Cross-species genomics identifies DLG2 as a tumor suppressor in osteosarcoma
Bibliographic record
Abstract
Leveraging the conserved cancer genomes across mammals has the potential to transform driver gene discovery in orphan cancers. Here, we combine cross-species genomics with validation across human-dog-mouse systems to uncover a new bone tumor suppressor gene. Comparative genomics of spontaneous human and dog osteosarcomas (OS) expose Disks Large Homolog 2 (DLG2) as a tumor suppressor candidate. DLG2 copy number loss occurs in 42% of human and 56% of canine OS. Functional validation through pertinent human and canine OS DLG2-deficient cell lines identifies a regulatory role of DLG2 in cell division, migration and tumorigenesis. Moreover, osteoblast-specific deletion of Dlg2 in a clinically relevant genetically engineered mouse model leads to acceleration of OS development, establishing DLG2 as a critical determinant of OS. This widely applicable cross-species approach serves as a platform to expedite the search of cancer drivers in rare human malignancies, offering new targets for cancer therapy.
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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 | 0.000 |
| 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 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".