Upregulation of IL-12 Signaling Genes Mediated by STAT4 Predicts a Complete Clinical Response in Patients with Squamous Cell Carcinoma of the Esophagus who Undergo Concurrent Chemoradiotherapy

Nattida Sapsamarn, M.D.1,2, Asada Methasate, M.D., Ph.D.3, Pariyada Tanjak, Ph.D.3,4, Thanawat Suwatthanarak, Ph.D.3,4, Onchira Acharayothin3, Kullanist Thanormjit3,4, Jirawat Swangsri, M.D., Ph.D.3, Thammawat Parakonthun, M.D.3, Tetsuya Taga, M.D., Ph.D.5, Vitoon Chinswangwatanakul, M.D., Ph.D.3,4,*

1Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok. 2Institute of Science Tokyo, Japan. 3Division of General Surgery, Department of

Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand. 4Siriraj Cancer Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand. 5Department of Stem Cell Regulation, Medical Research Institute, Institute of Science of Tokyo, Tokyo, Japan.

*Corresponding author: Vitoon Chinswangwatanakul E-mail: vchinswa@gmail.com

Received 20 December 2024 Revised 5 Jaunary 2025 Accepted 9 Jaunary 2025 ORCID ID:http://orcid.org/0000-0001-9662-1669 https://doi.org/10.33192/smj.v77i7.272789

All material is licensed under terms of the Creative Commons Attribution 4.0 International (CC-BY-NC-ND 4.0) license unless otherwise stated.

INTRODUCTION

Esophageal cancer is the seventh most common cancer worldwide. It ranks as the seventh most frequently occurring cancer in men and the thirteenth most common cancer in women.1 In Thailand, esophageal cancer is the thirteenth leading cause of cancer-related death. There are two main pathological types of esophageal cancer: squamous cell carcinoma (SCC), which can occur in the cervical, thoracic, or abdominal regions of the esophagus, and adenocarcinoma, which can develop at the junction of the esophagus and stomach (esophagogastric junction). There are various multimodal treatment options available for esophageal SCC, including endoscopic resection, primary esophagectomy, neoadjuvant chemotherapy or chemoradiotherapy (CRT) followed by surgery, and definitive CRT. The treatment of choice depends on the location and stage of the tumor.

There are two main types of immune resistance mechanisms: innate immunity and adaptive immunity. The mechanism that primarily targets tumors is adaptive immunity. Tumors can trigger specific adaptive immune resistance, which may prevent host immune response to limit their growth and spread. This immune response largely involves T cells, particularly CD8+ cytotoxic T lymphocytes (CTLs). Many tumors are surrounded by infiltrates of mononuclear cells, including T lymphocytes and macrophages. Activated lymphocytes and macrophages can also be found in the lymph nodes that drain the areas where tumors are located. The key cells involved in important cancer prevention mechanisms are T cells, macrophages, and natural killer cells.2

T cells, specifically T helper cells, are categorized into two types: Th1 and Th2 cells. Th1 cells secrete several cytokines, including IFN-gamma, IL-2, IL-3, TNF-alpha, TNF-beta, and the adjustment factor IFN-gamma. In contrast, Th2 cells produce cytokines such as IL-4, IL- 5, IL-9, IL-10, and IL-13, especially those that regulate IL-4, IL-5, and IL-13.2

Interleukin-12 (IL-12) is a heterodimeric cytokine composed of two subunits: p40 and p35.3 It is considered a proinflammatory cytokine and is produced by antigen- presenting cells such as dendritic cells and macrophages. IL-12 plays a significant role in facilitating effective antitumor immune responses. IL-12 signals through its receptors, IL-12R1 and IL-12R2, which are expressed on target cells. This signaling pathway activates downstream pathways involving Jak2 and Tyk2, leading to the phosphorylation and homodimerization of STAT4.3 These processes are essential for the recruitment and effector functions of CD8+ T cells and natural killer cells. Given its capabilities, IL-12 is a strong candidate for immunotherapy-based interventions, as it enhances the activity of tumor-specific cytotoxic natural killer and CD8+ T cells, both of which are critical for killing tumor cells. However, systemic administration of IL-12 can be quite toxic. Therefore, alternative administration methods are being explored.3-9 IL-12 signaling, which is mediated by STAT4, is a crucial pathway in the immune system that regulates the response to infections. IL-12 is produced by dendritic cells and macrophages and then binds to its receptor on T cells and natural killer cells before activating JAK kinases. This activation leads to the

phosphorylation of STAT4, which then dimerizes and translocates into the nucleus to promote gene expression. This pathway enhances the differentiation of Th1 cells and stimulates the release of IFN-γ, which is crucial for eliminating pathogens. Dysregulation of this pathway is associated with autoimmune diseases but can also enhance anticancer responses, indicating potential implications for cancer therapy in the future.10,11

A clinical complete response (cCR) occurs when no tumor remnants are detectable through nonsurgical methods, as proven by imaging studies such as esophagography, computed tomography (CT) scans, endoscopy, or positron emission tomography-CT following CRT. Neoadjuvant chemoradiotherapy increase survival rates in patients with locally advanced esophageal carcinoma. According to Monjazeb et al., achieving a cCR after concurrent CRT (CCRT) can lead to a more favorable prognosis.12 A pathological response study in another type of cancer, specifically CA rectum, reported that downstaging of the T-category was observed in 10 patients (55.6%), while downstaging of the N-category was noted in 14 patients (77.8%) following neoadjuvant chemoradiation.

MATERIALS AND METHODS

1. Study cohort

   A total of 800 patients with SCC of the esophagus were enrolled in this study. All patients were over 18 years of age at the time of diagnosis. Among these patients, 36 met the study criteria. The remaining patients were excluded from our analyses. The datasets collected included various aspects of patient information and clinical data, which were part of the inclusion criteria. These datasets comprised demographic details such as age at disease onset, sex, and underlying conditions; treatments received following CCRT, categorized as surgical or nonsurgical; and laboratory results obtained prior to CCRT, including complete blood count, absolute neutrophil count, absolute lymphocyte count, neutrophil-lymphocyte ratio, absolute eosinophil count, platelet count, and platelet-lymphocyte ratio. Additional information included tumor location within the thoracic region (classified as upper, middle, or lower), tumor differentiation based on biopsy results (poorly differentiated, moderately differentiated, or well- differentiated), tumor staging, and lymph node status as per the National Comprehensive Cancer Network (NCCN) guidelines, details on preoperative radiation (dose and fractionation), preoperative chemoradiotherapy, and recurrence following CCRT. All patients included in the study were diagnosed with Stage 3 disease, meeting the inclusion criteria as defined by the NCCN guidelines.

   Sample selection and specimen retrieval

   The protocol used for the selection and retrieval of specimens was as follows:

   • All samples must come from patients who meet the inclusion criteria.

   • Clinical data for potential samples will be collected through reviews of outpatient department and inpatient department documents.

   • Patient clinical information from operative records, as well as outpatient department and inpatient department documents, will be reviewed. Slides from formalin-fixed, paraffin-embedded (FFPE) blocks of tissue biopsies obtained from esophagogastroduodenoscopy will be obtained from the pathology department.

2. Treatment

   All patients underwent upper gastrointestinal endoscopy along with a biopsy of the lesion to confirm a diagnosis of SCC. Patients received a combination of chemotherapy and radiation therapy. Imaging studies were subsequently conducted via posttreatment sequential CT scanning at approximately 4- to 6-week intervals. After being diagnosed with esophageal SCC, patients were treated with a combination of chemotherapy and radiation therapy. The chemotherapy regimens were divided into three different formulas: Formula 1: paclitaxel and carboplatin; Formula 2: cisplatin and 5-FU; and Formula 3: carboplatin and 5-FU. Radiation therapy was administered at doses ranging from 50–60 Gy. Following the chemotherapy and radiation treatments, patients who underwent CT scans were categorized into two groups on the basis of their response: (1) clinical complete response (cCR) and (2) non-clinical complete response (non-cCR). These categories were further classified according to specific criteria.

   
   

3. Laboratory methods for RNA extraction from FFPE tissue

   Deparaffinization

   FFPE slides were cut into 5-micron-thick sections, resulting in a total of 5 cuts. Deparaffinization was performed by adding 1000 µL of xylene, followed by vortexing the mixture. Next, 400 µL of absolute ethanol was added, and the mixture was vortexed again. The sample was then centrifuged in RCF mode at 16000 × g for 2 minutes. The supernatant was discarded. Subsequently, 1000 µL of absolute ethanol was added to the pellet, which was vortexed. The sample was centrifuged a second time in RCF mode at 16,000 × g for 2 minutes, and the

   supernatant was discarded. Finally, the samples were dried in a heating box at 55 °C for 50 minutes.

   RNA extraction and qualification

   The process of extracting RNA from FFPE tissue via a Veracyte RNA Extraction Kit (Veracyte, San Francisco, CA, USA) began with the preparation of essential solutions. This involved adding ethanol to the wash solution and reconstituting DNase I with nuclease-free water, which was kept on ice. During the isolation phase, lysis buffer and proteinase K were added to the tissue sample. The mixture was then incubated at 56 °C and 80 °C to facilitate tissue breakdown and RNA release. Afterward, the sample was cooled on ice until it reached room temperature, followed by brief centrifugation to prepare for DNase treatment. Finally, DNase I was added to eliminate any DNA contaminants, ensuring high RNA purity for subsequent analyses. The RNA concentration and purity were measured via a Nanodrop 8000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). RNA purity was assessed via the A260/A280 ratio, which was required to be not less than 2.0.

   Gene expression and experiments

   This study utilized an nCounter Pan-Cancer Immune Profiling Panel (NanoString Technologies, Seattle, WA, USA). This panel includes 40 reference genes as well as 730 targets related to immuno-oncology that feature 109 cell surface markers and represent 14 immune cell types.

   After RNA extraction, immune transcriptome profiling was performed via the nCounter PanCancer Immune Panel. NanoString assays were performed with 100 ng of RNA, 8 µL of reporter probe, and 2 µL of capture probe per sample. The hybridization reaction was performed for 24 h at 65 °C in a thermocycler.

   NanoString data analysis

   The nSolver Analysis Software (version 4, NanoString Technologies) was utilized to assess quality control parameters, including binding density, limit of detection, and positive controls. No quality control issues were identified, allowing all 36 samples to proceed for further analysis. The advanced analysis module of the nSolver software was employed for housekeeping gene selection, data normalization, differential expression analysis, and calculation of immune-oncology-related scores. Differential gene expression analysis was conducted to identify the transcriptomic differences between patients with cCR and those with incomplete clinical responses. Rosalind software (NanoString Technologies) was subsequently

   used to compare the gene expression in the two patient groups. Differentially expressed genes were identified via the software. The threshold was set at a fold change greater than 1.5 combined with a p-value of < 0.05 to determine significant differentially expressed genes. To account for multiple t tests, the classical Benjamini–Hochberg method was applied to adjust p-values, controlling the false discovery rate and producing adjusted p-values.

   Gene set, pathway, and function analyses

   Gene set, pathway, and functional analyses were conducted via gene set analysis with a directed global significance score based on NanoString annotations version 46. Gene set analysis was used to evaluate the overall significance of functionally related gene sets, with the global significance score reflecting cumulative evidence for differential gene expression in a pathway through the square root of the mean squared t statistic. The directed global significance score considered the direction of over- or underexpression on the basis of t statistics. Biological networks were examined via Rosalind and Qiagen Ingenuity Pathway Analysis (Qiagen, Hilden, Germany). Fisher’s exact test was used to calculate enrichment p values, whereas Z scores were used to predict the activation or inhibition of molecular functions.

4. Statistical analysis

Non-normally distributed data were presented using the median and interquartile range (IQR) and analyzed with the Mann-Whitney test. Categorical data were analyzed using the Chi-square test and reported as proportions (percentages). All statistical analyses were performed using Stata version 19.1.

RESULTS

Patient characteristics

The characteristics of the eligible patients are summarized in Table 1. The median age of patients in the cCR group and the non-cCR group was 64 years. Many of these patients had underlying conditions, including hypertension and diabetes. In terms of treatment, the cCR group consisted of 10 patients, some of whom underwent surgery. Similarly, the non-cCR group consisted of 8 patients, with a comparable distribution of surgical and nonsurgical treatments.

Data collection from laboratory results revealed no statistically significant differences between the two groups. The laboratory findings included the complete blood count, absolute neutrophil count, absolute lymphocyte count, neutrophil‒lymphocyte ratio, absolute eosinophil count, platelet count, and platelet‒lymphocyte ratio.

TABLE 1. Characteristics of patients with a cCR and those with a non-cCR.

TABLE 1. Characteristics of patients with a cCR and those with a non-cCR. (Continue)

Abbreviations: 5FU, 5-Fluorouracil; CBC, Comeplete Blood Count; cM, clinical Metastasis; CMT, Chemotherapy; DM, Diabetes Mellitus; Fr, Fraction; Gr, Gray; HT, Hypertension; IQR, Interquatile Rang; LN, Lymph node; NLR, NeutrophiL‒Lymphocyte Ratio; PLR, Platele‒ Lymphocyte Ratio; RT, Radiotherpy; SD, Standard Deviation; U/D, Undelying Disease

In the cCR group, the majority of tumors were located in the upper and middle thoracic areas. In terms of tumor differentiation, moderate differentiation was most prevalent in the cCR group, with 16 patients, compared with 13 patients in the non-cCR group.

All patients in this study were at clinical stage 3. The most common tumor stage in both groups was T3. The lymph nodes were categorized into clinical lymph node–positive and lymph node–negative subgroups. There were 20 patients (55%) in the cCR group and 16 patients (44%) in the non-cCR group. None of the patients had metastasis before treatment.

The radiation dose for patients in the cCR group was 52 Gy. For those in the non-cCR group, the dose was 51 Gy. Three chemotherapy regimens were used:

(1) paclitaxel and carboplatin were administered to 9 patients (25%) in the cCR group and 4 patients (11%) in the non cCR group; (2) cisplatin and 5-FU were given to 6 patients (16.6%) in the cCR group and 9 patients (25%) in the non cCR group; and (3) carboplatin and

5-FU were provided to 5 patients (13.89%) in the cCR group and 3 patients (8.33%) in the non-cCR group.

In terms of follow-up after treatment, there was a statistically significant difference in local recurrence (p = 0.049) and single recurrence (p = 0.014). The incidence of recurrence was greater in the non-cCR group than in the cCR group.

Table 2 presents a list of differentially expressed genes organized into upregulated and downregulated groups. On the left side, the “Up” section features 72 genes whose expression increased (highlighted by the red background). Some of these genes are Interleukin 12 Receptor Subunit Alpha (IL12RA), C-X-C Motif Chemokine Receptor 6(CXCR6), C-C Motif Chemokine Receptor 5(CCR5), Tumor Necrosis Factor Receptor Superfamily Member 12A (TNFRSF12A), and Inducible T Cell Costimulator(ICOS). On the right side, the “Down” section highlights 13 genes with decreased expression (indicated by a green background). Examples of these genes are Chitinase 1(CHIT1), Interleukin 10(IL10), Fc Fragment of IgE

Receptor IA (FCER1A), and Semenogelin1(SEMG1). This table helps identify key genes that may contribute to different biological responses, highlighting them as potential targets for further research

The heatmap in Fig 1 displays the expression levels

of various genes across patient groups. The horizontal axis represents the patients, with the blue axis indicating those who achieved a cCR and the orange axis denoting the non-cCR group. The vertical axis represents the individual genes analyzed in the study. The blue shading

TABLE 2. In patients with a cCR, the differentially expressed genes were categorized into an upregulated group (72 genes, shown in red) and a downregulated group (13 genes, shown in green).

Fig 1. A heatmap displaying gene expression across patients, where blue represents clinical complete response (cCR) and orange indicates non-clinical complete response (non-cCR). The blue shading indicates downregulated genes, the orange shading represents upregulated genes, and the white shading signifies no change in expression.

(cool) indicates downregulated genes, whereas the orange shading (warm) indicates upregulated genes. The white cells presented no significant change in gene expression. The dendrograms on the top and left show clustering, with clustering for the top group based on the patient response profiles and clustering on the left based on the gene expression. The black markers highlight specific genes with notable expression changes and high statistical significance, suggesting potential key indicators for treatment response.

The volcano plot in Fig 2 compares gene expression between patients who achieved a cCR and those with a non-cCR. The x-axis represents the log2-fold change, indicating the ratio of gene expression between the cCR and non-cCR groups. Positive values on the right side of the x-axis represent genes with increased expression in the cCR group, whereas negative values on the left

side denote genes with decreased expression. The y-axis displays the -log10 p-value, which represents the probability that the observed changes occurred by chance. Higher points on this axis signify changes that are statistically more significant.

The pathway interaction database in Fig 3 illustrates the signaling pathways in immune cells, with bars highlighting changes in pathway activity that are either upregulated (shown in red) or downregulated (shown in green) on the basis of adjusted p-values. The analyzed pathways included “IL12 signaling mediated by STAT4,” “downstream signaling in naive CD8+ T cells,” “TCR signaling in naive CD8+ T cells,” “IL12-mediated signaling events,” “caspase cascade in apoptosis,” and “TCR signaling in naive CD4+ T cells.” The length of each bar represents the adjusted p value, indicating the significance level of each pathway. This visualization emphasized the activity

Fig 2. A volcano plot comparing gene expression between patients who achieved a clinical complete response (cCR) and those who did not achieve a complete response (non-cCR). Genes with increased expression in the cCR group are represented as green points on the right side of the plot, whereas genes with decreased expression are shown as purple points on the left side. The height of each point indicates the level of statistical significance, with taller points representing greater significance.

Fig 3. Pathway interaction database illustrating immune cell signaling pathways. The figure highlights upregulated activities in red and downregulated activities in green on the basis of adjusted p-values. The pathways that are represented include T-cell signaling and apoptosis, with the lengths of the bars indicating the significance levels of each pathway.

and suppression of specific pathways, particularly those involved in T-cell signaling and apoptosis.

The Pathways/BioPlanet chart in Fig 4 illustrates immune signaling pathways associated with the body’s immune response, which are categorized into upregulated pathways (in red) and downregulated pathways (in green). All the signals presented a p-value of less than 0.05, indicating statistical significance. The analyzed pathways included “T-cell activation co-stimulatory signal,” “leptin influence on the immune response,” “interleukin-12/STAT4 pathway,” “T-cell receptor and CD3 complex,” “CTL-mediated immune response against target cells,” and “PD-1 signaling”. The length of each bar represents the significance level of either upregulation or downregulation in each pathway. The chart reveals that the “leptin influence on immune response” pathway is the most upregulated pathway, whereas the “interleukin-12/ STAT4 pathway” is partially downregulated. This provides a comprehensive overview of variations in the immune response across different pathways, highlighting the importance of T-cell signaling and immune regulation.

Genes, biological processes, and signaling pathways

To further investigate the different molecular functions at each resection margin, we conducted a functional analysis of differentially expressed genes with p- values < 0.05 via IPA. Our analysis revealed a total of 85 significantly 72 upregulated genes (positive fold change values) and 13 downregulated genes (negative fold change values) compared with cCR and non-cCR. The analysis highlights key genes and pathways involved in immune regulation and inflammation. Central genes such as TNF, IL6, IL1A, and IL1B play pivotal roles in cytokine-mediated signaling, particularly in pathways such as cytokine storm signaling and Th1/Th2 activation.

These processes drive immune responses, including the activation of cytotoxic T cells, monocyte adhesion, and the release of reactive oxygen species, which are essential for inflammation and defense mechanisms. Additionally, genes such as RELA (part of the NF-kappa B pathway) and IFNG/IL17A underscore the importance of adaptive immunity. These are depicted in Fig 5.

DISCUSSION

This study demonstrated that patients who achieved a cCR had higher levels of IL-12 than those with a non- cCR. These findings suggest potential applications for these two groups of patients. The first group included patients who were not suitable for surgery due to their physical condition. For these patients, achieving a cCR could help reduce complications associated with the tumor mass, such as bleeding or difficulty swallowing. The second group consisted of patients eligible for surgery, where IL-12 could be used as an adjuvant therapy. As a result, the present research could improve treatment options for both groups of patients. Further studies are necessary because this research was retrospective and limited by the availability of biopsy tissue, which must be collected before CCRT. This limitation may reduce the number of patients who meet the criteria. Future research could expand the patient sample size and use a prospective design, enabling the collection of both fresh and paraffin-embedded biopsy tissues. These samples could then be utilized for RNA extraction and further analysis via Geomax technology, allowing for more

accurate calculations.

The current review highlights that IL-12 has been utilized in the treatment of several cancer types,13 including melanoma,4,14-22 based on certain studies, as well as in gynecologic cancer10,12,22-29 and breast cancer,8,14,25,30-40 as

Fig 4. The Pathways/BioPlanet chart illustrates the immune signaling pathways involved in the body’s immune response. Pathways that are upregulated are indicated in red, whereas those that are downregulated are shown in green. All signaling pathways had p-values less than 0.05. Notably, the “leptin influence on immune response” pathway was the most significantly upregulated pathway, whereas the “interleukin-12/STAT4 pathway” was partially downregulated.

Fig 5. The diagram highlights critical immune pathways and key genes, such as TNF, IL6, IL1A, and IL1B, which regulate cytokine storms, Th1/Th2 pathways, and inflammation. These processes activate T cells, monocyte adhesion, and reactive oxygen species essential for immune responses.

indicated by other research. However, the use of IL-12 should be approached with caution due to potential side effects, such as flu-like symptoms, which some patients may find intolerable.14 Various methods of administering IL-12 have been explored to mitigate these side effects.36 Due to the severe side effects associated with systemic treatment via intravenous injections, alternative methods for administering IL-12, including intravenous injection, subcutaneous injection, intramuscular injection, and intratumoral injection, have been developed.36 Future studies may focus on the intratumoral injection of IL-12, an adjuvant for cancer vaccines,37 which could increase the effectiveness of treatment and potentially improve outcomes in patients with SCC of the esophagus. This recent discovery could prove beneficial in the future by improving treatment effectiveness for patients, particularly by increasing the rate of cCR.

CONCLUSION

This study highlights significant differences in immune transcriptome profiles between patients with a cCR and those with a non-cCR to CRT in patients with SCC of the esophagus. The findings indicate that the upregulation of genes involved in IL-12 signaling, particularly those mediated by STAT4, may serve as

potential biomarkers for predicting favorable treatment responses. Patients who achieved cCR presented a lower incidence of distant recurrence than non- cCR patients did, indicating that immune transcriptomic characteristics may influence patient prognosis. The results provide a strong foundation for future research that leverages transcriptomic data to develop personalized treatment strategies, potentially improving patient outcomes in patients with esophageal cancer.

Data Availability Statement

The authors affirm that the data supporting the findings of this study are included within the article and its supplementary materials.

ACKNOWLEDGEMENTS

The authors would like to express their sincere gratitude to all individuals and organizations who contributed to the successful completion and publication of this research.

DECLARATION

Grants and Funding Information

No grants or funding applied.

Conflict of Interest

All authors declare no personal or professional conflicts of interest relating to any aspect of this study.

Registration Number of Clinical Trial

COA no. Si 203/2020

Author Contributions

Conceptualization and methodology, N.S, V.T., A.M., P.T., T.T.; Specimen collection, A.M., J.S. and

T.P.; Investigation, N.S., P.T., T.S., O.A., K.T.; Formal analysis, N.S. ,P.T. and T.S. ; Visualization and writing – original draft, N.S.; Writing – review and editing, N.S.,V.T. ; Funding acquisition, none ; Supervision, V.T. All authors have read and agreed to the final version of the manuscript.

Use of Artificial Intelligence

No artificial intelligence tools or technologies were utilized in the writing, analysis, or development of this research.

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