Driver Mutations and Malignant Pleural Effusion in Non-small Cell Lung Cancer

Driver Mutations and Malignant Pleural Effusion in Non-small Cell Lung Cancer | 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 Driver Mutations and Malignant Pleural Effusion in Non-small Cell Lung Cancer Ugur Temel, Onur Derdiyok This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6275845/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in BMC Medical Genomics → Version 1 posted 9 You are reading this latest preprint version Abstract Background Malignant pleural effusion (MPE) complicates approximately 50% of non-small cell lung cancer (NSCLC) cases, signaling advanced disease and poor patient outcomes. While driver mutations including programmed death-ligand 1 (PD-L1), anaplastic lymphoma kinase (ALK), proto-oncogene tyrosine-protein kinase-1 (ROS1), and threonine at amino acid position 790 (T790M) are critical in NSCLC progression, their relationship with MPE development remains inadequately characterized. Methods This retrospective cohort study examined 130 NSCLC patients (52 with MPE, 78 without MPE). Clinical characteristics and comprehensive molecular profiles were analyzed using next-generation sequencing. Statistical comparisons were performed, and a Least Absolute Shrinkage and Selection Operator (LASSO) regularized logistic regression model identified independent predictors of MPE. Model performance was evaluated using receiver operating characteristic (ROC) analysis. Results PD-L1 expression demonstrated a significant association with MPE development (Odds ratio = 2.78, p < 0.01), nearly tripling the likelihood of effusion. The presence of ALK, ROS1, and T790M mutations (combined OR = 2.41, p 50 pack-years), and right inferior lobe tumor location (all p < 0.05). The predictive model demonstrated robust performance with an area under the curve of 0.80. Conclusions These findings establish important associations between specific driver mutations, particularly PD-L1 expression, and MPE development in NSCLC patients. Identifying these genetic and clinical predictors may enhance risk stratification approaches and guide personalized treatment strategies, especially for those with advanced disease. Further prospective validation studies are needed to confirm these associations and explore their therapeutic implications. Driver mutations Malignant pleural effusion Non-small cell lung cancer Figures Figure 1 Introduction Lung cancer remains the leading cause of cancer-related mortality worldwide, with 1.8 million deaths annually and non-small cell lung cancer (NSCLC) comprising 85% of cases ( 1 , 2 ). Despite advances in detection and targeted therapies, prognosis remains poor, particularly for patients with malignant pleural effusion (MPE), a complication associated with disease progression and limited treatment options ( 3 , 4 ). MPE occurs in nearly 50% of lung cancer patients and indicates advanced-stage malignancy (stage IV), signaling systemic tumor spread and often precluding curative interventions ( 5 ). Its presence correlates with median survival of less than one year ( 6 ). Pleural metastasis pathogenesis follows the invasion-metastasis cascade involving epithelial-to-mesenchymal transition (EMT), tumor cell intravasation, immune evasion, extravasation, and adaptation to the pleural microenvironment ( 7 ). This begins when cancer cells undergo EMT, enhancing their migratory potential. These transformed cells detach from the primary tumor, infiltrate circulatory or lymphatic systems, and extravasate into the pleural space ( 8 ). The pleural microenvironment facilitates tumor cell survival by modulating immune responses, activating fibroblasts, and remodeling the extracellular matrix ( 9 , 10 ). NSCLC harbors a high frequency of targetable driver mutations critical in tumor development, metastatic potential, and treatment response. These genetic alterations drive oncogenesis by activating pathways that promote proliferation, inhibit apoptosis, and enhance metastatic dissemination ( 11 ). Key oncogenic mutations include epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), proto-oncogene tyrosine-protein kinase-1 (ROS1), B-raf proto-oncogene serine/threonine-protein kinase (BRAF), Kirsten rat sarcoma virus (KRAS), threonine at amino acid position 790 (T790M) mutations, and programmed death-ligand 1 (PD-L1) overexpression ( 12 , 13 ). Identifying these mutations has transformed NSCLC management through tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs), significantly improving survival outcomes ( 14 , 15 ). Despite the established role of driver mutations in tumor progression and therapeutic response, their association with pleural effusion development remains poorly understood. Evidence suggests specific mutations may increase pleural dissemination likelihood and some studies report EGFR-mutated tumors have higher propensity to develop MPE, possibly due to increased tumor cell plasticity, higher deoxyribonucleic acid (DNA) content, and enhanced mesothelial invasion capacity ( 16 , 17 ). Conversely, another study suggests mutation status alone may not reliably predict pleural involvement, highlighting the need for a multifactorial approach incorporating tumor biology, microenvironmental interactions, and clinical parameters ( 18 ). We hypothesize that specific driver mutations are more prevalent in NSCLC patients with pleural effusion and thus, this may serve as predictive markers for pleural dissemination. Understanding these molecular determinants could facilitate early risk stratification, guide therapeutic decision-making, and improve treatment outcomes. This study aims to evaluate the prevalence and distribution of key driver mutations (PD-L1, ALK, ROS1, and T790M) in NSCLC patients with and without MPE and determine their association with pleural involvement. Materials and Methods Study Design and Patient Selection This retrospective cohort study reviewed medical records of NSCLC patients treated at the Thoracic Surgery and Oncology departments of a tertiary referral center between January 2020 and September 2024. Patients were included if they were ≥ 18 years old, had a confirmed diagnosis of NSCLC, and had sufficient clinical, radiological, and molecular testing data. Patients were stratified by the presence or abscence of MPE. Patients were excluded if they had incomplete molecular test results, a history of another malignancy, or received systemic therapy prior to mutation testing. Clinical and Molecular Data Collection Demographic and clinical variables included age, gender, smoking history, tumor location, histological subtype, and presence of MPE. Tumor histology was classified into adenocarcinoma, squamous cell carcinoma, neuroendocrine, pleomorphic, and mucinous subtypes based on pathology reports. Smoking exposure was categorized as non-smokers, up to 40 pack-years, up to 50 pack-years, and heavy smokers (> 50 pack-years). MPE was diagnosed using chest radiography, computed tomography (CT), or ultrasound and confirmed by cytopathological analysis of pleural fluid specimens. MPE was defined as pleural fluid containing malignant cells. Molecular Testing Driving mutations were detected by anchored multiplex polymerase chain reaction (AMP) method ( 19 ). Briefly, comprehensive DNA and ribonucleic acid (RNA) based next-generation sequencing (NGS) was performed on the ArcherDX platform using AMP chemistry, detecting point mutations, copy number alterations, fusions, and exon-skipping variants with high sensitivity. DNA/RNA was extracted from formalin-fixed paraffin-embedded (FFPE) tumor tissue, fresh tumor biopsies, liquid biopsy samples, or pleural fluid cell blocks. Mutation profiling included PD-L1, ALK, ROS1, and T790M in all patients, while EGFR, BRAF, and KRAS mutations were assessed in a subset based on clinical indications. Reference genes for quality control included actin beta (ACTB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and tumor protein p53 (TP53). Ethical Considerations This study was conducted in accordance with the Declaration of Helsinki and approved by the local ethical committee (Approval No:2778, Date:14.01.2025). As this was a retrospective study, individual written informed consent was not newly obtained; however, all patients had previously provided written informed consent for their medical data to be used for research purposes at the time of hospital admission. Statistical Analyses All analyses were conducted using SPSS (Version 29; IBM SPSS, USA) and R (Version 4.4; R Core Team, 2024). Continuous variables were compared using the Mann-Whitney U test, while categorical variables were analyzed using the Chi-square test or Fisher’s exact test, as appropriate. A p-value < 0.05 was considered statistically significant. To identify independent predictors of pleural effusion, a Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model was applied. The model selected relevant clinical and molecular predictors, and results were reported as odds ratios (ORs) with 95% confidence intervals (CIs). Model assumptions, including multicollinearity variance inflatin factor (VIF) < 5, linearity of continuous predictors in the logit transformation, and proportional odds assumption for ordinal predictors, were tested before finalizing the analysis. Model performance was assessed using receiver operating characteristic (ROC) curve analysis, with classification metrics including sensitivity, specificity, precision, and F1-score. Internal validation was performed using an 80%-20% train-test split and k-fold cross-validation (k = 5) to ensure generalizability. Mutation effects were further evaluated using univariate and multivariate logistic regression models, assessing both individual and grouped mutation impacts. Results Table 1 presents the baseline characteristics of 130 lung cancer patients stratified by MPE status. The demographic and clinical variables were analyzed to identify potential associations with MPE development. Female gender was selected as the reference category for gender-based comparisons. The study population had a median age of 67 years (range: 35–90 years), with no significant difference in age distribution between patients with and without MPE (p = 0.763). In terms of anatomical considerations, the left superior lobe was designated as the reference location for tumor site analysis. While the distribution of tumor locations showed some variation between groups, with right inferior lobe involvement being more frequent in the presence of MPE (23.1% versus 6.4%, respectively), this difference did not reach statistical significance (p = 0.133). For histological classification, mucinous type served as the reference category, with adenocarcinoma being the predominant type in both groups (50.8% overall), showing similar distribution patterns (p = 0.956). Smoking status analysis, using non-smokers as the reference category, revealed significant differences between groups (p = 0.016). Notably, heavy smoking (above 50 pack-years) was more prevalent in patients with MPE compared to those without (32.7% versus 12.8%, respectively). Regarding mutation status, cases without detected mutations served as the reference category, and while PDL1 mutations were more frequent in the presence of MPE (21.2% versus 9.0%, respectively), the overall distribution of mutation status did not differ significantly between groups (p = 0.947). Treatment strategies, with chemotherapy alone serving as the reference category, showed varying distributions between groups. Although combination therapy (chemotherapy and immunotherapy) was more commonly employed in patients with MPE (46.2% versus 24.4%, respectively), the overall distribution of management strategies did not demonstrate statistical significance (p = 0.726). These findings suggest that among the analyzed characteristics, smoking history demonstrates the strongest association with MPE development in lung cancer patients, warranting particular attention in clinical assessment and risk stratification. Table 1 Selected clinical characteristics of the patients with NSCLC. Characteristic Total Population (N = 130) No Pleural Effusion (n = 78) Pleural Effusion (n = 52) P-value Age (years) Median (min-max) 67 (35–90) 67 (43–82) 67 (35–90) 0.763† Gender, n (%) 0.464‡ Male 109 (83.8) 64 (82.1) 45 (86.5) Female* 21 (16.2) 14 (17.9) 7 (13.5) Tumor Location, n (%) 0.133‡ Left Superior* 39 (30.0) 30 (38.5) 9 (17.3) Left Inferior 15 (11.5) 6 (7.7) 9 (17.3) Right Superior 38 (29.2) 24 (30.8) 14 (26.9) Right Middle 21 (16.2) 13 (16.7) 8 (15.4) Right Inferior 17 (13.1) 5 (6.4) 12 (23.1) Histological Type, n (%) 0.956‡ Adenocarcinoma 66 (50.8) 40 (51.3) 26 (50.0) Squamous Cell 58 (44.6) 36 (46.1) 22 (42.3) Neuroendocrine 2 (1.5) 1 (1.3) 1 (1.9) Pleomorphic 3 (2.3) 1 (1.3) 2 (3.8) Mucinous* 1 (0.8) 0 (0.0) 1 (1.9) Smoking Status, n (%) 0.016‡ No Smoking* 26 (20.0) 13 (16.7) 13 (25.0) Up to 40 pack-years 39 (30.0) 27 (34.6) 12 (23.1) Up to 50 pack-years 38 (29.2) 28 (35.9) 10 (19.2) Above 50 pack-years 27 (20.8) 10 (12.8) 17 (32.7) Mutation Status, n (%) 0.947‡ No Mutation* 104 (80.0) 68 (87.2) 36 (69.2) PDL1 18 (13.8) 7 (9.0) 11 (21.2) Others 8 (6.2) 3 (3.8) 5 (9.6) Management Strategy, n (%) 0.726‡ Chemotherapy* 79 (60.8) 56 (71.8) 23 (44.2) Chemotherapy and Immunotherapy 43 (33.1) 19 (24.4) 24 (46.2) Immunotherapy 8 (6.1) 3 (3.8) 5 (9.6) *Reference category for statistical analyses. †Mann-Whitney U test. ‡Chi-square test or Fisher's exact test when appropriate. A logistic regression model with LASSO regularization was developed to identify significant predictors of MPE in NSCLC patients. The model achieved 74.6% accuracy with an area under the curve (AUC)-ROC of 0.80 (Table 1 ), showing 75% precision/86% recall for the negative class and 73% precision/58% recall for the positive class. Genetic mutations were strong predictors: PD-L1 expression (β = 1.02, OR = 2.78, p = 0.01) nearly tripled pleural effusion likelihood, while ALK, ROS1, and T790M alterations (β = 0.71, OR = 2.04, p = 0.03) were also independently associated with its development. Table 2 showed other significant predictors: age (β = 0.11, OR = 1.12, p = 0.04) increased odds by 12% per year; heavy smoking history (β = 0.89, OR = 2.43, p = 0.02) nearly doubled the likelihood compared to non-smokers; adenocarcinoma histology (β = 0.75, OR = 2.12, p = 0.03) doubled the odds versus mucinous carcinoma; and right inferior lobe tumor location (β = 0.68, OR = 1.97, p = 0.04) suggested anatomical predisposition. The model showed no multicollinearity (VIF < 5) and maintained stability under cross-validation. These findings suggest combining clinical parameters with genetic mutations provides a predictive framework for MPE risk assessment, with implications for early diagnosis and personalized treatment strategies. Table 2 Logistic Regression Coefficients and Odds Ratios. Variable Coefficient (β) Odds Ratio (OR) p-value Age 0.15 1.16 50 pack-years) 0.92 2.51 < 0.01 Histological subtype (Adenocarcinoma vs. mucinous) 0.80 2.24 < 0.05 Tumor location (Right inferior vs. left superior) 0.73 2.08 < 0.05 PD-L1 Expression 1.02 2.78 < 0.01 Other Mutations (ALK, ROS1, T790M) 0.88 2.41 < 0.05 The ROC curve shows the logistic regression model's performance in predicting MPE in NSCLC patients (Fig. 1 ). With an AUC of 0.765 (95% CI: 0.680–0.850, SE: 0.0434, p < 0.0001), the model demonstrates strong discriminative ability. The diagonal dashed line indicates a non-discriminative model (AUC = 0.5), while the solid blue line plots sensitivity against false positive rate. The cohort comprised 78 control patients and 52 with MPE. Detailed mutation pattern analysis revealed distinct associations with MPE development (Table 3 ). PD-L1 mutations showed significant association (OR:2.78, 95% CI:1.14–6.48, p = 0.024), present in 21.2% of patients with MPE versus 9.0% without. While individual ALK, ROS1, and T790M mutations weren't statistically significant alone, their combined effect in the logistic regression model showed significant association with MPE (OR:2.41, p < 0.05) (Table 2 ), suggesting their collective presence might contribute to disease progression despite individual mutations not being strong predictors alone. Table 3 Association between Tumor Mutations and Pleural Effusion in patients with NSCLC. Mutation Type With Effusion (n = 52) n (%) Without Effusion (n = 78) n (%) Crude OR (95% CI) P-value No Mutation 36 (69.2) 68 (87.2) Reference - PDL1 11 (21.2) 7 (9.0) 2.78 (1.14–6.48) 0.024 ALK 2 (3.8) 1 (1.3) 3.08 (0.27–34.96) 0.367 ROS1 1 (1.9) 2 (2.6) 0.75 (0.07–8.48) 0.815 T790M 2 (3.8) 0 (0.0) NA 0.169 Discussion Our study identified significant clinical and molecular predictors of MPE in NSCLC patients. The logistic regression model (AUC = 0.80) effectively distinguished between patients with and without MPE. PD-L1 expression (OR = 2.78, p < 0.01) nearly tripled MPE-likelihood. ALK, ROS1, and T790M alterations (OR = 2.41, p < 0.05) were independently linked to MPE. Older age (OR = 1.12, p = 0.04), heavy smoking (OR = 2.43, p = 0.02), adenocarcinoma histology (OR = 2.12, p = 0.03), and right inferior lobe tumor location (OR = 1.97, p = 0.04) were key risk factors. Patients with MPE more commonly received combined chemotherapy and immunotherapy (46.2% versus 24.4%, respectively). These findings underscore the interplay between genetic mutations, tumor biology, and clinical factors in the MPE development. Several studies have established that driver mutations contribute significantly to MPE in NSCLC ( 13 ). The association between PD-L1 expression and MPE (22.9% versus 9.3%, respectively) aligns with previous reports demonstrating interactions between oncogenic pathways and immune checkpoint regulation ( 13 ). High PD-L1 expression doesn't always predict improved response to PD-1/PD-L1 inhibitors, particularly in tumors with EGFR or ALK mutations ( 14 , 20 , 21 ). This may be attributed to the pleural immune microenvironment, a sanctuary for immune-evasive tumor cells ( 20 ). Our findings support this hypothesis, as MPE-positive patients exhibited distinct molecular characteristics. Beyond immune checkpoints, mesothelial-mesenchymal transition, extracellular matrix remodeling, and immunosuppressive signaling facilitate tumor invasion into the pleural cavity ( 15 , 16 , 22 ), potentially explaining increased MPE in right inferior lobe tumors. The pleural space acts as an active metastatic niche with mesenchymal-like tumor cells, stromal fibroblasts, and immunomodulatory cells ( 15 , 16 , 23 ), suggesting both genetic and microenvironmental factors contribute to pleural involvement. Distinct molecular mechanisms explain how driver mutations facilitate pleural metastasis. KRAS-mutant tumors induce C-C motif chemokine ligand 2 secretion, mobilizing myeloid-derived suppressor cells and promoting a metastatic-supportive environment ( 5 , 24 ). EGFR-mutated tumors exhibit greater genomic instability, linked to higher DNA content and frequent aneuploid peaks ( 4 , 12 , 25 ). Our findings support these observations, as patients with ALK, ROS1, and T790M mutations demonstrated higher likelihood of MPE (OR:2.41, p < 0.05), corroborating studies showing chromosomally unstable tumors are more prone to pleural dissemination ( 18 ). Research into EGFR-TKI efficacy in patients with pleural effusion has yielded conflicting results. Some studies report EGFR-TKI-naïve patients with MPE had insignificantly shorter treatment durations (14.8 versus 19.8 months) ( 17 ). Our study found no significant difference in treatment failure rates between MPE-positive and negative patients. Additionally, EGFR exon 19 deletions versus exon 21 L858R mutations may influence treatment response, as studies report variable osimertinib efficacy based on mutation subtype ( 17 , 26 ). Recent advances in molecular testing show pleural effusion cfDNA provides higher sensitivity (97%) for EGFR mutation detection compared to plasma cfDNA (74%) ( 12 ). Pleural effusion-positive patients in our cohort were more likely to undergo comprehensive molecular testing. Additionally, patient-derived organoids have emerged as promising preclinical models for evaluating drug response in NSCLC patients with pleural effusion ( 12 ). Our findings support integrating molecular, immune, and microenvironmental profiling in managing NSCLC patients with MPE, providing insights into anatomical, histological, and clinical factors influencing pleural involvement. Strengths Our study comprehensively investigates the relationship between genetic alterations and MPE in NSCLC. The logistic regression model with LASSO regularization selected meaningful predictive variables, improving accuracy and reducing overfitting. Our dataset reflects real-world clinical settings, enhancing applicability to clinical practice. The integration of clinical and molecular data strengthens evidence that MPE is driven by genetic predispositions and anatomical factors. Limitations and Future Research Directions This retrospective study introduces selection bias and limits establishing causality. The sample size for certain mutations was relatively small, potentially affecting statistical power. Although our model demonstrated strong performance, external validation is necessary. Our dataset lacked detailed information on tumor burden, pleural fluid cytology, and immune microenvironmental markers. Besides, since treatment is ongoing, and the number of the patients are not high, we did not perform survival analysis. Thus, prospective multicenter studies with larger population are required to validate these genetic and clinical predictors in larger populations. Liquid biopsy techniques and pleural fluid genomic analysis may further elucidate mechanisms of pleural invasion. Exploring tumor microenvironment, immune checkpoint inhibitors, and inflammatory cytokines could provide novel therapeutic targets. Future studies should investigate how these factors influence response to targeted therapies and immunotherapy. Conclusions This study highlights significant associations between driver mutations, clinical characteristics, and MPE in NSCLC. PD-L1 expression and ALK, ROS1, and T790M mutations are independently linked to increased MPE risk. Age, smoking history, tumor location, and histological subtype emerged as crucial predictors, suggesting pleural effusion development is driven by genetic and anatomical factors. The strong association between PD-L1 expression and MPE suggests immunotherapy responses may be influenced by the tumor microenvironment. The independent effect of ALK, ROS1, and T790M mutations underscores the need for comprehensive molecular profiling. Given the variations in treatment patterns between patients with and without MPE, future clinical strategies should integrate molecular and clinical data to guide personalized therapy. As NSCLC treatment evolves, integrating molecular and microenvironmental profiling will be essential for improving outcomes in patients with MPE. Declarations Ethics Approval and Consent to Participate This study was conducted in accordance with the Declaration of Helsinki and approved by the Şişli Hamidiye Etfal Training and Research Hospital Health Application and Research Center Clinical Research Ethics Committee (Approval No:2778, Date:14.01.2025). As this was a retrospective study, individual written informed consent was not newly obtained; however, all patients had previously provided written informed consent for their medical data to be used for research purposes, which was verified through hospital records before data extraction. Consent for Publication Not applicable. No identifiable patient data or images are included in this study. Availability of Data and Materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request, in compliance with institutional and ethical guidelines. Competing Interests The authors declare that they have no competing interests relevant to this study. Funding This research received no external funding. All aspects of the study were supported by institutional resources. Author Contributions [Author 1 U. T.]: Conceptualization, Data Collection, Formal Analysis, Writing – Original Draft [Author 2 O. D.]: Methodology, Statistical Analysis, Writing – Review & Editing Acknowledgments The authors would like to thank [Name of Institution or Research Team] for their support and assistance in data collection and analysis. Use of Artificial Intelligence in Manuscript Preparation The authors acknowledge the use of artificial intelligence (AI)-based tools for language editing, structural refinement, and manuscript organization. AI-assisted platforms, including ChatGPT-4 and Claude AI, were employed to enhance the clarity and readability of the text while maintaining scientific accuracy. However, all scientific interpretations, data analyses, and conclusions were formulated by the authors to ensure the originality and integrity of the study. The statistical methods and computational scripts used in this study adhere to best-practice guidelines for reproducible research, ensuring transparency in data processing and model interpretation. The authors confirm that all analyses were manually reviewed to validate statistical outputs and avoid AI-generated biases in model selection. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49. Zhao Y, Yu L, Wang L, Wu Y, Chen H, Wang Q, et al. Current status of and progress in the treatment of malignant pleural effusion of lung cancer. Front Oncol. 2023;12:961440. Koegelenberg CFN, Shaw JA, Irusen EM, Lee YCG. Contemporary best practice in the management of malignant pleural effusion. Ther Adv Respir Dis. 2018;12:1753466618785098. Wu YY, Hsu YL, Huang YC, Su YC, Wu KL, Chang CY, et al. Characterization of the pleural microenvironment niche and cancer transition using single-cell RNA sequencing in EGFR-mutated lung cancer. Theranostics. 2023;13(13):4412–29. Agalioti T, Giannou AD, Stathopoulos GT. Pleural involvement in lung cancer. J Thorac Dis. 2015;7:1021–30. Changchien CY, Chen Y, Chang HH, Chang SY, Tsai WC, Tsai HC, et al. Effect of malignant-associated pleural effusion on endothelial viability, motility and angiogenesis in lung cancer. Cancer Sci. 2020;111(10):3747–58. Kunimasa K, Tamiya M, Inoue T, Kawamura T, Miyazaki A, Kojitani Y, et al. Clinical application of the Lung Cancer Compact Panel™ using various types of cytological specimens in patients with lung cancer. Lung Cancer. 2024;189:107498. Hendriks LE, Kerr KM, Menis J, et al. Oncogene-addicted metastatic non-small-cell lung cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol. 2023;34:339–57. Ji M, Liu Y, Li Q, Li X, Ning Z, Zhao W, et al. PD-1/PD-L1 expression in non-small-cell lung cancer and its correlation with EGFR/KRAS mutations. Cancer Biol Ther. 2016;17(4):407–13. Chevallier M, Borgeaud M, Addeo A, Friedlaender A. Oncogenic driver mutations in non-small cell lung cancer: Past, present and future. World J Clin Oncol. 2021;12(4):217–37. Harrison PT, Vyse S, Huang PH. Rare epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer. Semin Cancer Biol. 2020;61:167–79. Lee KWC, Li MSC, Gai W, Lau YM, Chan AKC, Chan OSH, et al. Testing for EGFR Variants in Pleural and Pericardial Effusion Cell-Free DNA in Patients With Non-Small Cell Lung Cancer. JAMA Oncol. 2022;8(4):563–9. LoRusso PM, Schalper K, Sosman J. Targeted therapy and immunotherapy: Emerging biomarkers in metastatic melanoma. Pigment Cell Melanoma Res. 2020;33(3):390–402. 10.1111/pcmr.12847 . Dantoing E, Piton N, Salaün M, Thiberville L, Guisier F, Yıl. Anti-PD1/PD-L1 immunotherapy for non-small cell lung cancer with actionable oncogenic driver mutations. Int J Mol Sci. 2021;22:6288. https://doi.org/10.3390/ijms22126288 . Wu J, Lin Z (Yıl), editors. Non-small cell lung cancer targeted therapy: Drugs and mechanisms of drug resistance. Int. J. Mol. Sci. 2022, 23, 15056. https://doi.org/10.3390/ijms232315056 Brazel D, Kroening G, Nagasaka M, Yıl.). Non-small cell lung cancer with EGFR or HER2 exon 20 insertion mutations: Diagnosis and treatment options. BioDrugs (2022) 36:717–729. Kiritani A, Amino Y, Uchibori K, Akita T, Harutani Y, Ogusu S, Tsugitomi R, Manabe R, Ariyasu R, Kitazono S, Yanagitani N, Nishio M, Yıl. Testing for EGFR variants in pleural and pericardial effusion cell-free DNA in efficacy of osimertinib in patients with EGFR-mutation positive non-small cell lung cancer with malignant pleural effusion. Thorac Cancer. 2024;15:402–9. Zheng Z, Liebers M, Zhelyazkova B, Cao Y, Panditi D, Lynch KD, Chen J, Robinson HE, Shim HS, Chmielecki J, Pao W, Engelman JA, Iafrate AJ, Le LP. Anchored multiplex PCR for targeted next-generation sequencing. Nat Med. 2014;20(12):1479–84. Du Y, Guo X, Wang R, Ma Y, Zhang Y, Liu Y, Dong L, Wu J, Ji X, Wang H. The correlation between EGFR mutation status and DNA content of lung adenocarcinoma cells in pleural effusion. J Cancer 2020, 11 2265–72. Kumar M, Guleria B, Swamy S, Soni S. Correlation of programmed death-ligand 1 expression with gene expression and clinicopathological parameters in Indian patients with non-small cell lung cancer. Lung India. March-April 2020;37(2):145–50. Azuma K, Ota K, Kawahara A, Hattori S, Iwama E, Harada T, Matsumoto K, Takayama K, Takamori S, Kage M, Hoshino T, Nakanishi Y, Okamoto I. Association of PD-L1 overexpression with activating EGFR mutations in surgically resected nonsmall-cell lung cancer. Ann Oncol. 2014;25(10):1935–40. Hiltbrunner S, Mannarino L, Kirschner MB, Opitz I, Rigutto A, Laure A, Lia M, Nozza P, Maconi A, Marchini S, D'Incalci M, Curioni-Fontecedro A, Grosso F. Tumor Immune Microenvironment and Genetic Alterations in Mesothelioma. Front Oncol. 2021;11:660039. https://doi.org/10.3389/fonc.2021.660039 . Qin S, Jiang J, Lu Y, Nice EC, Huang C, Zhang J, He W. Emerging role of tumor cell plasticity in modifying therapeutic response. Signal Transduct Target Ther. 2020;5:228. Altorki NK, Markowitz GJ, Gao D, Port JL, Saxena A, Stiles B, McGraw T, Mittal V. The lung microenvironment: an important regulator of tumour growth and metastasis. Nat Rev Cancer. 2019;19(1):9–31. Du Y, Guo X, Wang R, Ma Y, Zhang Y, Liu Y, Dong L, Wu J, Ji X, Wang H. The Correlation between EGFR Mutation Status and DNA Content of Lung Adenocarcinoma Cells in Pleural Effusion. J Cancer. 2020;11(8):2265–72. Grant MJ, Aredo JV, Starrett JH, Stockhammer P, van Alderwerelt IK, Wurtz A, Piper-Valillo AJ, Piotrowska Z, Falcon C, Yu HA, Aggarwal C, Scholes D, Patil T, Nguyen C, Phadke M, Li F, Neal J, Lemmon MA, Walther Z, Politi K, Goldberg SB. Efficacy of osimertinib in patients with lung cancer positive for uncommon EGFR exon 19 deletion mutations. Clin Cancer Res. 2023;29(11):2123–30. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in BMC Medical Genomics → Version 1 posted Editorial decision: Revision requested 09 May, 2025 Reviews received at journal 24 Apr, 2025 Reviewers agreed at journal 08 Apr, 2025 Reviews received at journal 04 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviewers invited by journal 31 Mar, 2025 Editor assigned by journal 31 Mar, 2025 Submission checks completed at journal 28 Mar, 2025 First submitted to journal 28 Mar, 2025 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. 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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-6275845","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":442339351,"identity":"2eea3a0a-9471-46cb-a3c2-5e0eec9350fd","order_by":0,"name":"Ugur Temel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDCCA3AWc+NjCM3cQIwWAyBmbDYGM5gZidfSJg1l4NfCd/uM8WvePX/kdNsb26oLKv5E87cDtfyo2IZTi+S5HDNrnmcGxmZnDrbdnnHGIHfGYcYGxp4zt3FqMTjDY2bMc8AgcduNxLbbvG0GuQ1ALcyMbYS11G+7/7CtGKRlPhFajB8DtSSY3WBsYwZp2UBIi+QZtjLGOQeMDbedSWyW5jljnLsRqOUgPr/wnWHe/OHNATl5s+OHD37mqZDLnXf+8MEHPypwawECNgkMoQP41AMB8wcCCkbBKBgFo2CkAwDyGFvE3uKDrwAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Ugur","middleName":"","lastName":"Temel","suffix":""},{"id":442339352,"identity":"029644a3-343a-4e2d-ae65-4244cbe07415","order_by":1,"name":"Onur Derdiyok","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Onur","middleName":"","lastName":"Derdiyok","suffix":""}],"badges":[],"createdAt":"2025-03-21 08:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6275845/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6275845/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12920-025-02180-x","type":"published","date":"2025-07-01T15:57:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81026921,"identity":"a55f7731-3752-4eab-b94a-5c57bb063202","added_by":"auto","created_at":"2025-04-21 10:45:05","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":306825,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver Operating Characteristic (ROC) Curve for Pleural Effusion Prediction.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6275845/v1/91602d8701ac5e13ff78a01b.jpeg"},{"id":86180164,"identity":"818f8289-e015-46bd-90bf-160e3fa95b3d","added_by":"auto","created_at":"2025-07-07 16:21:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1173849,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6275845/v1/c12dd674-437f-4314-bfbe-4bcb7d6f3532.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDriver Mutations and Malignant Pleural Effusion in Non-small Cell Lung Cancer\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer remains the leading cause of cancer-related mortality worldwide, with 1.8\u0026nbsp;million deaths annually and non-small cell lung cancer (NSCLC) comprising 85% of cases (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Despite advances in detection and targeted therapies, prognosis remains poor, particularly for patients with malignant pleural effusion (MPE), a complication associated with disease progression and limited treatment options (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). MPE occurs in nearly 50% of lung cancer patients and indicates advanced-stage malignancy (stage IV), signaling systemic tumor spread and often precluding curative interventions (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Its presence correlates with median survival of less than one year (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePleural metastasis pathogenesis follows the invasion-metastasis cascade involving epithelial-to-mesenchymal transition (EMT), tumor cell intravasation, immune evasion, extravasation, and adaptation to the pleural microenvironment (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This begins when cancer cells undergo EMT, enhancing their migratory potential. These transformed cells detach from the primary tumor, infiltrate circulatory or lymphatic systems, and extravasate into the pleural space (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The pleural microenvironment facilitates tumor cell survival by modulating immune responses, activating fibroblasts, and remodeling the extracellular matrix (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNSCLC harbors a high frequency of targetable driver mutations critical in tumor development, metastatic potential, and treatment response. These genetic alterations drive oncogenesis by activating pathways that promote proliferation, inhibit apoptosis, and enhance metastatic dissemination (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Key oncogenic mutations include epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), proto-oncogene tyrosine-protein kinase-1 (ROS1), B-raf proto-oncogene serine/threonine-protein kinase (BRAF), Kirsten rat sarcoma virus (KRAS), threonine at amino acid position 790 (T790M) mutations, and programmed death-ligand 1 (PD-L1) overexpression (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Identifying these mutations has transformed NSCLC management through tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs), significantly improving survival outcomes (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the established role of driver mutations in tumor progression and therapeutic response, their association with pleural effusion development remains poorly understood. Evidence suggests specific mutations may increase pleural dissemination likelihood and some studies report EGFR-mutated tumors have higher propensity to develop MPE, possibly due to increased tumor cell plasticity, higher deoxyribonucleic acid (DNA) content, and enhanced mesothelial invasion capacity (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Conversely, another study suggests mutation status alone may not reliably predict pleural involvement, highlighting the need for a multifactorial approach incorporating tumor biology, microenvironmental interactions, and clinical parameters (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe hypothesize that specific driver mutations are more prevalent in NSCLC patients with pleural effusion and thus, this may serve as predictive markers for pleural dissemination. Understanding these molecular determinants could facilitate early risk stratification, guide therapeutic decision-making, and improve treatment outcomes. This study aims to evaluate the prevalence and distribution of key driver mutations (PD-L1, ALK, ROS1, and T790M) in NSCLC patients with and without MPE and determine their association with pleural involvement.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Patient Selection\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study reviewed medical records of NSCLC patients treated at the Thoracic Surgery and Oncology departments of a tertiary referral center between January 2020 and September 2024. Patients were included if they were \u0026ge;\u0026thinsp;18 years old, had a confirmed diagnosis of NSCLC, and had sufficient clinical, radiological, and molecular testing data. Patients were stratified by the presence or abscence of MPE. Patients were excluded if they had incomplete molecular test results, a history of another malignancy, or received systemic therapy prior to mutation testing.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical and Molecular Data Collection\u003c/h3\u003e\n\u003cp\u003eDemographic and clinical variables included age, gender, smoking history, tumor location, histological subtype, and presence of MPE. Tumor histology was classified into adenocarcinoma, squamous cell carcinoma, neuroendocrine, pleomorphic, and mucinous subtypes based on pathology reports. Smoking exposure was categorized as non-smokers, up to 40 pack-years, up to 50 pack-years, and heavy smokers (\u0026gt;\u0026thinsp;50 pack-years).\u003c/p\u003e \u003cp\u003eMPE was diagnosed using chest radiography, computed tomography (CT), or ultrasound and confirmed by cytopathological analysis of pleural fluid specimens. MPE was defined as pleural fluid containing malignant cells.\u003c/p\u003e\n\u003ch3\u003eMolecular Testing\u003c/h3\u003e\n\u003cp\u003eDriving mutations were detected by anchored multiplex polymerase chain reaction (AMP) method (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Briefly, comprehensive DNA and ribonucleic acid (RNA) based next-generation sequencing (NGS) was performed on the ArcherDX platform using AMP chemistry, detecting point mutations, copy number alterations, fusions, and exon-skipping variants with high sensitivity. DNA/RNA was extracted from formalin-fixed paraffin-embedded (FFPE) tumor tissue, fresh tumor biopsies, liquid biopsy samples, or pleural fluid cell blocks. Mutation profiling included PD-L1, ALK, ROS1, and T790M in all patients, while EGFR, BRAF, and KRAS mutations were assessed in a subset based on clinical indications. Reference genes for quality control included actin beta (ACTB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and tumor protein p53 (TP53).\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki and approved by the local ethical committee (Approval No:2778, Date:14.01.2025). As this was a retrospective study, individual written informed consent was not newly obtained; however, all patients had previously provided written informed consent for their medical data to be used for research purposes at the time of hospital admission.\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eAll analyses were conducted using SPSS (Version 29; IBM SPSS, USA) and R (Version 4.4; R Core Team, 2024). Continuous variables were compared using the Mann-Whitney U test, while categorical variables were analyzed using the Chi-square test or Fisher\u0026rsquo;s exact test, as appropriate. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eTo identify independent predictors of pleural effusion, a Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model was applied. The model selected relevant clinical and molecular predictors, and results were reported as odds ratios (ORs) with 95% confidence intervals (CIs). Model assumptions, including multicollinearity variance inflatin factor (VIF)\u0026thinsp;\u0026lt;\u0026thinsp;5, linearity of continuous predictors in the logit transformation, and proportional odds assumption for ordinal predictors, were tested before finalizing the analysis.\u003c/p\u003e \u003cp\u003eModel performance was assessed using receiver operating characteristic (ROC) curve analysis, with classification metrics including sensitivity, specificity, precision, and F1-score. Internal validation was performed using an 80%-20% train-test split and k-fold cross-validation (k\u0026thinsp;=\u0026thinsp;5) to ensure generalizability. Mutation effects were further evaluated using univariate and multivariate logistic regression models, assessing both individual and grouped mutation impacts.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of 130 lung cancer patients stratified by MPE status. The demographic and clinical variables were analyzed to identify potential associations with MPE development. Female gender was selected as the reference category for gender-based comparisons. The study population had a median age of 67 years (range: 35\u0026ndash;90 years), with no significant difference in age distribution between patients with and without MPE (p\u0026thinsp;=\u0026thinsp;0.763). In terms of anatomical considerations, the left superior lobe was designated as the reference location for tumor site analysis. While the distribution of tumor locations showed some variation between groups, with right inferior lobe involvement being more frequent in the presence of MPE (23.1% versus 6.4%, respectively), this difference did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.133). For histological classification, mucinous type served as the reference category, with adenocarcinoma being the predominant type in both groups (50.8% overall), showing similar distribution patterns (p\u0026thinsp;=\u0026thinsp;0.956). Smoking status analysis, using non-smokers as the reference category, revealed significant differences between groups (p\u0026thinsp;=\u0026thinsp;0.016). Notably, heavy smoking (above 50 pack-years) was more prevalent in patients with MPE compared to those without (32.7% versus 12.8%, respectively). Regarding mutation status, cases without detected mutations served as the reference category, and while PDL1 mutations were more frequent in the presence of MPE (21.2% versus 9.0%, respectively), the overall distribution of mutation status did not differ significantly between groups (p\u0026thinsp;=\u0026thinsp;0.947). Treatment strategies, with chemotherapy alone serving as the reference category, showed varying distributions between groups. Although combination therapy (chemotherapy and immunotherapy) was more commonly employed in patients with MPE (46.2% versus 24.4%, respectively), the overall distribution of management strategies did not demonstrate statistical significance (p\u0026thinsp;=\u0026thinsp;0.726). These findings suggest that among the analyzed characteristics, smoking history demonstrates the strongest association with MPE development in lung cancer patients, warranting particular attention in clinical assessment and risk stratification.\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\u003eSelected clinical characteristics of the patients with NSCLC.\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=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eTotal \u003c/p\u003e \u003cp\u003ePopulation \u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;130)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo Pleural \u003c/p\u003e \u003cp\u003eEffusion \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePleural \u003c/p\u003e \u003cp\u003eEffusion \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (min-max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (35\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (43\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (35\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.763\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender, n (%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.464\u0026Dagger;\u003c/p\u003e \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\u003e109 (83.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (82.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (86.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e21 (16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor Location, n (%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.133\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Superior*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Inferior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight Superior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight Middle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight Inferior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistological Type, n (%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.956\u0026Dagger;\u003c/p\u003e \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\u003e66 (50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous Cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuroendocrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleomorphic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucinous*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking Status, n (%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.016\u0026Dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Smoking*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUp to 40 pack-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUp to 50 pack-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove 50 pack-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMutation Status, n (%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.947\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Mutation*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (87.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eManagement Strategy, n (%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.726\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (60.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (71.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy and Immunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e*Reference category for statistical analyses. \u0026dagger;Mann-Whitney U test. \u0026Dagger;Chi-square test or Fisher's exact test when appropriate.\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\u003eA logistic regression model with LASSO regularization was developed to identify significant predictors of MPE in NSCLC patients. The model achieved 74.6% accuracy with an area under the curve (AUC)-ROC of 0.80 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), showing 75% precision/86% recall for the negative class and 73% precision/58% recall for the positive class. Genetic mutations were strong predictors: PD-L1 expression (β\u0026thinsp;=\u0026thinsp;1.02, OR\u0026thinsp;=\u0026thinsp;2.78, p\u0026thinsp;=\u0026thinsp;0.01) nearly tripled pleural effusion likelihood, while ALK, ROS1, and T790M alterations (β\u0026thinsp;=\u0026thinsp;0.71, OR\u0026thinsp;=\u0026thinsp;2.04, p\u0026thinsp;=\u0026thinsp;0.03) were also independently associated with its development.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed other significant predictors: age (β\u0026thinsp;=\u0026thinsp;0.11, OR\u0026thinsp;=\u0026thinsp;1.12, p\u0026thinsp;=\u0026thinsp;0.04) increased odds by 12% per year; heavy smoking history (β\u0026thinsp;=\u0026thinsp;0.89, OR\u0026thinsp;=\u0026thinsp;2.43, p\u0026thinsp;=\u0026thinsp;0.02) nearly doubled the likelihood compared to non-smokers; adenocarcinoma histology (β\u0026thinsp;=\u0026thinsp;0.75, OR\u0026thinsp;=\u0026thinsp;2.12, p\u0026thinsp;=\u0026thinsp;0.03) doubled the odds versus mucinous carcinoma; and right inferior lobe tumor location (β\u0026thinsp;=\u0026thinsp;0.68, OR\u0026thinsp;=\u0026thinsp;1.97, p\u0026thinsp;=\u0026thinsp;0.04) suggested anatomical predisposition. The model showed no multicollinearity (VIF\u0026thinsp;\u0026lt;\u0026thinsp;5) and maintained stability under cross-validation. These findings suggest combining clinical parameters with genetic mutations provides a predictive framework for MPE risk assessment, with implications for early diagnosis and personalized treatment strategies.\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\u003eLogistic Regression Coefficients and Odds Ratios.\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOdds Ratio (OR)\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\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (\u0026gt;\u0026thinsp;50 pack-years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological subtype (Adenocarcinoma vs. mucinous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location (Right inferior vs. left superior)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1 Expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Mutations (ALK, ROS1, T790M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\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\u003eThe ROC curve shows the logistic regression model's performance in predicting MPE in NSCLC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). With an AUC of 0.765 (95% CI: 0.680\u0026ndash;0.850, SE: 0.0434, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), the model demonstrates strong discriminative ability. The diagonal dashed line indicates a non-discriminative model (AUC\u0026thinsp;=\u0026thinsp;0.5), while the solid blue line plots sensitivity against false positive rate. The cohort comprised 78 control patients and 52 with MPE.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDetailed mutation pattern analysis revealed distinct associations with MPE development (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). PD-L1 mutations showed significant association (OR:2.78, 95% CI:1.14\u0026ndash;6.48, p\u0026thinsp;=\u0026thinsp;0.024), present in 21.2% of patients with MPE versus 9.0% without. While individual ALK, ROS1, and T790M mutations weren't statistically significant alone, their combined effect in the logistic regression model showed significant association with MPE (OR:2.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), suggesting their collective presence might contribute to disease progression despite individual mutations not being strong predictors alone.\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\u003eAssociation between Tumor Mutations and Pleural Effusion in patients with NSCLC.\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=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutation Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith Effusion (n\u0026thinsp;=\u0026thinsp;52) n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout Effusion (n\u0026thinsp;=\u0026thinsp;78) n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eNo Mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (87.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.78 (1.14\u0026ndash;6.48)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.08 (0.27\u0026ndash;34.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75 (0.07\u0026ndash;8.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT790M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study identified significant clinical and molecular predictors of MPE in NSCLC patients. The logistic regression model (AUC\u0026thinsp;=\u0026thinsp;0.80) effectively distinguished between patients with and without MPE. PD-L1 expression (OR\u0026thinsp;=\u0026thinsp;2.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) nearly tripled MPE-likelihood. ALK, ROS1, and T790M alterations (OR\u0026thinsp;=\u0026thinsp;2.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were independently linked to MPE. Older age (OR\u0026thinsp;=\u0026thinsp;1.12, p\u0026thinsp;=\u0026thinsp;0.04), heavy smoking (OR\u0026thinsp;=\u0026thinsp;2.43, p\u0026thinsp;=\u0026thinsp;0.02), adenocarcinoma histology (OR\u0026thinsp;=\u0026thinsp;2.12, p\u0026thinsp;=\u0026thinsp;0.03), and right inferior lobe tumor location (OR\u0026thinsp;=\u0026thinsp;1.97, p\u0026thinsp;=\u0026thinsp;0.04) were key risk factors. Patients with MPE more commonly received combined chemotherapy and immunotherapy (46.2% versus 24.4%, respectively). These findings underscore the interplay between genetic mutations, tumor biology, and clinical factors in the MPE development.\u003c/p\u003e \u003cp\u003eSeveral studies have established that driver mutations contribute significantly to MPE in NSCLC (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The association between PD-L1 expression and MPE (22.9% versus 9.3%, respectively) aligns with previous reports demonstrating interactions between oncogenic pathways and immune checkpoint regulation (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). High PD-L1 expression doesn't always predict improved response to PD-1/PD-L1 inhibitors, particularly in tumors with EGFR or ALK mutations (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This may be attributed to the pleural immune microenvironment, a sanctuary for immune-evasive tumor cells (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Our findings support this hypothesis, as MPE-positive patients exhibited distinct molecular characteristics.\u003c/p\u003e \u003cp\u003eBeyond immune checkpoints, mesothelial-mesenchymal transition, extracellular matrix remodeling, and immunosuppressive signaling facilitate tumor invasion into the pleural cavity (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), potentially explaining increased MPE in right inferior lobe tumors. The pleural space acts as an active metastatic niche with mesenchymal-like tumor cells, stromal fibroblasts, and immunomodulatory cells (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), suggesting both genetic and microenvironmental factors contribute to pleural involvement.\u003c/p\u003e \u003cp\u003eDistinct molecular mechanisms explain how driver mutations facilitate pleural metastasis. KRAS-mutant tumors induce C-C motif chemokine ligand 2 secretion, mobilizing myeloid-derived suppressor cells and promoting a metastatic-supportive environment (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). EGFR-mutated tumors exhibit greater genomic instability, linked to higher DNA content and frequent aneuploid peaks (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Our findings support these observations, as patients with ALK, ROS1, and T790M mutations demonstrated higher likelihood of MPE (OR:2.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), corroborating studies showing chromosomally unstable tumors are more prone to pleural dissemination (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch into EGFR-TKI efficacy in patients with pleural effusion has yielded conflicting results. Some studies report EGFR-TKI-na\u0026iuml;ve patients with MPE had insignificantly shorter treatment durations (14.8 versus 19.8 months) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Our study found no significant difference in treatment failure rates between MPE-positive and negative patients. Additionally, EGFR exon 19 deletions versus exon 21 L858R mutations may influence treatment response, as studies report variable osimertinib efficacy based on mutation subtype (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent advances in molecular testing show pleural effusion cfDNA provides higher sensitivity (97%) for EGFR mutation detection compared to plasma cfDNA (74%) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Pleural effusion-positive patients in our cohort were more likely to undergo comprehensive molecular testing. Additionally, patient-derived organoids have emerged as promising preclinical models for evaluating drug response in NSCLC patients with pleural effusion (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings support integrating molecular, immune, and microenvironmental profiling in managing NSCLC patients with MPE, providing insights into anatomical, histological, and clinical factors influencing pleural involvement.\u003c/p\u003e\n\u003ch3\u003eStrengths\u003c/h3\u003e\n\u003cp\u003eOur study comprehensively investigates the relationship between genetic alterations and MPE in NSCLC. The logistic regression model with LASSO regularization selected meaningful predictive variables, improving accuracy and reducing overfitting. Our dataset reflects real-world clinical settings, enhancing applicability to clinical practice. The integration of clinical and molecular data strengthens evidence that MPE is driven by genetic predispositions and anatomical factors.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Research Directions\u003c/h2\u003e \u003cp\u003eThis retrospective study introduces selection bias and limits establishing causality. The sample size for certain mutations was relatively small, potentially affecting statistical power. Although our model demonstrated strong performance, external validation is necessary. Our dataset lacked detailed information on tumor burden, pleural fluid cytology, and immune microenvironmental markers. Besides, since treatment is ongoing, and the number of the patients are not high, we did not perform survival analysis. Thus, prospective multicenter studies with larger population are required to validate these genetic and clinical predictors in larger populations. Liquid biopsy techniques and pleural fluid genomic analysis may further elucidate mechanisms of pleural invasion. Exploring tumor microenvironment, immune checkpoint inhibitors, and inflammatory cytokines could provide novel therapeutic targets. Future studies should investigate how these factors influence response to targeted therapies and immunotherapy.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study highlights significant associations between driver mutations, clinical characteristics, and MPE in NSCLC. PD-L1 expression and ALK, ROS1, and T790M mutations are independently linked to increased MPE risk. Age, smoking history, tumor location, and histological subtype emerged as crucial predictors, suggesting pleural effusion development is driven by genetic and anatomical factors. The strong association between PD-L1 expression and MPE suggests immunotherapy responses may be influenced by the tumor microenvironment. The independent effect of ALK, ROS1, and T790M mutations underscores the need for comprehensive molecular profiling. Given the variations in treatment patterns between patients with and without MPE, future clinical strategies should integrate molecular and clinical data to guide personalized therapy. As NSCLC treatment evolves, integrating molecular and microenvironmental profiling will be essential for improving outcomes in patients with MPE.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics Approval and Consent to Participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and approved by the Şişli Hamidiye Etfal Training and Research Hospital Health Application and Research Center Clinical Research Ethics Committee (Approval No:2778, Date:14.01.2025). As this was a retrospective study, individual written informed consent was not newly obtained; however, all patients had previously provided written informed consent for their medical data to be used for research purposes, which was verified through hospital records before data extraction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for Publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. No identifiable patient data or images are included in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of Data and Materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request, in compliance with institutional and ethical guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests relevant to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding. All aspects of the study were supported by institutional resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e[Author 1 U. T.]: Conceptualization, Data Collection, Formal Analysis, Writing – Original Draft\u003c/p\u003e\n\u003cp\u003e[Author 2 O. D.]: Methodology, Statistical Analysis, Writing – Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank [Name of Institution or Research Team] for their support and assistance in data collection and analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUse of Artificial Intelligence in Manuscript Preparation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the use of artificial intelligence (AI)-based tools for language editing, structural refinement, and manuscript organization. AI-assisted platforms, including ChatGPT-4 and Claude AI, were employed to enhance the clarity and readability of the text while maintaining scientific accuracy. However, all scientific interpretations, data analyses, and conclusions were formulated by the authors to ensure the originality and integrity of the study. The statistical methods and computational scripts used in this study adhere to best-practice guidelines for reproducible research, ensuring transparency in data processing and model interpretation. The authors confirm that all analyses were manually reviewed to validate statistical outputs and avoid AI-generated biases in model selection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Yu L, Wang L, Wu Y, Chen H, Wang Q, et al. Current status of and progress in the treatment of malignant pleural effusion of lung cancer. Front Oncol. 2023;12:961440.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoegelenberg CFN, Shaw JA, Irusen EM, Lee YCG. Contemporary best practice in the management of malignant pleural effusion. Ther Adv Respir Dis. 2018;12:1753466618785098.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu YY, Hsu YL, Huang YC, Su YC, Wu KL, Chang CY, et al. Characterization of the pleural microenvironment niche and cancer transition using single-cell RNA sequencing in EGFR-mutated lung cancer. Theranostics. 2023;13(13):4412\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgalioti T, Giannou AD, Stathopoulos GT. Pleural involvement in lung cancer. J Thorac Dis. 2015;7:1021\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChangchien CY, Chen Y, Chang HH, Chang SY, Tsai WC, Tsai HC, et al. Effect of malignant-associated pleural effusion on endothelial viability, motility and angiogenesis in lung cancer. Cancer Sci. 2020;111(10):3747\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKunimasa K, Tamiya M, Inoue T, Kawamura T, Miyazaki A, Kojitani Y, et al. Clinical application of the Lung Cancer Compact Panel\u0026trade; using various types of cytological specimens in patients with lung cancer. Lung Cancer. 2024;189:107498.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHendriks LE, Kerr KM, Menis J, et al. Oncogene-addicted metastatic non-small-cell lung cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol. 2023;34:339\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi M, Liu Y, Li Q, Li X, Ning Z, Zhao W, et al. PD-1/PD-L1 expression in non-small-cell lung cancer and its correlation with EGFR/KRAS mutations. Cancer Biol Ther. 2016;17(4):407\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChevallier M, Borgeaud M, Addeo A, Friedlaender A. Oncogenic driver mutations in non-small cell lung cancer: Past, present and future. World J Clin Oncol. 2021;12(4):217\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrison PT, Vyse S, Huang PH. Rare epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer. Semin Cancer Biol. 2020;61:167\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee KWC, Li MSC, Gai W, Lau YM, Chan AKC, Chan OSH, et al. Testing for EGFR Variants in Pleural and Pericardial Effusion Cell-Free DNA in Patients With Non-Small Cell Lung Cancer. JAMA Oncol. 2022;8(4):563\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoRusso PM, Schalper K, Sosman J. Targeted therapy and immunotherapy: Emerging biomarkers in metastatic melanoma. Pigment Cell Melanoma Res. 2020;33(3):390\u0026ndash;402. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/pcmr.12847\u003c/span\u003e\u003cspan address=\"10.1111/pcmr.12847\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDantoing E, Piton N, Sala\u0026uuml;n M, Thiberville L, Guisier F, Yıl. Anti-PD1/PD-L1 immunotherapy for non-small cell lung cancer with actionable oncogenic driver mutations. Int J Mol Sci. 2021;22:6288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms22126288\u003c/span\u003e\u003cspan address=\"10.3390/ijms22126288\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu J, Lin Z (Yıl), editors. Non-small cell lung cancer targeted therapy: Drugs and mechanisms of drug resistance. Int. J. Mol. Sci. 2022, 23, 15056. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms232315056\u003c/span\u003e\u003cspan address=\"10.3390/ijms232315056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrazel D, Kroening G, Nagasaka M, Yıl.). Non-small cell lung cancer with EGFR or HER2 exon 20 insertion mutations: Diagnosis and treatment options. BioDrugs (2022) 36:717\u0026ndash;729.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiritani A, Amino Y, Uchibori K, Akita T, Harutani Y, Ogusu S, Tsugitomi R, Manabe R, Ariyasu R, Kitazono S, Yanagitani N, Nishio M, Yıl. Testing for EGFR variants in pleural and pericardial effusion cell-free DNA in efficacy of osimertinib in patients with EGFR-mutation positive non-small cell lung cancer with malignant pleural effusion. Thorac Cancer. 2024;15:402\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng Z, Liebers M, Zhelyazkova B, Cao Y, Panditi D, Lynch KD, Chen J, Robinson HE, Shim HS, Chmielecki J, Pao W, Engelman JA, Iafrate AJ, Le LP. Anchored multiplex PCR for targeted next-generation sequencing. Nat Med. 2014;20(12):1479\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu Y, Guo X, Wang R, Ma Y, Zhang Y, Liu Y, Dong L, Wu J, Ji X, Wang H. The correlation between EGFR mutation status and DNA content of lung adenocarcinoma cells in pleural effusion. J Cancer 2020, 11 2265\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar M, Guleria B, Swamy S, Soni S. Correlation of programmed death-ligand 1 expression with gene expression and clinicopathological parameters in Indian patients with non-small cell lung cancer. Lung India. March-April 2020;37(2):145\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzuma K, Ota K, Kawahara A, Hattori S, Iwama E, Harada T, Matsumoto K, Takayama K, Takamori S, Kage M, Hoshino T, Nakanishi Y, Okamoto I. Association of PD-L1 overexpression with activating EGFR mutations in surgically resected nonsmall-cell lung cancer. Ann Oncol. 2014;25(10):1935\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiltbrunner S, Mannarino L, Kirschner MB, Opitz I, Rigutto A, Laure A, Lia M, Nozza P, Maconi A, Marchini S, D'Incalci M, Curioni-Fontecedro A, Grosso F. Tumor Immune Microenvironment and Genetic Alterations in Mesothelioma. Front Oncol. 2021;11:660039. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fonc.2021.660039\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2021.660039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin S, Jiang J, Lu Y, Nice EC, Huang C, Zhang J, He W. Emerging role of tumor cell plasticity in modifying therapeutic response. Signal Transduct Target Ther. 2020;5:228.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAltorki NK, Markowitz GJ, Gao D, Port JL, Saxena A, Stiles B, McGraw T, Mittal V. The lung microenvironment: an important regulator of tumour growth and metastasis. Nat Rev Cancer. 2019;19(1):9\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu Y, Guo X, Wang R, Ma Y, Zhang Y, Liu Y, Dong L, Wu J, Ji X, Wang H. The Correlation between EGFR Mutation Status and DNA Content of Lung Adenocarcinoma Cells in Pleural Effusion. J Cancer. 2020;11(8):2265\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrant MJ, Aredo JV, Starrett JH, Stockhammer P, van Alderwerelt IK, Wurtz A, Piper-Valillo AJ, Piotrowska Z, Falcon C, Yu HA, Aggarwal C, Scholes D, Patil T, Nguyen C, Phadke M, Li F, Neal J, Lemmon MA, Walther Z, Politi K, Goldberg SB. Efficacy of osimertinib in patients with lung cancer positive for uncommon EGFR exon 19 deletion mutations. Clin Cancer Res. 2023;29(11):2123\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\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":"bmc-medical-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgnm","sideBox":"Learn more about [BMC Medical Genomics](http://bmcmedgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgnm/default.aspx","title":"BMC Medical Genomics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Driver mutations, Malignant pleural effusion, Non-small cell lung cancer","lastPublishedDoi":"10.21203/rs.3.rs-6275845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6275845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMalignant pleural effusion (MPE) complicates approximately 50% of non-small cell lung cancer (NSCLC) cases, signaling advanced disease and poor patient outcomes. While driver mutations including programmed death-ligand 1 (PD-L1), anaplastic lymphoma kinase (ALK), proto-oncogene tyrosine-protein kinase-1 (ROS1), and threonine at amino acid position 790 (T790M) are critical in NSCLC progression, their relationship with MPE development remains inadequately characterized.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis retrospective cohort study examined 130 NSCLC patients (52 with MPE, 78 without MPE). Clinical characteristics and comprehensive molecular profiles were analyzed using next-generation sequencing. Statistical comparisons were performed, and a Least Absolute Shrinkage and Selection Operator (LASSO) regularized logistic regression model identified independent predictors of MPE. Model performance was evaluated using receiver operating characteristic (ROC) analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePD-L1 expression demonstrated a significant association with MPE development (Odds ratio\u0026thinsp;=\u0026thinsp;2.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), nearly tripling the likelihood of effusion. The presence of ALK, ROS1, and T790M mutations (combined OR\u0026thinsp;=\u0026thinsp;2.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) also showed predictive value for MPE formation. Several clinical factors independently correlated with MPE, including advanced age, heavy smoking history (\u0026gt;\u0026thinsp;50 pack-years), and right inferior lobe tumor location (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The predictive model demonstrated robust performance with an area under the curve of 0.80.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThese findings establish important associations between specific driver mutations, particularly PD-L1 expression, and MPE development in NSCLC patients. Identifying these genetic and clinical predictors may enhance risk stratification approaches and guide personalized treatment strategies, especially for those with advanced disease. Further prospective validation studies are needed to confirm these associations and explore their therapeutic implications.\u003c/p\u003e","manuscriptTitle":"Driver Mutations and Malignant Pleural Effusion in Non-small Cell Lung Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 10:37:01","doi":"10.21203/rs.3.rs-6275845/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-09T05:08:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-25T03:51:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254541578684275681261099431222961974612","date":"2025-04-08T20:33:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-04T15:36:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259963128812973292494820745730320625016","date":"2025-04-03T19:02:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-31T05:56:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-31T05:20:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-28T13:23:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Genomics","date":"2025-03-28T13:22:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgnm","sideBox":"Learn more about [BMC Medical Genomics](http://bmcmedgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgnm/default.aspx","title":"BMC Medical Genomics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad04d206-4832-4058-ab99-7308ed3ab844","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:16:09+00:00","versionOfRecord":{"articleIdentity":"rs-6275845","link":"https://doi.org/10.1186/s12920-025-02180-x","journal":{"identity":"bmc-medical-genomics","isVorOnly":false,"title":"BMC Medical Genomics"},"publishedOn":"2025-07-01 15:57:10","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2025-04-21 10:37:01","video":"","vorDoi":"10.1186/s12920-025-02180-x","vorDoiUrl":"https://doi.org/10.1186/s12920-025-02180-x","workflowStages":[]},"version":"v1","identity":"rs-6275845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6275845","identity":"rs-6275845","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}