Gene expression signatures prognostic for relapse in stage I testicular germ cell tumours
Bibliographic record
Abstract
OBJECTIVES: To identify differentially expressed genes between relapsed and non-relapsed clinical stage I testicular germ cell tumours (TGCTs). MATERIALS AND METHODS: We reviewed patients with clinical stage I non-seminoma and seminoma from an institutional database (2000-2012) who were managed by active surveillance. Patients with non-relapsed non-seminoma and non-relapsed seminoma were defined as being relapse-free after 2 and 3 years of surveillance, respectively. RNA extraction and gene expression analysis was performed on archival primary tumour samples and gene-set enrichment analysis (GSEA) was conducted in order to identify differentiating biological pathways. RESULTS: A total of 57 patients (relapsed non-seminoma, n = 12; relapsed seminoma, n =15; non-relapsed non-seminoma, n = 15; non-relapsed seminoma, n = 15) were identified, with a median (range) relapse time of 5.6 (2.5-18.1) and 19.3 (4.7-65.3) months in the relapsed non-seminoma and relapsed seminoma cohorts, respectively. A total of 1 039 differentially expressed genes were identified that separated relapsed and non-relapsed groups. In patients with relapse, GSEA revealed enrichment in pathways associated with differentiation, such as skeletal development (i.e. FGFR1, BMP4, GLI2, SPARC, COL2A1), tissue (i.e. BMP4, SPARC, COL13A1) and bone remodelling (i.e. CARTPT, GLI2, MGP). A discriminative gene expression profile between relapsed and non-relapsed cases was discovered when combining non-seminoma and seminoma samples using 10- and 30-probe signatures; however, this profile was not observed in the seminoma and non-seminoma cohorts individually. CONCLUSION: A discriminating signature for relapsed disease was identified for clinical stage I TGCT that we were not able to identify by histology alone. Further validation is required to determine if this signature provides independent prognostic information to standard pathological risk factors.
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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".