penetrance and accelerate tumor growth. Ultimately, by developing these lineage tracing tools in both human RMS cell lines and our transgenic Zebrafish RMS model, we will identify new modulators of the mCSC transcriptional cell states and possible therapeutic targets. Citation Format: Tiffany Eng, Yun Wei, Qian Qin, Chuan Yan, Qiqi Yang, Alexandra Veloso, Karin McCarthy, David Langenau. Dynamic single cell imaging of cancer stem cells and clonality in fusion-negative rhabdomyosarcoma [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 A002.
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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) | 0.003 | 0.000 |
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".