Gene-expression signatures as prognostic for relapse in stage I testicular germ cell tumors (TGCT).
Bibliographic record
Abstract
493 Background: Genomic signatures may compliment pathological features in identifying appropriate patients who may benefit from adjuvant therapy in Stage I (SI) TGCT. This study aimed to identify a gene expression pattern to differentiate between relapsed (R) and non-relapsed (NR) SI TGCT. Methods: Patients with SI non-seminoma (NS) and seminoma (S) were identified from an institutional database from 2000 to 2012. All patients were managed with active surveillance. NR-NS and NR-S patients were defined as having no evidence of relapse after 2 and 3 years of surveillance respectively. Following pathology review, RNA extraction and gene expression analysis was performed on archived paraffin embedded tumor and normal testicular tissue using Illumina Whole Genome DASL Human HT-12 V4 BeadChip. Hierarchical clustering analysis, ANOVA and t-tests were used to evaluate candidate genes and expression patterns that could differentiate NR and R samples. Results: 57 patients (12 R-NS, 15 R-S, 15 NR-NS, 15 NR-S) were identified with median relapse time of 5.6 (2.5-18.1) and 19.3 (4.7-65.3) months in NS and S cohorts respectively. 3 additional normal testis samples were included. Poor prognostic factors were more frequent in R versus NR cases (NS: vascular invasion [5/12 vs 0/15]; S: median size [4cm vs 2.8cm]). Unsupervised hierarchical clustering of 22822 probes randomly separated S from NS, indicating no batch effect. One-way ANOVA revealed 4525 significantly varying probes (p < 0.05) however, no statistically significant gene expression profile differentiated the 4 cohorts. A discriminative gene expression profile between R and NR cases was discovered when combining NS and S samples using 10 (p = 0.03) and 30 (p = 0.03) probe signatures with a 10 fold cross-validation. However, this profile was not observed in the S and NS cohorts individually. Conclusions: A discriminating signature for R and NR was identified for SI testis tumors, but not separately for NS and S. Biological relevance of these signatures is to be determined. Further studies are required to corroborate this profile in NS and S. If validated, these expression patterns could help identify patients beyond standard pathological risk algorithms for optimal management.
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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 | 0.001 | 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".