Use of genetic instability to predict biochemical recurrence in intermediate-risk prostate cancer.
Bibliographic record
Abstract
42 Background: Biomarkers of local and systemic recurrence are needed to individualize patient risk categories and better define treatment. We hypothesized that genomic instability, as measured by percent genome alteration (PGA), can predict for biochemical failure in intermediate- risk prostate cancer. Methods: High-resolution array comparative genomic hybridization (arrayCGH) was used to identify PGA in frozen biopsies from 120 intermediate-risk prostate cancer patients. Our cohort included 39 T1c tumors, 78 T2 tumors and 2 T3 tumors. The Gleason score was 6 in 32 tumors, 7 in 82 tumors and 8–9 in 6 tumors. PSA ranged from 2.1–33 (median 8.0). Patients were treated with intensity-modulated radiotherapy (IMRT) with doses of 75.6–79.8 Gy in 1.8–2Gy fractions, or 60–66 Gy in 3 Gy fractions.. Twenty-five percent of patients also received neoadjuvant-concurrent bicalutamide (150mg po od). Biochemical failure, defined by Phoenix criteria or the initiation of salvage therapy, was observed in 35 patients after median follow-up of 5.4 years (range 0.9–8.8). Results: Array CGH showed variable PGA ranging from <1% to 35% (median 6.7%). PSA and the use of hormonal therapy independently influenced biochemical relapse, and formed a baseline clinical model to which PGA was added. PGA was found to be a strong predictor of biochemical relapse (p<0.0001) independent of the clinical prognostic factors (pre-treatment PSA, Gleason score and T-category). PGA was also found to be associated with unique tumour suppressor and oncogene gene loci clusters involved in genetic stability (e.g. loss of PTEN, p53, RB, NKX3.1, ATM, PARP-1 and gain of c-MYC; validated by in situ FISH). Conclusions: This is the first report to show genetic instability can independently predict for biochemical recurrence in intermediate-risk prostate cancer. Current studies are associating specific gene loci regions with clinical outcome. Our results could provide a way forward for individualized medicine for non-indolent prostate cancer based on initial daignostic biopsy material. Supported by Prostate Cancer Canada, The Terry Fox Foundation and the Canadian Cancer Society. No significant financial relationships to disclose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".