Clinical and Genomic Determinants of Survival in EGFR-Mutant Non-Small Cell Lung Cancer: An Integrated Analysis of UK Biobank and cBioPortal Cohorts

Clinical and Genomic Determinants of Survival in EGFR-Mutant Non-Small Cell Lung Cancer: An Integrated Analysis of UK Biobank and cBioPortal Cohorts | 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 Clinical and Genomic Determinants of Survival in EGFR-Mutant Non-Small Cell Lung Cancer: An Integrated Analysis of UK Biobank and cBioPortal Cohorts Kejun Liu, Guojian Huang, Baocheng Xie, Dongxia Wang, Zhuanghua Li, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9194785/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Prognostic heterogeneity in EGFR-mutant non-small cell lung cancer (NSCLC) remains incompletely understood. Although differences between EGFR mutation subtypes have been widely reported, the extent to which co-occurring genomic alterations contribute to survival variability remains unclear. Methods We conducted an integrated analysis combining population-based clinical data from the UK Biobank (UKB; n = 818) and genomic data from cBioPortal (n = 1,126). Kaplan–Meier analysis, Cox proportional hazards models, inverse probability of treatment weighting (IPTW), Fine–Gray competing-risk models, and restricted mean survival time (RMST) were used to evaluate survival outcomes. A prognostic nomogram was constructed and internally validated. Results In the UKB cohort, age at diagnosis was the only independent clinical predictor of overall survival. Treatment status, smoking status, and body mass index were not independently associated with survival after multivariable adjustment. IPTW, Fine–Gray, and RMST analyses consistently showed no clinically meaningful survival advantage associated with treatment status. In the cBioPortal cohort, the apparent survival difference between EGFR L858R and exon 19 deletions was attenuated after adjustment. TP53 was the most frequent co-mutation and was consistently associated with worse survival, including within subtype-specific analyses. Multivariable analysis identified TP53, PIK3CA, and CDKN2A as independent adverse prognostic factors, whereas EGFR L858R was associated with improved survival. The nomogram showed acceptable calibration but limited discriminatory performance. Conclusions Survival outcomes in EGFR-mutant NSCLC are jointly influenced by clinical characteristics and co-occurring genomic alterations. Age represents a key clinical determinant, while TP53-centered co-mutation patterns provide additional prognostic stratification. These findings may help improve risk stratification and support more individualized clinical management. EGFR-mutant NSCLC Co-mutation Genomic heterogeneity Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of all cases[1]. Epidermal growth factor receptor (EGFR) mutant NSCLC represents a major oncogenic subset, predominantly driven by two activating alterations, exon 19 deletions (Del19) and L858R substitutions[2]. Although both subtypes are highly sensitive to EGFR tyrosine kinase inhibitors (EGFR-TKIs), they have been reported to exhibit differences in survival outcomes, the underlying determinants of which remain incompletely understood[3, 4]. Conventional prognostic assessment in NSCLC is largely driven by clinical characteristics, including age, performance status, and comorbidity burden[5]. While these factors characterize baseline risk, they may not fully reflect the biological complexity underlying tumor behavior. Increasing evidence suggests that tumor-intrinsic molecular characteristics, particularly co-occurring genomic alterations, play a critical role in determining therapeutic response, resistance, and long-term survival in EGFR-mutant NSCLC[6, 7]. Beyond primary EGFR alterations, co-occurring genomic alterations in key pathways, including TP53, PI3K/AKT/mTOR signaling, DNA damage response (DDR), and cell cycle regulation, are increasingly recognized as critical drivers of tumor progression and therapeutic resistance[8, 9]. These co-mutation patterns may exert distinct effects on disease progression across EGFR subtypes[10]. However, most existing studies have evaluated clinical and molecular determinants separately, and a comprehensive framework integrating these factors to explain survival heterogeneity remains lacking. In this study, we integrated population-based clinical data from the UK Biobank (UKB) with high-resolution genomic profiles from cBioPortal to investigate the determinants of prognosis in EGFR-mutant NSCLC. We aimed to explore a hierarchical framework in which clinical characteristics establish baseline risk, while genomic heterogeneity further contributes to survival outcomes. We further developed an integrated prognostic nomogram for individualized risk stratification. Our findings provide mechanistic insight into prognostic heterogeneity and support a framework for precision management of EGFR-mutant NSCLC. Methods Study Populations and Data Sources Two independent datasets were utilized to investigate the clinical and genomic determinants of survival in non-small cell lung cancer (NSCLC). We identified 818 patients with a primary diagnosis of non-small cell lung cancer (NSCLC) from the Clinical Cohort (UK Biobank). Clinical variables included chronological age at diagnosis, sex, Body Mass Index (BMI), smoking status, and the Townsend Deprivation Index (TDI) as a measure of socioeconomic status. To dismantle the molecular drivers of survival, we retrieved genomic and survival data for 1,126 NSCLC patients harboring classical EGFR mutations (L858R or Exon 19 deletions) from the cBioPortal for Cancer Genomics, primarily integrating the TCGA and MSK-IMPACT datasets. According to the cBioPortal metadata, the cohort consisted of 66.8% White, 12.3% Asian, 2.7% Black or African American, and 18.2% Other/Unknown racial groups. Survival Endpoints and Statistical Modeling The primary endpoint was Overall Survival (OS), defined as the interval from diagnosis to death from any cause. Univariate and multivariable Cox proportional hazards (PH) models were constructed to estimate Hazard Ratios (HR) and 95% confidence intervals (CI) (Table S2 ). UKB models were adjusted for clinical and lifestyle metrics (age, sex, BMI, smoking, TDI). In the cBioPortal cohort, adjustments focused on tumor stage, smoking history, and race to control for genomic confounding. For the cBioPortal cohort (60.8% missing staging data), "Missing Stage" was treated as a distinct category in sensitivity analyses to ensure the stability of the EGFR subtype-specific findings. Competing Risk Validations To mitigate selection bias between treated (n = 777) and untreated (n = 41) individuals in the UKB, Inverse Probability of Treatment Weighting (IPTW) was applied. Propensity scores were estimated via logistic regression, and balance was confirmed using the Standardized Mean Difference (SMD < 0.1, Table S1 ). A Fine-Gray subdistribution hazards model was employed to differentiate lung cancer-specific mortality from competing causes of death. Restricted Mean Survival Time (RMST) over a 60-month horizon was calculated to provide a clinically interpretable measure of survival benefits, particularly for variables violating the PH assumption. Genomic and Pathway Analysis To unmask the intrinsic prognostic signals of EGFR sub-variants, frequencies of concurrent alterations were compared between L858R and Del19 subtypes using Fisher’s exact test. Multiple comparison corrections were performed using the Benjamini-Hochberg False Discovery Rate (FDR). Interaction terms were incorporated into Cox models to test for subtype-specific synergistic effects between EGFR variants and co-mutations Nomogram Analysis A clinical nomogram incorporating UKB-derived clinical variables was developed. Discrimination was evaluated using time-dependent Receiver Operating Characteristic (ROC) curves to calculate the Area Under the Curve (AUC) for 1, 3, and 5-year OS. Calibration plots were utilized to assess the agreement between predicted and observed survival probabilities. Results Clinical characteristics and survival analyses in the UKB cohort We first analyzed real-world clinical data from the UK Biobank. Baseline characteristics are summarized in Table 1 . Treatment distribution was markedly imbalanced (777 treated vs. 41 untreated), with baseline differences observed in age, sex, and Townsend deprivation index. Descriptive Kaplan-Meier curves showed limited separation by treatment status and no evident prognostic differentiation by smoking status or BMI, whereas age-group analysis revealed modest differences, suggesting a continuous rather than threshold dependent effect (Fig. 1 A-C, Figure S1 A-B). These findings suggest that treatment status, as recorded in the UKB dataset, was not independently associated with overall survival after adjustment. In univariable Cox analysis, age at diagnosis was significantly associated with worse overall survival, whereas treatment status was not. This finding remained unchanged in the multivariable model, in which age at diagnosis was the only independent clinical predictor of overall survival (HR 1.015, 95% CI 1.006–1.024; P = 0.00069), while treatment status, sex, BMI, smoking status, and Townsend deprivation index remained non-significant (Table 2 , Figure S1 C). Subgroup analyses did not demonstrate a consistent statistically significant treatment effect across clinical subgroups (Fig. 1 D, Table S8 ). IPTW-weighted sensitivity analysis produced similar results, further indicating that the UKB data do not support a meaningful overall-survival advantage associated with treatment status in this cohort (Table S3 and Table S4 ). The proportional hazards assumption was assessed and generally satisfied for all variables (Table S2 ). EGFR mutation subtype and survival heterogeneity We next examined whether EGFR mutation subtype contributed to survival heterogeneity in the cBioPortal cohort. In unadjusted Kaplan-Meier analysis, patients harboring L858R mutations showed longer overall survival than those with exon 19 deletions (Fig. 2 A). However, in the fully adjusted subtype-specific Cox model, the association between Del19 and worse survival was attenuated and no longer statistically significant (HR 1.700, 95% CI 0.702–4.112; P = 0.239; Table S5 ), indicating that EGFR subtype alone did not provide consistent prognostic discrimination following covariate adjustment. TP53 co-mutation as a major driver of survival heterogeneity We then evaluated recurrent co-mutations in the cBioPortal cohort. The overall oncoplot demonstrated that TP53 co-mutation was consistently associated with worse survival outcomes and remained significant across multiple analyses, occurring in 56% of EGFR-mutant tumors, whereas other recurrent events such as PIK3CA and RB1 were far less common (Fig. 3 A). The mutation landscape indicated a higher frequency of death events among TP53-altered cases. Consistent with this pattern, TP53 co-mutation was associated with significantly worse overall survival compared with EGFR-mutant tumors without TP53 alterations (Fig. 2 B). In subgroup analysis, this adverse association was also observed within the Del19 subgroup (Fig. 2 C). When patients were stratified into four groups according to EGFR subtype and TP53 status, the most favorable outcome was observed in the L858R/TP53-wild-type group, whereas the Del19/TP53-mutant group showed the worst prognosis (Fig. 2 D). Together, these results indicate that TP53 co-mutation improves prognostic stratification beyond EGFR subtype. co-mutation landscape and independent prognostic effects Comparison of recurrent co-mutation frequencies between L858R and Del19 tumors revealed broadly similar patterns across the two EGFR subtypes, with BRAF as the most distinct subtype-specific alteration, occurring exclusively in Del19 tumors (Fig. 3 B; Table S6 ). In univariable analysis, TP53 and RB1 were associated with worse survival after correction (Fig. 3 C; Table S7 ). In the multivariable co-mutation model, EGFR L858R remained associated with more favorable survival (HR 0.733, 95% CI 0.561–0.957; P = 0.0225), whereas TP53 (HR 2.248, 95% CI 1.695–2.981; P < 0.001), PIK3CA (HR 1.716, 95% CI 1.143–2.576; P = 0.009), and CDKN2A (HR 2.111, 95% CI 1.036–4.303; P = 0.040) independently predicted worse outcomes (Fig. 3 C; Table 3 ). These findings indicate that survival differences in EGFR-mutant NSCLC are influenced by both the primary EGFR variant and co-occurring genomic alterations. Clinical prognostic modeling and sensitivity analyses A clinical nomogram incorporating UKB-derived variables was constructed to estimate individualized survival (Fig. 4 A). Calibration at 1, 3, and 5 years was acceptable, whereas discrimination remained limited, with time-dependent AUCs of 0.559, 0.598, and 0.627 (Table S10 , Fig. 4 B-C). Competing-risk analysis did not show a significant association between treatment status and cancer-specific mortality (Fine-Gray HR 1.329, 95% CI 0.759–2.330; P = 0.320), and 60-month RMST estimates were similar between treated and untreated groups (40.28 vs. 41.61 months), further arguing against a robust treatment-related survival signal in the UKB dataset (Fig. 4 D, Table S4 and S9). Discussion In this study, we integrated population-based clinical data and public genomic datasets to investigate survival heterogeneity in EGFR-mutant NSCLC. The results support that age at diagnosis was the most consistent clinical variable associated with survival in the UKB cohort; second, treatment status as captured in UKB was not independently associated with overall survival. Third, TP53 co-mutation contributed to additional prognostic stratification beyond EGFR subtype alone. Finally, although a clinical nomogram can be constructed, its discriminatory ability appears limited. Our analysis highlights that chronological age at diagnosis remains a stable and consistent clinical predictor of outcome in EGFR-mutant NSCLC. Although Kaplan-Meier curves based on age stratification showed only limited separation[11–13], Cox regression consistently identified age as an independent predictor, suggesting a continuous rather than threshold-dependent effect[14, 15]. Together, these factors may limit treatment intensity and contribute to worse long-term outcomes in older patients[16]. The clinical management of older patients with EGFR-mutated NSCLC is further complicated by the heterogeneous biological impact of aging[17]. Functional decline, polypharmacy, and the accumulation of chronic comorbidities can alter drug pharmacokinetics and pharmacodynamics, thereby increasing the risk of adverse events during long-term targeted therapy[18, 19]. Although EGFR tyrosine kinase inhibitors (EGFR-TKIs) are generally better tolerated than conventional chemotherapy, older patients remain more vulnerable to treatment-associated toxicities, including cardiovascular complications and metabolic disturbances[14]. These factors may compromise treatment continuity and ultimately influence survival outcomes. At the clinical level, conventional lifestyle-related variables such as BMI and smoking status were not independently associated with survival. This finding should not be interpreted as evidence that these factors are biologically irrelevant, but rather that their effects may be overshadowed by stronger host-related determinants such as age or obscured by limited measurement resolution[20]. In oncogene-driven tumors such as EGFR-mutant NSCLC, disease progression and treatment response may be more strongly influenced by tumor-intrinsic molecular characteristics than by external lifestyle exposures[21]. Beyond clinical factors, our study highlights the critical role of molecular heterogeneity in driving survival outcomes in EGFR-mutant NSCLC. Unadjusted Kaplan–Meier analysis suggested a difference in overall survival between EGFR subtypes; however, this association was attenuated and no longer statistically significant after multivariable adjustment. Notably, the longer OS observed in L858R tumors contrasts with prior reports demonstrating superior EGFR-TKI efficacy in Del19[22, 23]. This apparent inconsistency likely reflects the distinction between treatment-specific response and overall survival. While Del19 mutations are consistently associated with enhanced sensitivity to EGFR-TKIs, long-term survival is influenced by a broader spectrum of factors, including co-mutation burden, resistance mechanisms, and subsequent therapeutic strategies. Emerging evidence further supports subtype-specific differences beyond first-line therapy[24, 25]. Patients with L858R mutations may derive greater benefit from immune checkpoint inhibitors compared with those harboring Del19 alterations, whereas Del19 tumors, particularly after acquired resistance, often exhibit limited responsiveness to immunotherapy[26–28]. These findings suggest that the prognostic impact of EGFR mutation subtypes is highly dependent on therapeutic context and line of treatment. Previous studies have established that co-mutation patterns critically influence prognosis in EGFR-mutant NSCLC, while our study further suggests that differential co-mutation burden may underlie subtype-specific survival differences[29]. Among all co-mutations, TP53 demonstrated the most consistent and robust association with adverse outcomes across analyses. Overall, co-mutation patterns were broadly similar between Del19 and L858R tumors, although selected alterations such as BRAF showed subtype-specific enrichment, suggesting that additional oncogenic events may contribute to tumor progression and therapeutic resistance. Among these alterations, activation of the PI3K/AKT/mTOR signaling axis emerged as a key feature[30]. As a central downstream effector of EGFR signaling, the PI3K pathway regulates cellular proliferation, survival, and metabolic adaptation[31]. Mutations in PIK3CA or loss of PTEN function can reactivate downstream signaling independently of EGFR inhibition, thereby promoting resistance through alternative signaling pathways[32, 33]. Consistent with previous reports, our finding that PIK3CA mutations independently predict poorer survival underscores the critical role of PI3K pathway dysregulation in driving therapeutic failure[34, 35]. Alterations in cell cycle regulatory genes were observed and may contribute to tumor progression. Disruption of checkpoint regulators such as RB1 and CDKN2A may promote uncontrolled proliferation and genomic instability[36, 37]. In particular, RB1 loss has been associated with aggressive tumor behavior and lineage plasticity, including histologic transformation following EGFR-TKI therapy[38]. These findings highlight the importance of cell cycle checkpoint integrity in maintaining genomic stability and suggest that its disruption may further contribute to tumor evolution and therapeutic resistance. Our pathway-level analysis further identified DDR disruption as another key determinant of prognosis. Tumors with impaired DNA repair capacity may exhibit increased sensitivity to DNA-damaging therapies, including radiotherapy and immune checkpoint blockade, partly due to increased tumor immunogenicity[39, 40]. Collectively, our findings support a model in which prognosis in EGFR-mutant NSCLC is determined not only by the primary oncogenic driver but also by the broader genomic context. Tumors characterized by TP53 mutations, PI3K pathway activation, DDR deficiency, and cell cycle disruption represent biologically aggressive subgroups with enhanced genomic instability and adaptive capacity. These may facilitate tumor evolution under therapeutic pressure while simultaneously providing opportunities for targeted intervention. Finally, the integrated prognostic nomogram developed in this study provides a practical framework for incorporating molecular features into individualized risk assessment. The nomogram developed in this study is based on clinical variables and demonstrates acceptable calibration but modest discrimination. Several limitations should be considered. First, the retrospective design of the UK Biobank analysis may introduce inherent bias. Second, genomic data were derived from public datasets rather than matched sequencing data, limiting direct clinico-genomic integration. Third, the functional heterogeneity of individual mutations was not fully explored. Future prospective studies incorporating multi-omics approaches are warranted to further elucidate the underlying biological mechanisms. Conclusion Survival outcomes in EGFR-mutant NSCLC are determined by the combined influence of clinical characteristics and co-occurring genomic alterations. Age represents a key clinical determinant, while TP53-centered co-mutation patterns provide additional prognostic value. Integrating clinical and genomic features may improve risk stratification and support more personalized management strategies. Declarations Author Contributions Linxuan Huang: Conceptualization, Methodology, Supervision, Writing-original draft, Writing-review & editing. Kejun Liu: Data curation, Formal analysis, Visualization, Writing-original draft. Guojian Huang: Formal analysis, Validation, Writing-review & editing. Baocheng Xie: Data curation, Investigation, Writing -review & editing. Zhuanghua Li: Software, Data curation. Shufeng Chen: Data acquisition, Resources. June Wang: Validation, Visualization. Zhaoxi Li: Investigation, Data curation. Ting Huang: Methodology, Supervision.All authors have read and approved the final manuscript. Funding This work was supported by funds from the Guangdong Basic and Applied Basic Research Foundation, China (No. 2021B1515140031). Dongguan Science and Technology of Social Development Program, China (No. 20231800936452). The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital) Dongguan People’s Hospital (No. Z202412). Ethics Statement This study was conducted using data from the UK Biobank under approved access. All participants provided informed consent, and the study was conducted in accordance with the Declaration of Helsinki. Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data that support the findings of our study are available on request from the corresponding author. References Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A: Cancer statistics, 2025 . 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Tables Table 1 Baseline clinical characteristics of patients in the UK Biobank cohort. level Overall Untreated Treated p n 818 41 777 sex (%) Male 367 (44.9) 24 (58.5) 343 (44.1) 0.1 Female 451 (55.1) 17 (41.5) 434 (55.9) age_at_diagnosis (mean (SD)) 59.21 (10.71) 61.41 (11.79) 59.10 (10.65) 0.177 ethnicity (%) White 730 (89.2) 35 (85.4) 695 (89.4) 0.408 Asian 40 (4.9) 4 (9.8) 36 (4.6) Black 18 (2.2) 0 (0.0) 18 (2.3) Mixed/Other 24 (2.9) 2 (4.9) 22 (2.8) Unknown 6 (0.7) 0 (0.0) 6 (0.8) bmi (mean (SD)) 27.55 (4.64) 27.08 (4.68) 27.57 (4.64) 0.505 bmi_cat (%) =30 264 (32.3) 13 (31.7) 251 (32.3) smoking (%) Never 110 (13.4) 5 (12.2) 105 (13.5) 0.951 Previous 441 (53.9) 23 (56.1) 418 (53.8) Current 267 (32.6) 13 (31.7) 254 (32.7) pack_years (mean (SD)) 23.15 (14.42) 22.28 (14.29) 23.20 (14.44) 0.712 alcohol (%) Never 30 (3.7) 1 (2.4) 29 (3.7) 0.884 Special occasions 25 (3.1) 1 (2.4) 24 (3.1) 1-3 times/month 763 (93.3) 39 (95.1) 724 (93.2) townsend (mean (SD)) -1.28 (3.05) -0.35 (3.47) -1.33 (3.02) 0.046 education (%) College/University 261 (31.9) 12 (29.3) 249 (32.0) 0.801 A/O levels/Vocational 265 (32.4) 12 (29.3) 253 (32.6) Other/None 135 (16.5) 9 (22.0) 126 (16.2) Unknown 157 (19.2) 8 (19.5) 149 (19.2) has_diabetes (%) FALSE 715 (87.4) 36 (87.8) 679 (87.4) 1 TRUE 103 (12.6) 5 (12.2) 98 (12.6) has_cvd (%) FALSE 734 (89.7) 36 (87.8) 698 (89.8) 0.878 TRUE 84 (10.3) 5 (12.2) 79 (10.2) has_copd (%) FALSE 726 (88.8) 36 (87.8) 690 (88.8) 1 TRUE 92 (11.2) 5 (12.2) 87 (11.2) event (%) 0 375 (45.8) 22 (53.7) 353 (45.4) 0.384 1 443 (54.2) 19 (46.3) 424 (54.6) surv_time_months (mean (SD)) 80.70 (83.80) 74.57 (78.22) 81.02 (84.12) 0.632 Table 2 Univariate and multivariate Cox regression analysis of overall survival in the UKB cohort Variable HR Lower Upper P treatmentTreated 1.144 0.723 1.812 0.565 sexFemale 1.096 0.908 1.322 0.34 age_at_diagnosis 1.015 1.006 1.024 0.000671 ethnicityAsian 1.206 0.792 1.837 0.382 ethnicityBlack 0.985 0.508 1.907 0.963 ethnicityMixed/Other 1.337 0.798 2.24 0.27 ethnicityUnknown 1.205 0.45 3.227 0.711 bmi 1.005 0.985 1.025 0.643 smokingPrevious 0.829 0.628 1.095 0.186 smokingCurrent 0.989 0.739 1.322 0.94 smokingUnknown NA NA NA NA pack_years 1.005 0.998 1.011 0.177 townsend 1.009 0.978 1.041 0.553 educationA/O levels/Vocational 0.966 0.761 1.225 0.772 educationOther/None 1.207 0.912 1.597 0.188 educationUnknown 1.25 0.961 1.626 0.0961 has_diabetesTRUE 0.895 0.668 1.198 0.456 has_cvdTRUE 0.815 0.589 1.129 0.218 has_copdTRUE 0.904 0.668 1.224 0.515 Variable HR Lower Upper P treatmentTreated 1.15 0.723 1.829 0.554 sexFemale 1.074 0.889 1.297 0.459 age_at_diagnosis 1.015 1.006 1.024 0.00069 bmi 1.005 0.984 1.025 0.659 smokingPrevious 0.816 0.617 1.078 0.153 smokingCurrent 0.989 0.739 1.325 0.943 smokingUnknown NA NA NA NA townsend 1.008 0.977 1.04 0.614 has_copdTRUE 0.919 0.679 1.245 0.586 has_cvdTRUE 0.833 0.601 1.155 0.274 has_diabetesTRUE 0.888 0.662 1.191 0.429 Table 3 Multivariable Cox regression analysis of recurrent co-mutations in EGFR-mutant NSCLC Variable HR Lower Upper P primary_egfrL858R 0.733 0.561 0.957 0.0225 TP53 2.248 1.695 2.981 1.88e-08 RB1 1.321 0.878 1.989 0.182 KRAS 2.649 0.971 7.229 0.0572 PIK3CA 1.716 1.143 2.576 0.00916 CDKN2A 2.111 1.036 4.303 0.0397 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures.docx TableS1Summaryofdatasetsincludedinthisstudy.csv TableS2.SchoenfeldresidualtestforproportionalhazardsassumptioninthemultivariableCoxmodel.csv TableS4.CovariatebalancebeforeandafterinverseprobabilityoftreatmentweightingIPTW..csv TableS8.Schoenfeldresidualtestforproportionalhazardsassumptionincomutationmodels.csv TableS7.SchoenfeldresidualtestforproportionalhazardsassumptioninEGFRsubtypemodels..csv TableS10.RestrictedmeansurvivaltimeRMSTanalysisat60months.csv TableS9.FineGraycompetingriskregressionanalysis.csv TableS5.FrequencyofrecurrentgenemutationsandcomparisonbetweenEGFRL858RandDel19subtypes..csv TableS3.IPTWweightedCoxregressionanalysisofoverallsurvival.csv TableS6.CoxregressionanalysisofEGFRmutationsubtypes.csv Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 May, 2026 Reviewers invited by journal 01 Apr, 2026 Editor invited by journal 24 Mar, 2026 Editor assigned by journal 23 Mar, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 22 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-9194785","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617527924,"identity":"cc06a0d0-f409-4466-96b7-71ad3d90c086","order_by":0,"name":"Kejun Liu","email":"","orcid":"","institution":"Dongguan Institute of Clinical Cancer Research, Southern Medical University (Dongguan People's Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Kejun","middleName":"","lastName":"Liu","suffix":""},{"id":617527925,"identity":"e46cd90a-2d21-41c3-af1e-86c739da16cf","order_by":1,"name":"Guojian Huang","email":"","orcid":"","institution":"Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guojian","middleName":"","lastName":"Huang","suffix":""},{"id":617527926,"identity":"268c5b51-c740-4d5b-b4db-6f8c1e230c10","order_by":2,"name":"Baocheng Xie","email":"","orcid":"","institution":"Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Baocheng","middleName":"","lastName":"Xie","suffix":""},{"id":617527927,"identity":"5ae6a768-f97c-4394-adb4-8b99af01f131","order_by":3,"name":"Dongxia Wang","email":"","orcid":"","institution":"Shenzhen Guangming District People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dongxia","middleName":"","lastName":"Wang","suffix":""},{"id":617527928,"identity":"54aa0931-4127-4bf0-996b-666fa845e8f4","order_by":4,"name":"Zhuanghua Li","email":"","orcid":"","institution":"Dongguan Institute of Clinical Cancer Research, Southern Medical University (Dongguan People's Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Zhuanghua","middleName":"","lastName":"Li","suffix":""},{"id":617527929,"identity":"e47bd961-5715-4231-b993-58addf70bf06","order_by":5,"name":"Shufeng Chen","email":"","orcid":"","institution":"Dongguan wangniudun hospital","correspondingAuthor":false,"prefix":"","firstName":"Shufeng","middleName":"","lastName":"Chen","suffix":""},{"id":617527930,"identity":"14771b82-9c04-49e9-8120-8ec8874ecbcd","order_by":6,"name":"June Wang","email":"","orcid":"","institution":"Southern Medical University, Dongguan People's Hospital)","correspondingAuthor":false,"prefix":"","firstName":"June","middleName":"","lastName":"Wang","suffix":""},{"id":617527931,"identity":"a2b7b82e-a182-4830-8a0e-4f555c9adfd6","order_by":7,"name":"Zhaoxi Li","email":"","orcid":"","institution":"Southern Medical University, Dongguan People's Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Zhaoxi","middleName":"","lastName":"Li","suffix":""},{"id":617527932,"identity":"b54ed44a-a92c-4072-af50-05a0fa9793ae","order_by":8,"name":"Ting Huang","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Huang","suffix":""},{"id":617527933,"identity":"33becf6b-1ad6-45da-a907-4f430f05b81b","order_by":9,"name":"Linxuan Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACAzBZwCDHz97/8AGKIH4tBgzGkj1nmA1QBAlpSTS4kcMmQZQWc4nkZw+/GBxOYLiRe6zyZ85hBn6J5A0MP3fg1mI5I83cWMYgLY+x513abd5thxkkZ6QVMPaeweOwGwlm0hIGNsXM7AlmtxmBWoAuNGBmbMOnJf0bUItEYhtDglnhT6AWe8JacswkPxjYJPZw5JgxgBxmIEFIy5k3ZdIMBmnGEjzHkqV5t6XzSJx5VnCwF5+W4+nbJH9UHJazP9588OPPbdZy/O3JGx/8xKMFBJh5kDhg9gH8GhgYGH8QUjEKRsEoGAUjGwAA0ldRACA2TDQAAAAASUVORK5CYII=","orcid":"","institution":"Dongguan Institute of Clinical Cancer Research, Southern Medical University (Dongguan People's Hospital)","correspondingAuthor":true,"prefix":"","firstName":"Linxuan","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2026-03-23 02:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9194785/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9194785/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106403583,"identity":"5107c2b9-f46d-48f9-b18e-fabd2a4fae55","added_by":"auto","created_at":"2026-04-08 09:14:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":274391,"visible":true,"origin":"","legend":"\u003cp\u003eClinical survival analyses in the UK Biobank cohort.\u003c/p\u003e\n\u003cp\u003e(A) Kaplan–Meier curves for overall survival stratified by treatment status.\u003c/p\u003e\n\u003cp\u003e(B) Kaplan–Meier curves for overall survival stratified by age groups.\u003c/p\u003e\n\u003cp\u003e(C) Forest plot of multivariable Cox proportional hazards analysis of clinical variables associated with overall survival.\u003c/p\u003e\n\u003cp\u003e(D) Subgroup analysis of treatment effects across clinically relevant subgroups.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9194785/v1/29d7ecaf8192ab3ebeec5743.png"},{"id":106346535,"identity":"462dc289-d511-4dbe-925b-e4c2e1a3bf8c","added_by":"auto","created_at":"2026-04-07 16:29:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304459,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis according to EGFR mutation subtype and TP53 co-mutation status.\u003c/p\u003e\n\u003cp\u003e(A) Kaplan–Meier curves comparing overall survival between EGFR L858R and exon 19 deletion (Del19) subtypes.\u003c/p\u003e\n\u003cp\u003e(B) Kaplan–Meier curves stratified by TP53 mutation status.\u003c/p\u003e\n\u003cp\u003e(C) Kaplan–Meier curves within the Del19 subgroup stratified by TP53 status.\u003c/p\u003e\n\u003cp\u003e(D) Kaplan–Meier curves stratified by combined EGFR subtype and TP53 mutation status.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9194785/v1/acfc8efa82cbe9ddc012cd1f.png"},{"id":106403391,"identity":"b35348f5-719c-44ce-a4c6-8f06d8629a8e","added_by":"auto","created_at":"2026-04-08 09:14:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":178009,"visible":true,"origin":"","legend":"\u003cp\u003eCo-mutation landscape and prognostic analysis in EGFR-mutant NSCLC.\u003c/p\u003e\n\u003cp\u003e(A) Oncoplot showing the distribution of recurrent genomic alterations in EGFR-mutant tumors.\u003c/p\u003e\n\u003cp\u003e(B) Comparison of co-mutation frequencies between EGFR L858R and Del19 subtypes.\u003c/p\u003e\n\u003cp\u003e(C) Forest plot of univariable and multivariable Cox proportional hazards analyses of recurrent co-mutations.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9194785/v1/3b4b43aef71362252b256704.png"},{"id":106403500,"identity":"a1587395-c38c-4a72-b6a4-ceb0eac06a3d","added_by":"auto","created_at":"2026-04-08 09:14:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":274737,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic modeling and sensitivity analyses.\u003c/p\u003e\n\u003cp\u003e(A) Nomogram for predicting 1-, 3-, and 5-year overall survival.\u003c/p\u003e\n\u003cp\u003e(B) Time-dependent receiver operating characteristic (ROC) curves for model discrimination.\u003c/p\u003e\n\u003cp\u003e(C) Calibration curves comparing predicted and observed survival probabilities.\u003c/p\u003e\n\u003cp\u003e(D) Restricted mean survival time (RMST) and competing-risk analyses.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9194785/v1/ec8fccac4e603fe7de12d3fb.png"},{"id":106405964,"identity":"ae39ab9f-b0f9-40a9-9019-a7bfe4ac1501","added_by":"auto","created_at":"2026-04-08 09:29:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3850776,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9194785/v1/2c188f85-efbe-483c-836f-7251c5629315.pdf"},{"id":106346532,"identity":"0cd9315a-8dae-4a85-85c5-d2fc3c769552","added_by":"auto","created_at":"2026-04-07 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09:17:00","extension":"csv","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":467,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.FrequencyofrecurrentgenemutationsandcomparisonbetweenEGFRL858RandDel19subtypes..csv","url":"https://assets-eu.researchsquare.com/files/rs-9194785/v1/1930fceef8a0d788b8593472.csv"},{"id":106403666,"identity":"19047be4-02dd-487e-a73a-7b88138b8541","added_by":"auto","created_at":"2026-04-08 09:14:44","extension":"csv","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":153,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.IPTWweightedCoxregressionanalysisofoverallsurvival.csv","url":"https://assets-eu.researchsquare.com/files/rs-9194785/v1/eb4ec02dba91c6a76560db55.csv"},{"id":106346545,"identity":"c1673058-b177-449d-9cde-92c2cd5da7cc","added_by":"auto","created_at":"2026-04-07 16:29:35","extension":"csv","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":174,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.CoxregressionanalysisofEGFRmutationsubtypes.csv","url":"https://assets-eu.researchsquare.com/files/rs-9194785/v1/8fc97978a47ada3add93cdff.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical and Genomic Determinants of Survival in EGFR-Mutant Non-Small Cell Lung Cancer: An Integrated Analysis of UK Biobank and cBioPortal Cohorts","fulltext":[{"header":"Background","content":"\u003cp\u003eLung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of all cases[1]. Epidermal growth factor receptor (EGFR) mutant NSCLC represents a major oncogenic subset, predominantly driven by two activating alterations, exon 19 deletions (Del19) and L858R substitutions[2]. Although both subtypes are highly sensitive to EGFR tyrosine kinase inhibitors (EGFR-TKIs), they have been reported to exhibit differences in survival outcomes, the underlying determinants of which remain incompletely understood[3, 4].\u003c/p\u003e \u003cp\u003eConventional prognostic assessment in NSCLC is largely driven by clinical characteristics, including age, performance status, and comorbidity burden[5]. While these factors characterize baseline risk, they may not fully reflect the biological complexity underlying tumor behavior. Increasing evidence suggests that tumor-intrinsic molecular characteristics, particularly co-occurring genomic alterations, play a critical role in determining therapeutic response, resistance, and long-term survival in EGFR-mutant NSCLC[6, 7].\u003c/p\u003e \u003cp\u003eBeyond primary EGFR alterations, co-occurring genomic alterations in key pathways, including TP53, PI3K/AKT/mTOR signaling, DNA damage response (DDR), and cell cycle regulation, are increasingly recognized as critical drivers of tumor progression and therapeutic resistance[8, 9]. These co-mutation patterns may exert distinct effects on disease progression across EGFR subtypes[10]. However, most existing studies have evaluated clinical and molecular determinants separately, and a comprehensive framework integrating these factors to explain survival heterogeneity remains lacking.\u003c/p\u003e \u003cp\u003eIn this study, we integrated population-based clinical data from the UK Biobank (UKB) with high-resolution genomic profiles from cBioPortal to investigate the determinants of prognosis in EGFR-mutant NSCLC. We aimed to explore a hierarchical framework in which clinical characteristics establish baseline risk, while genomic heterogeneity further contributes to survival outcomes. We further developed an integrated prognostic nomogram for individualized risk stratification. Our findings provide mechanistic insight into prognostic heterogeneity and support a framework for precision management of EGFR-mutant NSCLC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Populations and Data Sources\u003c/h2\u003e \u003cp\u003eTwo independent datasets were utilized to investigate the clinical and genomic determinants of survival in non-small cell lung cancer (NSCLC). We identified 818 patients with a primary diagnosis of non-small cell lung cancer (NSCLC) from the Clinical Cohort (UK Biobank). Clinical variables included chronological age at diagnosis, sex, Body Mass Index (BMI), smoking status, and the Townsend Deprivation Index (TDI) as a measure of socioeconomic status. To dismantle the molecular drivers of survival, we retrieved genomic and survival data for 1,126 NSCLC patients harboring classical EGFR mutations (L858R or Exon 19 deletions) from the cBioPortal for Cancer Genomics, primarily integrating the TCGA and MSK-IMPACT datasets. According to the cBioPortal metadata, the cohort consisted of 66.8% White, 12.3% Asian, 2.7% Black or African American, and 18.2% Other/Unknown racial groups.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSurvival Endpoints and Statistical Modeling\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint was Overall Survival (OS), defined as the interval from diagnosis to death from any cause. Univariate and multivariable Cox proportional hazards (PH) models were constructed to estimate Hazard Ratios (HR) and 95% confidence intervals (CI) (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). UKB models were adjusted for clinical and lifestyle metrics (age, sex, BMI, smoking, TDI). In the cBioPortal cohort, adjustments focused on tumor stage, smoking history, and race to control for genomic confounding. For the cBioPortal cohort (60.8% missing staging data), \"Missing Stage\" was treated as a distinct category in sensitivity analyses to ensure the stability of the EGFR subtype-specific findings.\u003c/p\u003e\n\u003ch3\u003eCompeting Risk Validations\u003c/h3\u003e\n\u003cp\u003eTo mitigate selection bias between treated (n\u0026thinsp;=\u0026thinsp;777) and untreated (n\u0026thinsp;=\u0026thinsp;41) individuals in the UKB, Inverse Probability of Treatment Weighting (IPTW) was applied. Propensity scores were estimated via logistic regression, and balance was confirmed using the Standardized Mean Difference (SMD\u0026thinsp;\u0026lt;\u0026thinsp;0.1, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A Fine-Gray subdistribution hazards model was employed to differentiate lung cancer-specific mortality from competing causes of death. Restricted Mean Survival Time (RMST) over a 60-month horizon was calculated to provide a clinically interpretable measure of survival benefits, particularly for variables violating the PH assumption.\u003c/p\u003e\n\u003ch3\u003eGenomic and Pathway Analysis\u003c/h3\u003e\n\u003cp\u003eTo unmask the intrinsic prognostic signals of EGFR sub-variants, frequencies of concurrent alterations were compared between L858R and Del19 subtypes using Fisher\u0026rsquo;s exact test. Multiple comparison corrections were performed using the Benjamini-Hochberg False Discovery Rate (FDR). Interaction terms were incorporated into Cox models to test for subtype-specific synergistic effects between EGFR variants and co-mutations\u003c/p\u003e\n\u003ch3\u003eNomogram Analysis\u003c/h3\u003e\n\u003cp\u003eA clinical nomogram incorporating UKB-derived clinical variables was developed. Discrimination was evaluated using time-dependent Receiver Operating Characteristic (ROC) curves to calculate the Area Under the Curve (AUC) for 1, 3, and 5-year OS. Calibration plots were utilized to assess the agreement between predicted and observed survival probabilities.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eClinical characteristics and survival analyses in the UKB cohort\u003c/h2\u003e\n \u003cp\u003eWe first analyzed real-world clinical data from the UK Biobank. Baseline characteristics are summarized in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Treatment distribution was markedly imbalanced (777 treated vs. 41 untreated), with baseline differences observed in age, sex, and Townsend deprivation index. Descriptive Kaplan-Meier curves showed limited separation by treatment status and no evident prognostic differentiation by smoking status or BMI, whereas age-group analysis revealed modest differences, suggesting a continuous rather than threshold dependent effect (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-C, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-B). These findings suggest that treatment status, as recorded in the UKB dataset, was not independently associated with overall survival after adjustment.\u003c/p\u003e\n \u003cp\u003eIn univariable Cox analysis, age at diagnosis was significantly associated with worse overall survival, whereas treatment status was not. This finding remained unchanged in the multivariable model, in which age at diagnosis was the only independent clinical predictor of overall survival (HR 1.015, 95% CI 1.006\u0026ndash;1.024; P\u0026thinsp;=\u0026thinsp;0.00069), while treatment status, sex, BMI, smoking status, and Townsend deprivation index remained non-significant (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003eSubgroup analyses did not demonstrate a consistent statistically significant treatment effect across clinical subgroups (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e). IPTW-weighted sensitivity analysis produced similar results, further indicating that the UKB data do not support a meaningful overall-survival advantage associated with treatment status in this cohort (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e and Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). The proportional hazards assumption was assessed and generally satisfied for all variables (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEGFR mutation subtype and survival heterogeneity\u003c/h3\u003e\n\u003cp\u003eWe next examined whether EGFR mutation subtype contributed to survival heterogeneity in the cBioPortal cohort. In unadjusted Kaplan-Meier analysis, patients harboring L858R mutations showed longer overall survival than those with exon 19 deletions (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). However, in the fully adjusted subtype-specific Cox model, the association between Del19 and worse survival was attenuated and no longer statistically significant (HR 1.700, 95% CI 0.702\u0026ndash;4.112; P\u0026thinsp;=\u0026thinsp;0.239; Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e), indicating that EGFR subtype alone did not provide consistent prognostic discrimination following covariate adjustment.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eTP53 co-mutation as a major driver of survival heterogeneity\u003c/h2\u003e\n \u003cp\u003eWe then evaluated recurrent co-mutations in the cBioPortal cohort. The overall oncoplot demonstrated that TP53 co-mutation was consistently associated with worse survival outcomes and remained significant across multiple analyses, occurring in 56% of EGFR-mutant tumors, whereas other recurrent events such as PIK3CA and RB1 were far less common (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The mutation landscape indicated a higher frequency of death events among TP53-altered cases. Consistent with this pattern, TP53 co-mutation was associated with significantly worse overall survival compared with EGFR-mutant tumors without TP53 alterations (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In subgroup analysis, this adverse association was also observed within the Del19 subgroup (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003eWhen patients were stratified into four groups according to EGFR subtype and TP53 status, the most favorable outcome was observed in the L858R/TP53-wild-type group, whereas the Del19/TP53-mutant group showed the worst prognosis (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Together, these results indicate that TP53 co-mutation improves prognostic stratification beyond EGFR subtype.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eco-mutation landscape and independent prognostic effects\u003c/h2\u003e\n \u003cp\u003eComparison of recurrent co-mutation frequencies between L858R and Del19 tumors revealed broadly similar patterns across the two EGFR subtypes, with BRAF as the most distinct subtype-specific alteration, occurring exclusively in Del19 tumors (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). In univariable analysis, TP53 and RB1 were associated with worse survival after correction (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC; Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn the multivariable co-mutation model, EGFR L858R remained associated with more favorable survival (HR 0.733, 95% CI 0.561\u0026ndash;0.957; P\u0026thinsp;=\u0026thinsp;0.0225), whereas TP53 (HR 2.248, 95% CI 1.695\u0026ndash;2.981; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PIK3CA (HR 1.716, 95% CI 1.143\u0026ndash;2.576; P\u0026thinsp;=\u0026thinsp;0.009), and CDKN2A (HR 2.111, 95% CI 1.036\u0026ndash;4.303; P\u0026thinsp;=\u0026thinsp;0.040) independently predicted worse outcomes (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC; Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These findings indicate that survival differences in EGFR-mutant NSCLC are influenced by both the primary EGFR variant and co-occurring genomic alterations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eClinical prognostic modeling and sensitivity analyses\u003c/h2\u003e\n \u003cp\u003eA clinical nomogram incorporating UKB-derived variables was constructed to estimate individualized survival (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Calibration at 1, 3, and 5 years was acceptable, whereas discrimination remained limited, with time-dependent AUCs of 0.559, 0.598, and 0.627 (Table \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003e, Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C). Competing-risk analysis did not show a significant association between treatment status and cancer-specific mortality (Fine-Gray HR 1.329, 95% CI 0.759\u0026ndash;2.330; P\u0026thinsp;=\u0026thinsp;0.320), and 60-month RMST estimates were similar between treated and untreated groups (40.28 vs. 41.61 months), further arguing against a robust treatment-related survival signal in the UKB dataset (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e and S9).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we integrated population-based clinical data and public genomic datasets to investigate survival heterogeneity in EGFR-mutant NSCLC. The results support that age at diagnosis was the most consistent clinical variable associated with survival in the UKB cohort; second, treatment status as captured in UKB was not independently associated with overall survival. Third, TP53 co-mutation contributed to additional prognostic stratification beyond EGFR subtype alone. Finally, although a clinical nomogram can be constructed, its discriminatory ability appears limited.\u003c/p\u003e \u003cp\u003eOur analysis highlights that chronological age at diagnosis remains a stable and consistent clinical predictor of outcome in EGFR-mutant NSCLC. Although Kaplan-Meier curves based on age stratification showed only limited separation[11\u0026ndash;13], Cox regression consistently identified age as an independent predictor, suggesting a continuous rather than threshold-dependent effect[14, 15]. Together, these factors may limit treatment intensity and contribute to worse long-term outcomes in older patients[16].\u003c/p\u003e \u003cp\u003eThe clinical management of older patients with EGFR-mutated NSCLC is further complicated by the heterogeneous biological impact of aging[17]. Functional decline, polypharmacy, and the accumulation of chronic comorbidities can alter drug pharmacokinetics and pharmacodynamics, thereby increasing the risk of adverse events during long-term targeted therapy[18, 19]. Although EGFR tyrosine kinase inhibitors (EGFR-TKIs) are generally better tolerated than conventional chemotherapy, older patients remain more vulnerable to treatment-associated toxicities, including cardiovascular complications and metabolic disturbances[14]. These factors may compromise treatment continuity and ultimately influence survival outcomes.\u003c/p\u003e \u003cp\u003eAt the clinical level, conventional lifestyle-related variables such as BMI and smoking status were not independently associated with survival. This finding should not be interpreted as evidence that these factors are biologically irrelevant, but rather that their effects may be overshadowed by stronger host-related determinants such as age or obscured by limited measurement resolution[20]. In oncogene-driven tumors such as EGFR-mutant NSCLC, disease progression and treatment response may be more strongly influenced by tumor-intrinsic molecular characteristics than by external lifestyle exposures[21].\u003c/p\u003e \u003cp\u003eBeyond clinical factors, our study highlights the critical role of molecular heterogeneity in driving survival outcomes in EGFR-mutant NSCLC. Unadjusted Kaplan\u0026ndash;Meier analysis suggested a difference in overall survival between EGFR subtypes; however, this association was attenuated and no longer statistically significant after multivariable adjustment. Notably, the longer OS observed in L858R tumors contrasts with prior reports demonstrating superior EGFR-TKI efficacy in Del19[22, 23]. This apparent inconsistency likely reflects the distinction between treatment-specific response and overall survival. While Del19 mutations are consistently associated with enhanced sensitivity to EGFR-TKIs, long-term survival is influenced by a broader spectrum of factors, including co-mutation burden, resistance mechanisms, and subsequent therapeutic strategies.\u003c/p\u003e \u003cp\u003eEmerging evidence further supports subtype-specific differences beyond first-line therapy[24, 25]. Patients with L858R mutations may derive greater benefit from immune checkpoint inhibitors compared with those harboring Del19 alterations, whereas Del19 tumors, particularly after acquired resistance, often exhibit limited responsiveness to immunotherapy[26\u0026ndash;28]. These findings suggest that the prognostic impact of EGFR mutation subtypes is highly dependent on therapeutic context and line of treatment.\u003c/p\u003e \u003cp\u003ePrevious studies have established that co-mutation patterns critically influence prognosis in EGFR-mutant NSCLC, while our study further suggests that differential co-mutation burden may underlie subtype-specific survival differences[29]. Among all co-mutations, TP53 demonstrated the most consistent and robust association with adverse outcomes across analyses. Overall, co-mutation patterns were broadly similar between Del19 and L858R tumors, although selected alterations such as BRAF showed subtype-specific enrichment, suggesting that additional oncogenic events may contribute to tumor progression and therapeutic resistance.\u003c/p\u003e \u003cp\u003eAmong these alterations, activation of the PI3K/AKT/mTOR signaling axis emerged as a key feature[30]. As a central downstream effector of EGFR signaling, the PI3K pathway regulates cellular proliferation, survival, and metabolic adaptation[31]. Mutations in PIK3CA or loss of PTEN function can reactivate downstream signaling independently of EGFR inhibition, thereby promoting resistance through alternative signaling pathways[32, 33]. Consistent with previous reports, our finding that PIK3CA mutations independently predict poorer survival underscores the critical role of PI3K pathway dysregulation in driving therapeutic failure[34, 35].\u003c/p\u003e \u003cp\u003eAlterations in cell cycle regulatory genes were observed and may contribute to tumor progression. Disruption of checkpoint regulators such as RB1 and CDKN2A may promote uncontrolled proliferation and genomic instability[36, 37]. In particular, RB1 loss has been associated with aggressive tumor behavior and lineage plasticity, including histologic transformation following EGFR-TKI therapy[38]. These findings highlight the importance of cell cycle checkpoint integrity in maintaining genomic stability and suggest that its disruption may further contribute to tumor evolution and therapeutic resistance.\u003c/p\u003e \u003cp\u003eOur pathway-level analysis further identified DDR disruption as another key determinant of prognosis. Tumors with impaired DNA repair capacity may exhibit increased sensitivity to DNA-damaging therapies, including radiotherapy and immune checkpoint blockade, partly due to increased tumor immunogenicity[39, 40].\u003c/p\u003e \u003cp\u003eCollectively, our findings support a model in which prognosis in EGFR-mutant NSCLC is determined not only by the primary oncogenic driver but also by the broader genomic context. Tumors characterized by TP53 mutations, PI3K pathway activation, DDR deficiency, and cell cycle disruption represent biologically aggressive subgroups with enhanced genomic instability and adaptive capacity. These may facilitate tumor evolution under therapeutic pressure while simultaneously providing opportunities for targeted intervention.\u003c/p\u003e \u003cp\u003eFinally, the integrated prognostic nomogram developed in this study provides a practical framework for incorporating molecular features into individualized risk assessment. The nomogram developed in this study is based on clinical variables and demonstrates acceptable calibration but modest discrimination.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered. First, the retrospective design of the UK Biobank analysis may introduce inherent bias. Second, genomic data were derived from public datasets rather than matched sequencing data, limiting direct clinico-genomic integration. Third, the functional heterogeneity of individual mutations was not fully explored. Future prospective studies incorporating multi-omics approaches are warranted to further elucidate the underlying biological mechanisms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSurvival outcomes in EGFR-mutant NSCLC are determined by the combined influence of clinical characteristics and co-occurring genomic alterations. Age represents a key clinical determinant, while TP53-centered co-mutation patterns provide additional prognostic value. Integrating clinical and genomic features may improve risk stratification and support more personalized management strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinxuan Huang: Conceptualization, Methodology, Supervision, Writing-original draft, Writing-review \u0026amp; editing. Kejun Liu: Data curation, Formal analysis, Visualization, Writing-original draft. Guojian Huang: Formal analysis, Validation, Writing-review \u0026amp; editing. Baocheng Xie: Data curation, Investigation, Writing -review \u0026amp; editing. Zhuanghua Li: Software, Data curation. Shufeng Chen: Data acquisition, Resources. June Wang: Validation, Visualization. Zhaoxi Li: Investigation, Data curation. Ting Huang: Methodology, Supervision.All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by funds from the Guangdong Basic and Applied Basic Research Foundation, China (No. 2021B1515140031). Dongguan Science and Technology of Social Development Program, China (No. 20231800936452). The Tenth Affiliated Hospital of Southern Medical University (Dongguan People\u0026apos;s Hospital) Dongguan People\u0026rsquo;s Hospital (No. Z202412).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted using data from the UK Biobank under approved access. All participants provided informed consent, and the study was conducted in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of our study are available on request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A: \u003cstrong\u003eCancer statistics, 2025\u003c/strong\u003e. \u003cem\u003eCA: a cancer journal for clinicians\u003c/em\u003e 2025, \u003cstrong\u003e75\u003c/strong\u003e(1):10-45.\u003c/li\u003e\n\u003cli\u003eWu F, Fan J, He Y, Xiong A, Yu J, Li Y, Zhang Y, Zhao W, Zhou F, Li W\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eSingle-cell profiling of tumor heterogeneity and the microenvironment in advanced non-small cell lung cancer\u003c/strong\u003e. \u003cem\u003eNAT COMMUN\u003c/em\u003e 2021, \u003cstrong\u003e12\u003c/strong\u003e(1):2540.\u003c/li\u003e\n\u003cli\u003eZhou F, Guo H, Xia Y, Le X, Tan DSW, Ramalingam SS, Zhou C: \u003cstrong\u003eThe changing treatment landscape of EGFR-mutant non-small-cell lung cancer\u003c/strong\u003e. \u003cem\u003eNature reviews. 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\u003cstrong\u003e70\u003c/strong\u003e(1):101-106.\u003c/li\u003e\n\u003cli\u003eHellyer JA, White MN, Gardner RM, Cunanan K, Padda SK, Das M, Ramchandran K, Neal JW, Wakelee HA: \u003cstrong\u003eImpact of Tumor Suppressor Gene Co-Mutations on Differential Response to EGFR TKI Therapy in EGFR L858R and Exon 19 Deletion Lung Cancer\u003c/strong\u003e. \u003cem\u003eCLIN LUNG CANCER\u003c/em\u003e 2022, \u003cstrong\u003e23\u003c/strong\u003e(3):264-272.\u003c/li\u003e\n\u003cli\u003eQiang M, Chen Z, Liu H, Dong J, Gong K, Zhang X, Huo P, Zhu J, Shao Y, Ma J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eTargeting the PI3K/AKT/mTOR pathway in lung cancer: mechanisms and therapeutic targeting\u003c/strong\u003e. \u003cem\u003eFRONT PHARMACOL\u003c/em\u003e 2025, \u003cstrong\u003e16\u003c/strong\u003e:1516583.\u003c/li\u003e\n\u003cli\u003eCokpinar S, Erdogdu IH, Orenay-Boyacioglu S, Boyacioglu O, Kahraman-Cetin N, Meteoglu I: \u003cstrong\u003ePIK3CA Mutations and Co-Mutations 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Martinelli G\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eWide Next-Generation Sequencing Characterization of Young Adults Non-Small-Cell Lung Cancer Patients\u003c/strong\u003e. \u003cem\u003eCANCERS\u003c/em\u003e 2022, \u003cstrong\u003e14\u003c/strong\u003e(10):2352.\u003c/li\u003e\n\u003cli\u003eHuang RSP, Harries L, Decker B, Hiemenz MC, Murugesan K, Creeden J, Tolba K, Stabile LP, Ramkissoon SH, Burns TF\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eClinicopathologic and Genomic Landscape of Non-Small Cell Lung Cancer Brain Metastases\u003c/strong\u003e. \u003cem\u003eThe oncologist\u003c/em\u003e 2022, \u003cstrong\u003e27\u003c/strong\u003e(10):839-848.\u003c/li\u003e\n\u003cli\u003eSun F, Banwait MK, Singhal S, Herrmann A, Piotrowska Z, Yun K, Bazhenova L, Ullah AT, Guo EW, Wakelee HA\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eClinical factors and molecular co-alterations impact outcomes in patients receiving first-line osimertinib for EGFR-mutated non-small cell lung cancer\u003c/strong\u003e. \u003cem\u003eLung cancer (Amsterdam, Netherlands)\u003c/em\u003e 2025, \u003cstrong\u003e208\u003c/strong\u003e:108747.\u003c/li\u003e\n\u003cli\u003eSun S, Wang K, Guo D, Zheng H, Liu Y, Shen H, Du J: \u003cstrong\u003eIdentification of the key DNA damage response genes for predicting immunotherapy and chemotherapy efficacy in lung adenocarcinoma based on bulk, single-cell RNA sequencing, and spatial transcriptomics\u003c/strong\u003e. \u003cem\u003eCOMPUT BIOL MED\u003c/em\u003e 2024, \u003cstrong\u003e171\u003c/strong\u003e:108078.\u003c/li\u003e\n\u003cli\u003eSankar K, Mercer J, Jaeger EB, Godden J, Williams E, Thompson MA, Patel SA, Figueiredo JC, Weinberg F, Reckamp KL: \u003cstrong\u003eDNA damage repair gene alterations influence the tumor immune microenvironment in advanced non-small cell lung cancer\u003c/strong\u003e. \u003cem\u003eLung cancer (Amsterdam, Netherlands)\u003c/em\u003e 2025, \u003cstrong\u003e201\u003c/strong\u003e:108444.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Baseline clinical characteristics of patients in the UK Biobank cohort.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"573\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elevel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUntreated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003esex (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e367 (44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e24 (58.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e343 (44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e451 (55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e17 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e434 (55.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eage_at_diagnosis (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e59.21 (10.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e61.41 (11.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e59.10 (10.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eethnicity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e730 (89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e35 (85.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e695 (89.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e40 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e4 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e36 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e18 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e18 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eMixed/Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e24 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e2 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e22 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e6 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003ebmi (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e27.55 (4.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e27.08 (4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e27.57 (4.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003ebmi_cat (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e19 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e19 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e18.5-24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e227 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e18 (43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e209 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e25-29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e308 (37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e10 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e298 (38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026gt;=30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e264 (32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e13 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e251 (32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003esmoking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e110 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e5 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e105 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003ePrevious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e441 (53.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e23 (56.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e418 (53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e267 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e13 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e254 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003epack_years (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e23.15 (14.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e22.28 (14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e23.20 (14.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003ealcohol (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e30 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e29 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eSpecial occasions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e25 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e24 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1-3 times/month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e763 (93.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e39 (95.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e724 (93.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003etownsend (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e-1.28 (3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.35 (3.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-1.33 (3.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eeducation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eCollege/University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e261 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e12 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e249 (32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eA/O levels/Vocational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e265 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e12 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e253 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eOther/None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e135 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e9 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e126 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e157 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e8 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e149 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003ehas_diabetes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eFALSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e715 (87.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e36 (87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e679 (87.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eTRUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e103 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e5 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e98 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003ehas_cvd (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eFALSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e734 (89.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e36 (87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e698 (89.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eTRUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e84 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e5 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e79 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003ehas_copd (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eFALSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e726 (88.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e36 (87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e690 (88.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eTRUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e92 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e5 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e87 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eevent (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e375 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e22 (53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e353 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e443 (54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e19 (46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e424 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003esurv_time_months (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e80.70 (83.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e74.57 (78.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e81.02 (84.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Univariate and multivariate Cox regression analysis of overall survival in the UKB cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003etreatmentTreated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003esexFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eage_at_diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.000671\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eethnicityAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eethnicityBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eethnicityMixed/Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eethnicityUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e3.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003ebmi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003esmokingPrevious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003esmokingCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003esmokingUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003epack_years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003etownsend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eeducationA/O levels/Vocational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eeducationOther/None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eeducationUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003ehas_diabetesTRUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003ehas_cvdTRUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003ehas_copdTRUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\" class=\"fr-table-selection-hover\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003etreatmentTreated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003esexFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eage_at_diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ebmi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003esmokingPrevious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003esmokingCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003esmokingUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003etownsend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehas_copdTRUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehas_cvdTRUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003ehas_diabetesTRUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Multivariable Cox regression analysis of recurrent co-mutations in EGFR-mutant NSCLC\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eprimary_egfrL858R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.88e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKRAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePIK3CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00916\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eCDKN2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e2.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.0397\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"EGFR-mutant NSCLC, Co-mutation, Genomic heterogeneity, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-9194785/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9194785/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrognostic heterogeneity in EGFR-mutant non-small cell lung cancer (NSCLC) remains incompletely understood. Although differences between EGFR mutation subtypes have been widely reported, the extent to which co-occurring genomic alterations contribute to survival variability remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted an integrated analysis combining population-based clinical data from the UK Biobank (UKB; n\u0026thinsp;=\u0026thinsp;818) and genomic data from cBioPortal (n\u0026thinsp;=\u0026thinsp;1,126). Kaplan\u0026ndash;Meier analysis, Cox proportional hazards models, inverse probability of treatment weighting (IPTW), Fine\u0026ndash;Gray competing-risk models, and restricted mean survival time (RMST) were used to evaluate survival outcomes. A prognostic nomogram was constructed and internally validated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the UKB cohort, age at diagnosis was the only independent clinical predictor of overall survival. Treatment status, smoking status, and body mass index were not independently associated with survival after multivariable adjustment. IPTW, Fine\u0026ndash;Gray, and RMST analyses consistently showed no clinically meaningful survival advantage associated with treatment status. In the cBioPortal cohort, the apparent survival difference between EGFR L858R and exon 19 deletions was attenuated after adjustment. TP53 was the most frequent co-mutation and was consistently associated with worse survival, including within subtype-specific analyses. Multivariable analysis identified TP53, PIK3CA, and CDKN2A as independent adverse prognostic factors, whereas EGFR L858R was associated with improved survival. The nomogram showed acceptable calibration but limited discriminatory performance.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSurvival outcomes in EGFR-mutant NSCLC are jointly influenced by clinical characteristics and co-occurring genomic alterations. Age represents a key clinical determinant, while TP53-centered co-mutation patterns provide additional prognostic stratification. These findings may help improve risk stratification and support more individualized clinical management.\u003c/p\u003e","manuscriptTitle":"Clinical and Genomic Determinants of Survival in EGFR-Mutant Non-Small Cell Lung Cancer: An Integrated Analysis of UK Biobank and cBioPortal Cohorts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 16:29:28","doi":"10.21203/rs.3.rs-9194785/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"329994213390933951566838496762223613204","date":"2026-05-11T15:42:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T02:59:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-24T18:00:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-23T13:08:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-23T13:08:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-03-23T02:43:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce1bb184-6d42-4421-ad48-fd7ce36bc006","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"329994213390933951566838496762223613204","date":"2026-05-11T15:42:21+00:00","index":53,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:29:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 16:29:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9194785","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9194785","identity":"rs-9194785","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}