Abstract PR011: Characterization of the precancerous and cancer microenvironment in a zebrafish sarcoma model
Bibliographic record
Abstract
Abstract Contributions of the microenvironment to soft tissue sarcoma progression are relatively undefined, representing a major impediment to identifying essential regulatory networks in sarcomagenesis. Furthermore, genetic and molecular characteristics that distinguish precancerous versus cancerous microenvironments are not well known across human cancer types. While animal models have the potential to reveal these complex processes, significant impediments to such inquiries include (1) the difficulty in distinguishing microenvironmental cells from precancerous or cancer cells in tissue specimens; and (2) the challenge in defining a discrete tissue with known cancer predilection that represents a precancerous microenvironment. We developed a unique zebrafish model that allows segregation of microenvironmental, precancerous, and cancerous cell populations by fluorescence-activated cell sorting. This model exhibits high predilection for malignant peripheral nerve sheath tumor (MPNST), a type of soft tissue sarcoma with a particularly poor prognosis due to aggressive growth, limited response to conventional treatment, and ineffective targeted therapy options. Using RNA-seq, we profiled the transcriptomes of microenvironmental cells from our zebrafish MPNST model and determined that the precancerous and cancerous microenvironments exhibit broad activation of inflammatory and immune-associated signaling networks. Markers for both M1 and M2 macrophage polarization were upregulated in precancerous and cancerous microenvironments, suggesting the presence of a mixed macrophage population during sarcomagenesis. Patterns of ligand and receptor expression based on a previously defined human ligand-receptor network identified significant upregulation of multiple tumor-promoting ligands in both precancerous and cancerous microenvironments. We also identified specific ligand-receptor pairs that may mediate key signaling events during sarcoma initiation and progression. Together this work provide new insight into distinguishing characteristics of the cancer-prone cellular microenvironment that may promote MPNST initiation and progression in vertebrates. Citation Format: Heather R. Shive, John S. House, Jordan L. Ferguson, Dereje D. Jima, Aubrie A. Selmek, Dillon T. Lloyd. Characterization of the precancerous and cancer microenvironment in a zebrafish sarcoma model [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 PR011.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".