High frequency of exon 20 <scp> S768I <i>EGFR</i> </scp> mutation detected in malignant pleural effusions: A poor prognosticator of <scp>NSCLC</scp>
Bibliographic record
Abstract
BACKGROUND: Lung cancer is the cause of a fourth of all cancer-related deaths. About a third of all lung adenocarcinoma tumours harbour mutations on exons 18 to 21 of the epidermal growth factor receptor (EGFR) gene. Detection of these mutations allows for targeted therapies in the form of EGFR Tyrosine kinase inhibitors. Recently, "liquid biopsies" have emerged as an alternative to conventional tissue mutation detection. AIM: In this pilot study, we attempted to optimize EGFR mutation detection from malignant pleural effusions (MPEs) as "liquid biopsies" when tissue biopsies were unavailable. Resulting mutations were then to be mapped on the EGFR gene and explored using cBioPortal, a public cancer genomic database. METHODS AND RESULTS: We first attempted a direct sequencing approach and showed that single nucleotide variants (SNVs) were likely to be missed in MPEs. We then switched to and optimized an EGFR mutant-specific quantitative polymerase chain reaction-based assay. This assay was piloted on n = 10 pleural effusion samples (one non-malignant pleural effusion as a negative control). 5/9 (55.55%) samples harboured EGFR mutations with 2/9 (22.22%) being exon 19 deletions and 3/9 (33.33%) the S768I mutation. The frequency of the S768I SNV in our study was significantly higher than that observed in other studies (~0.2%). Utilizing cBioPortal data, we report that patients with S768I have a shorter median survival time (6 months vs 38 months), progression-free survival time (8 months vs 44 months) and lower tumor mutation count compared to patients with other EGFR mutations. CONCLUSIONS: The shorter survival of patients with the S768I SNV predicts aggressive disease and poor prognosis as a result of this mutation. Studies in larger cohorts and/or animal models are necessary to confirm these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".