Expression and mutation analysis of the discoidin domain receptors 1 and 2 in non-small cell lung carcinoma
Bibliographic record
Abstract
The discoidin domain receptors, (DDR)1 and DDR2, have been linked to numerous human cancers. We sought to determine expression levels of DDRs in human lung cancer, investigate prognostic determinates, and determine the prevalence of recently reported mutations in these receptor tyrosine kinases. Tumour samples from 146 non-small cell lung carcinoma (NSCLC) patients were analysed for relative expression of DDR1 and DDR2 using quantitative real-time PCR (qRT-PCR). An additional 23 matched tumour and normal tissues were tested for differential expression of DDR1 and DDR2, and previously reported somatic mutations. Discoidin domain receptor 1 was found to be significantly upregulated by 2.15-fold (P=0.0005) and DDR2 significantly downregulated to an equivalent extent (P=0.0001) in tumour vs normal lung tissue. Discoidin domain receptor 2 expression was not predictive for patient survival; however, DDR1 expression was significantly associated with overall (hazard ratio (HR) 0.43, 95% CI=0.22-0.83, P=0.014) and disease-free survival (HR=0.56, 95% CI=0.33-0.94, P=0.029). Multivariate analysis revealed DDR1 is an independent favourable predictor for prognosis independent of tumour differentiation, stage, histology, and patient age. However, contrary to previous work, we did not observe DDR mutations. We conclude that whereas altered expression of DDRs may contribute to malignant progression of NSCLC, it is unlikely that this results from mutations in the DDR1 and DDR2 genes that we investigated.
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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 | 0.001 | 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".