Recurrent copy number alterations in prostate cancer: an in silico meta-analysis of publicly available genomic data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present a meta-analysis of somatic copy number alterations (CNAs) from 11 publications that examined 662 prostate cancer patient samples, which were derived from 546 primary and 116 advanced tumors. Normalization, segmentation, and identification of corresponding CNAs for meta-analysis was achieved using established commercial software. Unsupervised analysis identified five genomic subgroups in which approximately 90% of the samples were characterized by abnormal profiles with gains of 8q. The most common loss was 8p (NKX3.1). The CNA distribution in other genomic subgroups was characterized by losses at 2q, 3p, 5q, 6q, 13q, 16q, 17p, 18q, and PTEN (10q), and acquisition of 21q deletions associated with the TMPRSS2-ERG fusion rearrangement. Parallel analysis of advanced and primary tumors in the cohort indicated that genomic deletions of PTEN and the gene fusion were enriched in advanced disease. A supervised analysis of the PTEN deletion and the fusion gene showed that PTEN deletion was sufficient to impose higher levels of CNA. Moreover, the overall percentage of the genome altered was significantly higher when PTEN was deleted, suggesting that this important genomic subgroup was likely characterized by intrinsic chromosomal instability. Predicted alterations in expression levels of candidate genes in each of the recurrent CNA regions characteristic of each subgroup showed that signaling networks associated with cancer progression and genome stability were likely to be perturbed at the highest level in the PTEN deleted genomic subgroup.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it