<i>PTEN</i> genomic deletions that characterize aggressive prostate cancer originate close to segmental duplications
Bibliographic record
Abstract
Deletion of PTEN at 10q23.3 occurs in ∼40% of human prostate cancers and is associated with aggressive metastatic potential, poor prognosis, and androgen-independence. This high frequency of recurrent PTEN deletions in prostate cancer suggests there may be unusual genomic features close to this locus that facilitate DNA alteration at 10q23.3. To explore possible mechanisms for deletions in the PTEN region, a meta-analysis of 311 published human genome array datasets was conducted and determined that the minimal prostate cancer-associated deletion at 10q23.3 corresponds to ∼2.06 MB region flanked by BMPR1A and FAS. On a separate cohort comprising an additional 330 tumors, four-color fluorescence in situ hybridization analysis using probes for BMPR1A, FAS, cen(10), and PTEN showed that 132 of 330 (40%) tumors had PTEN loss, 50 (15%) of which were homozygous losses (comprising in total 100 deletion events). Breakpoints between PTEN and BMPR1A or FAS were subsequently mapped in 100 homozygous and 82 hemizygous PTEN losses, revealing that 125/182 PTEN microdeletions occurred within the 940 kB interval between BMPR1A and PTEN. Furthermore, this breakpoint interval coincides with a repeat-rich region of 414 kB containing the SD17 and SD18 segmental duplications, which contain at least 13 homologous inverted repeat sequences. Together, these data suggest that a strong selective growth advantage for loss of PTEN and upregulation of PI3K/AKT, combined with the close proximity of PTEN to a large unstable segment of repeated DNA comprising SD17 and SD18, can lead to recurrent microdeletions of the PTEN gene in prostate cancer. © 2011 Wiley Periodicals, Inc.
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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 | 0.001 | 0.001 |
| 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".