IgA-Dominated Humoral Immune Responses Govern Patients' Outcome in Endometrial Cancer

Spontaneous immune responses in epithelial cancers are associated with improved patients' outcome (1–4). Although most studies focus on uncovering the predictive value of T cells, recent studies support the importance of B-cell infiltrations in the tumor microenvironment, suggesting that T-cell responses do not work in isolation (4–7).

The role of B cells in the tumor microenvironment has been associated with both the production of antibodies against tumor antigens (5, 8) and their capacity to present antigens (9). Antibodies can bind to the antigens or receptors on cancer cells and promote antibody-dependent cell-mediated cytotoxicity/phagocytosis (5, 10, 11). At least, some epithelial cancer cells express the receptor for polymeric IgM or IgA antibodies, polymeric immunoglobulin receptor (pIgR; refs. 5, 12–16). pIgR expression has been correlated with opposite disease prognosis in multiple cancers, including hepatocellular cancer and ovarian cancer (5, 13, 16). We recently showed that the binding and subsequent transcytosis of IgA antibodies through ovarian cancer cells promote MHC-independent T-cell–mediated killing of cancer cells, probably by the upregulation of IFN receptors (5). However, the immune landscape of endometrial cancer, which is one of the major cancer types in women across the world (17, 18), remains incompletely understood.

In an effort to identify likely responders to different clinical interventions, endometrial carcinomas have been recently reclassified based on genomic features (19). Some of these classifications include mutation-induced DNA repair gene dysfunction, which are typically associated with increased cancer risk. For instance, an individual with a germline mutation in one or more of the methyl directed DNA mismatch repair (MMR) genes such as MLH1, MSH2, MSH6, or PMS2 is more susceptible to colorectal cancer, endometrial cancer (20, 21), and possibly, breast cancer (22), prostate cancer (23), pancreatic cancer (22), and cancers at many other locations (21). Accordingly, the immunogenicity of endometrial cancer was recently evaluated in terms of MMR defects in patients with endometrial cancer (24).

In this study, we evaluated the elusive role of coordinated humoral and cellular adaptive immune responses in endometrial cancer, including the extent of class-switched IgA and IgG antibodies production across multiple histological subtypes. Our results indicate that coordinated B-cell and T-cell responses predict superior survival of patients with endometrial cancer. Humoral responses are dominated by IgA in most patients, and its occupancy of pIgR in tumor cells is a major predictor of improved patient survival.

We have analyzed two tissue microarrays (TMA) procured from Moffitt Cancer Center Tissue Core Facility, each of which include endometrial cancer tissues (n = 107, total; clear cell, n = 18, endometrioid type high grade (grade 3), n = 28; endometrioid type low grade, n = 30; serous, n = 31) and some control tissues. The study protocol was approved by the Institutional Review Board at the Moffitt Cancer Center. Individual tissue blocks were reviewed to confirm histology and grade and make TMAs. Lymph node contamination was ruled out. TMAs were arrayed using 1-mm–sized needle and by taking two duplicate core biopsies from endometrial cancer tissue blocks or control tissues, and re-embedding the cores into a single block.

Formalin-fixed, paraffin-embedded TMAs were immunostained using the PerkinElmer OPAL TM 7-Color Automation IHC kit on the BOND RX autostainer (Leica Biosystems) and the following anti-human antibodies: CD3 (Dako, A0452), CD4 (Cell Marque, EP204, 104R-25), CD8 (Dako, C8/144B, M7103,), CD19 (Dako, LE-CD19, M7296), CD138 (Dako, MI15, M7228), pIgR (Abcam, ab96196), IgA (Abcam, EPR5367-76, ab124716), IgG (Abcam, EPR4421, ab109489), and pan-cytokeratin (PCK, Dako, AE1/AE3, M3515). Nuclei were stained with DAPI. Precisely, tissues were baked at 65°C for 2 hours and then transferred to the BOND RX (Leica Biosystems) followed by automated deparaffinization, antigen retrieval using OPAL IHC procedure (PerkinElmer). Autofluorescence slides (negative control) were included, which use primary and secondary antibodies omitting the OPAL fluors. Slides were scanned and imaged with the PerkinElmer Vectra3 Automated Quantitative Pathology Imaging System. Multilayer TIFF images were exported from InForm (PerkinElmer) and loaded into HALO (Indica Labs) for quantitative image analysis. Each fluorescent fluorophore is assigned to a dye color, and positivity thresholds were determined per marker based on published nuclear or cytoplasmic staining patterns. Quantitation in tumor islets and stroma was distinguished by PCK staining. Datasets were exported with cytoplasmic, nuclear, and total cell counts for each fluorescent marker from the sample set.

DNA repair molecules were independently stained using a Ventana Discovery XT automated system (Ventana Medical Systems) as per the manufacturer's protocol with proprietary reagents for MLH1 (M1, #790-5091, Ventana), MSH2 (G219-1129, #790-5093, Ventana), MSH6 (BC/44, CM265A, Biocare), and PMS2 (EPR3947, ab110638, Abcam) antibodies. The expression of these markers was evaluated by using 1% cutoff level. If any one of duplicate cores had 1% nuclear staining, the MMR protein was considered as “preserved.”

Optimization of multiplex immunofluorescence and immunohistochemistry staining experiments with appropriate positive and negative control tissues, including isotype control antibodies, are summarized in Supplementary Fig. S1. Sections of human tonsil tissues were used as a positive control for CD3, CD4, CD8, CD19, CD138, IgA, IgG, and PCK, and as a negative control for pIgR. Sections of healthy kidney tissues were used as a positive control for pIgR. Sections of glioblastoma tissues were used as a negative control for CD3, CD4, CD8, CD19, CD138, IgA, IgG, and PCK. Respective isotype control antibodies to rule out false-positive staining. Sections of human colon adenocarcinoma tissues were used as positive control for MLH1, MSH2, MSH6, and PMS2.

Each sample core had two slices taken in close proximity, each of which was stained using a different panel of markers. The DAPI channel was extracted from each slice's image and used to align the two slices as follows: (i) Each image's features were detected using BRISK, and then described with the VGG feature descriptor (25); (ii) features were matched using brute force, and the matched features subsequently used to find the similarity matrix, M, that can warp the matched features to align with one another; (iii) M was used to apply a rigid transformation to the image; (iv) The rigid alignments were improved using a nonrigid transformation, using the deformation fields (dx, dy) found using Deep Optical Flow (26). The positions of each cell could then be warped using these same transformations. All image registration was conducted in Python 3.7.

Using the image registration parameters (M, dx, dy) that aligned to the two images, the position of each cell was warped such that they also aligned. Spatial association networks were then found for the subset of samples that had an alignment error less than 25 μm. First, each TMA core was divided into 100 μm × 100 μm quadrats, and the number of each cell type was counted within each quadrat. The ecoCopula package for R was then used to find the spatial association network for each core (27). An average association network for each histological type was then created from the individual networks. The averaged spatial associations were then clustered using the Leiden community detection algorithm (one graph of average positive associations and second graph of average negative associations; ref. 28). Spatial communities contain cell types (i.e., “nodes”) that have strong spatial associations with one another (i.e., are “densely connected”), and weaker and/or negative spatial associations with other communities (i.e., are “loosely connected”). Grouping cell types into communities based on their spatial associations help to better understand the overall spatial structure of the tumor, as cells in one community tend to be found colocalized with one another, and potentially isolated from other communities. Combined with understanding of how cell types interact mechanistically (e.g., cytotoxic T-cell preying upon tumor cells), we can treat each community as a functional group. This, in turn, paints a bigger picture of the spatial interactions within the tumor, facilitating a better understanding of how and why cells are interacting with one another.

As image registration was not cell–cell perfect, summaries of spatial relationships based on cell positions (as opposed to quadrat counts) used the original unwarped cell positions. As such, each panel of markers was analyzed independently. Significance of departures from complete spatial randomness (CSR) was determined using the Diggle–Cressie–Loosmore–Ford test on the cross-type L-function for homogeneous point patterns (i.e., Besag's transformation of Ripley's K function; ref. 29). Clustering was considered significant when |P \le 0.05$| for the alternative hypothesis of “greater,” i.e., there were more cells within a radius r than expected under CSR. If the two-sided alternative hypothesis could not be rejected, the spatial pattern for sample was defined as CSR. The Fisher exact test for count data was used to determine if the number of samples with clustering was significantly different between each pairwise combination of histological types.

In addition to determining if there was significant clustering between pairs of phenotypes, we used simulation envelopes of the cross-type L-function to determine at which distances clustering is observed. In each core, the envelopes for each phenotype pair were determined using 100 simulated realizations of CSR, as described in ref. 30. Distances at which significant clustering occurred were those where the observed cross-type L-function was greater than the upper envelope. Points where the observed cross-type L-function fell between the lower and upper envelopes were considered to be associated with CSR.

We extracted cell segments per core to build a count matrix with cells as rows and known phenotypes as columns. The known phenotypes are defined as follows: Cytotoxic T cell: DAPI⁺/CD3⁺/CD8⁺/CD4⁻/CD19⁻/CD138⁻, Helper T cell: DAPI⁺/CD3⁺/CD4⁺/CD8⁻/CD19⁻/CD138⁻, nonplasma B cell: DAPI⁺/CD19⁺/CD138⁻/CD3⁻/CD4⁻/CD8⁻, plasma cell: DAPI⁺/CD19⁺/CD138⁺/CD3⁻/CD4⁻/CD8⁻, IgG: DAPI⁺/IgG⁺/IgA⁻/pIgR⁻/CD19⁻/CD138⁻, and IgA: DAPI⁺/IgA⁺/IgG⁻/pIgR⁻/CD19⁻/CD138⁻, pIgR: DAPI⁺/pIgR⁺/IgA⁻/IgG⁻/CD19⁻/CD138⁻.

A Euclidean distance matrix of dimension cells x cells is generated from this count matrix to compute the neighboring cells for each cell. We then build a spatial neighborhood for each cell where the phenotype expression of each cell is the average of six of its spatially nearest neighbors in Euclidean space. This approach is inspired by the cellular neighborhoods in ref. 31.

Cells along with their spatial neighbors from all the cores are merged and clustered using a Gaussian mixture model (32) to extract heterogeneous cell types that are also spatially distinct. The differentially expressed phenotypes used to annotate each cell type are those phenotypes with the highest z-score values for the log-normalized expression per cell type. The cells are further embedded in reduced 2D space using tSNE (33).

HEK293T and human endometrial cancer cell lines, KLE and HEC-1-A, were procured from the ATCC. HEK293T, KLE, and HEC-1-A cells were routinely cultured in RPMI-1640 (Sigma), DMEM/F12 (Gibco), and McCoy's 5A (Thermo) media, respectively, supplemented with 10% FBS, penicillin (100 IU/mL), streptomycin (100 μg/mL), l-glutamine (2 mmol/L), and sodium pyruvate (0.5 mmol/L; Thermo Scientific). Cell lines were routinely tested for negative Mycoplasma contamination. Human PIGR-coding sequence was cloned into pLVX-IRES-ZsGreen1 lentiviral vector (Genscript), and virus was produced by cotransfecting with viral packaging plasmids into HEK293T cells using Lipofectamine-3000 (Invitrogen). KLE cells were transduced with 0.45 μ filtered virus and checked under microscope for ZsGreen expression.

Cells were lysed in RIPA buffer (Thermo Scientific) with protease-phosphatase inhibitor cocktail (Sigma Aldrich) and cleared by centrifugation. Proteins were quantified by BCA assay (Thermo Scientific). Membranes were blotted with anti-pIgR (Abcam, ab96196), CHOP (CST, L63F7, and 2895), and anti–β-actin (CST, 13E5, and 5125) antibodies.

Sorting of PIGR^transduced KLE cells were performed by staining with DAPI (Thermo Scientific) viability dye, blocking with anti-CD16/32 (BioLegend), and staining for 30 minutes at 4°C with anti-human pIgR (Thermo Scientific, PA5-35340) antibodies, followed by incubation with Alexa Fluor-647–conjugated secondary antibodies (CST). Samples were subsequently fluorescence-activated cell sorting (FACS) sorted using BD FACS ARIA. Data were analyzed using FlowJo.

KLE (wild-type or PIGR^transduced) and HEC-1-A cell lines in 2% FBS containing DMEM/F12 or McCoy's 5A media, respectively, were treated with or without 0.5 μg/mL of natural human IgA or IgG for 72 hours. Total RNA were isolated from cultured cells using RNA isolation kit (QIAGEN) and analyzed for RINe. Next-generation RNA sequencing (RNA-seq) was performed by the Moffitt Cancer Center Molecular Genomics Facility. Paired-end RNA-seq reads were aligned to the GRCh37 human reference genome using STAR (version 2.5.3a; ref. 34) following adaptor trimming by cutadapt (https://doi.org/10.14806/ej.17.1.200; version 1.8.1). Uniquely mapped reads were counted by featureCounts (version 1.5.3; ref. 35) using Gencode V30 transcript annotations for human. Differential expression analysis was performed using DESeq2 (36). Heat maps were made using z-score transformed log 2 (1 + normalized count).

For every differential expression analysis comparing antibody-treated groups versus -untreated group, genes were ranked based on −log10(P value) * (sign of log2 (fold change)). The preranked gene list was used to perform preranked gene set enrichment analysis (GSEA version 4.0.2; ref. 37) to assess enrichment of hallmarks, curated gene sets, and gene ontology (38) terms in MSigDB (37). The resulting normalized enrichment score and FDR-controlled P values were used to assess the IgA-induced transcriptome changes. Pathways have been selected based on the magnitude of changes among the most significantly altered ones and commonalities of the effects of IgA on both the cell lines, and presented.

RNA was reverse transcribed to cDNAs using SuperScript-IV (Invitrogen) and oligo-dT (Invitrogen). Quantification of human PIGR, CLIP3, LAMP3, CCL20, CCL5, TICAM1, DDIT3, MMP1, CEBPG, VEGFA, ARHGEF2, GADD45A, and FEN1 mRNA was performed using SYBR Green mastermix reagent (Applied Biosystems). Expression was normalized by α-tubulin levels. Fold changes relative to average cycle threshold (C_T) values in control samples were calculated by the equation 2^−ΔΔC_T.

RNA-seq HTSeq raw count generated from Gencode v22 for The Cancer Genome Atlas (TCGA) Uterine Corpus Endometrial Carcinoma (UCEC) was downloaded from Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov/). Variance stabilization transformation implemented in DESeq2 (36) was performed on the raw gene count to create a log₂-scaled variance-stabilized gene expression matrix for downstream analyses. Clinical data were obtained from cBio Cancer Genomics Portal (http://www.cbioportal.org/). A total of 528 patients with matched clinical information and tumor RNA-seq data were used in this study. Another dataset of 547 patients with information for mutations or deletions of four MMR genes MLH1, MSH2, MSH6, and PMS2 and overall survival information was downloaded from cBioPortal and used. Precalculated RNA-seq–based enrichment for 64 immune and stromal cell types was downloaded for TCGA 33 cancer types from xcell (39). Enrichment scores of T cells, B cells, and plasma cells across TCGA pan-cancer were visualized by boxplots.

Unpaired two-tailed nonparametric Mann–Whitney test were performed between two groups, unless indicated otherwise. In addition to Kaplan–Meier plots, Cox regression model has been used for adjusting for covariates, wherever feasible. We limited survival analysis to univariate Mantel–Cox test in occasions where data from a single histology type are used, or between groups with low sample volumes. Analyses were carried out in Graph Pad Prism (v.9.0) or R (v.3.6.1) software. A significance threshold 0.05 for P values was used.

The data generated in this study are available within the article and its Supplementary Data Files. RNA-seq datasets generated in this study have been deposited in Gene Expression Omnibus under accession number GSE180455.

We recently showed that in serous ovarian cancer, IgA antibodies and, in a lesser extent, IgG responses, govern the magnitude of antitumor immunity (5). To understand the role of humoral immunity in endometrial cancer, we stained 107 endometrial cancer specimens from four histological subtypes for expression of IgA and IgG antibodies (Supplementary Fig. S2). Clinicopathologic information of tissue specimens are summarized in Table 1. We found similar densities of PCK⁺ tumor cells across different histological types (Supplementary Fig. S3A), which were quasi-universally coated by both IgA and IgG (Fig. 1A and B). High-grade endometrioid type tumors showed significantly higher density of IgA and IgG antibodies at PCK⁺ tumor islets, compared with other histology types, whereas clear cell endometrial cancers showed the lowest intra-epithelial IgA and IgG accumulation (Fig. 1B). Notably, virtually all endometrial tumors of any histology expressed pIgR (Supplementary Fig. S2), the IgA/IgM receptor associated with other epithelial cancers (5, 40–42). Accordingly, the density of coating of tumor cells by IgA is higher than IgG-coating in 63% of tumors (Fig. 1A). T-Distributed stochastic neighbor embedding (t-SNE) analysis of spatial interactions between different immune markers showed large clusters of PCK⁺ tumors cells expressing pIgR, and multiple clusters of IgA and IgG with pIgR-expressing cancer cells (Fig. 1C). The tSNE depicts 90 thousand PCK⁺ tumors cells, of which, the proportion of cells expressing pIgR is 23.6%, pIgR with IgA is 11.7%, pIgR with IgA and IgG is 22.4%, and pIgR with IgG is 33%. These clusters are consistent across all four histology types of endometrial cancer (Supplementary Fig. S3B). Endometrioid type tumors (both high- and low-grade) exhibited higher pIgR expression, compared with serous cancer, whereas clear cell endometrial tumors showed the lowest pIgR abundance (Fig. 1D). Interestingly, we found that higher pIgR⁺ cancer cell density predicts improved, albeit not significant, patient survival (Fig. 1E; Supplementary Fig. S3C), with corresponding results for PIGR mRNA expression in endometrial cancer TCGA dataset (n = 528; Fig. 1F; Supplementary Fig. S3C). Most importantly, IgA occupancy of pIgR at tumor cells, rather than mere IgA density, strongly predicts patients' survival (Fig. 1G; Supplementary Fig. S3C). As expected, endometrioid type tumors showed the highest percentages of pIgR occupancy by IgA (Fig. 1H). Together, these results indicate that, similar to ovarian cancer, IgA production at tumor beds determines endometrial cancer progression through interactions with pIgR, quasi-universally expressed in tumor cells.

To understand the effect of IgA binding to pIgR on endometrial cancer cells, we used pIgR⁺ HEC-1-A endometrial cancer cells, along with pIgR-negative KLE endometrial cancer cells, which we lentivirally transduced with pIgR or mock-transduced (Supplementary Fig. S3D–S3G). Treatment with irrelevant IgA, but not IgG, induced genome-wide transcriptional changes in both systems in a pIgR-dependent manner (Fig. 2A and B; Supplementary Fig. S3H and S3I). GSEA pathway analyses and subsequent qPCR analyses revealed IgA-driven activation of multiple inflammatory pathways, including TNF signaling and IFN production, among other cytokines. Furthermore, pIgR:IgA interactions induced the transcription of multiple genes associated with endoplasmic reticulum (ER)–stress and the unfolded protein response, which converge in CHOP/DDIT3-mediated apoptosis (Fig. 2C and D; Supplementary Figs. S3Jand S4). In addition, IgA signaling dampened DNA repair pathways in pIgR^transduced KLE and pIgR⁺ HEC-1-A cells (Fig. 2C; Supplementary Figs. S3J and S4), which positively correlate with the progression of multiple cancer types, and can be targeted with existing and emerging drugs (43–46). Therefore, the favorable prognostic value of pIgR:IgA interactions at tumor beds is determined by the activation of proapoptotic and inflammatory pathways in tumor cells, independently of antigen recognition.

Recent studies have underscored the importance of B-cell activity synchronized with T-cell activity at the tumor beds for a sustained, robust antitumor humoral response in multiple cancer types (4, 5, 47, 48). To understand the importance of coordinated T-cell and B-cell responses in the production of IgA and IgG antibodies, and subsequent immune protection, we also stained our cohort of patients with endometrial cancer, along with some healthy endometrium samples, with markers of B and T lymphocytes, and plasma cells (Supplementary Fig. S5). We observed significant infiltration by lymphocytes of both lineages, with distinct t-SNE clusters of cytotoxic T cells, helper T cells, B cells, and plasma cells in all histological types of endometrial cancer, consistent with TCGA enrichment scores (Fig. 3A; Supplementary Fig. S6). For the 84 thousand cells depicted on the tSNE, the proportion of cytotoxic T cells is 30.2%, helper T cells is 40.5%, plasma cells is 5%, and B cells is 10%. The highest density of T-cell and B-cell infiltration was found again in high-grade endometrioid type tumors (Fig. 3B). Supporting coordinated cellular and humoral adaptive immune responses, B-cell infiltration significantly correlated with T-cell accumulation (Fig. 3C; Supplementary Fig. S7A and S7B). Accordingly, the densities of IgA and IgG were maximum in tumors where denser concomitant T-cell and B-cell infiltrates are identified (Fig. 3D).

Spatial point pattern analyses revealed important differences in the spatial relationship of cytotoxic and helper T cells with nonplasma B cells. We observe that the serous histology type and endometrioid type low grades exhibited the earliest switch in spatial pattern, transitioning from CSR to spatial clustering, starting around 100 μm (Fig. 4A). However, the distribution of the distances at which spatial clustering was observed between nonplasma and plasma B cells with helper T cells was significantly different between serous type and endometrioid type low grade (Fig. 4B). The distribution of clustering distances between nonplasma B cells and helper T cells also similar between serous and clear cell endometrial tumors, but each of the two types was significantly different compared with low- and high-grade endometrial cancer (Fig. 4B). In contrast, the distribution of clustering distances between nonplasma and plasma B cells with cytotoxic T cells did not differ significantly between serous type and endometrioid type low grade samples (Fig. 4B). Interestingly, the only B-cell–T-cell clustering distribution that differed significantly between serous and clear cell endometrial tumors was that between nonplasma B cells and cytotoxic T cells (Fig. 4B).

Analysis of the spatial association networks within each histology type in PCK⁺ tumor epithelium or in total tumor tissues showed that each histology type has distinct spatial communities, detected by clustering the strength of spatial associations (Fig. 4C; Supplementary Fig. S7C). Notably, the tumor epithelium exhibits different spatial communities compared with respective total tumor areas (Fig. 4C; Supplementary Fig. S7C). Consistent with the clustering between B cells and T cells, spatial pattern and distribution analyses between IgA and pIgR at varying distances discovered maximum clustering as a function of the distances in the serous type (Fig. 4D and E; Supplementary Fig. S7D and S7E). Consequently, endometrial tumors with increased accumulation of B cells and CD19⁺CD138⁺ plasma cells and, to a lesser extent, T cells, exhibited improved overall survival (Fig. 5A–C; Supplementary Fig. S7F–S7I). Corresponding associations between increased abundance of pan–B-cell marker CD19 (a cell surface protein restricted to B-cell lymphocytes, and continuously and stably expressed on all stages of B lineage differentiation), and pan–T-cell marker CD3, and delayed malignant progression were also identified in 528 patients in endometrial cancer TCGA datasets (Fig. 5D and E; Supplementary Fig. S7J). Notably, in high-grade endometrioid type and serous tumors, superior outcome was associated with infiltration of CD19⁺ B cells, but not CD3⁺ T lymphocytes (Supplementary Fig. S8A–S8D), suggesting a predominant role of antibody-mediated protection in these patients. In contrast, the predictive value of T cells is stronger in clear cell endometrial cancer, compared with B-cell accumulation. Together, these data indicate that the production of IgA or IgG is consistently associated with concurrent infiltration of T and B lymphocytes in endometrial cancer, which predicts delayed malignant progression. This is suggestive of coordinated activation of both arms of the adaptive immune system, with a predominant role of humoral vs. cellular responses depending on histological subtypes.

Endometrial carcinomas have been recently reclassified based on genomic features (19). To gain additional insight about how defects in MMR proteins influence the immunogenicity of endometrial cancer, we stained our cohort for MLH1, MSH2, MSH6 and PMS2 proteins. Low-grade endometrioid type tumors showed the highest percentage of loss of one or more MMR protein expression (23.33%; n = 37), followed by high-grade endometrioid type carcinomas (21.43%; n = 34). In contrast, serous and clear cell endometrial cancers showed infrequent loss of MMR protein expression (9.68%; n = 34 and 11.11%; n = 20, respectively; Fig. 6A). We did not find a significant association between B-cell, plasma cell, or T-cell infiltration and loss of one or more MMR proteins (Fig. 6B–E). However, deregulated MMR protein expression is significantly associated with higher percentages of IgA bound to pIgR (Fig. 7A). This is associated with a significant increase in pIgR expression, but not IgA or IgG densities (Fig. 7B–D), suggesting an intrinsic property of MMR-deregulated endometrial cancer cells to upregulate pIgR and favor IgA-pIgR interaction. Similar to available TCGA data, combined or individual loss of one or more MMR protein expressions do not predict overall survival (Fig. 7E–I; Supplementary Fig. S8E–S8I).

Here we show that coordinated B-cell and T-cell responses can predict the outcome of patients with endometrial cancer of multiple combined histological subtypes, but only B and plasma cell infiltration predicts survival in patients specifically with serous and high-grade endometrioid type tumors. Humoral responses are dominated by IgA in terms of staining, followed by IgG. Accordingly, pIgR occupancy by IgA in tumor cells is consistently associated with better outcome and elicitation of proapoptotic pathways.

In an effort to inform adjuvant treatment, endometrial carcinomas have been recently reclassified based on molecular features into four categories: POLE ultramutated, microsatellite instability hypermutated, copy-number low, and copy-number high (19). Previous studies reported that immune responses correlate with endometrial cancer molecular subtype but does not carry independent prognostic significance (49). Although these studies did not include the analyses of antibody isotypes and used an alternative molecular classification, we also found a significant correlation between MMR protein deregulation and higher percentages of IgA bound to pIgR, along with overall increase in pIgR density. Moreover, we found that B and plasma cell infiltration has predictive value in specifically serous and high-grade endometrioid type tumors. In contrast, the outcome of patients with clear cell carcinomas, which were restricted to 5 samples in the aforementioned study (49), depends on tumor-infiltrating T cells, rather than humoral responses. Our study, therefore, suggests that different histological subtypes of endometrial cancer orchestrate different immune responses that spontaneously determine the patients' outcome, but it is also consistent with their dependence on the molecular make-up of each tumor.

A major finding of our study is that, as we reported for other gynecologic malignancies (5), pIgR is quasi-universally expressed in endometrial cancer cells. The highest expression levels correspond to endometrioid type tumors. An exception is the KLE cell line, which allowed us to investigate the effect of pIgR-mediated IgA signaling by restoring pIgR expression. Independently of antigen recognition, irrelevant IgA, but not IgG, elicited an array of inflammatory, ER stress and proapoptotic transcriptional pathways in tumor cells. This is consistent with the significant antitumor effects of control IgA in pIgR⁺ ovarian tumors in vivo (5), and provides a rationale for testing dimeric IgA as an alternative reagent for immunotherapeutic antibodies in patients with cancer. High-grade endometrioid type tumors also showed higher density of both IgA and IgG antibodies coating tumor cells. However, the density of IgA is higher than that of IgG-coating in the majority of tumors. IgA, therefore, dominates the antibody response in endometrial cancer, although IgG responses are also very strong in endometrioid type and serous tumors.

B cells infiltrating most established tumors have been traditionally associated with immunosuppressive activity, through the production of IL10, IL35 and/or TGFβ (50). However, a flurry of recent studies in human cancer consistently associates B-cell responses with better outcome and concomitant T-cell infiltration (5, 50). Beyond the elusive role of tertiary lymphoid structures (51), our study suggests that humoral responses, and in particular IgA responses, are as important in many gynecologic tumors as effector T cells. Modulating humoral responses that synergize with existing T-cell–centric immunotherapies could pave the way for novel interventions to control aggressive endometrial cancers, which could be applicable to other gynecologic malignancies.

J.R. Conejo-Garcia reports other support from Compass Therapeutics, personal fees and other support from Alloy Therapeutics, and grants, personal fees, and other support from Anixa Biosciences outside the submitted work. No disclosures were reported by the other authors.

G. Mandal: Data curation, formal analysis, investigation, visualization, methodology, writing–original draft, writing–review and editing. S. Biswas: Data curation, formal analysis, supervision, validation, investigation, visualization, writing–original draft, writing–review and editing. C.M. Anadon: Investigation, methodology, writing–review and editing. X. Yu: Software, formal analysis, supervision, project administration, writing–review and editing. C.D. Gatenbee: Software, formal analysis, methodology, writing–review and editing. S. Prabhakaran: Software, formal analysis, investigation, visualization, methodology, writing–review and editing. K.K. Payne: Investigation, writing–review and editing. R.A. Chaurio: Investigation, writing–review and editing. A. Martin: Investigation, methodology, writing–review and editing. P. Innamarato: Investigation, methodology, writing–review and editing. C. Moran: Visualization, methodology, writing–review and editing. J.J. Powers: Investigation, methodology, writing–review and editing. C.M. Harro: Investigation, methodology, writing–review and editing. J.A. Mine: Investigation, writing–review and editing. K.B. Sprenger: Investigation, writing–review and editing. K.E. Rigolizzo: Investigation, writing–review and editing. X. Wang: Formal analysis, investigation, methodology. T.J. Curiel: Investigation, writing–review and editing. P.C. Rodriguez: Supervision, visualization, methodology, writing–review and editing. A.R. Anderson: Software, formal analysis, supervision, visualization, methodology. O. Saglam: Resources, supervision, investigation, methodology, project administration, writing–review and editing. J.R. Conejo-Garcia: Conceptualization, resources, formal analysis, supervision, funding acquisition, validation, investigation, writing–original draft, project administration, writing–review and editing.

Support for Shared Resources was provided by Cancer Center Support Grant (CCSG) CA076292 to H. Lee Moffitt Cancer Center. This study was supported by R01CA157664, R01CA124515, R01CA178687, and R01CA211913 to J.R. Conejo-Garcia and by R01CA184185 to P.C. Rodriguez. K.K. Payne was supported by T32CA009140 and The American Cancer Society Postdoctoral Fellowship. C.D. Gatenbee, S. Prabhakaran, and A.R. Anderson gratefully acknowledge funding from both the Cancer Systems Biology Consortium and the Physical Sciences Oncology Network at the National Cancer Institute through grants U01CA232382 and U54CA193489 as well as support from the Moffitt Center of Excellence for Evolutionary Therapy. The authors are especially grateful to Advanced Analytical and Digital Pathology Laboratory, Molecular Genomics, Flow Cytometry, Biostatistics and Bioinformatics, Analytic Microscopy Core, and Tissue Core Facility Shared Resources at Moffitt Cancer Center for exceptional support.

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