Real-world first-line outcomes of alectinib and brigatinib in anaplastic lymphoma kinase-positive non-small cell lung cancer: a nationwide South Korean cohort study using the health insurance review and assessment data

Introduction

Background

Lung cancer remains one of the most prevalent malignancies globally, accounting for the highest number of cancer-related deaths worldwide (1). In South Korea, the 5-year relative survival rate for lung cancer improved from 32.4% in 2018 to 40.6% in 2022. Despite this progress, survival outcomes for lung cancer lag behind those of other major malignancies, underscoring the need for more effective therapeutic strategies (2). Lung cancer is broadly classified into non-small cell lung cancer (NSCLC), which comprises approximately 80–85% of all cases, and small cell lung cancer, accounting for the remaining 15–20%. These subtypes differ markedly in their biological behavior, prognosis, and treatment paradigms.

Treatment selection for patients with NSCLC is increasingly guided by molecular profiling, with targeted therapies directed at oncogenic driver mutations such as those in the epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), and ROS1 genes demonstrating significant clinical benefit (3). ALK rearrangements are present in approximately 5% of NSCLC cases and are associated with a high propensity for central nervous system (CNS) metastases, which adversely affect both prognosis and quality of life (4,5).

Crizotinib, the first ALK inhibitor approved for ALK-rearranged NSCLC, substantially improved objective response rates (ORR) and progression-free survival (PFS) compared with standard platinum-based chemotherapy, establishing it as the initial standard of care (6). However, the therapeutic benefit of crizotinib is often limited by the development of resistance within one to two years of treatment initiation. In response, second-generation ALK inhibitors, including ceritinib, alectinib, and brigatinib, have been developed (7,8). Notably, alectinib and brigatinib have demonstrated superior efficacy over crizotinib, particularly in delaying CNS progression and preventing CNS metastases (9). In the Phase III ALEX trial (ClinicalTrials.gov Identifier: NCT02075840), alectinib markedly prolonged median PFS from 10.4 months with crizotinib to 25.7 months and reduced the risk of CNS progression (9). Similarly, the Phase III ALTA-1L trial (ClinicalTrials.gov Identifier: NCT02737501) reported that brigatinib extended median PFS from 11.0 months (crizotinib) to 24.0 months (10).

In current clinical practice, treatment sequencing for advanced NSCLC generally begins with molecular testing to identify actionable driver alterations such as EGFR or ALK (3,8). Patients without such mutations often receive immune checkpoint inhibitors, either alone or in combination with platinum-based chemotherapy, as first-line therapy, followed by targeted or cytotoxic agents upon progression (3). For ALK-positive NSCLC, sequential administration of second- and third-generation ALK inhibitors—followed by systemic chemotherapy or immunotherapy in later lines—represents the prevailing therapeutic strategy in routine care (7,9,10).

Beyond targeted therapies, the therapeutic landscape of lung cancer has broadened with the introduction of immune checkpoint inhibitors and other systemic approaches that integrate immunologic, inflammatory, and nutritional biomarkers. Recent studies have highlighted the prognostic and predictive relevance of host-related factors such as programmed death ligand-1 (PD-L1) expression, systemic inflammatory indices (including the neutrophil-to-eosinophil ratio and cachexia index), albumin levels, and composite prognostic scores such as the Royal Marsden Hospital (RMH) score in patients with advanced NSCLC (11-15). These developments reflect a shift toward comprehensive, biology-driven therapeutic paradigms that combine tumor genomics, immune activation, and metabolic status to guide individualized therapy and sequencing decisions in modern clinical practice.

Rationale and knowledge gap

While the aforementioned results are promising, clinical trial data may not fully represent real-world outcomes owing to limitations such as highly selected patient populations, controlled treatment environments, and limited follow-up durations. In routine clinical practice, factors, including patient age, comorbidities, treatment adherence, resistance mechanisms, and treatment switching, may significantly influence therapeutic outcomes (16). Evaluation of real-world outcomes outside of clinical trial settings remains necessary. Moreover, to our knowledge, no clinical trials have directly compared brigatinib with alectinib in the first-line setting, further underscoring the need for real-world comparative analyses. In this context, several observational studies have provided important insights. A large US-based cohort described treatment patterns and real-world PFS with ALK tyrosine kinase inhibitors (TKIs) (4), while a European study compared the real-world effectiveness of first-line alectinib versus crizotinib and highlighted the impact of baseline CNS metastases on outcomes (5). In Japan, a nationwide post-marketing surveillance study demonstrated the real-world safety and effectiveness of alectinib, showing durable CNS control consistent with clinical trial data (17). Building upon these findings, this study extends the evidence base by providing a nationwide, population-based comparison of alectinib and brigatinib in South Korea.

Objective

This study aims to evaluate the clinical characteristics and compare real-world PFS and overall survival (OS) among patients with ALK-positive NSCLC who received alectinib or brigatinib as first-line therapy in South Korea. We present this article in accordance with the STROBE reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-983/rc).

Methods

Study design and data source

This retrospective, nationwide cohort study utilized anonymized health insurance claims data obtained from the Health Insurance Review and Assessment Service (HIRA) of South Korea. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Review Board of Ajou University Medical Center (approval No. AJOUIRB-EX-2023-562) and individual consent for this retrospective analysis was waived. No personally identifiable information was accessed.

South Korea operates a universal, mandatory health insurance system covering approximately 97% of the population. The HIRA database contains comprehensive clinical and administrative data, including unique patient identifiers, demographic characteristics, International Classification of Diseases, 10th Revision (ICD-10) diagnosis codes, prescription records, procedures, and diagnostic test results. Because the HIRA database encompasses claims from virtually all hospitals and clinics across South Korea, this study represents a nationwide population-based cohort rather than a conventional multi-center cohort. Data from January 1, 2007, through December 31, 2023, were extracted for this analysis.

Study population

Patients with a diagnosis of lung cancer [International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) code: C34x] were identified. Inclusion criteria were as follows: (I) first diagnosis of lung cancer between January 1, 2010, and December 31, 2022; (II) at least one prescription for an ALK inhibitor, including alectinib (code 656201ACH), brigatinib (codes 675701ATB, 675702ATB, 675703ATB), crizotinib (codes 617501ACH, 617502ACH), ceritinib (code 634401ACH), or lorlatinib (codes 699701ATB, 699702ATB); and (III) first-line monotherapy with either alectinib or brigatinib. Patients were excluded if they had a lung cancer diagnosis before the study period, had a prior diagnosis of another malignancy, or had undergone lung surgery, as identified by procedure codes. Concomitant radiotherapy (RT), including local therapies such as brain RT, was not considered an exclusion criterion.

The treatment initiation date was defined as the date of the first prescription of alectinib or brigatinib. Patients without a recorded death were censored at the earliest of the last claim, disenrollment, or data cutoff. Furthermore, a 6-month inactivity window after the last claim was applied to define the end of follow-up for censoring, consistent with prior claims-based research that used 90- or 180-day inactivity thresholds as proxies for follow-up termination when death information was unavailable (18,19). The follow-up period was calculated from the date of treatment initiation to death or censoring. As this study was based on nationwide claims data, all baseline demographic and clinical variables required for analysis were available.

No missing data were detected for the included covariates, and analyses were therefore conducted on complete cases (Figure S1).

Covariates

Baseline characteristics were extracted from the HIRA database and included age at diagnosis, older age (≥70 years), sex, follow-up duration (years), and comorbidities such as diabetes mellitus (DM), hypertension (HTN), coronary artery occlusive disease (CAOD), stroke [cerebrovascular accident (CVA)], chronic kidney disease (CKD), liver cirrhosis (LC), chronic obstructive pulmonary disease (COPD), and a history of other cancers. The multivariable Cox proportional hazards model was adjusted for the following covariates: age at diagnosis (continuous), sex, and comorbidities, including DM, HTN, CAOD, CVA, CKD, LC, COPD, and a history of other cancers.

Outcomes and statistical analysis

The primary study outcomes were OS, first PFS (PFS1), and second PFS (PFS2). OS was defined as the time from initiation of first-line ALK inhibitor therapy to death from any cause. Patients without a death event were right-censored at the last follow-up or claim date. In line with previous claims-based oncology studies that have operationalized inactivity windows of 90 or 180 days as proxies for mortality (18,19), a 6-month window was applied after the last claim to define censoring. This approach balances the risk of misclassifying patients with temporary care gaps as deceased and minimizes bias from prolonged loss to follow-up.

PFS1 and PFS2 were defined as the intervals from treatment initiation to the first and second documented disease progression events, respectively. As RECIST-based assessments were not available in the claims database, progression was defined primarily by the initiation of a new systemic therapy. Other potential triggers of progression, such as RT (including brain RT), hospice enrollment, or death, could not be reliably identified in the claims data and were therefore not incorporated into the algorithm.

Continuous variables were summarized as means [standard deviations (SDs)] and medians (ranges). Categorical variables were reported as counts and percentages. Survival distributions were estimated using the Kaplan-Meier method, and differences between treatment groups were compared using the log-rank test. Hazard ratios (HRs) for OS and PFS were estimated using Cox proportional hazards regression models. Logistic regression analysis was used to assess the association between baseline characteristics and study outcomes. A two-sided P value <0.05 was considered statistically significant. All statistical analyses were performed using SAS 9.4 (Enterprise Guide; SAS Institute Inc., Cary, NC, USA).

Results

Baseline characteristics

Of the 1,009 patients included in the analysis, 868 received alectinib and 141 received brigatinib as first-line therapy. Baseline characteristics are presented in Table 1. The overall median follow-up time was 1.79 years, with 1.98 years in the alectinib group and 1.51 years in the brigatinib group. The overall mean age at diagnosis was 61.56 years, with 62.15 and 57.19 years in the alectinib and brigatinib groups, respectively. A total of 317 patients (31.4%) were aged >70 years, including 283 (32.6%) in the alectinib group and 34 (24.1%) in the brigatinib group. In total, 499 patients were male, representing 49.5% of the study population. Of these, 437 received alectinib, and 62 received brigatinib, accounting for 50.4% and 44.0% of their respective treatment groups. DM was reported in 153 patients (15.2%), including 139 (16.0%) and 14 (9.9%) in the alectinib and brigatinib groups, respectively. HTN was present in 327 patients (32.4%), with 286 (33.0%) in the alectinib group and 41 (29.1%) in the brigatinib group. Other comorbidities included CAOD in 6 patients (0.6%), CVA in 8 (0.8%), CKD in 9 (0.9%), LC in 6 (0.6%), and COPD in 33 (3.3%). Additionally, 264 patients (26.2%) had a cancer history other than lung cancer. Significant differences in follow-up time, age at diagnosis, and the proportion of patients aged >70 years were observed between the two treatment groups.

Treatment patterns

Among the 1,009 patients with ALK-positive NSCLC, 646 received alectinib monotherapy, making it the most frequently used initial treatment regimen. Following alectinib, some patients transitioned to cytotoxic chemotherapy, with the most common subsequent regimens being pemetrexed monotherapy or carboplatin/cisplatin (n=135), followed by lorlatinib (n=48), gemcitabine monotherapy or carboplatin/cisplatin (n=22), and paclitaxel monotherapy or carboplatin/cisplatin (n=21). Among patients who received brigatinib as their initial treatment regimen, 111 were treated with brigatinib monotherapy, making it the most frequently used regimen in this group. Following brigatinib, 15 patients switched to pemetrexed monotherapy or carboplatin/cisplatin, and another 15 to lorlatinib. Table 2 presents an analysis of treatment patterns and mortality outcomes in patients with ALK-positive NSCLC who received either alectinib or brigatinib as their first-line therapy.

OS

A comparison of OS between alectinib and brigatinib monotherapy showed that the 1-year survival rate was 77% [95% confidence interval (CI): 74–80%] in the alectinib group and 86% (95% CI: 79–90%) in the brigatinib group. The 2-year survival rate was 68% (95% CI: 65–71%) and 81% (95% CI: 74–87%), respectively (Table S1). In this descriptive analysis, patients receiving brigatinib had higher observed survival rates than those receiving alectinib (log-rank P=0.01) (Figure 1A, Table 2). These are unadjusted comparisons and may reflect confounding by clinical variables not captured in the dataset. In contrast, patients who transitioned from alectinib to paclitaxel, pemetrexed, or gemcitabine had lower survival rates compared with those who remained on alectinib monotherapy. Notably, the 2-year survival rate of patients who switched from alectinib to gemcitabine was only 15%, the lowest among all subgroups (Figure 1B, Table 2). Similarly, lower survival rates were observed in patients who switched from brigatinib to pemetrexed compared with those who continued brigatinib monotherapy (Figure 1C, Table 2).

Figure 1 Unadjusted descriptive Kaplan-Meier estimates of OS. (A) OS of patients with ALK-positive NSCLC treated with first-line alectinib versus brigatinib. (B) Subgroup survival analysis among patients who received alectinib, stratified by second-line treatment. (C) Subgroup survival analysis among patients who received brigatinib, stratified by second-line treatment. A, alectinib; B, brigatinib; ALK, anaplastic lymphoma kinase; NSCLC, non-small cell lung cancer; OS, overall survival.

PFS

The PFS analyses of patients with ALK-positive NSCLC treated with alectinib or brigatinib were conducted separately for PFS1 and PFS2. PFS1 was defined as the time from the initiation of first-line ALK inhibitor therapy (alectinib or brigatinib) to the first documented disease progression or death from any cause, whichever occurred first. PFS2 was defined as the time from the initiation of second-line therapy—following progression on first-line alectinib or brigatinib—to the second documented disease progression or death from any cause, whichever occurred first.

PFS1

A comparison of PFS1 between alectinib and brigatinib monotherapy showed that the median PFS was 4.02 years (95% CI: 3.78–5.30) in the alectinib group (n=226; 26.0%) and 2.60 years (95% CI: 2.23 to not estimable) in the brigatinib group (n=30; 21.3%). The 1-year PFS rate was 82% (95% CI: 78–84%) for alectinib and 81% (95% CI: 73–87%) for brigatinib, with no statistically significant difference between the two groups (P=0.46) (Figure 2A, Table S2). In the alectinib group, patients who subsequently received lorlatinib demonstrated the longest PFS1, whereas those who transitioned to conventional cytotoxic chemotherapy experienced significantly shorter PFS1, showing clear differences in outcomes across subsequent treatment subgroups (Figure 2B, Table S2). In the brigatinib group, patients who later received lorlatinib (n=15) had a median PFS of 1.40 years (95% CI: 1.15–1.53) with a 1-year PFS rate of 87% (95% CI: 56–96%) (Figure 2C, Table S2). Patients who transitioned to pemetrexed (n=15) had a median PFS of 0.49 years (95% CI: 0.33–0.72) with a 1-year PFS rate of 7% (95% CI: 0–26%) (P<0.001) (Figure 2C, Table S2). These are descriptive subgroup comparisons and may be subject to residual confounding from variables not available in the claims data.

Figure 2 Unadjusted descriptive Kaplan-Meier estimates of PFS1. (A) PFS1 of patients treated with first-line alectinib versus brigatinib. (B) Subgroup PFS1 among patients in the alectinib group, stratified by subsequent treatment. (C) Subgroup PFS1 among patients in the brigatinib group, stratified by subsequent treatment. A, alectinib; B, brigatinib; PFS1, first progression-free survival.

PFS2

Among patients who remained on monotherapy, the alectinib group (n=94; 39.8%) had a median PFS of 0.69 years (95% CI: 0.57–0.86) and a 3-month PFS rate of 89% (95% CI: 84–94%). The brigatinib group (n=16; 36.4%) had a median PFS of 0.31 years (95% CI: 0.23–0.45) and a 3-month PFS rate of 67% (95% CI: 49–91%) (P<0.001) (Figure S2A, Table S3).

Among patients who switched treatments, the alectinib-to-paclitaxel group (n=10; 47.6%) had a median PFS of 0.79 years (95% CI: 0.39–0.97) and a 3-month PFS rate of 93% (95% CI: 80–100%). The alectinib-to-lorlatinib group (n=5; 10.4%) had a 3-month PFS rate of 85% (95% CI: 73–100%) (Figure S2B, Table S3).

In the brigatinib group, patients who switched to pemetrexed (n=11; 73.3%) had a median PFS of 0.31 years (95% CI: 0.18–0.45) and a 3-month PFS rate of 75% (95% CI: 54–100%) (P=0.79). The brigatinib-to-lorlatinib group (n=5; 33.3%) had a median PFS of 0.31 years (95% CI: 0.15 to not estimable) and a 3-month PFS rate of 55% (95% CI: 29–100%) (Figure S2C, Table S3). The findings reflect unadjusted comparisons and may be influenced by residual confounding from unmeasured clinical factors.

Univariable and multivariable analyses for OS using a Cox proportional hazards model

A Cox proportional hazards regression model was applied to evaluate the HR for mortality and disease progression in patients with ALK-positive NSCLC (Table 3). In the analysis, brigatinib monotherapy showed a trend toward lower mortality risk compared with alectinib monotherapy; however, this difference was not statistically significant in the multivariable analysis (HR, 0.69; 95% CI: 0.46–1.03; P=0.07). In contrast, patients who transitioned from alectinib or brigatinib monotherapy to subsequent treatments (alectinib-to-paclitaxel, alectinib-to-pemetrexed, alectinib-to-gemcitabine, brigatinib-to-pemetrexed) had a significantly increased mortality risk compared with those who remained on monotherapy. The highest mortality risk was observed in the alectinib-to-gemcitabine group (HR, 3.92; 95% CI: 2.46–6.24; P<0.001) and the brigatinib-to-pemetrexed group (HR, 5.07; 95% CI: 1.99–12.92; P<0.001).

In the PFS1 analysis, brigatinib monotherapy was associated with a significantly higher risk of disease progression than alectinib monotherapy (HR, 1.53; 95% CI: 1.10–2.13; P=0.01). A significant increase in disease progression risk was also observed in patients who switched to subsequent treatments, with the alectinib-to-gemcitabine group exhibiting the highest HR for PFS1 (HR, 13.22; 95% CI: 7.16–24.42; P<0.001). In the PFS2 analysis, brigatinib monotherapy remained associated with a significantly higher risk of a second disease progression than alectinib monotherapy (HR, 4.02; 95% CI: 2.22–7.27; P<0.001). However, no statistically significant differences were observed in PFS2 risk among patients who transitioned to subsequent treatments.

Discussion

Key findings

This nationwide real-world cohort study compared the clinical outcomes of alectinib and brigatinib as first-line treatments for patients with ALK-positive NSCLC. These findings suggest that alectinib was associated with longer PFS, whereas brigatinib showed a nonsignificant trend toward improved OS, although this advantage was attenuated after multivariable adjustment. Furthermore, patients who received lorlatinib following either alectinib or brigatinib demonstrated notably prolonged survival, whereas those who transitioned to conventional cytotoxic chemotherapies experienced significantly higher mortality, underscoring the importance of treatment sequencing. Given the observational design and limited availability of key prognostic factors, these findings should be interpreted cautiously as descriptive associations observed in real-world practice rather than definitive causal effects.

Strengths and limitations

A key strength of this work is its nationwide, population-based design using a comprehensive health insurance claims database that captures routine clinical practice across care settings, thereby enhancing generalizability within South Korea and providing adequate power for subgroup descriptions. Specifically, the HIRA database offers near-complete population coverage and standardized reimbursement coding, enabling representative ascertainment of diagnoses, procedures, and prescriptions across providers, which is advantageous for describing treatment patterns and subsequent sequencing in routine care.

However, several limitations merit emphasis. First, key clinical confounders such as baseline CNS metastases, Eastern Cooperative Oncology Group (ECOG) performance status, and smoking history were not available in the claims data, which may have introduced residual confounding that could bias the observed associations. Second, patient identification and outcome ascertainment relied on administrative data; inferring death from care gaps may misclassify individuals who moved, disenrolled, or received care outside the system, potentially biasing OS estimates. Third, the two treatment cohorts differed substantially in both sample size (868 patients received alectinib vs. 141 received brigatinib) and median follow-up duration (1.98 vs. 1.51 years). These imbalances may have led to unequal censoring patterns, particularly in the brigatinib group, where wide confidence intervals and limited follow-up reduced precision in survival estimation. Consequently, the observed differences in PFS and OS should be interpreted cautiously, as they may partly reflect disparities in observation time, cohort size, or selection bias rather than true differences in treatment effect. In South Korea, the smaller number of brigatinib users and contextual differences in drug accessibility and prescribing policy may also have contributed to the observed numerical imbalance between treatment groups. These methodological differences should be considered when interpreting comparative survival outcomes.

Because the database lacks radiologic information, this study did not apply the RECIST criteria to define disease progression. Instead, the initiation of a new systemic therapy was used as a proxy, which may overestimate PFS and limit comparability with clinical trial results. Finally, as no formal identification strategy was applied, the analyses cannot exclude selection bias and should not be interpreted causally. Specifically, no propensity score adjustment, inverse probability of treatment weighting (IPTW), covariate matching, or other quasi-experimental methods were employed to mitigate confounding or approximate randomization. As a result, the observed associations represent descriptive real-world patterns rather than causal treatment effects. The lack of formal adjustment increases the risk of residual and unmeasured confounding—particularly from unobserved factors influencing both treatment allocation and outcomes—while nonrandomized baseline heterogeneity and differential follow-up may further bias comparisons between treatment groups. Such quasi-experimental techniques were not feasible in the present analysis due to the inherent structure of claims-based data, which lack key clinical covariates—such as performance status, CNS involvement, and tumor burden—required to implement robust adjustment procedures.

Prior studies have demonstrated how quasi-experimental approaches in real-world ALK-positive NSCLC research—such as those applied in studies with PubMed IDs (PMIDs) 37025119, 40222406, and 39157676—can strengthen causal inference by improving comparability between treatment groups through propensity score matching or inverse probability weighting. Collectively, these methodological differences constrain internal validity and limit the interpretability of apparent survival differences as true comparative effects. Such challenges underscore the inherent limitations of causal inference in real-world observational studies, where treatment decisions are driven by clinical judgment and patient heterogeneity that cannot be fully captured in claims-based datasets. Prior methodological research has emphasized that the absence of formal identification strategies undermines causal interpretability (20), whereas more advanced quasi-experimental frameworks, including those implemented in recent ALK TKI real-world analyses (5,21,22), can better approximate causal effects and strengthen analytic rigor in non-randomized contexts. Finally, findings may not generalize beyond the Korean healthcare context or to settings with different access, reimbursement, or sequencing practices.

Comparison with similar research

The 1-year PFS rates observed in both treatment groups 82% for alectinib and 81% for brigatinib, exceeded those reported in randomized clinical trials such as ALEX and ALTA-1L, which documented 1-year PFS rates of 68.4% and 67.0%, respectively (9,10). These discrepancies may reflect differences in patient characteristics, treatment adherence, and follow-up practices between real-world and trial settings. The superior PFS observed with alectinib is consistent with prior indirect comparative studies, including those employing surface under the cumulative ranking curve (SUCRA) scores, in which alectinib ranked highest in delaying disease progression among first-line ALK inhibitors (23). Conversely, the nonsignificant OS trend in favor of brigatinib aligns with previous retrospective findings (24) and warrants further validation. Beyond trial comparisons, our findings align with and extend prior observational studies that have evaluated real-world outcomes of ALK TKI therapy across diverse regions. In US and Canadian population-based studies, median real-world PFS ranged from 7 to 17 months, and 1-year OS reached approximately 70–75%, consistent with the durability of benefit observed in our cohort (4,25). Comparative real-world analyses further showed that alectinib significantly prolonged PFS versus crizotinib and reduced new CNS metastases, reflecting its strong intracranial activity in routine practice (5). Additional claims and EHR-based studies demonstrated median OS of 25–37 months with second-generation ALK TKIs, confirming their effectiveness beyond trial settings, while also highlighting cost and access disparities across regimen (21,22). Moreover, sequencing analyses indicated that patients continuing ALK inhibition beyond progression achieved notably longer survival than those switching to non-TKI therapy, supporting the value of ongoing targeted therapy (26). By integrating these findings with this body of real-world evidence, the present study provides population-level data from South Korea, emphasizing the comparable effectiveness of second-generation ALK TKIs and regional nuances in practice patterns and accessibility.

Explanations of findings

Several factors may explain the observed differences in clinical outcomes. The observed OS trend favoring brigatinib should be interpreted cautiously, as it may reflect differences in baseline characteristics, cohort size, follow-up duration, or unmeasured confounding rather than true treatment effects. The association between post-progression lorlatinib use and prolonged survival aligns with previous reports of its intracranial activity and efficacy after second-generation ALK TKIs (27). Recent trial and real-world evidence, including the CROWN study, further confirm lorlatinib’s durable systemic and CNS control, supporting its use in sequential ALK inhibition strategies (27). Maintaining ALK inhibition beyond progression appears critical for sustaining survival benefits and delaying chemotherapy initiation. The superiority of ALK TKIs over platinum-based chemotherapy has been consistently demonstrated in randomized and real-world studies (6,21), marking a shift toward molecularly targeted therapy in ALK-rearranged NSCLC. The improved survival observed in our cohort likely reflects this sequential treatment paradigm rather than the isolated effect of any single agent. Discontinuation of ALK TKI therapy without timely transition to another targeted agent may induce a tumor flare phenomenon, emphasizing the need for continued ALK pathway suppression to prevent rapid disease progression. The survival advantage observed among patients who continued ALK inhibition beyond progression supports the clinical relevance of optimized sequencing in real-world practice. Collectively, these findings highlight the central role of sequencing in ALK-positive NSCLC and underscore the need for future prospective studies to address confounding and identify optimal sequencing strategies to improve long-term outcomes.

Implications and actions needed

Clinical variability in sequencing strategies, particularly regarding lorlatinib initiation, has been frequently observed across institutions. Recognizing this inconsistency in real-world practice motivated our investigation and underlines the importance of generating real-world evidence to inform optimal treatment planning in ALK-positive NSCLC.

These findings have important clinical implications. First, alectinib demonstrated consistent benefits in prolonging PFS in the first-line setting, reinforcing its role as a standard treatment option. However, it is important to note that no head-to-head trials have directly compared alectinib with brigatinib or lorlatinib in this setting. Second, the observed trend toward improved OS with brigatinib highlights the need for prospective comparative studies among ALK inhibitors in real-world practice. Third, recent evidence from the CROWN trial underscores the long-term potential of lorlatinib, with approximately 60% of patients remaining progression-free at five years, emphasizing its importance in treatment sequencing and the need to ensure patient access (28). Future research should explore biomarkers and resistance mechanisms to refine individualized sequencing strategies and determine the optimal first-line ALK inhibitor.

Conclusions

In this nationwide real-world cohort of patients with ALK-positive NSCLC, longer PFS was observed with alectinib and a trend toward improved OS with brigatinib in unadjusted analyses. Following disease progression, lorlatinib use was associated with favorable survival outcomes, whereas transitions to cytotoxic chemotherapy were linked to poorer outcomes. Although alectinib showed consistent patterns of longer PFS, to our knowledge, no head-to-head trials have directly compared alectinib with brigatinib or lorlatinib in the first-line setting. These descriptive real-world patterns align with findings from prior randomized trials but should not be interpreted as causal evidence. Further research—including prospective studies and causal inference methods—is needed to better understand comparative effectiveness and sequencing strategies for ALK-targeted therapies in clinical practice.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-983/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-983/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-983/prf

Funding: This study was supported in part by Ajou University Medical Center, Suwon, Korea.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-983/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Review Board of Ajou University Medical Center (approval No. AJOUIRB-EX-2023-562) and individual consent for this retrospective analysis was waived.

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

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