Development and validation of an ADT resistance signature to predict adjuvant hormone treatment failure.
Bibliographic record
Abstract
106 Background: Androgen deprivation therapy (ADT) is one of the main treatment options for locally advanced and metastatic prostate cancer. Neuroendocrine prostate cancer (NEPC) is inherently less sensitive or even resistant to ADT. NEPC can be observed de novo (e.g., small cell prostate cancer) but more commonly arises after exposure to ADT. We hypothesized that a gene expression signature of NEPC when measured in primary tumor specimens (RP) of prostatic adenocarcinoma may be useful for predicting patients with innate resistance to ADT. Methods: Expression profiles of 1023 PCa patients treated with RP were obtained from the Decipher GRID database. These were split into training (n=529) and validation (n=494) sets and stratified by the receipt of adjuvant ADT (n=243) or no adjuvant ADT (n=780). A literature review of ADT resistance and neuroendocrine genes identified 1,557 genes as candidates. This set was further filtered, using logistic regression to select a 52-gene ADT resistance signature (ARS). ARS was trained using a generalized linear model with lasso regularization. Survival c-index and Kaplan Meier was used to compare survival differences between treated and untreated patients with high and low ARS scores (defined by median split). Results: In validation cohorts, the ARS was predictive of metastasis in cohorts receiving adjuvant ADT (10-year metastasis free survival c-index of 0.69 (95% CI 0.59-0.78) as compared to 0.45 (95% CI 0.29-0.61) in patients not treated with ADT). Similarly in a separate cohort of untreated patients that received no ADT until after metastatic onset, ARS was not prognostic (c-index 0.53). Among ADT treated patients, those with low ARS scores had a 10 year MFS of 87%, versus 70% in those with high ARS scores (p<0.001). In the subset of men who received ADT after metastatic onset and who developed castrate-resistant prostate cancer (CRPC, n = 41), median time to treatment failure was 1 year in patients with high ARS compared to 2 years for those with low ARS scores (p=0.07). Conclusions: A 52-gene ADT resistance signature was developed which showed significant differences in metastasis-free survival among adjuvant hormone treated but not untreated patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".