Cooperation of dominant oncogenes with regulatory germline variants shapes clinical outcomes in childhood cancer
Bibliographic record
Abstract
INTRODUCTORY PARAGRAPH Deciphering principles of inter-individual tumor heterogeneity is essential for refinement of personalized anti-cancer therapy. Unlike cancers of adulthood, pediatric malignancies including Ewing sarcoma (EwS) feature a striking paucity of somatic alterations except for pathognomonic driver-mutations that cannot explain overt variations in clinical outcome. Here we demonstrate in the EwS model how cooperation of a dominant oncogene and regulatory variants determine tumor growth, patient survival and drug response. We show that binding of the oncogenic EWSR1-FLI1 fusion transcription factor to a polymorphic enhancer-like DNA element controls expression of the transcription factor MYBL2, whose high expression promotes poor patient outcome via activation of pro-proliferative signatures. Analysis of paired germline and tumor whole-genome sequencing data revealed that regulatory variability at this locus is inherited via the germline. CRISPR-mediated interference with this regulatory element almost abolished MYBL2 transcription, and MYBL2 knockdown decreased cell proliferation, cell survival and tumorigenicity of EwS cells. Combined RNA- and ChIP-seq analyses as well as functional experiments and clinical data identified CCNF, BIRC5 and AURKB as direct MYBL2 targets and critical mediators of its phenotype. In drug-response experiments, high MYBL2 levels sensitized EwS cells for inhibition of its activating cyclin dependent kinase CDK2 in vitro and in vivo, suggesting MYBL2 as a predictive biomarker for targeted anti-CDK2-therapy. Collectively, our findings establish cooperation of somatic mutations and regulatory germline variants as a major determinant of tumor progression and indicate the importance of integrating the regulatory genome in the process of developing new diagnostic and/or therapeutic strategies to fully harness the potential of precision medicine.
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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".