Transcriptome-wide analysis of matched biopsy and prostatectomy to measure genomic classifiers of prostate cancer progression and field effect.
Bibliographic record
Abstract
85 Background: Active surveillance (AS) allows patients with localized prostate cancer to delay and even avoid treatments that carry risk of significant side-effects. However, biomarkers are needed to more accurately risk-stratify patients and enhance acceptance of AS. Challenges with biomarkers measured from needle biopsy specimen that need to be address include limited material, tumor and specimen heterogeneity and fragmentation of RNA. Methods: A total of 23 patients with matched formalin-fixed paraffin-embedded (FFPE) biopsy and radical prostatectomy (RP) specimens were identified at University of California, San Francisco and Cedar Sinai. For each patient, samples were taken from tumor, non-neoplastic tissue adjacent to tumor (NAT) and non-neoplastic tissue contralateral to tumor (NCT) from both biopsy and RP specimens. RNA expression was measured in 130 samples using the 1.4 million feature Affymetrix Human Exon 1.0 ST arrays. Results: From 1 mm cylindrical cores punched from FFPE blocks, sufficient RNA was extracted for the assay from 63 out of 69 (91%) biopsy specimens and 69 out of 69 (100%) RP specimens. Median RNA yield from biopsies was lower than RP specimens but a similar quantity and quality of cDNA was amplified from the 100 ng RNA required for the assay. RNA from 62/63 (98%) of the biopsy specimens and 68/69 (99%) of the RP specimens generated expression data that passed quality control. Genomic classifiers (GC) predicting metastasis (GC1) and presence of high Gleason grade tumor (GC2) were highly correlated between matched biopsy and RP specimens with R=0.74 (p=0.0003) and R=0.63 (p=0.004), respectively. For GC2, matched tumor and NAT from RP had a higher correlation (R=0.57, p=0.0045) than matched tumor and NCT (R=0.10, p=0.67). There was no relationship between percentage of stromal contamination in the biopsy and the GC scores for either classifier, R=0.02 (p=0.91) and R=-0.16 (p=0.28) respectively. Conclusions: RNA expression levels can be measured in FFPE needle biopsy on a genome-wide scale with similar data quality compared to RP specimens. GC scores from biopsies and RP samples correlate well, are robust to stromal contamination, and are present in adjacent non-neoplastic tissue.
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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 | 0.001 |
| 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".