First-Line Pyrotinib Combination Therapy for HER2-Mutated Advanced NSCLC: A Retrospective Cohort Analysis
Bibliographic record
Abstract
Background: HER2 mutations are rare driver events in advanced NSCLC, with limited relief from current targeted therapies. This study aimed to characterize the molecular features of HER2-mutant NSCLC and to evaluate the clinical efficacy of pyrotinib-based combination therapy as a first-line treatment, providing evidence for optimizing treatment strategies. Methods: NSCLC patients diagnosed at Jiangsu Province People’s Hospital from 2016 to 2024 were enrolled. HER2-positive cases were screened by IHC/FISH and further profiled by NGS. Treatment response was assessed by RECIST 1.1, and survival analysis was performed using Kaplan–Meier and log-rank tests. Results: Among 144 HER2-mutant NSCLC cases confirmed by NGS, 10 insertion mutations, 26 missense mutations, and 2 fusion mutations were identified. The most common mutation was the exon 20 p.A775_G776insYVMA (47.9%), and TP53 was the most frequent co-mutation (10.4%). In terms of efficacy, the pyrotinib-based combination therapy demonstrated significant clinical benefit, with an ORR of 33.3%, DCR of 95.2%, median PFS (mPFS) of 11.3 months (95% CI: 10.27–12.26), and median OS (mOS) of 21.0 months (95% CI: 18.00–23.94). Subgroup analysis revealed no significant impact of mutation subtype or co-mutation status on the treatment efficacy, but patients with brain metastases had a significantly worse prognosis than those without metastasis (mPFS: 5.1 vs. 12.9 months, p < 0.01; mOS: 9.3 vs. 26.5 months, p < 0.01). All TRAEs were grade 1–3 (any grade: 90.5%; grade 3: 14.3%), with the most common TRAE being diarrhea (any grade: 85.7%; grade 3: 9.5%). Conclusions: Pyrotinib-based combination therapy is a feasible first-line treatment for HER2-mutant NSCLC, demonstrating significant survival benefits and manageable toxicity. However, brain metastasis patients require enhanced comprehensive management.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".