KLK3 and TMPRSS2 for molecular lymph-node staging in prostate cancer patients undergoing radical prostatectomy
Bibliographic record
Abstract
BACKGROUND: Lymph-node (LN) metastasis in prostate cancer (PC) is a main risk factor for tumor recurrence after radical prostatectomy (RP). Molecular analysis facilitates detection of small-volume LN metastases with higher sensitivity than histopathology. We aimed to prospectively evaluate six candidate gene markers for detection of pelvic LN metastases and to determine their ability to predict biochemical recurrence-free survival (bRFS) in patients treated with RP. METHODS: The expression of kallikrein 2, 3, and 4 (KLK2, KLK3, and KLK4), prostate-specific membrane antigen (PSMA), transmembrane serine protease 2 (TMPRSS2) and transient receptor potential cation channel subfamily M member 8 (TRPM8) was assessed using qPCR. We analyzed LNs from 111 patients (intermediate PC, n = 32 (29%); high-risk PC, n = 79 (71%)) who underwent RP and extended pelvic lymph-node dissection without neoadjuvant treatment. RESULTS: Overall, 2411 LNs were examined by molecular and histopathologic examination. Histopathology detected 69 LN metastases in 28 (25%) patients. KLK2 and KLK3 diagnostically performed best and classified all pN1-patients correctly as molecular node-positive (molN1/pN1). The concordance on LN level was best for KLK3 (96%). KLK2, KLK3, KLK4, PSMA, TMPRSS2, and TRPM8 reclassified 27 (24%), 32 (29%), 29 (26%), 8 (7%), 13 (12%), and 23 (21%) pN0-patients, respectively, as node-positive (pN0/molN1). On multivariable cox regression analysis molecular LN status (molN1 vs. molN0) using KLK3 (HR 4.0, p = 0.04) and TMPRSS2 (HR 5.1, p = 0.02) were independent predictors of bRFS. Median bRFS was shorter in patients with only molecular positive LNs (molN1/pN0) for KLK3 (24 months, p = 0.001) and for TMPRSS2 (12 months, p < 0.001) compared to patients with negative nodes (molN0/pN0) (median bRFS not reached). CONCLUSIONS: For diagnostic purposes, KLK3 showed highest concordance with histopathology for detection of LN metastases in PC patients undergoing RP. For prognostic purposes, KLK3 and TMPRSS2 expression were superior to histopathologic LN status and other transcripts tested for molecular LN status. We suggest a combined KLK3/TMPRSS2 panel as a valuable diagnostic and prognostic tool for molecular LN analysis.
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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 | 0.001 | 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".