FISH analysis of 107 prostate cancers shows that PTEN genomic deletion is associated with poor clinical outcome
Bibliographic record
Abstract
This study examines the clinical impact of PTEN genomic deletions using fluorescence in situ hybridisation (FISH) analysis of 107 prostate cancers, with follow-up information covering a period of up to 10 years. Tissue microarray analysis using interphase FISH indicated that hemizygous PTEN losses were present in 42/107 (39%) of prostatic adenocarcinomas, with a homozygous PTEN deletion observed in 5/107 (5%) tumours. FISH analysis using closely linked probes centromeric and telomeric to the PTEN indicated that subband microdeletions accounted for approximately 70% genomic losses. Kaplan-Meier survival analysis of PTEN genomic losses (hemizygous and homozygous deletion vs not deleted) identified subgroups with different prognosis based on their time to biochemical relapse after surgery, and demonstrated significant association between PTEN deletion and an earlier onset of disease recurrence (as determined by prostate-specific antigen levels). Homozygous PTEN deletion was associated with a much earlier onset of biochemical recurrence (P=0.002). Furthermore, PTEN loss at the time of prostatectomy correlated with clinical parameters of more advanced disease, such as extraprostatic extension and seminal vesicle invasion. Collectively, our data indicates that haploinsufficiency or PTEN genomic loss is an indicator of more advanced disease at surgery, and is predictive of a shorter time to biochemical recurrence of disease.
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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 | 0.001 |
| 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".