Clinical outcomes of EGFR kinase domain duplication to targeted therapies in NSCLC
Bibliographic record
Abstract
Kinase domain duplications of the epidermal growth factor receptor (EGFR-KDD) have been identified and implicated to be oncogenic in nonsmall cell lung cancers (NSCLCs). However, its prevalence and clinical contributions in lung cancer are largely unknown. Here, we conducted a multicenter record review of 10,759 NSCLC patients who underwent genetic testing using next-generation sequencing (NGS) targeting EGFR exons and the introns involved in EGFR-KDD rearrangements. EGFR-KDDs were identified in a total of 13 patients, which is approximately 0.12% of the total population reviewed, and also consisted of 0.24% (13/5394) of EGFR mutation-positive patients. A total of 85% of patients (11/13) were identified with the canonical EGFR-KDD duplication of exons 18-25, while the remaining two cases harbored duplications of EGFR exons 14-26 and exons 17-25, which have not been previously described. Importantly, none of the 13 patients had other coexisting driver mutations, highlighting the potential oncogenic role of this type of alteration. Three out of five patients who had exon 18-25 duplications showed partial antitumor responses to targeted therapies, while the other two patients demonstrated no clinical improvement. Furthermore, our data suggested that the EGFR T790 M mutation and EGFR amplification may represent the major resistance mechanisms against targeted therapies in tumors bearing EGFR-KDD. In summary, our findings provide valuable insight into the prevalence of EGFR-KDDs in NSCLCs and their clinical outcomes to targeted therapies.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".