hCAP-D3 Expression Marks a Prostate Cancer Subtype With Favorable Clinical Behavior and Androgen Signaling Signature
Bibliographic record
Abstract
Growing evidence suggests that only a fraction of prostate cancers detected clinically are potentially lethal. An important clinical issue is identifying men with indolent cancer who might be spared aggressive therapies with associated morbidities. Previously, using microarray analysis we defined 3 molecular subtypes of prostate cancer with different gene-expression patterns. One, subtype-1, displayed features consistent with more indolent behavior, where an immunohistochemical marker (AZGP1) for subtype-1 predicted favorable outcome after radical prostatectomy. Here we characterize a second candidate tissue biomarker, hCAP-D3, expressed in subtype-1 prostate tumors. hCAP-D3 expression, assayed by RNA in situ hybridization on a tissue microarray comprising 225 cases, was associated with decreased tumor recurrence after radical prostatectomy (P=0.004), independent of pathologic tumor stage, Gleason grade, and preoperative prostate-specific antigen levels. Simultaneous assessment of hCAP-D3 and AZGP1 expression in this tumor set improved outcome prediction. We have previously demonstrated that hCAP-D3 is induced by androgen in prostate cells. Extending this finding, Gene Set Enrichment Analysis revealed enrichment of androgen-responsive genes in subtype-1 tumors (P=0.019). Our findings identify hCAP-D3 as a new biomarker for subtype-1 tumors that improves prognostication, and reveal androgen signaling as an important biologic feature of this potentially clinically favorable molecular subtype.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".