<i>NKX3.1</i> Haploinsufficiency Is Prognostic for Prostate Cancer Relapse following Surgery or Image-Guided Radiotherapy
Bibliographic record
Abstract
BACKGROUND: Despite the use of prostate specific antigen (PSA), Gleason-score, and T-category as prognostic factors, up to 40% of patients with intermediate-risk prostate cancer will fail radical prostatectomy or precision image-guided radiotherapy (IGRT). Additional genetic prognosticators are needed to triage these patients toward intensified combination therapy with novel targeted therapeutics. We tested the role of the NKX3.1 gene as a determinant of treatment outcome given its reported roles in tumor initiating cell (TIC) renewal, the DNA damage response, and cooperation with c-MYC during prostate cancer progression. METHODS: Using high-resolution array comparative genomic hybridization (aCGH), we profiled the copy number alterations in TIC genes using tumor DNA from frozen needle biopsies derived from 126 intermediate-risk patients who underwent IGRT. These data were correlated to biochemical relapse-free rate (bRFR) by the Kaplan-Meier method and Cox proportional hazards models. RESULTS: A screen of the aCGH-IGRT data for TIC genes showed frequent copy number alterations for NKX3.1, PSCA, and c-MYC. NKX3.1 haploinsufficiency was associated with increased genomic instability independent of PSA, T-category, and Gleason-score. After adjusting for clinical factors in a multivariate model, NKX3.1 haploinsufficiency was associated with bRFR when tested alone (HR = 3.05, 95% CI: 1.46-6.39, P = 0.0030) or when combined with c-MYC gain (HR = 3.88, 95% CI: 1.78-8.49, P = 0.00067). A similar association was observed for patients following radical prostatectomy with a public aCGH database. NKX3.1 status was associated with positive biopsies post-IGRT and increased clonogen radioresistance in vitro. CONCLUSIONS: Our results support the use of genomic predictors, such as NKX3.1 status, in needle biopsies for personalized approaches to prostate cancer management.
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How this classification was reachedexpand
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".