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e20752 Background: Next-generation sequencing (NGS) is central to the diagnostic work-up in NSCLC by enabling identification of actionable genomic alterations. We report the first pilot experience of NGS testing in routine practice in a state-funded public hospital in Russia. Methods: We retrospectively analyzed consecutive patients with NSCLC treated at Moscow City Oncology Hospital No. 62 who underwent NGS testing prior to multidisciplinary tumor board discussion between August 2024 and December 2025. Demographic, clinical characteristics and NGS results were collected. Categorical variables were compared using χ² or Fisher’s exact test. Results: The cohort included 466 patients (median age 69 years; 56% male; 60% smokers; 89% adenocarcinoma). Median turnaround time was 11 days (IQR 7–14). NGS results were practice-changing in 25% of cases. The most frequent alterations were EGFR 21.9% (L858R = 46 [40%], Ex19del = 34 [29.5%], G719X = 9 [7.8%]), KRAS 20.6% (G12C 36 [36%], G12V 24 [24%], G12A 12 [12%]) and BRAF 3.0% (V600E 6 [40%], non-V600E 9 [60%]); fusions included ALK (4.1%), ROS1 (2.4%), RET (1.0%) and NTRK (0.24%). All MET alterations were exon 14 skipping mutations (3.2%). Overall, 61.2% of patients had no actionable driver mutations. EGFR mutations were more frequent in females than males (40% vs 7.3%, p < 0.001) and in non-smokers than smokers (47% vs 5.5%, p < 0.001), whereas KRAS mutations predominated in males (26.7% vs 13.2%, p < 0.001) and smokers (27.6% vs 10.1%, p < 0.001). ROS1 (5.3% vs 0.4%, p = 0.001) and MET (5.3% vs 0.8%, p = 0.008) alterations were significantly enriched in non-smokers. Conclusions: Universal targeted multigene NGS was feasible in a state-funded public hospital, with short turnaround time and clinically meaningful impact on treatment decisions. These data support implementation of NGS testing within publicly funded healthcare systems. . Alteration Frequency% p53% Male/Female Smoking No/Yes (p) Stage I–III/IV (p) EGFR 102(21.9) 45.1 19(7.3)/ 81(40) (p<0.001) 79(47.9)/ 14(5.4) (p<0.001) 61(24.5)/ 41(19.0) (p=0.167) KRAS 96(20.6) 45.4 70(26.7)/ 27(13.2) (p<0.001) 17(10.1)/ 71(27.6) (p<0.001) 45(18.1)/ 52(24.1) (p=0.101) ALK 19(4.1) 15.8 9(3.4)/ 10(4.9) (p=0.594) 12(7.1)/ 6(2.3) (p=0.017) 13(5.2)/ 6(2.8) (p=0.191) ERBB2 15(3.6) 46.7 4(1.5)/ 11(5.4) (p=0.020) 11(6.5)/ 3(1.2) (p=0.002) 7(2.8)/ 8(3.7) (p=0.574) PIK3CA 15(3.6) 66.7 7(2.7)/ 8(3.9) (p=0.462) 7(4.1)/ 8(3.1) (p=0.765) 10(4.0)/ 5(2.3) (p=0.309) BRAF 14(3.0) 53.3 11(4.2)/ 4(2.0) (p=0.168) 2(1.2)/ 12(4.7) (p=0.049) 6(2.4)/ 9(4.2) (p=0.277) MET 14(3.0) 28.6 5(1.9)/ 9(4.4) (p=0.119) 9(5.3)/ 2(0.8) (p=0.008) 9(3.6)/ 5(2.3) (p=0.428) ROS1 11(2.4) 45.5 3(1.1)/ 8(3.9) (p=0.067) 9(5.3)/ 1(0.4) (p=0.001) 6(2.4)/ 5(2.3) (p=0.959)
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Stroyakovskiy et al. (2026) studied this question.
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