Characterizing the Myc and Notch1 Genetic Cooperation Network Driving Breast Cancer Progression
Bibliographic record
Abstract
Breast cancers (BC) have some well-defined genetic drivers, however, for the most part, this list of drivers does not take into account copy number variation (CNV). Hyperactivation of Myc or Notch1 in BC are associated with poor patient outcomes and limited therapeutic targetability. To identify the network of genetic events that cooperate with increased Myc or Notch1 activity in mammary tumorigenesis, I conducted Sleeping Beauty (SB) transposon-based insertional mutagenesis screens in a mouse model for Myc overexpression based on stabilization with the T58A mutation and a mouse developed by the Egan lab expressing a ligand-dependent Notch1ΔPEST that extends the half-life of Notch intracellular domain, respectively. Compared to SB tumors, MycT58A shifted the histological profile of tumors from squamous to adenocarcinoma, and Notch1ΔPEST increased the proportion of papillary tumors. Most gene-centric Common Insertion Sites (gCIS) identified in these screens were driver-specific. Thus, each generated networks of genetic perturbations representing therapeutic vulnerabilities that can inform future development of targeted therapies for Myc-overexpressing and Notch1ΔPEST-driven BC.
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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) | 0.001 | 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".