ArticleInternational journal of cancer2026
Landscape of Chinese Lung Cancer Patients With Compound Mutations and Acquired Resistance Alterations: A Retrospective Study.
Article in International journal of cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Epidermal growth factor receptor (EGFR) mutations are key oncogenic drivers in non-small cell lung cancer (NSCLC). Rare and compound EGFR mutation-mediated resistance to tyrosine-kinase inhibitors (TKIs) is a major hurdle for NSCLC treatment. Here, we characterized compound EGFR mutations and elucidated mechanisms of acquired resistance to EGFR TKIs in Chinese NSCLC patients. Using next-generation sequencing (NGS) data, the mutation spectrum of 10 lung cancer-related genes was analyzed in 8849 patients. From this cohort, 7081 NSCLC tissue samples were assessed, revealing common EGFR mutations (19-Del, exon 21 p.L858R), rare EGFR mutations, variants of uncertain significance (VUSs), and compound EGFR mutations. Acquired resistance mutations were further evaluated in 77 paired baseline and post-progression samples. Among the 8849 patients, 4320 (48.82%) harbored common EGFR mutations, 973 (11.00%) carried Kirsten rat sarcoma viral oncogene (KRAS) hotspot mutations, 357 (4.03%) had phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) mutations, 246 (2.78%) had Erb-B2 receptor tyrosine kinase 2 (ERBB2) mutations, 102 (1.15%) had B-Raf proto-oncogene, serine/threonine kinase (BRAF) V600E mutations, and 95 (1.07%) had mesenchymal epithelial transition factor (MET) mutations. Compound EGFR mutations were identified in 7.99% of NSCLC patients, comprising 7.50% dual mutations and 0.49% multiple (> 2) mutations. Compared to single EGFR mutations, compound EGFR mutations were more frequently associated with EGFR exon 21 p.L858R and EGFR rare mutations, but not with EGFR 19-Del or VUSs (p < 0.001). Compared to patients with EGFR 19-Del, patients with EGFR exon 21 p.L858R mutation had significantly more rare point mutations in EGFR exon 7, 18, and 21, and fewer EGFR exon 20 p.T790M mutations. Additionally, EGFR exon 20 p.T790M-mediated acquired resistance was the most common mechanism (71.43%), followed by mutations in the RAS/RAF/MEK pathway (18.18%). These resistance-associated mutations frequently co-occurred with tumor protein p53 (TP53) variants (15.58%). Notably, the mean duration of disease progression following EGFR-TKI initiation did not differ significantly between patients with EGFR exon 21 p.L858R mutation and those with EGFR 19-Del. Our study reveals the heterogeneity of compound EGFR mutations, and characterizes the spectrum of acquired resistance mutations to EGFR TKIs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.