Database of somatic mutations in EGFR with analyses revealing indel hotspots but no smoking-associated signature
Bibliographic record
Abstract
We created an Epidermal Growth Factor Receptor (EGFR) Mutation Database (http://www.cityofhope.org/cmdl/egfr_db) that curates a convenient compilation of somatic EGFR mutations in non-small-cell lung cancer (NSCLC) and associated epidemiological and methodological data, including response to the tyrosine kinase inhibitors Gefitinib and Erlotinib. Herein, we analyze 809 mutations collected from 26 publications. Four super hotspots account for 70% of reported mutations while two-thirds of 131 unique mutations have been reported only once and account for only 11% of reported mutations. Consistent with strong biological selection for gain of function, the reported mutations are virtually all missense substitutions or in-frame microdeletions, microinsertions, or microindels (colocalized insertion and deletion with a net gain or loss of 1-50 nucleotides). Microdeletions and microindels are common in a region of exon 19. Microindels, which account for 8% of mutations, have smaller inserted sequences (95% are 1 to 5 bp) and are elevated 16-fold relative to mouse somatic microindels and to human germline microindels. Microdeletions/microindels are significantly more frequent in responders to Gefitinib or Erlotinib (P = 0.003). In addition, EGFR mutations in smokers do not carry signatures of mutagens in cigarette smoke. Otherwise, the mutation pattern does not differ significantly with respect to gender, age, or tumor histology. The EGFR Mutation Database is a central resource of EGFR sequence variant data for clinicians, geneticists, and other researchers. Authors are encouraged to submit new publications with EGFR sequence variants to be included in the database or to provide direct submissions via The WayStation submission and publication process (http://www.centralmutations.org).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".