Integrative analysis of copy number and gene expression data identifies potential oncogenic drivers that promote mammary tumor recurrence
Bibliographic record
Abstract
Tumor recurrence represents a significant clinical challenge in the treatment and management of breast cancer. To investigate whether copy number aberrations (CNAs) facilitate the re-emergence of tumor growth from residual disease, we performed array comparative genomic hybridization on primary and recurrent mammary tumors from an inducible mouse model of type-I insulin-like growth factor receptor driven breast cancer. This genome-wide analysis revealed primary and recurrent tumors harbored distinct CNAs with relapsed tumors containing an increased number of gene-level gains and losses. Remarkably, high-level CNAs detected in primary tumors were largely devoid of annotated cancer genes while the vast majority of recurrent tumors harbored at least one CNA containing a known oncogene or tumor suppressor. Specifically, 38% of recurrent tumors carried gains at 6qA2 and 9qA2 which encode the Met and Yap1 oncogenes, respectively. The most frequent CNA, occurring in 63% of recurrent tumors, was a focal deletion at 4qC5 involving the Cdkn2a/b tumor suppressor genes. Integrative analysis revealed positive correlations between gene copy number and mRNA expression suggesting Met, Yap1, and Cdkn2a/b may serve as potential drivers that promote tumor recurrence. Accordingly, cross-species analysis revealed gene-level murine CNAs were present in a subset of human breast cancers with high MET and YAP1 mRNA predictive of decreased relapse-free survival in basal-like breast cancers. Together, these findings indicate that tumor recurrence is facilitated by the acquisition of CNAs with oncogenic potential and provide a framework to dissect the molecular mechanisms that mediate tumor escape from dormancy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".