Serum tumor markers and outcomes in lung cancer patients with brain metastases: a retrospective longitudinal cohort study

Introduction

Lung cancer is the primary cause of cancer-related mortality globally (1-3). Over 50% of lung cancers are diagnosed as unresectable or at an advanced stage, and 15–20% of lung cancer patients have been found to have brain metastases (BM) at the time of diagnosis (4,5). With the development of treatment options, different approaches such as tyrosine kinase inhibitors (TKIs) and immune checkpoint therapy have emerged alongside traditional cytotoxic chemotherapy, improving the long-term survival rate of patients with BM, highlighting the importance of ongoing disease surveillance. In terms of disease monitoring in clinical practice, serum tumor markers (STMs) are widely used in lung cancer (6,7), with carcinoembryonic antigen (CEA), cytokeratin-19 fragment (CYFRA21-1), neuron-specific enolase (NSE), squamous cell carcinoma antigen (SCC-Ag), cancer antigen 125 (CA125) and carbohydrate antigen 199 (CA199), being most sensitive. However, their role in BM remains unclear, lacking clinical evidence.

Previous studies have also emphasized the utilization of STMs alone or in combination to monitor treatment effectiveness and prognosis (8,9). Typically, both CEA and other STMs have been reported as predictive factors at baseline or prognostic factors for disease recurrence, progression-free survival (PFS), or overall survival (OS) (6,10). Moreover, a few studies have suggested that the trajectory of STMs in colorectal cancer can reflect dynamic patterns of changes during the disease progression, providing more information on the relationship between STMs and outcomes (11,12). However, due to the limited number of measurements, the relationship between STMs and the prognosis of patients with BM is still uncertain, and the longitudinal trajectory of STMs is often overlooked. Specifically, there is a lack of understanding regarding the trajectory of STMs in lung cancer patients with BM, and its association with the OS of these patients.

Therefore, we employed a latent class growth mixture model using a large-sample retrospective longitudinal cohort to define the dynamic pattern of STMs, and simultaneously analyzed the role of baseline levels and dynamic changes in STMs in predicting prognosis of lung cancer patients with BM. We present this article in accordance with the STROBE reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-404/rc).

Methods

Ethics

This retrospective study was approved by the Ethics Committee of West China Hospital (No. 2022127). The study was conducted according to the guidelines of the Declaration of Helsinki (as revised in 2013). Individual consent for this analysis was waived due to retrospective nature.

Patients

Referring to our previous literature (13), we obtained the clinicopathological and follow-up data of lung cancer patients with BM from the linked electronic medical care records at West China Hospital during the period from December 1, 2013 to September 30, 2020. Lung cancer patients with BM were verified by pathology reports and brain imaging. The exclusion criteria for this study included: (I) patients aged below 18 years or above 80 years, (II) incomplete STMs determinations, (III) lack of survival information, and (IV) less than 3 visits. BM patients with STMs records from the first admission and follow-up were included for STMs trajectory analysis. Participants who met the inclusion criteria for all STMs trajectory analyses simultaneously were subjected to joint analysis. The treatment regimen (radiotherapy, chemotherapy, immunotherapy, and targeted therapy) for all our included patients was used after diagnosis of BM. Targeted therapy includes anti-angiogenesis treatment and small molecule targeted therapy.

STMs measurement

Serum CYFRA21-1, CEA and NSE were selected in this study. A total of 3 mL of peripheral venous blood was withdrawn, serum was separated at 4,000 r/min and stored at −80 ℃ for measurement. An electrochemiluminescence immunoassay (Roche Cobas e601) was used to evaluate the three STMs. Normal reference values of CEA, CYFRA21-1 and NSE are <3.4, <3.0, and <15.0 ng/mL, respectively. Each patient underwent at least three follow-up measurements before being included in the analysis.

Surveillance outcome

The primary outcome was to evaluate the prognostic roles of STMs in the OS of lung cancer patients with BM. Kaplan-Meier analysis, Cox proportional hazards model, and log-rank test were performed.

Statistical analysis

The relationship between the baseline levels of CEA, CYFRA21-1, as well as NSE and BM outcomes was evaluated by Kaplan-Meier analysis and Cox proportional hazard models, which included age, gender, smoking, status of key driver genes, Graded Prognostic Assessment (GPA) score of BM and treatment regimen (radiotherapy, chemotherapy, immunotherapy, and targeted therapy). Kaplan-Meier analysis and cox proportional hazard models were performed via the “survival” package in R (version 4.2.1).

We utilized longitudinal mode to analyze the patterns and trajectories of STMs in lung cancer patients with BM. The logarithms of CEA, CYFRA21-1, and NSE values were then used for trajectory fitting due to their non-normal distribution. A latent class growth mixed model (LCGMM) was utilized to identify different trajectory patterns, considering the heterogeneity of the population. This model included longitudinal measurements as linear or nonlinear functions of time, with 2–5 potential groups considered. The optimal number of groups and the best fitting shape were determined based on the Bayesian information criterion, ensuring a representative proportion in each group. LCGMM analysis was carried out using the “lcmm” package in R (version 4.2.1). The relationship between trajectory groups and BM outcomes was evaluated by Kaplan-Meier analysis and Cox proportional hazard models with age, sex, and baseline levels adjusted.

Results

Participant characteristics

During the period from December 2013 and September 2020, a total of 1,169 lung cancer patients with BM were included in this study. Of these, 702 were males and 467 were females. The age distribution was as follows: 323 patients were under 50 years old, 419 were aged 50–60 years old, and 427 were over 60 years old. Among the participants, 591 were never-smokers, 180 were current smokers, and 398 were former smokers. According to the World Health Organization/International Association for the Study of Lung Cancer (WHO/IASLC) classification criteria for lung tumors, SCC accounted for 10.9%, adenocarcinoma (AD) for 66.9%, small cell lung cancer (SCLC) for 14.5%, and not otherwise specified (NOS) for the remaining 7.4%. In terms of treatment, 485 cases (41.5%) underwent targeted therapy, 39 cases (3.3%) underwent immunotherapy, 627 cases (53.6%) underwent radiotherapy and 954 cases (81.6%) underwent chemotherapy. The baseline characteristics of patients are shown in Table 1.

Association of single STMs at baseline with BM survival

In order to investigate the clinical significance of individual STMs (CEA, CYFRA21-1, and NSE) baseline levels, we collected baseline STM data and utilized the median as the cutoff value to classify patients into high- and low-level groups. The baseline characteristics of patients were further analyzed in detail (Table 1). In the high NSE levels group (≥15.5 ng/mL), there was a higher proportion of male individuals (63.4% vs. 56.7%), a lower body mass index (BMI), a higher prevalence of SCLC pathology type (20.7% vs. 8.2%), a higher prevalence of smoking habits (27.1% vs. 19.9%), and a lower GPA score (40.7% vs. 30%). Similarly, in the high CYFRA21-1 levels group (≥4.66 ng/mL), there was a higher prevalence of male individuals (63.2% vs. 56.8%), a lower BMI, or a higher prevalence of SCC pathology types (13.8% vs. 7.9%), had more number of brain metastatic lesions (50.8% vs. 45%), and a lower GPA score (40.2% vs. 30.5%). Conversely, the high CEA levels group (≥9.35 ng/mL) shows a tendency towards female gender (43.6% vs. 36.3%), age over 60 years (40% vs. 33%), presence of AD pathology (76.8% vs. 57%), more number of brain metastatic lesions (52.6% vs. 43.2%), a higher prevalence of positive epidermal growth factor receptor (EGFR) (26.2% vs. 19.2%), and a lower GPA score (41.4% vs. 29.3%). Furthermore, we observed that a significant proportion of BM patients with AD exhibit elevated levels of CEA (76.8%), CYFRA21-1 (69.6%) or NSE (60%).

In terms of survival, Kaplan-Meier survival analysis revealed that all STMs (CEA, CYFRA21-1, and NSE) were negatively correlated with survival outcomes (Figure 1A-1C). Patients with low serum NSE levels (<15.5 ng/mL) exhibited a significantly extended median survival time (MST) (P<0.001) compared to those with high NSE levels (≥15.5 ng/mL) [29 months (95% CI: 27–31) vs. 19 months (95% CI: 18–21)], indicating a survival benefit (P<0.001) for patients with low baseline serum NSE levels. Similarly, patients with low baseline serum CYFRA21-1 levels (<4.66 ng/mL) had a longer MST of 27 months (95% CI: 24–29) in contrast to patients with high CYFRA21-1 levels (≥4.66 ng/mL) with an MST of 21 months (95% CI: 19–23). Additionally, patients with low baseline serum CEA levels (<9.35 ng/mL) displayed a prolonged MST (P=0.01) of 24 months (95% CI: 22–27) compared to patients with high CEA levels (≥9.35 ng/mL), who had an MST of 23 months (95% CI: 21–25).

Figure 1 Kaplan-Meier curves and multivariate Cox’s forest plots of overall survival in lung cancer patients with brain metastases based on baseline levels of different serum tumor markers. (A) Kaplan-Meier curves of patients stratified by baseline serum NSE levels. (B) Kaplan-Meier curves of patients stratified by baseline serum CYFRA21-1 levels. (C) Kaplan-Meier curves of patients stratified by baseline serum CEA levels. (D) Multivariable Cox regression analysis of patients stratified by baseline serum NSE levels. (E) Multivariable Cox regression analysis of patients stratified by baseline serum CYFRA21-1 levels. (F) Multivariable Cox regression analysis of patients stratified by baseline serum CEA levels. NSE, neuron-specific enolase; CYFRA21-1, cytokeratin-19 fragment; CEA, carcinoembryonic antigen; KPS, Karnofsky Performance Status; EM, extracranial metastases; MT_number, brain metastasis number; BMI, body mass index; EGFR, epidermal growth factor receptor; ALK, anaplastic lymphoma kinase; ROS, reactive oxygen species; GPA_C, Graded Prognostic Assessment score.

Multivariate Cox regression analysis to identify poor prognostic factors at baseline levels

We conducted a multivariate Cox regression analysis and observed a significant correlation between OS and CYFRA21-1 (P<0.001), CEA (P=0.005) as well as NSE (P<0.001), serving as independent prognostic factors (Figure 1D-1F). When all three were included in the Cox model as continuous variables, only CYFRA21-1 (P<0.001) as well as NSE (P=0.03) remained statistically different (Figure S1). In either scenario, the mutation status of anaplastic lymphoma kinase (ALK) (P<0.05) and EGFR (P<0.05) as well as receiving radiotherapy and targeted therapy were independent prognostic factors for patients with BM (Figure 1D-1F). Further analysis indicated that baseline CYFRA21-1, CEA, NSE as well as status of key driver genes were independent prognostic factors in non-small cell lung cancer (NSCLC) patients with BM, while for SCLC patients with BM, baseline NSE and receiving chemotherapy show independent correlations with survival (Figures S2-S4).

Net reclassification index (NRI) for individual STMs baseline levels

When each STM was individually integrated into the NRI clinical model, as compared to traditional GPA scores, each STM demonstrated an enhancement in discriminatory ability (Table 2). Among these, NSE exhibited the most substantial enhancement in NRI. The inclusion of NSE [NRI (categorical) 0.11, 95% CI: 0.04–0.2; NRI (continuous) 0.27, 95% CI: 0.1–0.38], CYFRA21-1 [NRI (categorical) 0.10, 95% CI: 0.03–0.2; NRI (continuous) 0.25, 95% CI: 0.09–0.38], or CEA [NRI (categorical) 0.08, 95% CI: 0.018–0.18; NRI (continuous) 0.21, 95% CI: 0.07–0.33] led to an increase in the accurate reclassification rate for both positive and negative outcomes.

Clinical value of STMs trajectory modeling

We conducted a longitudinal analysis to analyze the trajectories of STMs and understand how they evolve over time in the progression and treatment process of BM patients. Table 3 shows the baseline characteristics of participants in the trajectory groups of STMs. Based on LCGMM, the trajectories of STMs were classified by three classes. The distribution of the 1,169 patients with NSE across the three classes was 48 (class 1, persistently-rising trajectory), 1,014 (class 2, normal-level trajectory), and 107 (class 3, persistently-descending); the distribution of CYFRA21-1 was 933 (class 1, normal-level trajectory), 62 (class 2, early-rising trajectory), and 174 (class 3, later-rising trajectory); and the distribution of CEA was 914 (class 1, normal-level trajectory), 67 (class 2, baseline-high trajectory) and 188 (class 3, persistently-rising trajectory) (Figure 2A-2C).

Figure 2 Trajectory curves, Kaplan-Meier curves and multivariate Cox’s forest plots of overall survival in lung cancer patients with brain metastases based on dynamic changes of different serum tumor markers. (A) Dynamic trajectories based on serum NSE stratification. (B) Dynamic trajectories based on serum CYFRA21-1 stratification. (C) Dynamic trajectories based on serum CEA stratification. (D) Kaplan-Meier curves of patients stratified by NSE trajectories. (E) Kaplan-Meier curves of patients stratified by CYFRA21-1 trajectories. (F) Kaplan-Meier curves of patients stratified by CEA trajectories. (G) Multivariable Cox regression analysis of patients stratified by NSE trajectories. (H) Multivariable Cox regression analysis of patients stratified by CYFRA21-1 trajectories. (I) Multivariable Cox regression analysis of patients stratified by CEA trajectories. NSE, neuron-specific enolase; CYFRA21-1, cytokeratin-19 fragment; CEA, carcinoembryonic antigen; KPS, Karnofsky Performance Status; EM, extracranial metastases; MT_number, brain metastasis number; BMI, body mass index; EGFR, epidermal growth factor receptor; ALK, anaplastic lymphoma kinase; ROS, reactive oxygen species; GPA_C, Graded Prognostic Assessment score.

Relationship between BM survival and STM trajectories

Kaplan-Meier survival analyses showed that dynamic changes in all STMs (CEA, CYFRA21-1 and NSE) correlated with prognosis. In terms of NSE, class 1 (persistently-rising trajectory) had the worst prognosis, with a median time of 11 months (95% CI: 10–19). In contrast, the class 2 (normal-level trajectory) had the best prognosis, with an MST of 25 months (95% CI: 23–27), and the class 3 (persistently-descending) had a more intermediate median time of 18 months (95% CI: 15–23). In the dynamic analysis of serum CYFRA21-1 levels, the class 1 (normal-level trajectory) group had the best prognosis (P<0.001), with an MST of 25 months (95% CI: 23–27), and the class 2 (early-rising trajectory) and 3 (later-rising trajectory) groups all had a worse prognosis, with an MST of 17 months (95% CI: 14–23) and 19.5 months (95% CI: 17–23), respectively. In dynamic analysis of serum CEA levels, class 1 (normal-level trajectory) had the best prognosis, with a MST of 23 months (95% CI: 22–25); class 2 (baseline-high trajectory) had the worst prognosis, with a MST of 20 months (95% CI: 17–23); and class 3 (persistently-rising trajectory) had an intermediate prognosis, with a median time of 23 months (95% CI: 20–26) (Figure 2D-2F).

Multivariate Cox regression analysis and subgroup analysis

After excluding confounding factors using multivariate Cox regression analysis, dynamic changes of STMs were not significantly correlated with survival. In either scenario, status of driver genes (the mutation status of ALK and EGFR), receiving targeted therapy, baseline NSE level were independent prognostic factors for patients with BM (Figure 2G-2I). The joining of STM analysis showed that for both NSCLC and SCLC, there was no statistically significant difference in the dynamic trajectory of STMs (CEA, CYFRA21-1 and NSE), while NSCLC patients with positive driver genes (P<0.05) have better survival, and SCLC patients with chemotherapy (P<0.001) have better survival (Figure 3). Further analysis indicated that among BM patients lacking key driver genes, NSE trajectory(P<0.05), CYFRA21-1 trajectory (P<0.05) and receiving chemotherapy (P<0.001) were independent prognostic factors (Figure S5).

Figure 3 Joint multivariate Cox’s forest plots of overall survival in lung cancer patients with brain metastases. (A) Joint multivariate Cox’s forest plots in non-small cell lung cancer (NSCLC) patients with brain metastases. (B) Joint multivariate Cox’s forest plots in small cell lung cancer (SCLC) patients with brain metastases; (C) Joint multivariate Cox’s forest plots in all patients with brain metastases. KPS, Karnofsky Performance Status; EM, extracranial metastases; BMI, body mass index; MT_number, brain metastasis number; CYFRA21-1, cytokeratin-19 fragment; CEA, carcinoembryonic antigen; NSE, neuron-specific enolase; EGFR, epidermal growth factor receptor; ALK, anaplastic lymphoma kinase; ROS, reactive oxygen species; GPA_C, Graded Prognostic Assessment score.

Discussion

We report for the first time the relationship between baseline levels and dynamic changes of STMs (CEA, CYFRA21-1 and NSE) in lung cancer patients with BM and prognosis. We found that serum CEA, CYFRA21-1 as well as NSE at baseline levels were correlated with OS of patients, serving as independent prognostic factors. Moreover, the performance of the GPA model was significantly improved with the addition of STMs at baseline levels. More specifically, we delineated the dynamic evolution trajectories of STMs. Each type of STMs can be divided into three different types based on changes in disease course. However, the dynamic alterations of STMs levels lacked prognostic significance. For patients with non-driver gene, the dynamic trajectory of CYFRA21-1 and NSE, as well as receiving chemotherapy were independent prognostic factors.

Several studies have shown that STMs are biomarkers and associated with BM development in lung cancer (14,15). After reviewing data from over 1,000 patients with BM from lung cancer from December 2013 and September 2020, we found that, the baseline levels of serum CEA (cut-off: 9.35 ng/mL), CYFRA21-1 (cut-off: 4.66 ng/mL) and NSE (cut-off: 15.5 ng/mL) were independent prognostic factors related to the outcomes of BM, representing a previously unexplored discovery. Normal reference values of CEA, CYFRA21-1 and NSE are <3.4, <3.0, and <15.0 ng/mL, respectively. In our study, the cutoff values of CYFRA21-1 and NSE did not differ much from previous normal reference ranges. However, the situation is different for CEA (which is elevated). This also explains why, after including STMs as continuous variables in Cox regression, only CEA did not show statistical significance, which suggesting that the baseline level of CEA should have a higher value when used as a prognostic indicator. Furthermore, the status of key driver gene mutations (EGFR and ALK) was considered an independent prognostic indicator for patients with BM, consistent with previous reports (16,17). Individuals with positive mutation of driver gene benefit from personalized targeted therapy, which has the characteristics of low molecular weight and effective penetration of the blood-brain barrier, thereby enhancing therapeutic efficacy in brain tissue (18,19). Moreover, this analysis showed that GPA scores did not demonstrate predictive significance. GPA was first published in 2008 (20), including age, Karnofsky Performance Status (KPS), number of BM, and extracranial metastases, based on data from 1,960 patients from five randomly assigned Radiation Therapy Oncology Group (RTOG) trials. However, we collected data from patients with BM after 2010, with an increasing proportion receiving targeted therapy, radiotherapy and immunotherapy. The advancements in targeted therapy and immunotherapy have improved patient outcomes, necessitating further updates and research into prognostic models for BM.

A LCGMM was used for the first time to determine the dynamic trajectory of STMs of lung cancer patients with BM using a large sample of cohort. Indeed, we found that after for lung cancer patients with BM receiving treatments, serum STM level showed dynamic changes. And we identified for the first time the distinct dynamic trajectories of CEA, CYFRA21-1, and NSE in lung cancer patients with BM, and analyzed their relationship with prognosis. Li et al. reported that the simultaneously dynamic measurements of CEA, CA199, and CA125 can be as independent prognostic factors of colorectal cancer patients (11), indicating the importance of dynamic monitoring of STMs. Interestingly, for patients with BM, no matter whether CYFRA21-1 rises dynamically earlier or later, patients with high baseline levels have poor prognosis, which is consistent with the close relationship between baseline levels of CYFRA21-1 and prognosis. After excluding confounding factors, dynamic changes of STMs during disease course were not significantly associated with the final outcome of BM patients, while status of driver gene and receiving targeted therapy is an absolute prognostic factor for BM patients. The mechanisms of these interactions identified are not yet clear and need further exploration in future studies. For patients with lung cancer BM, the combined use of anti-angiogenic targeted drugs has shown promising anti-tumor activity and tolerable safety (21). Of course, with the widespread adoption of next-generation sequencing (NGS) over the past decade, a positive driver gene status means the accessibility to targeted therapy and better control of BM disease progression (22-24). This is consistent with the findings of our study, emphasizing the importance of driver gene testing and targeted therapy in the management of BM patients, as well as the need for future research to delve into exploring their interaction mechanisms. In subgroup analysis, we observed that the dynamic trajectory of CYFRA21-1 and NSE as well as receiving chemotherapy were closely related to prognosis in driver gene-negative patients. Due to the limitation of the number of research subjects, this conclusion still needs further verification in future studies.

Nevertheless, there are several limitations in this study. The data set evaluated was a heterogeneous cohort of patients with BM. We collected the survival data of patients with BM from different treatment models and histology, and we did not study the prognosis of patients with different treatment models separately. There would be selection bias in the final results.

Conclusions

Baseline levels of serum CEA, CYFRA21-1 and NSE are recommended for evaluating the prognosis of lung cancer patients with BM. Dynamic changes of STMs during disease course were not significantly associated with the final outcome of BM patients. More attention to driver gene testing and targeted therapy may be needed in the management of BM patients.

Acknowledgments

Funding: The work was supported by the National Key Research and Development Program of China (2021YFE0206600), the International Science and Technology Cooperation Program of Chengdu Science and Technology Bureau (2024-YF06-00011-HZ and 2022-GH03-00004-HZ), the Strategic Cooperation Special Fund of Sichuan University-Dazhou Municipal People’s Government (2021CDDZ-25), the Science and Technology Cooperation Special Fund of Sichuan University-Zigong (2021CDZG-24), the Health Research Project of Chengdu Eastern New Area Management Committee (202304), Talent Excellence Development Project of West China Hospital (ZYYC23006), the Achievement Transformation Fund of West China Hospital (CGZH19002), Science and Technology Project of Sichuan Provincial Health Commission (Clinical Research Special Project JH2023082), Clinical Research Incubation Project of West China Hospital (23HXFH001), the National Natural Sciences Foundation of China (82172842), Sichuan Provincial Science and Technology Department Key Research and Development Program (2022YFSY0012), the Ministry of Education University-Industry Collaborative Education Program (230720523707281), Miaozi Project in Science and Technology Innovation Program of Sichuan Province (23-YCG034) and 1.3.5 project for Disciplines of Excellence, West China Hospital, Sichuan University (ZYGD23023). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-404/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-404/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-404/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-404/coif). All authors report funding from the National Key Research and Development Program of China (2021YFE0206600), the International Science and Technology Cooperation Program of Chengdu Science and Technology Bureau (2024-YF06-00011-HZ and 2022-GH03-00004-HZ), the Strategic Cooperation Special Fund of Sichuan University-Dazhou Municipal People’s Government (2021CDDZ-25), the Science and Technology Cooperation Special Fund of Sichuan University-Zigong (2021CDZG-24), and the Health Research Project of Chengdu Eastern New Area Management Committee (202304), Talent Excellence Development Project of West China Hospital (ZYYC23006), the Achievement Transformation Fund of West China Hospital (CGZH19002), Science and Technology Project of Sichuan Provincial Health Commission (Clinical Research Special Project JH2023082), Clinical Research Incubation Project of West China Hospital (23HXFH001), the National Natural Sciences Foundation of China (82172842), Sichuan Provincial Science and Technology Department Key Research and Development Program (2022YFSY0012), the Ministry of Education University-Industry Collaborative Education Program (230720523707281), and Miaozi Project in Science and Technology Innovation Program of Sichuan Province (23-YCG034). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors have no other 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 (as revised in 2013) and was approved by the Ethics Committee of West China Hospital (No. 2022127). Individual consent for this analysis was waived due to retrospective nature.

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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