Array CGH as a potential predictor of radiocurability in intermediate risk prostate cancer
Why this work is in the frame
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Bibliographic record
Abstract
Prostate cancer is the most common male cancer and up to one fifth of diagnosed patients will die of their disease. Current prognostic variables including T-category (of the TNM staging), the absolute or kinetics of prostatic specific antigen (PSA) and the pathologic Gleason score (GS) are utilized to place men in low, intermediate and high-risk prostate cancer risk groupings. There is great heterogeneity within the non-indolent intermediate risk group with respect to clinical response. It is therefore imperative that further genetic and other prognostic factors be identified to better individualize treatment. Somatic alterations in prostate cancer. Herein, we review the potential for somatic alterations in tumor-associated genes (based on comparative genomic hybridization (CGH) in prostate cancers to be novel prognostic, and possibly predictive, factors for prostate cancer radiotherapy response. Intermediate risk prostate cancers show alterations in a number of genes thought to be involved in radiosensitivity, DNA repair, cell death and stem cell renewal. These include deletions at 21q (TMPRSS2: ERG), 13q (RB1), 10q (PTEN), 8p (NKX3.1), additions at 8q21 (containing c-Myc)) and haplo-insufficiency for p53, PARP1, ATM and DNA-PKcs. Conclusions. The use of high-resolution CGH for fine-mapping of deletions and amplifications in pre-radiotherapy prostate cancer biopsies is feasible. Genetic alterations may delineate localized prostate cancer from systemic disease and be used as a predictive factor in that patients would be individually triaged to local (surgery versus radiotherapy) and/or adjuvant (adjuvant androgen ablation or post-operative radiotherapy) therapies in a prospective fashion to improve outcome. The knowledge of abnormal DNA repair pathways within in a given patient could allow for the judicious use of targeted agents (PARP/ATM inhibitors) as personalized medicine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it