Abstract B007: Patterns and quantitation of migration and metastasis in a zebrafish xenograft model of ewing sarcoma
Bibliographic record
Abstract
Abstract Ewing sarcoma (ES) is one of the most common and lethal pediatric cancers with a 5-year survival rate of less than 30% for those with metastatic disease. Approximately 25% of ES patients have metastatic disease at time of diagnosis. Current treatment for ES rests on an intensive regimen of five chemotherapeutic agents with a high level of toxicity. Recurrent ES is incurable. Hence, there is a desperate need for more effective and less toxic treatments. A major obstacle in developing new, more effective therapies against ES is the lack of animal models. Zebrafish offers a robust vertebrate model for investigating cancer and evaluating the efficacy of anti-cancer drugs. Many genes, signaling pathways, and mechanisms of carcinogenesis and metastasis are highly conserved between humans and zebrafish. As zebrafish do not develop an adaptive immune response until approximately four weeks of age, they present an ideal system for xenograft tolerance in an immunocompetent organism. In addition, the transparency of zebrafish embryos permits high-resolution optical imaging in vivo and allows for easy tracking of tumor cell migration and metastasis. Our lab has created a ES xenograft model in zebrafish by injecting mCherry-labeled ES cell lines into the transgenic strain Tg(fli:GFP). This strain marks the zebrafish vasculature green, allowing for effective tracking of the injected red cancer cells. Our lab transduced the human ES cell lines A673 and MHH-ES-1, both of which contain the EWSR1:FLI fusion and TP53 mutations, with the mCherry construct. The top 10% brightest cells were selected to allow for easy visualization within the zebrafish. Approximately 200 labeled cells were injected into the yolk at two days post fertilization (2 dpf). Cell growth and migration were tracked until 5 dpf. Using fluorescent microscopy, images of each injected fish were collected at one day post injection (1 dpi), corresponding to 3 dpf, and at 3dpi, corresponding to 5 dpf. Using ImageJ software, these images were carefully analyzed for tumor growth, cell migration, and metastasis. To quantify tumor invasion and metastatic dissemination, we used flow cytometry to compare total ES cell numbers between 1 dpi and 3 dpi within the zebrafish. Our results show that metastasis within the zebrafish, which is indicated by ES cell growth within the tail region, occurs in approximately 15% of injected fish. Data from fluorescent microscopy and flow cytometry demonstrate primary tumor growth, within the zebrafish yolk, occurs in nearly all injected fish. Flow cytometry work is ongoing to quantify the percent increase in tumor growth within injected fish at 5dpf. Large–scale drug screens can be easily performed in the zebrafish due to their small size and ease of drug delivery, which consists simply of immersing fish into drug solutions. Ongoing work includes testing our xenograft model using two known chemotherapeutic agents against ES, doxorubicin and ifosfamide, with the goal of providing a benchmark for novel drugs’ responses on cell migration and metastases. Citation Format: Rebecca A. Anderson, Usua Oyarbide Cuervas-Mon, Seth Corey. Patterns and quantitation of migration and metastasis in a zebrafish xenograft model of 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 B007.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".