Molecular characterisation of <scp><i>TP53</i></scp> mutated squamous cell carcinomas of the lung to identify putative targets for therapy
Bibliographic record
Abstract
Personalised cancer treatment depends on identification of therapeutically relevant biological subgroups of patients for assessing effect of treatment and to discover new therapeutic options. By analyses in heterogeneous patient populations, the effects may be lost in noise. Squamous cell carcinoma of the lung is a major killer worldwide. Despite recent advances, mortality is high and response to therapies varies greatly from patient to patient. Target search in biologically relevant subgroups may identify treatment options not so far discovered. A total of 198 patients undergoing surgery for squamous cell carcinomas of the lung were included in the study. The tumours were analysed for copy number alterations (n = 152) and gene expression from tumour (n = 188) and normal lung (n = 21), with both data levels present in 140 patients. We studied alterations in tumours harbouring mutations in TP53 and in previously published gene expression subtypes. Genes with consistent alterations in both genomic levels were identified as putative biomarkers. Results were validated in TCGA. The most convincing biomarker in TP53 mutated squamous cell carcinomas of the lung was BIRC5 with amplification in 36% of mutated samples, 5% in wild-type samples and a 17%-fold change of expression between TP53 mutated tumours and normal lung tissue. BIRC5 was significantly altered in the classical and primitive subtypes. We suggest BIRC5 as a putative predictive biomarker and putative druggable target in squamous cell lung carcinomas harbouring TP53 mutation or classified as classical and primitive subtypes.
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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 | 0.001 |
| 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".