RNAseq Analysis of FFPE Radical Prostatectomy Specimens Identifies Predictors of Biochemical Recurrence
Bibliographic record
Abstract
To identify effective predictors of recurrence following radical prostatectomy, RNAseq libraries were prepared from RNA derived from 71 Formalin Fixed Paraffin Embedded (FFPE) prostatectomy samples from two institutions, and high‐throughput sequencing was performed on an Illumina HiSeq2000 instrument. Subjects were divided into high and low risk groups based on the optimal predictive score cutoff from ROC analysis, and we performed log rank test to compare biochemical recurrence (BCR) between the two risk groups. For the 70 samples analyzed, clinical variables alone were highly predictive of recurrence (log‐rank test p=2.55e‐04); however the panel of 11 genes from the RNAseq analysis outperformed clinical variables alone (log rank test p=1.82e‐13). Prediction using this model achieved an ROC AUC of 0.959 with 85% sensitivity and 97% specificity. This set of RNA biomarkers may be useful in the prediction of BCR following radical prostatectomy, irrespective of stage, grade, race, or pre‐operative PSA. Patients with tumors having increased expression of these markers may benefit from close follow‐up after surgery. Research supported by DOD IDEA Award W81XWH‐010–1‐0090.
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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".