Understanding the Impact of Spatial Immunophenotypes on the Survival of Endometrial Cancer Patients through the ProMisE Classification

doi:10.21203/rs.3.rs-5241013/v1

Objectives

We focused on how the immunophenotypes based on the distribution of CD8-positive tumor-infiltrating lymphocytes (TILs) relate to the endometrial cancer (EC) molecular subtypes and patients’ prognosis.

Patients and Methods:

Two cohorts of EC patients (total n = 145) were analyzed and categorized using the Molecular Risk Classifier for Endometrial cancer (ProMisE): POLEmut (POLE mutation), MMRd (mismatch repair deficiency), NSMP (no specific molecular profile), and p53abn (p53 abnormality). CD8-positive TILs, within the central tumor and the invasive margin, were examined by using immunohistochemical staining and advanced image-analysis software. It was investigated whether these immunophenotypes correlate with the patients' survival and molecular subtypes. RNA sequencing (RNA-seq) was used to explore tumor-derived factors influencing these immunophenotypes.

Results

Three distinct immunophenotypes (inflamed, excluded, and desert) based on the CD8-positive TIL patterns were identified in EC patients. The prognosis was markedly poorer in the patients with the non-inflamed (desert or excluded) phenotype than in those with the inflamed phenotype. Notably, the majority of POLEmut subtypes showed the inflamed phenotype and all p53abn subtypes showed the non-inflamed phenotype, while three immunophenotypes were observed in MMRd and NSMP subtypes, although there was a significant distribution bias. The RNA-seq data showed that the expression of MYC target genes and type-1 interferon response genes was enriched in the non-inflamed phenotype in MMRd and NSMP subtypes respectively.

Conclusion

Evaluating not only the molecular classification but also the immunophenotype may lead to more personalized immunotherapy in EC and elucidating the mechanisms that underlie the formation of the three immunophenotypes could lead to the discovery of new immunotherapy targets.

endometrial cancer

tumor-infiltrating lymphocyte

ProMisE classification

immunophenotype

personalized immunotherapy

As of 2024, endometrial cancer (EC) ranks as the sixth most common malignancy in women globally, contributing significantly to cancer-related morbidity and mortality [1]. Despite recent advancements in the early detection of EC and its treatment modalities, a significant number of patients with EC continue to suffer from poor prognoses, particularly those with advanced-stage and high-grade disease [2, 3]. This persistent challenge underscores the limitations of current prognostic tools and therapies, thereby necessitating a more nuanced understanding of the molecular and immunological aspects of EC.

The Cancer Genome Atlas (TCGA) has been instrumental in identifying four key genomic subgroups in EC: POLE (ultramutated), microsatellite instability (MSI) (hypermutated), copy-number low (CN low) (endometrioid), and copy-number high (CN high) (serous-like) [4]. These findings have significantly advanced our approach to personalized EC treatments and prognoses. Notably, tumors with POLE mutations have shown better outcomes, even in high-grade cases, whereas the CN high subtype, particularly with TP53 mutations, often indicates a poorer prognosis [5, 6]. This diversity in outcomes among the EC subgroups underscores the necessity for additional markers to refine the risk stratification and personalize treatments [4]. The Proactive Molecular Risk Classifier for Endometrial cancer (ProMisE), based on TCGA's genomic subgroups, represents a significant step forward in this context [7, 8]. By integrating POLE exonuclease domain mutations and immunohistochemistry for mismatch repair proteins and TP53, the ProMisE categorizes EC into four distinct groups: POLE-EDM, MMR-D, p53wt, and p53abn [7, 8]. It extends TCGA's foundational work by providing comprehensive prognostic information, enhancing patient management within the International Federation of Gynecology and Obstetrics (FIGO) 2023 staging system [9, 10].

The distribution of tumor-infiltrating lymphocytes (TILs) was reported to be associated with the prognosis of patients with colorectal cancer in 2006 [11]. The concept of immunophenotypes (i.e., inflamed, excluded, and desert) based on the spatial distribution of CD8⁺ TILs, which recognized as vital players in modulating tumor progression and influencing responses to immunotherapies, was introduced in 2016 [12]. These immunophenotypes are known to affect the effectiveness of treatments, particularly treatment with immune checkpoint inhibitors (ICIs) in some solid tumors [12, 13]. In EC, the amount of CD8⁺ TILs has been reported to be associated with patients’ prognosis [14], however the association between the distribution of CD8⁺ TILs and clinical outcomes has not been investigated. Although ICIs are increasingly being applied clinically in the treatment of EC, research on the tumor immune microenvironment of EC is lacking.

We sought to bridge this knowledge gap by exploring the distribution and prognostic relevance of CD8⁺ TIL-based immunophenotypes in EC, stratified by ProMisE molecular classification. We hypothesized that each molecular subtype may be associated with a distinct immunophenotype, which could have significant implications for patient prognoses and treatment responses. Our goal is to integrate genomic and immunological data to provide a more holistic view of the EC landscape, potentially paving the way for novel, targeted immunotherapeutic strategies for EC.

Study design and patient cohorts

Cohort 1: Retrospective analysis regarding survival outcomes

We retrospectively analyzed the cases of a separate consecutive cohort of patients with high-grade EC (n = 85, including endometrioid grade 3, serous, and clear cell histology) treated between January 2002 and December 2017 at Nagoya University Hospital (Nagoya, Japan). Formalin-fixed paraffin-embedded (FFPE) tumor tissues were collected for analysis.

Cohort 2: Pros pective analysis regarding the immunophenotype-molecular classification relationship

The cases of 60 patients with EC treated during the period from January 2019 to December 2022 at Nagoya University Hospital were prospectively enrolled. Following a definitive pathological diagnosis, these patients underwent surgical intervention. Ethical approval of this study was granted by the Institutional Review Board of Nagoya University (IRB approval no.: 2018 − 0400). Both fresh frozen and FFPE tumor tissues were collected for analysis.

Diagnosis and treatment

All 145 of the patients underwent a simple or semi-radical hysterectomy with a bilateral salpingo-oophorectomy, pelvic and/or para-aortic lymphadenectomy. The surgical specimens were staged according to the FIGO 2008 staging system. Postoperative treatment followed the Japan Society of Gynecologic Oncology guidelines [15, 16]. Risk-based adjuvant therapy was administered; low-risk patients received no additional treatment, and the intermediate- to high-risk patients received radiotherapy or platinum-based chemotherapy.

Sanger sequencing for POLE exonuclease domain

Hotspot mutations in exons 9, 13, and 14 of the POLE gene were identified using Sanger sequencing. Genomic DNA extraction was conducted in accord with the manufacturers' protocols for both fresh frozen (NucleoSpin® DNA Rapidlyse, Macherey-Nagel, Düren, Germany) and FFPE tissues (QIAamp DNA FFPE Advanced UNG Kit, Qiagen, Hilden, Germany). The samples were enriched using the Blend Taq Plus (Toyobo, Osaka, Japan). The polymerase chain reaction (PCR) amplification and conditions were as described [17]. A portion of the PCR products was run on a 2% agarose gel in 1× TAE buffer to verify the presence of a single band approx. 200–300 base pairs in size. The rest of the PCR products was then purified using the QIAquick Gel Extraction Kit (Qiagen), following the manufacturer's instructions.

The DNA concentrations were measured with a NanoDrop One spectrophotometer (Thermo Fisher Scientific, Waltham, USA). The subsequent DNA sequencing was outsourced to Eurofins Genomics (Tokyo). For the sequence analysis, we used SnapGene Viewer ver. 6.1.2 (GSL Biotech, San Diego, CA) to examine the waveform patterns. Mutation identification was conducted using the Nucleotide BLAST tool (Basic Local Alignment Search Tool) from the U.S. National Center for Biotechnology Information (NCBI). In this study, we defined 'POLE pathogenic mutations' as the nine mutations on exons 9, 13, and 14: P286R (exon9), M295R (exon9), S297F (exon9), V411L (exon13), L424I (exon13), P436R (exon13), M444K (exon13), A456P (exon14), and S459F (exon14) [18].

Immunohistochemistry analysis

Immunohistochemistry (IHC) was conducted on 4-µm sections of FFPE tumor tissues, which included sections from both the central tumor (CT) and the invasive margin (IM). The primary antibodies used for the IHC included anti-human CD8 (clone C8/144b, Dako, Glostrup, Denmark; 1:100), PMS2 (clone A16-4, Biocare Medical, Walnut Creek, CA; 1:100), MSH6 (clone BC/44, Biocare Medical, 1:100), and p53 (clone DO-7, Dako; 1:100). The sections were deparaffinized and rehydrated, subjected to antigen retrieval in 10 mM sodium citrate (pH 6.0) or 1× Immunoactive (pH 9.0, Matsunami, Osaka, Japan) for 20 min at 95°C in a microwave, and treated with 0.3% hydrogen peroxide in methanol for 20 min after being washed with phosphate-buffered saline (PBS). Blocking was performed using the Histofine SAB-PO kit (Nichirei, Tokyo), followed by overnight incubation at 4°C with the diluted primary antibodies. After the primary antibody incubation, sections were washed in PBS, incubated with biotin-labeled secondary antibody, peroxidase-labeled streptavidin, and developed using 3,3'-diaminobenzidine (DAB) substrate-chromogen for specific time durations: 3 min for p53, 5 min for CD8, and 60 min respectively for PMS2 and MSH6. After DAB development, the sections were rinsed, counterstained with hematoxylin, dehydrated, and mounted.

Our application of immunohistochemistry for PMS2 and MSH6 was based on reports suggesting their effectiveness in screening for mismatch repair deficiency (MMRd) [19]. MMRd was identified by the complete absence of nuclear staining for either protein with internal positive controls including unaltered nuclear staining in adjacent normal endometrium, stromal cells, and inflammatory cells. Abnormal p53 staining (p53abn) was characterized as either a strong, diffuse nuclear staining pattern in > 80% of carcinoma cells, or a complete lack of staining ("null pattern"), using adjacent non-tumor cells as an internal control. Wild-type tumor cells exhibited weak and heterogeneous staining patterns [20].

Immunophenotyping

The assessment of TILs in this study followed the guidelines established by the International Immuno-Oncology Biomarker Working Group [21]. We did not differentiate between intra-tumoral TILs and stromal TILs during this evaluation. After capturing stained slide images with a VS120-S5 (Evident, Tokyo, Japan), CD8⁺ TILs were quantified automatically in both the CT and IM with the use of QuPath ver. 0.3.0 [22], as illustrated in Fig. 3A,B. This quantification was performed over five distinct areas, each measuring 0.25 mm² (0.0625 mm² per square). The average number of CD8⁺ TILs per square millimeter was calculated for these regions.

Tumors with a CD8⁺ TIL density ≥ 1,000 cells/mm² in both the CT and IM regions were classified as 'inflamed' phenotype. The tumors with a CD8⁺ TIL density < 1,000 cells/mm² in the CT but > 1,000 cells/mm² at the IM were designated as the 'excluded' phenotype. Conversely, tumors were classified as the 'desert' phenotype when the density of CD8⁺ TILs was < 1,000 cells/mm² in both the CT and IM areas.

The ProMisE molecular classification

We conducted the molecular classification of tumors using an adapted approach from the ProMisE methodology, aligned with the steps outlined in the World Health Organization (WHO) classification [8]. This approach involved a sequential assessment of specific molecular markers. Initially, all tumor samples underwent Sanger sequencing to identify mutations in the POLE exonuclease domain, specifically targeting exons 9, 13, and 14 as noted above. Tumors harboring pathogenic mutations in these regions were classified as 'POLEmut.' Next, the tumors exhibiting a complete absence of nuclear staining for PMS2 or MSH6 protein by IHC were categorized as 'MMRd.' Then, the tumors demonstrating abnormal p53 expression patterns by IHC were classified as 'p53abn.' Finally, tumors that did not exhibit any of the aforementioned molecular characteristics were classified as 'NSMP,' indicating a no specific molecular profile.

Survival analysis

We defined progression-free survival (PFS) as the duration from the initiation of a patient's treatment to the point of observed disease progression. Overall survival (OS) was determined as the time span from the commencement of treatment to either the death of a patient due to any reason or the patient's last confirmed survival status, with data collected up until March 2023. We used the Kaplan–Meier method to estimate the 145 patients' PFS and OS rates. This approach allowed us to generate survival curves, providing a visual and statistical representation of the survival probabilities over time for the patients in Cohort 1. The survival analysis was conducted exclusively for cohort 1 due to limitations in Cohort 2, i.e., the short follow-up period and the low number of recorded events.

RNA-sequencing analysis

The RNA-sequencing analysis was carried out on samples from the 40 of the 60 patients in Cohort 2 from whom RNA samples were available. The extraction of RNA from fresh frozen tumor tissue was done using the NucleoSpin RNA Plus kit (Macherey-Nagel), following the manufacturer's guidelines. We measured the total RNA concentration with the NanoDrop One spectrophotometer. RNA sequencing was then performed by Novogene Japan (Tokyo). The obtained raw FASTQ data were uploaded to Galaxy, an open-source web-based platform tailored for data-intensive biomedical research. Quality control of the data was executed using FastQC and Trimmomatic. The clean, paired-end data were then processed for gene expression quantification using the Kallisto quant tool, referencing the GENCODE GRC38.p13 transcript (genecode. v41.transcript).

Post-processing, the data were aggregated using the tximport package (ver. 1.18.0) in R software (ver. 4.0.3) and RStudio. For the subsequent analyses, scaled transcripts per million (TPM) counts were used. The TPM counts were processed with the use of the web portal for integrated differential expression and pathway analysis (iDEP) (iDEP 2.01; http://bioinformatics.sdstate.edu/idep, accessed April 30, 2024). We also used iDEP 2.01 for a principal component analysis (PCA). A gene set enrichment analysis (GSEA) was conducted employing the GSEA software (ver. 4.3.2), allowing for the identification of significantly altered pathways and gene sets in the dataset.

Statistical analyses

We used GraphPad Prism software, ver. 9.2.0 (GraphPad Software, San Diego, CA) for the statistical analyses. To compare the relationships between different groups, we applied two distinct statistical tests depending on the data structure and distribution: the Wilcoxon matched-pairs signed rank test was used for paired data comparisons, and the nonparametric Mann-Whitney U-test was applied for unpaired data sets. To compare distributions between the observed and expected data, we used the χ²-test (parts-of-whole section).

The Kaplan-Meier method was used for the survival analyses, i.e., the PFS and OS rates. This allowed us to plot survival curves and estimate survival probabilities over time. The differences in survival rates between groups were evaluated by the log-rank test. Throughout the analyses, a p-value threshold < 0.05 was set for determining statistical significance. This threshold helped ensure that the observed differences or associations were not due to chance, thereby bolstering the reliability of our findings.

Survival analysis of the 85 EC patients with high-grade histology stratified by the ProMisE classification, and their immunophenotypes

We first focused on the prognostic significance of ProMisE classification and immunophenotypes in Cohort 1 comprising 85 EC patients with high-grade histology treated prior to 2017. These patients' clinicopathological characteristics are summarized in Supplementary Table 1. The median follow-up period was 75.8 months (range 0.7–173.3 months). Most of the Cohort 1 patients (70.6%) presented with endometrioid G3 histology; the other patients presented serous, clear, and mixed histology. Each patient in Cohort 1 had an intermediate or high risk of recurrence (Supplementary Table S1).

The ProMisE classification revealed that 7.1% of the 60 Cohort 1 patients were the POLEmut subtype, 22.3% had the MMRd subtype, 42.4% showed NSMP subtype, and 28.2% were classified as p53abn subtype (Supplementary Table S2). The detailed POLE pathogenic mutations are presented in Supplementary Table S3. The survival analysis indicated distinct prognostic differences across the ProMisE classifications and immunophenotypes. The POLEmut and MMRd subtypes exhibited favorable PFS and OS rates, whereas the p53abn and NSMP subtypes were associated with poorer outcomes (Fig. 1A, B). Moreover, the patients with the excluded or desert phenotypes demonstrated significantly worse survival rates compared to those with the inflamed phenotype (Fig. 1C, D), highlighting the prognostic relevance of this immunophenotypic categorization. The detailed results of the survival analyses are depicted in Supplementary Figure S1.

The clinicopathological characteristics and ProMisE classification of Cohort 2

We next conducted a prospective study (Cohort 2) over a 4-year period, enrolling consecutive 60 EC patients representing a full spectrum of histology. The clinicopathological characteristics of these patients are provided in Table 1. The cohort had a median follow-up of 20.0 months (range 0.4–51.5 months) and a median age of 58 years (range 32–84 years). Most of the patients (61.7%) presented with endometrioid G1/2 histology, and 65% were diagnosed at early stages (FIGO stage I–II). The ProMisE classification revealed that 11.7% of these patients fell into the POLEmut subtype, 25.0% into MMRd, 48.3% into NSMP, and 15.0% into p53abn (Table 2). The detailed POLE pathogenic mutations are presented in Supplementary Table S4. The distribution of these categories aligns with another investigation of Japanese patients with EC [17]. Notably, higher ages were observed in the p53abn subtype, and the NSMP subtype predominantly consisted of endometrioid G1 and G2 histology. The serous histology tumors were exclusively categorized as p53abn.

Table 1. Cohort 2; EC patients in 2019–2022
Total patients, n	60
Follow–up period, mos.; median (range)	20.0 (0.4–51.5)

Age, yrs; median (range)	58.0 (32–84)

Histology:
Endometrioid G1/2	37	(61.7)
Endometrioid G3	20	(33.3)
Serous	3	(5.0)
Stage (FIGO 2008):
Ⅰ	35	(58.3)
Ⅱ	5	(8.3)
Ⅲ	18	(30.0)
Ⅳ	2	(3.3)
Risk of recurrence:
Low	13	(21.7)
Intermediate	18	(30.0)
High	29	(48.3)
The data are numbers and percentages.
Abbreviations: EC, endometrial cancer; FIGO, International Federation of Gynecology and Obstetrics

Table 2. ProMisE molecular classification in Cohort 2
	POLEmut	MMRd	NSMP	p53abn
	(n=7)	(n=15)	(n=29)	(n=9)
Proportion, %	11.7	25.0	48.3	15.0

Age, yrs; median	57.0	58.0	58.0	66.0
(range)	(48–75)	(50–65)	(32–84)	(52–75)

Histology:
Endometrioid G1/2	5	6	23	3
Endometrioid G3	2	9	6	3
Serous	0	0	0	3

Stage (FIGO 2008):
Ⅰ	6	7	19	3
Ⅱ	0	1	4	0
Ⅲ	1	6	6	5
Ⅳ	0	1	0	1

Risk of recurrence:
Low	2	2	8	1
Intermediate	4	4	10	0
High	1	9	11	8
Abbreviations: ProMisE, Proactive Molecular Risk Classifier for Endometrial cancer; POLEmut, polymerase-epsilon mutation; MMRd, mismatch-repair deficiency; NSMP, no specific molecular profile; p53abn, p53 abnormality; FIGO, International Federation of Gynecology and Obstetrics

Figure 2 provides representative histological images of the three immunophenotypes. The classification was based on the distribution patterns of CD8⁺ TILs. The inflamed phenotype showed abundant CD8⁺TILs in both the CT and the IM, whereas the excluded phenotype had a higher concentration in the IM than the CT. The desert phenotype was characterized by sparse CD8⁺TILs in both areas.

The relationship between the ProMisE classification and the immunophenotypes

Figure 3C presents the results of immunophenotyping in Cohort 2. The densities of CD8⁺ TILs in the CT and IM are shown. Among all 60 patients, 17 (28.3%) were classified as the inflamed phenotype, 22 (36.7%) as the excluded phenotype, and 21 (35%) as the desert phenotype. The median density of CD8⁺ TILs in the CT was significantly lower than that in the IM in the inflamed phenotype (1,712/mm² vs. 2,563/mm², p<0.0001) (Fig. 3D), the excluded phenotype (487/mm² vs. 2,330/mm², p<0.0001) (Fig. 3E), and the desert phenotype (172/mm² vs. 318/mm², p=0.0025) (Fig. 3F).

Figure 3G depicts the density of CD8⁺TILs in the CT in each ProMisE category. The median density of CD8⁺ TILs in the CT was not significantly different between the POLEmut and MMRd subtypes (1522/mm² vs. 855/mm², p=0.2666), or between the NSMP and p53abn subtypes (315/mm² vs. 442/mm², p=0.2841). This value was significantly higher in the POLEmut subtype compared to the NSMP and p53abn subtypes (p=0.0004 and p=0.0003, respectively) and was significantly higher in the MMRd subtype versus the NSMP and p53abn subtypes (p=0.0023 and p=0.0148, respectively).

Figure 3H illustrates the density of CD8⁺ TILs in the IM in each of the ProMisE categories. The median density of CD8⁺ TILs in the IM was not significantly different between the POLEmut and MMRd subtypes (2,469/mm² vs. 3,125/mm²; p=0.3322) but was significantly higher in p53abn subtype than in NSMP subtype (2,265/mm² vs. 429/mm²; p=0.0002) unlike the densities in the CT. In terms of the median density of CD8⁺ TILs in the CT and IM, the POLEmut and MMRd subtypes showed the inflamed phenotype with more CD8⁺ TILs in both areas; the NSMP subtype showed the desert phenotype with fewer CD8⁺ TILs in both areas; and the p53abn subtype showed the excluded phenotype with fewer CD8⁺TILs in the CT and more in the IM. However, looking at individual cases, some of the POLEmut and MMRd subtypes showed the non-inflamed phenotypes, and some of the NSMP subtype showed the inflamed phenotype. Table 3 summarizes the distribution of the three immunophenotypes in each ProMisE category in Cohort 2. All three immunophenotypes were observed in MMRd and NSMP subtypes, although there was a significant distribution bias. Supplementary Table S5 summarizes the distribution of the three immunophenotypes in each ProMisE category in Cohort 1 which only included EC patients with high-grade histology.

Table 3. Relationship between the ProMisE classifications and immunophenotypes in Cohorts 1 and 2
	POLEmut	MMRd	NSMP	p53abn	χ2
	(n=13)	(n=34)	(n=65)	(n=33)	χ2
Inflamed	6	22	9	0	p<0.0001
Excluded	6	9	13	23	p=0.0009
Desert	1	3	43	10	p<0.0001
Abbreviations: ProMisE, Proactive Molecular Risk Classifier for Endometrial cancer; POLEmut, polymerase-epsilon mutation; MMRd, mismatch-repair deficiency; NSMP, no specific molecular profile; p53abn, p53 abnormality; FIGO, International Federation of Gynecology and Obstetrics

Comparison of gene expression between the non-inflamed (excluded and desert) and inflamed phenotypes

Finally, to investigate factors that produce the different distribution patterns of CD8⁺ TILs between the non-inflamed (excluded and desert) and inflamed phenotypes, we performed an RNA-sequencing (RNA-seq) analysis. The principal component analysis (PCA) was performed with RNA-seq data of 40 EC samples from Cohort 2. The first two principal components (PCs) are plotted and colored according to the ProMisE classification (Fig. 4A), or immunophenotype (Fig. 4B). The GSEA of the MMRd subtype with the non-inflamed phenotypes compared to that with the inflamed phenotype revealed that the expressions of MYC target gene sets were more enriched in the non-inflamed phenotypes versus the inflamed phenotype (Fig. 4C). Figure 4D shows the enrichment plots for the top two datasets that were enriched in the GSEA hallmark analysis. The heat map of the top 50 marker genes for each phenotype in the MMRd subtype with the non-inflamed phenotypes and that with the inflamed phenotype is provided as Figure 4E.

Our GSEA of the NSMP subtype with the non-inflamed phenotypes compared to that with the inflamed phenotype revealed that the expressions of type-1 interferon response gene sets were more enriched in the non-inflamed phenotypes versus the inflamed phenotype (Fig. 5A). The enrichment plots for the top two datasets enriched in the GSEA hallmark analysis are given in Figure 5B. The heat map of the top 50 marker genes for each phenotype in the NSMP subtype with the non-inflamed phenotypes and that with the inflamed phenotype is shown in Figure 5C.

Our investigation into the distribution and prognostic significance of CD8⁺ TILs in EC according to a molecular classification unveiled crucial findings. Most notably, we identified three distinct immunophenotypes — inflamed, excluded, and desert — in EC patients. These immunophenotypes provide a more granular understanding of the immune landscape in EC, reflecting the diverse immunological responses triggered by tumor development. The existence of such distinct phenotypes underscores the complexity and heterogeneity of EC, further highlighting the need for personalized diagnostic and therapeutic approaches that consider both molecular and immunological tumor characteristics.

Interestingly, we observed that the EC patients with the excluded or desert phenotypes had poorer prognoses than those exhibiting inflamed phenotypes. This observation aligns with the increasing body of evidence suggesting that a robust anti-tumor immune response, represented by a high level of CD8⁺ TILs, is associated with better outcomes in various cancers, including EC [14]. This finding emphasizes the prognostic value of assessing the immunophenotype, in addition to the molecular classification, in EC patients. It also stresses the importance of developing therapeutic strategies aimed at modifying the tumor microenvironment in order to contribute to a more robust immune response, particularly for patients with the excluded or desert phenotypes.

When evaluated by the median density of CD8⁺ TILs in the CT and IM, the POLEmut and MMRd subtypes showed the inflamed phenotype; the NSMP subtype showed the desert phenotype; and the p53abn subtype showed the excluded phenotype. This insight suggests a potential correlation between the genomic background and CD8⁺ T cell anti-tumor response in EC. It has been reported that POLEmut and MMRd subtypes have high tumor mutation burdens which contribute to the expression of neoantigens and they cause a strong anti-tumor response by CD8⁺ T cell [23]. The limited CD8⁺ T cell response in the NSMP subtype suggests that this subtype is less likely to elicit a CD8⁺ T cell response due to its low immunogenicity and may contribute to its resistance to immunotherapy strategies. Our observation that the p53abn subtype exhibited the excluded phenotype suggests that this subtype, despite eliciting an immune response, is resistant to immunotherapy because it has mechanisms that exclude CD8⁺TILs from the central tumor. However, looking at individual cases, some of the POLEmut and MMRd subtypes showed the non-inflamed phenotypes, and some of the NSMP subtype showed the inflamed phenotype. It is thought that the formation of different immunophenotypes is due to some factors other than the number of genetic mutations or differences in immunogenicity.

Our RNA-seq analysis provided another layer of insight, suggesting that MYC target genes or type-1 interferon response genes might play a role in determining these immunophenotypes. The MYC oncogene is a well-documented driver of tumorigenesis in many cancers, and the type-1 interferon pathway is an important pathway in the antiviral response; however, their roles in the formation of the tumor immunosuppressive microenvironment remain unclear. Our findings suggest that the MYC signaling pathway or type-1 interferon pathway may, directly or indirectly, influence the distribution CD8⁺ TILs and the form a different immunophenotype, representing a novel avenue for future research.

A limitation of our study is that we only assessed CD8⁺ TILs. To clarify the immune microenvironment, it is also necessary to assess the distribution patterns of immune cells other than CD8⁺ TILs and the interaction of each immune cell, which is a future task. Furthermore, because the number of tumors in which RNA-seq was performed was limited, it was not possible to find the factors that cause the difference between the excluded and desert phenotypes.

In conclusion, our results suggests that evaluating not only the molecular classification but also the immunophenotype may lead to more accurate patients’ prognosis prediction in EC. Future studies exploring the role of the MYC signaling pathway or type-1 interferon pathway in shaping the immune landscape will undoubtedly provide further insights into the complex biology of EC. Elucidating the mechanisms that underlie the formation of the three immunophenotypes could lead to the discovery of new immunotherapy targets.

Acknowledgment

The authors to acknowledge Division for Medical Research Engineering, Nagoya University Graduate School of Medicine for usage of VS120-S5, and KN International, Inc. for English revisions.

Funding

This work supported by the Ministry of Education, Culture, Sports, Science and Technology, Japan Society for Promotion of Science (22K16876) and Seiichi Imai Memorial Foundation.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author contributions

Conceptualization: S.H, N.Y, H.K, Project administration, methodology, and software: S.H, N.Y, W.L,

Validation: T.M, M.K, K.Y, M.Y, S.T, Y.I, Y.S, A.Y, Writing original draft: S.H,

Editing draft: N.Y, Y.I, A.Y, K.N, H.K, Supervision: N.Y, A.Y, K.N, H.K

Data Availability

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Nagoya University (2018-0400)

Consent to participate

Cohort 1; An informational summary was presented on our hospital's website and the patients who did not consent were excluded. Cohort 2; All participants providing written informed consent.

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No competing interests reported.

Understanding the Impact of Spatial Immunophenotypes on the Survival of Endometrial Cancer Patients through the ProMisE Classification

Status:

Version 1

Abstract

Objectives

Patients and Methods:

Results

Conclusion

Figures

Introduction

Patients and Methods

Study design and patient cohorts

Cohort 1: Retrospective analysis regarding survival outcomes

Diagnosis and treatment

Sanger sequencing for POLE exonuclease domain

Immunohistochemistry analysis

Immunophenotyping

The ProMisE molecular classification

Survival analysis

RNA-sequencing analysis

Statistical analyses

Results

The clinicopathological characteristics and ProMisE classification of Cohort 2

The relationship between the ProMisE classification and the immunophenotypes

Comparison of gene expression between the non-inflamed (excluded and desert) and inflamed phenotypes

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1