Independent and functional validation of a multi-tumour-type proliferation signature
Bibliographic record
Abstract
BACKGROUND: Previously we demonstrated that an mRNA signature reflecting cellular proliferation had strong prognostic value. As clinical applicability of signatures can be controversial, we sought to improve our marker's clinical utility by validating its biological relevance, reproducibility in independent data sets and applicability using an independent technique. METHODS: To facilitate signature evaluation with quantitative PCR (qPCR) a novel computational procedure was used to reduce the number of signature genes without significant information loss. These genes were validated in different human cancer cell lines upon serum starvation and in a 168 xenografts panel. Analyses were then extended to breast cancer and non-small-cell lung cancer (NSCLC) patient cohorts. RESULTS: Expression of the qPCR-based signature was dramatically decreased under starvation conditions and inversely correlated with tumour volume doubling time in xenografts. The signature validated in breast cancer (hazard ratio (HR)=1.63, P<0.001, n=1820) and NSCLC adenocarcinoma (HR=1.64, P<0.001, n=639) microarray data sets. Lastly, qPCR in a node-negative, non-adjuvantly treated breast cancer cohort (n=129) showed that patients assigned to the high-proliferation group had worse disease-free survival (HR=2.25, P<0.05). CONCLUSION: We have developed and validated a qPCR-based proliferation signature. This test might be used in the clinic to select (early-stage) patients for specific treatments that target proliferation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".