Germline Variants in Asporin Vary by Race, Modulate the Tumor Microenvironment, and Are Differentially Associated with Metastatic Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Prostate cancers incite tremendous morbidity upon metastatic growth. We previously identified Asporin (ASPN) as a potential mediator of metastatic progression found within the tumor microenvironment. ASPN contains an aspartic acid (D)-repeat domain and germline polymorphisms in D-repeat-length have been associated with degenerative diseases. Associations of germline ASPN D polymorphisms with risk of prostate cancer progression to metastatic disease have not been assessed. EXPERIMENTAL DESIGN: Germline ASPN D-repeat-length was retrospectively analyzed in 1,600 men who underwent radical prostatectomy for clinically localized prostate cancer and in 548 noncancer controls. Multivariable Cox proportional hazards models were used to test the associations of ASPN variations with risk of subsequent oncologic outcomes, including metastasis. Orthotopic xenografts were used to establish allele- and stroma-specific roles for ASPN D variants in metastatic prostate cancer. RESULTS: Variation at the ASPN D locus was differentially associated with poorer oncologic outcomes. ASPN D14 [HR, 1.72; 95% confidence interval (CI), 1.05-2.81, P = 0.032] and heterozygosity for ASPN D13/14 (HR, 1.86; 95% CI, 1.03-3.35, P = 0.040) were significantly associated with metastatic recurrence, while homozygosity for the ASPN D13 variant was significantly associated with a reduced risk of metastatic recurrence (HR, 0.44; 95% CI, 0.21-0.94, P = 0.035) in multivariable analyses. Orthotopic xenografts established biologic roles for ASPN D14 and ASPN D13 variants in metastatic prostate cancer progression that were consistent with patient-based data. CONCLUSIONS: We observed associations between ASPN D variants and oncologic outcomes, including metastasis. Our data suggest that ASPN expressed in the tumor microenvironment is a heritable modulator of metastatic progression.
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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".