Exploring Predictors of Response to Dacomitinib in <i>EGFR</i>-Amplified Recurrent Glioblastoma
Bibliographic record
Abstract
PURPOSE Despite the high frequency of EGFR genetic alterations in glioblastoma (GBM), EGFR-targeted therapies have not had success in this disease. To improve the likelihood of efficacy, we targeted adult patients with recurrent GBM enriched for EGFR gene amplification, which occurs in approximately half of GBM, with dacomitinib, a second-generation, irreversible epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that penetrates the blood-brain barrier, in a multicenter phase II trial. PATIENTS AND METHODS We retrospectively explored whether previously described EGFR extracellular domain (ECD)–sensitizing mutations in the context of EGFR gene amplification could predict response to dacomitinib, and in a predefined subset of patients, we measured post-treatment intratumoral dacomitinib levels to verify tumor penetration. RESULTS We found that dacomitinib effectively penetrates contrast-enhancing GBM tumors. Among all 56 treated patients, 8 (14.3%) had a clinical benefit as defined by a duration of treatment of at least 6 months, of whom 5 (8.9%) remained progression free for at least 1 year. Presence of EGFRvIII or EGFR ECD missense mutation was not associated with clinical benefit. We evaluated the pretreatment transcriptome in circulating extracellular vesicles (EVs) by RNA sequencing in a subset of patients and identified a signature that distinguished patients who had durable benefit versus those with rapid progression. CONCLUSION While dacomitinib was not effective in most patients with EGFR-amplified GBM, a subset experienced a durable, clinically meaningful benefit. Moreover, EGFRvIII and EGFR ECD mutation status in archival tumors did not predict clinical benefit. RNA signatures in circulating EVs may warrant investigation as biomarkers of dacomitinib efficacy in GBM.
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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 | 0.001 | 0.002 |
| 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".