Biomarkers of Osimertinib Response in Patients with Refractory, EGFR-T790M–positive Non–Small Cell Lung Cancer and Central Nervous System Metastases: The APOLLO Study
Bibliographic record
Abstract
Abstract Purpose: Dynamic biomarker monitoring may inform pathways for treating EGFR-T790M–positive non–small cell lung cancer (NSCLC) and central nervous system (CNS) metastases with osimertinib. This study aimed to determine the efficacy and safety of osimertinib for real-world patients with EGFR-T790M NSCLC and CNS metastases and to explore potential circulating biomarkers of therapeutic response. Patients and Methods: APOLLO (ClinicalTrials.gov registration: NCT02972333) was a prospective, single-arm, open-label trial which ran from January 2017 to April 2019. Eligible patients had confirmed EGFR-T790M–positive NSCLC, prior treatment with an EGFR-tyrosine kinase inhibitor, and CNS metastases. All enrolled patients received oral osimertinib 80 mg once daily until disease progression or intolerable toxicity. Primary outcome was overall progression-free survival (PFSo) and secondary outcomes included objective response rate (ORR) and adverse events (AE). Exploratory biomarker analysis involved collection of plasma and cerebrospinal fluid (CSF) samples for next-generation sequencing and drug penetration analysis. Results: From January to September 2017, 38 patients were enrolled. After a median follow-up of 8.2 months (range, 0.07–15.6), 23 (60.5%) of 38 patients had disease progression or death. Median PFSo was 8.4 months [95% confidence interval (CI), 5.8–10.9]. Overall ORR was 39.4%. Twelve (31.6%) of 38 patients had ≥1 grade 3–4 AE. Median osimertinib CSF penetration rate was 31.7%. Patients with undetectable plasma EGFR mutations at week 6 had improved PFSo compared with those with detectable mutations (not reached vs. 4.5 months; 95% CI, 0.0–1.1; P < 0.05). Conclusions: Osimertinib had potent activity against EGFR-T790M–positive NSCLC with CNS metastases. Dynamic monitoring of plasma EGFR may suffice for predicting clinical responses, mitigating the need for repeat CSF biopsy. See related commentary by Marmarelis and Bauml, p. 6077
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".