Long-term outcomes of ALK inhibitors in metastatic ALK-positive non-small cell lung cancer: an updated indirect comparison using reconstructed patient-level data

ALK rearrangements define a biologically distinct subset of metastatic non-small cell lung cancer (mNSCLC), characterized by early onset, limited smoking exposure, and a strong propensity for central nervous system (CNS) involvement (1). In this setting, treatment is firmly based on ALK tyrosine kinase inhibitors (ALKi), which represent the established standard of care.

Randomized phase III trials have demonstrated substantial improvements in progression-free survival (PFS) and intracranial disease control with second-generation ALKi (such as alectinib, brigatinib ensartinib and envolakib) and third-generation ALKi (such as lorlatinib) compared with the first-generation ALK inhibitor crizotinib (2-10). However, despite the availability of multiple highly active options, their relative efficacy remains incompletely defined due to the absence of direct head-to-head comparisons.

Indirect comparative analyses based on reconstructed individual patient data (IPD) provide a robust approach to address this gap. Building on our previous work, which identified alectinib and lorlatinib as the most effective agents in terms of PFS but was limited by immature follow-up (11), the present study primarily aims to update indirect comparisons using extended follow-up from pivotal trials, enabling a more refined assessment of long-term systemic and CNS control in ALK-positive mNSCLC.

To this end, a systematic literature search was performed in the PubMed database (last search on 15 October 2025) using a predefined search strategy designed to identify randomized controlled trials evaluating first-line ALKi in ALK-positive non-small cell lung cancer (NSCLC). The full search strategy has been previously reported. The search retrieved 708 records, of which 47 were identified after automated filtering. The inclusion criteria had already been detailed in the previous study (11). To avoid duplicate inclusion of patient populations, only the most recent publication of each randomised controlled trial (RCT) was included in the present analysis.

IPD were reconstructed from published Kaplan-Meier (KM) curves using the IPDfromKM methodology. KM curves were digitized with WebPlotDigitizer (version 4.7, accessed November 8, 2025). Digitalized coordinates, together with the reported number at risk and event counts, were subsequently entered into IPDfromKM software (version 1.2.3.0, updated March 22, 2022). This procedure generated reconstructed individual survival times (from enrollment to last follow-up) and classified patient status as event or censored, yielding patient-level datasets for each treatment arm.

Across all studies, crizotinib served as the reference comparator. Heterogeneity test was performed using the likelihood ratio test. In absence of significant heterogeneity, patients treated with crizotinib from all studies were pooled to constitute the common control group, enabling indirect comparisons across different ALKi.

Treatment efficacy for PFS and overall survival (OS) was assessed using Cox proportional hazards regression models, with each treatment arm compared against the pooled crizotinib control group. Assessment of the proportional hazards assumption was performed using Schoenfeld residuals. Results were reported as hazard ratios (HRs) with corresponding 95% confidence intervals (CIs). Homogeneity of the pooled control group was evaluated using likelihood ratio testing and concordance measures. Indirect head-to-head treatment comparisons between ALKi were performed using Cox regression models and complemented by restricted mean survival time (RMST) analyses. RMST for PFS was calculated at prespecified truncation times of 35, 48, and 65 months in the overall population, and 41 months in patients with baseline brain metastases (BMs). In addition, OS was analyzed, with RMST calculated at a truncation time of 35 months. All statistical analyses were conducted using the survival package in R software (version 4.3.2).

Seven RCTs met the inclusion criteria. The updated analysis incorporated newly available long-term follow-up data from the CROWN and ALESIA trials (approximately 5 years of follow-up) (12,13) and from J-ALEX trial (approximately 4 years of follow-up) (14) RCT, while data from the remaining trials were unchanged from the previous analysis. Patient characteristics are reported in Table S1. As in the previous analysis (11), heterogeneity was assessed across the control arms treated with crizotinib to evaluate whether populations from the included trials showed comparable responses to the common comparator. No significant heterogeneity was observed for PFS [likelihood ratio test =9.05, 6 degrees of freedom (df), P=0.20] or OS (likelihood ratio test =10.22, 5 df, P=0.07) in the overall population, nor for PFS among patients with baseline BMs (likelihood ratio test =7.54, 6 df, P=0.30). The proportional hazards assumption was assessed using Schoenfeld residuals, with no evidence of violation observed either for the treatment variable or globally in the PFS analysis of the overall population (P=0.27), in the PFS analysis of patients with baseline BMs (P=0.71), or in the OS analysis (P=0.98).

With longer follow-up, all second- and third-generation ALKi maintained statistically significant improvements in PFS compared with crizotinib. Updated HRs versus crizotinib were: lorlatinib, HR 0.23 (95% CI: 0.17–0.31); alectinib, HR 0.37 (95% CI: 0.32–0.44); ensartinib, HR 0.38 (95% CI: 0.29–0.50); brigatinib, HR 0.47 (95% CI: 0.37–0.61); and envonalkib, HR 0.55 (95% CI: 0.42–0.71). All agents significantly reduced the risk of disease progression relative to crizotinib (Figure 1).

Figure 1 Progression-free survival of first-line ALK tyrosine kinase inhibitors. Kaplan-Meier curves are shown for alectinib (blue), brigatinib (turquoise), crizotinib (control) (red), ensartinib (gold), envonalkib (pink), and lorlatinib (green).

In inter-treatment comparisons, lorlatinib demonstrated superior PFS compared with brigatinib and envonalkib (11). With extended follow-up, this advantage was confirmed and further strengthened versus brigatinib (HR 0.48; 95% CI: 0.33–0.71) and envonalkib (HR 0.42; 95% CI: 0.28–0.62). Lorlatinib also achieved statistically significant superiority over ensartinib (HR =0.60; 95% CI: 0.40–0.90) and, notably, versus alectinib (HR 0.61; 95% CI: 0.43–0.85). In addition, alectinib remained superior to envonalkib (HR 0.69; 95% CI: 0.51–0.93), while no other significant inter-treatment differences were observed.

Because the shorter follow-up of the envonalkib trial limited the RMST analysis to 35 months, additional RMST analyses were performed at longer time horizons using only trials with adequate follow-up. The minimum follow-up was 48 months for comparisons including brigatinib and 65 months for comparisons between alectinib and lorlatinib. At a truncation time of 48 months, RMSTs for PFS were 34.66 months (95% CI: 31.46–37.86) for lorlatinib, 31.08 months (95% CI: 28.66–33.51) for alectinib, 28.16 months (95% CI: 24.80–31.51) for brigatinib, and 16.83 months (95% CI: 15.41–18.24) for controls. Compared with crizotinib, lorlatinib provided the largest absolute gain in PFS-RMST (+17.83 months), exceeding that observed with alectinib and brigatinib.

When extending the observation period to 65 months, lorlatinib achieved a PFS-RMST of 46.16 months (95% CI: 41.9–50.5), corresponding to an absolute gain of 28 months versus controls, while alectinib provided nearly 20 months of absolute gain (Figure 2).

Figure 2 Progression-free survival of alectinib and lorlatinib compared with crizotinib. Kaplan-Meier curves are shown for alectinib (blue), crizotinib (control) (red), and lorlatinib (green).

The RMST difference between lorlatinib and alectinib increased progressively with longer follow-up, widening by 1.82 months between 35 and 48 months and by a further 4.4 months between 48 and 65 months.

Among patients with baseline BMs, all ALKi significantly improved PFS compared with crizotinib. Lorlatinib demonstrated the strongest effect (HR 0.04; 95% CI: 0.01–0.10), followed by alectinib (HR 0.26; 95% CI: 0.19–0.36), envonalkib (HR 0.28; 95% CI: 0.17–0.46), brigatinib (HR 0.35; 95% CI: 0.23–0.52), and ensartinib (HR 0.61; 95% CI: 0.41–0.91) (Figure 3).

Figure 3 Progression-free survival in patients with baseline brain metastases treated with first-line ALK tyrosine kinase inhibitors. Kaplan-Meier curves are shown for alectinib (blue), brigatinib (turquoise), crizotinib (control) (red), ensartinib (gold), envonalkib (pink), and lorlatinib (green).

Inter-treatment comparisons confirmed lorlatinib’s superiority over envonalkib (HR 0.14; 95% CI: 0.04–0.42), brigatinib (HR 0.11; 95% CI: 0.04–0.32), alectinib (HR 0.14; 95% CI: 0.05–0.41), and ensartinib (HR 0.06; 95% CI: 0.02–0.18). Envonalkib also demonstrated superiority over ensartinib (HR 0.46; 95% CI: 0.24–0.88). At 41 months, lorlatinib achieved a PFS-RMST of 37.74 months (95% CI: 34.60–40.89), corresponding to an absolute gain of 26.54 months versus controls.

OS analyses are newly reported. Alectinib was the only agent to show a statistically significant OS benefit versus crizotinib (HR 0.74; 95% CI: 0.58–0.95). No significant differences versus controls were observed for lorlatinib, brigatinib, or ensartinib. Inter-treatment OS comparisons did not reveal significant differences. Both lorlatinib and alectinib showed numerical advantage over brigatinib and ensartinib in terms of OS, while the HR comparing lorlatinib with alectinib was close to unity (HR 1.03; 95% CI: 0.66–1.73) (Figure 4).

Figure 4 Overall survival of first-line ALK tyrosine kinase inhibitors. Kaplan-Meier curves are shown for alectinib (pink), brigatinib (turquoise), crizotinib (control) (red), ensartinib (gold), and lorlatinib (green).

At a truncation time of 34 months, OS-RMST analysis showed the largest absolute gain with alectinib (+1.44 months versus control), followed by lorlatinib (+1.21 months), ensartinib (+0.42 months), and brigatinib (−0.08 months).

Building upon our previously published indirect comparison (11), the present study provides an updated and more comprehensive evaluation of first-line next-generation ALK inhibitors in metastatic ALK-positive NSCLC, integrating extended follow-up from pivotal randomized trials and enabling a refined assessment of long-term disease control. (2,6,9,10,12-14). With longer observation, clinically meaningful differences among available agents become more clearly delineated, particularly with respect to durability of benefit and intracranial efficacy.

Across analyses, lorlatinib consistently demonstrated the greatest capacity to delay disease progression, while alectinib and ensartinib provided substantial but comparatively smaller benefit. Notably, RMST analyses showed a progressive widening of the separation between lorlatinib and alectinib with extended follow-up, suggesting increasing durability of benefit over time. In the present study, median PFS and OS were not reached, limiting their ability to capture the full magnitude of treatment effect. By contrast, RMST allows quantification of PFS benefit even in the setting of immature medians, providing a clinically meaningful and statistically robust measure of treatment effect.

A defining biological feature of ALK-positive NSCLC is its marked tropism for the CNS, with BMs representing a major driver of morbidity, impaired quality of life, and treatment failure (1). In patients with baseline BMs, lorlatinib consistently emerged as the most effective agent, with alectinib ranking second and other next-generation ALKi providing meaningful, albeit smaller, benefit. These findings underscore the importance of CNS penetrance and sustained intracranial target inhibition as key biological discriminators among ALKi and likely contributors to differences in long-term disease control.

More mature OS data allowed extension of efficacy analyses beyond progression-based endpoints; however, OS results did not fully mirror the PFS hierarchy. While numerical differences were observed, most OS comparisons among next-generation ALKi were not statistically significant. This divergence is not unexpected and likely reflects the confounding effects of subsequent lines of therapy, treatment crossover, and heterogeneous follow-up across trials. In particular, lorlatinib retains clinically relevant activity in patients progressing on second-generation ALKi (15), whereas therapeutic options following lorlatinib failure remain limited, complicating the interpretation of OS as a discriminator between upfront treatment strategies.

Beyond efficacy, differences in tolerability profiles play a central role in real-world treatment selection. Second-generation ALK inhibitors, particularly alectinib, are generally characterized by favorable long-term tolerability, with predominantly manageable hepatic, musculoskeletal, and gastrointestinal toxicities (2,13,14). In contrast, lorlatinib is associated with a distinct adverse event profile, including metabolic disturbances such as hyperlipidemia and weight gain, peripheral edema, and neurocognitive or mood-related effects (12,16). Although these toxicities are often manageable with appropriate monitoring and supportive care, they may meaningfully affect quality of life and long-term treatment adherence, especially in patients with pre-existing metabolic, cardiovascular, or neuropsychiatric comorbidities. Cognitive adverse events, in particular, may be underrecognized in routine practice and can contribute to prescribing cascades that further amplify neurocognitive burden (17).

From a translational perspective, increasing molecular granularity within ALK-positive NSCLC may provide additional context for first-line treatment decisions. Beyond TP53 co-mutations, which are consistently associated with a poorer prognosis, growing evidence indicates that ALK fusion variants are linked to distinct biological behavior and variable sensitivity to ALK inhibition. In particular, the EML4-ALK variant 3 has been associated with a more aggressive clinical phenotype, higher genomic instability, increased propensity for CNS dissemination, and inferior outcomes compared with variant 1 (18,19). In this setting, deeper and more sustained ALK inhibition may be of particular relevance. Available clinical and translational data suggest that third-generation inhibitors such as lorlatinib may mitigate, at least in part, the adverse prognostic impact of EML4-ALK variant 3 and TP53 alterations, achieving longer disease control than second-generation inhibitors in these molecularly high-risk subgroups. However, these molecular features were not available in the reconstructed datasets and were not directly evaluated in the present analysis. Therefore, these considerations should be regarded as hypothesis-generating, providing biological context rather than supporting conclusions from the current study, and representing an area for future investigation.

Taken together, these results contribute to the ongoing discussion regarding the optimal use of upfront versus sequential ALK inhibition. Initiating treatment with the most potent ALK inhibitor, particularly in patients at increased risk of CNS progression or with adverse molecular features, may support more sustained disease control. Notably, evidence from first-line randomized trials suggests that a considerable fraction of patients—approximately 40%—do not proceed to additional systemic therapies following disease progression (20). This observation highlights the importance of carefully selecting first-line treatment to maximize PFS, in line with the frequently advocated concept of “the best first”. Nonetheless, a stepwise approach starting with a well-tolerated second-generation ALK inhibitor and reserving lorlatinib for subsequent lines may still represent a reasonable option for carefully selected patients, especially when long-term tolerability and comorbidity burden are key considerations.

The present analysis employed an IPD-based pooled Cox regression approach, a methodology increasingly applied across oncology and broader medical research, with validity supported by prior dedicated validation studies (21). A key strength of this approach lies in its ability to preserve time-to-event information—an element systematically lost in conventional meta-analyses based on dichotomous outcomes—while also enabling the generation of pooled survival curves for direct visual comparison across treatment arms. Nonetheless, several limitations should be acknowledged. All efficacy estimates are derived from reconstructed rather than original IPD, which may introduce approximation errors and precludes full adjustment for trial-level stratification factors. In addition, the indirect nature of the comparisons and heterogeneity in trial design, eligibility criteria, and patient characteristics across studies represent inherent constraints on the generalizability and precision of the findings. Furthermore, although patients with baseline BMs were generally eligible if asymptomatic or clinically stable and prior local treatments such as radiotherapy were allowed, Kaplan-Meier curves stratified by prior radiotherapy were not available. Consequently, the impact of prior radiotherapy on intracranial outcomes could not be assessed within the IPD reconstruction framework.

In conclusion, this indirect comparison highlights clinically meaningful differences among next-generation ALKi in metastatic ALK-positive NSCLC. Lorlatinib is associated with the most durable PFS and intracranial disease control, as captured by RMST analyses, whereas OS differences remain less definitive and will require further maturation. In the absence of direct head-to-head comparisons, first-line treatment selection is typically guided by multiple factors, including PFS outcomes from pivotal trials, activity against resistance mutations, intracranial efficacy, and toxicity profile. Optimal treatment choice should therefore integrate these dimensions within a personalized, biology-driven therapeutic framework.

Acknowledgments

The authors gratefully acknowledge Prof. Andrea Messori for his constant methodological support and invaluable scientific discussion throughout the development of this work.

Footnote

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0077/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0077/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.

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/.

References

1. Shaw AT, Yeap BY, Mino-Kenudson M, et al. Clinical features and outcome of patients with non-small-cell lung cancer who harbor EML4-ALK. J Clin Oncol 2009;27:4247-53. [Crossref] [PubMed]
2. Mok T, Camidge DR, Gadgeel SM, et al. Updated overall survival and final progression-free survival data for patients with treatment-naive advanced ALK-positive non-small-cell lung cancer in the ALEX study. Ann Oncol 2020;31:1056-64. [Crossref] [PubMed]
3. Gadgeel S, Peters S, Mok T, et al. Alectinib versus crizotinib in treatment-naive anaplastic lymphoma kinase-positive (ALK+) non-small-cell lung cancer: CNS efficacy results from the ALEX study. Ann Oncol 2018;29:2214-22. [Crossref] [PubMed]
4. Hida T, Nokihara H, Kondo M, et al. Alectinib versus crizotinib in patients with ALK-positive non-small-cell lung cancer (J-ALEX): an open-label, randomised phase 3 trial. Lancet 2017;390:29-39. [Crossref] [PubMed]
5. Zhou C, Kim SW, Reungwetwattana T, et al. Alectinib versus crizotinib in untreated Asian patients with anaplastic lymphoma kinase-positive non-small-cell lung cancer (ALESIA): a randomised phase 3 study. Lancet Respir Med 2019;7:437-46. [Crossref] [PubMed]
6. Horn L, Wang Z, Wu G, et al. Ensartinib vs Crizotinib for Patients With Anaplastic Lymphoma Kinase-Positive Non-Small Cell Lung Cancer: A Randomized Clinical Trial. JAMA Oncol 2021;7:1617-25. [Crossref] [PubMed]
7. Shaw AT, Bauer TM, de Marinis F, et al. First-Line Lorlatinib or Crizotinib in Advanced ALK-Positive Lung Cancer. N Engl J Med 2020;383:2018-29. [Crossref] [PubMed]
8. Solomon BJ, Bauer TM, Mok TSK, et al. Efficacy and safety of first-line lorlatinib versus crizotinib in patients with advanced ALK-positive non-small-cell lung cancer: updated analysis of the CROWN study. Lancet Respir Med 2023;11:354-66. [Crossref] [PubMed]
9. Camidge DR, Kim HR, Ahn MJ, et al. Brigatinib versus crizotinib in ALKi-naive advanced ALK-positive NSCLC: final results of phase 3 ALTA-1L trial. J Thorac Oncol 2021;16:2091-108. [Crossref] [PubMed]
10. Yang Y, Min J, Yang N, et al. Envonalkib versus crizotinib for treatment-naive ALK-positive non-small cell lung cancer: a randomized, multicenter, open-label, phase III trial. Signal Transduct Target Ther 2023;8:301. [Crossref] [PubMed]
11. Damuzzo V, Gasperoni L, Del Bono L, et al. Treatment of metastatic ALK-positive non-small cell lung cancer: indirect comparison of different ALK inhibitors using reconstructed patient data. Front Oncol 2025;15:1566816. [Crossref] [PubMed]
12. Solomon BJ, Liu G, Felip E, et al. Lorlatinib Versus Crizotinib in Patients With Advanced ALK-Positive Non-Small Cell Lung Cancer: 5-Year Outcomes From the Phase III CROWN Study. J Clin Oncol 2024;42:3400-9. [Crossref] [PubMed]
13. Zhou C, Lu Y, Kim SW, et al. Alectinib Versus Crizotinib in Asian Patients With Treatment-Naïve Advanced ALK-Positive NSCLC: Five-Year Update From the Phase 3 ALESIA Study. JTO Clin Res Rep 2024;5:100700. [Crossref] [PubMed]
14. Nakagawa K, Hida T, Nokihara H, et al. Final progression-free survival results from the J-ALEX study of alectinib versus crizotinib in ALK-positive non-small-cell lung cancer. Lung Cancer 2020;139:195-9. [Crossref] [PubMed]
15. Solomon BJ, Besse B, Bauer TM, et al. Lorlatinib in patients with ALK-positive non-small-cell lung cancer: results from a global phase 2 study. Lancet Oncol 2018;19:1654-67. [Crossref] [PubMed]
16. Priantti JN, Vilbert M, de Moraes FCA, et al. Neurocognitive Adverse Events Related to Lorlatinib in Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis. Cancers (Basel) 2024;16:2611. [Crossref] [PubMed]
17. Liu G, Mazieres J, Stratmann J, et al. A pragmatic guide for management of adverse events associated with lorlatinib. Lung Cancer 2024;191:107535. [Crossref] [PubMed]
18. Bearz A, Martini JF, Jassem J, et al. Efficacy of Lorlatinib in Treatment-Naive Patients With ALK-Positive Advanced NSCLC in Relation to EML4::ALK Variant Type and ALK With or Without TP53 Mutations. J Thorac Oncol 2023;18:1581-93. [Crossref] [PubMed]
19. Camidge DR, Dziadziuszko R, Peters S, et al. Updated Efficacy and Safety Data and Impact of the EML4-ALK Fusion Variant on the Efficacy of Alectinib in Untreated ALK-Positive Advanced Non-Small Cell Lung Cancer in the Global Phase III ALEX Study. J Thorac Oncol 2019;14:1233-43. [Crossref] [PubMed]
20. Bearz A, Bertoli E, Stanzione B, et al. EML4-ALK: Update on ALK Inhibitors. Int J Mol Sci 2025;26:308. [Crossref] [PubMed]
21. Rogula B, Lozano-Ortega G, Johnston KM. A Method for Reconstructing Individual Patient Data From Kaplan-Meier Survival Curves That Incorporate Marked Censoring Times. MDM Policy Pract 2022;7:23814683221077643. [Crossref] [PubMed]