Revisiting Genomic Instability, Tumor Microenvironment and Immune Response in High-Grade Serous Ovarian Cancer

Abstract

High-grade serous tubo-ovarian cancer is the most common and aggressive type of ovarian cancer characterized by extensive genomic instability and marked inter- and intra-patient tumor heterogeneity. Tumor-site specific signaling crosstalk between cancer cells and the tumor microenvironment influences different tumor ecosystems that drive therapy response and disease progression. Cancer cell-intrinsic genomic aberrations further contribute to the diversity of the tumor immune landscape. Homologous recombination deficiency is considered a key oncogenic driver in 50% of the cases underlying distinctive mechanisms of tumor evolution. The heterogenous character of the tumor microenvironment represents a major challenge to identify predictive biomarkers of therapy response and to stratify subgroups amenable to immunotherapies.

Zusammenfassung

Das hochgradige seröse Tubo-Ovarialkarzinom ist die häufigste und aggressivste Art des Ovarialkarzinoms. Gekennzeichnet ist diese Tumorart durch genomische Instabilität sowie eine ausgeprägte Tumorheterogenität zwischen verschiedenen Patientinnen und verschiedenen Tumorlokalisationen innerhalb einer Patientin. In unterschiedlichen Tumorlokalisationen beeinflussen spezifische Signalwege zwischen Krebszellen und dem Tumormikromilieu verschiedene Tumorekosysteme, die sich auf das Therapieansprechen und die Krankheitsprogression auswirken. Krebszellenspezifische genomische Defekte tragen zusätzlich zur Diversität der Tumor-Immunantwort bei. Eine homologe Rekombinationsdefizienz gilt in 50% der Fälle als wichtiger onkogener Faktor für unterschiedliche Mechanismen der Tumorevolution. Der heterogene Charakter dieser Tumormikromilieus stellt eine große Herausforderung dar bei der Identifizierung von prädiktiven Biomarkern für das Therapieansprechen und bei der Stratifikation von Untergruppen, die auf eine Immuntherapie ansprechen.

Keywords

high-grade serous ovarian cancer - genomic instability - immune response - tumor heterogeneity

Schlüsselwörter

hochgradiges seröses Ovarialkarzinom - genomische Instabilität - Immunreaktion - Tumorheterogenität

Introduction

High-grade serous tubo-ovarian cancer (HGSC) is the most common and aggressive type of ovarian cancer, and the second leading cause of death among gynecological cancers [1]. Intra-abdominal dissemination and non-specific late symptoms are major obstacles to early disease detection leading to 75% of cases diagnosed at advanced stages with a 5-year survival rate of 30% [2]. At the mutational level, HGSC is characterized by chromosomal aberrations, copy number alterations, nearly ubiquitous TP53 mutation, frequent loss of RB1, NF1, and PTEN, and a very low tumor mutational burden (TMB) [3] [4] [5] [6]. Loss of p53 function is a key early event in HGSC tumorigenesis, leading to chromosomal instability. HGSC exhibits extensive genomic rearrangements, including telomeric allelic imbalances, large-scale structural transitions, and loss of heterozygosity, creating a permanent “genomic scar” [7] [8] [9]. Approximately 50% of HGSCs show homologous recombination deficiency (HRD), considered an early oncogenic driver that further provokes chromosomal instability. Indeed, ovarian cancer has the highest HRD score in a pan-cancer analysis [10]. The HRD phenotype results from BRCA1/2 loss-of-function mutations in 30% of cases. In addition, alterations in other genes involved in homologous recombination (HR)-mediated DNA repair (ATM, ATR, BAP1, BARD1, BRIP1, CHEK1, CHEK2, MRE11A, NBN, PALB2, RAD50, RAD51, XRCC2) contribute to the HRD-positive phenotype ([Fig. 1]). Aberrations in non-HR DNA repair pathways are prevalent in distinct ovarian cancer histotypes with different therapeutic implications [11].

Fig. 1 Homologous recombination repair pathway of double-strand breaks (for a detailed description see info box). Created with Biorender.com [rerif]

BRCA1/2 mutations disrupt the high-fidelity HR DNA repair pathway. In turn these cells depend on error-prone DNA repair mechanisms such as non-homologous end-joining (NHEJ), polymerase Theta (POLQ)-mediated end-joining (also referred to as alternative end-joining, altEJ), or single-strand annealing (SSA) [23] [24]. HRD imparts sensitivity to DNA-damaging platinum-based chemotherapy, favoring better progression-free and overall survival [10]. It also enables the use of PARP (Poly [ADP-ribose] polymerase) inhibitors as a synthetic lethal treatment by preventing the repair of single-strand DNA breaks. These breaks persist and ultimately convert to double-strand DNA breaks that lead to cell death in HR-deficient cells [25]. Patients with BRCA1/2 mutations who achieved a complete or partial response to first-line platinum-based chemotherapy have shown a significant survival benefit with seven years overall survival of 67% from PARP inhibition (PARPi) with olaparib, as demonstrated in the SOLO1 trial [26]. PARPi implementation delayed disease recurrence and the need for second chemotherapy, and has now shifted to earlier application in the course of the disease as frontline maintenance therapy after surgical debulking and chemotherapy based on the SOLO1, PAOLA-1, ATHENA-MONO and PRIMA trials [27] [28] [29] [30].

Despite advances, many patients experience disease progression or recurrence under standard of care first-line treatment. Molecular markers predicting therapy resistance are under investigation but not yet in clinical use. Of note, heterogenous disease courses are governed by marked inter- and intra-patient and tumor microenvironment heterogeneity, affecting both therapy response and disease progression. Here, we aim to revisit the multifaceted aspects of tumor heterogeneity in HGSC at the level of tumor mutational context and the tumor microenvironment.

Mechanisms and Markers of Therapy Resistance in HGSC

Platinum resistance

Patients with platinum-resistant HGSC represent a heterogeneous group. The definition of platinum responsiveness after relapse has been revisited and is now based on the treatment-free interval after platinum therapy (TFIp) [31]. Shorter TFIp (< 6 months) is linked to reduced survival due to limited treatment options [32] [33]. Diverse mechanisms contribute to these variable treatment responses [34] [35] [36]. For example, resistance can arise via clonal selection of quiescent, drug-resistant cells from a pool of genomically heterogenous sub-clones in the chemo-naïve state, secondary mutations, or adaptive epigenetic changes [36] [37]. The latter may represent a transient resistant state induced by potentially druggable targets. Further, reversion mutations in BRCA1/2 or loss of BRCA1 promoter methylation can restore DNA repair capacity.

About 10–20% of patients with HGSC are platinum-refractory, showing no initial response [38]. Even so, validated predictive markers or genomic signatures of platinum resistance remain absent from clinical use [35]. Partial BRCA function correlates with poorer survival and may go undetected in standard BRCA mutation testing [39]. Genes forming other DNA repair pathways that resolve platinum DNA-adducts, for example nucleotide excision repair genes (CDK7, CETN2, LIG3, TFIIH), are often overexpressed in resistant HGSC [40]. PARP4, among the most overexpressed DNA repair genes in platinum-resistant HGSC, has been linked to multidrug resistance, although its role remains unclear [40] [41]. Further investigations into PARP4 as a marker and target for platinum-resistant HGSC are warranted, especially in light of already existing PARP inhibitors.

Copy number analysis of tumors from platinum-resistant patients revealed more frequent amplification of CCNE1, KRAS and PIK3CA compared to platinum-sensitive patients [40] [42]. CCNE1 and CDK2 facilitate cell cycle progression, replication initiation and centrosome duplication [43]. CCNE1 amplification induces replication stress and chromosomal instability [44], and CDK2 inhibition sensitizes CCNE1-amplified ovarian cancer cells to DNA damaging agents [45]. CCNE1 is one of the most common driver gene amplifications [46] and should be further evaluated as a negative predictor of response to chemotherapy and PARPi. High KRAS signaling disrupts G2 and mitotic checkpoint controls, causing chromosomal instability platinum-resistance in vitro [47] [48]. PI3K signaling dysregulation also promotes chemoresistance, and PI3KCA gene amplification may contribute to oncogenic signaling [49].

Tumors overexpressing negative cell cycle regulators (PTEN, APC) show poor chemotherapy responsiveness, possibly due to slower proliferation allowing enhanced DNA repair [40]. Conversely, high levels of proliferation markers (PCNA, MKI67) correlate with better long-term survival [50]. Single-analyte biomarker studies may fail to capture the full range of resistance mechanisms. However, a multi-protein prediction model integrating proteomics, whole genome sequencing, and bulk RNA-sequencing on a cohort of primary chemo-naïve HGSC tumors revealed heterogeneous resistance mechanisms [51]. In this model, enriched pathways included translational and rRNA processing, cell-cycle, metabolism (oxidative phosphorylation and tricarboxylic acid cycle), hypoxia, EMT, and TGFβ, suggesting subtype-specific treatment approaches [51].

PARPi resistance

Roughly 50% of patients with a BRCA1/2 mutated (BRCAm) ovarian cancer relapse within seven years following PARPi maintenance therapy [26]. Mechanisms of resistance to PARPi overlap only partly with those for platinum resistance [37] [52]. Clinically relevant relapse mechanisms include reversion mutations in BRCA1, BRCA2, RAD51B, RAD51C and RAD51D, and loss of BRCA1 promoter methylation, all of which restore HR repair [36] [53] [54] [55]. The BRCA1-Δ11q isoform promotes resistance to platinum and PARPi by skipping transcription of the germline mutation and is linked to poor overall survival [56] [57]. Sequence alterations such as MRE11 amplification, which leads to increased DNA end-resection and HR, and TP53BP1 loss can compensate for BRCA1 loss, but may present opportunities for therapeutic targeting [56] [58].

Despite improved patient prognosis with current therapies, post-relapse treatment selection remains critical. Several studies suggest that prior PARPi treatment may reduce subsequent chemotherapy efficacy [59] [60] [61] due to overlapping resistance mechanisms. In the phase III ARIEL4 trial, 7% of patients had BRCA1/2 reversion mutations after platinum-based chemotherapy [62], compared to 26% after PARPi treatment [63]. Reversion mutation impact varies by location within different BRCA1/2 protein domains. Alternative DSB repair pathways like NHEJ and MMEJ, may drive these mutations and could be targeted to prevent or delay PARPi resistance [62] [64]. Other resistance mechanisms under PARPi included upregulation of the multidrug efflux pump gene ABCB1, CCNE1 amplification, and downregulation of SLFN11, a DNA/RNA helicase that contributes to resistance to replication stress-inducing agents [65] [66]. Further molecular analysis is needed to understand post-PARPi chemotherapy response.

Of note, prior PARPi exposure does not always preclude benefit from PARPi retreatment. For example, the OReO trial showed a modest significant improvement of progression-free survival with PARPi rechallenge in both the BRCAm and non-BRCAm cohorts [67]. For this trial, eligible patients had prior partial or complete responses to platinum. Ongoing preclinical and clinical studies are testing novel combinations of PARPi with inhibitors of AKT, PI3K, ATR, CHK1, WEE1, and HDAC, which may provide new options for HGSC patients [68] [69].

Determinants of the Anti-Tumor Immune Response

While PD-1/PD-L1-targeting immunotherapies show clinical success in many cancer types, their efficacy in unselected ovarian cancers remains limited [70] [71]. This is largely attributed to distinct mutational signatures and heterogenous tumor immune phenotypes, making ovarian cancer particularly resistant to immunotherapy. Although biomarkers like tumor-infiltrating lymphocytes (TILs), PD-L1 expression, high microsatellite instability/defects in mismatch repair and TMB have been established in other cancers, clinical trials have shown their limited predictive value in ovarian cancer [72].

The presence of TILs, especially CD8+ T cells, correlates with improved prognosis in ovarian cancer and long-term survivorship in selected subgroups [73]. HGSC tumors display variable TIL patterns that correspond to known molecular HGSC subtypes: epithelial immune-infiltrated (“inflamed”), stromal restricted immune-infiltrated (“excluded”) and non-infiltrated (“desert”) [74] [75]. However, HGSC classification by gene expression signatures and TIL presence lacks cross-study robustness and clinical use [76] [77] [78]. This is partly due to the diversity within the intra-tumoral CD8+ T cell compartment, including naïve (CD8+PD-1−TOX−TCF1−), activated/predysfunctional (CD8+PD-1+TOX−), exhausted/dysfunctional (CD8+PD-1+TOX+), and heterogenous memory (CD45RA−/CD45RO+) T cells, as well as various transitional states. Moreover, TIL presence does not always indicate anti-tumor reactivity, with many T cells identified as bystanders lacking cytotoxic function [79] [80]. Immune cells residing within the malignant compartment also appear to differ transcriptionally from those in the stromal compartment [80].

The outcome of the anti-tumor immune response is further shaped by spatially distinct tumor microenvironments (TME). Variability in immune cell composition and functional states within the TME complicates the use of PD-L1 expression as a reliable biomarker [76] [77]. Interaction networks between malignant, stromal, and immune cell lineages and signaling pathways guiding T cell infiltration remain incompletely understood but are essential for immunotherapy response and biomarker development. The following sections discuss how spatial immune heterogeneity in HGSC underlies disease progression and therapeutic outcomes.

HGSC is Governed by Spatially Distinct Immune Ecosystems

HGSC typically spreads intra-abdominally, leading to multiple tumor sites with distinct TMEs. Comparative analyses of tumors from the ovary, omentum and ascites revealed significant variation in tumor and immune cell composition ([Table 1]). Limited migration of T cell clusters between sites further underscores the non-representative nature of single-site sampling and its implications for treatment planning [81] [82]. Intriguingly, tumor site-specific immune dynamics appear to depend on underlying mutational processes.

Table 1 Comparison of immune changes in different tumor niches.

Tumor niche	Immune characteristics
Primary tumor	Chronic antigen exposure leading to dysfunctional CD4+ and CD8+ T cells JAK-STAT signaling PD-L1 upregulation on cancer cells and macrophages suppressing T cell cytotoxicity Immunosuppressive cells Regulatory T cells M2-macrophages Myeloid-derived suppressor cells
Peritoneal implants	Reduced antigen recognition Reduced interaction between cancer cells and immune cells
Omental implants	High T cell infiltration pattern correlated with pro-inflammatory immune cell infiltrates: M1-like PD-L1+ macrophages CD4+ T cell Tissue resident memory CD8+ T cells Plasmablasts, plasma B cells Low T cell infiltration pattern resembling an immune-excluded phenotype: Cancer-associated fibroblasts Stromal localization of CD4+ and non-exhausted CD8+ T cells Few interactions of T cells and macrophages with PD-L1+ cancer cells Reduced interferon signaling Reduced MHC class I expression
Ascites	Lack of chronic antigen exposure Low MHC-I and II expression on cancer cells Increased naive/stem-like (TCF1+LEF1+) CD8+ T cells Increased central memory (IL7R+TCF1+) CD4+ T cells Fibroblast-derived IL-6 signaling Low-anti-tumor immunity

Primary tumor sites

Adnexal tumors, often the primary tumor sites, are enriched in dysfunctional CD4+ and CD8+ T cells, indicating chronic cancer antigen exposure and adoptive immune exhaustion during early carcinogenesis [81] [82]. MHC-mediated antigen presentation activates type I interferon signaling, triggering the JAK-STAT pathway activation and PD-L1 upregulation in cancer cells and macrophages [82]. PD-L1/PD-1 engagement in turn suppresses T cell cytotoxicity as part of an immune regulatory feedback loop. Primary tumor sites also contain immunosuppressive regulatory CD4+CD25+FOXP3+ T cells (Tregs), tumor-associated/M2 macrophages, and myeloid-derived suppressor cells (MDSCs), further reinforcing immune evasion [81] [82]. Tregs, expressing CTLA-4, inhibit cytotoxic T cells [83], and their proximity to CD8+ TILs correlates with poor prognosis [83] [84].

Metastatic niches

Tumor cell dissemination to secondary sites is associated with site-specific TME evolution patterns. High CD8+ T cell abundance but reduced antigen recognition were evident in intraperitoneal tumors compared to primary tumors. Bowel metastatic sites showed increased interaction between PD-L1-expressing cancer cells and activated/predysfunctional T cells, along with JAK-STAT signaling [82]. In contrast, ascites samples were enriched in naive/stem-like (TCF1+LEF1+) CD8+ and central memory (IL7R+TCF1+) CD4+ T cells, but contained fewer dysfunctional CD4+ and CD8+ T cells or dendritic cells, indicating limited antigen exposure and low MHC-I/II expression [82]. The ascites microenvironment is further dominated by fibroblast-derived IL-6, activating JAK/STAT signaling and promoting tumor growth and drug resistance [85]. Although ascites appears to be an immune-infiltrated TME, its low antigen presentation suggests reduced anti-tumor reactivity and immune characteristics that are hostile for an adaptive immune response. Ascitic fluid, for example, suppressed the major glucose transporter GLUT1 in CD4+ T cells leading to activation of the unfolded protein response, and ultimately to endoplasmic reticulum (ER) stress. ER stress activates the IRE1α-XBP1 signaling pathway inducing metabolic dysfunction and reduced mitochondrial activity in CD4+ T cells correlating with reduced IFNγ production and reduced T cell infiltration [86]. Additional immunoregulatory metabolites, such as 1-methylnicotinamide (MNA), contribute to intratumoral T cell dysfunction in HGSC, reduced IFNγ production and diminished antitumor activity in vitro [87]. Mature neutrophils in the ascitic TME exert T cell suppression that is initiated by complement pathway signaling (via complement receptor 3 and C1q), and subsequent inhibition of CD8+ effector T cell differentiation [88]. Ovarian cancer clustered into an ‘upregulated complement’ group in TCGA data indicating that complement activation plays a role in ovarian carcinogenesis [89].

The omentum, a common metastatic niche, displays heterogenous T cell infiltration patterns. Highly infiltrated samples show co-infiltration of pro-inflammatory immune cells, including M1-like PD-L1+ macrophages, CD8+ tissue-resident memory T cells (TRM), CD4+ T cells, plasmablasts, and plasma B cell clusters supporting an effective anti-tumor CD8+ TIL response [90]. TRM T cells (CD8+CD103+CD69+CD62L−) can be reactivated by anti-PD-L1 therapy, exert cytotoxic function, and are associated with longer survival [90] [91] [92] [93]. B cells colocalize with CD8+ T cells in tertiary lymphoid structures (TLSs) and enhance immune activation through a robust, prognostically favorable CD8+ TIL response [94] [95] that includes increased chemokine signaling and coordinated antigen presentation between M1 macrophages or dendritic cells and T and B cells [90] [96] [97] [98]. However, TLSs in HGSC are often immature, marginally located, and contain CD8+TIM3+PD-1+ T cells associated with resistance to immune checkpoint inhibition – factors that could partially explain the limited responsiveness of HGSC to anti-PD-1/PD-L1 treatment [95].

Conversely, omental samples with low T cell infiltration exhibit an immune-excluded phenotype, with stromal CD4+ and non-exhausted (CD8+PD-1−GZMB−) CD8+ T cells, reduced interferon signaling, and impaired antigen presentation [81] [82]. These regions are dominated by cancer-associated fibroblasts (CAFs), which regulate extracellular matrix remodeling, immune cell distribution, and intercellular signaling by PDGFB, IL-6, TGFB1/2 and CXCL12-CXCR4 signaling [97] [99] [100] [101] [102] [103]. CAF-rich TMEs correlate with poor disease outcomes, therapy resistance and early relapse [96] [97] [104]. Stroma-dominated tumors respond poorly to neoadjuvant chemotherapy (NACT), whereas immune-rich tumors show better responses, underscoring the contribution of cytotoxic immune cells to successful chemotherapy [102]. Fibroblast-mediated regional barriers generate immune privileged sites that dampen tumor-immune cell engagement, contributing to immunotherapy failure and highlighting that TIL abundance alone is insufficient for predicting immune response in HGSC. Elevated tumor immune dysfunction and exclusion, T cell exclusion, and CAF scores in HGSC further support a high degree of immune escape in this cancer type [105] [106]. Better characterization of common stromal phenotypes may improve HGSC classification. These exemplary differences in TME composition and cellular activity states underscore that the immune TME in ascites samples does not represent solid tumors.

Mutational Processes are Associated to Distinct HGSC Immunophenotypes

Cancer cell-intrinsic genomic aberrations contribute to the heterogeneity of the tumor immune landscape. While the molecular regulators of tumor immunity in HGSC remain incompletely understood, recent studies highlight how genomic instability influences TME composition and cellular networks with implications for therapy response and resistance.

Copy number alterations, including gene deletions and amplifications, were shown to disrupt immune regulation and promote immune evasion. A cancer-cell intrinsic transcriptional program was linked to T and NK cell infiltration across HGSC samples and tumor sites [80]. Chemokines (CCL5, CXCL10, CXCL9 and CXCL16), oxidative stress genes (GPX3 and SOD2), and immune response genes – such as those involved in antigen presentation (MHCI, CIITA, HLA-A/HLA-B/HLA-C), interferon gamma signaling (IDO1, JAK1, STAT1), and cell adhesion (ICAM1, ITGAV and ITGB2) – were associated with an “immune-infiltrated” phenotype and may serve as future biomarkers for immunotherapy [80]. Conversely, expression of genes involved in Wnt signaling (CTNNB1, FZD3/FZD4/FZD6, FGFR2, WNT7A), epigenetic regulation (DNMT3A, HDAC1/HDAC11/HDAC4/HDAC5), and cell differentiation (BMP7, BMPR1A, ETV4, FGFR1/FGFR2, S100A4, SMAD4) negatively correlated with T and NK cell infiltration, suggesting a link between these pathways and immune evasion [80].

Tumor evolution

The pronounced tumor heterogeneity observed in HGSC stems in part from polyclonal tumor evolution, with subclones diverging after early clonal alterations in TP53, BRCA1/2, PTEN, RB1 and NF1 [56] [107] [108]. Emerging evidence suggests that the current HGSC molecular subtypes may reflect late subclonal events, which has therapeutic implications, as targeted therapies may miss heterogenous, resistant subclones [107] [108]. Subclonal heterogeneity also impacts TME composition through variable expression of signaling molecules that shape local immune networks [109]. Rather than discrete subtypes, HGSC may follow a continuous evolutionary trajectory from early “differentiated” to late “proliferative” tumors [107] [110]. Differentiated tumors exhibit low copy number alterations, near-normal ploidy, high lymphocyte infiltration, fallopian tube gene signatures, and correlate with lower patient age [107]. In contrast, proliferative tumors show high ploidy, frequent genome duplication, extensive amplifications, loss of epithelial features, immune evasion, and correlation to higher patient age [107]. Autopsy samples from end-stage HGSC, often highly aneuploid, display reduced infiltration of CD8+PD-1+ TILs, Tregs, plasma and myeloid cells, indicating a shift towards an immunosuppressive, protumorigenic microenvironment in advanced disease [56].

Whole genome doubling

HGSC exhibits one of the highest rates of whole genome doubling (WGD) (40%), correlating with poor overall survival [111]. WGD is associated with decreased immune cell infiltration, IFNγ-signaling, STING and MHC-II expression – all of which are hallmarks of an immunosuppressive TME [111] [112] [113] [114]. MHC-II-expressing ovarian cancer cells generate immune-active hotspots, enhancing IFNγ expression and cellular crosstalk [115]. Such hotspots are linked to improved immunotherapy in other cancers [116] [117]. Histone deacetylase inhibition (e.g. entinostat) has been shown to restore MHC-II expression in ovarian cancer cells and suppressed tumor growth in vitro [118]. As WGD increases TMB and, potentially, immunogenicity, reduced MHC-II expression may allow immune evasion in genetically unstable tumors, providing a potential therapeutic target in HGSC.

Tumor mutational burden and neoantigens

Despite wide-spread genomic instability, HGSC generally has low TMB [119] [120]. While DNA repair defects can increase TMB, levels remain low compared to highly immunogenic tumors such as lung cancer and melanoma, and even among gynecological cancers [6] [121]. Unlike cervical cancer, where TMB predicts immune checkpoint inhibitor response, this marker lacks predictive value in ovarian cancers [122] [123].

HRD signature and BRCA1/2 mutation status

HRD-positive HGSCs, particularly those with BRCA1/2 mutations, often show higher neoantigen load, lymphoid cell infiltration and improved overall survival ([Table 2]) [124]. BRCA1/2-mutant tumors exhibit elevated inflammatory signaling (e.g. CCL5, CXCL9/10, interferon-related gene expression) [74] [115] [125] [126] and active immune signaling, including PD-L1+ cancer cells engaging with CD8+ and CD4+ T cells [82] [126]. Interactions were also found between CD8+ T cells and antigen-presenting (IBA1+ CD11c+) macrophages, between CD4+ T cells and (CD11c+) antigen presenting cells, and both CD8+ T cells and CD4+ T cells with B cells, indicating a coordinated immune response [126]. However, the immune response varies between BRCA1 and BRCA2 mutations [127], partly due to a dual role of cGAS-STING signaling. This pathway senses cytoplasmic DNA that is released at increased levels by chromosomal instability. It activates interferon signaling and NF-kB responses that can be tumor-suppressive or -promoting depending on extent and duration of STING-activation [128] [129] [130] [131].

Table 2 Immune differences between HRD and non-HRD tumors.

HRD ovarian cancer	Non-HRD ovarian cancer
Increased lymphoid cell infiltration	Immune cell exclusion
Increased interaction of PD-L1+ cancer cells with CD8+ and CD4+ T cells	Low interaction between cancer cells and CD8+ T cells
Increased interaction between CD8+ T cells and APCs	Increased interaction between M2-macrophages and stromal cells
Increased interaction between CD8+ T cells and B cells	Increased expression of TGFβ
Increased expression of CCL5, CXCL9/10, interferon-related genes

Non-HRD HGSC and CCNE1 amplification

Non-HRD HGSC typically presents with an immunosuppressive TME, featuring a high-proliferative stroma, immune exclusion, scarce CD8+ T cell-tumor interactions, increased naïve T cells, and predominant engagement of M2-like (IBA1+CD163+CD11c+) macrophages with the stroma [82] [126] [132]. In HRD-negative tumors with CCNE1 amplification, heightened cancer cell-intrinsic TGFβ signaling at metastatic sites suggests a role in driving immune suppression and metastasis [82] [126]. As TGFβ signaling promotes T cell exclusion, CAF activation, and chemoresistance, it represents a promising target to modulate tumor immune composition [101] [133]. In vivo, TGFβ inhibition restores MHC-I expression on ovarian cancer cells [101], increases CD8+ T cell and NK cell activation and infiltration, and induces M1-polarization of TAMs [133]. These effects support ongoing clinical trials exploring TGFβ inhibition in HGSC [134].

Immunoregulatory Role of Standard-of-Care Treatments and its Combination with Immunotherapies

Standard treatments of HGSC, including cytoreductive surgery, chemotherapy, PARPi, and anti-angiogenesis treatment, modulate the immune TME with potential to impact immunotherapy efficacy ([Table 3]).

Table 3 Influence of standard-of-care treatment on immune environment of ovarian cancer.

Tumor niche	Immune characteristics
Surgery	Unfavorable tumor-intrinsic characteristics and immune TME composition promoting tumor metastasis, unfavorable dissemination pattern
Sub-optimal tumor removal	Increased regulatory T cells Increased TGFβ levels
Complete tumor removal	Increased CD8+ T cell cytotoxicity
Chemotherapy	Reduced regulatory T cells Increased number of CD8+ T cells Increased CD8+ T cell cytotoxicity Granzyme A, perforin 1, IFNγ Increased interaction between antigen presenting cells and T cells Increased interaction between PD-L1+ macrophages and CD8+ T cells Stimulation of B cell differentiation Reduced levels of TNF, IL-6, IL-8 Increased expression of MHCI, PD-L1+ on tumor cells
PARP inhibition	Increased number of CD8+ and CD4+ T cells Increased activation of cGAS-STING Increased expression of PD-1/PD-L1
VEGF inhibition	Endothelial normalization Reduced hypoxia Increased number of CD8+ and CD4+ T cells Increased CD8+ to CD4+FOXP3+ T cell ratio

Surgery

Surgery is a main pillar in HGSC treatment since complete tumor resection has significant prognostic benefits. Ongoing research aims to identify cancer-intrinsic markers predicting the presence of miliary peritoneal metastasis that impede maximal tumor resectability and surgical success [135] [136]. Differences in tumor cell-intrinsic characteristics and immune cell composition impact miliary versus non-miliary dissemination patterns [137] [138]. Incorporating radiomic tumor quantitative analyses, diagnostic biopsies and unfavorable tumor immune characteristics may enhance predictive models.

Further, timing and extent of cytoreductive surgery critically affect patient prognosis, through reduction of tumor cell load and modulation of the immune microenvironment [139] [140] [141]. Elevated TGFβ levels and increased numbers of circulating CD4+ Tregs (CD4+/CD25+/FOXP3+) were observed following sub-optimal tumor removal compared to optimal primary debulking [140]. Primary cytoreductive surgery also enhanced the potential of CD8+ T cells to secrete IFNγ [139]. The favorable prognostic impact of CD8+ T cells was restricted to patients undergoing complete tumor resection and was absent in those with residual disease [142]. Notably, the immunomodulatory benefits of surgery were more pronounced when cytoreduction was performed as primary treatment modality [139].

Chemotherapy

Chemotherapy induces immunosuppression due to bone marrow cytotoxicity and has further immunomodulatory effects. NACT consisting of carboplatin/paclitaxel reshapes the immune landscape mostly by reducing CD4+ Tregs, increasing CD8+ T cell counts, and promoting spatial redistribution and interaction of CD8+ T cells with macrophages [139] [143] [144] [145]. Good responders exhibit stronger cytotoxic T cell responses, characterized by coexpression of granzyme A and perforin-1, elevated IFNγ production [143], and higher T cell receptor clonality, suggesting that chemotherapy induces or unmask (neo)antigens in post-NACT patients [146]. This tumor antigen release likely boosts B cell activation and differentiation, as indicated by an increase in class-switched memory B cells [147]. Chemotherapy-induced immunoregulation also involves elevated levels of dendritic cells, PD-L1+ macrophages, and increased antigen presentation signaling [148] [149] [150]. In responders, pro-tumorigenic cytokines such as TNF, IL-6, IL-8 are reduced, while IL-6 among other immune-related genes are more highly expressed in non-responders [143] [148]. Tumor cells upregulate MHC-I and PD-L1 in response to chemotherapy treatment [144] [146] [149] [150]. Overall, these effects promote an orchestrated immune response that could favor immune checkpoint inhibition. However, clinical trial results remain mixed. The phase III JAVELIN Ovarian 100 trial found no benefit from adding anti-PD-L1 (avelumab) therapy to carboplatin and paclitaxel in the first-line setting [151], while the phase II NeoPembrOv trial, which combined pembrolizumab (anti-PD-1) with NACT showed modest improvements in response rate, resectability, and overall survival [152]. Of note, spatial and molecular analyses from NeoPembrOv revealed notable TME changes in responders, including increased CD8+PD-1+ T cell density within tumor islets and reduced distance to tumor cells [153]. A high monocyte signature, low endothelial signature, and high CD8B/FOXP3 expression ratio were associated with better survival and may serve as predictive biomarkers for immunotherapy response [153].

PARP inhibition

PARPi modulates the tumor microenvironment by enhancing IFNγ-mediated STING response and upregulating PD-L1 expression, and increasing microvascular density and VEGFA expression in BRCA1-deficient in vivo models [130] [154]. It also induces antitumor immunity through induction of effector CD4+ and CD8+ T cells [154]. In PARPi-resistant BRCA1-deficient HGSC, macrophages shift toward an immunosuppressive, pro-tumorigenic M2 phenotype via STAT3 signaling [155] [156]. Enhancing DNA damage through PARPi activates cGAS/STING and improves anti-PD-1/anti-PD-L1 efficacy, with strong results in preclinical [154] [157] [158] and early phase I/II clinical studies [159] [160]. However, the phase III ANITA/ENGOT-Ov41/GEICO 69-O clinical trial found no added benefit from combining niraparib maintenance treatment and atezolizumab (anti-PD-L1) following platinum-based chemotherapy in patients with recurrent ovarian cancer and a platinum-free interval > 6 months [161].

VEGF inhibition

Angiogenic factors like VEGF and TGFβ in the HGSC TME promote tumor progression, ascites formation and immunosuppression [162] [163]. VEGF recruits immunosuppressive Tregs and pro-tumorigenic M2 macrophages, inhibits dendritic cell maturation, and reduces lymphocyte infiltration by inducing endothelial dysfunction, and downregulating cell adhesion molecules and chemokines [163] [164]. The VEGF receptor VEGFR2 was identified as a resistance factor to anti-PD-1 treatment in the NeoPembrOv trial [153]. Bevacizumab (anti-VEGF) normalizes tumor microvasculature, reduces hypoxia-driven signaling [163], and enhances immune infiltration by boosting cytotoxic CD8+ and CD4+ T cells, and increasing the CD8+/CD4+/FOXP3+ T cell ratio, which correlates with improved progression-free survival [165]. Although combining anti-angiogenesis therapy with immune checkpoint inhibition was hypothesized to be synergistic, the phase III clinical trial AGO-OVAR 2.29 failed to demonstrate significant improvements in progression-free and overall survival (ClinicalTrials.gov: NCT03353831, [166]). Similarly, in the ongoing ATALANTE/ENGOT OV-29 phase III clinical trial for platinum-sensitive recurrent ovarian cancer, combining atezolizumab with platinum-based chemotherapy and bevacizumab did not improve progression-free survival. Here, overall survival follow-up data are still pending [167].

Triple combination therapy

Triple therapy is under investigation in the first-line setting as well as for maintenance therapy. The KEYLYNK-001 trial (ClinicalTrials.gov: NCT03740165) demonstrated a benefit in progression-free survival for pembrolizumab (anti-PD-1) and olaparib after upfront chemotherapy in newly diagnosed BRCA wildtype patients. Triple maintenance therapy for platinum-sensitive relapsed ovarian cancer was investigated in the MEDIOLA phase II trial. Germline BRCAm patients treated with olaparib and durvalumab (anti-PD-L1) showed excellent overall response rate of 92% [160]. In the non-BRCAm subgroups, triple maintenance therapy with bevacizumab in addition to olaparib and durvalumab showed an overall response rate of 87% compared to 34% without bevacizumab, indicating that especially for patients with non-BRCAm platinum sensitive relapsed ovarian cancer, triple maintenance therapy could be of clinical relevance regardless of previous therapy with bevacizumab. Also, durvalumab and olaparib treatment in combination with bevacizumab maintenance therapy after chemotherapy showed improved progression-free survival compared to bevacizumab only maintenance therapy in newly-diagnosed ovarian cancer patients with non-BRCAm tumors in the phase III DUO-O trial (ClinicalTrials.gov: NCT03737643). The benefit was slightly higher for HRD-positive patients than for HRD-negative patients. Since this trial lacked an olaparib/bevacizumab control arm, individual contributions of PARPi and anti-PD-L1 treatment or a synergistic effect of the triplet maintenance therapy remained inconclusive.

HRD Score and Predictive Markers of Immunotherapy Revisited

Currently, HRD and BRCA1/2 mutation status are the only validated genomic markers predicting response to platinum-based and PARPi therapy [11] [47]. However, they do not correlate with clinical benefit from immune checkpoint inhibition [168] [169]. Clinical HRD assays measure the “genomic scar” from accumulated mutations, insertions/deletions, and genome rearrangements, but various non-equivalent HRD assays exist [170]. Importantly, HRD scores reflect past, not functional current HR capacity in a tumor [55] [171]. PARPi resistance due to reversion mutations despite high HRD scores yields inconsistent outcomes. Further, HRD scores remained stable after NACT, while mutational and immune signatures changed, further underscoring their limited utility in capturing tumor dynamics or predicting the response to immune checkpoint inhibition [172]. Hence, debate continues on which surrogate best represents functional HRD status. Current candidates are classification of HRD status discriminated by mechanism (genetic/epigenetic), immune-related gene signatures, immunogenomic profiling and immune cell phenotype analysis [69] [72] [108] [173] [174] [175]. For example, antigen presenting cells are key interactors of CD8+PD-1+ T cells, and were more abundant in patients with recurrent ovarian cancer responding to anti-PD-1 therapy. Further validation of clinical application for candidate markers is necessary.

Future Prospects in Immunomodulatory Therapy for HGSC

Adoptive immunotherapies, including TIL therapy and genetically engineered chimeric antigen receptor (CAR) immune cells, represent promising future treatment strategies. Targets under investigation in advanced preclinical and phase I clinical trials include Mesothelin (ClinicalTrials.gov: NCT03054298) [176] [177], MUC16 (NCT03907527) [178] [179], HER2 [180], NY-ESO-1 (NCT01567891, NCT03691376, NCT02457650, NCT02869217, NCT03159585), and Claudin 6 (NCT04503278). CD137, a co-stimulatory T cell receptor used in CAR constructs, is being studied for its role in enhancing the efficacy of adoptive T cell therapy [181] [182] [183]. It marks a tumor-reactive CD3+ TIL subset and may serve as a prognostic marker, with potential for better T cell sub-phenotyping [181] [182] [183] [184].

Folate receptor alpha (FRα), targeted by the EMA approved antibody-drug conjugate mirvetuximab soravtansine, is being explored as target of a chimeric costimulatory receptor (CoStAR). TILs engineered to express an extracellular anti-FRα-specific receptor domain and an intracellular CD28/CD40 costimulatory signaling domain showed enhanced T cell effector function, tumor control, and survival in a HGSC patient-derived xenograft model [185]. Preclinical studies include oncolytic adenovirus expressing IL2 in combination with autologous TIL or NK cell injections to reshape the immunosuppressive TME [186] [187]. Whether adoptive cell therapy can overcome tumor heterogeneity and immunosuppressive TME in HGSC remains to be seen in clinical trials. Combinatorial CAR-T cell strategies may enhance tumor cell targeting [188], but improved design of CAR-T cell constructs and administration protocols are needed to reduce off-tumor effects and improve safety profiles. Several ongoing clinical trials are addressing these issues (ClinicalTrials.gov: NCT02498912, NCT05568680).

Understanding T cell metabolism is further critical to identify dysfunctional states during cancer progression and therapy response. CD8+ T cell dysfunction in HGSC has been linked to impaired lipid uptake and metabolic programming due to faulty FABP5 transport, which is mediated by TAGLN2 [189]. Targeting this pathway may restore immune function in ovarian cancer and improve the efficacy of T cell-based immunotherapies. Lastly, dysregulated innate immunological mechanisms are exploited as potential treatment targets since they can dampen immune checkpoint inhibition or adoptive cell therapies [88]. Targeting ER stress by IRE1α ablation in neutrophils in a HGSC mouse model unleashed endogenous and anti-PD-L1-elicited anti-tumor immunity [184]. Complement inhibition to abrogate the suppressive effects of mature neutrophils in the TME represents further therapeutic opportunities, but caution should be taken regarding the complex roles of the complement system in tumor immunity.

Conclusion

Disease outcome and treatment success in HGSC are shaped by the interplay between tumor-intrinsic DNA repair capacity, mutational and gene expression signatures, and interactions among tumor, stromal and immune cells within the TME ([Fig. 2]). Recent technological advancements in single-cell analysis and spatial imaging turned the spotlight on cellular diversity, phenotypic states, and the dynamics of immune cell networks that are crucial to the quality of the anti-tumor immune response. Co-existence of multiple distinct TMEs within a patient could explain failure of current immunotherapy approaches. Integration of relative quantity of anatomic tumor location in combination with novel molecular tests might improve the performance of predictive models for immunotherapies. Since TIL density is an insufficient predictive marker for immunotherapies, additional measures of a coordinated immune response including B cells and myeloid lineage cells more faithfully represent susceptibility to adaptive immunotherapy in recent clinical trials.

Fig. 2 Graphical summary of factors determining tumor heterogeneity in HGSC. Spatial and temporal heterogeneity in HGSC is shaped by the tumor-intrinsic capacity to repair DNA damaged sites, mutational and gene expression signatures that are associated with HR repair gene defects, and crosstalk between tumor, stromal and immune cells in the tumor microenvironment with implications for prognosis and therapy outcome. Created with Biorender.com [rerif]

Targeting the PD-1/PD-L1 engagement between cytotoxic T cells and tumor cells has been central to preclinical and clinical studies. However, immunosuppressive pathways in the TME beyond adaptive immune checkpoints dampen current immunotherapy approaches in HGSC. Innate immune components – such as neutrophils, macrophages, NK cells, and cGAS-STING signaling – significantly affect adaptive immunity. Single immunotherapeutic approaches may be insufficient to reinvigorate anti-tumor immunity in immune-escaped HGSC. Synchronous targeting of innate and adaptive immune regulators may hold promise for future treatment strategies.

Contributorsʼ Statement

L.R. researched data for the article and wrote the article. All authors contributed substantially to discussion of the content. L.R. and S.P. reviewed and edited the manuscript before submission.

Conflict of Interest

The authors declare that they have no conflict of interest.

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Publication History

Received: 11 January 2025

Accepted after revision: 11 May 2025

Article published online:
11 June 2025