Selecting optimal immunotherapy based on inflammation in stage IV PD-L1 ≥50% gene mutation-negative non-small cell lung cancer: pembrolizumab monotherapy versus combination chemoimmunotherapy

Introduction

Background

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of advanced non-small cell lung cancer (NSCLC). Clinical trials in advanced NSCLC patients without driver gene mutations, but with high programmed death-ligand 1 (PD-L1) expression (PD-L1 ≥50%), have demonstrated that pembrolizumab monotherapy significantly prolongs progression-free survival (PFS) and overall survival (OS) compared to platinum-based chemotherapy. ICIs added to chemotherapy have also been shown to significantly prolong PFS and OS compared to chemotherapy. Based on these clinical trial outcomes, pembrolizumab monotherapy or combination chemoimmunotherapy is recommended as first-line therapy for advanced NSCLC without driver gene mutations and with high PD-L1 (PD-L1 ≥50%) (1-6).

Systemic inflammation is associated with the therapeutic effects of ICI, and changes in the tumor microenvironment (TME) due to inflammation are attributed to resistance to ICI treatment (7). Several inflammatory markers reflecting systemic inflammation, such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), Glasgow Prognostic Score (GPS) or modified GPS, and systemic immune-inflammation index (SII), have been proposed as independent prognostic factors for cancer patients (8,9). Among these, SII has been suggested to function as a prognostic marker in NSCLC and may be associated with tumor invasion and lymph node metastasis (10). Previous meta-analyses have reported that higher SII values are associated with shorter PFS and OS in cancer patients (11). Furthermore, the pretreatment SII may be a useful prognostic indicator for advanced NSCLC and may have a greater prognostic capacity than other inflammatory markers, such as NLR and PLR (12,13).

Rationale and knowledge gap

Despite the growing evidence linking systemic inflammation with cancer prognosis and treatment response, few studies have examined the association between systemic inflammation and the therapeutic efficacy of ICIs in advanced NSCLC patients with PD-L1 ≥50%. In addition, the superiority of ICI monotherapy or combination chemoimmunotherapy in this patient group has not been established.

Objective

This study aimed to evaluate the utility of SII in guiding treatment selection by comparing the efficacy of ICI monotherapy and combination chemoimmunotherapy in patients with advanced driver gene mutation-negative NSCLC with PD-L1 ≥50%, using SII as an indicator of systemic inflammation. We present this article in accordance with the STROBE reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-873/rc).

Methods

Study population and clinical data

This retrospective study was conducted at 13 institutions in Japan. Patients with PD-L1 ≥50% advanced NSCLC [stage IVA or IVB according to the American Joint Committee on Cancer Staging Manual, version 8 (14)] who received pembrolizumab monotherapy or combination chemoimmunotherapy as initial treatment between March 2017 and October 2021 were included. As this was a retrospective observational study, the sample size was determined by the number of eligible patients treated during the study period at the participating institutions. No formal sample size calculation was performed. To ensure a more homogeneous study population for evaluating systemic inflammation and treatment efficacy, we focused on patients with stage IV NSCLC, who have been reported to have poorer prognoses than those with postoperative recurrence, likely due to a higher tumor burden and poorer general condition at diagnosis (15,16). Patients with driver gene mutations were excluded. The data cutoff date for follow-up was December 28, 2023. The median follow-up duration was 21.8 months.

Pretreatment hematological clinical data were collected from electronic medical records. SII was calculated as platelet count × neutrophil count/lymphocyte count. Pretreatment SII cutoff values were established using previously published data, with SII ≥1,444 defined as high inflammation and SII <1,444 defined as low inflammation (17). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the ethics review board of Kyoto Prefectural University of Medicine (approval No. ERB-C-2113) and was conducted with the consent of the ethics review board of each hospital involved in the study. Informed consent was waived in this retrospective study.

Assessment of survival outcomes

Therapeutic responses to pembrolizumab and combination chemoimmunotherapy were evaluated according to the Response Evaluation Criteria in Solid Tumors guidelines (version 1.1). PFS was measured from the start of first-line treatment until the date of tumor progression or death, whichever occurred first. OS was measured from the start of the first-line treatment until all-cause death.

Statistical analysis

Continuous and nominal variables for patient characteristics were analyzed using the Mann-Whitney U test and Fisher’s exact test. Survival outcomes were estimated using the Kaplan-Meier method and compared using the log-rank test. Cox proportional hazard models were used to determine the association between inflammatory markers and survival outcomes. Multivariate models included Eastern Cooperative Oncology Group performance status (ECOG PS), age, histology, stage, and metastasis as covariates to adjust for potential confounding. The selected variables were all incorporated as established prognostic determinants in advanced NSCLC (18). Multivariate analyses incorporating covariates including age, histology, stage, metastasis, and SII were performed for the overall population, the pembrolizumab group, and the combination chemoimmunotherapy group. ECOG PS is known to be a confounding factor with SII and was excluded from the covariates to avoid confounding (19). The results are expressed as hazard ratios (HRs) with 95% confidence intervals (CIs), as appropriate. All statistical analyses were performed using EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan), a graphical user interface for R (The R Foundation for Statistical Computing, Vienna, Austria) and statistical significance was determined as P<0.05 (20). There were no missing data for the variables analyzed in this study.

Results

Baseline characteristics

A total of 347 patients were included in the study. Their median age was 71 (range, 36–90) years, and 277 (79.8%) were male. A total of 201 (57.9%) patients had adenocarcinoma, and 93 (26.8%) had squamous cell carcinoma. Most patients had an ECOG PS 0–1 (80.6%). Of the overall population, 212 (61.1%) patients were in the pembrolizumab group and 135 (38.9%) were in the combination chemoimmunotherapy group. The median age was 72 (range, 45–90) years in the pembrolizumab group and 68 (range, 36–86) years in the combination chemoimmunotherapy group. Compared with the combination chemoimmunotherapy group, the pembrolizumab group had more former smokers and patients with poor ECOG PS (≥2). Smoking history and ECOG PS showed statistically significant differences between the two groups (Table S1).

Association between pretreatment SII and PFS/OS in advanced NSCLC with PD-L1 ≥50%

The overall population was divided into two groups according to SII cutoff values, where SII ≥1,444 was defined as the high inflammation group and SII <1,444 as the low inflammation group. PFS and OS were compared between the two groups. As shown in Figure 1, there was no statistically significant difference in PFS between the two groups (high inflammation vs. low inflammation; 7.8 [95% CI: 5.1–11.4) vs. 10.5 (95% CI: 7.8–13.0) months, HR =1.12 (95% CI: 0.88–1.43), P=0.34], but there was a statistically shorter OS in the high inflammation group [high inflammation vs. low inflammation; 17.8 (95% CI: 13.2–24.1) vs. 37.3 (95% CI: 26.6–44.1) months, HR =1.43 (95% CI: 1.08–1.88), P=0.01]. In the univariate analysis, SII [HR =1.43 (95% CI: 1.08–1.88), P=0.01] was associated with OS. In the multivariate analysis, SII [HR =1.40 (95% CI: 1.06–1.85), P=0.02] was an independent predictor of OS (Table S2).

Figure 1 Kaplan-Meier curves of (A) PFS and (B) OS using SII cutoff value [1,444] in advanced NSCLC with PD-L1 ≥50%. There was no statistically significant difference in PFS between the two groups (high inflammation vs. low inflammation; 7.8 [95% CI: 5.1–11.4) vs. 10.5 (95% CI: 7.8–13.0) months, HR =1.12 (95% CI: 0.88–1.43), P=0.34]. OS was statistically shorter in the high inflammation group than in the low inflammation group [high inflammation vs. low inflammation; 17.8 (95% CI: 13.2–24.1) vs. 37.3 (95% CI: 26.6–44.1) months, HR =1.43 (95% CI: 1.08–1.88), P=0.01]. CI, confidence interval; HR, hazard ratio; m, median; NSCLC, non-small cell lung cancer; OS, overall survival; PD-L1, programmed death-ligand 1; PFS, progression-free survival; SII, systemic immune-inflammation index.

A total of 212 patients received pembrolizumab monotherapy, including 121 patients in the low inflammation group (SII <1,444) and 91 patients in the high inflammation group (SII ≥1,444). Statistically significant differences between the two groups were observed in tumor histology and ECOG PS (Table S3). In the pembrolizumab group, multivariate analysis showed that liver metastasis was significantly associated with PFS, while age, tumor histology, stage, and liver metastasis were independent predictors of OS. However, SII was not significantly associated with either PFS or OS (Table S4). In the combination chemoimmunotherapy group, multivariate analysis showed that brain metastasis was significantly associated with PFS, while tumor histology and SII were independent prognostic predictors for OS (Table S5).

Therapeutic efficacy of pembrolizumab vs. combination chemoimmunotherapy in high inflammation population

The characteristics of patients with high inflammation in the pembrolizumab and combination chemoimmunotherapy groups are shown in Table 1. The median age in the pembrolizumab group was 73 years and that in the combination chemoimmunotherapy group was 68 years. More squamous cell carcinoma and poor ECOG PS (≥2) in the pembrolizumab group compared to the combination chemoimmunotherapy group. There were no significant differences between the two groups in terms of sex, smoking history, and disease stage. Regarding combination chemoimmunotherapy regimens, 34 (54.8%) patients received cisplatin or carboplatin plus pemetrexed plus pembrolizumab, and 17 (27.4%) patients received carboplatin plus nab-paclitaxel plus pembrolizumab. In the high inflammation (SII ≥1,444) population, as shown in Figure 2, there were no statistically significant differences in PFS and OS between the pembrolizumab and combination chemoimmunotherapy groups [median PFS: 5.7 (95% CI: 4.1–12.0) vs. 10.4 (95% CI: 6.4–15.5) months, HR =0.86 (95% CI: 0.59–1.25), P=0.43; median OS: 19.5 (95% CI: 10.8–25.2) vs. 17.8 (95% CI: 11.4–42.6) months, HR =0.85 (95% CI: 0.56–1.28), P=0.44]. In univariate analysis using Cox proportional hazards regression models, ECOG PS ≥2 [HR =1.85 (95% CI: 1.24–2.77), P=0.003] and non-squamous histology [HR =0.64 (95% CI: 0.43–0.95), P=0.03] were associated with PFS. Similarly, ECOG PS ≥2 [HR =1.98 (95% CI: 1.30–3.01), P=0.001], age ≥75 years [HR =1.53 (95% CI: 1.01–2.33), P=0.04], and non-squamous histology [HR =0.54 (95% CI: 0.35–0.82), P=0.004] were associated with OS. Multivariate analysis using Cox proportional hazards regression models showed that ECOG PS ≥2 [HR =2.01 (95% CI: 1.30–3.10), P=0.002] and non-squamous histology [HR =0.51 (95% CI: 0.33–0.78), P=0.002] were significantly associated with PFS. ECOG PS ≥2 [HR =2.46 (95% CI: 1.55–3.91), P<0.001], age ≥75 years [HR =1.66 (95% CI: 1.07–2.60), P=0.02], non-squamous [HR =0.41 (95% CI: 0.26–0.65), P<0.001], stage IVB [HR =1.82 (95% CI: 1.15–2.88), P=0.01], and liver metastasis [HR =1.72 (95% CI: 1.01–2.91), P=0.04] were independent predictors of OS. SII was not associated with PFS or OS in univariate and multivariate analyses (Table S6).

Figure 2 Kaplan-Meier curves of (A) PFS and (B) OS for pembrolizumab compared with combination chemoimmunotherapy in high inflammation population. There were no statistically significant differences in PFS and OS between the pembrolizumab and combination chemoimmunotherapy groups [mPFS: 5.7 (95% CI: 4.1–12.0) vs. 10.4 (95% CI: 6.4–15.5) months, HR =0.86 (95% CI: 0.59–1.25), P=0.43; mOS: 19.5 (95% CI: 10.8–25.2) vs. 17.8 (95% CI: 11.4–42.6) months, HR =0.85 (95% CI: 0.56–1.28), P=0.44]. CI, confidence interval; HR, hazard ratio; m, median; NR, not reached; OS, overall survival; PFS, progression-free survival.

Therapeutic efficacy of pembrolizumab vs. combination chemoimmunotherapy in low inflammation population

The characteristics of patients with low inflammation in the pembrolizumab and combination chemoimmunotherapy groups are shown in Table 2. The median age in the pembrolizumab group was 72 and 69 years in the combination chemoimmunotherapy group. The pembrolizumab group included a higher proportion of patients with adenocarcinoma compared to the combination chemoimmunotherapy group. There were no significant differences between the two groups in terms of sex, smoking history, disease stage, or ECOG PS. Among patients receiving combination chemoimmunotherapy regimens, 30 (41.0%) patients were treated with carboplatin plus paclitaxel or nab-paclitaxel plus pembrolizumab, while 29 (39.7%) received cisplatin or carboplatin plus pemetrexed and pembrolizumab. As shown in Figure 3, there was a statistically significant improvement in PFS and OS in the combination chemoimmunotherapy group [median PFS: 8.8 (95% CI: 6.6–12.1) vs. 16.0 (95% CI: 8.6–20.5) months, HR =0.69 (95% CI: 0.49–0.98), P=0.04; median OS: 29.4 (95% CI: 19.8–37.8) months vs. not reached (NR) (95% CI: 38.6–NR), HR =0.55 (95% CI: 0.35–0.85), P=0.007]. In the univariate analysis using Cox proportional hazards regression models, ECOG PS ≥2 [HR =2.16 (95% CI: 1.35–3.43), P=0.001], combination chemoimmunotherapy [HR =0.69 (95% CI: 0.49–0.98), P=0.04] were associated with PFS. Similarly, ECOG PS ≥2 [HR =4.13 (95% CI: 2.49–6.87), P<0.001], age ≥75 years [HR =1.61 (95% CI: 1.07–2.42), P=0.02], combination chemoimmunotherapy [HR =0.55 (95% CI: 0.35–0.85), P=0.008] were associated with OS. Furthermore, multivariate analysis using Cox proportional hazards regression models showed that ECOG PS ≥2 [HR =2.08 (95% CI: 1.26–3.42), P=0.004] and combination chemoimmunotherapy [HR =0.68 (95% CI: 0.46–0.99), P=0.047] were statistically associated with PFS; ECOG PS ≥2 [HR =3.52 (95% CI: 1.98–6.24), P<0.001] and combination chemoimmunotherapy [HR =0.56 (95% CI: 0.34–0.90), P=0.02] were independent predictors of OS (Table 3).

Figure 3 Kaplan-Meier curves of (A) PFS and (B) OS for pembrolizumab compared with combination chemoimmunotherapy in low inflammation population. There was a statistically significant improvement in PFS and OS in the combination chemoimmunotherapy group [mPFS: 8.8 (95% CI: 6.6–12.1) vs. 16.0 (95% CI: 8.6–20.5) months, HR =0.69 (95% CI: 0.49–0.98), P=0.04; mOS: 29.4 (95% CI: 19.8–37.8) months vs. NR (95% CI: 38.6–NR), HR =0.55 (95% CI: 0.35–0.85), P=0.007]. CI, confidence interval; HR, hazard ratio; m, median; NR, not reached; OS, overall survival; PFS, progression-free survival.

Discussion

Key findings

To our knowledge, few studies have investigated the therapeutic efficacy of pembrolizumab monotherapy versus combination chemoimmunotherapy in advanced NSCLC with PD-L1 ≥50%, stratified by the level of inflammation. Our study confirms that the SII serves as a prognostic predictor of ICI-based therapy. We found no significant difference in treatment outcomes between ICI monotherapy and combination chemoimmunotherapy in the high inflammation population, whereas combination chemoimmunotherapy was associated with improved outcomes in the low inflammation population.

Strengths and limitations

This study provides novel insights into the utility of systemic inflammation as a biomarker to guide immunotherapy selection in well-defined NSCLC population. However, this study has some limitations. First, this was a retrospective study with a small sample size conducted exclusively in Japanese institutions, which may limit the generalizability of our findings to larger trials and to other ethnic or geographic populations. Second, inflammatory markers were evaluated based only on existing blood test data. Although we used the SII as a marker in this study, other markers may have led to different groupings of high and low inflammation, potentially altering the results. Finally, some patients may have been treated with immunotherapy alone or combination chemoimmunotherapy for reasons unrelated to inflammation, and because of the retrospective nature of the study, we were unable to fully control the treatment selection across the groups.

Comparison with similar research

The therapeutic efficacy of ICIs has been shown to be influenced by systemic inflammation present at the initiation of therapy. Feng et al. demonstrated that SII were significantly associated with PFS and OS in patients treated with programmed death-1 (PD-1)/PD-L1 inhibitors, suggesting that systemic inflammation may impair the effectiveness of ICI therapy (21). However, in this study, no association between SII defined inflammation status and either PFS or OS was observed in the ICI monotherapy group. Several factors may explain the discrepancy from previous reports. First, the present analysis was limited to patients with high PD-L1 expression. On the other hand, in line with our findings, a previous study restricted to PD-L1-high populations also reported that SII was not an independent prognostic factor for OS in ICI monotherapy (22). Second, the cutoff values for SII varied across studies. Third, different indicators of systemic inflammation were employed in each study, and markers other than SII may have greater prognostic relevance in our cohort. In the overall population, no significant difference in PFS was observed between the high and low inflammation groups; however, OS was poorer in the high inflammation group.

In addition, several previous studies have reported that the addition of chemotherapy is effective in patients with high levels of systemic inflammation (23). In contrast, in our study, combination chemoimmunotherapy was more effective than ICI monotherapy in the low inflammation population, whereas it conferred no significant benefit in the high inflammation population. Multivariable analysis of patients receiving chemoimmunotherapy demonstrated that SII was an independent prognostic factor for OS, with greater treatment efficacy observed in the low inflammation population. Several factors may explain the discrepant effects of adding chemotherapy to ICI in the high inflammation population compared with previous reports. First, the retrospective nature of this study and the limitation of the analysis to patients with high PD-L1 expression. Second, the potential impact of the type and cutoff values of inflammatory markers employed.

These findings indicate that SII is a prognostic factor in immunotherapy for advanced NSCLC, and assessment of systemic inflammation may contribute to the development of ICI treatment strategies.

Explanations of findings

In this study, the level of systemic inflammation, as indicated by SII, was associated with differences in treatment efficacy between ICI monotherapy and combination chemoimmunotherapy. This difference may be partly explained by the biological impact of systemic inflammation on the tumor immune microenvironment. Systemic inflammation not only promotes cancer cell proliferation and metastasis, but also inhibits the therapeutic efficacy of ICIs by altering the TME and suppressing antitumor immunity (24,25). Chronic inflammation is known to increase the expression of immune checkpoint molecules by cytokines such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) in the TME. These phenomena suggest that cancer cells escape the immune system, which may weaken the effectiveness of ICIs (25). Furthermore, in patients with advanced NSCLC receiving carboplatin-based chemotherapy, high systemic inflammation has been reported to affect the pharmacokinetics of carboplatin, leading to reduced treatment intensity and subsequently resulting in shorter PFS and OS (26). In patients with advanced NSCLC and elevated NLR, no significant differences have been reported between ICI monotherapy and combination chemoimmunotherapy (27). These findings suggest that adding platinum-based chemotherapy to ICI monotherapy in the context of significant systemic inflammation may not enhance therapeutic efficacy and could potentially lead to poorer clinical outcomes. On the other hand, Moik et al., in a prospective observational cohort study of patients with metastatic NSCLC receiving first-line chemotherapy, found that low systemic inflammation was associated with better treatment responses (28). Cytotoxic drugs are known to induce pyroptosis mediated gasdermin E (GSDME) via caspase-3 activation, thereby enhancing antitumor immunity (29). Moreover, NLRP3 inflammasome-dependent cleavage of GSDMD represents a promising mechanism to promote pyroptotic cell death and may enhance the efficacy of immunotherapy (30). These mechanisms may provide a biologically plausible rationale for the greater efficacy of combination chemoimmunotherapy observed in the low inflammation population in our study. However, several mechanisms, including the unfolded protein response (UPR), can limit effective pyroptosis and contribute to therapeutic resistance (31). These findings highlight the need to further investigate inflammasome-dependent pyroptosis in PD-L1-high immunogenic tumors as part of future translational research.

Implications and actions needed

Our study suggests that pretreatment assessment of systemic inflammation may help guide the choice between ICI monotherapy and combination chemoimmunotherapy in advanced NSCLC with PD-L1 ≥50%. Incorporating inflammation markers such as the SII into clinical decision-making could improve treatment strategies. However, prospective validation in larger cohorts is needed.

Conclusions

In patients with advanced NSCLC and PD-L1 ≥50%, combination chemoimmunotherapy improved PFS and OS in the low systemic inflammation population compared to immunotherapy alone. In contrast, this trend was not observed in the high inflammation group. These results suggest that evaluating inflammatory responses at the time of treatment intervention may be useful in guiding treatment decisions for patients with advanced NSCLC and PD-L1 ≥50%.

Acknowledgments

We thank the patients, their families, and all investigators involved in this study. We also thank Editage (http://www.editage.jp) for their help with English language editing.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-873/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-873/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-873/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-2025-873/coif). T.Y. serves as an unpaid editorial board member of Translational Lung Cancer Research from October 2025 to September 2027. N.N. received personal fees from Chugai Pharmaceutical Co. Ltd., AstraZeneca KK., Eli Lilly Japan KK, and MSD KK outside the purview of the submitted work. T.Y. received research grants from Ono Pharmaceutical, Janssen, AstraZeneca, and Takeda Pharmaceutical, and has received speaking honoraria from Eli Lilly and Chugai Pharmaceutical outside the purview of the submitted work. H.K. received personal fees from Ono Pharmaceutical Co. Ltd., Chugai Pharmaceutical Co. Ltd., AstraZeneca KK, Taiho Pharmaceutical Co. Ltd., Eli Lilly Japan KK, and MSD KK outside the purview of the submitted work. M.T. received research grants from Boehringer Ingelheim, Ono Pharmaceutical, Bristol-Myers Squibb, MSD, Daiichi-Sankyo, Eisai, Chugai Pharmaceutical and Janssen, and personal fees from Chugai Pharmaceutical, Boehringer Ingelheim, AstraZeneca, Taiho Pharmaceutical, Eli Lilly, Novartis, Pfizer, Asahi Kasei Pharmaceutical, Ono Pharmaceutical, Bristol-Myers Squibb, MSD, Bayer, Amgen, Kyowa-Kirin, and Nippon Kayaku, outside the purview of the submitted work. A.O. has received personal fees from Chugai-Roshe, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Japan, Nippon Kayaku, and Bristol-Myers Squibb outside the purview of the submitted work. T.K. received personal fees from Chugai Pharmaceutical Co., Ltd. and MSD KK outside the purview of the submitted work. K.T. received research grants from Chugai Pharmaceutical and Ono Pharmaceutical and personal fees from AstraZeneca, Chugai Pharmaceutical, MSD-Merck, Eli Lilly, Boehringer-Ingelheim, and Daiichi-Sankyo outside the purview of the submitted work. The other 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 ethics review board of Kyoto Prefectural University of Medicine (approval No. ERB-C-2113) and was conducted with the consent of the ethics review board of each hospital involved in the study. Informed consent was waived in this retrospective study.

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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