Abstract 722: A cancer-associated fibroblast model to discover mechanisms of growth, survival, and treatment sensitivity in PAX3/7-FKHR fusion-positive alveolar rhabdomyosarcoma
Bibliographic record
Abstract
Abstract Introduction: Rhabdomyosarcoma (RMS) is the most common pediatric soft tissue sarcoma. Among various subtypes, alveolar RMS (ARMS) is generally more aggressive and usually carries the fusion transcription factor, PAX3/7-FKHR. High-risk ARMS has an extremely poor prognosis, with ~25% five-year survival. Hence, novel therapeutic approaches are urgently needed. However, potential therapies are often hindered by the immunosuppressive tumor microenvironment (TME) associated with sarcomas. The TME is highly dynamic and includes fibroblasts, immune cells, and extracellular matrix. Cancer-associated fibroblasts (CAFs) may promote tumor growth and survival by secreting distinct factors to regulate proliferation, metastasis, and resistance to therapy. In this study, we investigated mechanisms involved in this phenomenon and identified a novel therapeutic strategy targeting the TME of ARMS. Methods: Cells were isolated from a PAX3-FKHR fusion-positive ARMS tumor specimen, following ethics approval and informed consent. The PAX3-FKHR fusion status and mesenchymal biomarkers were analyzed by immunoblotting. The patient’s CAFs (designated KCCF14), RMS lines (RD, RH30, RH41), and control fibroblasts (BJ, HS68, WI-38) were cultured in vitro and their respective supernatant media was harvested for subsequent experiments. Cell proliferation and viability were determined by hemocytometer and alamar blue assay, respectively. The scratch wound healing assay was used to assess cell migration. Cytokine analysis was performed with a 96-plex array. For drug screening, cells were treated with a panel of CXCR4 antagonists to generate dose-response curves. Results: KCCF14 CAFs isolated from an ARMS tumor exhibit elongated spindle morphology and are positive for mesenchymal biomarkers, including PDGFR-β and smooth muscle α-actin. As expected, these cells lack characteristics of the patient’s tumor, notably the expression of PAX3-FKHR fusion and N-myc. KCCF14-conditioned media significantly enhances ARMS cell proliferation and migration in a concentration-dependent manner. Analysis of the conditioned media revealed upregulation of key growth factors and cytokines, including PDGF-AA, VEGF-A, MCP-1, and IL-11, that promote tumor aggressiveness through proliferative signaling, angiogenesis, activation of migration, and immune evasion. Because SDF-1 (CXCR4 ligand) was also increased, we tested targeted inhibition of CXCR4, which induced cytotoxicity with increased sensitivity (IC50 = 0.5 µM) in KCCF14 cells compared to control fibroblasts. Conclusions: This study provides, for the first time, findings from a continuously growing CAF model system for RMS. Our data describes key tumor growth and survival mechanisms supported by the TME and establishes a method to identify treatments with novel therapeutic strategies. Citation Format: Patrick Sipila, Son Tran, Chunfen Zhang, Anne–Marie Langevin, Aru Narendran. A cancer-associated fibroblast model to discover mechanisms of growth, survival, and treatment sensitivity in PAX3/7-FKHR fusion-positive alveolar rhabdomyosarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 722.
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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 | 0.001 |
| 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".