Minimum altered regions in early prostate cancer progression identified by high resolution whole genome tiling path BAC array comparative hybridization
Bibliographic record
Abstract
BACKGROUND: Carcinoma of the prostate (CaP) is a serious health problem. The altered molecular mechanisms that lead to this disease are poorly understood. METHODS: Specimens from radical prostatectomies and blood were collected from 18 CaP surgery patients. For CGH studies, 20 CaP-related samples (16 Gleason grade 3, 3 higher grades, 1 BPH sample) and 18 samples of patient-matched normal epithelial cells were obtained by laser-assisted microdissection from frozen sections of the 18 prostatectomy specimens. High resolution SMRT aCGH was used to compare genomic profiles of prostatic samples to patient-matched blood and pooled female DNA. TMPRSS2-ERG fusion transcript analysis was performed by RT-PCR in relation to alterations detected at the TMPRSS2 locus. RESULTS: Our comprehensive aCGH approach allowed us to define 35 regions of recurrent alterations while excluding germline copy number polymorphisms. Novel regions identified include 2q14.2, containing INHBB, and 17q21.31. The TMPRSS2 locus at 21q22.3 may be a hotspot for rearrangements with 75% of the alterations resulting in the expression of a TMPRSS2-ERG fusion transcript. Differences in fusion expression in different areas in an individual tumor focus and expression in adjacent normal epithelium supported intrafocal heterogeneity and field cancerization, respectively. Both features challenge our efforts to develop more objective markers for diagnosis and prediction of the severity of CaP. CONCLUSION: The high-density array enabled precise mapping of genomic alterations and consequently definition of minimum altered regions smaller than previously reported thus facilitating identification of those genes that contribute to the cancer transformation process.
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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".