Skip to main content
International Journal of Molecular Sciences logoLink to International Journal of Molecular Sciences
. 2023 Jun 29;24(13):10859. doi: 10.3390/ijms241310859

Current Understanding on Why Ovarian Cancer Is Resistant to Immune Checkpoint Inhibitors

Anna Pawłowska 1, Anna Rekowska 2, Weronika Kuryło 2, Anna Pańczyszyn 3, Jan Kotarski 1,*, Iwona Wertel 1
Editor: Carmela Ricciardelli
PMCID: PMC10341806  PMID: 37446039

Abstract

The standard treatment of ovarian cancer (OC) patients, including debulking surgery and first-line chemotherapy, is unsatisfactory because of recurrent episodes in the majority (~70%) of patients with advanced OC. Clinical trials have shown only a modest (10–15%) response of OC individuals to treatment based on immune checkpoint inhibitors (ICIs). The resistance of OC to therapy is caused by various factors, including OC heterogeneity, low density of tumor-infiltrating lymphocytes (TILs), non-cellular and cellular interactions in the tumor microenvironment (TME), as well as a network of microRNA regulating immune checkpoint pathways. Moreover, ICIs are the most efficient in tumors that are marked by high microsatellite instability and high tumor mutation burden, which is rare among OC patients. The great challenge in ICI implementation is connected with distinguishing hyper-, pseudo-, and real progression of the disease. The understanding of the immunological, molecular, and genetic mechanisms of OC resistance is crucial to selecting the group of OC individuals in whom personalized treatment would be beneficial. In this review, we summarize current knowledge about the selected factors inducing OC resistance and discuss the future directions of ICI-based immunotherapy development for OC patients.

Keywords: ovarian cancer, immune checkpoints, PD-1/PD-L1, TIGIT, immunotherapy, resistance, microRNA

1. Heterogeneity and Prognosis of Ovarian Cancer (OC)

Despite the progress made in the treatment of solid malignancies, ovarian cancer (OC) continues to be the most lethal gynecological cancer. In 2020, OC was diagnosed in 313,959 women, and as many as 207,252 patients died because of the disease. According to the prediction of the World Health Organization (WHO), in 2025, the number of newly diagnosed OC patients will total 823,315 [1].

The prognosis for patients with OC is poor because the disease symptoms are unspecific at early stages (stages I–II of the International Federation of Gynecology and Obstetrics—FIGO). Thus, OC in <70% of all cases is diagnosed at FIGO stages III and IV when the tumor has already spread to distant organs. Ovarian cancer at FIGO stages I and II is accounted as curable, and the five-year survival rates total 90% and 70%, respectively. In comparison, at the advanced stages of OC, the five-year survival rate drops below 30% [2,3,4,5].

Moreover, heterogeneity has an impact on the diagnosis and high mortality rate. Based on different morphology, the WHO classifies OC cases into several subtypes, including transitional-cell Brenner tumors, serous, mucinous, clear-cell, endometrioid carcinomas, and mixed and undifferentiated types. However, this classification is insufficient because it does not take into account the molecular background, the prognosis, or the etiology of the disease [6,7]. Histologically, high-grade serous ovarian carcinoma (HGSOC) accounts for approximately 80% of epithelial OC cases. Up to 75% of HGSOC cases are diagnosed at the advanced stages (FIGO stages III and IV) of the disease [8].

Kurman and Shih classified OC into two types, allowing for the dualistic model of carcinogenesis. Their classification includes genetic mutations, molecular biology, and histopathological background. Type I includes endometrioid, low-grade serous, seromucinous, mucinous, malignant Brenner tumor, and clear cell carcinoma. These are genetically stable carcinomas that are mostly diagnosed at early stages (FIGO stages I and II). The prognosis for patients with type I OC is favorable (with a mortality rate of 10% and a slow rate of disease progression) [9,10].

Unfortunately, the prognosis for patients with type II tumors is poor. The majority of cases (~75%) are diagnosed at advanced FIGO stages. Type II OC includes carcinosarcomas, undifferentiated carcinomas, and high-grade serous carcinomas. In contrast to type I, they are highly aggressive and develop rapidly. Ascites are a frequently occurring symptom. Type I and type II OC cases also differ in somatic mutations. In type I, the most frequent mutations include phosphatase and tensin homolog deleted on chromosome 10 (PTEN), extracellular signal-regulated kinase (ERK), AT-rich interactive domain-containing protein 1A (ARID1A), B-Raf proto-oncogene, serine/threonine kinase (BRAF), mitogen-activated protein (MAP), phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit α (PIK3CA), and Kirsten rat sarcoma viral oncogene homolog (KRAS). In type II OC, mutations in RB1 (gene encoding retinoblastoma protein), TP53 (Tumor protein P53), FOXM1 (forkhead box M1), genes encoding cyclin E, and Notch3 are the most frequent [9,10].

2. Treatment of Ovarian Cancer

The standard treatment of OC patients includes debulking surgery and carboplatin and paclitaxel chemotherapy as part of first-line treatment [11,12,13]. However, the majority of patients (~70%) suffering from advanced OC experience recurrences. As a result, the disease becomes non-sensitive to platinum-based chemotherapy [3,14]. The efficiency of second-line therapy that includes gemcitabine or pegylated liposomal doxorubicin (PLD) is poor. Thus, it is necessary to develop other treatment strategies with a view to improving long-term clinical outcomes [13,15].

Clinical trials have demonstrated that targeted molecular drugs improve the outcomes of patients with advanced OC [13]. The Food and Drug Administration (FDA) approved two biological medical preparations, namely bevacizumab (vascular endothelial growth factor inhibitor; VEGFi) in 2018 and olaparib (poly(ADP-ribose) polymerase inhibitor; PARPi) in 2014. The combination of these drugs was approved by the FDA in 2020 for breast cancer gene (BRCA)-mutated OC [16].

Despite the progress made in OC treatment, the prognosis for patients remains poor. This is related to the lack of screening biomarkers in clinical practice and the heterogeneity of the disease. The diagnosis of OC is primarily based on the presence of cancer antigen 125 (CA-125) in serum, diagnostic imaging, and laparoscopy. However, these approaches are insufficient to detect the disease at early stages [3,14,17,18,19,20].

Considering the unsatisfactory efficacy of standard therapies, interactions in the tumor microenvironment (TME) seem to be potential targets in OC treatment. The signals derived from TME manipulate the activity and functions of immune cells and lead to immune evasion by cancer cells via various mechanisms, including immune checkpoints (ICPs), such as programmed cell death pathways and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) [5,21,22,23,24,25,26,27]. It should be stressed that the activity of these molecules prevents autoimmunity in normal conditions, but their upregulation leads to the suppression of immune response [28]. Targeting immune checkpoints and their blockade by monoclonal antibodies (mAbs) lead to restoring the sensitization of the immune system to cancer cells [29].

Programmed cell death receptor 1 (PD-1) belongs to the CD27 immunoglobulin superfamily and is encoded by the programmed cell death 1 (PDCD1) gene (chromosome 2) [30]. It is expressed on CD4+ and CD8+ T cells and antigen-presenting cells (APCs), including B cells, dendritic cells (DCs), and monocytes/macrophages (MO/MA). The PD-1 receptor (CD279) ligands include programmed death-ligand 1 (PD-L1; CD274, B7-H1) and programmed death-ligand 2 (PD-L2; CD273, B7-DC). They are both expressed on APCs and tumor cells. The binding of the ligand (PD-L1 or PD-L2) on a tumor cell with PD-1 receptor on a T cell leads to the exhaustion of the T cell and the inhibition of its effector activity. Moreover, the interaction results in the enhanced secretion of proinflammatory cytokines, including interferon γ (IFN-γ), tumor necrosis factor α (TNF-α), and interleukin 2 (IL-2) [5,23,31,32]. Consequently, the ability of T cells to eliminate cancer cells is decreased, and they are able to escape immune surveillance. The PD-L1 expression in tumor cells, which is upregulated by chemopreventive factors, results in a decreased T cell activity targeting cancer cells and in promoting the tumor cells’ evasion of surveillance by immune cells. This suggests a relationship between immune resistance and chemotherapy in OC patients [33,34].

Another co-inhibitory molecule that plays a crucial role in OC progression and tumorigenesis is CTLA-4 [35]. It is a membrane protein expressed by activated T cells, constitutively regulatory T cells (Tregs), and is considered to be homologous to CD28, which is involved in the second step of T cell activation after the binding of an antigen and T cell receptor (TCR). Notably, CTLA-4 and CD28 share the same ligands, CD80 (B7-1) and CD86 (B7-2). However, the affinity of CTLA-4 for each ligand is 500–2500 times higher in comparison with that of CD28. In contrast to CD28 activity, the result of CTLA-4 and CD80/86 binding is the suppression of immune response [21]. The TCR signaling is suppressed, and T cells’ activity is inhibited by interactions between CTLA-4 expressed T cells and its ligands expressed on APCs in lymph nodes. In consequence, the anti-tumor immune response is suppressed by inhibiting the effector activity of T cells at an early stage of T cell activation [31,36].

The role of both ICPs, i.e., the PD-1/PD-L1/PD-L2 pathway and CTLA-4, in inhibiting anti-tumor response are similar. However, CTLA-4 regulates the immune response at an early stage in lymph nodes, whereas the PD-1/PD-L1/PD-L2 pathway regulates anticancer immune response at later stages in peripheral tissues [31].

Immunotherapies based on ICPs targeted against PD-1 and its ligands (PD-L1, PD-L2), as well as CTLA-4, turned out to be game-changers in the treatment of various malignancy types [37]. These immune checkpoint inhibitors (ICIs) improve the overall survival (OS) rate in malignancies with inflamed TME, non-small-cell lung cancer (NSCLC) [38,39,40], melanoma [41,42], renal cancer [43], head and neck squamous cell carcinoma (HNSCC) [44], and urothelial carcinoma [45].

Despite the fact that ICI immunotherapy is not as effective as in other solid malignancies (with the response rate to monotherapy in OC patients totaling 10–15%) [46,47], the clinical trials that are currently being conducted determine its impact in monotherapy and/or in combination with other agents, such as biological drugs or standard therapy, to improve OC patients’ outcomes [48,49]. The modes of action of selected ICPs and ICIs are presented in Figure 1.

Figure 1.

Figure 1

The modes of action of selected ICPs and ICIs.

It is noteworthy that OC, similar to breast cancer, is a hormone-dependent tumor in which steroid hormones (estrogen, progesterone) and their receptors (estrogen receptor (ER) and progesterone receptor (PR)) influence the disease progression. In addition to the fact that ERs are potential targets in OC treatment, their modulators and enzymes are also involved in estrogen synthesis [50,51].

Aromatase is an enzyme (synthetase) that is crucial in estrogen synthesis and its circulation. Its inhibitors, such as exemestane, letrozole, and anastrozole, inhibit the shift from androgen to estrogen, downregulating the circulating estrogen level [52,53].

Hormone therapy, including anti-estrogen treatment (tamoxifen) and aromatase [54] inhibitors, is efficient for ER-positive OC patients with recurrence episodes or advanced stages of the disease. Additionally, it appears as a treatment characterized by low toxicity. However, due to the heterogeneity of OC, studies conducted on small samples, variable expression of hormones on OC cells, and the lack of biomarkers, the therapeutic value of this kind of OC treatment is inconclusive. It should be highlighted that the network of interrelationships of hormonal modulation is complex and concerns not only estrogen, progesterone, their receptors, and aromatase but also signaling cascades, including the Janus kinase/signal transducers and activators of transcription (JAK-STAT), mitogen-activated protein kinases (MAPK), Src, and receptor tyrosine kinase [50,51,55,56]. Further multicenter clinical studies are necessary to confirm the efficacy of the treatment. However, the hormonal modulation is not the subject matter of this paper.

3. Clinical Trials in Ovarian Cancer

Currently, there are nine ICIs approved by the FDA for use in cancer treatment. These are divided into three groups: anti-PD-1/PD-L1 mAbs (pembrolizumab, nivolumab, cemiplimab, atezolizumab, durvalumab, avelumab), anti-CTLA-4 mAbs (ipilimumab, tremelimumab), and anti-LAG-3 mAbs (relatlimab) [54].

To date, ICIs have been approved for various types of malignancies. Pembrolizumab, nivolumab, and cemiplimab are anti-PD-1 mAbs approved for the treatment of melanoma, NSCLC, malignant mesothelioma, HNSCC, classical Hodgkin Lymphoma (cHL), primary mediastinal large B-cell lymphoma (PMLBCL), urothelial cancer, microsatellite instability-high (MSI-H) or mismatch repair (dMMR) deficient colorectal cancer (CRC), hepatocellular carcinoma (HCC), renal cell carcinoma (RCC), esophageal cancer, gastric cancer, gastroesophageal junction cancer, cervical cancer, endometrial cancer, high tumor mutational burden (TMB-H) cancers, cutaneous squamous cell carcinoma (cSCC), and triple-negative breast cancer [57,58,59].

Anti-PD-L1 mAbs, i.e., atezolizumab, durvalumab, and avelumab, are approved for the treatment of melanoma, NSCLC, small cell lung cancer (SCLC), HCC, urothelial carcinoma, biliary tract cancers (BTC), and metastatic Merkel cell carcinoma (MCC) [60,61,62].

CTLA-4 blocking mAbs are approved for the treatment of HCC, NSCLC, melanoma, renal cell carcinoma, mesothelioma, CRC, and cutaneous squamous cell carcinoma [55,56]. However, the incidence of immune-related adverse events (irAEs) is higher in cancer patients treated with ipilimumab as a single agent (86%) compared with the treatment with nivolumab alone (78%) and with combined therapy using both agents (95%) [57,58,63,64,65,66].

Relatlimab is a LAG-3 blocking mAb approved in combination with nivolumab for the treatment of unresectable or metastatic melanoma [67].

To date, OC has been one of the few tumors for which ICI-based treatment has not been approved, either as part of combined therapy or as monotherapy [68]. According to the European Society For Medical Oncology (ESMO) guidelines, the use of ICIs is not applicable in OC [69]. However, the National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines resonate and recommend using ICIs in certain cases, i.e., dostarlimab-gxly for recurrent or advanced dMMR or MSI-H tumors and pembrolizumab for MSI-H or dMMR solid tumors, as well as for patients with TMB-H tumors with ≥10 mutations/megabase [70].

The combination of immunotherapies based on ICPs, VEGFi, and PARPi, and the development of biomarkers of ICI efficiency appears to provide a promising strategy for OC treatment [68]. Researchers have recently focused on folate receptor alpha (FRα) which was found to be overexpressed in 70–90% of OC cases and, therefore, became a promising target for anticancer drug development [71]. In November 2022, a novel FRα-directed antibody and microtubule inhibitor conjugate (mirvetuximab soravtansine) was granted accelerated approval for use in FRα positive, platinum-resistant epithelial ovarian, fallopian tube, and peritoneal cancer [72].

According to ClinicalTrials.gov, there are 125 ongoing clinical trials concerning anti-PD-1 agents, 111 studies of anti-PD-L1 mAbs, and 26 research projects concerning anti-CTLA-4 mAbs in the treatment of OC [73]. Most of them are combined with other drugs and/or biological agents. The advanced-phase clinical trials (phases 3 and 4) concerning ICIs in OC treatment are presented in Table 1.

Table 1.

The advanced-phase clinical trials (phases 3 and 4) concerning ICIs in OC treatment.

NCT Number Acronym Condition mAbs
Anti-ICPs
Additional Drugs Participants Phase Company Ref.
NCT03598270 ANITA Recurrent
ovarian carcinoma
Atezolizumab placebo carboplatin
paclitaxel
niraparib
gemcitabine PLD
414 3 Grupo Español de Investigación en Cáncer de Ovario [74]
NCT03522246 ATHENA Epithelial ovarian cancer Nivolumab rucaparib
placebo
oral tablet placebo IV infusion
1000 3 Clovis Oncology, Inc. [75]
NCT03740165 - Epithelial ovarian cancer Pembrolizumab placebo for pembrolizumab
carboplatin
paclitaxel
olaparib
placebo for olaparib bevacizumab docetaxel
1367 3 Merck Sharp & Dohme LLC [76]
NCT03602859 FIRST First-line treatment of stage III/IV non-mucinous epithelial OC Dostarlimab (TSR-042) niraparib
standard care
dostarlimab-placebo
niraparib-placebo
1403 3 Tesaro, Inc. [77]
NCT05116189 - Platinum-resistant recurrent ovarian cancer Pembrolizumab paclitaxel bevacizumab
placebo for pembrolizumab
docetaxel
616 3 Merck Sharp & Dohme LLC [78]
NCT03353831 - early relapse ovarian cancer Atezolizumab bevacizumab
chemotherapy
placebos
550 3 AGO Research GmbH [79]
NCT02580058 JAVELIN OVARIAN 200 Platinum resistant/
refractory ovarian cancer
Avelumab PLD 566 3 Pfizer [80]
NCT03642132 JAVELIN OVARIAN PARP - Untreated advanced ovarian cancer Avelumab chemotherapy + avelumab followed by avelumab + talazoparib
chemotherapy + bevacizumab followed by bevacizumab
chemotherapy, followed by talazoparib maintenance
79 3 Pfizer [81]
NCT02718417 JAVELIN OVARIAN 100 Previously untreated patients with epithelial ovarian cancer Avelumab carboplatin paclitaxel 998 3 Pfizer [82]
NCT03038100 IMagyn050 Newly-diagnosed stage III or stage IV ovarian cancer Atezolizumab paclitaxel
carboplatin
bevacizumab
atezolizumab placebo
1301 3 Hoffmann-La Roche [83]
NCT02891824 ARCAGY/GINECO GROUP Late relapse ovarian cancer Atezolizumab atezolizumab + avastin + platinum-based chemotherapy
placebo + avastin + platinum-based chemotherapy
614 3 ARCAGY/GINECO GROUP [84]
NCT02839707 - Recurrent ovarian
cancer
Atezolizumab bevacizumab
computed tomography PLD hydrochloride
quality-of-life assessment
444 2/3 National Cancer Institute (NCI) [85]
NCT03755739 - Ovarian cancer Pembrolizumab,
ipilimumab
immune checkpoint inhibitors such as pembrolizumab, ipilimumab plus chemotherapy 200 2/3 Second Affiliated Hospital of Guangzhou Medical University [86]
NCT03651206 ROCSAN Recurrent ovarian carcinosarcoma Dostarlimab niraparib
niraparib + dostarlimab chemotherapy drugs
196 2/3 ARCAGY/GINECO GROUP [87]
NCT04679064 NItCHE-MITO33 Recurrent ovarian cancer patients not a candidate for platinum retreatment Dostarlimab niraparib
pegylated liposomal doxorubicin
paclitaxel
gemcitabine
topotecan
bevacizumab
427 3 Fondazione Policlinico Universitario Agostino Gemelli IRCCS [88]

It should be highlighted that there are also clinical trials that concern other ICIs in the treatment of OC, including lymphocyte activation gene 3 (LAG-3), i.e., relatlimab, INCAGN02385, and T cell immunoglobulin and ITIM domain (TIGIT) inhibitors (COM902, etigilimab). The current research examines the use of bispecific mAbs in OC treatment, such as XmAb®22841 (anti-CTLA-4 and anti-LAG-3) [89] and tebotelimab (anti-PD-1 and anti-LAG-3) [90]. However, these studies are in the early phases (phases 1–2). Selected early-phase clinical trials concerning ICIs in OC treatment are presented in Table 2.

Table 2.

Selected early-phase clinical trials concerning ICIs in OC treatment.

NCT Number Acronym Condition mAbs
Anti-ICPs
Additional Drugs Participants Phase Company Ref.
NCT04611126 - Metastatic ovarian cancer Ipilimumab
Nivolumab Relatlimab
cyclophosphamid
fludarabine phosphate
tumor-infiltrating lymphocytes infusion
18 1/2 Inge Marie Svane [91]
NCT03219268 - Ovarian cancer Tebotelimab
Margetuximab
- 353 1 MacroGenics [90]
NCT03538028 - Advanced ovarian cancer INCAGN02385 - 22 1 Incyte Biosciences International Sàrl [92]
NCT03849469 DUET-4 Advanced ovarian cancer Xmab®22841
Pembrolizumab
- 78 1 Xencor, Inc. [89]
NCT04354246 - Advanced ovarian cancer COM902 COM701
(antiCD112R) pembrolizumab.
- 110 1 Compugen Ltd. [93]
NCT05026606 - Recurrent ovarian clear cell adenocarcinoma
Recurrent platinum-resistant ovarian carcinoma
Etigilimab
nivolumab
- 20 2 M.D. Anderson Cancer Center [94]

According to ClinicalTrials.gov (accessed on 2 June 2023), there are 217 ongoing clinical trials focusing on ICIs in the treatment of OC [95]. These studies concern ICIs in monotherapy, as well as in combination with other agents, such as biological drugs and standard therapy. Selected trials are summarized in Table 3.

Table 3.

Selected clinical trials concerning ICIs in monotherapy and combined therapy.

mAbs
Anti-ICPs
Additional Drugs NCT Number
Pembrolizumab - (monotherapy) NCT05368207
NCT04575961
NCT03732950
NCT04602377
NCT03430700
NCT04375956
NCT02644369
NCT03012620
chemotherapy NCT03734692
NCT05467670
NCT04387227
NCT02766582
NCT03410784
NCT03755739
NCT02520154
NCT03126812
VEGFi + chemotherapy NCT03596281
NCT03275506
NCT05116189
VEGFi + PARPi + chemotherapy NCT03740165
NCT05158062
PARPi NCT04417192
VEGFi + PARPi NCT04361370
PY314 NCT04691375
KVA12123 NCT05708950
Anti-CTLA4 NCT04140526
Modified vaccinia
virus Ankara vaccine
expressing p53
NCT03113487
Nivolumab PARPi (Rucaparib) NCT03522246
PARPi + VEGFi NCT02873962
Chemotherapy + PARPi NCT03245892
Etigilimab NCT05715216
NY-ESO-1
peptide vaccine
NCT05479045
Atezolizumab Chemotherapy + PARPi NCT03598270
Chemotherapy + VEGFi NCT03353831
NCT02891824
NCT02839707
VEGFi NCT04510584
Durvalumab Olaparib + Bevacizumab NCT04015739
Durvalumab
+ Tremelimumab
(ICIs combination) NCT03026062
Tremelimumab PARPi NCT04034927
Nivolumab
+ Ipilimumab
(ICIs combination) NCT03355976
NCT03508570
NCT02498600
Ipilimumab
+Pembrolizumab
+Durvalumab
(ICIs combination) NCT05187338

4. Mechanisms of Immunotherapy Resistance in Ovarian Cancer

Despite the successful use of ICIs in the treatment of other solid malignancies, their efficacy in OC therapy is insufficient. Thus, the understanding of biological, molecular, and genetic mechanisms of immunotherapy resistance in OC patients plays a crucial role in developing response biomarkers. It would be helpful in selecting a group of OC individuals for whom this kind of treatment would be beneficial, as well as projecting efficient targeted (immuno)therapies. It should be highlighted that the majority of clinical trials concerning the use of ICIs in OC treatment focus on heavily pretreated individuals, including patients with the disease recurrence. Drakes et al. [96] have shown higher PD-1 expression on T cells and PD-L1 expression on tumor cells at early OC stages in comparison with the advanced stages of the disease. Thus, clinical trials using ICIs in the first line of OC treatment seem to be crucial in the pursuit of implementing it in clinical practice. To date, numerous factors that determine the response of OC patients to ICI-based immunotherapy have been identified, including the heterogeneity of TME, as well as the molecular and genetic background.

4.1. Significance of Tumor Infiltrating Lymphocytes (TILs)

There are several variables that influence the success of ICIs in OC treatment, including their interactions with and influence on tumor-infiltrating lymphocytes (TILs). The cells belonging to this subset express multiple molecules, including immune checkpoints such as T cell immunoglobulin, mucin domain-containing protein 3 (Tim-3), LAG-3, CTLA-4, and PD-1 [48]. It should be emphasized that the response of cancer patients to ICIs depends on TME heterogeneity, including inflamed (hot) tumors with high infiltration of T cells and low immune-reactive tumors, i.e., non-inflamed (cold) tumors with low infiltration by T cells, ‘immune-excluded’ tumors where TILs are observed only in stromal space, or ‘immune desert’ tumors with no TILs present in TME.

The inflammatory tumors display an effective response to the immunomodulatory compounds and have a favorable prognosis. However, OC is considered a cold or warm tumor with low to intermediate infiltration by T cells [30,36,97,98,99]. Such malignancies as prostate, breast, pancreatic, and colorectal cancers are also regarded as cold tumors [30]. Cancer cells from non-inflamed tumors display only a modest level of neoantigens and have a low mutational burden and a negative/low PD-L1 expression. As a result, effector cells of the immune system are not able to distinguish them from normal cells, thereby prompting cancer cells to evade the immune system [29,30]. Thus, the presence of TILs and PD-1 expression are considered to be positive prognostic factors [8,100,101,102]. The response to ICIs is higher in PD-L1 positive tumors, but the high PD-L1 level is related to poor prognosis [30]. The functions of TILs are inhibited by immunosuppressive TME, which leads to an ineffective elimination of tumor cells [27]. The main features of hot, intermediate, and cold tumors are presented in Figure 2.

Figure 2.

Figure 2

The main features of hot, intermediate, and cold tumors.

4.2. Dual Role of Tumor-Associated Macrophages (TAMs)

Tumor-associated macrophages (TAMs) comprise the main subset of immune system cells in OC TME and arise either from bone marrow monocytes or tissue-resident macrophages [103,104]. It should be stressed that TAMs have a dual nature depending on their phenotypes. There are two phenotypes of TAMs: the first one is the tumor-suppressive M1 type, and the second one is the tumor-promoting M2 type. The M2 macrophages, in addition to producing vascular endothelial growth factor (VEGF), epidermal growth factor (EGF), transforming growth factor β (TGF-β), and hepatocyte growth factor (HGF), also enhance the maturation of regulatory T cells via TGF-β and the infiltration of the tumor by M2 TAMs via chemokine (C-C motif) ligand 2 (CCL2) and colony-stimulating factor 1 (CSF-1). Moreover, TAMs secrete IL-6 and IL-10 that upregulate B7-H4 and, consequently, block T cell functions [103,105,106].

M2-like macrophages are a population of immune system cells playing a key role in the creation of immunosuppressive TME in metastatic OC. They are involved in cytokine and chemokine signaling, such as IL-10, C-C motif chemokine 22 (CCL22), IL-4 (components), and IL-13 (components) signaling pathways, leading to T cell exhaustion. It has been proven that M2-like TAMs suppress immune responses in HGSOCs [107]. Yin et al. [108] have shown that, in the peritoneal fluid of OC patients during the disease progression, TAMs are polarized into an M2-like population that leads to the enhancement of OC cell migration and proliferation [108]. Mei Song et al. [106] have revealed that ubiquitin-protein ligase E3 component n-recognin 5 (UBR5), a gene that is frequently overexpressed in OC, plays an important role in the creation of immunosuppressive TME. The authors have further demonstrated that UBR5 deficiency impairs TAMs recruitment. Moreover, mice with an ovarian tumor subjected to treatment targeting tumor-derived UBR5, concurrently with anti-PD-1 mAbs, responded to the therapy, whereas mice treated only with anti-PD-1 agents did not [106].

Another protein expressed by TAMs is the transmembrane protein triggering receptor on myeloid cells 2 (TREM2). Notably, TREM2 causes T cell exhaustion and anti-PD-1 resistance. Binnewies et al. [109] have reported that TME in which TAMs express TREM2 displays immunosuppressive properties, which results in maintaining resistance to anti-PD-1 treatment [109]. The authors have found that the level of TREM2+ TAMs is correlated with exhausted CD8+ TILs in the murine and human models of solid tumors [109]. The implementation of anti-TREM2 mAbs enhances anticancer immunity via the modulation and elimination of TAMs. The result is the stimulated infiltration of CD8+ TILs and the enhancement of their effector functions. It is worth noting that TREM2+ TAMs are especially enriched in OC patients in whom TREM2 expression is associated with the disease grade and poorer recurrence-free survival. These findings indicate that TREM2 appears as a potential target in OC immunotherapy, especially in OC patients with TAM-rich TME [109].

Moreover, Ardighieri et al. [110] have shown that, in most cases of clear cell carcinomas (CCC) that demonstrate poor prognosis and resistance to platinum-based chemotherapy, the high density of TAMs is related to poor T cell infiltration as a result of C-X-C motif chemokine ligand 10 (CXCL10) produced by M1-type macrophages. In addition, HGSOC infiltration by immune cells contains the M1 subtype of TAMs that also express TREM2 [110].

The angiogenesis factors such as angiopoietin 2 (Ang-2) and VEGF are also able to contribute to immune suppression in OC TME by repressing anticancer immune effector cells, including APCs, and to enhance the activity of Tregs, M2 type TAMs, and myeloid-derived suppressor cells (MDSCs) [111]. The implementation of antiangiogenic factors inhibits the creation of blood vessels, which plays a crucial part in cancer progression and the decrease in the level of ICPs. The result is the increasing ratio of anti- and protumoral subsets of immune cells. The proper management of antiangiogenic factors, such as bevacizumab, may be helpful in reducing immunosuppression, restoring immunity, and improving the efficiency of the ICP blockade [112]. In addition, the implementation of ICIs results in the stimulation of antiangiogenic treatment via recruiting angiomodulatory immune cells [111,113].

4.3. Significance of Microsatellite Instability (MSI)

Another factor that affects the response to ICIs is microsatellite instability. Microsatellites, also known as ‘short tandem repeats’, are small repetitive DNA sequences. Because of their structure, they are most likely the effect of a replication error, indicating MSI or dMMR [114]. The occurrence of dMMR marks the loss of at least one of mismatch repair (MMR)-related proteins: MutL homolog 1 (MLH1), MutS homolog 2 (MSH2), MutS homolog 6 (MSH6), and PMS1 homolog 2 (PMS2) [114,115]. Mismatch repair deficiency in cancer leads to the accumulation of genetic abnormalities and elevated tumor mutational burden (TMB) levels, as well as stimulates tumors to be highly immunogenic. These features appear to be a good foothold for ICI-based therapy [116]. Nonomura et al. [117] have found that high MSI is associated with higher CD8+ T cells tumor infiltration and enhanced immune response in ovarian endometrioid carcinomas [117]. The PD-1 blockade has also been proven effective and induces a permanent response in patients with either MSI-H/dMMR metastatic or unresectable non-colorectal cancer. However, only 1.6–20% of OCs are dMMR. Therefore, the implementation of ICIs does not bring the expected benefits in OC patients and is not tested on a routine basis [114,115,117,118]. Nevertheless, dihydropyrimidinase-like 2 (DPYSL2) and alpha kinase 2 (ALPK2) genes have been found to be affected by MSI frameshift events in OC and could be prospectively used to identify the occurrence of MSI [115]. Nonomoura et al. did not observe any significant correlation between MSI-H and PD-1/PD-L1 expression [117]. Sui et al. [119] have shown the association between poor response to the ICIs blocking PD-1 in MSI-H colorectal cancer and inflammation caused by neutrophils through CD80/CD86-CTLA-4 signaling in the immunosuppressive microenvironment [119].

One of the most frequently mutated oncogenes is ARID1A which interacts with MSH2 and promotes MMR, while its knockdown participates in dMMR and mutation-phenotype. Shen et al. [120] have demonstrated that tumors formed by the OC cell line with ARID1A deficiency in syngeneic mice display elevated TIL density values, PD-L1 expression, higher mutation load, and longer OS after anti-PD-L1-antibody implementation. This suggests that ARID1A inhibition could enhance the effectiveness of ICIs [120].

The FDA approved pembrolizumab (anti-PD-1 mAbs) in the treatment of solid malignancies with MSI-H or MMR. The accumulation of genetic mutations in tumors results both in their recognition as non-self and in the recruitment of immune cells. Thus, ICIs appear beneficial in solid malignancies with dMMR and MSI-H. It should be highlighted that MSI-H occurs in the minority of OC cases. Thus, ICI-based monotherapy proves beneficial for a low percentage of patients. Combination therapy appears to be a promising approach [121].

4.4. Significance of Tumor Mutation Burden

The effectiveness of ICIs is also determined by tumor mutation burden. It is defined as the total number of DNA somatic mutations accumulated in a tumor cell. TMB is usually measured by the number of mutations per DNA megabase (Mb). A higher TMB is generally linked to an increased number of neoantigens, which may be caused by a high number of mutations. Therefore, cancer cells can be recognized more easily by the host immune system and then attacked. In general, high TMB values (≥20 mutations/Mb) are associated with a good response to ICIs in multiple tumors such as melanoma [122,123,124,125,126]. However, TMB in OC is estimated at only 1–3.5 mutations/Mb, and OC is considered to be a tumor with low TMB and expectedly low responsiveness to immunotherapy [103]. Fan et al. [127] have shown that TMB-H OC patients have higher levels of infiltrating CD8+ T cells, Th1 cells, Th2 cells, and Th17. Previously published studies have already associated tumor infiltration via CD8+ T cells with immunotherapy responsiveness [128]. Additionally, Fan et al. [127] have found a positive correlation between immune infiltration and human leukocyte antigen class II histocompatibility antigen, DO beta chain (HLA-DOB), and interferon-stimulated gene of 20 kDa protein (ISG20), on the one hand, and a negative correlation with calcium/calmodulin-dependent protein kinase IG (CAMK1G) and ubiquitin specific peptidase 51 (USP51) in OC patients [127], on the other. Nevertheless, no significant correlation between TMB and ICI response was established [98,127]. McGrail et al. [129] have analyzed data regarding over 10,000 various tumors, finding that CD8+ T cell infiltration in ovarian serous cystadenocarcinomas is not associated with neoantigen load and, in this group, TMB-H does not predict a beneficial impact of ICI implementation [128,129]. However, the possibility of some indirect anti-PD1 therapy response by TMB-related signature is not excluded [98,127].

4.5. The Regulation of ICPs by microRNA Net

MicroRNAs (miRNAs) are small molecules (20–22 nucleotides) that play a significant role in multiple biological pathways, including the regulation of immune system response and their dual activity as a tumor suppressor or oncogene activity in TME [33,118,119,120,121,122,123,130,131,132,133,134,135]. Together with transfer RNA (tRNA), ribosomal RNA (rRNA), and other regulatory RNAs, they belong to non-coding RNAs (ncRNAs) [136,137,138,139,140]. Overall, ncRNAs account for 98% of the eukaryotic genome transcript, while the remaining 2% are translated into proteins. Primary miRNAs are usually the products of RNA polymerase II transcription in the nucleus, and subsequently, they undergo multiple transformation processes to ultimately become mature miRNAs in the cytoplasm [138]. Even though miRNAs are mostly intracellular, there are also populations of circulating miRNAs and extracellular miRNAs that are displaced in the extracellular milieu, such as blood plasma or follicular fluid [139].

MiRNAs are the predominant epigenetic modulators. Their main role consists in post-transcriptional regulation and degradation of mRNA [124,129,130]. Available data suggest that miRNAs regulate almost 70% of all genes in the human genome, and their dysregulation leads to genome instability. They can also influence multiple transcripts, including the expression of oncogenes and suppressors, hence the occurrence of malignant transformation and carcinogenesis. In addition, miRNAs can control other non-coding RNAs [141,142,143,144,145,146] and play a crucial role in cellular communication, TME modification, and the promotion of cell immune escape [124,128,131,132,133,135,147].

Moreover, miRNAs are capable of modulating gene expression, in a post-transcriptional way, via ligation to the 3′-untranslated region (3′-UTR) [33,136]. It is common that each miRNA targets various transcripts, and mRNA may be targeted by a pool of miRNAs. These dependencies create a complex net of interactions [33].

Recent studies have indicated that miRNAs take part in the regulation of anti-tumor immune response. For instance, miR-200, miR-34a, and miR-513 translationally regulate the expression of PD-L1 [33,34,137,138,148,149,150]. MiRNAs also downregulate PD-L1 expression on cancer cells and CD8+ T cell infiltration, as well as reduce the angiogenesis factors in TME and increase the sensitivity to BRAF inhibitors. The combination of a BRAF inhibitor and miR-200c reportedly prevents drug resistance, boosts the host immune response against the tumor, and makes anti-tumor treatment effective at decreased dosages [151]. In various cancer types, miR-21 represents up to 10% of total miRNA. Xi et al. [152] have demonstrated that the increased percentage of miR-21-negative macrophages is associated with increased PD-L1 expression, the result of which is the inhibition of anticancer immune response [152]. Moreover, miR-28 silences PD-1, regulates cytokine secretion in cancer cells, decreases exhaustion, and improves ICI efficiency [141].

Other miRNAs, such as miR-513, miR-34a, and miR-424, take part in the regulation of PD-L1 as well. It appears that miR-424, which regulates not only PD-L1 activity but also CD80, is particularly interesting. The elevated expression of miR-424 is positively correlated with the progression-free survival (PFS) of patients with OC [33]. The decreased level of miR-424 and the increased PD-L1 expression are associated with chemoresistant phenotypes of OC tissues and cells. Moreover, PD-L1 and CD80 may be blocked by restoring the level of miR-424 via its direct ligation to 3′-UTR of their genes. The result of the PD-L1 blockade is the activation of T cells and the restoration of tumor sensitivity to chemotherapy. Thus, miR-424 is a potential factor that may enhance the OC cell’s chemo-sensitivity through the ICP blockade [118,119,120,123].

In addition, miR34a also has an impact on OC progression because it is directly transactivated by p53, which is a well-known tumor suppressor. In OC patients, TP53 mutations are very common, especially in HGSOC (the mutation frequency is even 95%). Schmid et al. [153] have shown the inverse relationship between miR-34a expression and clinicopathological data, such as the OC type according to the Kurman and Shih classification, the overall survival rate, grading, and the status of TP53 mutation. Moreover, the results have indicated that miR-34a exhibits an inhibitory effect on the invasion and proliferation of OC cells [63].

Guyon et al. [154] have demonstrated that the T cells exposed to anti-PD-1 agents enhance the production of exosomal miR-4315 that induces resistance to the chemotherapy-induced apoptosis in tumor cells. This phenomenon at a molecular level is related to the downregulation of proapoptotic protein Bim via exosomal miR-4315. Thus, miR-4315 could be used as a blood biomarker to detect patients that would not respond to the combination of anti-PD-1 and chemotherapy [154].

Kousar et al. [138] have classified multiple cancer-derived miRNAs that are linked to tumor evasion by upregulating PD-L1, including miR-197, miR-873, miR-16, miR-140, miR-142, miR18a, miR-138, miR34a, miR-195, miR-3609, mi-193a-3p, miR-200, miR-93, miR-15a, miR-383, miR-340, miR-17-5p, miR-93, and miR106b. Other sources also mention miR-570 and miR-513 as particles involved in the PD-L1 expression regulation [155]. The authors indicate that miRNAs participate in the processes either by binding to the 3′ UTR of PD-L1 or by targeting programmed cell death 4 (PDCD4) via the phosphoinositide 3-kinase/protein kinase B (PI3K/Akt) pathway [138,155]. Although their impact on ICI effectiveness has not been investigated in OC yet, further analysis could provide new insights that might allow a more profound understanding of the miRNA impact on OC immunotherapy. Interestingly, miR-15a and miR-15b in neuroblastoma, and miR-140 in osteosarcoma, display certain tumor suppressive properties which are gained by the PD-L1 signaling involvement [126,143,144,156,157].

The exosomal miRNAs derived from cancer cells also play a regulatory role in TME. Their immunosuppressive properties result from their capability to induce the polarization of M1-type to M2-type macrophages. Moreover, they diminish the differentiation of T helper cells via their interaction with dendritic cells [138]. The impact of miRNAs in inducing resistance to therapy was closely analyzed in previous years in relation to chemotherapy. However, the available literature data allows us to bring attention to these molecules also in regard to immunotherapy and their prospective use to improve cancer treatment [158,159].

Considering that miRNAs are highly stable in the cytoplasm and multifarious types of body fluids, such as peritoneal fluid and blood, they can be potentially used in early cancer diagnosis and in predictions of response to implemented treatment [118,146,147,148,149]. Circulating cell-free miRNAs are the source of tumor-derived data and appear to be a useful biomarker that may help identify the premalignant stages of the disease, support OC diagnosis at the early stages, and select the group of patients in which the implementation of ICIs would be beneficial. The identification of miRNAs which are involved in the progression of OC and which regulate the ICP pathways provides a novel insight into molecular mechanisms underlying ICI resistance. Considering the influence of miRNAs on ICPs, they are potentially noninvasive biomarkers to be used for selecting the proper group of OC patients in which immunotherapies based on ICIs will prove beneficial [153,160,161,162,163,164]. Consequently, the non-responders would be detected early enough to replace the time- and cost-intensive therapy with more efficient treatment options [150,151]. The factors triggering the OC resistance to ICI-based immunotherapy are presented in Figure 3.

Figure 3.

Figure 3

The factors triggering the OC resistance to ICI-based immunotherapy.

5. Hyperprogression

Some patients with solid tumors experience rapid and/or complete responses to ICIs within 12 weeks. There is also a group of patients that experience delayed response, even after 36 weeks [165,166]. The response to implementing ICIs in clinical practice has resulted in the occurrence of heterogenic and unconventional response patterns, such as hyper- and pseudoprogression [37,153].

Hyperprogression is the acceleration of tumor development as a side effect of immunotherapy [19,154]. The phenomenon was described for the first time by Chubachi et al. in a 54-year-old patient with stage IIB NSCLC treated with nivolumab. Six weeks after the administration of nivolumab (in three cycles), abrupt tumor progression was observed. Moreover, in addition to the growth of the primary tumor, multiple new nodules on the patient’s lungs and brain metastases were observed [167]. A hyperprogressive disease (HPD) may occur in various types of malignancies, including OC [19]. It should be stressed that patients with HPD have worse OS than patients experiencing standard progression [168,169,170]. The HPD incidence is reported to range from 4–29% [171,172,173,174]. A retrospective study conducted on OC patients (n = 89) who had received ICIs as part of clinical trials showed that 51.6% of the participants (n = 46) experienced HPD. As a result, ICI-based treatment was discontinued after ≤12 weeks based on the patients’ clinical or radiographic disease progression [175]. The biological mechanisms underlying hyperprogression, including senescent CD4+ T cells, mouse double minute 2 (MDM2), mouse double minute 4 (MDM4), and epidermal growth factor receptor (EGFR) amplification, as well as the antigen-binding Fc fragment (FcAb) regions, still remain unclear [176,177,178].

The premises concerning HPD risk factors are ambiguous. In their study, Champiat et al. showed that the age of >65 years was a risk factor for HPD [179]. However, this finding was not confirmed in other studies [158,160,161]. Moreover, Kanjanapan et al. [24] demonstrated an increased hyperprogression rate among women [24]. The studies by Kim et al. [169] and Kanjanapan et al. [180] showed that the increased number of metastasis sites was positively correlated with HPD. To date, no strong predictive factors for HPD have been identified. Notwithstanding the above, ICI-based treatment should not be limited to cancer patients based on the described factors because of the low level of proof, and the group of patients displaying HPD risk factors should be rigorously monitored to promptly identify hyperprogression [37].

6. Pseudoprogression

The phenomenon of initial progression followed by an objective response to the same kind of treatment is called pseudoprogression [37]. It manifests itself as an increase in tumor burden or the occurrence of new lesions that are caused by inflammation deriving from an initial response of the immune system and T cell recruitment to the tumor site as a reaction to immunotherapy based on ICPs. As a result, the tumor size is falsely increased as effector immune cells exhibit their anticancer activity [165].

However, the mechanism of pseudoprogression still remains unclear [165]. Pseudoprogression was described for the first time in melanoma after ipilimumab treatment implementation [168,169,181] and then after anti-PD-1 mAbs application (nivolumab, pembrolizumab) [182].

Further investigations have demonstrated that this phenomenon also occurs in other types of malignancies. The pseudoprogression rates vary by cancer type [165]. However, they rarely exceed 10%, e.g., in NSCLC (4–7%) and renal carcinoma (9–15%) [183].

Li et al. [184] described the case of a 47-year-old OC patient. Based on the immunohistochemistry test results that showed 10% of tumor cells expressing PD-L1, the patient received nivolumab (100mg/2 weeks). After two months, the tumor size was found to increase in computer tomography (CT). Moreover, based on the elevated levels of aspartate aminotransferase (AST) and alanine aminotransferase (ALT), the patient was diagnosed with immune-related hepatitis. The occurrence of the irAEs related to the liver suggests that the implemented ICI-based treatment is beneficial for the patient despite the enlarged tumor size. The decreased concentration of CA-125 was also observed (103.1 vs. 50.2 U/mL). Considering the clinical outcome of the patient, pseudoprogression was taken into account, and the treatment was continued. After four months, the size of the tumor decreased (50.7%), and an improvement in the patient’s outcome was observed [184].

In contrast, Passler et al. [185] demonstrated the case of a 47-year-old woman with recurrent OC. Nivolumab was implemented every three weeks in four cycles (3.0 mg/kg). The lymph node in the left groin with metastasis was twice the size compared with the values that the classic progression would suggest. However, there was no other evidence for standard progression. A stable level of CA-125 and local inflammation indicated pseudoprogression. Thus, the treatment was continued, and the size of the described lymph node with metastasis decreased, which also suggested pseudoprogression. After six treatment cycles, rectal bleeding occurred in the patient. Moreover, based on new tumor lesions occurring in the rectum, the progressive disease type was diagnosed. It should be highlighted that the size of the tumor might increase in both standard and pseudoprogression. However, the occurrence of new lesions and the infiltration of other tissues is related only to standard progression [185].

The described cases are insufficient to establish predictive or diversifying factors. Investigations should be conducted on larger OC patient groups to account for the heterogeneity of the disease. However, the cited studies determine the direction of future research.

In contrast to hyperprogression, the initial disease development in the course of pseudoprogression is followed by a positive response to ICIs. In a group of patients with pseudoprogression, the treatment should be continued. It should be highlighted that the OS of patients with pseudoprogression is improved in comparison with standard progression. To date, no clinical factors and features (i.e., CA-125, CEA, lactate dehydrogenase (LDH), age, gender) have been determined to distinguish between pseudo- and standard progression [165].

Unfortunately, there is no approach to selecting the group of patients in which pseudoprogression may occur. Selected biomarkers (i.e., cell-free DNA (cfDNA), X-ray repair cross-complementing gene (XRCC1), Ki67 expression, interferon regulatory factor (IRF9), small extracellular vesicles, O6-methylguanine-DNA methyltransferase methylation (MGMT), isocitrate dehydrogenase 1 (IDH1), and IL-8) and imaging approaches are helpful in the distinction of true progression from pseudoprogression. However, they are controversial and insufficient to implement in clinical practice. Presently, pseudoprogression is identified via retrospective imaging data, which results in premature discontinuation of efficient therapy. Although biopsy is an efficient diagnostic tool that is used before retrospective imaging analysis, it is an invasive method [186].

It is necessary to identify the mechanisms underlying hyper- and pseudoprogression, along with their predictive factors, to improve the implementation of treatment offered to cancer patients and to decide in which cases ICI-based treatment should be discontinued at an early stage. The need to differentiate between these two phenomena is predominant in the management of ICI-based treatment [37,187]. The identification of OC patients in whom HPD may occur is particularly crucial to preventing the rapid progression of the disease [19].

7. Future Directions

7.1. Double and Triple ICP Blockade

Similar to other malignancies, there is evidence that the implementation of a dual or triple immune checkpoint blockade may be beneficial for OC patients and may help overcome immunoresistance [5,173,174].

Although T cell activity is mostly controlled by the PD-1/PD-L1/PD-L2 pathway, other co-expressed ICPs, such as LAG-3, TIM-3, TIGIT, and DNAX accessory molecule-1 (DNAM-1; CD226), also regulate T cell activity, whether directly or indirectly [188,189,190,191].

It is well established that anti-PD-1 mAbs synergize with anti-CTLA-4 agents to fully restore T cell activity [192,193,194]. The synergistic effect is displayed as a priming and expansion of tumor-specific T cells in TME. Moreover, the dual blockade of these ICPs is a strategy to inhibit the promotion of another inhibitory axis while only one co-inhibitory molecule is blocked [192]. In spite of the synergistic effect of the dual blockade of PD-1 and CTLA-4, their simultaneous implementation leads to an increased rate of irAEs in comparison with single-agent application [178,181,182,195,196].

The TIGIT receptor is a negative regulator of T cells and NK cells [197,198,199,200]. Similar to PD-1, TIGIT is considered to be an exhaustion marker of CD8+ T cells. The receptor is able to regulate the anti-tumor response through CD4+ Tregs that are associated with tumor burden in OC patients [201]. In their preclinical studies conducted on a murine model, Chen et al. [202] demonstrated that TIGIT expression is increased in immune cells, such as Tregs. The blockade of TIGIT in mice with OC results in their beneficial survival rates as a result of Treg activity inhibition. These findings indicate that TIGIT is able to stimulate the Treg activity and plays a significant role in the creation of immunosuppression signatures in OC TME [202].

It has been shown that the TIGIT/CD155/DNAM-1 axis synergizes with the PD-1/PD-L1/PD-L2 pathway [189,190,191]. The double blockade of both pathways stimulates the effector activity of CD8+ T cells [202,203,204]. Banta et al. [201] have shown that costimulatory receptor DNAM-1 is a common factor of both these pathways. Both TIGIT and PD-1 are able to suppress the activity of DNAM-1, so the dual blockade is intrinsic to restoring the costimulatory signaling of DNAM-1. Thus, the single blockade of PD-1 or TIGIT via mAbs is insufficient to restore DNAM-1 functions. Hoogstad-van Evert et al. [205] have demonstrated that the OC patients with decreased DNAM-1 expression on NK cells derived from ascites have shorter survival times in comparison to the OC patients with increased DNAM-1 expression. The complex activity of the TIGIT/CD155/DNAM-1 axis, its synergistic mode of action with the PD-1/PD-L1/PD-L2 pathway, and the clinical trials on OC were described in detail in our previous paper [5].

Ongoing clinical trials also focus on the implementation of a triple ICPs blockade. The phase 1/2 study (NCT05187338) involves the combination of three mAbs, i.e., anti-PD-1 (pembrolizumab), anti-PD-L1 (durvalumab), and anti-CTLA-4 (ipilimumab), in the treatment of advanced solid tumors, including OC. The aim of the study is to establish the efficacy, safety, and survival benefits for cancer patients. The results have not been published yet [206]. Anderson et al. [207] have demonstrated, using a murine model of OC, that the triple ICP blockade (anti-PD-1, anti-TIM-3, and anti-LAG-3 mAbs) is more efficient than anti-PD-1 mAbs in monotherapy. The interactions of inhibitory receptors or ligands in TME lead to the impairment of the effector functions of T cells. This suggests that cancer cells can evade immune response via upregulating PD-L1 and ligands for LAG-3 and TIM-3. In the murine model of advanced OC, the implementation of triple ICIs results in a significant improvement of outcomes and the activity of transferred engineered T cells in comparison with the lack of significant effect after single blockade implementation [207]. Considering the complexity of a combinatory ICIs blockade, this kind of treatment may pose the risk of secondary events, including irAEs. Thus, it is important to find a suitable combination of ICIs for OC treatment. The main challenge is to develop an efficient treatment strategy without increasing the risk of irAEs occurrence [208,209].

7.2. Vaccines

Both vaccines and ICIs are aimed at fighting the disease via the modulation of host immune response mechanisms [194,195]. A cancer vaccine is usually understood as a vaccine against tumor-associated antigens with the addition of adjuvants activating DCs or DCs in general [210,211,212]. The first report on the potential OC vaccine development dates back to 2013 and describes a dendritic cell vaccine pulsed with autologous hypochlorous acid-oxidized OC lysate. The study showed some promising results in both mice and human preclinical experiments and prompted an attempt to adapt it to clinical practice with favorable outcomes [213]. Subsequent studies expanded the scope of the study and reported positive effects of a whole-tumor lysate-pulsed dendritic cell vaccine (OCDC) combined with bevacizumab (VEGFi) and cyclophosphamide elicited neoantigen-specific T cells on the OS rates in OC patients. Then, new evidence showed that the addition of acetylsalicylic acid (ASA) and low-dose IL-2 to OCDC, bevacizumab, and cyclophosphamide positively correlated with prolonged OS and time to progression rates [214,215]. Since numerous trials have proven the safety and potential benefits of DC vaccines, these agents could positively contribute to OC treatment outcomes [216]. Conversely, Martin-Lluesma et al. [217] have suggested that in addition to dendritic cell vaccines, B cells and macrophages could become the next agents playing a crucial role in the development of novel anti-cancer vaccines [217]. Moreover, according to Brentville et al. [218], the Modi-1 peptide vaccine consisting of a combination of citrullinated vimentin and enolase peptides could be an effective vaccine in OC patients [218].

Since FRα expression is almost exclusive for cancer tissue, and its epitopes have the capability to enhance T cell response in OC, the idea of vaccine development became reasonable and potentially achievable [219]. An attempt was made to determine whether the use of a multi-epitope anti-folate receptor vaccine (TPIV200) combined with durvalumab, a PD-L1 antibody, could improve the immunotherapy outcomes and help overcome ICI resistance. The TPIV200 vaccine consists of five highly antigenic human leukocyte antigen (HLA) peptides from FRα that are immunogenic and can evoke T cell response. The study results published by Zamarin et al. [219] in 2020 revealed that, despite the fact that vaccine-specific T cells had been produced, they were not effective enough to induce an anti-tumor response. According to the results of the phase 2 trial, there was no correlation between the response level and the antigens and PFS or OS. Therefore, the study was discontinued after phase 1 accrual. Nevertheless, the authors strongly suggest that this vaccination could potentially positively influence treatment outcomes [219].

7.3. Machine Learning as a Hope for Ovarian Cancer Patients

Machine learning is a subfield of artificial intelligence (AI) that succeeded in arousing interest in versatile scientific fields, including medicine. Machine learning is based on algorithms and statistical models that give computers the capability to learn and later recognize and analyze data patterns and relationships to make decisions, predictions, and recommendations based on previously unknown data [220,221,222]. There are various learning methods and models comprised in the term machine learning. In the following chapter, we will analyze its potential application in OC diagnostics. AI could provide support not only in respect of the early detection of OC but it could also help specify the genetic properties of OC [189,190,191,192].

A deep convolutional neural network (DCNN) is a machine learning algorithm that can be used for tasks such as image recognition. It learns from uploaded data during training and then makes predictions on previously unseen data. A DCNN model could potentially be suitable for distinguishing between benign and malignant adnexal tumors based on ultrasound images. The technique is capable of interpreting the nature of the ultrasound scans provided using an algorithm originating from numerous previous scan analyses and diagnoses. This tool was developed by Gao et al. [223] and is based on retrospective images of adnexal masses from multiple healthcare centers in China. The DCNN-assisted tumor evaluation has displayed certain advantages in terms of the distinction between subtle image details and features, easily overlooked by the human eye, along with better efficiency, a versatile database used in algorithm development, and smooth distribution in less experienced healthcare centers. This tool could also be used by medical professionals as a support in their real-time ultrasound examination in clinical practice. However, there are several factors that add up to malignancy risk evaluation, such as genomic characteristics, BRCA mutation status, or histological subtype. Since molecular testing in many cases is not easily accessible, the authors have emphasized that further investigation and development of DCNN could help determine the OC subtype only with an AI-based application [223].

Another study has demonstrated the application of machine learning models and statistics in the classification models aimed at developing efficient blood biomarkers for the early diagnosis of OC [224]. The database contains laboratory blood results consisting of three subgroups: routine blood count (1), general blood chemistry (2), and tumor markers (3), including carbohydrate antigen 72-4 (CA72-4), alpha-fetoprotein (AFP), carbohydrate antigen 19-9 (CA19-9), CA-125, carcinoembryonic antigen (CEA), human epididymis protein 4 (HE4), and clinical features such as menopause status and age. The authors used various machine learning tools, including Random Forest (RF), Gradient Boosting Machines (GBM), and light gradient boosting machines (LGBM) that, when combined with statistical tests, were capable of processing the datasets provided in terms of significant feature finding, feature association finding, and OC prediction [224]. This low-cost diagnostic tool could be a quality assistance for physicians, shortening the entire diagnostic process. The accuracy attributed to this method of malignant-or-benign differentiation is estimated at over 90% [224].

Another application of machine learning refers to second-harmonic generation (SHG) imaging that provides a quick and non-invasive method of OC diagnosis. More specifically, SHG provides a visualization of tissue structures, including collagen. Collagen remodeling is linked to OC carcinogenesis and progression, and the characteristics of collagen fibers vary depending on ovarian tissue origin. When combined with a machine learning model, SHG is useful in distinguishing borderline tumors from malignant and benign ones [225].

Machine learning algorithms and bioinformatics can also be used to analyze multiple large gene datasets to identify and validate genes with a potential diagnostic value. Liu et al. [226] have focused on OC genome exploration based on Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA), and the Genotype-Tissue Expression (GTEx) cohort datasets, with the application of machine learning algorithms. Moreover, the authors have investigated the function and pathways that are involved in the interdependence between those characteristics, diagnosis-related genes, and immune cell infiltration in OC to be analyzed at a later stage [226].

Firstly, they developed a tool to provide insight and detect differentially expressed genes (DEGs) in OC and non-OC tissues. Pieces of information about the selected genes relevant for OC were supplemented by numerous datasets to eventually undergo validation in terms of diagnostic relevance. Additionally, the authors investigated whether DEGs and immune cell infiltration could be related. According to the study results, out of 590 identified DEGs, 10 genes, i.e., budding uninhibited by benzimidazoles 1 (BUB1), adenosine 5′-triphosphate–binding cassette subfamily B member 1 (ABCB1), secreted frizzled-related protein 1 (SFRP1), innate immunity activator (INAVA), transmembrane protein 139 (TMEM139), mitotic checkpoint serine/threonine-protein kinase BUB1 beta (BUB1B), phosphoserine aminotransferase 1 (PSAT1), phosphodiesterase 8B (PDE8B), folate receptor alpha (FOLR1), and homeobox A13 (HOXA13), are involved in biological cell functions and could affect immune infiltration levels in OC [226].

Numerous attempts to directly apply machine learning in ICI response prediction have already been made. Even though OC-specific algorithms are yet to be developed, there are some promising study results in respect of other cancer types, including melanoma, glioblastoma, and hepatocellular carcinoma [227,228,229,230,231,232]. Johannet et al. [227] have created a DCNN classifying whole-slide images to predict which melanoma patients would more likely benefit from ICIs or progress during the therapy, and the nuclei characteristics were found to be crucial in the construction of algorithms [227]. In 2019, Harder et al. [229] presented a DCNN model that successfully predicted ipilimumab response in malignant melanoma. Their model used whole-slide images of different materials, such as lymph nodes and skin, to identify their cells with emphasis on immune cell densities and distances between them. The superior model developed in the process turned out to be a decision tree and included the concepts of distribution and density of CD8+ and CD3+ in TME. The study revealed that a high ratio of intratumoral CD8+ infiltration to CD8+ and CD3+ densities in surrounding tissues indicated a good therapy response [229].

Another machine learning model to predict ICI response was created by Zhang et al. [230] for glioblastoma, and their method analyzed the tumor-infiltrating immune cell-associated long noncoding ribonucleic acids (TIIClnc) signature using purified immune cells, glioblastoma cell lines and glioblastoma tissues transcriptome data. The developed TIIClnc signature was a marker of immune infiltration correlated with CD8+, PD-1, and PD-L1 [230]. A paper by Wang et al. [231], dated 2020, was the starting point for examining the role of cancer stem cells in tumorigenesis and resistance to therapy in glioblastoma. The authors performed an integrated multiomic analysis using the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm to study the correlation between stemness and immuno- and chemotherapy response in glioblastoma patients. Based on their findings, they established a novel stemness classification that helped them identify which patients would more likely benefit from ICIs [231]. With a view to predicting ICI effectiveness in hepatocellular carcinoma, Chen et al. [232] created cancer-stem-cell-related clusters using machine learning algorithms that combined datasets of genome information. After that, it was possible to categorize patients according to stemness subtypes, which were found to be strongly related to immune infiltration modulation and able to predict their immunogenomic expressions, tumor immune microenvironment status, and thus immunotherapy susceptibility [232].

Recently, Kong et al. have built a machine learning framework that could make an accurate prognosis of ICI-based treatment effectiveness, relying on network-based biomarkers. Moreover, according to the results established for ICI treatment outcomes in melanoma, metastatic gastric cancer, and bladder cancer, the authors have found that predictions made using network-based biomarkers are more precise than those based on the expression levels of ICI targets, including PD1, PD-L1, or CTLA-4 [228].

8. Conclusions

Given the limited efficacy of the current treatment options for OC patients, novel therapeutic approaches are urgently required. The immunotherapies based on ICIs have turned out to be game-changers in the treatment of cancer types with poor prognoses, such as melanoma. Thus, this kind of therapy appears to be a promising approach to breaking immunosuppression in OC TME. Unfortunately, the preclinical studies and clinical trials conducted to date have shown that OC tumors are non-inflamed, and the response to ICIs among OC patients is insufficient, especially if monotherapy is applied. Thus, the combination of ICIs with other biological drugs, such as PARPi or antiangiogenic factors (VEGFi), aimed at sensitizing the tumor to this kind of treatment seems to be a promising approach. Moreover, it is crucial to examine various combinations of ICIs, also in double and triple blockades, to break the immunosuppression in OC TME and to overcome immunoresistance.

Considering that the majority of studies are conducted on recurrent OC patients that previously received several treatment lines, further investigation of their efficiency as first-line treatment is highly needed to break the ICI immunosuppression. Another challenge is posed by the proper selection of OC patients and the development of predictive biomarkers that would help identify the OC individuals in whom this treatment would prove beneficial. The understanding of the mechanisms underlying immunoresistance, including immunological, genetic, and molecular aspects, is crucial to developing efficient immunotherapy for OC patients and improving their clinical outcomes.

Acknowledgments

Anna Pawłowska has received annual support (scholarship—START) from the Foundation for Polish Science (FNP) in 2022 for the most talented young scientists.

Abbreviations

3′-UTR 3′-untranslated region
ABCB1 adenosine 5′-triphosphate–binding cassette subfamily B member 1
AFP alpha-fetoprotein
AI artificial intelligence
ALPK2 alpha kinase 2
ALT alanine aminotransferase
Ang-2 angiopoietin 2
APCs antigen-presenting cells
ARID1A AT-rich interactive domain-containing protein 1A
ASA acetylsalicylic acid
AST aspartate aminotransferase
BRAF B-Raf proto-oncogene, serine/threonine kinase
BRCA breast cancer gene
BTC biliary tract cancers
BUB1 budding uninhibited by benzimidazoles 1
BUB1B mitotic checkpoint serine/threonine-protein kinase BUB1 beta
CA-125 cancer antigen 125
CA19-9 carbohydrate antigen 19-9
CA72-4 carbohydrate antigen 72-4
CAMK1G calcium/calmodulin-dependent protein kinase 1G
CCC clear cell carcinomas
CCL2 chemokine (C-C motif) ligand 2
CCL22 C-C motif chemokine 22
CD cluster of differentiation
CEA carcinoembryonic antigen
cfDNA cell-free DNA
cHL classical Hodgkin Lymphoma
CRC colorectal cancer
cSCC cutaneous squamous cell carcinoma
CSF-1 colony stimulating factor 1
CT computer tomography
CTLA-4 cytotoxic T-lymphocyte-associated antigen 4
CXCL10 C-X-C motif chemokine ligand 10
DCNN deep convolutional neural network
DCs dendritic cells
DEG differentially expressed gene
dMMR deficient mismatch repair
DNAM-1 DNAX accessory molecule-1
DPYSL2 dihydropyrimidinase like 2
EGF epidermal growth factor
EGFR epidermal growth factor receptor
ER estrogen receptor
ERK extracellular signal-regulated kinase
ESMO European Society for Medical Oncology
FcAb antigen-binding Fc fragment
FDA Food and Drug Administration
FIGO International Federation of Gynecology and Obstetrics
FOLR1 folate receptor alpha
FOXM1 forkhead box M1
FRα folate receptor alpha
GBM Gradient Boosting Machines
GEO Gene Expression Omnibus
GTEx The Genotype-Tissue Expression
HCC hepatocellular carcinoma
HE4 human epididymis protein 4
HGF hepatocyte growth factor
HGSOC high-grade serous ovarian carcinoma
HLA human leukocyte antigen
HLA-DOB histocompatibility antigen, DO beta chain
HNSCC head and neck squamous cell carcinoma
HOXA13 homeobox A13
HPD hyperprogressive disease
ICI immune checkpoint inhibitor
ICP immune checkpoint
IDH1 isocitrate dehydrogenase 1
IFN-γ interferon γ
IL interleukin
INAVA innate immunity activator
irAEs immune-related adverse events
IRF9 interferon regulatory factor 9
ISG20 interferon-stimulated gene of 20 kDa protein
JAK-STAT Janus kinase/signal transducers and activators of transcription
KRAS Kirsten rat sarcoma viral oncogene homolog
LAG-3 lymphocyte activation gene 3
LDH lactate dehydrogenase
LGBM light gradient boosting machine
mAbs monoclonal antibodies
MAP mitogen-activated protein
MAPK mitogen-activated protein kinase
Mb megabase
MCC Merkel cell carcinoma
MDM2 mouse double minute 2
MDM4 mouse double minute 4
MDSC myeloid-derived suppressor cells
MGMT O6-methylguanine-DNA methyltransferase methylated
miRNA microRNA
MLH1 MutL homolog 1
MMR mismatch repair
MO/MA monocytes/macrophages
MSH2 MutS homolog 2
MSH6 MutS homolog 6
MSI microsatellite instability
MSI-H microsatellite instability-high
MSI-H high microsatellite instability
NCCN National Comprehensive Cancer Network
ncRNAs non-coding RNAs
NK cell natural killer cell
NSCLC non-small-cell lung cancer
OC ovarian cancer
OCDC whole-tumor lysate-pulsed dendritic cell vaccine
OS overall survival
PARPi poly(ADP-ribose) polymerase inhibitor
PD-1 Programmed cell death receptor 1
PDCD1 Programmed Cell Death 1
PDCD4 programmed cell death 4
PDE8B phosphodiesterase 8B
PD-L1 Programmed death-ligand 1
PD-L2 Programmed death-ligand 2
PFS progression-free survival
PI3K/Akt phosphoinositide 3-kinase/protein kinase B
PIK3CA phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit α
PLD pegylated liposomal doxorubicin
PMLBCL primary mediastinal large B-cell lymphoma
PMS2 PMS1 homolog 2
PR progesterone receptor
PSAT1 phosphoserine aminotransferase 1
PTEN phosphatase and tensin homolog deleted on chromosome ten
RCC renal cell carcinoma
RF random forest
rRNA ribosomal RNA
SCLC small cell lung cancer
SFRP1 secreted frizzled-related protein 1
SHG second-harmonic generation
TAM Tumor-associated macrophage
TCGA The Cancer Genome Atlas
TCR T cell receptor
TGF-β transforming growth 276 factor β
TIDE Tumor Immune Dysfunction and Exclusion algorithm
TIGIT T cell immunoglobulin and ITIM domain
TIIClnc tumor-infiltrating immune cell-associated long noncoding ribonucleic acids
TIL tumor-infiltrating lymphocytes
TIM-3 T cell immunoglobulin, mucin domain-containing protein 3
TMB tumor mutational burden
TMB-H high tumor mutational burden
TME tumor microenvironment
TMEM139 transmembrane protein 139
TNF-α tumor necrosis factor α
TP53 Tumor protein P53
TPIV200 a multi-epitope anti-folate receptor vaccine
TREM2 triggering receptor expressed on myeloid cells 2
tRNA transfer RNA
UBR5 ubiquitin 288 protein ligase E3 component n-recognin 5
USP51 ubiquitin specific peptidase
VEGF vascular endothelial growth factor
VEGFi vascular endothelial growth factor inhibitor
WHO World Health Organization
XRCC1 X-ray repair cross-complementing gene

Author Contributions

Conceptualization, A.P. (Anna Pawłowska) and I.W.; formal analysis, A.R., W.K. and A.P. (Anna Pańczyszyn); writing—original draft preparation, A.P. (Anna Pawłowska) and A.R.; writing—review and editing, A.P. (Anna Pańczyszyn), J.K. and I.W.; visualization, A.P. (Anna Pawłowska) and W.K.; supervision, J.K. and I.W. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Funding Statement

This work was supported by the Medical University of Lublin, grant number DS124, and in part by the National Science Centre, Poland, Preludium grant number 2021/41/N/NZ6/01727. For the purpose of Open Access, the author has applied a CC-BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

References

  • 1.Cancer (IARC), T.I.A. for R. on Global Cancer Observatory. [(accessed on 12 February 2022)]. Available online: https://gco.iarc.fr/
  • 2.Bonifácio V.D.B. Ovarian Cancer Biomarkers: Moving Forward in Early Detection. In: Serpa J., editor. Tumor Microenvironment: The Main Driver of Metabolic Adaptation. Springer International Publishing; Cham, Switzerland: 2020. pp. 355–363. Advances in Experimental Medicine and Biology. [DOI] [PubMed] [Google Scholar]
  • 3.Nebgen D.R., Lu K.H., Bast R.C. Novel Approaches to Ovarian Cancer Screening. Curr. Oncol. Rep. 2019;21:75. doi: 10.1007/s11912-019-0816-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Terp S.K., Stoico M.P., Dybkær K., Pedersen I.S. Early Diagnosis of Ovarian Cancer Based on Methylation Profiles in Peripheral Blood Cell-Free DNA: A Systematic Review. Clin. Epigenetics. 2023;15:24. doi: 10.1186/s13148-023-01440-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Pawłowska A., Skiba W., Suszczyk D., Kuryło W., Jakubowicz-Gil J., Paduch R., Wertel I. The Dual Blockade of the TIGIT and PD-1/PD-L1 Pathway as a New Hope for Ovarian Cancer Patients. Cancers. 2022;14:5757. doi: 10.3390/cancers14235757. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Kossaï M., Leary A., Scoazec J.-Y., Genestie C. Ovarian Cancer: A Heterogeneous Disease. Pathobiology. 2018;85:41–49. doi: 10.1159/000479006. [DOI] [PubMed] [Google Scholar]
  • 7.Kurman R.J., Carcangiu M.L., Herrington C.S., Young R.H. WHO Classification of Tumours of Female Reproductive Organs. IARC Press; Lyon, France: 2014. [Google Scholar]
  • 8.Awada A., Ahmad S., McKenzie N.D., Holloway R.W. Immunotherapy in the Treatment of Platinum-Resistant Ovarian Cancer: Current Perspectives. Onco Targets Ther. 2022;15:853–866. doi: 10.2147/OTT.S335936. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Kurman R.J., Shih I.-M. The Dualistic Model of Ovarian Carcinogenesis: Revisited, Revised, and Expanded. Am. J. Pathol. 2016;186:733–747. doi: 10.1016/j.ajpath.2015.11.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Shih I.-M., Kurman R.J. Ovarian Tumorigenesis: A Proposed Model Based on Morphological and Molecular Genetic Analysis. Am. J. Pathol. 2004;164:1511–1518. doi: 10.1016/S0002-9440(10)63708-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Zhang Y., Cui Q., Xu M., Liu D., Yao S., Chen M. Current Advances in PD-1/PD-L1 Blockade in Recurrent Epithelial Ovarian Cancer. Front. Immunol. 2022;13:901772. doi: 10.3389/fimmu.2022.901772. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Huo X., Zhang X., Li S., Wang S., Sun H., Yang M. Identification of Novel Immunologic Checkpoint Gene Prognostic Markers for Ovarian Cancer. J. Oncol. 2022;2022:8570882. doi: 10.1155/2022/8570882. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Hamanishi J., Mandai M., Konishi I. Immune Checkpoint Inhibition in Ovarian Cancer. Int. Immunol. 2016;28:339–348. doi: 10.1093/intimm/dxw020. [DOI] [PubMed] [Google Scholar]
  • 14.Stewart C., Ralyea C., Lockwood S. Ovarian Cancer: An Integrated Review. Semin. Oncol. Nurs. 2019;35:151–156. doi: 10.1016/j.soncn.2019.02.001. [DOI] [PubMed] [Google Scholar]
  • 15.Terlikowska K.M., Dobrzycka B., Terlikowski S.J. Chimeric Antigen Receptor Design and Efficacy in Ovarian Cancer Treatment. Int. J. Mol. Sci. 2021;22:3495. doi: 10.3390/ijms22073495. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Commissioner of the U.S. Food and Drug Administration. [(accessed on 4 November 2022)]; Available online: https://www.fda.gov/home.
  • 17.Rutten M.J., Leeflang M.M.G., Kenter G.G., Mol B.W.J., Buist M. Laparoscopy for Diagnosing Resectability of Disease in Patients with Advanced Ovarian Cancer. Cochrane Database Syst. Rev. 2014;2014:CD009786. doi: 10.1002/14651858.CD009786.pub2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Westergaard M.C.W., Milne K., Pedersen M., Hasselager T., Olsen L.R., Anglesio M.S., Borch T.H., Kennedy M., Briggs G., Ledoux S., et al. Changes in the Tumor Immune Microenvironment during Disease Progression in Patients with Ovarian Cancer. Cancers. 2020;12:3828. doi: 10.3390/cancers12123828. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Yang C., Xia B.-R., Zhang Z.-C., Zhang Y.-J., Lou G., Jin W.-L. Immunotherapy for Ovarian Cancer: Adjuvant, Combination, and Neoadjuvant. Front. Immunol. 2020;11:2595. doi: 10.3389/fimmu.2020.577869. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Lheureux S., Braunstein M., Oza A.M. Epithelial Ovarian Cancer: Evolution of Management in the Era of Precision Medicine. CA A Cancer J. Clin. 2019;69:280–304. doi: 10.3322/caac.21559. [DOI] [PubMed] [Google Scholar]
  • 21.Świderska J., Kozłowski M., Kwiatkowski S., Cymbaluk-Płoska A. Immunotherapy of Ovarian Cancer with Particular Emphasis on the PD-1/PDL-1 as Target Points. Cancers. 2021;13:6063. doi: 10.3390/cancers13236063. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Liu X., Hou M., Liu Y. TIGIT, A Novel Therapeutic Target for Tumor Immunotherapy. Immunol. Investig. 2017;46:172–182. doi: 10.1080/08820139.2016.1237524. [DOI] [PubMed] [Google Scholar]
  • 23.Pawłowska A., Suszczyk D., Okła K., Barczyński B., Kotarski J., Wertel I. Immunotherapies Based on PD-1/PD-L1 Pathway Inhibitors in Ovarian Cancer Treatment. Clin. Exp. Immunol. 2019;195:334–344. doi: 10.1111/cei.13255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Maiorano B.A., Maiorano M.F.P., Lorusso D., Maiello E. Ovarian Cancer in the Era of Immune Checkpoint Inhibitors: State of the Art. and Future Perspectives. Cancers. 2021;13:4438. doi: 10.3390/cancers13174438. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Doo D.W., Norian L.A., Arend R.C. Checkpoint Inhibitors in Ovarian Cancer: A Review of Preclinical Data. Gynecol. Oncol. Rep. 2019;29:48–54. doi: 10.1016/j.gore.2019.06.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Chardin L., Leary A. Immunotherapy in Ovarian Cancer: Thinking Beyond PD-1/PD-L1. Front. Oncol. 2021;11:795547. doi: 10.3389/fonc.2021.795547. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Agyemang A.F., Lele S. The Use of Immunotherapy for Treatment of Gynecologic Malignancies. In: Lele S., editor. Ovarian Cancer. Exon Publications; Brisbane, Australia: 2022. [PubMed] [Google Scholar]
  • 28.Ning F., Cole C.B., Annunziata C.M. Driving Immune Responses in the Ovarian Tumor Microenvironment. Front. Oncol. 2020;10:604084. doi: 10.3389/fonc.2020.604084. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Cassar E., Kartikasari A.E.R., Plebanski M. Regulatory T Cells in Ovarian Carcinogenesis and Future Therapeutic Opportunities. Cancers. 2022;14:5488. doi: 10.3390/cancers14225488. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Majidpoor J., Mortezaee K. The Efficacy of PD-1/PD-L1 Blockade in Cold Cancers and Future Perspectives. Clin. Immunol. 2021;226:108707. doi: 10.1016/j.clim.2021.108707. [DOI] [PubMed] [Google Scholar]
  • 31.Lee S.-M., Lee S., Cho H.-W., Min K.-J., Hong J.-H., Song J.-Y., Lee J.-K., Lee N.-W. Application of Immune Checkpoint Inhibitors in Gynecological Cancers: What Do Gynecologists Need to Know before Using Immune Checkpoint Inhibitors? Int. J. Mol. Sci. 2023;24:974. doi: 10.3390/ijms24020974. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Revythis A., Limbu A., Mikropoulos C., Ghose A., Sanchez E., Sheriff M., Boussios S. Recent Insights into PARP and Immuno-Checkpoint Inhibitors in Epithelial Ovarian Cancer. Int. J. Environ. Res. Public Health. 2022;19:8577. doi: 10.3390/ijerph19148577. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Xu S., Tao Z., Hai B., Liang H., Shi Y., Wang T., Song W., Chen Y., OuYang J., Chen J., et al. MiR-424(322) Reverses Chemoresistance via T-Cell Immune Response Activation by Blocking the PD-L1 Immune Checkpoint. Nat. Commun. 2016;7:11406. doi: 10.1038/ncomms11406. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Wang X., Li J., Dong K., Lin F., Long M., Ouyang Y., Wei J., Chen X., Weng Y., He T., et al. Tumor Suppressor MiR-34a Targets PD-L1 and Functions as a Potential Immunotherapeutic Target in Acute Myeloid Leukemia. Cell. Signal. 2015;27:443–452. doi: 10.1016/j.cellsig.2014.12.003. [DOI] [PubMed] [Google Scholar]
  • 35.Chen J., Kang S., Wu J., Zhao J., Si W., Sun H., Li Y. CTLA-4 Polymorphism Contributes to the Genetic Susceptibility of Epithelial Ovarian Cancer. J. Obstet. Gynaecol. Res. 2022;48:1240–1247. doi: 10.1111/jog.15186. [DOI] [PubMed] [Google Scholar]
  • 36.Siminiak N., Czepczyński R., Zaborowski M.P., Iżycki D. Immunotherapy in Ovarian Cancer. Arch. Immunol. Ther. Exp. 2022;70:19. doi: 10.1007/s00005-022-00655-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Frelaut M., du Rusquec P., de Moura A., Le Tourneau C., Borcoman E. Pseudoprogression and Hyperprogression as New Forms of Response to Immunotherapy. BioDrugs. 2020;34:463–476. doi: 10.1007/s40259-020-00425-y. [DOI] [PubMed] [Google Scholar]
  • 38.Herbst R.S., Baas P., Kim D.-W., Felip E., Pérez-Gracia J.L., Han J.-Y., Molina J., Kim J.-H., Arvis C.D., Ahn M.-J., et al. Pembrolizumab versus Docetaxel for Previously Treated, PD-L1-Positive, Advanced Non-Small-Cell Lung Cancer (KEYNOTE-010): A Randomised Controlled Trial. Lancet. 2016;387:1540–1550. doi: 10.1016/S0140-6736(15)01281-7. [DOI] [PubMed] [Google Scholar]
  • 39.Borghaei H., Paz-Ares L., Horn L., Spigel D.R., Steins M., Ready N.E., Chow L.Q., Vokes E.E., Felip E., Holgado E., et al. Nivolumab versus Docetaxel in Advanced Nonsquamous Non–Small-Cell Lung Cancer. N. Engl. J. Med. 2015;373:1627–1639. doi: 10.1056/NEJMoa1507643. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Brahmer J., Reckamp K.L., Baas P., Crinò L., Eberhardt W.E.E., Poddubskaya E., Antonia S., Pluzanski A., Vokes E.E., Holgado E., et al. Nivolumab versus Docetaxel in Advanced Squamous-Cell Non–Small-Cell Lung Cancer. N. Engl. J. Med. 2015;373:123–135. doi: 10.1056/NEJMoa1504627. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Robert C., Long G.V., Brady B., Dutriaux C., Maio M., Mortier L., Hassel J.C., Rutkowski P., McNeil C., Kalinka-Warzocha E., et al. Nivolumab in Previously Untreated Melanoma without BRAF Mutation. N. Engl. J. Med. 2015;372:320–330. doi: 10.1056/NEJMoa1412082. [DOI] [PubMed] [Google Scholar]
  • 42.Hodi F.S., O’Day S.J., McDermott D.F., Weber R.W., Sosman J.A., Haanen J.B., Gonzalez R., Robert C., Schadendorf D., Hassel J.C., et al. Improved Survival with Ipilimumab in Patients with Metastatic Melanoma. N. Engl. J. Med. 2010;363:711–723. doi: 10.1056/NEJMoa1003466. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Motzer R.J., Escudier B., McDermott D.F., George S., Hammers H.J., Srinivas S., Tykodi S.S., Sosman J.A., Procopio G., Plimack E.R., et al. Nivolumab versus Everolimus in Advanced Renal-Cell Carcinoma. N. Engl. J. Med. 2015;373:1803–1813. doi: 10.1056/NEJMoa1510665. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Ferris R.L., Blumenschein G., Fayette J., Guigay J., Colevas A.D., Licitra L., Harrington K., Kasper S., Vokes E.E., Even C., et al. Nivolumab for Recurrent Squamous-Cell Carcinoma of the Head and Neck. N. Engl. J. Med. 2016;375:1856–1867. doi: 10.1056/NEJMoa1602252. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Bellmunt J., de Wit R., Vaughn D.J., Fradet Y., Lee J.-L., Fong L., Vogelzang N.J., Climent M.A., Petrylak D.P., Choueiri T.K., et al. Pembrolizumab as Second-Line Therapy for Advanced Urothelial Carcinoma. N. Engl. J. Med. 2017;376:1015–1026. doi: 10.1056/NEJMoa1613683. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Cascio S., Chandler C., Zhang L., Sinno S., Gao B., Onkar S., Bruno T.C., Vignali D.A.A., Mahdi H., Osmanbeyoglu H.U., et al. Cancer-Associated MSC Drive Tumor Immune Exclusion and Resistance to Immunotherapy, Which Can Be Overcome by Hedgehog Inhibition. Sci. Adv. 2021;7:eabi5790. doi: 10.1126/sciadv.abi5790. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Luyckx M., Squifflet J.L., Bruger A.M., Baurain J.F. Recurrent High Grade Serous Ovarian Cancer Management. In: Lele S., editor. Ovarian Cancer. Exon Publications; Brisbane, Australia: 2022. pp. 87–105. Chapter 6. [DOI] [PubMed] [Google Scholar]
  • 48.Hudry D., Le Guellec S., Meignan S., Bécourt S., Pasquesoone C., El Hajj H., Martínez-Gómez C., Leblanc É., Narducci F., Ladoire S. Tumor-Infiltrating Lymphocytes (TILs) in Epithelial Ovarian Cancer: Heterogeneity, Prognostic Impact, and Relationship with Immune Checkpoints. Cancers. 2022;14:5332. doi: 10.3390/cancers14215332. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Shen J., Liu T., Bei Q., Xu S. Comprehensive Landscape of Ovarian Cancer Immune Microenvironment Based on Integrated Multi-Omics Analysis. Front. Oncol. 2021;11:2180. doi: 10.3389/fonc.2021.685065. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.McHann M.C., Blanton H.L., Guindon J. Role of sex hormones in modulating breast and ovarian cancer associated pain. Mol. Cell. Endocrinol. 2021;533:111320. doi: 10.1016/j.mce.2021.111320. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Li H., Liu Y., Wang Y., Zhao X., Qi X. Hormone Therapy for Ovarian Cancer: Emphasis on Mechanisms and Applications (Review) Oncol. Rep. 2021;46:223. doi: 10.3892/or.2021.8174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Anbarasu S., Anbarasu A. Cancer-Biomarkers Associated with Sex Hormone Receptors and Recent Therapeutic Advancements: A Comprehensive Review. Med. Oncol. 2023;40:171. doi: 10.1007/s12032-023-02044-3. [DOI] [PubMed] [Google Scholar]
  • 53.Langdon S.P., Herrington C.S., Hollis R.L., Gourley C. Estrogen Signaling and Its Potential as a Target for Therapy in Ovarian Cancer. Cancers. 2020;12:1647. doi: 10.3390/cancers12061647. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Shiravand Y., Khodadadi F., Kashani S.M.A., Hosseini-Fard S.R., Hosseini S., Sadeghirad H., Ladwa R., O’Byrne K., Kulasinghe A. Immune Checkpoint Inhibitors in Cancer Therapy. Curr. Oncol. 2022;29:3044–3060. doi: 10.3390/curroncol29050247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Andersen C.L., Sikora M.J., Boisen M.M., Ma T., Christie A., Tseng G., Park Y., Luthra S., Chandran U., Haluska P., et al. Active Estrogen Receptor-Alpha Signaling in Ovarian Cancer Models and Clinical Specimens. Clin. Cancer Res. 2017;23:3802–3812. doi: 10.1158/1078-0432.CCR-16-1501. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Gjorgoska M., Rižner T.L. Estrogens and the Schrödinger’s Cat in the Ovarian Tumor Microenvironment. Cancers. 2021;13:5011. doi: 10.3390/cancers13195011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.KEYTRUDA® (Pembrolizumab)—Official Site. [(accessed on 17 April 2023)]. Available online: https://www.keytruda.com/
  • 58.OPDIVO® (Nivolumab) [(accessed on 17 April 2023)]. Available online: https://www.opdivo.com/
  • 59.LIBTAYO® (Cemiplimab-Rwlc): Official Patient Website. [(accessed on 24 April 2023)]. Available online: https://www.libtayo.com/
  • 60.BAVENCIO® (Avelumab)|For Healthcare Professionals. [(accessed on 17 April 2023)]. Available online: https://www.bavencio.com/en_US/hcp.html.
  • 61.TECENTRIQ® (Atezolizumab) HCP|Efficacy, Safety, PI & MOA. [(accessed on 17 April 2023)]. Available online: https://www.tecentriq-hcp.com/
  • 62.Immunotherapy for BTC, UHCC, NSCLC & ES-SCLC—IMFINZI® (Durvalumab) [(accessed on 17 April 2023)]. Available online: https://www.imfinzi.com/
  • 63.EMA Imjudo. [(accessed on 17 April 2023)]. Available online: https://www.ema.europa.eu/en/medicines/human/EPAR/imjudo.
  • 64.YERVOY®(Ipilimumab)|Consumer|Gateway. [(accessed on 17 April 2023)]. Available online: https://www.yervoy.com/
  • 65.Kähler K.C., Hassel J.C., Heinzerling L., Loquai C., Thoms K.-M., Ugurel S., Zimmer L., Gutzmer R., for the committee on “Cutaneous Adverse Events“ of the German Working Group for Dermatological Oncology (Arbeitsgemeinschaft Dermatologische Onkologie, ADO) Side Effect Management during Immune Checkpoint Blockade Using CTLA-4 and PD-1 Antibodies for Metastatic Melanoma—An Update. J. Der Dtsch. Dermatol. Ges. 2020;18:582–609. doi: 10.1111/ddg.14128. [DOI] [PubMed] [Google Scholar]
  • 66.Hassel J.C., Heinzerling L., Aberle J., Bähr O., Eigentler T.K., Grimm M.-O., Grünwald V., Leipe J., Reinmuth N., Tietze J.K., et al. Combined Immune Checkpoint Blockade (Anti-PD-1/Anti-CTLA-4): Evaluation and Management of Adverse Drug Reactions. Cancer Treat. Rev. 2017;57:36–49. doi: 10.1016/j.ctrv.2017.05.003. [DOI] [PubMed] [Google Scholar]
  • 67.Walsh R.J., Sundar R., Lim J.S.J. Immune Checkpoint Inhibitor Combinations—Current and Emerging Strategies. Br. J. Cancer. 2023;128:1415–1417. doi: 10.1038/s41416-023-02181-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Zou Y., Xu Y., Chen X., Zheng L. Advances in the Application of Immune Checkpoint Inhibitors in Gynecological Tumors. Int. Immunopharmacol. 2023;117:109774. doi: 10.1016/j.intimp.2023.109774. [DOI] [PubMed] [Google Scholar]
  • 69.Colombo N., Sessa C., du Bois A., Ledermann J., McCluggage W.G., McNeish I., Morice P., Pignata S., Ray-Coquard I., Vergote I., et al. ESMO-ESGO Consensus Conference Recommendations on Ovarian Cancer: Pathology and Molecular Biology, Early and Advanced Stages, Borderline Tumours and Recurrent Disease†. Ann. Oncol. 2019;30:672–705. doi: 10.1093/annonc/mdz062. [DOI] [PubMed] [Google Scholar]
  • 70.Guidelines Detail. [(accessed on 17 April 2023)]. Available online: https://www.nccn.org/guidelines/guidelines-detail.
  • 71.Heo Y.-A. Mirvetuximab Soravtansine: First Approval. Drugs. 2023;83:265–273. doi: 10.1007/s40265-023-01834-3. [DOI] [PubMed] [Google Scholar]
  • 72.U.S. Food & Drug Administration: FDA Grants Accelerated Approval to Mirvetuximab Soravtansine-Gynx for FRα Positive, Platinum-Resistant Epithelial Ovarian, Fallopian Tube, or Peritoneal Cancer. [(accessed on 4 June 2023)]; Available online: https://www.fda.gov/drugs/resources-information-approved-drugs/fda-disco-burst-edition-fda-approval-elahere-mirvetuximab-soravtansine-gynx-fra-positive-platinum.
  • 73.Home—ClinicalTrials.Gov. [(accessed on 25 February 2022)]; Available online: https://www.clinicaltrials.gov/ct2/home.
  • 74.Grupo Español de Investigación en Cáncer de Ovario A Phase III Randomized, Double-Blinded Trial of Platinum-Based Chemotherapy with or without Atezolizumab Followed by Niraparib Maintenance with or without Atezolizumab in Patients with Recurrent Ovarian, Tubal or Peritoneal Cancer and Platinum Treatment-Free. Interval (TFIp) >6 Months. [(accessed on 3 June 2023)];2022 doi: 10.1136/ijgc-2020-001633. Available online: https://clinicaltrials.gov/ct2/show/NCT03598270. [DOI] [PubMed]
  • 75.Clovis Oncology, Inc ATHENA (A Multicenter, Randomized, Double-Blind, Placebo- Controlled Phase 3 Study in Ovarian Cancer Patients Evaluating Rucaparib and Nivolumab as Maintenance Treatment Following Response to Front-Line Platinum-Based Chemotherapy) [(accessed on 3 June 2023)];2023 Available online: https://clinicaltrials.gov/ct2/show/NCT03522246.
  • 76.Merck Sharp & Dohme LLC A Randomized Phase 3, Double-Blind Study of Chemotherapy with or without Pembrolizumab Followed by Maintenance with Olaparib or Placebo for the First-Line Treatment of BRCA Non-Mutated Advanced Epithelial Ovarian Cancer (EOC) (KEYLYNK-001/ENGOT-Ov43/GOG-3036) [(accessed on 3 June 2023)];2023 Available online: https://clinicaltrials.gov/ct2/show/NCT03740165.
  • 77.Tesaro, Inc A Randomized, Double-Blind, Phase 3 Comparison of Platinum-Based Therapy with TSR-042 and Niraparib Versus Standard of Care Platinum-Based Therapy as First-Line Treatment of Stage III or IV Nonmucinous Epithelial Ovarian Cancer. [(accessed on 3 June 2023)];2023 Available online: https://clinicaltrials.gov/ct2/show/NCT03602859.
  • 78.Merck Sharp & Dohme LLC A Phase 3, Randomized, Double-Blind Study of Pembrolizumab Versus Placebo in Combination With Paclitaxel With or Without Bevacizumab for the Treatment of Platinum-Resistant Recurrent Ovarian Cancer (KEYNOTE-B96/ENGOT-Ov65) [(accessed on 3 June 2023)];2023 Available online: https://clinicaltrials.gov/ct2/show/NCT05116189.
  • 79.AGO Research GmbH Atezolizumab in Combination with Bevacizumab and Chemotherapy Versus Bevacizumab and Chemotherapy in Recurrent Ovarian Cancer—A Randomized Phase III Trial. [(accessed on 3 June 2023)];2022 Available online: https://clinicaltrials.gov/ct2/show/NCT03353831.
  • 80.Pfizer A Phase 3, Multicenter, Randomized, Open-Label Study of Avelumab (MSB0010718C) Alone or in Combination with Pegylated Liposomal Doxorubicin Versus Pegylated Liposomal Doxorubicin alone in Patients with Platinum-Resistant/Refractory Ovarian Cancer. [(accessed on 3 June 2023)];2022 Available online: https://clinicaltrials.gov/ct2/show/NCT02580058.
  • 81.Pfizer A Randomized, Open-Label, Multicenter, Phase 3 Study to Evaluate the Efficacy and Safety of Avelumab in Combination with Chemotherapy Followed by Maintenance Therapy of Avelumab in Combination with the Poly (Adenosine Diphosphate [ADP]-Ribose) Polymerase (PARP) Inhibitor Talazoparib in Patients with Previously Untreated Advanced Ovarian Cancer (Javelin Ovarian PARP100) [(accessed on 3 June 2023)];2023 Available online: https://clinicaltrials.gov/ct2/show/NCT03642132.
  • 82.Pfizer A Randomized, Open-Label, Multicenter, Phase 3 Study to Evaluate the Efficacy and Safety of Avelumab (MSB0010718C) in Combination with and/or Following Chemotherapy in Patients with Previously Untreated Epithelial Ovarian Cancer Javelin Ovarian 100. [(accessed on 3 June 2023)];2020 Available online: https://clinicaltrials.gov/ct2/show/NCT02718417.
  • 83.Hoffmann-La Roche A Phase III, Multicenter, Randomized, Study of Atezolizumab Versus Placebo Administered in Combination with Paclitaxel, Carboplatin, and Bevacizumab to Patients with Newly-Diagnosed Stage III or Stage IV Ovarian, Fallopian Tube, or Primary Peritoneal Cancer. [(accessed on 3 June 2023)];2023 doi: 10.1136/ijgc-2018-000071. Available online: https://clinicaltrials.gov/ct2/show/NCT03038100. [DOI] [PubMed]
  • 84.ARCAGY/GINECO Group A Randomized, Double-Blinded, Phase III Study of Atezolizumab Versus Placebo in Patients with Late Relapse of Epithelial Ovarian, Fallopian Tube, or Peritoneal Cancer Treated by Platinum-Based Chemotherapy and Bevacizumab. [(accessed on 3 June 2023)];2023 Available online: https://clinicaltrials.gov/ct2/show/NCT02891824.
  • 85.National Cancer Institute (NCI) A Randomized, Phase II/III Study of Pegylated Liposomal Doxorubicin and CTEP-Supplied Atezolizumab Versus Pegylated Liposomal Doxorubicin, CTEP-Supplied Bevacizumab and CTEP-Supplied Atezolizumab Versus Pegylated Liposomal Doxorubicin and CTEP-Supplied Bevacizumab in Platinum Resistant Ovarian Cancer. [(accessed on 3 June 2023)];2023 Available online: https://clinicaltrials.gov/ct2/show/NCT02839707.
  • 86.Second Affiliated Hospital of Guangzhou Medical University A Phase II/III Trial of Comparison of Benefit of Administration of Checkpoint Inhibitors Plus Chemodrug Via Artery or Fine Needle to Tumor Versus Vein for Immunotherapy of Advanced Solid Tumors. [(accessed on 3 June 2023)];2023 Available online: https://clinicaltrials.gov/ct2/show/NCT03755739.
  • 87.ARCAGY/GINECO Group A Multicentric Randomized Phase II/III Evaluating TSR-042 (Anti-PD-1 MAb) in Combination with Niraparib (Parpi) Versus Niraparib Alone Compared to Chemotherapy in the Treatment of Metastatic or Recurrent Endometrial or Ovarian Carcinosarcoma after at Least One Line of Chemotherapy. [(accessed on 3 June 2023)];2023 Available online: https://clinicaltrials.gov/ct2/show/NCT03651206.
  • 88.Fondazione Policlinico Universitario Agostino Gemelli IRCCS Randomized Phase III Trial on NIraparib-TSR-042 (Dostarlimab) vs Physician’s Choice CHEmotherapy in Recurrent, Ovarian, Fallopian Tube or Primary Peritoneal Cancer Patients Not Candidate for Platinum Retreatment: NItCHE Trial (MITO 33) [(accessed on 4 June 2023)];2021 doi: 10.1136/ijgc-2021-002593. Available online: https://clinicaltrials.gov/ct2/show/NCT04679064. [DOI] [PubMed]
  • 89.Xencor, Inc A Phase 1 Multiple-Dose Study to Evaluate the Safety and Tolerability of XmAb®22841 Monotherapy and in Combination with Pembrolizumab in Subjects with Selected Advanced Solid Tumors (DUET-4) [(accessed on 4 June 2023)];2023 Available online: https://clinicaltrials.gov/ct2/show/NCT03849469.
  • 90.MacroGenics A Phase 1, First-in-Human, Open-Label, Dose Escalation Study of MGD013, A Bispecific DART® Protein Binding PD-1 and LAG-3 in Patients with Unresectable or Metastatic Neoplasms. [(accessed on 4 June 2023)];2023 Available online: https://clinicaltrials.gov/ct2/show/NCT03219268.
  • 91.Svane I.M. T-Cell Therapy in Combination with Nivolumab, Relatlimab and Ipilimumab for Patients with Advanced Ovarian-, Fallopian Tube- and Primary Peritoneal Cancer. [(accessed on 4 June 2023)];2021 Available online: https://clinicaltrials.gov/ct2/show/NCT04611126.
  • 92.Incyte Biosciences International Sàrl A Phase 1 Open-Label, Dose-Escalation, Safety and Tolerability Study of INCAGN02385 in Participants with Select Advanced Malignancies. [(accessed on 4 June 2023)];2020 Available online: https://clinicaltrials.gov/ct2/show/NCT03538028.
  • 93.Compugen Ltd A Phase 1 Study of The Safety and Tolerability of COM902 in Subjects with Advanced Malignancies. [(accessed on 4 June 2023)];2023 Available online: https://clinicaltrials.gov/ct2/show/NCT04354246.
  • 94.M.D. Anderson Cancer Center EON: A Single-Arm Phase II Study of Etigilimab (OMP-313M32) in Combination with Checkpoint Inhibition (Nivolumab) in Patients with Platinum-Resistant, Recurrent Epithelial Ovarian Cancer. [(accessed on 4 June 2023)];2023 Available online: https://www.clinicaltrials.gov/ct2/show/NCT05715216/
  • 95.Search Results|Beta ClinicalTrials.Gov. [(accessed on 1 June 2023)]; Available online: https://beta.clinicaltrials.gov/search?cond=Ovarian%20Cancer&term=immune%20checkpoint.
  • 96.Drakes M.L., Mehrotra S., Aldulescu M., Potkul R.K., Liu Y., Grisoli A., Joyce C., O’Brien T.E., Stack M.S., Stiff P.J. Stratification of Ovarian Tumor Pathology by Expression of Programmed Cell Death-1 (PD-1) and PD-Ligand- 1 (PD-L1) in Ovarian Cancer. J. Ovarian Res. 2018;11:43. doi: 10.1186/s13048-018-0414-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 97.Drakes M.L., Czerlanis C.M., Stiff P.J. Immune Checkpoint Blockade in Gynecologic Cancers: State of Affairs. Cancers. 2020;12:3301. doi: 10.3390/cancers12113301. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98.Pirš B., Škof E., Smrkolj V., Smrkolj Š. Overview of Immune Checkpoint Inhibitors in Gynecological Cancer Treatment. Cancers. 2022;14:631. doi: 10.3390/cancers14030631. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 99.Santoiemma P.P., Powell D.J. Tumor Infiltrating Lymphocytes in Ovarian Cancer. Cancer Biol. Ther. 2015;16:807–820. doi: 10.1080/15384047.2015.1040960. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 100.Zhang L., Conejo-Garcia J.R., Katsaros D., Gimotty P.A., Massobrio M., Regnani G., Makrigiannakis A., Gray H., Schlienger K., Liebman M.N., et al. Intratumoral T Cells, Recurrence, and Survival in Epithelial Ovarian Cancer. N. Engl. J. Med. 2003;348:203–213. doi: 10.1056/NEJMoa020177. [DOI] [PubMed] [Google Scholar]
  • 101.Hwang W.-T., Adams S.F., Tahirovic E., Hagemann I.S., Coukos G. Prognostic Significance of Tumor-Infiltrating T Cells in Ovarian Cancer: A Meta-Analysis. Gynecol. Oncol. 2012;124:192–198. doi: 10.1016/j.ygyno.2011.09.039. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 102.Sato E., Olson S.H., Ahn J., Bundy B., Nishikawa H., Qian F., Jungbluth A.A., Frosina D., Gnjatic S., Ambrosone C., et al. Intraepithelial CD8+ Tumor-Infiltrating Lymphocytes and a High CD8+/Regulatory T Cell Ratio Are Associated with Favorable Prognosis in Ovarian Cancer. Proc. Natl. Acad. Sci. USA. 2005;102:18538–18543. doi: 10.1073/pnas.0509182102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 103.Bronger H. Immunology and Immune Checkpoint Inhibition in Ovarian Cancer—Current Aspects. Geburtshilfe Frauenheilkd. 2021;81:1128–1144. doi: 10.1055/a-1475-4335. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 104.Tumor Derived UBR5 Promotes Ovarian Cancer Growth and Metastasis through Inducing Immunosuppressive Macrophages|Nature Communications. [(accessed on 12 April 2023)]. Available online: https://www.nature.com/articles/s41467-020-20140-0. [DOI] [PMC free article] [PubMed]
  • 105.Pan Y., Yu Y., Wang X., Zhang T. Tumor-Associated Macrophages in Tumor Immunity. Front. Immunol. 2020;11:583084. doi: 10.3389/fimmu.2020.583084. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 106.Song M., Yeku O.O., Rafiq S., Purdon T., Dong X., Zhu L., Zhang T., Wang H., Yu Z., Mai J., et al. Tumor Derived UBR5 Promotes Ovarian Cancer Growth and Metastasis through Inducing Immunosuppressive Macrophages. Nat. Commun. 2020;11:6298. doi: 10.1038/s41467-020-20140-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 107.Hensler M., Kasikova L., Fiser K., Rakova J., Skapa P., Laco J., Lanickova T., Pecen L., Truxova I., Vosahlikova S., et al. M2-like Macrophages Dictate Clinically Relevant Immunosuppression in Metastatic Ovarian Cancer. J. Immunother. Cancer. 2020;8:e000979. doi: 10.1136/jitc-2020-000979. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 108.Yin M., Li X., Tan S., Zhou H.J., Ji W., Bellone S., Xu X., Zhang H., Santin A.D., Lou G., et al. Tumor-Associated Macrophages Drive Spheroid Formation during Early Transcoelomic Metastasis of Ovarian Cancer. J. Clin. Investig. 2016;126:4157–4173. doi: 10.1172/JCI87252. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 109.Binnewies M., Pollack J.L., Rudolph J., Dash S., Abushawish M., Lee T., Jahchan N.S., Canaday P., Lu E., Norng M., et al. Targeting TREM2 on Tumor-Associated Macrophages Enhances Immunotherapy. Cell. Rep. 2021;37:109844. doi: 10.1016/j.celrep.2021.109844. [DOI] [PubMed] [Google Scholar]
  • 110.Ardighieri L., Missale F., Bugatti M., Gatta L.B., Pezzali I., Monti M., Gottardi S., Zanotti L., Bignotti E., Ravaggi A., et al. Infiltration by CXCL10 Secreting Macrophages Is Associated With Antitumor Immunity and Response to Therapy in Ovarian Cancer Subtypes. Front. Immunol. 2021;12:690201. doi: 10.3389/fimmu.2021.690201. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 111.Zhu L., Yu X., Wang L., Liu J., Qu Z., Zhang H., Li L., Chen J., Zhou Q. Angiogenesis and Immune Checkpoint Dual Blockade in Combination with Radiotherapy for Treatment of Solid Cancers: Opportunities and Challenges. Oncogenesis. 2021;10:47. doi: 10.1038/s41389-021-00335-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 112.Fukumura D., Kloepper J., Amoozgar Z., Duda D.G., Jain R.K. Enhancing Cancer Immunotherapy Using Antiangiogenics: Opportunities and Challenges. Nat. Rev. Clin. Oncol. 2018;15:325–340. doi: 10.1038/nrclinonc.2018.29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 113.Yi M., Jiao D., Qin S., Chu Q., Wu K., Li A. Synergistic Effect of Immune Checkpoint Blockade and Anti-Angiogenesis in Cancer Treatment. Mol. Cancer. 2019;18:60. doi: 10.1186/s12943-019-0974-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 114.Evrard C., Alexandre J. Predictive and Prognostic Value of Microsatellite Instability in Gynecologic Cancer (Endometrial and Ovarian) Cancers. 2021;13:2434. doi: 10.3390/cancers13102434. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 115.Deshpande M., Romanski P.A., Rosenwaks Z., Gerhardt J. Gynecological Cancers Caused by Deficient Mismatch Repair and Microsatellite Instability. Cancers. 2020;12:3319. doi: 10.3390/cancers12113319. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 116.Atwal A., Snowsill T., Dandy M.C., Krum T., Newton C., Evans D.G., Crosbie E.J., Ryan N.A.J. The Prevalence of Mismatch Repair Deficiency in Ovarian Cancer: A Systematic Review and Meta-Analysis. Int. J. Cancer. 2022;151:1626–1639. doi: 10.1002/ijc.34165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 117.Nonomura Y., Nakayama K., Nakamura K., Razia S., Yamashita H., Ishibashi T., Ishikawa M., Sato S., Nakayama S., Otsuki Y., et al. Ovarian Endometrioid and Clear Cell Carcinomas with Low Prevalence of Microsatellite Instability: A Unique Subset of Ovarian Carcinomas Could Benefit from Combination Therapy with Immune Checkpoint Inhibitors and Other Anticancer Agents. Healthcare. 2022;10:694. doi: 10.3390/healthcare10040694. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 118.Marabelle A., Le D.T., Ascierto P.A., Di Giacomo A.M., De Jesus-Acosta A., Delord J.-P., Geva R., Gottfried M., Penel N., Hansen A.R., et al. Efficacy of Pembrolizumab in Patients with Noncolorectal High Microsatellite Instability/Mismatch Repair-Deficient Cancer: Results From the Phase II KEYNOTE-158 Study. J. Clin. Oncol. 2020;38:1–10. doi: 10.1200/JCO.19.02105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 119.Sui Q., Zhang X., Chen C., Tang J., Yu J., Li W., Han K., Jiang W., Liao L., Kong L., et al. Inflammation Promotes Resistance to Immune Checkpoint Inhibitors in High Microsatellite Instability Colorectal Cancer. Nat. Commun. 2022;13:7316. doi: 10.1038/s41467-022-35096-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 120.Shen J., Ju Z., Zhao W., Wang L., Peng Y., Ge Z., Nagel Z.D., Zou J., Wang C., Kapoor P., et al. ARID1A Deficiency Promotes Mutability and Potentiates Therapeutic Antitumor Immunity Unleashed by Immune Checkpoint Blockade. Nat. Med. 2018;24:556–562. doi: 10.1038/s41591-018-0012-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 121.Yamashita H., Nakayama K., Ishikawa M., Ishibashi T., Nakamura K., Sawada K., Yoshimura Y., Tatsumi N., Kurose S., Minamoto T., et al. Relationship between Microsatellite Instability, Immune Cells Infiltration, and Expression of Immune Checkpoint Molecules in Ovarian Carcinoma: Immunotherapeutic Strategies for the Future. Int. J. Mol. Sci. 2019;20:5129. doi: 10.3390/ijms20205129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 122.Wang H., Liu J., Yang J., Wang Z., Zhang Z., Peng J., Wang Y., Hong L. A Novel Tumor Mutational Burden-Based Risk Model Predicts Prognosis and Correlates with Immune Infiltration in Ovarian Cancer. Front. Immunol. 2022;13:943389. doi: 10.3389/fimmu.2022.943389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 123.Mi J.-L., Xu M., Liu C., Wang R.-S. Interactions between Tumor Mutation Burden and Immune Infiltration in Ovarian Cancer. Int. J. Clin. Exp. Pathol. 2020;13:2513–2523. [PMC free article] [PubMed] [Google Scholar]
  • 124.Chan T.A., Yarchoan M., Jaffee E., Swanton C., Quezada S.A., Stenzinger A., Peters S. Development of Tumor Mutation Burden as an Immunotherapy Biomarker: Utility for the Oncology Clinic. Ann. Oncol. 2019;30:44–56. doi: 10.1093/annonc/mdy495. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 125.Sha D., Jin Z., Budzcies J., Kluck K., Stenzinger A., Sinicrope F.A. Tumor Mutational Burden (TMB) as a Predictive Biomarker in Solid Tumors. Cancer Discov. 2020;10:1808–1825. doi: 10.1158/2159-8290.CD-20-0522. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 126.Riviere P., Goodman A.M., Okamura R., Barkauskas D.A., Whitchurch T.J., Lee S., Khalid N., Collier R., Mareboina M., Frampton G.M., et al. High Tumor Mutational Burden Correlates with Longer Survival in Immunotherapy-Naïve Patients with Diverse Cancers. Mol. Cancer Ther. 2020;19:2139–2145. doi: 10.1158/1535-7163.MCT-20-0161. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 127.Fan S., Gao X., Qin Q., Li H., Yuan Z., Zhao S. Association between Tumor Mutation Burden and Immune Infiltration in Ovarian Cancer. Int. Immunopharmacol. 2020;89:107126. doi: 10.1016/j.intimp.2020.107126. [DOI] [PubMed] [Google Scholar]
  • 128.Wang Q., Qin Y., Li B. CD8+ T Cell Exhaustion and Cancer Immunotherapy. Cancer Lett. 2023;559:216043. doi: 10.1016/j.canlet.2022.216043. [DOI] [PubMed] [Google Scholar]
  • 129.McGrail D.J., Pilié P.G., Rashid N.U., Voorwerk L., Slagter M., Kok M., Jonasch E., Khasraw M., Heimberger A.B., Lim B., et al. High Tumor Mutation Burden Fails to Predict Immune Checkpoint Blockade Response across All Cancer Types. Ann. Oncol. 2021;32:661–672. doi: 10.1016/j.annonc.2021.02.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 130.Ukai M., Yokoi A., Yoshida K., Suzuki S., Shibata K., Kikkawa F., Nakatsura T., Kajiyama H. Extracellular MiRNAs as Predictive Biomarkers for Glypican-3-Derived Peptide Vaccine Therapy Response in Ovarian Clear Cell Carcinoma. Cancers. 2021;13:550. doi: 10.3390/cancers13030550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 131.Png K.J., Halberg N., Yoshida M., Tavazoie S.F. A MicroRNA Regulon That Mediates Endothelial Recruitment and Metastasis by Cancer Cells. Nature. 2011;481:190–194. doi: 10.1038/nature10661. [DOI] [PubMed] [Google Scholar]
  • 132.Suzuki H.I., Katsura A., Matsuyama H., Miyazono K. MicroRNA Regulons in Tumor Microenvironment. Oncogene. 2015;34:3085–3094. doi: 10.1038/onc.2014.254. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 133.Matsuyama H., Suzuki H.I., Nishimori H., Noguchi M., Yao T., Komatsu N., Mano H., Sugimoto K., Miyazono K. MiR-135b Mediates NPM-ALK-Driven Oncogenicity and Renders IL-17-Producing Immunophenotype to Anaplastic Large Cell Lymphoma. Blood. 2011;118:6881–6892. doi: 10.1182/blood-2011-05-354654. [DOI] [PubMed] [Google Scholar]
  • 134.Au Yeung C.L., Co N.-N., Tsuruga T., Yeung T.-L., Kwan S.-Y., Leung C.S., Li Y., Lu E.S., Kwan K., Wong K.-K., et al. Exosomal Transfer of Stroma-Derived MiR21 Confers Paclitaxel Resistance in Ovarian Cancer Cells through Targeting APAF1. Nat. Commun. 2016;7:11150. doi: 10.1038/ncomms11150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 135.Nanbakhsh A., Malarkannan S. The Role of MicroRNAs in NK Cell Development and Function. Cells. 2021;10:2020. doi: 10.3390/cells10082020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 136.Weiss C.N., Ito K. A Macro View of MicroRNAs: The Discovery of MicroRNAs and Their Role in Hematopoiesis and Hematologic Disease. Int. Rev. Cell. Mol. Biol. 2017;334:99–175. doi: 10.1016/bs.ircmb.2017.03.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 137.Wang Q., Lin W., Tang X., Li S., Guo L., Lin Y., Kwok H.F. The Roles of MicroRNAs in Regulating the Expression of PD-1/PD-L1 Immune Checkpoint. Int. J. Mol. Sci. 2017;18:2540. doi: 10.3390/ijms18122540. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 138.Kousar K., Ahmad T., Abduh M.S., Kanwal B., Shah S.S., Naseer F., Anjum S. MiRNAs in Regulation of Tumor Microenvironment, Chemotherapy Resistance, Immunotherapy Modulation and MiRNA Therapeutics in Cancer. Int. J. Mol. Sci. 2022;23:13822. doi: 10.3390/ijms232213822. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 139.Sohel M.H. Extracellular/Circulating MicroRNAs: Release Mechanisms, Functions and Challenges. Achiev. Life Sci. 2016;10:175–186. doi: 10.1016/j.als.2016.11.007. [DOI] [Google Scholar]
  • 140.He B., Zhao Z., Cai Q., Zhang Y., Zhang P., Shi S., Xie H., Peng X., Yin W., Tao Y., et al. MiRNA-Based Biomarkers, Therapies, and Resistance in Cancer. Int. J. Biol. Sci. 2020;16:2628–2647. doi: 10.7150/ijbs.47203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 141.Li Q., Johnston N., Zheng X., Wang H., Zhang X., Gao D., Min W. MiR-28 Modulates Exhaustive Differentiation of T Cells through Silencing Programmed Cell Death-1 and Regulating Cytokine Secretion. Oncotarget. 2016;7:53735–53750. doi: 10.18632/oncotarget.10731. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 142.El-Daly S.M., Bayraktar R., Anfossi S., Calin G.A. The Interplay between MicroRNAs and the Components of the Tumor Microenvironment in B-Cell Malignancies. Int. J. Mol. Sci. 2020;21:3387. doi: 10.3390/ijms21093387. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 143.Diener C., Keller A., Meese E. Emerging Concepts of MiRNA Therapeutics: From Cells to Clinic. Trends Genet. 2022;38:613–626. doi: 10.1016/j.tig.2022.02.006. [DOI] [PubMed] [Google Scholar]
  • 144.Ferragut Cardoso A.P., Banerjee M., Nail A.N., Lykoudi A., States J.C. MiRNA Dysregulation Is an Emerging Modulator of Genomic Instability. Semin. Cancer Biol. 2021;76:120–131. doi: 10.1016/j.semcancer.2021.05.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 145.Wang Z., Yin H., Zhang Y., Feng Y., Yan Z., Jiang X., Bukhari I., Iqbal F., Cooke H.J., Shi Q. MiR-214-Mediated Downregulation of RNF8 Induces Chromosomal Instability in Ovarian Cancer Cells. Cell Cycle. 2014;13:3519–3528. doi: 10.4161/15384101.2014.958413. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 146.Hill M., Tran N. Global MiRNA to MiRNA Interactions: Impacts for MiR-21. Trends Cell Biol. 2021;31:3–5. doi: 10.1016/j.tcb.2020.10.005. [DOI] [PubMed] [Google Scholar]
  • 147.Terkelsen T., Russo F., Gromov P., Haakensen V.D., Brunak S., Gromova I., Krogh A., Papaleo E. Secreted Breast Tumor Interstitial Fluid MicroRNAs and Their Target Genes Are Associated with Triple-Negative Breast Cancer, Tumor Grade, and Immune Infiltration. Breast Cancer Res. 2020;22:73. doi: 10.1186/s13058-020-01295-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 148.Felekkis K., Touvana E., Stefanou C., Deltas C. MicroRNAs: A Newly Described Class of Encoded Molecules That Play a Role in Health and Disease. Hippokratia. 2010;14:236–240. [PMC free article] [PubMed] [Google Scholar]
  • 149.Gong A.-Y., Zhou R., Hu G., Li X., Splinter P.L., O’Hara S.P., LaRusso N.F., Soukup G.A., Dong H., Chen X.-M. MicroRNA-513 Regulates B7-H1 Translation and Is Involved in IFN-γ-Induced B7-H1 Expression in Cholangiocytes. J. Immunol. 2009;182:1325–1333. doi: 10.4049/jimmunol.182.3.1325. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 150.Chen L., Gibbons D.L., Goswami S., Cortez M.A., Ahn Y.-H., Byers L.A., Zhang X., Yi X., Dwyer D., Lin W., et al. Metastasis Is Regulated via MicroRNA-200/ZEB1 Axis Control of Tumour Cell PD-L1 Expression and Intratumoral Immunosuppression. Nat. Commun. 2014;5:5241. doi: 10.1038/ncomms6241. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 151.Nguyen H.T., Phung C.D., Tran T.H., Pham T.T., Pham L.M., Nguyen T.T., Jeong J.-H., Choi H.-G., Ku S.K., Yong C.S., et al. Manipulating Immune System Using Nanoparticles for an Effective Cancer Treatment: Combination of Targeted Therapy and Checkpoint Blockage MiRNA. J. Control. Release. 2021;329:524–537. doi: 10.1016/j.jconrel.2020.09.034. [DOI] [PubMed] [Google Scholar]
  • 152.Xi J., Huang Q., Wang L., Ma X., Deng Q., Kumar M., Zhou Z., Li L., Zeng Z., Young K.H., et al. MiR-21 Depletion in Macrophages Promotes Tumoricidal Polarization and Enhances PD-1 Immunotherapy. Oncogene. 2018;37:3151–3165. doi: 10.1038/s41388-018-0178-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 153.Schmid G., Notaro S., Reimer D., Abdel-Azim S., Duggan-Peer M., Holly J., Fiegl H., Rössler J., Wiedemair A., Concin N., et al. Expression and Promotor Hypermethylation of MiR-34a in the Various Histological Subtypes of Ovarian Cancer. BMC Cancer. 2016;16:102. doi: 10.1186/s12885-016-2135-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 154.Guyon N., Garnier D., Briand J., Nadaradjane A., Bougras-Cartron G., Raimbourg J., Campone M., Heymann D., Vallette F.M., Frenel J.-S., et al. Anti-PD1 Therapy Induces Lymphocyte-Derived Exosomal MiRNA-4315 Release Inhibiting Bim-Mediated Apoptosis of Tumor Cells. Cell. Death Dis. 2020;11:1048. doi: 10.1038/s41419-020-03224-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 155.Chen J., Jiang C.C., Jin L., Zhang X.D. Regulation of PD-L1: A Novel Role of pro-Survival Signalling in Cancer. Ann. Oncol. 2016;27:409–416. doi: 10.1093/annonc/mdv615. [DOI] [PubMed] [Google Scholar]
  • 156.Pathania A.S., Prathipati P., Olwenyi O.A., Chava S., Smith O.V., Gupta S.C., Chaturvedi N.K., Byrareddy S.N., Coulter D.W., Challagundla K.B. MiR-15a and MiR-15b Modulate Natural Killer and CD8+T-Cell Activation and Anti-Tumor Immune Response by Targeting PD-L1 in Neuroblastoma. Mol. Ther.-Oncolytics. 2022;25:308–329. doi: 10.1016/j.omto.2022.03.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 157.Ji X., Wang E., Tian F. MicroRNA-140 Suppresses Osteosarcoma Tumor Growth by Enhancing Anti-Tumor Immune Response and Blocking MTOR Signaling. Biochem. Biophys. Res. Commun. 2018;495:1342–1348. doi: 10.1016/j.bbrc.2017.11.120. [DOI] [PubMed] [Google Scholar]
  • 158.Di Martino M.T., Riillo C., Scionti F., Grillone K., Polerà N., Caracciolo D., Arbitrio M., Tagliaferri P., Tassone P. MiRNAs and LncRNAs as Novel Therapeutic Targets to Improve Cancer Immunotherapy. Cancers. 2021;13:1587. doi: 10.3390/cancers13071587. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 159.Yokoi A., Matsuzaki J., Yamamoto Y., Yoneoka Y., Takahashi K., Shimizu H., Uehara T., Ishikawa M., Ikeda S., Sonoda T., et al. Integrated Extracellular MicroRNA Profiling for Ovarian Cancer Screening. Nat. Commun. 2018;9:4319. doi: 10.1038/s41467-018-06434-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 160.Nakamura K., Sawada K., Yoshimura A., Kinose Y., Nakatsuka E., Kimura T. Clinical Relevance of Circulating Cell-Free MicroRNAs in Ovarian Cancer. Mol. Cancer. 2016;15:48. doi: 10.1186/s12943-016-0536-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 161.Kosaka N., Iguchi H., Ochiya T. Circulating MicroRNA in Body Fluid: A New Potential Biomarker for Cancer Diagnosis and Prognosis. Cancer Sci. 2010;101:2087–2092. doi: 10.1111/j.1349-7006.2010.01650.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 162.Rapado-González Ó., Álvarez-Castro A., López-López R., Iglesias-Canle J., Suárez-Cunqueiro M.M., Muinelo-Romay L. Circulating MicroRNAs as Promising Biomarkers in Colorectal Cancer. Cancers. 2019;11:898. doi: 10.3390/cancers11070898. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 163.Quandt D., Dieter Zucht H., Amann A., Wulf-Goldenberg A., Borrebaeck C., Cannarile M., Lambrechts D., Oberacher H., Garrett J., Nayak T., et al. Implementing Liquid Biopsies into Clinical Decision Making for Cancer Immunotherapy. Oncotarget. 2017;8:48507–48520. doi: 10.18632/oncotarget.17397. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 164.Mari R., Mamessier E., Lambaudie E., Provansal M., Birnbaum D., Bertucci F., Sabatier R. Liquid Biopsies for Ovarian Carcinoma: How Blood Tests May Improve the Clinical Management of a Deadly Disease. Cancers. 2019;11:774. doi: 10.3390/cancers11060774. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 165.Failing J.J., Dudek O.A., Marin Acevedo J.A., Chirila R.M., Dong H., Markovic S.N., Dronca R.S. Biomarkers of Hyperprogression and Pseudoprogression with Immune Checkpoint Inhibitor Therapy. Future Oncol. 2019;15:2645–2656. doi: 10.2217/fon-2019-0183. [DOI] [PubMed] [Google Scholar]
  • 166.Wang Q., Gao J., Wu X. Pseudoprogression and Hyperprogression after Checkpoint Blockade. Int. Immunopharmacol. 2018;58:125–135. doi: 10.1016/j.intimp.2018.03.018. [DOI] [PubMed] [Google Scholar]
  • 167.Chubachi S., Yasuda H., Irie H., Fukunaga K., Naoki K., Soejima K., Betsuyaku T. A Case of Non-Small Cell Lung Cancer with Possible “Disease Flare” on Nivolumab Treatment. Case Rep. Oncol. Med. 2016;2016:e1075641. doi: 10.1155/2016/1075641. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 168.Matos I., Martin-Liberal J., Hierro C., Ochoa De Olza M., Viaplana C., Costa M., Felip-Falg’s E., Mur-Bonet G., Vieito M., Brana I., et al. Incidence and Clinical Implications of a New Definition of Hyperprogression (HPD) with Immune Checkpoint Inhibitors (ICIs) in Patients Treated in Phase 1 (Ph1) Trials. J. Clin. Oncol. 2018;36:3032. doi: 10.1200/JCO.2018.36.15_suppl.3032. [DOI] [Google Scholar]
  • 169.Kim C.G., Kim K.H., Pyo K.-H., Xin C.-F., Hong M.H., Ahn B.-C., Kim Y., Choi S.J., Yoon H.I., Lee J.G., et al. Hyperprogressive Disease during PD-1/PD-L1 Blockade in Patients with Non-Small-Cell Lung Cancer. Ann. Oncol. 2019;30:1104–1113. doi: 10.1093/annonc/mdz123. [DOI] [PubMed] [Google Scholar]
  • 170.Sasaki A., Nakamura Y., Mishima S., Kawazoe A., Kuboki Y., Bando H., Kojima T., Doi T., Ohtsu A., Yoshino T., et al. Predictive Factors for Hyperprogressive Disease during Nivolumab as Anti-PD1 Treatment in Patients with Advanced Gastric Cancer. Gastric Cancer. 2019;22:793–802. doi: 10.1007/s10120-018-00922-8. [DOI] [PubMed] [Google Scholar]
  • 171.Ferrara R., Mezquita L., Texier M., Lahmar J., Audigier-Valette C., Tessonnier L., Mazieres J., Zalcman G., Brosseau S., Le Moulec S., et al. Hyperprogressive Disease in Patients with Advanced Non–Small Cell Lung Cancer Treated with PD-1/PD-L1 Inhibitors or with Single-Agent Chemotherapy. JAMA Oncol. 2018;4:1543–1552. doi: 10.1001/jamaoncol.2018.3676. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 172.Kato S., Goodman A., Walavalkar V., Barkauskas D.A., Sharabi A., Kurzrock R. Hyperprogressors after Immunotherapy: Analysis of Genomic Alterations Associated with Accelerated Growth Rate. Clin. Cancer Res. 2017;23:4242–4250. doi: 10.1158/1078-0432.CCR-16-3133. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 173.Frelaut M., Le Tourneau C., Borcoman E. Hyperprogression under Immunotherapy. Int. J. Mol. Sci. 2019;20:2674. doi: 10.3390/ijms20112674. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 174.Saâda-Bouzid E., Defaucheux C., Karabajakian A., Coloma V.P., Servois V., Paoletti X., Even C., Fayette J., Guigay J., Loirat D., et al. Hyperprogression during Anti-PD-1/PD-L1 Therapy in Patients with Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma. Ann. Oncol. 2017;28:1605–1611. doi: 10.1093/annonc/mdx178. [DOI] [PubMed] [Google Scholar]
  • 175.Boland J.L., Zhou Q., Martin M., Callahan M.K., Konner J., O’Cearbhaill R.E., Friedman C.F., Tew W., Makker V., Grisham R.N., et al. Early Disease Progression and Treatment Discontinuation in Patients with Advanced Ovarian Cancer Receiving Immune Checkpoint Blockade. Gynecol. Oncol. 2019;152:251–258. doi: 10.1016/j.ygyno.2018.11.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 176.Arasanz H., Zuazo M., Bocanegra A., Gato M., Martínez-Aguillo M., Morilla I., Fernández G., Hernández B., López P., Alberdi N., et al. Early Detection of Hyperprogressive Disease in Non-Small Cell Lung Cancer by Monitoring of Systemic T Cell Dynamics. Cancers. 2020;12:344. doi: 10.3390/cancers12020344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 177.Lo Russo G., Moro M., Sommariva M., Cancila V., Boeri M., Centonze G., Ferro S., Ganzinelli M., Gasparini P., Huber V., et al. Antibody–Fc/FcR Interaction on Macrophages as a Mechanism for Hyperprogressive Disease in Non–Small Cell Lung Cancer Subsequent to PD-1/PD-L1 Blockade. Clin. Cancer Res. 2019;25:989–999. doi: 10.1158/1078-0432.CCR-18-1390. [DOI] [PubMed] [Google Scholar]
  • 178.Sahin I., Zhang S., Navaraj A., Zhou L., Dizon D., Safran H., El-Deiry W.S. AMG-232 Sensitizes High MDM2-Expressing Tumor Cells to T-Cell-Mediated Killing. Cell Death Discov. 2020;6:57. doi: 10.1038/s41420-020-0292-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 179.Champiat S., Dercle L., Ammari S., Massard C., Hollebecque A., Postel-Vinay S., Chaput N., Eggermont A., Marabelle A., Soria J.-C., et al. Hyperprogressive Disease Is a New Pattern of Progression in Cancer Patients Treated by Anti-PD-1/PD-L1. Clin. Cancer Res. 2017;23:1920–1928. doi: 10.1158/1078-0432.CCR-16-1741. [DOI] [PubMed] [Google Scholar]
  • 180.Kanjanapan Y., Day D., Wang L., Al-Sawaihey H., Abbas E., Namini A., Siu L.L., Hansen A., Razak A.A., Spreafico A., et al. Hyperprogressive Disease in Early-Phase Immunotherapy Trials: Clinical Predictors and Association with Immune-Related Toxicities. Cancer. 2019;125:1341–1349. doi: 10.1002/cncr.31999. [DOI] [PubMed] [Google Scholar]
  • 181.Di Giacomo A.M., Danielli R., Guidoboni M., Calabrò L., Carlucci D., Miracco C., Volterrani L., Mazzei M.A., Biagioli M., Altomonte M., et al. Therapeutic Efficacy of Ipilimumab, an Anti-CTLA-4 Monoclonal Antibody, in Patients with Metastatic Melanoma Unresponsive to Prior Systemic Treatments: Clinical and Immunological Evidence from Three Patient Cases. Cancer Immunol. Immunother. 2009;58:1297–1306. doi: 10.1007/s00262-008-0642-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 182.Chiou V.L., Burotto M. Pseudoprogression and Immune-Related Response in Solid Tumors. J. Clin. Oncol. 2015;33:3541–3543. doi: 10.1200/JCO.2015.61.6870. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 183.Queirolo P., Spagnolo F. Atypical Responses in Patients with Advanced Melanoma, Lung Cancer, Renal-Cell Carcinoma and Other Solid Tumors Treated with Anti-PD-1 Drugs: A Systematic Review. Cancer Treat. Rev. 2017;59:71–78. doi: 10.1016/j.ctrv.2017.07.002. [DOI] [PubMed] [Google Scholar]
  • 184.Li H., Zhou X., Zhang D., Wang G., Cheng X., Xu C., Yao B., Pang L., Chen J. Early Onset Immune-Related Adverse Event to Identify Pseudo-Progression in a Patient With Ovarian Cancer Treated With Nivolumab: A Case Report and Review of the Literature. Front. Med. 2020;7:366. doi: 10.3389/fmed.2020.00366. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 185.Passler M., Taube E.T., Sehouli J., Pietzner K. Pseudo- or Real Progression? An Ovarian Cancer Patient under Nivolumab: A Case Report. World J. Clin. Oncol. 2019;10:247–255. doi: 10.5306/wjco.v10.i7.247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 186.Ma Y., Wang Q., Dong Q., Zhan L., Zhang J. How to Differentiate Pseudoprogression from True Progression in Cancer Patients Treated with Immunotherapy. Am. J. Cancer Res. 2019;9:1546–1553. [PMC free article] [PubMed] [Google Scholar]
  • 187.Nero C., Ciccarone F., Pietragalla A., Duranti S., Daniele G., Salutari V., Carbone M.V., Scambia G., Lorusso D. Ovarian Cancer Treatments Strategy: Focus on PARP Inhibitors and Immune Check Point Inhibitors. Cancers. 2021;13:1298. doi: 10.3390/cancers13061298. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 188.Nguyen L.T., Ohashi P.S. Clinical Blockade of PD1 and LAG3--Potential Mechanisms of Action. Nat. Rev. Immunol. 2015;15:45–56. doi: 10.1038/nri3790. [DOI] [PubMed] [Google Scholar]
  • 189.Sanchez-Correa B., Valhondo I., Hassouneh F., Lopez-Sejas N., Pera A., Bergua J.M., Arcos M.J., Bañas H., Casas-Avilés I., Durán E., et al. DNAM-1 and the TIGIT/PVRIG/TACTILE Axis: Novel Immune Checkpoints for Natural Killer Cell-Based Cancer Immunotherapy. Cancers. 2019;11:877. doi: 10.3390/cancers11060877. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 190.Chauvin J.-M., Zarour H.M. TIGIT in Cancer Immunotherapy. J. Immunother. Cancer. 2020;8:e000957. doi: 10.1136/jitc-2020-000957. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 191.Ge Z., Peppelenbosch M.P., Sprengers D., Kwekkeboom J. TIGIT, the Next Step Towards Successful Combination Immune Checkpoint Therapy in Cancer. Front. Immunol. 2021;12:699895. doi: 10.3389/fimmu.2021.699895. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 192.Mariniello A., Novello S., Scagliotti G.V., Ramalingam S.S. Double Immune Checkpoint Blockade in Advanced NSCLC. Crit. Rev. Oncol./Hematol. 2020;152:102980. doi: 10.1016/j.critrevonc.2020.102980. [DOI] [PubMed] [Google Scholar]
  • 193.Curran M.A., Montalvo W., Yagita H., Allison J.P. PD-1 and CTLA-4 Combination Blockade Expands Infiltrating T Cells and Reduces Regulatory T and Myeloid Cells within B16 Melanoma Tumors. Proc. Natl. Acad. Sci. USA. 2010;107:4275–4280. doi: 10.1073/pnas.0915174107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 194.Baumeister S.H., Freeman G.J., Dranoff G., Sharpe A.H. Coinhibitory Pathways in Immunotherapy for Cancer. Annu. Rev. Immunol. 2016;34:539–573. doi: 10.1146/annurev-immunol-032414-112049. [DOI] [PubMed] [Google Scholar]
  • 195.Boutros C., Tarhini A., Routier E., Lambotte O., Ladurie F.L., Carbonnel F., Izzeddine H., Marabelle A., Champiat S., Berdelou A., et al. Safety Profiles of Anti-CTLA-4 and Anti-PD-1 Antibodies Alone and in Combination. Nat. Rev. Clin. Oncol. 2016;13:473–486. doi: 10.1038/nrclinonc.2016.58. [DOI] [PubMed] [Google Scholar]
  • 196.Simpson T.R., Li F., Montalvo-Ortiz W., Sepulveda M.A., Bergerhoff K., Arce F., Roddie C., Henry J.Y., Yagita H., Wolchok H.D., et al. Fc-Dependent Depletion of Tumor-Infiltrating Regulatory t Cells Co-Defines the Efficacy of Anti-CTLA-4 Therapy against Melanoma. J. Exp. Med. 2013;210:1695–1710. doi: 10.1084/jem.20130579. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 197.Yu X., Harden K., C Gonzalez L., Francesco M., Chiang E., Irving B., Tom I., Ivelja S., Refino C.J., Clark H., et al. The Surface Protein TIGIT Suppresses T Cell Activation by Promoting the Generation of Mature Immunoregulatory Dendritic Cells. Nat. Immunol. 2009;10:48–57. doi: 10.1038/ni.1674. [DOI] [PubMed] [Google Scholar]
  • 198.Whelan S., Ophir E., Kotturi M.F., Levy O., Ganguly S., Leung L., Vaknin I., Kumar S., Dassa L., Hansen K., et al. PVRIG and PVRL2 Are Induced in Cancer and Inhibit CD8+ T-Cell Function. Cancer Immunol. Res. 2019;7:257–268. doi: 10.1158/2326-6066.CIR-18-0442. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 199.Solomon B.L., Garrido-Laguna I. TIGIT: A Novel Immunotherapy Target Moving from Bench to Bedside. Cancer Immunol. Immunother. 2018;67:1659–1667. doi: 10.1007/s00262-018-2246-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 200.Manieri N.A., Chiang E.Y., Grogan J.L. TIGIT: A Key Inhibitor of the Cancer Immunity Cycle. Trends Immunol. 2017;38:20–28. doi: 10.1016/j.it.2016.10.002. [DOI] [PubMed] [Google Scholar]
  • 201.Banta K.L., Xu X., Chitre A.S., Au-Yeung A., Takahashi C., O’Gorman W.E., Wu T.D., Mittman S., Cubas R., Comps-Agrar L., et al. Mechanistic Convergence of the TIGIT and PD-1 Inhibitory Pathways Necessitates Co-Blockade to Optimize Anti-Tumor CD8+ T Cell Responses. Immunity. 2022;55:512–526.e9. doi: 10.1016/j.immuni.2022.02.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 202.Chen F., Xu Y., Chen Y., Shan S. TIGIT Enhances CD4+ Regulatory T-Cell Response and Mediates Immune Suppression in a Murine Ovarian Cancer Model. Cancer Med. 2020;9:3584–3591. doi: 10.1002/cam4.2976. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 203.Wu M., Chen X., Lou J., Zhang S., Zhang X., Huang L., Sun R., Huang P., Pan S., Wang F. Changes in Regulatory T Cells in Patients with Ovarian Cancer Undergoing Surgery: Preliminary Results. Int. Immunopharmacol. 2017;47:244–250. doi: 10.1016/j.intimp.2017.04.004. [DOI] [PubMed] [Google Scholar]
  • 204.Kurtulus S., Sakuishi K., Ngiow S.-F., Joller N., Tan D.J., Teng M.W.L., Smyth M.J., Kuchroo V.K., Anderson A.C. TIGIT Predominantly Regulates the Immune Response via Regulatory T Cells. [(accessed on 17 August 2022)]. Available online: https://www.jci.org/articles/view/81187/pdf.
  • 205.Hoogstad-van Evert J.S., Maas R.J., van der Meer J., Cany J., van der Steen S., Jansen J.H., Miller J.S., Bekkers R., Hobo W., Massuger L., et al. Peritoneal NK Cells Are Responsive to IL-15 and Percentages Are Correlated with Outcome in Advanced Ovarian Cancer Patients. Oncotarget. 2018;9:34810–34820. doi: 10.18632/oncotarget.26199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 206.Second Affiliated Hospital of Guangzhou Medical University Triplex CTLA4/PD1/PDL1 Checkpoint Inhibitors Combination Therapy for Advanced Solid. Tumors. [(accessed on 4 June 2023)];2023 Available online: https://clinicaltrials.gov/ct2/show/NCT05187338.
  • 207.Anderson K., Su Y., Burnett M., Bates B., Suarez M.R., Ruskin S., Vakil A., Voillet V., Gottardo R., Greenberg P. 561 Triple Checkpoint Blockade, but Not Anti-PD1 Alone, Enhances the Efficacy of Engineered Adoptive T Cell Therapy in Advanced Ovarian Cancer. J. Immunother. Cancer. 2021;9:A590. doi: 10.1136/jitc-2021-SITC2021.561. [DOI] [Google Scholar]
  • 208.Archilla-Ortega A., Domuro C., Martin-Liberal J., Muñoz P. Blockade of Novel Immune Checkpoints and New Therapeutic Combinations to Boost Antitumor Immunity. J. Exp. Clin. Cancer Res. 2022;41:62. doi: 10.1186/s13046-022-02264-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 209.Huang R.-Y., Francois A., McGray A.R., Miliotto A., Odunsi K. Compensatory Upregulation of PD-1, LAG-3, and CTLA-4 Limits the Efficacy of Single-Agent Checkpoint Blockade in Metastatic Ovarian Cancer. Oncoimmunology. 2017;6:e1249561. doi: 10.1080/2162402X.2016.1249561. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 210.Vetter V., Denizer G., Friedland L.R., Krishnan J., Shapiro M. Understanding Modern-Day Vaccines: What You Need to Know. Ann. Med. 2018;50:110–120. doi: 10.1080/07853890.2017.1407035. [DOI] [PubMed] [Google Scholar]
  • 211.Kimiz-Gebologlu I., Gulce-Iz S., Biray-Avci C. Monoclonal Antibodies in Cancer Immunotherapy. Mol. Biol. Rep. 2018;45:2935–2940. doi: 10.1007/s11033-018-4427-x. [DOI] [PubMed] [Google Scholar]
  • 212.Saxena M., van der Burg S.H., Melief C.J.M., Bhardwaj N. Therapeutic Cancer Vaccines. Nat. Rev. Cancer. 2021;21:360–378. doi: 10.1038/s41568-021-00346-0. [DOI] [PubMed] [Google Scholar]
  • 213.Chiang C.L.-L., Kandalaft L.E., Tanyi J., Hagemann A.R., Motz G.T., Svoronos N., Montone K., Mantia-Smaldone G.M., Smith L., Nisenbaum H.L., et al. A Dendritic Cell Vaccine Pulsed with Autologous Hypochlorous Acid-Oxidized Ovarian Cancer Lysate Primes Effective Broad Antitumor Immunity: From Bench to Bedside. Clin. Cancer Res. 2013;19:4801–4815. doi: 10.1158/1078-0432.CCR-13-1185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 214.Tanyi J.L., Bobisse S., Ophir E., Tuyaerts S., Roberti A., Genolet R., Baumgartner P., Stevenson B.J., Iseli C., Dangaj D., et al. Personalized Cancer Vaccine Effectively Mobilizes Antitumor T Cell Immunity in Ovarian Cancer. Sci. Transl. Med. 2018;10:eaao5931. doi: 10.1126/scitranslmed.aao5931. [DOI] [PubMed] [Google Scholar]
  • 215.Tanyi J.L., Chiang C.L.-L., Chiffelle J., Thierry A.-C., Baumgartener P., Huber F., Goepfert C., Tarussio D., Tissot S., Torigian D.A., et al. Personalized Cancer Vaccine Strategy Elicits Polyfunctional T Cells and Demonstrates Clinical Benefits in Ovarian Cancer. NPJ Vaccines. 2021;6:36. doi: 10.1038/s41541-021-00297-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 216.Zhang X., He T., Li Y., Chen L., Liu H., Wu Y., Guo H. Dendritic Cell Vaccines in Ovarian Cancer. Front. Immunol. 2021;11:613773. doi: 10.3389/fimmu.2020.613773. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 217.Martin-Lluesma S., Graciotti M., Grimm A.J., Boudousquié C., Chiang C.L., Kandalaft L.E. Are Dendritic Cells the Most Appropriate Therapeutic Vaccine for Patients with Ovarian Cancer? Curr. Opin. Biotechnol. 2020;65:190–196. doi: 10.1016/j.copbio.2020.03.003. [DOI] [PubMed] [Google Scholar]
  • 218.Brentville V.A., Metheringham R.L., Daniels I., Atabani S., Symonds P., Cook K.W., Vankemmelbeke M., Choudhury R., Vaghela P., Gijon M., et al. Combination Vaccine Based on Citrullinated Vimentin and Enolase Peptides Induces Potent CD4-Mediated Anti-Tumor Responses. J. Immunother. Cancer. 2020;8:e000560. doi: 10.1136/jitc-2020-000560. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 219.Zamarin D., Walderich S., Holland A., Zhou Q., Iasonos A.E., Torrisi J.M., Merghoub T., Chesebrough L.F., Mcdonnell A.S., Gallagher J.M., et al. Safety, Immunogenicity, and Clinical Efficacy of Durvalumab in Combination with Folate Receptor Alpha Vaccine TPIV200 in Patients with Advanced Ovarian Cancer: A Phase II Trial. J. Immunother. Cancer. 2020;8:e000829. doi: 10.1136/jitc-2020-000829. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 220.Edgar T.W., Manz D.O. Chapter 6—Machine Learning. In: Edgar T.W., Manz D.O., editors. Research Methods for Cyber Security. Syngress; Burlington, MA, USA: 2017. pp. 153–173. [Google Scholar]
  • 221.Schneider P., Xhafa F. Chapter 8—Machine Learning: ML for EHealth Systems. In: Schneider P., Xhafa F., editors. Anomaly Detection and Complex Event Processing Over IoT Data Streams. Academic Press; Cambridge, MA, USA: 2022. pp. 149–191. [Google Scholar]
  • 222.Deo R.C. Machine Learning in Medicine. Circulation. 2015;132:1920–1930. doi: 10.1161/CIRCULATIONAHA.115.001593. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 223.Gao Y., Zeng S., Xu X., Li H., Yao S., Song K., Li X., Chen L., Tang J., Xing H., et al. Deep Learning-Enabled Pelvic Ultrasound Images for Accurate Diagnosis of Ovarian Cancer in China: A Retrospective, Multicentre, Diagnostic Study. Lancet Digit. Health. 2022;4:e179–e187. doi: 10.1016/S2589-7500(21)00278-8. [DOI] [PubMed] [Google Scholar]
  • 224.Ahamad M.M., Aktar S., Uddin M.J., Rahman T., Alyami S.A., Al-Ashhab S., Akhdar H.F., Azad A.K.M., Moni M.A. Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches. J. Pers. Med. 2022;12:1211. doi: 10.3390/jpm12081211. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 225.Wang G., Sun Y., Jiang S., Wu G., Liao W., Chen Y., Lin Z., Liu Z., Zhuo S. Machine Learning-Based Rapid Diagnosis of Human Borderline Ovarian Cancer on Second-Harmonic Generation Images. Biomed. Opt. Express. 2021;12:5658–5669. doi: 10.1364/BOE.429918. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 226.Liu J., Liu L., Antwi P.A., Luo Y., Liang F. Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning. Front. Genet. 2022;13:858466. doi: 10.3389/fgene.2022.858466. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 227.Johannet P., Coudray N., Donnelly D.M., Jour G., Bochaca I.I., Xia Y., Johnson D.B., Wheless L., Patrinely J.R., Nomikou S., et al. Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma. Clin. Cancer Res. 2021;27:131–140. doi: 10.1158/1078-0432.CCR-20-2415. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 228.Kong J., Ha D., Lee J., Kim I., Park M., Im S.-H., Shin K., Kim S. Network-Based Machine Learning Approach to Predict Immunotherapy Response in Cancer Patients. Nat. Commun. 2022;13:3703. doi: 10.1038/s41467-022-31535-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 229.Harder N., Schönmeyer R., Nekolla K., Meier A., Brieu N., Vanegas C., Madonna G., Capone M., Botti G., Ascierto P.A., et al. Automatic Discovery of Image-Based Signatures for Ipilimumab Response Prediction in Malignant Melanoma. Sci. Rep. 2019;9:7449. doi: 10.1038/s41598-019-43525-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 230.Zhang H., Zhang N., Wu W., Zhou R., Li S., Wang Z., Dai Z., Zhang L., Liu Z., Zhang J., et al. Machine Learning-Based Tumor-Infiltrating Immune Cell-Associated LncRNAs for Predicting Prognosis and Immunotherapy Response in Patients with Glioblastoma. Brief. Bioinform. 2022;23:bbac386. doi: 10.1093/bib/bbac386. [DOI] [PubMed] [Google Scholar]
  • 231.Wang Z., Wang Y., Yang T., Xing H., Wang Y., Gao L., Guo X., Xing B., Wang Y., Ma W. Machine Learning Revealed Stemness Features and a Novel Stemness-Based Classification with Appealing Implications in Discriminating the Prognosis, Immunotherapy and Temozolomide Responses of 906 Glioblastoma Patients. Brief. Bioinform. 2021;22:bbab032. doi: 10.1093/bib/bbab032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 232.Chen D., Liu J., Zang L., Xiao T., Zhang X., Li Z., Zhu H., Gao W., Yu X. Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients. Int. J. Biol. Sci. 2022;18:360–373. doi: 10.7150/ijbs.66913. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Not applicable.


Articles from International Journal of Molecular Sciences are provided here courtesy of Multidisciplinary Digital Publishing Institute (MDPI)

RESOURCES