Predictive biomarkers of response to immune checkpoint inhibitors in mismatch repair-deficient endometrial cancer

Abstract

The introduction of immune checkpoint inhibitors (ICIs) has represented a major therapeutic breakthrough for patients with mismatch repair-deficient (MMRd) endometrial cancer (EC). However, despite initial clinical success, a considerable subset of patients does not experience meaningful clinical benefit from these therapies. The lack of accurate predictive biomarkers to differentiate responders from non-responders remains a key clinical challenge. There is a pressing need for robust predictors of response that can more reliably identify patients with MMRd EC who are unlikely to benefit from ICIs, thereby guiding treatment decisions in routine practice and refining patient stratification in future clinical trials. A range of potential biomarkers has been explored in this context, including genomic, epigenomic, transcriptomic, and proteomic features of both the tumor and its microenvironment. In this review, we evaluate the predictive utility of conventional biomarkers, namely, programmed death-ligand 1 expression and tumor mutation burden, and survey emerging candidates, including proteomic immune signatures, for predicting response or resistance to ICIs in the MMRd EC population. We also examine machine-learning approaches that integrate multi-omics and clinicopathological data to improve stratification, and consider how mechanistic insights into ICI resistance may inform novel therapeutic strategies.

Immunogenic subtypes of endometrial cancer and the biological rationale for immune checkpoint blockade

Endometrial cancer (EC) represents the sixth most frequently diagnosed cancer in women, with an incidence of 4.3% and 420.242 new cases worldwide in 2022. ¹ The highest incidence rates have been observed in North America and Eastern and Northern Europe, while the lowest were reported in middle-income countries. Notably, the past decade has seen marked increases in countries undergoing rapid socioeconomic transition, with the steepest rises reported in South Africa and several Asian nations. ² Despite favorable outcomes in early-stage disease, primary advanced or recurrent cases have poor long-term survival; a large real-world cohort reported a median overall survival (OS) of 14.1–31.8 months. ³ Treatment choices have historically been limited for patients with advanced or recurrent disease, particularly for those whose disease progresses during or after initial platinum-based chemotherapy.

The treatment paradigm for EC has shifted markedly in recent years owing to new therapies, among which immune checkpoint inhibitors (ICIs) have shown particular promise.

Both Cancer Genome Atlas Research Project (TCGA) and the ProMisE classifier delineate two highly immunogenic EC subgroups, mismatch repair-deficient/microsatellite instability-high (MMRd/MSI-H) and POLE-mutant, comprising ~30% and ~7% of cases, respectively.^4–6 In EC, MMR deficiency is most commonly due to MLH1 promoter hypermethylation accounting for approximately 75% of cases. Up to 10% of MMR deficiency cases are due to an underlying germline mutation in MLH1, MSH2, MSH6, or PMS2, associated with Lynch syndrome. The majority of remaining cases are driven by somatic MMR gene mutations. ⁷

ECs with MMRd exhibit a defective DNA repair mechanism that fails to correct mismatches, including base substitutions and short insertions and deletions (indels), that arise during DNA replication across the genome, particularly in microsatellite regions (short and repetitive DNA sequences). This deficiency leads to the accumulation of somatic mutations and contributes to a hypermutated genomic landscape. A detectable consequence of MMRd is MSI, characterized by alterations in the length of these repetitive sequences (microsatellites) due to uncorrected indels. Many of the mutations arising in MMRd tumors result in highly immunogenic neoantigens, which can enhance tumor recognition by the immune system. Consequently, these tumors exhibit greater CD8⁺ cytotoxic T-cell infiltration compared with their mismatch repair-proficient (MMRp)/non-POLE-mutant counterparts. This immune-reactive microenvironment induces adaptive immune resistance, characterized by upregulation of inhibitory co-regulators such as programmed death-ligand 1 (PD-L1), rendering this EC subgroup a strong candidate for ICI therapy. ⁸

Clinical efficacy of ICIs in MMRd EC and challenges in predicting response

Over the last few years, various anti-programmed cell death protein 1 (PD-1) or anti-PD-L1 have shown meaningful clinical activity, either as monotherapy or in combination with lenvatinib, in the post-platinum setting in patients with advanced or recurrent MMRd/MSI-H EC, with overall response rates (ORR) ranging from 40% to 50%. Importantly, these responses were remarkably durable. In KEYNOTE-158, pembrolizumab achieved a median duration of response of 63.2 months among patients with MMRd EC, with a median follow-up of 54.5 months. By contrast, both dostarlimab (GARNET) and pembrolizumab plus lenvatinib (KEYNOTE-775) did not reach the median duration of response, with median follow-up durations of 27.6 and 18.7 months, respectively.^9–12

Long-term efficacy data from an academic, multicenter retrospective study demonstrated that patients with pre-treated advanced or recurrent MMRd EC who received anti-PD-(L)1 monotherapy and remained progression-free for at least 24 months had a subsequent risk of progression as low as 7%, with a median follow-up of 30 months beyond the 24-month mark. ¹³ These findings further support the potential for durable disease control and even suggest the possibility of cure in a subset of patients with metastatic MMRd EC.

The positive efficacy results from KEYNOTE-158 and GARNET trials ultimately led to the approval of pembrolizumab and dostarlimab, respectively, by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) for women with advanced or recurrent MSI-H/MMRd EC who have progressed after platinum-based therapy. In addition, based on the results from KEYNOTE-775, the combination of pembrolizumab and lenvatinib was approved by the EMA for the treatment of advanced or recurrent following prior systemic therapy, regardless of MMR status, and by the FDA for patients with MMRp EC, exclusively.

In the frontline setting, four phase III randomized clinical trials have consistently demonstrated that adding anti-PD-(L)1 monoclonal antibodies (dostarlimab, pembrolizumab, Atezolizumab, and durvalumab) to carboplatin and paclitaxel and then continuing as maintenance therapy significantly improved progression-free survival (PFS) in patients with newly diagnosed, advanced, or recurrent EC. This benefit was particularly notable in the MMRd/MSI-H EC subgroup.^14–17 In addition, the combination of dostarlimab with carboplatin–paclitaxel has recently demonstrated a significant improvement in OS in the overall population, with a more pronounced reduction in the risk of death observed in patients with MMRd disease. ¹⁸

The positive efficacy outcomes of ENGOT-EN-6-NSGO/GOG-3031/RUBY and NRG-GY018 trials led to the approval of dostarlimab and pembrolizumab, respectively, in combination with carboplatin and paclitaxel, followed by anti-PD-1 monotherapy maintenance, for the frontline treatment of advanced or recurrent EC, regardless of MMR status, by both the FDA and the EMA. In addition, durvalumab in combination with carboplatin and paclitaxel, followed by durvalumab maintenance therapy (GOG-3041/ENGOT-EN10/DUO-E), was approved by both agencies, only for patients with MMRd disease.

To date, MMRd status is considered one of the most robust predictive biomarkers of response to ICI across multiple solid tumors, including EC, ¹⁹ although important limitations remain. Indeed, approximately 50% of patients with advanced or recurrent MMRd EC treated with ICIs in the post-platinum setting fail to achieve an objective response.^9,10,12 In the first-line setting, approximately 40% of patients with MMRd EC receiving chemoimmunotherapy combinations progressed within the first 12 months, whereas the remaining ~60% exhibited a durable plateau beyond 12 months despite extended follow-up.^17,20,21

Collectively, the available efficacy data indicate heterogeneous outcomes in patients with MMRd EC treated with ICIs: a subset experiences early disease progression, whereas another subset achieves deep and durable benefit, in both the post-platinum and frontline settings. This pattern, characterized by an early event rate followed by a plateau, is consistent with underlying biological heterogeneity within the MMRd EC subgroup. Given this variability, there is a pressing need for more precise predictive biomarkers, beyond MMRd status alone, to identify patients at risk of early progression on ICIs and to prioritize their enrollment in clinical trials evaluating alternative or combinatorial therapeutic strategies.

In this review, we evaluate the predictive value of established biomarkers, including Tumor Mutation Burden (TMB) and PD-L1 expression, for response to ICIs in the MMRd EC population. We further appraise emerging biomarkers across genomic, epigenomic, transcriptomic, and proteomic features of the tumor and its microenvironment, and examine machine-learning (ML) approaches to refine patient selection and monitor ICI response (Figure 1). Finally, we briefly consider how insights into mechanisms of ICI resistance may be translated into novel therapeutic strategies.

Figure 1.

Overview of predictive biomarkers of response to anti-PD-(L)1 therapy in MMRd EC.

Tumor mutation burden

TMB, measured as the number of non-synonymous somatic mutations per megabase, has emerged as a putative predictor of ICI benefit across malignancies. High TMB can be driven by endogenous DNA repair impairment or exogenous mutagenic exposures (UV light, tobacco). Yet, clinical implementation is challenged by nonstandardized thresholds and platform-dependent differences in TMB estimation. ²² In EC, high TMB frequently co-occurs with MMRd status, where an impaired DNA mismatch repair pathway yields an accumulation of insertions/deletions and frameshift alterations. High or ultra-high TMB can also result from polymerase proofreading defects (e.g., POLE). ⁴ Across etiologies, increased TMB generally corresponds to greater neoantigen load and tumor immunogenicity, potentially increasing the likelihood of benefit from ICIs. ⁸

In the prospective biomarker analysis of the multicohort, open-label, phase II KEYNOTE-158 trial, the association between TMB and outcomes was evaluated in patients with previously treated advanced solid tumors receiving pembrolizumab. Using a prespecified cutoff of ⩾10 mutations/Megabase by FoundationOne CDx, TMB-high status correlated with a greater likelihood of objective response versus non-TMB-high in a large cohort (n = 790) spanning 10 tumor types (ORR 29% vs 6%). The median duration of response was not reached in the TMB-high group and was 33.1 months in the non-TMB-high group. These results supported FDA approval of pembrolizumab for TMB-high solid tumors.^21,22 In the evaluable EC subset (n = 82), 18% were TMB-high; the ORR was 46.6% in TMB-high versus 4.9% in non-TMB-high disease. ²¹

In a post hoc biomarker subgroup analysis of the GARNET trial, dostarlimab monotherapy efficacy was examined across biomarkers, including TMB. TMB data were available for 62.7% of patients with MMRd/MSI-H EC. Among these, 86.5% were TMB-high (⩾10 mutations/Mb by FoundationOne) and 13.5% were TMB-low. Within the MMRd cohort, ORR was 47.8% in TMB-high versus 21.4% in TMB-low. ⁹ These findings indicate substantial overlap between TMB-high and MMRd/MSI-H in EC, limiting the additive predictive value of TMB. Nonetheless, TMB may identify a small subset of MMRd EC with low TMB and poorer responses to immune checkpoint blockade. Notably, a proportion of MMRd/TMB-high tumors do not respond to ICIs. Multiple factors may contribute to this resistance, spanning from tumor-intrinsic genomic and epigenomic alterations and tumor-microenvironmental features, several of which remain incompletely elucidated. We analyze these determinants and the available evidence in the sections that follow, focusing on MMRd EC and incorporating data from other MMRd malignancies.

PD-L1 immunohistochemistry

PD-L1 has emerged as a potential predictor for ICI efficacy across multiple malignancies, in particular non-small cell lung cancer and bladder cancer. However, the use of PD-L1 immunohistochemistry (IHC) as a predictive biomarker is confounded by multiple factors, including differing IHC assays and cut-offs, processing variability, temporal and spatial heterogeneity, oncogene-driven versus induced PD-L1 expression, and staining of tumor versus immune cells. ²³ Indeed, the predictive value of PD-L1 expression remains notably limited across FDA-approved indications for ICIs. ²⁴

In the EC population, PD-L1 expression has been reported more frequently in MMRd tumors compared to MMRp counterparts. ²³ A systematic review of 10 studies evaluating PD-1/PD-L1 expression in EC stratified by MSI status found that PD-L1 was expressed in 49% of MSI tumors versus 33.5% of microsatellite-stable tumors. It is important to note that these studies showed substantial methodological variability in PD-L1 assessment, including differences in antibodies, scoring, and cut-offs. Notably, substantial intrapatient heterogeneity in PD-L1 expression has been reported between primary tumors and their corresponding metastatic lesions,^25,26 further undermining its predictive value in this context.

PD-L1 expression as a predictive biomarker of response to ICIs has not been widely adopted in clinical practice for patients with EC, primarily due to its limited predictive performance. Indeed, clinical efficacy observed among patients with PD-L1-positive EC, regardless of MMRd status, treated with ICIs has been generally modest.

In a phase Ib (KEYNOTE-028) study of pembrolizumab in advanced PD-L1-positive advanced solid tumors, the 24 patients with recurrent metastatic EC had an ORR of 13% (3 patients with partial response), and an additional 13% (3 patients) achieved stable disease. Of the three patients achieving a partial response, one had non-MSI-H EC with a POLE mutation, one had non-MSI-H EC, and the other had an unknown status. In this study, the expression of PD-L1 was assessed using a laboratory-developed prototype IHC assay and the 22C3 antibody, and PD-L1 positivity was defined as membranous staining in at least 1% of the tumor and inflammatory cells or positive staining in the stroma. ²⁷

Similarly, the efficacy of atezolizumab and nivolumab monotherapy was explored in phase I clinical trials in EC populations, regardless of MMRd status, with PD-L1 expression assessed in exploratory biomarker analyses.

In the atezolizumab study, the reported ORR in the whole uterine cancer cohort (15 patients) was 13.3%. As part of an exploratory endpoint, PD-L1 expression was assessed using the Ventana PD-L1 SP142 IHC assay, with expression evaluated separately on tumor and immune cells and classified based on the percentage of positive cells. Overall, 33% patients had PD-L1 ⩾ 5% on tumor-infiltrating immune cells. Notably, the only two partial responses occurred in tumors with PD-L1-positive (PD-L1 ⩾5% on tumor-infiltrating immune cells), one of which was confirmed to be MSI-H. ²⁸

In the nivolumab study, the ORR in the uterine corpus cancer cohort (22 patients) was 23%. PD-L1 expression was assessed using the PD-L1 IHC 28-8 pharmDx assay, with positivity defined by a tumor proportion score ⩾1%. Notably, the ORR was similar between PD-L1-positive (25%) and PD-L1-negative (21%) subgroups. Both MSI-H tumors in the cohort were responders, one PD-L1-positive and the other PD-L1-negative. ²⁹

As stated above, MMRd tumors are often correlated with elevated PD-L1 expression, likely reflecting a highly immunogenic microenvironment. The post hoc biomarker analysis of the GARNET trial evaluated the association of PD-L1 expression and MMRd status and the efficacy of dostarlimab. PD-L1 expression was assessed using the VENTANA PD-L1 assay (SP263), with Combined Positive Score (CPS) ⩾1 corresponding to positive PD-L1 expression. CPS is calculated as follows: PD-L1-positive tumor cells + immune cells/total viable tumor cells × 100. Among patients with MMRd/MSI-H EC and available CPS status, the study reported PD-L1 positivity (CPS ⩾1) in 72% of cases. The ORR in the MMRd CPS ⩾1 EC subgroup was 54.9%, compared to 31.3% in those with CPS <1. Given the substantial overlap between PD-L1 and MMRd status, the additional predictive utility of PD-L1 alone in this context appears limited. However, when CPS ⩾1 was combined with TMB-high status within the MMRd EC subgroup, present in 44.5% of patients, the predictive performance seemed to improve, with an ORR of 60.4% compared to 20% in the subgroup with TMB-low and CPS <1. Those with a single marker, either TMB-high or CPS ⩾1, demonstrated intermediate ORR of 47.8% and 54.9%, respectively. ⁹ These findings suggest that an increased tumor neoantigen load as a consequence of TMB-H status, coupled with an immunologically permissive tumor microenvironment due to CPS ⩾1, may enhance the likelihood of response to dostarlimab in the MMRd EC population.

Additionally, an exploratory PFS subgroup analysis of the first-line, phase III NRG-GY018 trial found that the benefit of adding pembrolizumab to carboplatin–paclitaxel did not seem to differ across PD-L1 strata within the MMRd cohort: hazard ratio (HR) 0.27 (95% confidence interval (CI), 0.16–0.47) for CPS ⩾1 and 0.30 (95% CI, 0.11–0.83) for CPS <1. The CPS <1 subgroup comprised only 16.4% of the MMRd cohort and included few events, limiting precision. Overall, these data support the conclusion that PD-L1 status did not meaningfully impact the magnitude of PFS benefit from pembrolizumab in this subset. ²¹

MLH1 promoter methylation

Emerging evidence suggests that not all mechanisms of mismatch repair deficiency confer equivalent sensitivity to ICIs. In particular, MLH1 promoter methylation and germline or somatic MMR gene mutations may be associated with differing response patterns.

MLH1-methylated EC constitutes a distinct clinicopathologic and immunologic subtype compared to ECs harboring germline or somatic MMR mutations. Several studies have consistently demonstrated that MLH1-methylated tumors present with advanced-stage disease at diagnosis, high-risk pathological features, and tend to have a higher risk of recurrence and poorer global survival outcomes.^30–33 From a genomic standpoint, MLH1-methylated ECs exhibit significantly lower TMB compared to germline or somatic MMR-mutated EC.^30,34 Furthermore, multiple studies have also demonstrated that MLH1-methylated ECs display a distinct immune landscape, characterized by significantly lower T-cell tumor infiltration and immune co-regulatory molecules expression compared to MMR-mutated tumors.^{28,31,33–37} Collectively, these observations indicate attenuated antitumor immune activation in MLH1-methylated EC, which may limit the likelihood of benefit from ICIs compared with MMR-mutated disease. These clinicopathological and immune distinctions motivate a mechanistic consideration of how MLH1 methylation might shape MMR function and ICI responsiveness.

Intratumoral heterogeneity of MLH1 methylation has been reported in MMRd EC.^38–40 Such heterogeneity may generate regional variation in MMR function (mixed MMRd/MMRp areas) and could contribute to variability in ICI outcomes. By contrast, MMR gene-mutated MMRd ECs (Lynch syndrome or double-somatic inactivation) more commonly exhibit uniform loss of mismatch repair, which may underlie differences in clinicopathological features and response to ICI compared with MLH1-methylated counterparts. Nevertheless, direct evidence linking MLH1-methylation heterogeneity to ICI efficacy is lacking and warrants prospective evaluation.

Two small-cohort studies suggested that MLH1 promoter methylation may be associated with reduced benefit from pembrolizumab. Bellone et al. ⁴¹ reported a lower ORR in MLH1-methylated tumors compared with MMR gene-mutated dMMR cases (44% vs 100%), as well as inferior 3-year outcomes (PFS: 30% vs 100%; OS: 43% vs 100%). In line with this, Borden et al. ⁴² observed a lower ORR in the methylated subgroup than in the mutated subgroup (41.7% vs 83.3%) and significantly shorter recurrence-free survival and OS. Additionally, a larger study showed a trend toward reduced OS in patients with MLH1-methylated ECs following ICI therapy. ³³ However, these observations have not been confirmed in larger biomarker exploratory analyses conducted across three different clinical trials. In the post hoc analysis of the GARNET trial, among patients with MMRd EC, 66% patients exhibited a MLH1/PMS2 loss and 11.2% had a loss of MSH2/MSH6. Reported ORR was 48.9% and 56.2%, respectively. Among patients with available MMR gene mutation data (n = 101), no notable differences in ORR were observed based on the underlying mechanism of MMR deficiency. The ORR with dostarlimab monotherapy was 41.7% for MLH1 loss with MMR gene mutation and 39.4% in those with MLH1 loss without a detectable MMR gene mutation. It is important to note that MLH1 promoter methylation status was not directly assessed in this study. ⁴³ Similarly, a post hoc analysis of the RUBY trial, among MMRd EC patients with available MMR gene mutation data (n = 88), no significant differences in PFS or OS were observed between those with MMRd EC due to gene mutations and those with presumed epigenetic MMRd EC, in the dostarlimab arm. In this analysis, MMR loss-of-function mutations were identified through whole-exome sequencing (WES), and MMR protein loss in the absence of mutations in MMR genes was used as a surrogate marker for epigenetic MMR deficiency. ⁴⁴ Moreover, in an exploratory analysis of NRG-GY018, among 222 patients with centrally confirmed MMRd tumors, 84% had MLH1 promoter methylation and 16% did not. The mechanism of MMR deficiency did not significantly impact the PFS benefit from adding pembrolizumab: the HR for investigator-assessed PFS was 0.30 (95% CI, 0.18–0.50) in the methylated subgroup and 0.17 (95% CI, 0.04–0.79) in the non-methylated subgroup, noting the small size and event count in the latter subgroup. ²¹

Beyond its potential clinical impact on ICI response, the mechanism underlying MMR deficiency in EC may impact the mode of immune response to ICI. Longitudinal single-cell RNA-sequencing (scRNA-seq) of circulating immune cells revealed that effector CD8⁺ T cells correlated with the regression of MMR-mutated ECs. In contrast, activated CD16⁺ NK cells correlated with ICI response in epigenetic MMRd tumors. These findings underscore the interplay between tumor-intrinsic and extrinsic factors in shaping the response to ICI in MMRd EC. ³⁴

In summary, the predictive value of MLH1 promoter methylation for ICI efficacy in MMRd EC remains uncertain, with conflicting evidence across studies. As such, it should not currently guide clinical decision-making. Clarifying its role as a predictive biomarker will require larger, well-powered studies using standardized, quantitative assays to assess MLH1 methylation, prospective comparisons of methylated versus non-methylated MMRd EC, and deeper mechanistic investigations of biological differences that may modulate ICI sensitivity.

Genomic determinants of response/resistance to ICI in MMRd EC

Despite the high TMB that characterizes MMRd EC, a subset of patients does not respond to ICIs, prompting investigations into additional genomic determinants involved in immune response beyond TMB. These include the extent of MSI, indel load, mutational signatures, clonality of neoantigen burden, as well as specific point mutations.

A study integrating both human and murine data demonstrated that the genome-wide extent of MSI and the resulting mutational load influence response to anti-PD-1 therapy and shape tumor evolution in MMRd settings. In this context, therapeutic efficacy was particularly associated with the accumulation of indel mutational load. However, these findings require validation in larger MMRd and anti-PD-1-treated patient cohorts, including MMRd EC. ⁴⁵

Another study employed a mutational signature-based classification of MMRd tumors and showed that this approach could distinguish ICI-responders (ICI-Rs) from ICI Non-Responders (ICI-NRs). Tumors exhibiting single base substitutions signatures (SBS) signatures 26 or 54 (mutational signatures enriched for T > C substitutions) were associated with reduced benefit from ICIs. Tumors with high SBS26 burden exhibited overexpression and recurrent mutations in genes involved in double-strand break repair and other DNA repair pathways, alongside features of chromosomal instability (CIN). This CIN likely stems from replication fork instability, contributing to copy number losses and downstream immune evasion mechanisms. In contrast, SBS54 was not associated with CIN but rather with increased transcriptional activity, defining a biologically distinct subtype. ⁴⁶ As most of these data were derived from colorectal and esophagogastric cancers, further validation in other tumor types, such as MMRd EC, is warranted before generalizing these findings.

Notably, in MMRd gastrointestinal cancers, genomic data analyses from patients treated with ICIs suggested that clonal neoantigen load, rather than subclonal neoantigen burden, was associated with improved responses. However, the relevance of clonal versus subclonal neoantigen load in MMRd EC and its impact on ICI response remains unexplored. ⁴⁷

Several studies have characterized the mutational landscape of MMRd EC, with a focus on genetic alterations that may confer resistance to ICIs, particularly those affecting interferon-gamma (IFNγ) signaling and the tumor antigen presentation machinery. Notably, loss-of-function mutations in JAK1/2 (Janus kinase 1/2) can impair IFNγ pathway signal transduction, thereby enabling tumor cells to evade CD8⁺ T-cell-mediated cytotoxicity. Similarly, mutations in β2-microglobulin (B2M), a gene involved in major histocompatibility complex class I folding and transport to the cell surface, compromise antigen presentation capacity and contribute to immune escape. Various clinical studies have associated these mutations with ICI resistance across different tumor types, especially melanoma and MMRd colorectal cancer.^48,49

In EC, a study using WES of MMRd tumors from patients treated with pembrolizumab in a phase II clinical trial identified genetic alterations potentially linked to resistance to immune checkpoint blockade. In one case of primary resistance, an in-frame deleterious mutation in the B2M gene was identified in the lung metastasis but was absent in the primary EC. In a case of secondary resistance, an abdominal metastasis showed an acquired homozygous truncating mutation in the JAK3 gene, suggesting this alteration emerged under therapeutic pressure. ⁴¹

Another study investigated the genomic determinants of de novo resistance to ICI from patients who were enrolled in a phase II clinical trial of avelumab. Among evaluable MMRd EC cases (n = 10), all seven ICI-NRs to avelumab harbored either JAK1 (six tumors) or B2M mutations (one tumor), while only one of the three ICI-Rs harbored a JAK1 mutation. ⁴⁹

Similarly, WES of MSI-H/MMRd EC samples (n = 24) revealed that 42% of the cases (10 of 24) exhibited pathogenic/deleterious mutations in JAK1 before treatment. Contrary to prior reports, pretreatment JAK1 mutations were not associated with primary resistance to pembrolizumab in this cohort: 70% (7 of 10) of the JAK1-mutant tumors responded to pembrolizumab, compared to 50% (7 of 14) of the non-mutant tumors. To further understand the relationship between JAK1 alterations and ICI response, WES was also conducted on eight epigenetic MMRd EC samples collected during pembrolizumab treatment. Three of these tumors (two ICI-NRs and one ICI-R) acquired JAK1 frameshift mutations that were not detected in the pretreatment samples. These emergent mutations may reflect clonal selection under immune pressure induced by ICI, consistent with acquired resistance. ³⁴

Further studies in larger cohorts are warranted to validate the role of JAK1/2 and B2M mutations as determinants of primary or acquired resistance to ICI in MMRd EC, with the ultimate goal of establishing these alterations as predictive biomarkers of resistance in this population.

Transcriptomic and proteomic signatures predictive of response to ICIs in MMRd EC

Various studies have recently explored the potential utility of transcriptomic and proteomic signatures in predicting response to ICI in MMRd EC.

An RNA sequencing study of MMRd ECs (n = 23 pre-treatment tumor samples; 14 ICI-Rs and 7 ICI-NRs) treated with pembrolizumab revealed a significant enrichment of fibroblast and endothelial cell transcriptomic signatures in ICI-NRs compared to ICI-Rs. Notably, expression of the fibroblast marker TAGLN and several endothelial-associated genes (EMCN, KDR, MMRN1, MYCT1, PEAR1, PTPRB, and TEK) was elevated in association with primary resistance to pembrolizumab. ⁵⁰ These findings were further supported by imaging mass cytometry, which showed a trend toward higher densities of activated fibroblasts (SMA⁺, MFAP5⁺) and endothelial cells (CD31⁺) in ICI-NRs. Additionally, ICI-NRs exhibited significantly higher total and activated regulatory T cells in both tumor and stromal compartments. ⁵¹

A study based on High-plex spatial transcriptomics of 45 MMRd ECs identified a 14-gene signature that stratifies tumors into “hot,” “intermediate,” and “cold” subtypes according to their distinct immune profiles, aligning with CD8⁺ T-cell infiltration status. Loss of HLA-I and elevated DNA (cytosine-5)-methyltransferase 3 alpha (DNMT3A) expression correlated with lower CD8⁺ T-cell presence. Validation analyses further supported the existence of a coregulatory network involving HLA-I and DNMT3A, potentially linked through dynamic crosstalk mediated by the chemokine CCL5. ⁵² These findings warrant further investigation into the predictive value of these biomarkers in MMRd ECs treated with ICIs, as well as into the mechanistic role of DNMT3A in modulating antitumor immunity to inform rational combination therapies.

Recent proteomic studies in MMRd EC have defined immune-related signatures that characterize the tumor immune microenvironment and are associated with differential response or resistance to ICIs.

Our laboratory conducted a biomarker-discovery study using spatial multiplexed profiling to compare the immune landscape of ICI-R versus ICI-NR MMRd ECs, profiling up to 16 immune markers. Overall, ICI-NRs showed significantly lower CD8⁺ T cell density, absence of terminally differentiated T cells, lack of mature tertiary lymphoid structures (CD23⁺), mature dendritic cells (DC-LAMP⁺), and loss of HLA-I. Moreover, 80% of ICI-NRs lacked PD-L1 expression on intraepithelial immune cells, though 60% upregulated alternative immune checkpoints. Clustering analysis of these 16 immune markers revealed three distinct immune phenotypes: high T-cell infiltrated, intermediate T-cell infiltrated, and immune desert, with marked differences in ICI response. To translate these findings into clinical practice, a ML approach (Brute-Force Feature Selection and Learning Vector Quantization Model) was applied to reduce dimensionality, resulting in a four-marker immune signature (intraepithelial CD8⁺, stromal CD8⁺, HLA-I, and PD-L1) that retained strong predictive power. This simplified model accurately identified immune clusters and reliably predicted ICI response, including a 100% non-response rate in the immune desert subgroup. ⁵¹ Further studies are needed to validate the predictive performance of this multiparametric immune biomarker in a larger and independent MMRd EC population.

Similarly, a multiplexed immunofluorescence analysis of MMRd uterine and ovarian cancers treated with nivolumab in a phase II clinical trial demonstrated that the presence of dysfunctional (CD8⁺PD-1⁺) or terminally exhausted (CD8⁺PD-1⁺TOX⁺) T cells, as well as their interaction with PD-L1⁺ immune cells, was independently and positively associated with PFS at 24 weeks. ⁵³

ML approaches to predict ICI benefit in EC

Predicting benefit from ICIs requires integrating tumor-intrinsic signals (e.g., TMB, MSI/MMRd status, antigen-presentation pathways), the state of the tumor immune microenvironment (T-cell infiltration/activation and myeloid programs), and clinical covariates. ML approaches are well-suited to combine these heterogeneous inputs into more discriminative models than any single biomarker.

Pan-cancer studies that included EC cases illustrate this. A random-forest model (“RF16”) trained on a large, ICI-treated cohort across 16 cancers combined genomics (e.g., TMB, HLA metrics, copy-number/aneuploidy) with laboratory and clinical data, consistently outperformed TMB alone for predicting benefit and survival. EC cases were part of the training set, but disease-specific, and especially MMRd-specific, performance was not reported and still needs dedicated validation. ⁵⁴ An early proof-of-concept study used a support-vector machine trained on TCGA pan-cancer genomic and transcriptomic profiles (whole-exome DNA-seq and bulk RNA-seq across 29 cancers) with a surrogate “response” label (high TMB plus low TGF-β signaling). This is conceptually useful for integrating tumor-intrinsic and microenvironmental biology, but it was not validated in ICI-treated cohorts and should be considered hypothesis-generating. ⁵⁵

EC-focused studies mostly derive prognostic signatures and then infer ICI sensitivity rather than measure it directly. Wang et al. analyzed TCGA-UCEC bulk RNA-seq and somatic mutation data with single-sample Gene Set Enrichment Analysis (ssGSEA) and Weighted Gene Co-Expression Network Analysis for pathway scoring and co-expression grouping, respectively, and used LASSO to build a six-gene immune response score (LTB, GPR18, BATF, ACAP1, GRAP2, CTSW). The score separated outcomes and immune activation. “Low-risk” tumors appeared immunologically “hotter” and were predicted (via TIDE/SubMap) to benefit more from PD-1/CTLA-4. Notably, the authors highlighted SLC38A3 as a pro-tumor candidate with in vitro and in vivo supportive experiments. ⁵⁶ Zhou et al. pooled multiple cohorts (public RNA-seq and microarray datasets, including TCGA-UCEC), used unsupervised clustering of inflammatory genes plus LASSO-Cox regression to generate a 12-gene risk score. The low-risk group had better survival, higher CD8⁺ infiltration, and predicted ICI sensitivity. The study also provided functional follow-up on P2RX4 (e.g., knockdown in Ishikawa cells) as a mechanistic lead. ⁵⁷ Liu et al. generated a simple two-gene immune score derived from TCGA-UCEC RNA-seq following ssGSEA-based stratification. This signature was prognostic and indicated putative benefit from ICIs and chemotherapy, in silico, with only limited qPCR/IHC validation of its gene components. ⁵⁸ Wu et al. integrated TCGA-UCEC RNA-seq, somatic mutation calls (whole-exome), copy-number data, and DNA-methylation profiles into a multi-omics LASSO model that predicted OS. The “low-risk” class was enriched for MSI-H (indirectly linking to MMRd) and was again predicted to be more ICI-sensitive. The analysis emphasized ARID1A alterations within the signature, though without experimental validation. ⁵⁹ Finally, Jiang et al. combined scRNA-seq from ECs and TCGA-UCEC bulk RNA-seq data to build a T-cell-exhaustion-associated long non-coding RNA model. The favorable-risk group showed a more inflamed immune milieu and predicted ICI responsiveness, with functional work implicating MIEN1 in tumor biology. ⁶⁰

In sum, transcriptomic and multi-omics ML-based signatures in EC suggest that composite models outperform single biomarkers for risk stratification and putative ICI prediction. The current evidence base is largely retrospective, TCGA-derived, and inference-based. Critically, MMRd-specific model development and validation are missing. Moving these ML predictive models into practice will require prospective, MMRd-stratified evaluation in ICI-treated EC cohorts, standardized analysis pipelines with external validation, and reporting not only discrimination (C-index/AUC) but also calibration and clinical utility (e.g., decision-curve analysis).

From resistance prediction to intervention: Strategies to reverse ICI resistance

As understanding of ICI resistance in MMRd EC grows, including contributions from suboptimal neoantigen quality, impaired antigen processing/presentation, attenuated interferon signaling, and an immunosuppressive tumor microenvironment, multiple therapeutic strategies are being explored in EC to restore antitumor immunity or bypass immune escape. Although a full therapeutic review is beyond the scope of this article, key global approaches include checkpoint intensification and exhaustion rescue, rational combinations with targeted agents, and DNA damage response modulators to improve immune infiltration and tumor antigenicity, cytokine- or immune agonist-based strategies to enhance immune activation, and advanced immunotherapy platforms such as vaccines and adoptive cellular therapies. Checkpoint intensification and exhaustion rescue strategies are actively being explored, notably dual immune checkpoint blockade with nivolumab ± ipilimumab in recurrent MMRd EC (NRG-GY025; NCT05112601), additional dual-checkpoint combinations (durvalumab plus tremelimumab; NCT06680739), next-generation CTLA-4 antibodies (botensilimab; NCT03860272), and biomarker-driven approaches targeting alternative inhibitory receptors such as TIGIT in MSI-H disease (atezolizumab plus tiragolumab; AFT-50 EndoMAP; NCT04486352). In parallel, combinations aiming to reprogram the tumor microenvironment and overcome immune exclusion include anti-angiogenic and tyrosine kinase inhibitor-based approaches such as pembrolizumab plus lenvatinib in the frontline setting (LEAP-001; NCT03884101), nivolumab plus cabozantinib in advanced or recurrent EC (NCT03367741), and atezolizumab plus bevacizumab in recurrent disease (NCT03526432), as well as emerging regimens integrating chemotherapy and VEGF blockade with novel immunotherapies (e.g., QL1706 plus chemotherapy and bevacizumab; NCT06917092). Beyond angiogenesis targeting, strategies designed to enhance tumor antigenicity and immunogenicity through DNA damage response modulation are also under investigation, including the platinum-free triplet of atezolizumab, bevacizumab, and rucaparib in EndoBARR (NCT03694262) and ATR/PARP-based combinations with immunotherapy (ceralasertib plus olaparib and durvalumab; NCT04065269), alongside approaches aimed at expanding the repertoire of immunogenic peptides in highly mutated tumors (ATAPEMBRO: pembrolizumab plus ataluren; NCT04014530). Additional strategies to increase immune activation include cytokine-based immunotherapies such as IL-2 pathway agonism (MDNA11 ± pembrolizumab; ABILITY-1; NCT05086692) and other emerging agents (e.g., STAR0602; NCT05592626; NP137 (anti-Netrin-1) plus chemotherapy and pembrolizumab; NCT04652076). Finally, advanced immunotherapy modalities aimed at generating or providing immune effector function are also being explored in treatment-resistant EC, including vaccine and oncolytic virus approaches (AdHER2DC vaccine plus pembrolizumab and lenvatinib; NCT06253494; oncolytic virus R130; NCT05812677), cellular therapies such as tumor-infiltrating lymphocytes (NCT06481592; NCT01174121), anti-HER2 CAR macrophages (NCT04660929), neoantigen-specific TCR-T cells (NCT05194735), and immune-engaging bispecific antibodies designed to redirect T-cell activity toward tumor antigens (e.g., CC-3 (CD276 × CD3)).^61,62

Conclusion

ICIs have transformed the management of MMRd EC, yet nearly half of patients fail to achieve an objective response or progress early in both post-platinum and frontline settings. Robust predictors of resistance, therefore, remain an unmet clinical need. Established biomarkers, including TMB and PD-L1 expression, provide limited predictive value owing to overlap with MMRd status, non-standardized assays, and pronounced temporal and spatial heterogeneity. MLH1 promoter methylation has shown inconsistent associations with ICI response and is not suitable for guiding clinical decisions. Beyond TMB, more granular genomic features, such as MSI extent, indel load, and clonal neoantigen content, may offer incremental predictive value. Transcriptomic and proteomic signatures, particularly those capturing tumor–microenvironment interplay, and emerging ML models that integrate multi-omic and clinicopathological data, are promising avenues to improve patient selection.

A deeper understanding of ICI resistance biology, including immune exclusion, compensatory checkpoint pathways, metabolic adaptation, and epigenetic reprogramming, will be essential to optimize treatment strategies and inform rational combination approaches. Ultimately, prospective validation in well-annotated MMRd cohorts, with harmonized analytic methods and longitudinal sampling, is required before these biomarkers can be adopted into routine practice.