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Record W3046994859 · doi:10.1158/1538-7445.pedca19-b10

Abstract B10: Rhabdomyosarcoma requires MYC family genomic events to pathogenically subvert core-regulatory circuitry

2020· article· en· W3046994859 on OpenAlexaboutno aff
Adam D. Durbin, Guillaume Kugener, Mark W. Zimmerman, Chuan Yan, Neekesh V. Dharia, Elizabeth S. Frank, Xiang Chen, Ken N. Ross, Brenton R. Paolella, Michael J. Krill-Burger, David E. Root, Jesse S. Boehm, Francisca Vázquez, Andrew L. Hong, Aviad Tsherniak, David M. Langenau, William C. Hahn, Todd R. Golub, Brian J. Abraham, Richard A. Young, A. Thomas Look, Kimberly Stegmaier

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyEnhancerCancer researchChromatin immunoprecipitationRhabdomyosarcomaCarcinogenesisTranscription factorPediatric cancerGeneCRISPRGeneticsCancerComputational biologyGene expressionPromoterSarcomaMedicinePathology

Abstract

fetched live from OpenAlex

Abstract Rhabdomyosarcoma (RMS) is the most common soft-tissue sarcoma of childhood. Despite multimodality therapy and trials of molecularly targeted agents, limited improvements in overall survival have been realized for patients with high-risk disease. Thus, we aimed to determine the landscape of tumor-specific gene dependencies that underlie tumorigenesis in RMS and therefore provide a valuable group of targets for the development of novel therapeutics. Using unbiased genome-scale CRISPR-Cas9 approaches, we identified a set of RMS-specific tumor dependencies involved in tumor cell growth and survival. RMS dependencies were enriched for nucleic acid binding proteins, including transcription factors (TFs). We then used genome-wide chromatin-immunoprecipitation coupled to high-throughput sequencing analysis to demonstrate that a small number of essential TFs—MYCN, MYOD1, TCF12, SOX8, ZEB2, ZNF217, and SIX1—are members of the transcriptional core regulatory circuitry (CRC) that maintains the malignant cell state of RMS. Both c-MYC and MYCN were associated with gene and enhancer copy number increases in cell lines and primary tumors and represented strong dependencies in the RMS cell lines screened. c-MYC and MYCN function to similarly invade and regulate the CRC in respectively dependent cells. To disable the CRC, we tested A485, an inhibitor of the histone acetyltransferase enzymes involved in the establishment of super-enhancer elements that are associated with high level expression of the CRC factors. A485 caused a reversible and rapid loss of CRC factor and c-MYC and/or MYCN expression, and prolonged treatment resulted in cell cycle arrest, differentiation, and apoptosis in vitro and in vivo. This phenotype is rescued by exogenous re-expression of either c-MYC or MYCN in a manner insensitive to A485, indicating a mechanism by which these genes subvert a myogenic CRC to produce an oncogenic fate. This study defines a common set of critical dependency genes in RMS and identifies key genomic events surrounding the c-MYC and MYCN loci that lead to elevated expression and tumorigenesis. Citation Format: Adam D. Durbin, Guillaume Kugener, Mark W. Zimmerman, Chuan Yan, Neekesh V. Dharia, Elizabeth S. Frank, Xiang Chen, Ken N. Ross, Brenton Paolella, Michael Krill-Burger, David E. Root, Jesse S. Boehm, Francisca Vazquez, Andrew L. Hong, Aviad Tsherniak, David M. Langenau, William C. Hahn, Todd R. Golub, Brian J. Abraham, Richard A. Young, A. Thomas Look, Kimberly Stegmaier. Rhabdomyosarcoma requires MYC family genomic events to pathogenically subvert core-regulatory circuitry [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B10.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.186
GPT teacher head0.415
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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