Radiogenomic Profiling of Prostate Tumors prior to External Beam Radiotherapy Converges on a Transcriptomic Signature of TGF-β Activity Driving Tumor Recurrence
Bibliographic record
Abstract
PURPOSE: Clinical risk grouping based on PSA, tumor grade, and disease extent guides treatment intensity for localized prostate cancer. However, many patients with intermediate- or high-risk disease treated with external beam radiotherapy (EBRT) and androgen deprivation therapy (ADT) still develop biochemical recurrence (BCR). Early identification of patients at high risk for BCR could enable personalized treatment strategies. EXPERIMENTAL DESIGN: We prospectively enrolled 29 patients with intermediate- or high-risk prostate cancer undergoing EBRT and ADT. Pretreatment biopsies (n = 60) underwent whole-transcriptome microarray and whole-exome sequencing. Patients received multiparametric MRI at baseline and 6 months after treatment, with a median follow-up of 6 years. Gene expression differences between patients with and without BCR were analyzed using pathway tools and validated in external datasets. A novel TGF-β gene signature was derived and tested across multiple cohorts (median follow-up: 5-11 years). RESULTS: TGF-β activity was significantly associated with BCR in the discovery cohort (P = 0.0081) and correlated with PTEN/TP53 alterations (P = 0.0246) and baseline multiparametric MRI tumor volume (P = 0.026). TGF-β activity also predicted metastasis-free survival (P = 0.037) and, in an independent cohort (n = 265), was prognostic for BCR-free (P = 0.05), metastasis-free (P < 0.001), and overall survival (P < 0.001). CONCLUSIONS: TGF-β activity is a dominant feature of intermediate- to unfavorable-risk prostate tumors prone to biochemical failure after EBRT with ADT and may serve as an independent prognostic biomarker beyond existing clinical criteria.
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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 | 0.000 |
| 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) | 0.001 | 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".