Brief Report: Combination of Osimertinib and Dacomitinib to Mitigate Primary and Acquired Resistance in <i>EGFR</i> -Mutant Lung Adenocarcinomas
Bibliographic record
Abstract
PURPOSE: Primary and acquired resistance to osimertinib remain significant challenges for patients with EGFR-mutant lung cancers. Acquired EGFR alterations such as EGFR T790M or C797S mediate resistance to EGFR tyrosine kinase inhibitors (TKI) and combination therapy with dual EGFR TKIs may prevent or reverse on-target resistance. PATIENTS AND METHODS: We conducted two prospective, phase I/II trials assessing combination osimertinib and dacomitinib to address on-target resistance in the primary and acquired resistance settings. In the initial therapy study, patients received dacomitinib and osimertinib in combination as initial therapy. In the acquired resistance trial, dacomitinib with or without osimertinib was administered to patients with EGFR-mutant lung cancers with disease progression on osimertinib alone and evidence of an acquired EGFR second-site mutation. RESULTS: Cutaneous toxicities occurred in 93% (any grade) of patients and diarrhea in 72% (any grade) with the combination. As initial therapy, the overall response to the combination was 73% [95% confidence interval (CI), 50%-88%]. No acquired secondary alterations in EGFR were observed in any patients at progression. In the acquired resistance setting, the overall response was 14% (95% CI, 1%-58%). CONCLUSIONS: We observed no acquired secondary EGFR alterations with dual inhibition of EGFR as up-front treatment, but this regimen was associated with greater toxicity. The combination was not effective in reversing acquired resistance after development of a second-site acquired EGFR alteration. Our study highlights the need to develop better strategies to address on-target resistance in patients with EGFR-mutant lung cancers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".