Abstract A026: PROX1 transcription factor regulates rhabdomyosarcoma growth, myogenic properties and therapeutic targets
Bibliographic record
Abstract
Abstract Rhabdomyosarcoma (RMS) is an aggressive pediatric soft tissue cancer with features of skeletal muscle. Because of poor survival of RMS patients and severe long-term side effects of RMS therapies, novel RMS therapies are urgently needed. Here we show that the PROX1 transcription factor is highly expressed in RMS tumors regardless of their cell type of origin. We demonstrate that PROX1 is needed for RMS cell clonogenicity, growth and tumor formation. PROX1 gene silencing repressed several myogenic and tumorigenic transcripts and altered the RD (ERMS) transcriptome to resemble that of benign mesenchymal stem cells. Importantly, we found that fibroblast growth factor receptors (FGFR) mediated the growth effects of PROX1 in RMS. Because of receptor cross-compensation, inhibition of either alone did not mimic the effects of PROX1 silencing, whereas a pan-FGFR inhibitor ablated RMS cell proliferation and decreased tumor xenograft growth. Our findings uncover the critical role of PROX1 in RMS and offer new insights to the mechanisms that regulate RMS development and growth. As FGFR inhibitors have already been tested in unrelated clinical trials, our findings provide a new promising option for RMS treatment. Citation Format: Nebeyu Y. Gizaw, Pauliina Kallio, Erika Gucciardo, Caj Haglund, Tom Bohling, Kaisa Lehti, Mika Sampo, Kari Alitalo, Riikka Kivela. PROX1 transcription factor regulates rhabdomyosarcoma growth, myogenic properties and therapeutic targets [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 A026.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".