Somatic Mutation Profiling by NGS-Based Liquid Biopsy in Advanced Non- Small Cell Lung Cancer: Frequency, Clinical Correlates, and Prognostic Significance

Somatic Mutation Profiling by NGS-Based Liquid Biopsy in Advanced Non- Small Cell Lung Cancer: Frequency, Clinical Correlates, and Prognostic Significance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Somatic Mutation Profiling by NGS-Based Liquid Biopsy in Advanced Non- Small Cell Lung Cancer: Frequency, Clinical Correlates, and Prognostic Significance Çağrı DOĞAN, Cengiz Akosman, Müge Sönmez, Bahaddin Yılmaz, Güzin Demirağ, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9396662/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background: Liquid biopsy-based next-generation sequencing (NGS) enables non-invasive, comprehensive mutational profiling in advanced non-small cell lung cancer (NSCLC). However, data on multi-gene cfDNA panels in Turkish populations remain limited. Methods: One hundred consecutive stage IV NSCLC patients (42 newly diagnosed, 58 with progression/recurrence) who underwent cfDNA-based NGS analysis between January and November 2019 were retrospectively analyzed. Somatic mutations across 19 genes were assessed using the GeneRead QIAact Lung DNA UMI Panel (Qiagen) on the GeneReader platform. Results: Pathogenic mutations were detected in 25 patients (25%), yielding 34 PMs across seven genes. EGFR was most frequently mutated (52.9%), followed by KRAS (20.5%) and PIK3CA (14.5%). Multimetastatic patients had significantly higher cfDNA levels (10.0 ± 3.9 vs. 3.8 ± 1.0 ng; p = 0.01) and shorter OS (17.2 ± 5.5 vs. 49.5 ± 5.7 months; p < 0.001). Among EGFR wild-type patients with progression, resistance mutation carriers had markedly shorter OS (16.5 ± 0.5 vs. 48.7 ± 6.6 months; p = 0.001). KRAS G12C was the predominant KRAS variant (71%). Conclusions: NGS-based liquid biopsy effectively identifies actionable and resistance-associated mutations in advanced NSCLC. cfDNA concentration correlates with metastatic burden. The high prevalence of KRAS G12C and EGFR T790M underscores the clinical utility of multi-gene cfDNA panels for guiding personalized treatment. liquid biopsy NSCLC cfDNA NGS EGFR KRAS T790M Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. INTRODUCTION Lung cancer remains the leading cause of cancer-related mortality worldwide, with approximately 2.1 million new diagnoses and 1.8 million deaths reported annually [ 1 ]. More than half of patients are diagnosed between ages 55 and 74, and the overall 5-year survival rate remains approximately 19% [ 2 , 3 ]. Cigarette smoking is the most prominent etiological factor, with approximately 85% of patients having a history of smoking [ 4 ]. Non-small cell lung cancer (NSCLC) accounts for 80–85% of all cases and has become a paradigm for precision oncology [ 5 ]. Numerous actionable driver mutations have been identified, including EGFR mutations (15–40%), KRAS mutations (15–25%), and alterations in PIK3CA, BRAF, NRAS, ALK, ROS1, and MET [ 5 , 6 , 7 ]. Targeted therapies—including EGFR tyrosine kinase inhibitors (TKIs), crizotinib for ALK/ROS1 rearrangements, and dabrafenib for BRAF V600E mutations—have substantially improved progression-free survival in molecularly defined subgroups [ 5 ]. However, acquired resistance inevitably emerges [ 8 ]. Tissue biopsy remains the diagnostic standard but is limited by procedural invasiveness, tumor inaccessibility, and inability to capture intratumoral heterogeneity [ 9 , 10 ]. Liquid biopsy, based on circulating cell-free DNA (cfDNA) analysis, has emerged as a minimally invasive alternative enabling real-time monitoring of somatic mutations and resistance [ 10 ]. Next-generation sequencing (NGS) platforms offer simultaneous multi-gene profiling from cfDNA with molecular barcoding for accurate low-frequency variant detection [ 11 ]. In this study, we aimed to determine the frequency and distribution of somatic mutations detected by NGS-based liquid biopsy across 19 cancer-related genes in stage IV NSCLC patients, and to evaluate their associations with metastatic burden, cfDNA levels, treatment resistance, and overall survival. 2. MATERIALS AND METHODS 2.1. Study Design and Patient Selection This retrospective observational study included 100 consecutive stage IV NSCLC patients between January and November 2019 at the Department of Medical Genetics, Ondokuz Mayıs University, Samsun, Turkey. The cohort comprised 42 newly diagnosed and 58 progressive/recurrent patients from the Central and Eastern Black Sea regions. Patients were classified by metastatic burden: no distant metastasis (n = 18), oligometastatic (1–3 distant sites; n = 71), and multimetastatic (≥ 3 sites; n = 11) [ 12 ]. 2.2. cfDNA Isolation and Quantification Peripheral blood (30 mL) was collected into two PAXgene Blood ccfDNA tubes (PreAnalytiX GmbH). cfDNA was isolated using the QIAamp Circulating Nucleic Acid Kit with QIAvac 24 Plus (Qiagen) and quantified by Qubit dsDNA HS Assay on a Qubit 3.0 Fluorometer (Thermo Fisher Scientific). 2.3. NGS Library Preparation and Sequencing Samples with cfDNA ≥ 2 ng/µL were processed using the GeneRead QIAact Lung DNA UMI Panel (Qiagen), covering hotspot regions of 19 genes (AKT1, ALK, BRAF, DDR2, EGFR, ERBB2, ESR1, FGFR1, KIT, KRAS, MAP2K1, MET, NRAS, NTRK1, PDGFRA, PIK3CA, PTEN, RICTOR, ROS1). Samples below threshold were processed with the GeneRead QIAact Actionable Insights Tumor Panel. Library preparation included UMI-tagged adapter ligation and target enrichment. Sequencing was performed on the GeneReader platform (Qiagen). 2.4. Bioinformatic Analysis Quality parameters (Q-score ≥ 25, coverage ≥ 500×) were assessed using QCI Analyze. Variants with allele fraction ≥ 1% were retained [ 13 , 14 ]. Pathogenicity was determined using QCI Analyze, COSMIC, ClinVar, ACMG/AMP guidelines, and in silico tools (MutationTaster, PROVEAN, VarSome, SIFT). 2.5. Statistical Analysis Analyses were performed using SPSS 15.0. Kruskal–Wallis test with Bonferroni-corrected Mann–Whitney U (p ≤ 0.016), Kaplan–Meier survival analysis with log-rank test (p ≤ 0.05). 2.6. Ethics he study protocol was approved by the Clinical Research Ethics Committee of Ondokuz Mayıs University (Approval No.: B.30.2.ODM.0.20.08/861; Date: 14 November 2019). The research was carried out in accordance with the guidelines of the Clinical Research Ethics Committee of Ondokuz Mayıs University and the principles of the Declaration of Helsinki (2013 revision). The present study is a retrospective analysis of de-identified clinical and molecular data obtained from 100 consecutive patients who had been referred to our center for clinical liquid biopsy (cfDNA next-generation sequencing) testing as part of their routine oncologic workup; clinical information was retrieved from the Nukleus Hospital Information System and from the archive of the Department of Medical Genetics of Ondokuz Mayıs University. In view of this retrospective design and the exclusive use of de-identified data, the ethics committee formally waived the requirement for additional written informed consent for research use of the data. 3. RESULTS 3.1. Patient Characteristics The study included 100 stage IV NSCLC patients. Demographics and clinical characteristics are summarized in Table 1 . Table 1 Baseline Demographics and Clinical Characteristics (N = 100) Characteristic n (%) Sex Male 66 (66.0) Female 34 (34.0) Age, years Mean ± SD 63.7 ± 9.7 Median (range) 65 (37–86) Histopathology Adenocarcinoma 80 (80.0) Squamous cell carcinoma 15 (15.0) NSCLC-NOS 5 (5.0) Stage IV 100 (100) Clinical status Newly diagnosed 42 (42.0) Progressive/recurrent 58 (58.0) Smoking status (n = 66) Never-smoker 29 (43.9) Smoker (mean 44 pack-years) 37 (56.1) NSCLC-NOS, non-small cell lung cancer not otherwise specified. 3.2. Metastatic Burden, cfDNA Levels, and Overall Survival At enrollment, 82 patients had distant metastases. The distribution of metastatic sites is shown in Table 2 . cfDNA levels and OS data stratified by metastatic burden are presented in Table 3 . Multimetastatic patients had significantly higher cfDNA levels (10.0 ± 3.9 vs. 3.8 ± 1.0 ng; p = 0.01) and shorter OS (17.2 ± 5.5 vs. 49.5 ± 5.7 months; p < 0.001). Table 2 Distribution of Distant Metastatic Sites at Enrollment Metastatic site n % Bone 34 34.0 Contralateral lung 27 27.0 Adrenal glands 24 24.0 Brain 16 16.0 Pleura 15 15.0 Liver 11 11.0 Pericardium 1 1.0 No distant metastasis 18 18.0 Some patients had metastases at multiple sites simultaneously. Table 3 cfDNA Concentration and Overall Survival Stratified by Metastatic Burden Metastatic group n cfDNA (ng) mean ± SD OS (months) mean ± SD Status No distant metastasis 18 3.8 ± 1.0 — All alive Oligometastatic (1–3 sites) 71 4.4 ± 0.4 49.5 ± 5.7 — Multimetastatic (≥ 3 sites) 11 10.0 ± 3.9 17.2 ± 5.5 — p-value p = 0.01ᵃ p < 0.001ᵇ ᵃ Multimetastatic vs. no distant metastasis (Mann–Whitney U test). ᵇ Kruskal–Wallis test across all groups. OS, overall survival. 3.3. Somatic Mutation Landscape Pathogenic mutations were detected in 25/100 patients (25%), yielding 34 PMs across seven genes (Table 4 , Fig. 3 ). Mean VAF was 8.3% (range 1–71%). Of 34 PMs, 23 (68%) were SNVs, 5 (15%) deletions, 1 duplication, 2 insertions, and 3 (9%) indels. Among 25 mutation-positive patients, 17 (68%) carried a single PM and 8 (32%) carried multiple PMs. Table 4 Distribution of 34 Pathogenic Mutations by Gene Gene Patients n (%) Mutations n (%) Most common variant(s) EGFR 12 (12) 18 (52.9) Exon 19 del (33%), T790M (22%), L858R (17%) KRAS 7 (7) 7 (20.6) G12C (71%), G13C (14%), G60D (14%) PIK3CA 4 (4) 5 (14.7) S553fs*7 (40%), E545A, E542K, P381fs*11 BRAF 1 (1) 1 (2.9) G469A KIT 1 (1) 1 (2.9) V559F NRAS 1 (1) 1 (2.9) Q61H RAF1 1 (1) 1 (2.9) S257L Total 25 (25) 34 (100) Percentages in the "Mutations" column refer to proportion of total 34 PMs. Gene names in italics per HUGO nomenclature. 3.4. EGFR Mutations Twelve patients (12%) harbored 18 EGFR PMs across exons 19, 20, and 21 (Fig. 4 ). Exon 19 deletions were most frequent (33%), followed by T790M (22%) and L858R (17%). All four T790M-positive patients had progressed on erlotinib (mean 9 months) and were alive at analysis (mean OS 18 ± 6 months). Table 5 Clinical Characteristics of Patients with EGFR T790M/T790A Resistance Mutations Pt Resistance mutation Sensitizing mutation Sex Age cfDNA (ng) TKI duration (months) TKI agent Metastatic sites 22 T790M L858R M 65 8.0 6 Erlotinib Lung, Bone 88 T790M L858R F 52 2.0 12 Erlotinib Lung 95 T790M Ex19 del M 67 4.75 9 Erlotinib Adrenal, Bone 98 T790M L858R F 63 2.5 9 Erlotinib Liver, Bone 97 T790A L858R F 73 1.6 24 Erlotinib None All patients had adenocarcinoma histology. Mean TKI duration: 9 months (excluding patient 97 on TKI for 24 months). M, male; F, female. 3.5. KRAS and Other Gene Mutations Seven patients (7%) harbored KRAS PMs—G12C predominated (5/7, 71%). PIK3CA mutations were found in 4 patients (5 PMs). Single PMs were detected in BRAF (G469A), KIT (V559F), NRAS (Q61H), and RAF1 (S257L). 3.6. Progressive/Recurrent Disease and Resistance Among 58 progressive/recurrent patients, 13 (22%) had PMs on liquid biopsy. Ten (77%) harbored resistance mutations (Fig. 5 ). Among wtEGFR patients, resistance mutation carriers had significantly shorter OS (16.5 ± 0.5 vs. 48.7 ± 6.6 months; p = 0.001). Table 6 Overall Survival in EGFR Wild-Type Progressive/Recurrent Patients by Resistance Mutation Status Subgroup n OS (months) mean ± SD p-value Resistance mutation positive 4 16.5 ± 0.5 EGFR A767_V769dup 1 PIK3CA P381fs*11 1 NRAS Q61H 1 KRAS G12C 1 Resistance mutation negative 25 48.7 ± 6.6 p = 0.001 Log-rank test. OS, overall survival. All patients were on conventional chemotherapy prior to liquid biopsy. 3.7. Smoking Status Among 29 never-smokers, EGFR mutations predominated (92%). Among 37 smokers, PIK3CA (43%) and KRAS (43%) predominated (Fig. 6 ). 4. DISCUSSION This study evaluated the clinical utility of NGS-based liquid biopsy using a 19-gene cfDNA in 100 consecutive stage IV NSCLC patients from Turkey’s Central and Eastern Black Sea region. The overall pathogenic mutation (PM) detection rate was 25%, which is notably lower than rates reported in several comparable studies. Büyükşimşek et al. reported a 42% detection rate using a similar cfDNA-NGS approach in a Turkish cohort [ 15 ], while Ottestad et al. identified somatic mutations in 52% of advanced NSCLC patients in Norway [ 16 ]. Sabari et al. documented a 45.7% mutation detection rate US cohort [ 17 ], and a recently Yang et al. reported detection rates exceeding 60% using a comprehensive 168-gene panel [ 18 ]. More recently, Doval et al. reported a 65% detection rate in the first Indian liquid biopsy experience [ 28 ]. These discrepancies likely reflect methodological differences: our stringent 1% minimum allele frequency (MAF) threshold, the narrower 19-gene panel scope, and the high proportion of patients (58%) on active systemic therapy at the time of sampling—a factor known to suppress ctDNA shedding through treatment-induced tumor cell depletion [ 29 ]. A 2025 systematic review by Jahani et al. confirmed that NGS provides the most comprehensive mutational profiling among cfDNA technologies but emphasized that detection sensitivity is highly dependent on panel breadth, sequencing depth, and bioinformatic filtering thresholds [ 19 ]. EGFR mutations were detected in 12% of our cohort, consistent with the reported 10–17% frequency in Turkish tissue-based studies. Calibasi-Kocal et al. reported an overall EGFR mutation frequency of 16.6% in 409 Turkish NSCLC patients [ 30 ], while a more recent single-center analysis by Aksakal et al. found EGFR mutations in 11.5% of 182 Turkish NSCLC cases [ 31 ]. These rates align with European NSCLC populations (7–20%) rather than Asian populations (40–60%), a pattern consistent with ethnicity-based variation extensively documented by Midha et al. in a global EGFR mutation map [ 20 ]. Our cfDNA-based detection rate of 12% is slightly lower than tissue-based Turkish studies, which is expected given the known sensitivity gap between plasma and tissue genotyping. A meta-analysis by Yu et al. demonstrated concordance rates of 60–80% between cfDNA and tissue EGFR testing, with sensitivity losses primarily attributable to low tumor shedding and subclonal heterogeneity [ 32 ]. Nevertheless, the 2024 NCCN guidelines (v5.2024) have elevated plasma cfDNA testing from a salvage option to an equivalent alternative for initial genotyping, reflecting accumulating evidence that plasma NGS identifies clinically actionable targets with sufficient reliability [ 22 ]. Detection of the EGFR T790M resistance mutation in four patients who had progressed on erlotinib (mean TKI duration 9 months) underscores the value of liquid biopsy for non-invasive resistance monitoring. T790M emerges as the dominant acquired resistance mechanism in approximately 50–60% of patients treated with first- or second-generation EGFR TKIs [ 8 , 33 ]. All T790M-positive patients in our cohort were alive at the time of analysis (mean OS 18 ± 6 months), reflecting the availability of effective subsequent therapy with osimertinib, a third-generation EGFR TKI. The FLAURA trial demonstrated that first-line osimertinib achieves a median OS of 38.6 months versus 31.8 months with earlier-generation TKIs (HR 0.80; p = 0.046) [ 21 ], while the AURA3 trial established osimertinib as standard second-line therapy in T790M-positive NSCLC with a median PFS of 10.1 months versus 4.4 months for platinum–pemetrexed [ 34 ]. Moreover, the FLAURA2 trial recently demonstrated that the addition of chemotherapy to first-line osimertinib further extended median PFS by 8.8 months compared with osimertinib monotherapy (HR 0.62; p < 0.0001) [ 35 ]. These evolving data reinforce the clinical imperative of cfDNA-based T790M surveillance to identify patients eligible for osimertinib and to guide sequential treatment strategies. KRAS mutations were detected in 7% of patients, with G12C constituting the predominant variant (5/7, 71%). This allelic distribution is consistent with large-scale genomic profiling studies reporting that G12C accounts for approximately 40% of all KRAS mutations in lung adenocarcinoma, making it the most prevalent KRAS subtype in Western populations [ 36 ]. The predominance of G12C in our cohort aligns with the known association between this specific transversion mutation and tobacco carcinogen exposure, as the majority of our KRAS-mutant patients were current or former smokers. Notably, real-world prevalence data indicate significant geographic variation in KRAS G12C frequency, ranging from 8.9–19.5% of NSCLC cases in the United States and 9.3–18.4% in Europe to 1.4–4.3% in Asia [ 37 ]. Our finding of 5% KRAS G12C prevalence in a Turkish Black Sea cohort provides novel population-specific data that contributes to the growing global map of KRAS G12C distribution. With the recent regulatory approval of selective KRAS G12C inhibitors, cfDNA-based identification of this variant has become directly actionable, and all seven KRAS-mutant patients in our cohort — including the five harboring G12C — represent candidates who could benefit from targeted therapy or clinical trial enrollment [ 37 ]. These findings underscore the importance of extending liquid biopsy panels beyond EGFR to capture the full spectrum of therapeutically relevant alterations. PIK3CA mutations were identified in four patients (5 PMs), making it the third most frequently mutated gene in our cohort. PIK3CA alterations were predominantly found in smokers (43% of mutations in the smoking subgroup), consistent with prior reports associating PIK3CA mutations with tobacco exposure and squamous histology in NSCLC [ 38 ]. The frameshift variant S553fs*7, detected in two patients, and the well-characterized hotspot mutations E545A and E542K represent genomic alterations with potential implications for treatment stratification, as PIK3CA mutations have been associated with resistance to EGFR-TKI therapy and may modulate sensitivity to immune checkpoint inhibitors [ 26 ]. Single pathogenic mutations in BRAF (G469A), KIT (V559F), NRAS (Q61H), and RAF1 (S257L) further illustrate the genomic heterogeneity captured by multi-gene cfDNA panels. Although individually rare, these variants collectively accounted for 11.8% (4/34) of all detected pathogenic mutations and may have clinical implications for treatment selection, including eligibility for basket trial enrollment. Large-scale cfDNA profiling studies have similarly reported a long tail of low-frequency but potentially actionable mutations beyond the canonical EGFR and KRAS drivers, reinforcing the value of broad panel-based approaches over single-gene assays in advanced NSCLC [ 18 ]. Taken together, these findings demonstrate that liquid biopsy captures a clinically meaningful mutation spectrum that extends well beyond EGFR, enabling comprehensive molecular characterization from a single blood draw. Total cfDNA concentration showed a significant positive correlation with metastatic burden in our cohort: multimetastatic patients (≥ 3 sites) had substantially higher cfDNA levels (10.0 ± 3.9 ng) compared with oligometastatic (4.4 ± 0.4 ng) and non-metastatic (3.8 ± 1.0 ng) groups (p = 0.01). This finding is consistent with the landmark observations of Newman et al., who demonstrated that ctDNA levels correlated with tumor volume in NSCLC using CAPP-Seq technology [ 23 ], and with Zhu et al., who reported that elevated cfDNA independently predicted shorter OS in advanced NSCLC [ 24 ]. More recently, a 2021 study by Li et al. confirmed that baseline cfDNA positively correlates with tumor burden and that cfDNA kinetics (the ratio of post-treatment to baseline cfDNA) can predict treatment response with high accuracy [ 39 ]. A 2025 prospective real-world study further validated ctDNA tumor fraction ≥ 1% as a robust prognostic biomarker: patients above this threshold had a median OS of 17.6 months compared with not reached for those below [ 40 ]. Taken together, these data support the dual utility of cfDNA as both a qualitative tool for mutation detection and a quantitative biomarker for disease burden stratification and prognostic assessment. Among 58 progressive/recurrent patients, 13 (22%) harbored pathogenic mutations on liquid biopsy, and resistance mutation carriers among wtEGFR patients had markedly shorter OS (16.5 ± 0.5 vs. 48.7 ± 6.6 months; p = 0.001). This prognostic dichotomy is consistent with prior studies demonstrating the adverse impact of acquired resistance mutations on survival. Zhao et al. reported that concurrent KRAS and PIK3CA mutations confer significantly worse OS in advanced NSCLC [ 25 ], while Ludovini et al. showed that multiple co-occurring oncogenic drivers in cfDNA are independently associated with poor clinical outcomes [ 26 ]. The detection of resistance-associated mutations (EGFR A767_V769dup, PIK3CA P381fs*11, NRAS Q61H, and KRAS G12C) in four progressive wtEGFR patients—all of whom had substantially shorter survival—highlights the clinical importance of liquid biopsy in identifying resistance mechanisms that would be missed without molecular profiling. The 2025 LIBRA study extended this concept further by demonstrating that ctDNA-based molecular progression precedes radiographic progression by a median of 4.9 weeks, suggesting a potential window for preemptive treatment modification [ 27 ]. Longitudinal ctDNA monitoring studies, including a 2023 machine-learning-based model from the IMpower150 trial, have confirmed that ctDNA dynamics robustly predict survival independently of radiographic response, supporting the integration of serial cfDNA assessment into routine clinical practice [ 41 ]. The mutation landscape in our cohort was strongly influenced by smoking status: EGFR mutations predominated in never-smokers (92% of mutations in 29 never-smokers), while PIK3CA (43%) and KRAS (43%) mutations predominated among 37 current/former smokers. This dichotomy replicates the well-established molecular epidemiology of NSCLC, where EGFR mutations are enriched in never-smokers, females, and adenocarcinoma histology, while KRAS mutations are strongly associated with tobacco carcinogenesis [6,20,47]. The concentration of EGFR mutations in never-smokers within our Black Sea cohort is clinically important because it suggests that patients in this demographic subgroup should be prioritized for reflex liquid biopsy testing at diagnosis, even when tissue availability is limited. Furthermore, the mutual exclusivity of EGFR and KRAS mutations observed in our cohort is consistent with the established paradigm of distinct oncogenic driver pathways in NSCLC [ 43 ]. Our study has several notable strengths. First, it represents one of the few liquid biopsy studies from Turkey’s Black Sea region, providing population-specific mutation frequency data for an underrepresented geographic area. Second, the consecutive enrollment of 100 patients with comprehensive clinical annotation—including metastatic burden stratification, smoking status, and long-term survival follow-up—enhances the clinical relevance of our findings. Third, the use of a UMI (unique molecular identifier)-based NGS panel with molecular barcoding minimizes sequencing artifacts and provides reliable low-frequency variant detection, addressing one of the key technical limitations of earlier cfDNA studies [ 11 , 19 ]. Finally, our cohort included both newly diagnosed (42%) and progressive/recurrent (58%) patients, enabling evaluation of liquid biopsy utility across different clinical scenarios. Several limitations should be acknowledged. First, the absence of concurrent tissue genotyping precluded assessment of plasma–tissue concordance, which is recognized as a critical quality metric for liquid biopsy validation studies [ 9 , 32 ]. Second, the modest cohort size (n = 100) limits the statistical power for subgroup analyses, particularly for rare mutations such as BRAF, KIT, and NRAS. Third, the 19-gene panel does not assess gene amplifications (MET, HER2), gene fusions (ALK, ROS1, RET, NTRK), or copy number alterations—all of which are therapeutically relevant in NSCLC and recommended for routine testing by current NCCN guidelines [ 22 ]. Fourth, the 1% MAF threshold, while reducing false positives, may have caused underdetection of subclonal mutations present at very low allele frequencies, particularly in patients receiving systemic therapy. Fifth, the potential confounding effect of clonal hematopoiesis of indeterminate potential (CHIP) was not systematically evaluated through matched white blood cell sequencing, which could lead to false-positive somatic mutation calls, especially for variants in genes such as PIK3CA [ 44 ]. Future studies incorporating larger cohorts, concurrent tissue and plasma genotyping, broader gene panels including fusion detection, serial longitudinal sampling, and CHIP filtering through paired leukocyte sequencing will be necessary to fully characterize the clinical utility of cfDNA-based precision oncology in this population. In conclusion, NGS-based liquid biopsy using a 19-gene cfDNA panel effectively identified actionable somatic mutations in 25% of stage IV NSCLC patients from Turkey’s Black Sea region, with EGFR (12%), KRAS (7%), and PIK3CA (4%) as the most frequently mutated genes. cfDNA concentration correlated with metastatic burden, and resistance mutation status independently predicted shorter survival in progressive patients. The high prevalence of KRAS G12C (71% of KRAS mutations) and EGFR T790M (22% of EGFR mutations) underscores the clinical utility of multi-gene cfDNA panels for identifying patients eligible for targeted therapies including osimertinib, sotorasib, and adagrasib. These findings support the integration of liquid biopsy into routine clinical practice for NSCLC patients in Turkey, complementing tissue-based molecular testing to enable timely, personalized therapeutic decision-making. Declarations Author Contribution Ç.D. and Ü.A. contributed to study conceptualization, patient data collection, NGS data analysis, genetic data interpretation, clinical–genomic correlation, and drafting and finalizing the manuscript. B.Y., G.D., C.A., and M.S. contributed to oncological diagnosis and patient evaluation, collection of oncological data, comparison of oncological findings with genetic analysis results, and critical review of the manuscript draft. 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Detection of cell-free tumor DNA in blood and urine of patients with non-small cell lung cancer. Balkan J Med Genet. 2019;22(2):17-24. doi:10.2478/bjmg-2019-0026 Ottestad AL, Wahl SGF, Gronberg BH, et al. The relevance of tumor mutation profiling in interpretation of NGS data from cell-free DNA in non-small cell lung cancer patients. Exp Mol Pathol. 2020;112:104347. doi:10.1016/j.yexmp.2019.104347 Sabari JK, Offin M, Stephens D, et al. A prospective study of circulating tumor DNA to guide matched targeted therapy in lung cancers. J Natl Cancer Inst. 2019;111(6):575-583. doi:10.1093/jnci/djy156 Yang X, Gao S, Ju R, et al. Implementing liquid biopsy NGS in stage III/IV NSCLC: clinical utility assessment from a real-world Chinese cohort. BMC Cancer. 2025;25:1192. doi:10.1186/s12885-025-15227-0 Jahani MM, Azadmehr A, Sedighi S, Pournajaf S, Amiri M. Efficacy of liquid biopsy for genetic mutations determination in non-small cell lung cancer: a systematic review. 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Lung Cancer. 2017;109:124-127. doi:10.1016/j.lungcan.2017.05.007 Zhao J, Han Y, Li J, Chai R, Bai C. Prognostic value of KRAS/TP53/PIK3CA in non-small cell lung cancer. Oncol Lett. 2019;17(3):3233-3240. doi:10.3892/ol.2019.10012 Ludovini V, Bianconi F, Pistola L, et al. Phosphoinositide-3-kinase catalytic alpha and KRAS mutations are important predictors of resistance to therapy with epidermal growth factor receptor tyrosine kinase inhibitors in patients with advanced non-small cell lung cancer. J Thorac Oncol. 2011;6(4):707-715. doi:10.1097/JTO.0b013e31820a3a6b Cancers. 2025;17(21):3474. LIBRA study, ctDNA resistance tracking in NSCLC. Doval DC, Rauthan A, Sarin A, et al. Next-generation sequencing-based liquid biopsy in Indian NSCLC patients with insufficient tissue: the first experience with Guardant360. Eur Soc Med. 2025. doi:10.5281/zenodo.14345 Lam VK, Zhang J, Wu CC, et al. Genotype-specific differences in circulating tumor DNA levels in advanced NSCLC. J Thorac Oncol. 2021;16(4):601-609. doi:10.1016/j.jtho.2020.12.011 Calibasi-Kocal G, Amirfallah A, Sever T, et al. EGFR mutation status in a series of Turkish non-small cell lung cancer patients. Biomed Rep. 2020;13(2):2. doi:10.3892/br.2020.1308 Aksakal O, Yilmaz E, Bodur S, et al. The epidermal growth factor, anaplastic lymphoma kinase, and ROS proto-oncogene 1 mutation profile of non-small cell lung carcinomas in the Turkish population. Turk J Med Sci. 2024;54(3):612-621. Yu C, Han Y, Wang M, Hua P, Zhang Y, Wang B. Concordance of ctDNA and tissue mutations in NSCLC: a meta-analysis. Cell Mol Biol (Noisy-le-grand). 2023;69:89-95. Soria JC, Ohe Y, Vansteenkiste J, et al. Osimertinib in untreated EGFR-mutated advanced non-small-cell lung cancer. N Engl J Med. 2018;378(2):113-125. doi:10.1056/NEJMoa1713137 Mok TS, Wu YL, Ahn MJ, et al. Osimertinib or platinum-pemetrexed in EGFR T790M-positive lung cancer. N Engl J Med. 2017;376(7):629-640. doi:10.1056/NEJMoa1612674 Planchard D, Janne PA, Cheng Y, et al. Osimertinib with or without chemotherapy in EGFR-mutated advanced NSCLC. N Engl J Med. 2023;389(21):1935-1948. doi:10.1056/NEJMoa2306434 Prior IA, Hood FE, Hartley JL. The frequency of Ras mutations in cancer. Cancer Res. 2020;80(14):2969-2974. doi:10.1158/0008-5472.CAN-19-3682 Lim TKH, Skoulidis F, Kerr KM, et al. KRAS G12C in advanced NSCLC: prevalence, co-mutations, and testing. Lung Cancer. 2023;184:107293. doi:10.1016/j.lungcan.2023.107293 Scheffler M, Bos M, Gardizi M, et al. PIK3CA mutations in non-small cell lung cancer (NSCLC): genetic heterogeneity, prognostic impact and incidence of prior malignancies. Oncotarget. 2015;6(2):1315-1326. doi:10.18632/oncotarget.2834 Li Y, Zhang Y, Li W, et al. Kinetics of plasma cfDNA predicts clinical response in non-small cell lung cancer patients. Sci Rep. 2021;11(1):7633. doi:10.1038/s41598-021-85797-z Passaro A, Sabari JK, Garon EB, et al. Role of circulating tumor DNA tumor fraction in advanced non-small cell lung cancer and its impact on patient treatment outcomes: a prospective real-world study. JCO Precis Oncol. 2025;9:e2500376. Assaf ZJF, Zou W, Fine AD, et al. A longitudinal circulating tumor DNA-based model associated with survival in metastatic non-small-cell lung cancer. Nat Med. 2023;29(4):859-868. doi:10.1038/s41591-023-02226-6 Komurcuoglu B, Karakurt G, Kaya OO, et al. Investigation of EGFR and ALK mutation frequency and treatment results in advanced non-small cell lung cancer. J Cancer Res Ther. 2023;19(Suppl):S183-S190. doi:10.4103/jcrt.JCRT_1766_20 Gainor JF, Varghese AM, Ou SH, et al. ALK rearrangements are mutually exclusive with mutations in EGFR or KRAS: an analysis of 1,683 patients with non-small cell lung cancer. Clin Cancer Res. 2013;19(15):4273-4281. doi:10.1158/1078-0432.CCR-13-0318 Hu Y, Ulrich BC, Supplee J, et al. False-positive plasma genotyping due to clonal hematopoiesis. Clin Cancer Res. 2018;24(18):4437-4443. doi:10.1158/1078-0432.CCR-18-0143 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Apr, 2026 Editor assigned by journal 17 Apr, 2026 Submission checks completed at journal 17 Apr, 2026 First submitted to journal 12 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9396662","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625778578,"identity":"fdf9502c-eaba-4de2-956c-82a2c15ea729","order_by":0,"name":"Çağrı DOĞAN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYFCCBCjNzHwASErIkKKFDcSS4CFBCwOPAZgkqEG3Pf3i44IaO7vt7TyfX92oseBhYD98dAM+LWZn3hQbzziWnDznMO8265xjQIfxpKXdwKvlRk6aNG8Dc7IEM+824xw2oBYJHjNCWtJ/8zbUA7XwPDPO+UeUlvRjzLwNh+2AWpgf57YRo+XMG2ZpnmPHEySY2cyYc/skeNgI+uV4+sPPPDXV9hL8hx9/zvlWJ8fPfvgYXi2w6EhsYGBgkwCx2PArBwH2ByDSHoiZPxBWPQpGwSgYBSMRAACHcEOxp/4jygAAAABJRU5ErkJggg==","orcid":"","institution":"Ordu University","correspondingAuthor":true,"prefix":"","firstName":"Çağrı","middleName":"","lastName":"DOĞAN","suffix":""},{"id":625778579,"identity":"1d1b90e8-9ccf-49ac-9083-2f8482e68669","order_by":1,"name":"Cengiz Akosman","email":"","orcid":"","institution":"Istinye University","correspondingAuthor":false,"prefix":"","firstName":"Cengiz","middleName":"","lastName":"Akosman","suffix":""},{"id":625778580,"identity":"3fbcf7cb-0758-4c2a-9a56-a8abd63b693a","order_by":2,"name":"Müge Sönmez","email":"","orcid":"","institution":"Ordu University","correspondingAuthor":false,"prefix":"","firstName":"Müge","middleName":"","lastName":"Sönmez","suffix":""},{"id":625778581,"identity":"29f32dda-189e-4ec3-9f9d-d69a590e94e9","order_by":3,"name":"Bahaddin Yılmaz","email":"","orcid":"","institution":"Ondokuz Mayıs University","correspondingAuthor":false,"prefix":"","firstName":"Bahaddin","middleName":"","lastName":"Yılmaz","suffix":""},{"id":625778582,"identity":"160e7018-4016-41f0-98fc-71980468593b","order_by":4,"name":"Güzin Demirağ","email":"","orcid":"","institution":"Ondokuz Mayıs University","correspondingAuthor":false,"prefix":"","firstName":"Güzin","middleName":"","lastName":"Demirağ","suffix":""},{"id":625778583,"identity":"9152f4d1-9eac-4e46-a8e0-66cb28af2438","order_by":5,"name":"Ümmet Abur","email":"","orcid":"","institution":"Ondokuz Mayıs University","correspondingAuthor":false,"prefix":"","firstName":"Ümmet","middleName":"","lastName":"Abur","suffix":""}],"badges":[],"createdAt":"2026-04-12 20:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9396662/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9396662/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108408826,"identity":"45985636-91ac-4c84-b409-841898622a3d","added_by":"auto","created_at":"2026-05-04 09:56:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":173212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStudy flowchart and patient classification schema. One hundred stage IV NSCLC patients were classified as newly diagnosed (n = 42) or progressive/recurrent (n = 58). Progressive/recurrent patients were further stratified by prior EGFR mutation status (mEGFR vs. wtEGFR). Pathogenic mutations were detected in 25% of the total cohort. PM, pathogenic mutation; mEGFR, EGFR-mutant; wtEGFR, EGFR wild-type; TKI, tyrosine kinase inhibitor.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9396662/v1/a6cced9fa96f553c5c54c8e0.png"},{"id":108408958,"identity":"5c554d1f-8cb4-418d-8970-c844a49b9afb","added_by":"auto","created_at":"2026-05-04 09:56:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61854,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ecfDNA concentration and overall survival stratified by metastatic burden. (A) Mean cfDNA concentration was significantly higher in multimetastatic patients compared with those without distant metastasis (10.0 ± 3.9 vs. 3.8 ± 1.0 ng; p = 0.01). (B) Mean overall survival was significantly shorter in multimetastatic patients compared with oligometastatic patients (17.2 ± 5.5 vs. 49.5 ± 5.7 months; p \u0026lt; 0.001). All patients without distant metastasis were alive at analysis. Error bars represent standard deviation.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9396662/v1/46ea128cddd65204696db6a6.png"},{"id":108408829,"identity":"ff370d2c-3299-4bcd-b216-0e59fde9a608","added_by":"auto","created_at":"2026-05-04 09:56:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69822,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of 34 pathogenic mutations across seven genes, stratified by mutation type. EGFR was the most frequently mutated gene (n = 18 mutations, 52.9%), followed by KRAS (n = 7, 20.6%) and PIK3CA (n = 5, 14.7%). Mutation types included single nucleotide variants (SNV), deletions, insertions/indels, and duplications. SNV, single nucleotide variant.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9396662/v1/c442574112110effc4af15ab.png"},{"id":108408925,"identity":"bf7d2463-72e2-460d-908a-90df6db48564","added_by":"auto","created_at":"2026-05-04 09:56:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69091,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEGFR mutation spectrum (n = 18 mutations in 12 patients) by variant and exon location. Activating mutations (exon 19 deletions, L858R) are shown in green, resistance mutations (T790M, T790A) in red, and other variants in gray. Exon 19 deletions were the most common activating alteration (33%), followed by T790M (22%) as the predominant resistance mutation.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9396662/v1/17a55c6068827b7e65d9b2c5.png"},{"id":108408825,"identity":"ea23151a-6128-40df-8963-62778528b003","added_by":"auto","created_at":"2026-05-04 09:56:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":51406,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eOverall survival comparison in EGFR wild-type progressive/recurrent patients by resistance mutation status. Patients harboring resistance mutations (n = 4) had significantly shorter mean OS compared with mutation-negative patients (16.5 ± 0.5 vs. 48.7 ± 6.6 months; p = 0.001, log-rank test). Resistance mutations included EGFR A767_V769dup, PIK3CA P381fs*11, NRAS Q61H, and KRAS G12C.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9396662/v1/93e692640603fefb38c7b513.png"},{"id":108409005,"identity":"93386fe0-c25c-4c48-90a8-fa0e630fed49","added_by":"auto","created_at":"2026-05-04 09:56:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":57018,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGene-specific pathogenic mutation frequencies stratified by smoking status. Among never-smokers (n = 29), EGFR mutations predominated (24.1%), while KRAS and PIK3CA mutations were absent or rare. Among smokers (n = 37), KRAS (8.1%) and PIK3CA (8.1%) were the most commonly mutated genes, while EGFR mutations were rare (2.7%). No PIK3CA mutations were detected in never-smokers.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9396662/v1/819bde34d2fc464bbe483fc4.png"},{"id":108492696,"identity":"d48126f5-4b0e-4e8e-b035-66d354ddde26","added_by":"auto","created_at":"2026-05-05 09:58:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":758286,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9396662/v1/9572c68c-6e41-4bbe-8a16-d1701d76f891.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Somatic Mutation Profiling by NGS-Based Liquid Biopsy in Advanced Non- Small Cell Lung Cancer: Frequency, Clinical Correlates, and Prognostic Significance","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eLung cancer remains the leading cause of cancer-related mortality worldwide, with approximately 2.1\u0026nbsp;million new diagnoses and 1.8\u0026nbsp;million deaths reported annually [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. More than half of patients are diagnosed between ages 55 and 74, and the overall 5-year survival rate remains approximately 19% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Cigarette smoking is the most prominent etiological factor, with approximately 85% of patients having a history of smoking [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNon-small cell lung cancer (NSCLC) accounts for 80\u0026ndash;85% of all cases and has become a paradigm for precision oncology [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Numerous actionable driver mutations have been identified, including EGFR mutations (15\u0026ndash;40%), KRAS mutations (15\u0026ndash;25%), and alterations in PIK3CA, BRAF, NRAS, ALK, ROS1, and MET [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Targeted therapies\u0026mdash;including EGFR tyrosine kinase inhibitors (TKIs), crizotinib for ALK/ROS1 rearrangements, and dabrafenib for BRAF V600E mutations\u0026mdash;have substantially improved progression-free survival in molecularly defined subgroups [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, acquired resistance inevitably emerges [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTissue biopsy remains the diagnostic standard but is limited by procedural invasiveness, tumor inaccessibility, and inability to capture intratumoral heterogeneity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Liquid biopsy, based on circulating cell-free DNA (cfDNA) analysis, has emerged as a minimally invasive alternative enabling real-time monitoring of somatic mutations and resistance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Next-generation sequencing (NGS) platforms offer simultaneous multi-gene profiling from cfDNA with molecular barcoding for accurate low-frequency variant detection [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we aimed to determine the frequency and distribution of somatic mutations detected by NGS-based liquid biopsy across 19 cancer-related genes in stage IV NSCLC patients, and to evaluate their associations with metastatic burden, cfDNA levels, treatment resistance, and overall survival.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design and Patient Selection\u003c/h2\u003e \u003cp\u003eThis retrospective observational study included 100 consecutive stage IV NSCLC patients between January and November 2019 at the Department of Medical Genetics, Ondokuz Mayıs University, Samsun, Turkey. The cohort comprised 42 newly diagnosed and 58 progressive/recurrent patients from the Central and Eastern Black Sea regions. Patients were classified by metastatic burden: no distant metastasis (n\u0026thinsp;=\u0026thinsp;18), oligometastatic (1\u0026ndash;3 distant sites; n\u0026thinsp;=\u0026thinsp;71), and multimetastatic (\u0026ge;\u0026thinsp;3 sites; n\u0026thinsp;=\u0026thinsp;11) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. cfDNA Isolation and Quantification\u003c/h2\u003e \u003cp\u003ePeripheral blood (30 mL) was collected into two PAXgene Blood ccfDNA tubes (PreAnalytiX GmbH). cfDNA was isolated using the QIAamp Circulating Nucleic Acid Kit with QIAvac 24 Plus (Qiagen) and quantified by Qubit dsDNA HS Assay on a Qubit 3.0 Fluorometer (Thermo Fisher Scientific).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. NGS Library Preparation and Sequencing\u003c/h2\u003e \u003cp\u003eSamples with cfDNA\u0026thinsp;\u0026ge;\u0026thinsp;2 ng/\u0026micro;L were processed using the GeneRead QIAact Lung DNA UMI Panel (Qiagen), covering hotspot regions of 19 genes (AKT1, ALK, BRAF, DDR2, EGFR, ERBB2, ESR1, FGFR1, KIT, KRAS, MAP2K1, MET, NRAS, NTRK1, PDGFRA, PIK3CA, PTEN, RICTOR, ROS1). Samples below threshold were processed with the GeneRead QIAact Actionable Insights Tumor Panel. Library preparation included UMI-tagged adapter ligation and target enrichment. Sequencing was performed on the GeneReader platform (Qiagen).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Bioinformatic Analysis\u003c/h2\u003e \u003cp\u003eQuality parameters (Q-score\u0026thinsp;\u0026ge;\u0026thinsp;25, coverage\u0026thinsp;\u0026ge;\u0026thinsp;500\u0026times;) were assessed using QCI Analyze. Variants with allele fraction\u0026thinsp;\u0026ge;\u0026thinsp;1% were retained [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Pathogenicity was determined using QCI Analyze, COSMIC, ClinVar, ACMG/AMP guidelines, and in silico tools (MutationTaster, PROVEAN, VarSome, SIFT).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical Analysis\u003c/h2\u003e \u003cp\u003eAnalyses were performed using SPSS 15.0. Kruskal\u0026ndash;Wallis test with Bonferroni-corrected Mann\u0026ndash;Whitney U (p\u0026thinsp;\u0026le;\u0026thinsp;0.016), Kaplan\u0026ndash;Meier survival analysis with log-rank test (p\u0026thinsp;\u0026le;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Ethics\u003c/h2\u003e \u003cp\u003ehe study protocol was approved by the Clinical Research Ethics Committee of Ondokuz Mayıs University (Approval No.: B.30.2.ODM.0.20.08/861; Date: 14 November 2019). The research was carried out in accordance with the guidelines of the Clinical Research Ethics Committee of Ondokuz Mayıs University and the principles of the Declaration of Helsinki (2013 revision). The present study is a retrospective analysis of de-identified clinical and molecular data obtained from 100 consecutive patients who had been referred to our center for clinical liquid biopsy (cfDNA next-generation sequencing) testing as part of their routine oncologic workup; clinical information was retrieved from the Nukleus Hospital Information System and from the archive of the Department of Medical Genetics of Ondokuz Mayıs University. In view of this retrospective design and the exclusive use of de-identified data, the ethics committee formally waived the requirement for additional written informed consent for research use of the data.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Patient Characteristics\u003c/h2\u003e \u003cp\u003eThe study included 100 stage IV NSCLC patients. Demographics and clinical characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Demographics and Clinical Characteristics (N\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (37\u0026ndash;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSCLC-NOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNewly diagnosed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgressive/recurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (58.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status (n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker (mean 44 pack-years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNSCLC-NOS, non-small cell lung cancer not otherwise specified.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Metastatic Burden, cfDNA Levels, and Overall Survival\u003c/h2\u003e \u003cp\u003eAt enrollment, 82 patients had distant metastases. The distribution of metastatic sites is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. cfDNA levels and OS data stratified by metastatic burden are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Multimetastatic patients had significantly higher cfDNA levels (10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 vs. 3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 ng; p\u0026thinsp;=\u0026thinsp;0.01) and shorter OS (17.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5 vs. 49.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7 months; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Distant Metastatic Sites at Enrollment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContralateral lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdrenal glands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePericardium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo distant metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSome patients had metastases at multiple sites simultaneously.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ecfDNA Concentration and Overall Survival Stratified by Metastatic Burden\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecfDNA (ng)\u003c/p\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOS (months)\u003c/p\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo distant metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAll alive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOligometastatic (1\u0026ndash;3 sites)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultimetastatic (\u0026ge;\u0026thinsp;3 sites)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.01ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001ᵇ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eᵃ Multimetastatic vs. no distant metastasis (Mann\u0026ndash;Whitney U test). ᵇ Kruskal\u0026ndash;Wallis test across all groups. OS, overall survival.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Somatic Mutation Landscape\u003c/h2\u003e \u003cp\u003ePathogenic mutations were detected in 25/100 patients (25%), yielding 34 PMs across seven genes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Mean VAF was 8.3% (range 1\u0026ndash;71%). Of 34 PMs, 23 (68%) were SNVs, 5 (15%) deletions, 1 duplication, 2 insertions, and 3 (9%) indels. Among 25 mutation-positive patients, 17 (68%) carried a single PM and 8 (32%) carried multiple PMs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of 34 Pathogenic Mutations by Gene\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMutations\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMost common variant(s)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExon 19 del (33%), T790M (22%), L858R (17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKRAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG12C (71%), G13C (14%), G60D (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIK3CA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS553fs*7 (40%), E545A, E542K, P381fs*11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG469A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV559F\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNRAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ61H\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS257L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ePercentages in the \"Mutations\" column refer to proportion of total 34 PMs. Gene names in italics per HUGO nomenclature.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. EGFR Mutations\u003c/h2\u003e \u003cp\u003eTwelve patients (12%) harbored 18 EGFR PMs across exons 19, 20, and 21 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Exon 19 deletions were most frequent (33%), followed by T790M (22%) and L858R (17%). All four T790M-positive patients had progressed on erlotinib (mean 9 months) and were alive at analysis (mean OS 18\u0026thinsp;\u0026plusmn;\u0026thinsp;6 months).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Characteristics of Patients with EGFR T790M/T790A Resistance Mutations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResistance\u003c/p\u003e \u003cp\u003emutation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitizing\u003c/p\u003e \u003cp\u003emutation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ecfDNA\u003c/p\u003e \u003cp\u003e(ng)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTKI duration\u003c/p\u003e \u003cp\u003e(months)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTKI agent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMetastatic sites\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT790M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL858R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eErlotinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLung, Bone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT790M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL858R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eErlotinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT790M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEx19 del\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eErlotinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAdrenal, Bone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT790M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL858R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eErlotinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLiver, Bone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT790A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL858R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eErlotinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAll patients had adenocarcinoma histology. Mean TKI duration: 9 months (excluding patient 97 on TKI for 24 months). M, male; F, female.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5. KRAS and Other Gene Mutations\u003c/h2\u003e \u003cp\u003eSeven patients (7%) harbored KRAS PMs\u0026mdash;G12C predominated (5/7, 71%). PIK3CA mutations were found in 4 patients (5 PMs). Single PMs were detected in BRAF (G469A), KIT (V559F), NRAS (Q61H), and RAF1 (S257L).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Progressive/Recurrent Disease and Resistance\u003c/h2\u003e \u003cp\u003eAmong 58 progressive/recurrent patients, 13 (22%) had PMs on liquid biopsy. Ten (77%) harbored resistance mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Among wtEGFR patients, resistance mutation carriers had significantly shorter OS (16.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 vs. 48.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6 months; p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverall Survival in EGFR Wild-Type Progressive/Recurrent Patients by Resistance Mutation Status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOS (months)\u003c/p\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResistance mutation positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e16.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR A767_V769dup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIK3CA P381fs*11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNRAS Q61H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKRAS G12C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResistance mutation negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e48.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eLog-rank test. OS, overall survival. All patients were on conventional chemotherapy prior to liquid biopsy.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Smoking Status\u003c/h2\u003e \u003cp\u003eAmong 29 never-smokers, EGFR mutations predominated (92%). Among 37 smokers, PIK3CA (43%) and KRAS (43%) predominated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThis study evaluated the clinical utility of NGS-based liquid biopsy using a 19-gene cfDNA in 100 consecutive stage IV NSCLC patients from Turkey\u0026rsquo;s Central and Eastern Black Sea region. The overall pathogenic mutation (PM) detection rate was 25%, which is notably lower than rates reported in several comparable studies. B\u0026uuml;y\u0026uuml;kşimşek et al. reported a 42% detection rate using a similar cfDNA-NGS approach in a Turkish cohort [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], while Ottestad et al. identified somatic mutations in 52% of advanced NSCLC patients in Norway [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Sabari et al. documented a 45.7% mutation detection rate US cohort [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and a recently Yang et al. reported detection rates exceeding 60% using a comprehensive 168-gene panel [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. More recently, Doval et al. reported a 65% detection rate in the first Indian liquid biopsy experience [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These discrepancies likely reflect methodological differences: our stringent 1% minimum allele frequency (MAF) threshold, the narrower 19-gene panel scope, and the high proportion of patients (58%) on active systemic therapy at the time of sampling\u0026mdash;a factor known to suppress ctDNA shedding through treatment-induced tumor cell depletion [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A 2025 systematic review by Jahani et al. confirmed that NGS provides the most comprehensive mutational profiling among cfDNA technologies but emphasized that detection sensitivity is highly dependent on panel breadth, sequencing depth, and bioinformatic filtering thresholds [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEGFR mutations were detected in 12% of our cohort, consistent with the reported 10\u0026ndash;17% frequency in Turkish tissue-based studies. Calibasi-Kocal et al. reported an overall EGFR mutation frequency of 16.6% in 409 Turkish NSCLC patients [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], while a more recent single-center analysis by Aksakal et al. found EGFR mutations in 11.5% of 182 Turkish NSCLC cases [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These rates align with European NSCLC populations (7\u0026ndash;20%) rather than Asian populations (40\u0026ndash;60%), a pattern consistent with ethnicity-based variation extensively documented by Midha et al. in a global EGFR mutation map [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our cfDNA-based detection rate of 12% is slightly lower than tissue-based Turkish studies, which is expected given the known sensitivity gap between plasma and tissue genotyping. A meta-analysis by Yu et al. demonstrated concordance rates of 60\u0026ndash;80% between cfDNA and tissue EGFR testing, with sensitivity losses primarily attributable to low tumor shedding and subclonal heterogeneity [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Nevertheless, the 2024 NCCN guidelines (v5.2024) have elevated plasma cfDNA testing from a salvage option to an equivalent alternative for initial genotyping, reflecting accumulating evidence that plasma NGS identifies clinically actionable targets with sufficient reliability [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDetection of the EGFR T790M resistance mutation in four patients who had progressed on erlotinib (mean TKI duration 9 months) underscores the value of liquid biopsy for non-invasive resistance monitoring. T790M emerges as the dominant acquired resistance mechanism in approximately 50\u0026ndash;60% of patients treated with first- or second-generation EGFR TKIs [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. All T790M-positive patients in our cohort were alive at the time of analysis (mean OS 18\u0026thinsp;\u0026plusmn;\u0026thinsp;6 months), reflecting the availability of effective subsequent therapy with osimertinib, a third-generation EGFR TKI. The FLAURA trial demonstrated that first-line osimertinib achieves a median OS of 38.6 months versus 31.8 months with earlier-generation TKIs (HR 0.80; p\u0026thinsp;=\u0026thinsp;0.046) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], while the AURA3 trial established osimertinib as standard second-line therapy in T790M-positive NSCLC with a median PFS of 10.1 months versus 4.4 months for platinum\u0026ndash;pemetrexed [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, the FLAURA2 trial recently demonstrated that the addition of chemotherapy to first-line osimertinib further extended median PFS by 8.8 months compared with osimertinib monotherapy (HR 0.62; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These evolving data reinforce the clinical imperative of cfDNA-based T790M surveillance to identify patients eligible for osimertinib and to guide sequential treatment strategies.\u003c/p\u003e \u003cp\u003eKRAS mutations were detected in 7% of patients, with G12C constituting the predominant variant (5/7, 71%). This allelic distribution is consistent with large-scale genomic profiling studies reporting that G12C accounts for approximately 40% of all KRAS mutations in lung adenocarcinoma, making it the most prevalent KRAS subtype in Western populations [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The predominance of G12C in our cohort aligns with the known association between this specific transversion mutation and tobacco carcinogen exposure, as the majority of our KRAS-mutant patients were current or former smokers. Notably, real-world prevalence data indicate significant geographic variation in KRAS G12C frequency, ranging from 8.9\u0026ndash;19.5% of NSCLC cases in the United States and 9.3\u0026ndash;18.4% in Europe to 1.4\u0026ndash;4.3% in Asia [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Our finding of 5% KRAS G12C prevalence in a Turkish Black Sea cohort provides novel population-specific data that contributes to the growing global map of KRAS G12C distribution. With the recent regulatory approval of selective KRAS G12C inhibitors, cfDNA-based identification of this variant has become directly actionable, and all seven KRAS-mutant patients in our cohort \u0026mdash; including the five harboring G12C \u0026mdash; represent candidates who could benefit from targeted therapy or clinical trial enrollment [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These findings underscore the importance of extending liquid biopsy panels beyond EGFR to capture the full spectrum of therapeutically relevant alterations.\u003c/p\u003e \u003cp\u003ePIK3CA mutations were identified in four patients (5 PMs), making it the third most frequently mutated gene in our cohort. PIK3CA alterations were predominantly found in smokers (43% of mutations in the smoking subgroup), consistent with prior reports associating PIK3CA mutations with tobacco exposure and squamous histology in NSCLC [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The frameshift variant S553fs*7, detected in two patients, and the well-characterized hotspot mutations E545A and E542K represent genomic alterations with potential implications for treatment stratification, as PIK3CA mutations have been associated with resistance to EGFR-TKI therapy and may modulate sensitivity to immune checkpoint inhibitors [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Single pathogenic mutations in BRAF (G469A), KIT (V559F), NRAS (Q61H), and RAF1 (S257L) further illustrate the genomic heterogeneity captured by multi-gene cfDNA panels. Although individually rare, these variants collectively accounted for 11.8% (4/34) of all detected pathogenic mutations and may have clinical implications for treatment selection, including eligibility for basket trial enrollment. Large-scale cfDNA profiling studies have similarly reported a long tail of low-frequency but potentially actionable mutations beyond the canonical EGFR and KRAS drivers, reinforcing the value of broad panel-based approaches over single-gene assays in advanced NSCLC [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Taken together, these findings demonstrate that liquid biopsy captures a clinically meaningful mutation spectrum that extends well beyond EGFR, enabling comprehensive molecular characterization from a single blood draw.\u003c/p\u003e \u003cp\u003eTotal cfDNA concentration showed a significant positive correlation with metastatic burden in our cohort: multimetastatic patients (\u0026ge;\u0026thinsp;3 sites) had substantially higher cfDNA levels (10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 ng) compared with oligometastatic (4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 ng) and non-metastatic (3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 ng) groups (p\u0026thinsp;=\u0026thinsp;0.01). This finding is consistent with the landmark observations of Newman et al., who demonstrated that ctDNA levels correlated with tumor volume in NSCLC using CAPP-Seq technology [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and with Zhu et al., who reported that elevated cfDNA independently predicted shorter OS in advanced NSCLC [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. More recently, a 2021 study by Li et al. confirmed that baseline cfDNA positively correlates with tumor burden and that cfDNA kinetics (the ratio of post-treatment to baseline cfDNA) can predict treatment response with high accuracy [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. A 2025 prospective real-world study further validated ctDNA tumor fraction\u0026thinsp;\u0026ge;\u0026thinsp;1% as a robust prognostic biomarker: patients above this threshold had a median OS of 17.6 months compared with not reached for those below [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Taken together, these data support the dual utility of cfDNA as both a qualitative tool for mutation detection and a quantitative biomarker for disease burden stratification and prognostic assessment.\u003c/p\u003e \u003cp\u003eAmong 58 progressive/recurrent patients, 13 (22%) harbored pathogenic mutations on liquid biopsy, and resistance mutation carriers among wtEGFR patients had markedly shorter OS (16.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 vs. 48.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6 months; p\u0026thinsp;=\u0026thinsp;0.001). This prognostic dichotomy is consistent with prior studies demonstrating the adverse impact of acquired resistance mutations on survival. Zhao et al. reported that concurrent KRAS and PIK3CA mutations confer significantly worse OS in advanced NSCLC [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], while Ludovini et al. showed that multiple co-occurring oncogenic drivers in cfDNA are independently associated with poor clinical outcomes [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The detection of resistance-associated mutations (EGFR A767_V769dup, PIK3CA P381fs*11, NRAS Q61H, and KRAS G12C) in four progressive wtEGFR patients\u0026mdash;all of whom had substantially shorter survival\u0026mdash;highlights the clinical importance of liquid biopsy in identifying resistance mechanisms that would be missed without molecular profiling. The 2025 LIBRA study extended this concept further by demonstrating that ctDNA-based molecular progression precedes radiographic progression by a median of 4.9 weeks, suggesting a potential window for preemptive treatment modification [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Longitudinal ctDNA monitoring studies, including a 2023 machine-learning-based model from the IMpower150 trial, have confirmed that ctDNA dynamics robustly predict survival independently of radiographic response, supporting the integration of serial cfDNA assessment into routine clinical practice [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe mutation landscape in our cohort was strongly influenced by smoking status: EGFR mutations predominated in never-smokers (92% of mutations in 29 never-smokers), while PIK3CA (43%) and KRAS (43%) mutations predominated among 37 current/former smokers. This dichotomy replicates the well-established molecular epidemiology of NSCLC, where EGFR mutations are enriched in never-smokers, females, and adenocarcinoma histology, while KRAS mutations are strongly associated with tobacco carcinogenesis [6,20,47]. The concentration of EGFR mutations in never-smokers within our Black Sea cohort is clinically important because it suggests that patients in this demographic subgroup should be prioritized for reflex liquid biopsy testing at diagnosis, even when tissue availability is limited. Furthermore, the mutual exclusivity of EGFR and KRAS mutations observed in our cohort is consistent with the established paradigm of distinct oncogenic driver pathways in NSCLC [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study has several notable strengths. First, it represents one of the few liquid biopsy studies from Turkey\u0026rsquo;s Black Sea region, providing population-specific mutation frequency data for an underrepresented geographic area. Second, the consecutive enrollment of 100 patients with comprehensive clinical annotation\u0026mdash;including metastatic burden stratification, smoking status, and long-term survival follow-up\u0026mdash;enhances the clinical relevance of our findings. Third, the use of a UMI (unique molecular identifier)-based NGS panel with molecular barcoding minimizes sequencing artifacts and provides reliable low-frequency variant detection, addressing one of the key technical limitations of earlier cfDNA studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Finally, our cohort included both newly diagnosed (42%) and progressive/recurrent (58%) patients, enabling evaluation of liquid biopsy utility across different clinical scenarios.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, the absence of concurrent tissue genotyping precluded assessment of plasma\u0026ndash;tissue concordance, which is recognized as a critical quality metric for liquid biopsy validation studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Second, the modest cohort size (n\u0026thinsp;=\u0026thinsp;100) limits the statistical power for subgroup analyses, particularly for rare mutations such as BRAF, KIT, and NRAS. Third, the 19-gene panel does not assess gene amplifications (MET, HER2), gene fusions (ALK, ROS1, RET, NTRK), or copy number alterations\u0026mdash;all of which are therapeutically relevant in NSCLC and recommended for routine testing by current NCCN guidelines [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Fourth, the 1% MAF threshold, while reducing false positives, may have caused underdetection of subclonal mutations present at very low allele frequencies, particularly in patients receiving systemic therapy. Fifth, the potential confounding effect of clonal hematopoiesis of indeterminate potential (CHIP) was not systematically evaluated through matched white blood cell sequencing, which could lead to false-positive somatic mutation calls, especially for variants in genes such as PIK3CA [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Future studies incorporating larger cohorts, concurrent tissue and plasma genotyping, broader gene panels including fusion detection, serial longitudinal sampling, and CHIP filtering through paired leukocyte sequencing will be necessary to fully characterize the clinical utility of cfDNA-based precision oncology in this population.\u003c/p\u003e \u003cp\u003eIn conclusion, NGS-based liquid biopsy using a 19-gene cfDNA panel effectively identified actionable somatic mutations in 25% of stage IV NSCLC patients from Turkey\u0026rsquo;s Black Sea region, with EGFR (12%), KRAS (7%), and PIK3CA (4%) as the most frequently mutated genes. cfDNA concentration correlated with metastatic burden, and resistance mutation status independently predicted shorter survival in progressive patients. The high prevalence of KRAS G12C (71% of KRAS mutations) and EGFR T790M (22% of EGFR mutations) underscores the clinical utility of multi-gene cfDNA panels for identifying patients eligible for targeted therapies including osimertinib, sotorasib, and adagrasib. These findings support the integration of liquid biopsy into routine clinical practice for NSCLC patients in Turkey, complementing tissue-based molecular testing to enable timely, personalized therapeutic decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\u0026Ccedil;.D. and \u0026Uuml;.A. contributed to study conceptualization, patient data collection, NGS data analysis, genetic data interpretation, clinical\u0026ndash;genomic correlation, and drafting and finalizing the manuscript. B.Y., G.D., C.A., and M.S. contributed to oncological diagnosis and patient evaluation, collection of oncological data, comparison of oncological findings with genetic analysis results, and critical review of the manuscript draft. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to patient privacy and ethical restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394-424. doi:10.3322/caac.21492\u003c/li\u003e\n\u003cli\u003eTorre LA, Siegel RL, Jemal A. Lung cancer statistics. 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Clin Cancer Res. 2018;24(18):4437-4443. doi:10.1158/1078-0432.CCR-18-0143\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"liquid biopsy, NSCLC, cfDNA, NGS, EGFR, KRAS, T790M","lastPublishedDoi":"10.21203/rs.3.rs-9396662/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9396662/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eLiquid biopsy-based next-generation sequencing (NGS) enables non-invasive, comprehensive mutational profiling in advanced non-small cell lung cancer (NSCLC). However, data on multi-gene cfDNA panels in Turkish populations remain limited.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eOne hundred consecutive stage IV NSCLC patients (42 newly diagnosed, 58 with progression/recurrence) who underwent cfDNA-based NGS analysis between January and November 2019 were retrospectively analyzed. Somatic mutations across 19 genes were assessed using the GeneRead QIAact Lung DNA UMI Panel (Qiagen) on the GeneReader platform.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003ePathogenic mutations were detected in 25 patients (25%), yielding 34 PMs across seven genes. EGFR was most frequently mutated (52.9%), followed by KRAS (20.5%) and PIK3CA (14.5%). Multimetastatic patients had significantly higher cfDNA levels (10.0 ± 3.9 vs. 3.8 ± 1.0 ng; p = 0.01) and shorter OS (17.2 ± 5.5 vs. 49.5 ± 5.7 months; p \u0026lt; 0.001). Among EGFR wild-type patients with progression, resistance mutation carriers had markedly shorter OS (16.5 ± 0.5 vs. 48.7 ± 6.6 months; p = 0.001). KRAS G12C was the predominant KRAS variant (71%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eNGS-based liquid biopsy effectively identifies actionable and resistance-associated mutations in advanced NSCLC. cfDNA concentration correlates with metastatic burden. The high prevalence of KRAS G12C and EGFR T790M underscores the clinical utility of multi-gene cfDNA panels for guiding personalized treatment.\u003c/p\u003e","manuscriptTitle":"Somatic Mutation Profiling by NGS-Based Liquid Biopsy in Advanced Non- Small Cell Lung Cancer: Frequency, Clinical Correlates, and Prognostic Significance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 09:55:44","doi":"10.21203/rs.3.rs-9396662/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-19T16:56:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-17T12:09:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T12:09:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Biology Reports","date":"2026-04-12T20:16:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3abde3b5-5d7c-4e22-99ae-ebeae829311f","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T08:57:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 09:55:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9396662","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9396662","identity":"rs-9396662","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}