Abstract B042: Identifying single-cell transcriptomic signatures and changes underlying the evolution of fusion-negative rhabdomyosarcoma in Li-Fraumeni Syndrome mice
Bibliographic record
Abstract
Abstract Soft tissue sarcomas (STS) comprise ∼7% of pediatric cancers, with over half classified as rhabdomyosarcoma (RMS), equating to approximately 4.5 cases per 1 million children. Germline mutations in the TP53 tumor suppressor gene (MutTP53), most notably in Li-Fraumeni Syndrome (LFS), represent the strongest heritable risk factor for RMS and are associated with poorer overall survival. Despite this, a transcriptomic signature specific to LFS-associated RMS (LFS-RMS) remains undefined. To address this, we performed droplet-based single-cell RNA sequencing (scRNA-seq; 10x Genomics) on RMS tumors and matched healthy muscle tissue from Trp53 R172H/WT mice, harboring a mutation analogous to the human TP53 R175H hotspot. Tumors were collected at endpoint from male (n = 2) and female (n = 3) mice. Cell types were identified through differential gene expression (DGE) analysis and annotated via consensus across machine learning-based tools. Trajectory analysis using Monocle3 revealed lineage transitions, while Generalized Additive Models (GAMs) identified genes associated with the progression from non-malignant to cancer-associated states. We observed consistent alterations in cell subtype proportions between tumor and healthy tissue, including a significant reduction in the M1/M2 macrophage proportion. Trajectory analysis identified key transcripts driving the transition from muscle-associated to cancer-associated fibroblasts and myogenic cells. Pathways enriched in GO analysis suggest that during the transition to cancer-associated phenotypes, cells in LFS-RMS adopt gene programs that reduce susceptibility to immune cell invasion in the tumor microenvironment. Our findings provide the first single-cell map of LFS-RMS, revealing dynamic transcriptional states and biomarkers of cells states associated with tumorigenesis. These insights pave the way for improved therapeutic approaches tailored to the complex cellular heterogeneity of LFS tumors. Further, these findings may broadly enhance RMS diagnostics and precision oncology approaches. Citation Format: Ashby Kissoondoyal, Paula R. Quaglietta, Brianne Laverty, Safa Majeed Grant, David Malkin. Identifying single-cell transcriptomic signatures and changes underlying the evolution of fusion-negative rhabdomyosarcoma in Li-Fraumeni Syndrome mice [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B042.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".