EGFR Mutation Subtype and Risk of Brain Metastases from Non-Small Cell Lung Cancer in the Osimertinib Era

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Patel, Victor Lee, Henry S. Park This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7992311/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Mutations in the EGFR gene are common in lung adenocarcinoma and can be targeted by tyrosine kinase inhibitors (TKIs). However, many patients eventually develop brain metastases, which are difficult to treat because the blood–brain barrier (BBB) restricts the entry of most TKIs into the central nervous system. Previous studies have suggested that tumors with the EGFR L858R mutation have a higher risk of brain metastasis than those with Exon 19 deletions (Ex19Del), possibly reflecting biological differences between these subtypes. Objectives: We aimed to evaluate whether the higher risk of brain metastasis associated with the L858R mutation, compared with the Exon 19 deletion (Ex19Del) mutation, was present among patients treated with early-generation TKIs. We also examined whether this difference was reduced among those treated with osimertinib, a third-generation, brain-penetrant TKI with improved BBB permeability and central nervous system activity. Methods: We used MSK-CHORD, a clinicogenomic database that applies natural language processing (NLP) to extract treatment and metastasis data from electronic health records. Among 7,809 NSCLC cases, we studied 601 patients with EGFR mutations without brain metastasis at diagnosis and treated with either early-generation TKIs or osimertinib. Kaplan-Meier analysis and Cox proportional hazards models were used to evaluate brain-metastasis-free survival (BMFS) with hazard ratios (HR) and 95% confidence intervals. Results: Overall, BMFS was shorter for patients with L858R vs. Ex19Del mutations (49.3% vs 58.0% at 5 years, HR 1.38 [1.02–1.87], p = 0.038). For patients receiving early-generation TKIs (n = 225, 37.4%), BMFS was shorter for those with L858R vs. Ex19Del mutations (44.8% vs 65.5% at 5 years, HR 1.95 [1.21–3.14], p = 0.006). For patients on osimertinib (n = 376, 62.6%), no significant association was observed between mutation subtype and BMFS (50.3% vs 51.2% at 5 years, HR 1.11 [0.74–1.65], p = 0.616). Conclusions: Differences in brain metastasis development between EGFR L858R and Ex19Del mutations appeared to be mitigated among patients on osimertinib compared to early-generation TKIs. Future research is necessary to determine optimal brain metastasis screening, prevention, and management strategies for patients undergoing osimertinib with either EGFR mutation subtype. Oncology Personalized Medicine EGFR-mutant non–small cell lung cancer Brain metastases EGFR L858R mutation Exon 19 deletion osimertinib NLP-derived data Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Brain metastases are common in patients with EGFR-mutant non-small cell lung cancer (NSCLC) and are a major cause of morbidity and mortality. In advanced NSCLC, brain metastases are found in about 20 percent of patients at diagnosis and up to 40 percent over the disease course, with higher baseline rates reported in genomically defined subgroups including EGFR-mutant disease 1 – 4 . Therefore, preventing and controlling spread to the central nervous system (CNS) is a critical goal. Tyrosine kinase inhibitors (TKIs) that target oncogenic EGFR mutations are highly effective against systemic disease, but the blood–brain barrier limits CNS drug penetration for many of these agents, making the brain a frequent site of relapse 5 , 6 . Two common EGFR mutation subtypes, exon 19 deletions (Ex19del) and exon 21 L858R, are associated with different clinical outcomes, but their relative risk for brain metastasis is not uniform across studies. In published cohorts, EGFR-mutant NSCLC shows a higher cumulative incidence of subsequent brain metastases than wild type but reported differences between Ex19del and L858R are inconsistent 3 , 4 , 6 , 7 . Differences across studies likely reflect treatment era and the CNS penetration of the TKI used, suggesting genotype-specific risk may be modified by therapy selection. Osimertinib, a third-generation EGFR TKI, achieves better CNS control than early-generation TKIs in randomized trials. In a prespecified CNS analysis of the FLAURA clinical trial, osimertinib reduced the risk of CNS progression and prolonged CNS progression-free survival compared with gefitinib or erlotinib 5 , 8 . These data support the idea that both genotype and initial therapy choice influence CNS outcomes in EGFR-mutant NSCLC 6 . In this study, we evaluated incident brain metastasis after TKI initiation in EGFR-mutant lung adenocarcinoma and to test whether the risk differs between L858R and Ex19del. We also examined whether any subtype-related difference would vary based on use of osimertinib compared with earlier-generation TKIs, given the established CNS activity of osimertinib in trials 5 , 8 and its growing real-world use 8 – 10 . METHODS Data Source We used data from the MSK-CHORD ( M emorial S loan K ettering C linicogenomic, H armonized O ncologic R eal-world D ataset) database, a publicly available clinicogenomic resource integrating structured and unstructured electronic health record data downloaded from cBioPortal 11 – 14 . The database includes longitudinal treatment and metastasis information extracted through natural language processing (NLP) algorithms, as well as tumor genomic profiling data derived from the MSK-IMPACT targeted sequencing panel. The MSK-CHORD cohort includes over 25,000 patients with diverse malignancies, of whom 7,809 had non–small cell lung cancer (NSCLC). Cohort Selection From the 7,809 NSCLC cases, we identified 1,657 patients with EGFR-mutant lung adenocarcinoma. Patients were excluded if they had evidence of brain metastasis within 60 days of their initial lung cancer diagnosis (considered baseline brain metastasis). The final analysis cohort comprised 601 patients with EGFR-mutant NSCLC who were treated with either early-generation tyrosine kinase inhibitors (TKIs; erlotinib, gefitinib, afatinib, or dacomitinib) or the third-generation TKI osimertinib, and who were free of brain metastases at baseline. A CONSORT diagram depicting the final cohort selection is provided in Fig. 1. Variable Definitions The anchor date for time-to-event analysis was defined as the (1) the start date of EGFR TKI therapy or (2) 60 days after the lung cancer diagnosis date (whichever was later), to allow sufficient time for detection of baseline brain metastases. The censoring date was defined as the earlier of (1) the date of death or last follow-up or (2) the date of treatment class switch between osimertinib and earlier-generation TKIs. Patients whose censoring date preceded their anchor date were excluded. Brain-metastasis-free survival (BMFS) was defined as the time from the anchor date to the date of new brain metastasis or censoring, whichever occurred first. Statistical Analysis Baseline demographic and clinical characteristics were summarized using medians with interquartile ranges for continuous variables and frequencies with percentages for categorical variables. Group comparisons between EGFR exon 19 deletion (Ex19del) and EGFR L858R mutation subtypes were performed using the table one package 15 , which applies Wilcoxon rank-sum tests for continuous variables and chi-square or Fisher’s exact tests for categorical variables as appropriate. Kaplan–Meier survival analyses were used to estimate BMFS, and the log-rank test was used to compare survival distributions between EGFR subtypes. Univariable and multivariable Cox proportional hazards models were constructed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between EGFR mutation subtype and risk of brain metastasis. Multivariable models adjusted for sex, race, smoking history, and stage at diagnosis. Analyses were performed for the overall cohort as well as for stratified subgroups of patients treated with early-generation TKIs versus osimertinib. All analyses were conducted using Python 3.7 and R 4.4.2. A two-sided p-value of < 0.05 was considered statistically significant. RESULTS Patient Characteristics A total of 601 patients with EGFR-mutant lung adenocarcinoma and no evidence of baseline brain metastasis were included in the study. Among these, 358 (59.6%) harbored EGFR exon 19 deletions (Ex19del) and 243 (40.4%) carried EGFR L858R mutations. The median age at tumor sequencing was 69 years (interquartile range [IQR], 60–77), and 67.6% of patients were female. Most patients were White (59.9%) or Asian (27.3%), and 60.7% were never-smokers. Stage IV disease at diagnosis was present in 63.4% of patients. Osimertinib was the first TKI received by 62.6% of patients, while 37.4% received an early-generation TKI (erlotinib, gefitinib, afatinib, or dacomitinib). Baseline characteristics were generally balanced between Ex19del and L858R groups except for median age (median 71 years for L858R vs. 67 years for Ex19del, p = 0.001) (Table 1 ). Table 1 Baseline demographic and clinical characteristics of 601 patients with EGFR-mutant lung adenocarcinoma, stratified by EGFR subtype (Ex19del vs. L858R). Overall Ex19del L858R P-Value n 601 358 243 Age at Tumor Sequencing, median [Q1,Q3] 69.0 [60.0,77.0] 67.0 [59.0,76.0] 71.0 [63.0,78.0] 0.001 Sex, n (%) Female 406 (67.6) 233 (65.1) 173 (71.2) 0.139 Male 195 (32.4) 125 (34.9) 70 (28.8) Race, n (%) Asian-Far East/Indian Subcont. 164 (27.3) 96 (26.8) 68 (28.0) 0.801 Black Or African American 39 (6.5) 27 (7.5) 12 (4.9) Native American-Am Ind/Alaska 1 (0.2) 1 (0.4) Native Hawaiian or Pacific Isl. 2 (0.3) 1 (0.3) 1 (0.4) Other 10 (1.7) 7 (2.0) 3 (1.2) Pt Refused to Answer 21 (3.5) 12 (3.4) 9 (3.7) Unknown 4 (0.7) 2 (0.6) 2 (0.8) White 360 (59.9) 213 (59.5) 147 (60.5) Smoking History, n (%) Former/Current Smoker 214 (35.6) 125 (34.9) 89 (36.6) 0.318 Never 365 (60.7) 223 (62.3) 142 (58.4) Unknown 22 (3.7) 10 (2.8) 12 (4.9) Stage At Diagnosis, n (%) III 220 (36.6) 132 (36.9) 88 (36.2) 0.938 IV 381 (63.4) 226 (63.1) 155 (63.8) First TKI Group, n (%) Early-generation 225 (37.4) 139 (38.8) 86 (35.4) 0.442 Osimertinib 376 (62.6) 219 (61.2) 157 (64.6) Specific TKI, n (%) Afatinib 18 (3.0) 12 (3.4) 6 (2.5) 0.625 Dacomitinib 1 (0.2) 1 (0.4) Erlotinib 204 (33.9) 126 (35.2) 78 (32.1) Gefitinib 2 (0.3) 1 (0.3) 1 (0.4) Osimertinib 376 (62.6) 219 (61.2) 157 (64.6) Brain Metastasis-Free Survival In the overall cohort, the 5-year brain metastasis-free survival (BMFS) rate was lower in patients with EGFR L858R mutations compared with Ex19del mutations (49.3% vs. 58.0%, log-rank p = 0.038, Fig. 2). In univariable Cox regression, EGFR L858R was associated with a higher risk of developing brain metastases (HR, 1.38; 95% CI, 1.02–1.87; p = 0.038). This association remained significant after adjustment for sex, race, smoking history, and stage at diagnosis in multivariable analysis (HR, 1.51; 95% CI, 1.11–2.06; p = 0.009). Impact of TKI Generation When stratified by the generation of EGFR TKI received, differences in BMFS were most pronounced among patients treated with early-generation TKIs. Among these patients, those with EGFR L858R mutations had substantially shorter BMFS than those with Ex19del mutations (5-year BMFS 44.8% vs. 65.5%, log-rank p = 0.005, Fig. 3). The risk of developing brain metastases was nearly doubled for L858R-mutant patients (univariable HR, 1.95; 95% CI, 1.21–3.14; p = 0.006; multivariable HR, 2.14; 95% CI, 1.30–3.51; p = 0.003). In contrast, among patients treated with osimertinib, BMFS was similar between EGFR subtypes (Ex19del 51.2%, L858R 50.3%, log-rank p = 0.617, Fig. 4). Correspondingly, EGFR mutation subtype was not significantly associated with brain metastasis risk in either univariable (HR, 1.11; 95% CI, 0.74–1.65; p = 0.616) or multivariable models (HR, 1.15; 95% CI, 0.77–1.74; p = 0.491). DISCUSSION In this study leveraging the MSK-CHORD clinicogenomic database, we validated prior observations that patients with EGFR L858R–mutant lung adenocarcinoma have a higher risk of developing brain metastases compared to those with EGFR exon 19 deletion (Ex19del) mutations. This disparity was most evident among patients treated with first- or second-generation tyrosine kinase inhibitors (TKIs), but was not observed in those receiving osimertinib, a third-generation EGFR inhibitor with improved central nervous system (CNS) penetration. These findings suggest that the excess brain metastasis risk associated with L858R-mutant disease may be mitigated in patients receiving osimertinib compared to early-generation TKIs. Several prior studies have examined how EGFR subtype relates to CNS risk and outcomes, with findings that depend on disease setting and treatment era. In a large single-institution analysis from 2017, EGFR-mutant NSCLC had a higher risk of brain metastasis than EGFR wild type, but exon 19 deletion and L858R showed similar subsequent brain-metastasis rates and no significant difference between the two subtypes 4 . In contrast, a 2025 retrospective study of resected stage I–III NSCLC reported that any patient with an EGFR mutation carried a higher cumulative incidence of brain metastasis than wild type, and within EGFR-mutant disease the cumulative incidence was somewhat higher for exon 19 deletion than for L858R during postoperative follow-up 3 . Another recent multicenter real-world cohort found that brain metastasis status and the generation of EGFR-TKI jointly influenced outcomes, while overall survival was more closely associated with EGFR subtype 6 . In that analysis, L858R was linked to worse overall survival than exon 19 deletion, and patterns of progression differed by subtype and by whether patients received first- or second-generation TKIs 6 . Finally, a recent single-institution study focused on advanced EGFR-mutant NSCLC treated with first-line TKIs. The authors reported inferior intracranial outcomes for L858R compared with exon 19 deletion, using intracranial progression-free survival as the primary endpoint 7 . Taken together, these reports suggest that L858R often tracks with poorer systemic outcomes and, in some contemporary series, worse intracranial control, while other settings such as postoperative surveillance can show equal or even higher brain-metastasis incidence with exon 19 deletion. Based on these observations, CNS behavior appears to reflect both tumor genotype and the initial therapy used, particularly the generation and CNS activity of the TKI. This heterogeneity across settings underscores the value of analyzing incident brain metastasis after therapy start, and of stratifying results by both EGFR subtype and frontline regimen. The improved CNS control seen in our study with osimertinib likely reflects its enhanced pharmacologic properties, including superior blood–brain barrier penetration and sustained CNS drug exposure compared to earlier EGFR inhibitors. The attenuation of subtype-related differences in brain metastasis risk among osimertinib-treated patients suggests that pharmacologic CNS activity, rather than intrinsic biological differences between L858R and Ex19del tumors alone, plays a major role in driving brain metastasis outcomes. A major strength of this study is the use of the MSK-CHORD database, which integrates natural language processing (NLP) techniques to extract longitudinal clinical events, such as treatment start and stop dates, metastasis onset, and radiologic progression, from unstructured medical records. This approach enables high-resolution temporal modeling of metastasis development in a real-world setting, overcoming a key limitation of most genomic datasets that lack detailed treatment and outcome timelines. Several limitations should be acknowledged. First, as a retrospective analysis of real-world data, unmeasured confounding and incomplete ascertainment of clinical events are possible despite NLP-based curation. Second, the MSK cohort primarily reflects patients treated at a tertiary cancer center, which may limit generalizability. Finally, detailed imaging and radiation treatment data were not uniformly available, precluding assessment of the impact of CNS-directed local therapy on metastasis-free survival. Despite these limitations, our findings provide evidence that osimertinib effectively reduces the elevated risk of brain metastases historically observed among patients with EGFR L858R–mutant lung adenocarcinoma. By leveraging NLP-derived longitudinal data, this study demonstrates the utility of artificial intelligence-assisted clinical databases such as MSK-CHORD in uncovering clinically meaningful patterns of metastasis and treatment response. Declarations AUTHOR CONTRIBUTIONS : Conception of study: All authors Curation and Harmonization of Data: ATP Statistical Analysis: VL, HSP Generation of Figures: All authors Writing of Manuscript: All authors Supervision of Study: HSP DATA AVAILABLILITY STATEMENT : All data analyzed in this study were downloaded from the publicly available MSK-CHORD database though the cBioPortal website. This data is available under the Creative Commons BY-NC-ND 4.0 license. All patient data is de-identified and lacks PHI. Data for the final cohort analyzed in this study are provided as a CSV file in Supplementary Materials (Table S1). COMPETING INTERESTS STATEMENT : The authors declare no competing interests. References Ruíz-Patiño A et al (2025) Molecular and clonal evolution of primary lesions vs. brain metastasis in EGFR-mutated NSCLC: a retrospective cohort study. Transl Lung Cancer Res 14:3824–3835 Huijs JWJ et al (2025) Screening for brain metastases in patients with advanced non-small cell lung cancer and an actionable genomic alteration: A structured literature review. Neuro-Oncol Pract 12:545–570 Zhang J et al (2025) Association between EGFR mutation types and incidence of brain metastases in postoperative patients with stage I-III NSCLC. Tumori 111:200–209 Hsiao S-H et al (2017) Brain metastases in patients with non-small cell lung cancer: the role of mutated- EGFRs with an exon 19 deletion or L858R point mutation in cancer cell dissemination. Oncotarget 8:53405–53418 Gourd E (2018) CNS efficacy of osimertinib in EGFR-mutated advanced NSCLC. Lancet Oncol 19:e516 Ju J-S et al (2023) Brain metastasis, EGFR mutation subtype and generation of EGFR-TKI jointly influence the treatment outcome of patient with EGFR-mutant NSCLC. Sci Rep 13:20323 Rios-Garcia E et al (2025) Elucidating the Role of EGFRL858R in Brain Metastasis Among Patients With Advanced NSCLC Undergoing TKI Therapy. Clin. Lung Cancer 26, e199-e206.e2 Ramalingam SS et al (2020) Overall Survival with Osimertinib in Untreated, EGFR-Mutated Advanced NSCLC. N Engl J Med 382:41–50 Hendriks LEL et al (2025) Updated treatment recommendations for systemic treatment: from the ESMO oncogene-addicted metastatic NSCLC living guideline. Ann Oncol 36:1227–1231 Sabari JK et al (2025) Overall Survival in EGFR-Mutant Advanced NSCLC Treated With First-Line Osimertinib: A Cohort Study Integrating Clinical and Biomarker Data in the United States. J Thorac Oncol 20:1268–1278 Jee J et al (2024) Automated real-world data integration improves cancer outcome prediction. Nature 636:728–736 Cerami E et al (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2:401–404 Gao J et al (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6:pl1 de Bruijn I et al (2023) Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal. Cancer Res 83:3861–3867 Pollard TJ, Johnson AEW, Raffa JD, Mark RG (2018) tableone: An open source Python package for producing summary statistics for research papers. JAMIA Open 1:26–31 Additional Declarations The authors declare no competing interests. Supplementary Files TableS1PatientCohortn601.csv Table S1. Cohort from MSK-CHORD Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7992311","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":537678704,"identity":"e720a30d-e641-42ad-a403-703ccf7a6c77","order_by":0,"name":"Ameya T. 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(log-rank p=0.038)\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7992311/v1/b0688b2d1e39a34a8547ef15.jpg"},{"id":94981401,"identity":"6b83ef8d-c740-4671-a827-0aa72037961d","added_by":"auto","created_at":"2025-11-03 05:36:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":35508,"visible":true,"origin":"","legend":"\u003cp\u003eBMFS based on EGFR subtype among patients who received early-generation TKIs first. (log-rank p=0.005)\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7992311/v1/7cc65a3070951cfb7e8ce43c.jpg"},{"id":94989395,"identity":"1604c051-44c2-454f-b70c-d026af0c979b","added_by":"auto","created_at":"2025-11-03 07:12:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33437,"visible":true,"origin":"","legend":"\u003cp\u003eBMFS based on EGFR subtype among patients who received osimertinib first. (log-rank p=0.617)\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7992311/v1/990baae44c9be5400457b0a5.jpg"},{"id":94991021,"identity":"5fd74673-51b8-4812-b39b-3604dcfc7c7a","added_by":"auto","created_at":"2025-11-03 07:19:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":871016,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7992311/v1/8d981362-fc94-43b9-8763-ebca7428f0fc.pdf"},{"id":94981397,"identity":"33f87579-a87e-4b07-8ee2-0b856909ba3a","added_by":"auto","created_at":"2025-11-03 05:36:33","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":140432,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1. \u0026nbsp;Cohort from MSK-CHORD\u003c/p\u003e","description":"","filename":"TableS1PatientCohortn601.csv","url":"https://assets-eu.researchsquare.com/files/rs-7992311/v1/dc1411de9a63e28220d0d24d.csv"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEGFR Mutation Subtype and Risk of Brain Metastases from Non-Small Cell Lung Cancer in the Osimertinib Era\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBrain metastases are common in patients with EGFR-mutant non-small cell lung cancer (NSCLC) and are a major cause of morbidity and mortality. In advanced NSCLC, brain metastases are found in about 20 percent of patients at diagnosis and up to 40 percent over the disease course, with higher baseline rates reported in genomically defined subgroups including EGFR-mutant disease\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Therefore, preventing and controlling spread to the central nervous system (CNS) is a critical goal. Tyrosine kinase inhibitors (TKIs) that target oncogenic EGFR mutations are highly effective against systemic disease, but the blood\u0026ndash;brain barrier limits CNS drug penetration for many of these agents, making the brain a frequent site of relapse\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTwo common EGFR mutation subtypes, exon 19 deletions (Ex19del) and exon 21 L858R, are associated with different clinical outcomes, but their relative risk for brain metastasis is not uniform across studies. In published cohorts, EGFR-mutant NSCLC shows a higher cumulative incidence of subsequent brain metastases than wild type but reported differences between Ex19del and L858R are inconsistent\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Differences across studies likely reflect treatment era and the CNS penetration of the TKI used, suggesting genotype-specific risk may be modified by therapy selection.\u003c/p\u003e\u003cp\u003eOsimertinib, a third-generation EGFR TKI, achieves better CNS control than early-generation TKIs in randomized trials. In a prespecified CNS analysis of the FLAURA clinical trial, osimertinib reduced the risk of CNS progression and prolonged CNS progression-free survival compared with gefitinib or erlotinib\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. These data support the idea that both genotype and initial therapy choice influence CNS outcomes in EGFR-mutant NSCLC\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, we evaluated incident brain metastasis after TKI initiation in EGFR-mutant lung adenocarcinoma and to test whether the risk differs between L858R and Ex19del. We also examined whether any subtype-related difference would vary based on use of osimertinib compared with earlier-generation TKIs, given the established CNS activity of osimertinib in trials\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and its growing real-world use\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Source\u003c/h2\u003e\u003cp\u003eWe used data from the MSK-CHORD (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eM\u003c/span\u003eemorial \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eS\u003c/span\u003eloan \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eK\u003c/span\u003eettering \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC\u003c/span\u003elinicogenomic, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eH\u003c/span\u003earmonized \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eO\u003c/span\u003encologic \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eR\u003c/span\u003eeal-world \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eD\u003c/span\u003eataset) database, a publicly available clinicogenomic resource integrating structured and unstructured electronic health record data downloaded from cBioPortal\u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The database includes longitudinal treatment and metastasis information extracted through natural language processing (NLP) algorithms, as well as tumor genomic profiling data derived from the MSK-IMPACT targeted sequencing panel. The MSK-CHORD cohort includes over 25,000 patients with diverse malignancies, of whom 7,809 had non\u0026ndash;small cell lung cancer (NSCLC).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCohort Selection\u003c/h3\u003e\n\u003cp\u003eFrom the 7,809 NSCLC cases, we identified 1,657 patients with EGFR-mutant lung adenocarcinoma. Patients were excluded if they had evidence of brain metastasis within 60 days of their initial lung cancer diagnosis (considered baseline brain metastasis). The final analysis cohort comprised 601 patients with EGFR-mutant NSCLC who were treated with either early-generation tyrosine kinase inhibitors (TKIs; erlotinib, gefitinib, afatinib, or dacomitinib) or the third-generation TKI osimertinib, and who were free of brain metastases at baseline. A CONSORT diagram depicting the final cohort selection is provided in Fig.\u0026nbsp;1.\u003c/p\u003e\n\u003ch3\u003eVariable Definitions\u003c/h3\u003e\n\u003cp\u003eThe anchor date for time-to-event analysis was defined as the (1) the start date of EGFR TKI therapy or (2) 60 days after the lung cancer diagnosis date (whichever was later), to allow sufficient time for detection of baseline brain metastases.\u003c/p\u003e\u003cp\u003eThe censoring date was defined as the earlier of (1) the date of death or last follow-up or (2) the date of treatment class switch between osimertinib and earlier-generation TKIs. Patients whose censoring date preceded their anchor date were excluded.\u003c/p\u003e\u003cp\u003eBrain-metastasis-free survival (BMFS) was defined as the time from the anchor date to the date of new brain metastasis or censoring, whichever occurred first.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eBaseline demographic and clinical characteristics were summarized using medians with interquartile ranges for continuous variables and frequencies with percentages for categorical variables. Group comparisons between EGFR exon 19 deletion (Ex19del) and EGFR L858R mutation subtypes were performed using the table one package\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, which applies Wilcoxon rank-sum tests for continuous variables and chi-square or Fisher\u0026rsquo;s exact tests for categorical variables as appropriate.\u003c/p\u003e\u003cp\u003eKaplan\u0026ndash;Meier survival analyses were used to estimate BMFS, and the log-rank test was used to compare survival distributions between EGFR subtypes. Univariable and multivariable Cox proportional hazards models were constructed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between EGFR mutation subtype and risk of brain metastasis. Multivariable models adjusted for sex, race, smoking history, and stage at diagnosis. Analyses were performed for the overall cohort as well as for stratified subgroups of patients treated with early-generation TKIs versus osimertinib. All analyses were conducted using Python 3.7 and R 4.4.2. A two-sided p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePatient Characteristics\u003c/h2\u003e\u003cp\u003eA total of 601 patients with EGFR-mutant lung adenocarcinoma and no evidence of baseline brain metastasis were included in the study. Among these, 358 (59.6%) harbored EGFR exon 19 deletions (Ex19del) and 243 (40.4%) carried EGFR L858R mutations. The median age at tumor sequencing was 69 years (interquartile range [IQR], 60\u0026ndash;77), and 67.6% of patients were female. Most patients were White (59.9%) or Asian (27.3%), and 60.7% were never-smokers. Stage IV disease at diagnosis was present in 63.4% of patients. Osimertinib was the first TKI received by 62.6% of patients, while 37.4% received an early-generation TKI (erlotinib, gefitinib, afatinib, or dacomitinib). Baseline characteristics were generally balanced between Ex19del and L858R groups except for median age (median 71 years for L858R vs. 67 years for Ex19del, p\u0026thinsp;=\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline demographic and clinical characteristics of 601 patients with EGFR-mutant lung adenocarcinoma, stratified by EGFR subtype (Ex19del vs. L858R).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEx19del\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eL858R\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at Tumor Sequencing, median [Q1,Q3]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.0 [60.0,77.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.0 [59.0,76.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.0 [63.0,78.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSex, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e406 (67.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e233 (65.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e173 (71.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e195 (32.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125 (34.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70 (28.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u003cb\u003eRace, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAsian-Far East/Indian Subcont.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e164 (27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96 (26.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68 (28.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.801\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBlack Or African American\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNative American-Am Ind/Alaska\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNative Hawaiian or Pacific Isl.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOther\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePt Refused to Answer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eWhite\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e360 (59.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e213 (59.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e147 (60.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eSmoking History, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eFormer/Current Smoker\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e214 (35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125 (34.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89 (36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.318\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNever\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e365 (60.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e223 (62.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e142 (58.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eStage At Diagnosis, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eIII\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e220 (36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e132 (36.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88 (36.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eIV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e381 (63.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e226 (63.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e155 (63.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eFirst TKI Group, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eEarly-generation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e225 (37.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139 (38.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e86 (35.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOsimertinib\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e376 (62.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e219 (61.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e157 (64.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eSpecific TKI, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAfatinib\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eDacomitinib\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eErlotinib\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e204 (33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126 (35.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78 (32.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eGefitinib\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOsimertinib\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e376 (62.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e219 (61.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e157 (64.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBrain Metastasis-Free Survival\u003c/h3\u003e\n\u003cp\u003eIn the overall cohort, the 5-year brain metastasis-free survival (BMFS) rate was lower in patients with EGFR L858R mutations compared with Ex19del mutations (49.3% vs. 58.0%, log-rank p\u0026thinsp;=\u0026thinsp;0.038, Fig.\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eIn univariable Cox regression, EGFR L858R was associated with a higher risk of developing brain metastases (HR, 1.38; 95% CI, 1.02\u0026ndash;1.87; p\u0026thinsp;=\u0026thinsp;0.038). This association remained significant after adjustment for sex, race, smoking history, and stage at diagnosis in multivariable analysis (HR, 1.51; 95% CI, 1.11\u0026ndash;2.06; p\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e\n\u003ch3\u003eImpact of TKI Generation\u003c/h3\u003e\n\u003cp\u003eWhen stratified by the generation of EGFR TKI received, differences in BMFS were most pronounced among patients treated with early-generation TKIs. Among these patients, those with EGFR L858R mutations had substantially shorter BMFS than those with Ex19del mutations (5-year BMFS 44.8% vs. 65.5%, log-rank p\u0026thinsp;=\u0026thinsp;0.005, Fig.\u0026nbsp;3). The risk of developing brain metastases was nearly doubled for L858R-mutant patients (univariable HR, 1.95; 95% CI, 1.21\u0026ndash;3.14; p\u0026thinsp;=\u0026thinsp;0.006; multivariable HR, 2.14; 95% CI, 1.30\u0026ndash;3.51; p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e\u003cp\u003eIn contrast, among patients treated with osimertinib, BMFS was similar between EGFR subtypes (Ex19del 51.2%, L858R 50.3%, log-rank p\u0026thinsp;=\u0026thinsp;0.617, Fig.\u0026nbsp;4). Correspondingly, EGFR mutation subtype was not significantly associated with brain metastasis risk in either univariable (HR, 1.11; 95% CI, 0.74\u0026ndash;1.65; p\u0026thinsp;=\u0026thinsp;0.616) or multivariable models (HR, 1.15; 95% CI, 0.77\u0026ndash;1.74; p\u0026thinsp;=\u0026thinsp;0.491).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study leveraging the MSK-CHORD clinicogenomic database, we validated prior observations that patients with EGFR L858R\u0026ndash;mutant lung adenocarcinoma have a higher risk of developing brain metastases compared to those with EGFR exon 19 deletion (Ex19del) mutations. This disparity was most evident among patients treated with first- or second-generation tyrosine kinase inhibitors (TKIs), but was not observed in those receiving osimertinib, a third-generation EGFR inhibitor with improved central nervous system (CNS) penetration. These findings suggest that the excess brain metastasis risk associated with L858R-mutant disease may be mitigated in patients receiving osimertinib compared to early-generation TKIs.\u003c/p\u003e\u003cp\u003eSeveral prior studies have examined how EGFR subtype relates to CNS risk and outcomes, with findings that depend on disease setting and treatment era. In a large single-institution analysis from 2017, EGFR-mutant NSCLC had a higher risk of brain metastasis than EGFR wild type, but exon 19 deletion and L858R showed similar subsequent brain-metastasis rates and no significant difference between the two subtypes\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In contrast, a 2025 retrospective study of resected stage I\u0026ndash;III NSCLC reported that any patient with an EGFR mutation carried a higher cumulative incidence of brain metastasis than wild type, and within EGFR-mutant disease the cumulative incidence was somewhat higher for exon 19 deletion than for L858R during postoperative follow-up\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAnother recent multicenter real-world cohort found that brain metastasis status and the generation of EGFR-TKI jointly influenced outcomes, while overall survival was more closely associated with EGFR subtype\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In that analysis, L858R was linked to worse overall survival than exon 19 deletion, and patterns of progression differed by subtype and by whether patients received first- or second-generation TKIs\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Finally, a recent single-institution study focused on advanced EGFR-mutant NSCLC treated with first-line TKIs. The authors reported inferior intracranial outcomes for L858R compared with exon 19 deletion, using intracranial progression-free survival as the primary endpoint\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTaken together, these reports suggest that L858R often tracks with poorer systemic outcomes and, in some contemporary series, worse intracranial control, while other settings such as postoperative surveillance can show equal or even higher brain-metastasis incidence with exon 19 deletion. Based on these observations, CNS behavior appears to reflect both tumor genotype and the initial therapy used, particularly the generation and CNS activity of the TKI. This heterogeneity across settings underscores the value of analyzing incident brain metastasis after therapy start, and of stratifying results by both EGFR subtype and frontline regimen.\u003c/p\u003e\u003cp\u003eThe improved CNS control seen in our study with osimertinib likely reflects its enhanced pharmacologic properties, including superior blood\u0026ndash;brain barrier penetration and sustained CNS drug exposure compared to earlier EGFR inhibitors. The attenuation of subtype-related differences in brain metastasis risk among osimertinib-treated patients suggests that pharmacologic CNS activity, rather than intrinsic biological differences between L858R and Ex19del tumors alone, plays a major role in driving brain metastasis outcomes.\u003c/p\u003e\u003cp\u003eA major strength of this study is the use of the MSK-CHORD database, which integrates natural language processing (NLP) techniques to extract longitudinal clinical events, such as treatment start and stop dates, metastasis onset, and radiologic progression, from unstructured medical records. This approach enables high-resolution temporal modeling of metastasis development in a real-world setting, overcoming a key limitation of most genomic datasets that lack detailed treatment and outcome timelines.\u003c/p\u003e\u003cp\u003eSeveral limitations should be acknowledged. First, as a retrospective analysis of real-world data, unmeasured confounding and incomplete ascertainment of clinical events are possible despite NLP-based curation. Second, the MSK cohort primarily reflects patients treated at a tertiary cancer center, which may limit generalizability. Finally, detailed imaging and radiation treatment data were not uniformly available, precluding assessment of the impact of CNS-directed local therapy on metastasis-free survival.\u003c/p\u003e\u003cp\u003eDespite these limitations, our findings provide evidence that osimertinib effectively reduces the elevated risk of brain metastases historically observed among patients with EGFR L858R\u0026ndash;mutant lung adenocarcinoma. By leveraging NLP-derived longitudinal data, this study demonstrates the utility of artificial intelligence-assisted clinical databases such as MSK-CHORD in uncovering clinically meaningful patterns of metastasis and treatment response.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e: \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConception of study: All authors\u003c/p\u003e\n\u003cp\u003eCuration and Harmonization of Data: ATP\u003c/p\u003e\n\u003cp\u003eStatistical Analysis: VL, HSP\u003c/p\u003e\n\u003cp\u003eGeneration of Figures: All authors\u003c/p\u003e\n\u003cp\u003eWriting of Manuscript: All authors\u003c/p\u003e\n\u003cp\u003eSupervision of Study: HSP\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABLILITY STATEMENT\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eAll data analyzed in this study were downloaded from the publicly available MSK-CHORD database though the cBioPortal website. This data is available under the Creative Commons BY-NC-ND 4.0 license. All patient data is de-identified and lacks PHI. \u0026nbsp;Data for the final cohort analyzed in this study are provided as a CSV file in Supplementary Materials (Table S1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS STATEMENT\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRu\u0026iacute;z-Pati\u0026ntilde;o A et al (2025) Molecular and clonal evolution of primary lesions vs. brain metastasis in EGFR-mutated NSCLC: a retrospective cohort study. Transl Lung Cancer Res 14:3824\u0026ndash;3835\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuijs JWJ et al (2025) Screening for brain metastases in patients with advanced non-small cell lung cancer and an actionable genomic alteration: A structured literature review. Neuro-Oncol Pract 12:545\u0026ndash;570\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang J et al (2025) Association between EGFR mutation types and incidence of brain metastases in postoperative patients with stage I-III NSCLC. Tumori 111:200\u0026ndash;209\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHsiao S-H et al (2017) Brain metastases in patients with non-small cell lung cancer: the role of mutated- EGFRs with an exon 19 deletion or L858R point mutation in cancer cell dissemination. Oncotarget 8:53405\u0026ndash;53418\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGourd E (2018) CNS efficacy of osimertinib in EGFR-mutated advanced NSCLC. Lancet Oncol 19:e516\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJu J-S et al (2023) Brain metastasis, EGFR mutation subtype and generation of EGFR-TKI jointly influence the treatment outcome of patient with EGFR-mutant NSCLC. Sci Rep 13:20323\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRios-Garcia E et al (2025) Elucidating the Role of EGFRL858R in Brain Metastasis Among Patients With Advanced NSCLC Undergoing TKI Therapy. \u003cem\u003eClin. Lung Cancer\u003c/em\u003e 26, e199-e206.e2\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamalingam SS et al (2020) Overall Survival with Osimertinib in Untreated, EGFR-Mutated Advanced NSCLC. N Engl J Med 382:41\u0026ndash;50\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHendriks LEL et al (2025) Updated treatment recommendations for systemic treatment: from the ESMO oncogene-addicted metastatic NSCLC living guideline. Ann Oncol 36:1227\u0026ndash;1231\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSabari JK et al (2025) Overall Survival in EGFR-Mutant Advanced NSCLC Treated With First-Line Osimertinib: A Cohort Study Integrating Clinical and Biomarker Data in the United States. J Thorac Oncol 20:1268\u0026ndash;1278\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJee J et al (2024) Automated real-world data integration improves cancer outcome prediction. Nature 636:728\u0026ndash;736\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCerami E et al (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2:401\u0026ndash;404\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao J et al (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6:pl1\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Bruijn I et al (2023) Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal. Cancer Res 83:3861\u0026ndash;3867\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePollard TJ, Johnson AEW, Raffa JD, Mark RG (2018) tableone: An open source Python package for producing summary statistics for research papers. JAMIA Open 1:26\u0026ndash;31\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Yale University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"EGFR-mutant non–small cell lung cancer, Brain metastases, EGFR L858R mutation, Exon 19 deletion, osimertinib, NLP-derived data","lastPublishedDoi":"10.21203/rs.3.rs-7992311/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7992311/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMutations in the EGFR gene are common in lung adenocarcinoma and can be targeted by tyrosine kinase inhibitors (TKIs). However, many patients eventually develop brain metastases, which are difficult to treat because the blood–brain barrier (BBB) restricts the entry of most TKIs into the central nervous system. Previous studies have suggested that tumors with the EGFR L858R mutation have a higher risk of brain metastasis than those with Exon 19 deletions (Ex19Del), possibly reflecting biological differences between these subtypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe aimed to evaluate whether the higher risk of brain metastasis associated with the L858R mutation, compared with the Exon 19 deletion (Ex19Del) mutation, was present among patients treated with early-generation TKIs. We also examined whether this difference was reduced among those treated with osimertinib, a third-generation, brain-penetrant TKI with improved BBB permeability and central nervous system activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used MSK-CHORD, a clinicogenomic database that applies natural language processing (NLP) to extract treatment and metastasis data from electronic health records. Among 7,809 NSCLC cases, we studied 601 patients with EGFR mutations without brain metastasis at diagnosis and treated with either early-generation TKIs or osimertinib. Kaplan-Meier analysis and Cox proportional hazards models were used to evaluate brain-metastasis-free survival (BMFS) with hazard ratios (HR) and 95% confidence intervals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, BMFS was shorter for patients with L858R vs. Ex19Del mutations (49.3% vs 58.0% at 5 years, HR 1.38 [1.02–1.87], p = 0.038). For patients receiving early-generation TKIs (n = 225, 37.4%), BMFS was shorter for those with L858R vs. Ex19Del mutations (44.8% vs 65.5% at 5 years, HR 1.95 [1.21–3.14], p = 0.006). For patients on osimertinib (n = 376, 62.6%), no significant association was observed between mutation subtype and BMFS (50.3% vs 51.2% at 5 years, HR 1.11 [0.74–1.65], p = 0.616).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferences in brain metastasis development between EGFR L858R and Ex19Del mutations appeared to be mitigated among patients on osimertinib compared to early-generation TKIs. Future research is necessary to determine optimal brain metastasis screening, prevention, and management strategies for patients undergoing osimertinib with either EGFR mutation subtype.\u003c/p\u003e","manuscriptTitle":"EGFR Mutation Subtype and Risk of Brain Metastases from Non-Small Cell Lung Cancer in the Osimertinib Era","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 05:36:28","doi":"10.21203/rs.3.rs-7992311/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"08b77099-e976-4e48-81b5-d169716d32b7","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57183462,"name":"Oncology"},{"id":57183463,"name":"Personalized Medicine"}],"tags":[],"updatedAt":"2025-11-03T05:36:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-03 05:36:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7992311","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7992311","identity":"rs-7992311","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}