Unveiling PIK3CA mutation: a prognostic beacon in endometrial carcinoma and its immune implications

Introduction

Endometrial carcinoma (EC) represents a significant health concern, being the most prevalent gynecological malignancy in developed countries (1,2). The disease not only imposes a considerable burden on patients, manifesting in both physical and psychological distress, but also incurs substantial economic costs to healthcare systems due to its rising incidence and associated treatment expenses (3,4). According to the analysis data from The Cancer Genome Atlas (TCGA) database, multiple literature articles have reported 4 molecular subtypes of endometrial cancer: POLE/ultramutated (POLE), microsatellite-instable (MSI)/hypermutated, copy-number-low (CNL)/TP53-wild-type, and copy-number-high (CNH)/TP53-mutant (5). This underscores the necessity for further research aimed at elucidating the implications of PIK3CA mutations on patient survival and treatment efficacy, thereby enhancing the precision of prognostic models and informing targeted therapeutic strategies (6). Previous research has established a correlation between PIK3CA mutations and distinct clinical features, suggesting a potential impact on patient prognosis and therapeutic response (7,8). PIK3CA encodes the p110α catalytic subunit of PI3K and activating mutations constitutively activate PI3K/AKT/mTOR signaling (9). The previous work on molecular mechanism of EC has indicated that PIK3CA played a crucial role in development of tumor (7,10).

The prognostic effect of PIK3CA mutation on EC is controversial, and lack of systematic analysis and elaboration (11-14). This study employed a comprehensive bioinformatics approach to investigate the molecular and immune landscape of EC with a specific focus on PIK3CA mutations. The advantages of this approach lie in its ability to leverage large-scale genomic datasets, facilitating the identification of prognostic markers and immune characteristics associated with EC. The primary objective of this research was to elucidate the relationship between PIK3CA mutations and clinical outcomes, as well as to assess the immune microenvironment’s role in influencing prognosis and therapeutic responses. By employing advanced statistical analyses, including Cox regression and differential expression analysis, this study aimed to construct a predictive risk model and nomogram that can enhance clinical decision-making and improve patient stratification in endometrial cancer management. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-704/rc).

Methods

Data acquisition

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Clinical information related to EC were obtained from cBioPort For Cancer Genomics Platform (https://www.cbioportal.org/). We downloaded STAR-counts data and corresponding clinical information for EC from TCGA database (https://portal.gdc.cancer.gov). We then extracted data in transcripts per million (TPM) format and performed normalization using the log₂(TPM +1) transformation. After retaining samples that included both RNAseq data and clinical information, we ultimately selected 525 samples for further analysis. In addition, a validation set comprising 1,882 EC samples sourced from cBioPort [Memorial Sloan Kettering Cancer Center (MSK, Cancer Discov 2023)] was utilized, which included 844 instances exhibiting PIK3CA mutations. The correlation between mutation status and molecular characteristics was assessed by analyzing the disparities in tumor mutational burden (TMB), mutation frequency, and the distribution of molecular subtypes between the mutation (MUT) and wild type (WT) samples.

Immune landscape analysis and prediction of immunotherapy response

We assessed the abundance of infiltration for 24 distinct immune cell types in EC samples utilizing the single sample Gene Set Enrichment Analysis (ssGSEA) methodology (15-17). This approach evaluates immune cell infiltration by measuring the relative enrichment of predefined immune cell gene sets within the gene expression profile of each sample. Following this, we examined the differences in immune infiltration between groups characterized by PIK3CA MUT and those with WT status. The ESTIMATE algorithm, which analyzes transcriptomic data, is employed to evaluate the levels of infiltration of both immune and stromal cells in malignant tumors (18). By applying the ESTIMATE algorithm, we compute the Stromal Score and Immune Score for each sample, subsequently integrating these scores to derive the overall ESTIMATE Score.

Differential expression analysis

An analysis of differential gene expression between PIK3CA mutated samples and wild-type samples from the TCGA cohort was conducted using the DESeq2 R package. The identification of differentially expressed genes (DEGs) was carried out by implementing criteria that included an adjusted P value threshold of less than 0.05 and an absolute log fold change (logFC) exceeding 0.58.

Functional enrichment analysis

Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were explored through functional enrichment analysis using the R package clusterProfiler (19,20). Significant enrichment of GO terms and KEGG-defined pathways was determined by a P<0.05.

Construction of PIK3CA-related risk model

A cohort of 525 patients diagnosed with endometrial cancer was randomly allocated in a 1:1 ratio to training and validation subsets. Utilizing the Cox regression framework, we employed the R package “glmnet” to execute least absolute shrinkage and selection operator (LASSO) regression for the purpose of variable selection within the training dataset. To ascertain the most suitable regularization parameter, we performed 10-fold cross-validation, which allowed us to identify variables that exhibited non-zero coefficients. These identified variables were considered to have a substantial influence on survival outcomes. Subsequently, we developed a risk assessment model predicated, and the risk score for each EC sample was computed through a specific formula. The term “Coef” denotes the coefficient index corresponding to the LASSO regression coefficients for each mRNA. Following this, we stratified the endometrial cancer patients from both the training and testing cohorts into high-risk and low-risk categories based on the defined optimal truncation score.

Development of a predictive nomogram

A nomogram was constructed to estimate the likelihood of disease occurrence risk, utilizing the results derived from Cox univariate regression analysis within the R package rms.

Statistical analysis

To identify genes associated with overall survival (OS), Cox univariate analyses were conducted. Kaplan-Meier (KM) analysis was used to investigate the effect of various factors on survival time and to estimate patient survival. All data were analyzed and represented through R version 4.3.2. The Wilcoxon test was employed to evaluate the disparities between the two groups. For categorical variables, the differences were assessed utilizing either the Fisher’s exact test or the Chi-squared test. A P value of less than 0.05 was deemed to denote statistical significance.

Results

High-frequency mutated genes in patients with EC and the relationship with prognosis

We conducted an analysis of the top 10 frequently mutated genes in 525 EC patients from the TCGA database. PTEN emerged as the most frequently mutated gene with 59.5%, while CSMD3 was the least mutated at 25.1% (Figure 1A). In Figure 1B, we explored the correlation between mutations in these 10 genes and OS. The results revealed that mutations in PTEN, PIK3CA, ARID1A, TP53, MUC16, and CTCF were significantly associated with prognosis (P<0.05; Figure 1B). The results showed that endometrial cancer patients with TP53 mutations had a poor prognosis [hazard ratio (HR) >1], while those with the remaining five gene mutations had a better prognosis and the remaining five gene mutations were protective factors (HR <1). Further evaluation of these six genes using receiver operating characteristic (ROC) curves revealed that only TP53 and PIK3CA mutations achieved area under the curve (AUC) values exceeding 0.5, indicating modest predictive capacity for survival outcomes (Figure 1C). Notably, TP53 mutations showed a stronger association with poorer prognosis, whereas PIK3CA, despite its unclear mechanistic role in endometrial carcinogenesis, displayed a distinct predictive pattern that warranted deeper investigation. Given the limited understanding of PIK3CA functional impact in this malignancy, we prioritized this gene for subsequent mechanistic and clinical studies.

Figure 1 The mutation waterfall plot of 525 samples and the association with mutation and prognosis. (A) The top 10 landscapes with the highest frequency of mutation. (B) Prognostic forest map of gene mutations. (C) Prognostic accuracy of genetic mutations with ROC. AUC, area under the curve; CI, confidence interval; FPR, false positive rate; MUT, mutation; ROC, receiver operating characteristic; TPR, true positive rate; WT, wild type.

The relationship between PIK3CA mutation and clinical features

We further analyzed the relationship between PIK3CA gene mutation and clinical features. The patients carrying the mutation were younger (Figure 2A). Compared with the WT, patients with the mutation had a higher proportion of uterine corpus endometrial carcinoma-DNA polymerase epsilon (UCEC-POLE) and a lower proportion of CN-High (Figure 2B). Mutant patients had a higher mutation count and a lower aneuploidy score, and there was a significant negative correlation between the number of mutations and aneuploidy score (R=−0.041; P<0.001; Figure 2C-2E). EC patients with PIK3CA mutation had higher TMB (Figure 2F). The Kaplan-Meier curve further confirmed that mutant patients had longer OS (P=0.01; Figure 2G), and with a non-significant trend toward prolonged progression-free survival (PFS; P=0.09; Figure 2H). These findings were validated in the MSK cohort, confirming the consistent association between PIK3CA mutation and enhanced POLE subtype frequency, diminished CN-High proportion, high mutation counts, and elevated TMB (Figure 2I-2K). The observed survival advantage in mutation carriers may reflect the unique molecular context of hypermutant POLE-associated tumors, characterized by increased immunogenicity and potential responsiveness to immune checkpoint inhibition. The inverse relationship between mutation burden and chromosomal instability suggests distinct evolutionary pathways in PIK3CA-mutant tumors, potentially influencing therapeutic vulnerability and clinical outcomes.

Figure 2 The relationship between PIK3CA mutation status and clinical features. PIK3CA mutation status was associated with diagnosis age, molecular subtype, mutation count, and aneuploid score (A-D). (E) The correlation between the mutation count and aneuploid score with spearman. (F) The comparison of TMB in the MUT and WT groups. (G,H) The relationship between PIK3CA gene mutation and overall survival and progression-free survival was analyzed by Kaplan-Meier survival curve. (I-K) The effect of PIK3CA mutations on molecular subtypes, mutation count, and TMB was verified in the MSK database. **, P<0.01; ***, P<0.001. CI, confidence interval; CN-H, copy-number-high; CN-L, copy-number-low; HR, hazard ratio; MSI-H, microsatellite-instable/hypermutated; MSK, Memorial Sloan Kettering; MUT, mutation; TMB, tumor mutational burden; WT, wild type.

Results of immune characteristic analysis for PIK3CA-mutated and wild type groups

To evaluate the quality and quantity of tumor-infiltrating lymphocytes (TILs), we used the ssGSEA algorithm to analyze the composition and proportions of 24 immune cell types in the TME (Figure 3A). Analysis of the relevant matrix of immune cells showed that their abundance was correlated with each other, with NK CD56 bright cells and Th2 cells showing the most negative correlation (r=−0.41), and T cells and cytotoxic cells showing the most positive correlation (r=0.86; Figure 3B). The scores of activated dendritic cells (aDC), B cell, cytotoxic cells, DC, immature DC (iDC), T cells, T follicular helper (TFH), Th1 cells, regulatory T cells (Treg) were higher in the mutant group than in the wild group (Figure 3C,3D). The analysis of ESTIMATE indicated that the PIK3CA-mutated group had higher immune score, stromal score and ESTIMATE score (Figure 3E). These results all suggested that the immune state was activated in the samples with mutation, which may have potential implications for improved prognosis and better immunotherapy outcome.

Figure 3 Analysis of immunoinfiltrating cells. (A) Immunoinfiltrating cell analysis of 525 EC patients from TCGA database. (B) Correlation analysis of immune infiltrating cells. (C) Differential analysis of immune infiltrating cells for mutation and wild type groups. (D) Immunoinfiltrating cell analysis of mutation and wild-type groups. (E) Comparison of stromal score, immune score and ESTIMATE score. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. aDC, activated dendritic cell; DC, dendritic cell; EC, endometrial cancer; iDC, immature dendritic cell; MUT, mutation; NK, natural killer; pDC, plasmacytoid dendritic cell; TCGA, The Cancer Genome Atlas; Tcm, T central memory; Tem, T effector memory; TFH, T follicular helper; Tgd, T gamma delta; Treg, regulatory T cells; WT, wild-type.

Gene expression analysis of TCGA-UCEC cohort

We analyzed the DEGs between the mutant group and the wild type group, and obtained 50 DEGs, including 47 up-regulated genes and three down-regulated genes (Figure 4A). The expression heat map of these 50 genes in 525 samples was shown in Figure 4B. These 50 differential genes receive GO and KEGG pathway enrichment analysis. BP in GO analysis enriched a variety of immune-related pathways, especially B cell related functions, including B cell activation, B cell mediated immunity, regulation of B cell activation, positive regulation of B cell activation, B cell receptor signaling pathway (Figure 4C). The biological processes and signaling pathways enriched for molecular function (MF), cellular component (CC), and KEGG were shown in Figure 4D. We successfully screened the DEGs of PIK3CA gene mutation in EC and preliminarily revealed its function and related pathways.

Figure 4 Functional analysis of differentially expressed genes. (A) Volcano map of differentially expressed genes. (B) Heat map of differentially expressed genes. (C,D) Gene Ontology analysis of differentially expressed genes and Kyoto Encyclopedia of Genes and Genomes. BP, biological process; CC, cellular component; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function; MUT, mutation; WT, wild-type.

Construction and validation of risk model for prognosis

Cox univariate regression analysis screened out 11 key PIK3CA mutation-related genes that were significantly associated with OS (Figure 5A). A prognostic risk model including three genes was constructed by LASSO-Cox regression algorithm. Finally, three key genes were identified-immunoglobulin heavy chain α1 (IGHA1), cellular retinoid-binding protein 1 (CRABP1), and somatostatin (SST) (Figure 5B,5C). Risk score =−0.00334034603595414 × IGHA1 + 0.0918110297278766 × CRABP1 + 0.0580053380259694 × SST. The training cohort was divided into high and low risk groups with the optimal cut-off value, and KM analysis showed that the high risk group had worse prognosis (P<0.001; Figure 5D). The risk model predicts a 1-, 3-, and 5-year AUC of 0.77, 0.74, and 0.81, respectively (Figure 5E). Heat map of the survival states of the three genes in the risk model were also shown in Figure 5F. The high risk group in the validation set had significantly shorter OS (P=0.005; Figure 5G).The AUC values of the risk model in the validation set were 0.73, 0.69 and 0.72 respectively for the predicted survival of 1-, 3- and 5-year (Figure 5H). The expression trend of the three genes in the validation set was consistent with that in the training set (Figure 5I). A prognostic risk model based on three key genes of PIK3CA gene mutation was constructed, which can effectively stratify the risk of patients, and patients in the high-risk group have worse prognosis.

Figure 5 Construction of a prognostic risk model in EC. (A) Genetic screening of constructed model. (B,C) LASSO regression to construct prognostic signature. (D-F) KM curve, ROC, expression heat map of the risk model in training set. (G-I) The KM curve, ROC curve, and expression heat map of the risk model in the validation set. AUC, area under the curve; CI, confidence interval; DEG, differentially expressed gene; EC, endometrial carcinoma; H, high; HR, hazard ratio; KM, Kaplan-Meier; L, low; LASSO, least absolute shrinkage and selection operator; OS, overall survival; ROC, receiver operating characteristic.

Construction of a nomogram model

We collected samples with age, mutation information, molecular subtypes, TMB information from 525 patients, and finally screened 502 samples. Univariate regression analysis identified significant differences between age, mutation status of PIK3CA, molecular subtype, TMB, and risk model with OS (Table 1). Clinical features and risk score were integrated to construct a nomogram, 1-, 3- and 5-year outcomes of prognosis can be predicted (Figure 6A). Notably, the calibration curves exhibited an excellent level of goodness-of-fit (Figure 6B). Based on the Nomo model, the samples were divided into high and low groups according to the optimal cut-off value. Patients with high scores had significantly worse prognosis (P<0.001; Figure 6C). The ROC curve also proved that the nomogram model had high accuracy in predicting 1-, 3- and 5-year prognosis, with AUC above 0.7 (Figure 6D). Nomo model had the largest AUC value and the highest accuracy of the six factors in predicting prognosis (Figure 6E). These results showed that our constructed nomogram was helpful to predict the prognosis of patients with EC.

Figure 6 Construction of the nomogram. (A) Nomogram for predicting the probability of 1-, 3-, and 5-year OS for EC patients. (B) Calibration plot of the nomogram for predicting the probability of OS at 1-, 3-, and 5-year. (C) Kaplan-Meier curves of OS in nomogram risk score. (D) Time-dependent ROC curve analyses of the nomogram model. (E) The nomogram model had better prognostic value than other clinical features. AUC, area under curve; CI, confidence interval; EC, endometrial carcinoma; FPR, false positive rate; H, high; HR, hazard ratio; L, low; MSI, microsatellite-instable; MUT, mutation; OS, overall survival; ROC, receiver operating characteristic; TMB, tumor mutational burden; TPR, true positive rate; WT, wild-type.

Discussion

The survival analysis conducted in this study highlights the significant prognostic implications of specific gene mutations in EC. Molecular and genomic profiling analysis of endometrial cancer is becoming increasingly popular. It has been reported in the literature that the L1 cell adhesion molecule (L1CAM) often mutates in endometrial cancer and is related to prognosis (21,22). In this study, mutations in TP53 were associated with a poor prognosis, as indicated by a HR greater than one. This finding aligned with existing literature that underscored the role of TP53 as a critical tumor suppressor gene, where its inactivation was frequently linked to aggressive tumor behavior and unfavorable clinical outcomes in various malignancies, including EC (23,24). Conversely, mutations in PIK3CA, ARID1A, MUC16, and CTCF were identified as protective factors, suggesting that these alterations may confer a survival advantage to patients (25). The analysis of mutation-related results in our study highlighted the significant role of PIK3CA mutations in EC and their implications for patient prognosis. Notably, PIK3CA emerged as a gene of interest due to its association with favorable OS outcomes, contrasting with the adverse prognostic implications of TP53 mutations. In endometrial cancer, the four classic types of classification have relatively clear meanings. POLE suggests a good prognosis and may be exempt from adjuvant therapy. TP53 indicates strong invasiveness and requires intensive treatment (4). The PIK3CA mutation is common but highly heterogeneous, and has weak guiding significance for prognosis and treatment selection. Usually, it needs to be comprehensively evaluated in combination with other markers (such as PTEN, MSI). This dichotomy underscored the complexity of the mutational landscape in EC, where certain mutations may confer protective effects while others exacerbate disease severity. The observed correlation between PIK3CA mutations and increased TMB suggested a potential link to heightened immunogenicity, which may enhance the efficacy of immunotherapeutic strategies. The higher immune scores and stromal scores in PIK3CA-mutated tumors, as evidenced by the ESTIMATE algorithm, further support the notion that these tumors may possess a more favorable immune microenvironment, potentially leading to improved outcomes following immunotherapy. The differential expression analysis revealed a robust immune-related gene signature associated with PIK3CA mutations, particularly in B cell activation pathways. This finding aligned with the hypothesis that PIK3CA mutations may enhance the immune response, thereby providing a rationale for exploring targeted immunotherapeutic approaches in this subset of patients (13). Collectively, these results not only elucidated the prognostic significance of PIK3CA mutations in EC but also paved the way for future investigations aimed at harnessing the immune landscape for therapeutic benefit. The integration of mutation status into clinical decision-making could ultimately refine prognostic models and guide personalized treatment strategies for patients with EC. The findings of our study elucidated a potential causal relationship between PIK3CA mutations and the immune landscape in EC, which may have significant implications for patient prognosis and therapeutic strategies. The observed association between PIK3CA mutations and enhanced TMB aligned with previous research indicating that hypermutant tumors often exhibit increased immunogenicity, thereby potentially improving responses to immunotherapy (26). This was particularly relevant given the higher immune scores and stromal scores observed in the PIK3CA-mutated group, suggesting an activated immune microenvironment that may facilitate better clinical outcomes (27,28). The correlation between PIK3CA mutations and the frequency of the POLE subtype further supported the notion that specific genetic alterations can influence tumor biology and patient survival (29,30). Moreover, the differential expression analysis revealed a significant enrichment of immune-related pathways, particularly those associated with B cell activation and function. This finding was consistent with the hypothesis that PIK3CA mutations may enhance the immune response through the modulation of immune cell infiltration, as evidenced by the higher abundance of various immune cell types in the mutant group. The interplay between PIK3CA mutations and immune cell dynamics warrants further investigation, as it may uncover novel therapeutic targets and strategies for improving immunotherapy efficacy in EC patients.

Moreover, the identification of three key genes—IGHA1, CRABP1, and SST within our risk model highlighted the intricate interplay between genetic alterations and clinical outcomes in EC. IGHA1 had been relatively poorly studied in endometrial cancer. However, the role of other members of the immunoglobulin family in cancer suggested that IGHA1 may be involved in the onset and development of endometrial cancer by influencing the immune response and inflammatory processes. Further studies were needed to uncover the specific mechanism of action of IGHA1 in endometrial cancer. CRABP1 was thought to have an important biological role in a variety of cancers. Although the specific role of CRABP1 in endometrial cancer had not been fully defined, its studies in other cancers suggested that CRABP1 may affect cancer progression by regulating cell proliferation, migration and apoptosis (31). In breast cancer, CRABP2 (a protein related to CRABP1) has been found to regulate drug sensitivity, suggesting that CRABP1 may have a similar function in EC (32). Studies on SST had shown that its expression level in EC was closely related to patient prognosis (33). In addition, SST may be involved in the development and progression of endometrial cancer by regulating cell adhesion molecules and chemokine signaling pathways (33). The inclusion of these genes, particularly in the context of PIK3CA mutations, suggested a potential mechanistic pathway that warrants further exploration. The differential expression of these genes, as evidenced by our analysis, may reflect underlying biological processes that contribute to tumor behavior and patient prognosis. The integration of clinical features and molecular data into a nomogram model further enhance the precision of prognosis prediction, as evidenced by the excellent calibration curves and significant differences in OS between high and low-risk groups.

The discovery of PIK3CA mutations challenged the results of EC, and may prevent unnecessary treatment, some patients as a result of our data is based on the analysis and research of public databases, remains to be further validation in clinical. This approach not only reinforces the prognostic value of PIK3CA mutations but also emphasizes the importance of personalized medicine in the management of EC. Collectively, these findings advocate for the incorporation of PIK3CA mutation status into clinical decision-making frameworks, thereby facilitating tailored therapeutic interventions that could improve patient outcomes in EC.

Our results suggested that PIK3CA mutations not only serve as prognostic markers but also play a pivotal role in shaping the immune landscape of EC. This relationship underscores the importance of integrating genetic profiling with immune characterization to develop personalized treatment approaches that leverage the unique molecular features of each tumor. Future studies should aim to elucidate the mechanistic pathways linking PIK3CA mutations to immune modulation, which could ultimately enhance the therapeutic arsenal against EC. In the validation of our risk model not only reinforces the prognostic implications of PIK3CA mutations in EC but also opens avenues for personalized treatment strategies. ARID1A is also a highly mutated gene in endometrial cancer. In subsequent studies, we defined patients with co-mutation of PIK3CA and ARID1A as a unique subtype and found that they have a better prognosis and are highly correlated with immunotherapy (34). Future investigations should focus on the functional roles of the identified genes and their potential as therapeutic targets, thereby enhancing the clinical management of EC patients.

Conclusions

Our study highlighted the PIK3CA mutations as potential biomarkers of EC patients stratified. Patients with EC who carry PIK3CA mutations have a better prognosis, with higher TMB scores and higher infiltration of immune cells in the microenvironment.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-704/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-704/prf

Funding: This study was supported by the National Natural Science Foundation of China (grant No. 81602020), the Tianjin Medical University Cancer Institute & Hospital Research Project (grant No. 1805), and Tianjin Binhai New Area Health Research Project (grant No. 2024BWKZ09).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-704/coif). All authors report that this study was supported by the National Natural Science Foundation of China (grant No. 81602020), the Tianjin Medical University Cancer Institute & Hospital Research Project (grant No. 1805), and Tianjin Binhai New Area Health Research Project (grant No. 2024BWKZ09).

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. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

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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