Analysis of clinicopathological characteristics in postoperative molecular residual disease-positive stage I non-small cell lung cancer patients

Introduction

Multiple primary lung cancers (MPLCs) are clinically rare and mechanistically complex, characterized by the occurrence of independent primary tumors, either synchronous or metachronous (1). Studies have revealed that these tumors exhibit a wide spectrum of driver gene mutations at the molecular level, with EGFR and KRAS gene alterations being particularly common, highlighting significant genetic heterogeneity. Crucially, dysregulation of the Wnt/β-catenin signaling pathway serves as a core pathogenic mechanism by conferring cancer stem cell-like traits to tumors, substantially increasing the difficulty of clinical diagnosis and treatment (1).

Next-generation sequencing (NGS) has become a cornerstone of molecular diagnostics in non-small cell lung cancer (NSCLC), crucially differentiating synchronous primary lung cancers (SPLCs) from intrapulmonary metastases (IPMs). This molecular stratification directly influences therapeutic decision making. Current studies reveal that SPLC lesions typically exhibit distinct driver mutations and histological features, while approximately 75% of IPM cases share identical mutational signatures (2). Conventional imaging techniques remain limited in resolving tumor molecular heterogeneity and detecting molecular residual disease (MRD). Invasive biopsies face inherent spatial restrictions and sampling bias, resulting in an inability to monitor cancer clonal evolutionary trajectories (3).

Importantly, MRD analysis in early-stage lung cancer has emerged as a valuable tool in several key clinical scenarios. These include postoperative risk stratification, where MRD status can help identify patients at higher risk of recurrence; guiding adjuvant therapy decisions, enabling clinicians to tailor treatment intensity according to residual tumor burden; and early relapse detection, which allows for timely intervention before clinical or radiographic progression becomes evident. By integrating MRD monitoring into these clinical workflows, patient management can become more personalized and potentially more effective, particularly in the context of multifocal or genetically heterogeneous disease (3).

Current prognostic evaluation of MPLC primarily relies on clinicopathological features and molecular markers. Recent studies reveal that patient survival outcomes are closely associated with tumor molecular characteristics and the immune microenvironment (4,5). Although NGS-based circulating tumor DNA (ctDNA) analysis has become a routine tool for prognostic assessment in solitary lung cancer (6), its application in MPLC remains exploratory—current research focuses predominantly on tissue molecular subtyping techniques (5,7). Notably, liquid biopsy has demonstrated potential for monitoring/guiding immunotherapy in NSCLC (8). However, due to tumor heterogeneity and the complexity of clonal evolution, standardization of MRD detection in MPLC remains challenging (6). It is precisely this dilemma that compels us to identify MPLC-specific MRD markers: tracing clonal origins and capturing evolutionary trajectories through multi-omics approaches hold the key to breakthrough (5,8).

ctDNA is currently primarily used for postoperative MRD monitoring. As tiny DNA fragments released into the bloodstream through apoptosis, necrosis, or active secretion by tumor cells, ctDNA provides a non-invasive solution for situations where tissue biopsies are limited or insufficient (9). These fragments contain tumor-specific genomic alterations (10,11). Their concentration fluctuations dynamically reflect the clonal evolutionary trajectory and overall tumor burden (12,13), directly influencing treatment decisions and recurrence risk assessment (14-18). It must be emphasized that MRD itself represents residual tumor cells lurking after radical treatment; these “molecular undercurrents” may ultimately trigger clinical recurrence (19). In NSCLC, ctDNA-guided MRD detection has proven its prognostic power: prospective trials confirm significantly prolonged recurrence-free survival in MRD-negative patients, even without adjuvant therapy (6). Yet, a critical blind spot persists for MPLCs. Tumor heterogeneity across distinct nodules drives divergent evolutionary paths and ctDNA shedding kinetics. Moreover, contemporary NSCLC trial designs routinely omit patients with MPLCs—notably in post-chemoradiation MRD validation studies (20). This exclusion reflects the interpretative challenges posed by multifocal heterogeneity, which complicates both therapeutic response evaluation and longitudinal surveillance. Consequently, no clinically applicable paradigms currently guide MRD interpretation in multinodular disease, compelling clinicians to navigate personalized management without evidence-based frameworks.

To bridge these gaps, we deploy NGS on paired tissue-plasma specimens to track nodule-specific mutations. This approach leverages NGS’s established high sensitivity/specificity for detecting low-frequency variants and mapping clonal heterogeneity (21,22). Customized NGS panels may enhance MRD detection in limited cell-free DNA (cfDNA) contexts by broadening mutational surveillance, thereby compensating for plasma DNA constraints through intensified tumor genome sampling (23,24). Nevertheless, comprehensive ctDNA-based genomic characterization of multifocal nodules remains scarce, with longitudinal subclonal MRD inadequately documented.

Incorporating ctDNA analysis into clinical workflows represents a translational strategy for pulmonary nodule management. This retrospective cohort study conducted comprehensive molecular profiling on 12 patients undergoing curative-intent resection at our institution, aiming to: (I) delineate discriminatory molecular features between solitary and multifocal nodules, and (II) optimize ctDNA-guided MRD surveillance in resectable disease. Through integrated sequencing-bioinformatics pipelines, we characterize nodule-specific molecular landscapes and their clinical correlations. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1990/rc).

Methods

Study subjects and samples

This study enrolled 12 patients with pulmonary nodules treated at The Second Affiliated Hospital of Army Medical University. Classification of multiple pulmonary nodules was performed using a combined clinicopathological and molecular approach. Multi-nodular cases were considered consistent with independent primary tumors when nodules exhibited discordant driver mutations and lacked molecular evidence of a shared clonal origin, in the absence of radiological or pathological findings suggestive of IPM. Five patients presented with solitary nodules and seven with multiple nodules. Blood specimens for ctDNA analysis were collected at multiple perioperative timepoints, alongside surgical or biopsy tissue samples. All participants underwent comprehensive clinical and imaging assessments to determine nodule number, size, and location. Inclusion criteria comprised: age ≥18 years, imaging-confirmed pulmonary nodules, and written informed consent. Exclusion criteria were prior malignancy diagnosis or unwillingness to undergo MRD testing. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. It was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Army Medical University, PLA (Approval No. 2024-205-01). Written informed consent was obtained from all participants.

NGS and ctDNA testing

All molecular analyses—DNA extraction, library preparation, and NGS—were conducted by CAP/CLIA-certified Genecast Biotechnology Co., Ltd. (Wuxi, China). Genomic DNA (gDNA) was isolated from tumor tissue using TIANGEN TIANamp Genomic DNA Kit (TIANGEN Biotech, Beijing, China) and from peripheral blood using the TGuide S32 Magnetic Blood Genomic DNA Kit (TIANGEN Biotech). For ctDNA analysis, circulating cfDNA was purified from 3.5–4 mL plasma with Thermo Fisher’s MagMAX Cell-Free DNA Isolation Kit (Waltham, MA, USA). DNA concentration and integrity were quantified using Thermo Fisher’s Qubit dsDNA HS Assay Kit and Agilent’s 2100 Bioanalyzer, respectively. Tissue or blood-derived gDNA (30–300 ng) was subsequently fragmented to 200 bp using a Covaris LE220 ultrasonicator (Woburn, MA, USA). Libraries were then prepared with KAPA Hyper Prep Kit (Kapa Biosystems, Boston, MA, USA) following standard protocols.

Targeted capture employed Roche’s HyperCap Target Enrichment Kit (Basel, Switzerland). The custom-designed 769-gene panel (Genecast Biotechnology Co., Ltd.) encompasses approximately 2.4 Mb of cancer-associated genomic regions. Sequencing utilized the Illumina NovaSeq 6000 platform (San Diego, CA, USA) with strict QC thresholds: tissue specimens required ≥1,000× depth and plasma samples ≥30,000× depth to ensure analytical sensitivity and specificity.

Bioinformatic analysis

We applied Trimmomatic (v0.36) for sequencing read quality control, performing adapter trimming and low-quality base filtration. Cleaned reads were aligned to the hg19 reference genome using BWA (v0.7.17), followed by Picard (v2.23.0) for read sorting and duplicate marking. Somatic variant detection employed VarDict (v1.5.1) for single nucleotide variants (SNVs) and indels and FreeBayes (v1.2.0) for complex variants. Post-detection, raw variants underwent standard QC filtering assessing quality metrics and strand bias. Hard-filtering excluded: (I) variants in low-complexity regions or segmental duplications, and (II) variants in ENCODE-defined low-mappability regions. We further eliminated variants matching sequence-specific error profiles benchmarked through internal empirical validation.

Plasma mutation analysis employed the tumor-informed strategy. MRD-positive loci were defined as tissue-derived somatic variants detected in plasma with sample positivity determined at a combined P value threshold of <0.01. MRD status was assessed at the locus level rather than the patient level. To eliminate technical false positives, ctDNA alterations underwent statistical filtering using an internal background reference library. The analytical sensitivity of ctDNA detection was approximately 0.007% variant allele frequency (VAF), supported by ultra-deep sequencing and statistical background correction. ANNOVAR facilitated somatic mutations annotation, while the R package ComplexHeatmap was used to generate mutational landscape visualizations. Distributional concordance of mutation frequencies across matched tissue and plasma specimens was assessed through histogram analysis.

To facilitate clarity in ctDNA-based MRD interpretation, patients and detected variants were classified into specific tracking groups based on the concordance between tumor tissue and plasma sequencing results. Tissue-traced variants were defined as somatic mutations identified exclusively in resected tumor tissue but not detected in postoperative plasma samples. Blood-traced variants referred to tumor-derived somatic mutations that were detectable in plasma ctDNA following surgery and met the predefined statistical significance threshold. At the patient level, cases were categorized as traced when at least one tissue-derived somatic variant could be successfully detected in plasma ctDNA during postoperative monitoring. Conversely, patients were classified as not traced when no tumor-informed variants were detectable in plasma across all collected time points.

Statistical analysis

Statistical analyses were performed using R (version v. 3.6.1). Continuous variables were analyzed using Student’s t-test or Mann-Whitney U test, as appropriate. Categorical data were compared using the chi-squared test. A P value <0.05 was considered statistically significant.

Results

Comparative analysis of clinical-molecular features in solitary versus multiple pulmonary nodule patients

Molecular profiling of patients with solitary pulmonary nodules (n=5) and multiple pulmonary nodules (n=7) identified the five most frequently mutated genes, which were detailed in supplementary table 1 (online at https://cdn.amegroups.cn/static/public/jtd-2025-1990-1.xlsx) and illustrated in Figure 1A. These genes were EGFR (50%), TP53 (33%), BRAF (33%), TSC1 (25%), and WT1 (17%). In the group with multiple nodules, over 80% of mutations were missense, and no oncogenic gene fusions, such as EML4-ALK, were identified in tumor tissue or plasma samples by DNA-based NGS in this cohort. This group also exhibited a greater tumor mutational burden (TMB) compared to patients with solitary nodules. When categorized by sample types, mutations were divided into blood-derived (blood-traced) and tissue-derived (tissue-traced) groups. Notably, the tissue-traced group showed a higher TMB than the blood-traced group, as shown in Figure 1B. Mutations in genes such as EGFR, TSC1 and NTRK3 were present in both groups, while mutations in PTPRS, ATRX and BRAF were found exclusively in the tissue-traced group.

Figure 1 SNV landscape and gene mutation characteristics in single-primary and multiple-primary lung nodules. (A) Groups stratified by tumor type (multiple primary vs. single primary), demonstrating missense mutations as the predominant SNV type (>80%). (B) Mutated gene count comparison between the blood-traced and tissue-traced cohorts. (C) Association analysis of heterogeneity-related clinical factors, revealing borderline significance for STAS (P=0.07). AIS, adenocarcinoma in situ; APA, acinar predominant adenocarcinoma; hGE, haploid genome equivalents; MIA, minimally invasive adenocarcinoma; MPA, micropapillary predominant adenocarcinoma; MRD, molecular residual disease; PD-L1, programmed death-ligand 1; PPA, papillary predominant adenocarcinoma; SNV, single-nucleotide variant; STAS, spread through air spaces; TNM, tumor, node, metastasis.

Baseline characteristics (Table 1, Figure 1C) revealed no statistically significant differences between solitary and multiple pulmonary nodule groups in age (51±6.89 vs. 53.57±15.81 years, P=0.71), sex distribution (40% females vs. 66.7% males, P>0.99), or TNM staging (predominantly stage IA1 in both cohorts, P=0.80). Wedge resection constituted the primary surgical approach (60% solitary vs. 71.4% multiple, P>0.99). However, the multiple-nodule group demonstrated significantly higher mean blood-tracked somatic loci (13.0 vs. 4.8 in the solitary group, P=0.09), suggesting broader clonal heterogeneity and enhanced detectability in multifocal tumors. Additionally, longitudinal MRD monitoring showed a higher first-positive MRD rate in the multiple group (28.6%, 2/7) compared to the solitary group (20.0%, 1/5). This observed difference was not statistically significant (P>0.99), which was probably attributable to the reduced statistical power resulting from the small sample size of the cohort.

Patients were stratified into the not traced (n=5) and traced (n=11) cohorts based on ctDNA-based MRD tracking success (Table 2). No significant intergroup differences emerged in pathological subtypes: minimally invasive adenocarcinoma (MIA) prevalence was 40.0% (not traced) vs. 54.5% (traced) (P=0.68). PD-L1 expression showed no divergence (P=0.73), nor did tumor laterality (0% vs. 45.5% left lung involvement, P=0.12). Notably, part-solid nodules predominated in the traced cohort (36.4%, 4/11) vs. ground-glass opacity dominance in the not traced cohort (80.0%, 4/5). This suggested enhanced ctDNA shedding with solid components despite statistical non-significance (P=0.29).

Molecular profiling revealed higher MRD-positive loci in traced (6.91±6.06) vs. not traced (4.20±3.49; P=0.28). Tissue-derived loci were 14.82 (traced) vs. 10.8 (not traced; P=0.45). Critically, ctDNA concentration (haploid genome equivalents/mL) demonstrated significantly elevated median levels in traced (0.75) compared to not traced (0) (P=0.004).

Mutation profiling (Figure 2A; supplementary table 2, online at https://cdn.amegroups.cn/static/public/jtd-2025-1990-2.xlsx) identified EGFR (44%), TSC1 (25%), FUBP1 (19%), BRAF (19%), and RBM10 (19%) as predominant alterations in the MRD-tracking group. POLD1/POLE mutations were restricted to non-tracking cases (20%; P=0.31).

Figure 2 Landscape and MRD traced loci in patients with single and multiple lung nodules. (A) Variation type distribution in the MRD not traced tissue versus the MRD traced tissue cohorts. (B) Identical analysis for the MRD all positive against MRD partially positive groups. (C) Right lung lobe schematic mapping per-patient nodule topography in multi-nodular cases; adjacent bar plot delineates MRD-positive site tissue origins. The y-axis represents the variant allele frequency. MRD, molecular residual disease.

Relationship between MRD heterogeneity and tumor clonal evolution

Patients were stratified into MRD all-positive (n=3) and partially positive (n=4) cohorts based on complete tissue-based MRD tracking success (Table 3). The partially positive group demonstrated higher part-solid nodule prevalence (75.0% vs. 33.3%; P=0.66), implying potential associations between solid components, tumor microenvironment complexity, and MRD heterogeneity. Longitudinal MRD monitoring revealed conversion to negative status in 66.7% (2/3) of all-positive patients versus none in partially positive (P=0.14), suggesting enhanced treatment response in the former. All-positive cases exhibited higher blood-tracked somatic loci (mean 14.0 vs. 12.25), indicating possible correlation between mutational burden and sustained ctDNA shedding.

Mutational profiling (Figure 2B) demonstrated RBM10 alterations in 27.3% (3/11) of MRD All positive group vs. 0% (0/4) of partially positive cases (P=0.06). This differential prevalence implies potential involvement of RBM10 mutations in early clonal dissemination.

Genetic origins of mutations in multiple pulmonary nodules

Within the multi-nodular pulmonary subgroup (n=7), mutation frequencies and cellular origins were analyzed per patient (Figure 2C). Two distinct ctDNA tracking patterns emerged.

Pattern I demonstrated MRD-positive assignment when identical driver mutations (e.g., EGFR L858R in P259123) were detected in both tissue and plasma. This molecular concordance confirmed that ctDNA accurately mirrors tumor genetic profiles, reflecting significant tumor proliferation and residual disease. Clinically, these findings validated the tissue-informed approach: single-lesion genotyping provided actionable biomarkers for MRD monitoring to directly inform adjuvant decisions.

Pattern II exhibited spatial discordance between plasma and tissue mutations. In patient P263816, GRIN2A mutations detected in the right middle lobe nodule matched plasma findings, whereas EGFR mutations from the right lower lobe nodule were undetectable in plasma. This inter-lesional heterogeneity reflected tumor clonal complexity, potentially reflecting differential ctDNA shedding or subclonal variants below assay detection limits.

Distinct ctDNA tracking patterns revealed inherent limitations of plasma-based detection in capturing multifocal tumor heterogeneity. To enhance sensitivity, clinicians required comprehensive multi-site tissue sampling during MRD monitoring for multi-nodular pulmonary cases. This strategy enhanced tumor-associated mutation profiling accuracy, enabling actionable molecular profiles for targeted therapy optimization and improved clinical outcomes.

Discussion

Pulmonary nodules constitute the primary manifestation of lung cancer, which remains the leading cause of cancer mortality globally (25,26). Early detection through low-dose computed tomography substantially reduces mortality by enabling stage-shift diagnosis, though its clinical utility is limited by high false-positive rates complicating nodule management. Liquid biopsy, particularly ctDNA analysis, emerges as a promising non-invasive approach that enables dynamic tumor genomic profiling for early detection and MRD monitoring (27,28).

This study analyzed a cohort of lung nodule patients, identifying significant variations in demographics, mutational landscapes, and clonal heterogeneity between solitary and multiple nodule groups. Mean age demonstrated no statistical difference (solitary: 51±6.89 years; multiple: 53.57±15.81 years; P>0.05), aligning with prior reports (29,30). No statistically significant differences were observed between the groups; however, the interpretability of these results is limited by the small sample size. But gender distribution revealed notable female predominance in multiple-nodule cases (66.7% vs. 40%). These demographic divergences underscore the necessity for nodule-type-adapted therapeutic strategies and warrant investigation into gender-specific disease mechanisms.

Molecular profiling revealed distinct genetic landscapes between solitary and multiple lung nodules. In solitary nodules, dominant mutations included EGFR (50%), TP53 (33%), BRAF (33%), TSC1 (25%), and WT1 (17%)—consistent with established EGFR-targeted therapy indications (31,32). Conversely, multiple nodules exhibited significantly elevated mutation burdens dominated by missense variants (>80%) with absent driver fusions, correlating with enhanced treatment resistance risks (33,34). The 33% co-occurrence of TP53/BRAF mutations suggests alternative therapeutic pathways required to overcome chemotherapy resistance (35-37). These findings collectively indicate that higher mutational loads in multiple nodules are associated with adverse clinical outcomes, reflecting tumor heterogeneity-driven treatment challenges (38,39).

ctDNA analysis yielded clinically actionable insights, supporting its utility as a non-invasive biomarker for MRD monitoring in pulmonary nodules. Patients with multiple nodules demonstrated significantly higher MRD positivity rates and increased blood tracking loci counts compared to solitary nodule cases, indicating greater clonal heterogeneity and complex tumor dynamics. These findings underscore ctDNA’s potential to enhance MRD surveillance and guide therapeutic decisions, particularly in heterogeneous disease presentations. Early MRD detection enables timely therapeutic intensification, with prior studies confirming that prompt MRD eradication correlates with improved clinical outcomes through targeted clonal population management (40,41).

Building on this, we focused on how molecular findings predict outcomes in patients undergoing MRD tracking. Our observations showed that the average number of MRD-positive loci and the median level of hGE were both significantly higher in the MRD tracking group than in those not tracked. These results suggest that molecular findings can predict the effectiveness of MRD tracking and serve as genetic markers for treatment response and disease prognosis. This correlation suggests that patients with higher MRD positivity have an increased risk of relapse, as supported by recent studies identifying MRD positivity as a key prognostic factor in lung cancer (42,43). Furthermore, a multicenter study demonstrated that ctDNA-based MRD analysis offered superior predictions of recurrence-free survival compared to traditional clinicopathologic variables (44). Therefore, incorporating genetic markers into clinical decision-making could enable more personalized treatment strategies and potentially improve outcomes for high-risk patients.

Analysis of ctDNA in multi-nodule patients revealed two distinct profiles. Pattern one exhibited identical tissue-plasma mutations indicating active malignancy, while pattern two showed partial concordance reflecting significant inter-lesion heterogeneity. Consistent mutation profiles warrant aggressive therapeutic strategies, whereas discordant ctDNA signals genetic diversity implicating resistant subclones. This polyclonal landscape complicates intervention, as monotherapy often fails against divergent clonal variants, permitting therapeutic escape. Studies demonstrate that MPLCs evolve through evolutionary contingency rather than adaptive convergence, exhibiting inter-patient-level genetic divergence. Consequently, lesion-specific targeting is essential to address inherent MPLC heterogeneity, with clonal evolution understanding being fundamental to optimized therapeutic protocols.

Enhancing ctDNA detection sensitivity is critical for optimizing therapeutic efficacy and disease monitoring. Current methodologies, while effective, often fail to capture tumoral heterogeneity comprehensively. Targeted digital sequencing and advanced bioinformatics significantly improve low-frequency mutation detection, increasing ctDNA analysis sensitivity (40,41). NGS techniques detect ctDNA at low allele frequencies, a capability critical for early residual disease identification (45,46), and reveal distinct mutation patterns with clonal dissemination associated with RBM10 alterations. NGS profiling reveals distinct mutational signatures and clonal dissemination patterns linked to specific genomic alterations like RBM10 mutations. These molecular insights facilitate the development of personalized treatment strategies targeting patient-specific tumor genetics. Integrating ctDNA analysis with conventional imaging permits comprehensive assessment of tumor dynamics, enabling timely clinical intervention upon the emergence of therapy-resistant subclones (17,47,48). This multimodal approach refines ctDNA tracking accuracy and informs clinical decision-making for multi-nodular disease management.

In addition to the molecular and clinical insights discussed above, the adequacy of biological samples represents a critical factor in interpreting MRD results. Tissue representativeness is particularly important in multi-nodular or heterogeneous tumors, as insufficient sampling may fail to capture all relevant subclonal populations, leading to underestimation of mutation burden or missed actionable variants. Similarly, plasma ctDNA shedding varies across patients and tumor sites, influencing detection sensitivity and potentially contributing to false-negative MRD results. These biological variabilities underscore the need for comprehensive multi-site sampling and longitudinal plasma monitoring.

Important insights emerge from this research, though several limitations warrant acknowledgment. The limited cohort of 12 patients reduces generalizability, particularly regarding observed demographic and molecular distinctions between solitary and multiple pulmonary nodules. Unmeasured confounders such as smoking history and comorbid conditions may confound relationships between molecular findings and clinical outcomes. The study is also limited by its short follow-up, precluding robust survival analyses. Insufficient longitudinal outcome data impedes validation of ctDNA-based MRD detection prognostic utility. Bronkhorst et al. emphasized that clinical integration of ctDNA analysis requires ongoing methodological refinement and validation across malignancies (49), while MRD tracking methodologies necessitate standardization for cross-institutional reproducibility. Single-center recruitment risks population-specific bias; incorporating diverse datasets would enhance conclusion robustness and therapeutic applicability. Future multicenter studies should validate these findings in expanded cohorts while investigating long-term clinical impacts of ctDNA-guided MRD monitoring. Addressing these limitations will advance understanding of ctDNA, supporting its establishment as a standard biomarker for pulmonary nodule management to optimize patient outcomes. In addition, the absence of detected gene fusions should be interpreted with caution, as comprehensive fusion profiling typically requires RNA-based sequencing approaches.

Delivering new insights into molecular trajectories, serial monitoring efficacy, and clonal evolution in pulmonary nodules, this investigation underscores ctDNA’s critical role in enhancing disease surveillance. It further delineates clinically relevant molecular and phenotypic distinctions between solitary and multiple nodules, revealing promising biomarkers for MRD tracking. These findings reveal the complexity of ctDNA in multifocal presentations while identifying actionable pathways for tailored therapeutic regimens. Subsequent research should prioritize recruitment of larger, multi-center cohorts representing diverse populations and implement longitudinal designs to comprehensively define ctDNA’s utility in MRD surveillance, prognostic stratification, and treatment optimization for pulmonary nodule management.

Conclusions

This study identifies significant genomic and ctDNA disparities between solitary and multifocal stage I NSCLC. MPLC exhibits higher genomic complexity and more robust ctDNA shedding. We identified two distinct tracking patterns and demonstrated that single-lesion sampling often fails to capture the full clonal landscape. Consequently, multi-site tissue analysis combined with deep liquid biopsy is essential for sensitive MRD monitoring. These findings provide a framework for ctDNA-guided risk stratification and personalized postoperative management in early-stage lung cancer.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1990/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1990/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1990/prf

Funding: This work was supported by Noncommunicable Chronic Diseases-National Science and Technology Major Project (Nos. 2024ZD0529400 and 2024ZD0529406); Hematopoietic Acute Radiation Syndrome Medical and Pharmaceutical Basic Research Innovation Center, Ministry of Education of the People’s Republic of China (No. RSBIC-B-202405); Chongqing Science and Health Joint Medical Science and Technology Innovation and Key Project (No. 2025GGXM001); and New Chongqing Youth Innovation Talent Project (Grant No. CSTB2024NSCQ-QCXMX0031).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1990/coif). W.S. and Q.L. declare employment with Genecast Biotechnology Co. Ltd. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. It was approved by the Medical Ethics Committee of The Second Affiliated Hospital of Army Medical University, PLA (Approval No. 2024-205-01). Written informed consent was obtained from all participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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