<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 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) | 0.002 | 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 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".