Abstract IA020: Reprogramming of myogenic transcription factors in rhabdomyosarcoma
Bibliographic record
Abstract
Abstract Rhabdomyosarcoma, a pediatric malignancy with partial resemblance to undifferentiated skeletal muscle, is characterized by high expression of myogenic-lineage transcription factors such as MYOD1 and MYOG. Despite high expression of these transcription factors, which in normal muscle result in differentiation, RMS cells fail to differentiate, suggesting the presence of factors that inhibit their normal differentiation-promoting functions. In this talk, I will present data that the key muscle transcriptional regulator, SIX1, which in development activates the myogenic regulatory factors (MRFs) and promotes muscle differentiation, in fact inhibits differentiation in fusion-negative (FN) RMS. SIX1 holds FN-RMS cells in a progenitor-like state by altering the chromatin landscape and causing MYOD1, a key MRF, to preferentially bind to regulatory regions of genes permissive to growth rather than differentiation. Loss of SIX1 results in re-localization of MYOD1 to promoters/enhancers of genes associated with differentiation, and further results in increased binding of MYOG at such loci. Altered binding of MYOD1 and MYOG in response to SIX1 loss results in marked inhibition of RMS growth in vivo, via induction of differentiation. These data suggest that SIX1 acts as a master regulatory factor controlling the fate of RMS cells. Data will be presented that suggest dynamic actions of SIX1 and its co-factors throughout normal muscle differentiation, whereby high levels of SIX1 expressed in early muscle differentiation may mimic a transcriptional state seen in RMS. We hypothesize that the specific levels of SIX1, combined with a unique combination of transcriptional co-factors, reprogram genome-wide binding of MRFs to different promoter/enhancer sites at specific developmental time points, and that RMS is trapped in an early developmental state where SIX1 represses differentiation via genome-wide alterations in MRF binding that favor growth. Understanding the co-factors that work with SIX1 to alter chromatin state and MRF binding may enable the discovery of novel targets whose inhibition could serve as a relatively non-toxic treatment to restore normal developmental processes and inhibit RMS progression. Citation Format: Heide L. Ford, Jessica Y. Hsu, Etienne P. Danis, Stephanie Nance, Jenean H. O’Brien, Annika L. Gustafson, Veronica M. Wessells, Andrew E. Goodspeed, Jared C. Talbot, Sharon L. Amacher, Paul Jedlicka, Joshua C. Black, James C. Costello, Adam D. Durbin, Kristin B. Artinger. Reprogramming of myogenic transcription factors in rhabdomyosarcoma [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 IA020.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".