Biomarkers to predict the benefits of immune‑checkpoint blockade‑based therapy in patients with malignant peritoneal mesothelioma (Review)
- Authors:
- Published online on: October 9, 2024 https://doi.org/10.3892/ol.2024.14733
- Article Number: 600
-
Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Malignant peritoneal mesothelioma (MPeM) is a rare and highly lethal type of cancer. Approximately 500 to 700 new cases are diagnosed annually in the United States and median overall survival (OS) is 6 months to 1 year. Platinum + pemetrexed with or without bevacizumab is the standard first-line therapy for patients with advanced disease; however, its efficacy is limited (1,2).
Immune checkpoint blockade (ICB)-based therapy has revolutionized the treatment of several types of solid tumors. Currently, approved ICB treatments worldwide include anti-programmed death-1 (PD-1)/anti-programmed death-ligand 1 (PD-L1), anti-cytotoxic T lymphocyte-associated protein 4 (CTLA-4) and anti-lymphocyte activation gene-3 (LAG-3) treatment (3). Given the promising results reported for malignant pleural mesothelioma (MPM), certain small studies have focused on the efficacy of ICB-based therapy for MPeM (4–6). In a real-world study, 29 patients with MPeM were treated with nivolumab + ipilimumab or a single immune checkpoint inhibitor as the second-line treatment (7). The objective response rate (ORR) was 19.2% (5/26; 95% CI, 6.6–39.4). In a phase II single-center study (8), 20 patients with MPeM were treated with atezolizumab + bevacizumab as the second-line treatment and the ORR was 40% (8/20; 95% CI 19.1–64.0). Both studies demonstrated the encouraging clinical activity of ICB treatment for MPeM.
However, it is evident that only a subset of patients with MPeM may benefit from ICB-based therapy. Identifying patients who may benefit from ICB-based therapy is a subject of research. To date, tumor mutation burden (TMB), expression of PD-L1 and microsatellite instability (MSI) are the three biomarkers validated for predicting the response to ICB; however, there is no widely accepted method for prediction based on biomarkers, and their applications vary by disease site (9–11). Certain patients with TMB-low and PD-L1 negative tumors also exhibit marked treatment responses; meanwhile, other patients with MSI-high tumors show primary or secondary resistance to ICB therapy (12,13). Other biomarkers, including the tumor microenvironment (TME) and the components in the TME, host immune response patterns based on transcriptomic and proteomic analysis, as well as specific mutations (such as gene fusions) or clonal mutations, are currently being explored. Malignant mesothelioma has been traditionally assumed to be a TMB-low tumor with extremely low MSI prevalence (14,15). Therefore, there is an urgent need to develop appropriate biomarkers for predicting the efficacy of ICB-based therapy (including, but not limited to, anti-PD-1/anti-PD-L1 treatment) for patients with MPeM. The present review focuses on five types of promising biomarkers currently available, highlighting evidence that supports the predictive role of these biomarkers for ICB-based therapy for MPeM (Fig. 1 and Table I). Additionally, the limitations of each type of biomarker are discussed and possible methods for addressing these problems are mentioned.
Table I.Promising biomarkers for predicting the efficacy of immune-checkpoint blockade-based therapy for malignant peritoneal mesothelioma. |
Tertiary lymphoid structures (TLS)
It is well established that the TME is a complex system. It includes multiple cellular and non-cellular components and serves an important role in several stages of tumor development and progression, including metastasis, and evasion of immune monitoring and treatment. Initially, the majority of tumor immunology research focused on how the function and relative abundance of T cells and macrophages within the TME mediated effector responses (16–19). Recently, it was reported that other tumor-infiltrating immune subsets, such as B cells and mast cells, were essential for effector responses (20,21).
TLS are organized aggregates of immune cells, characterized by an inner zone of CD20+ B cells surrounded by CD3+ T cells. In addition to B and T cell populations, TLS are also populated by dendritic cells (DC), macrophages and other immune cell types (22–24). Several studies have reported that TLS were detectable in certain types of tumors, such as cutaneous angiosarcoma, colorectal cancer, hepatocellular carcinoma, gastric cancer, gastrointestinal stromal tumor, breast cancer and lung squamous cell carcinoma (25–31). However, studies focusing on TLS in MPeMs are limited. In a study published in 2005, it was reported that there was a large degree of infiltration of lymphocytes and plasma cells, including lymphoid aggregates and follicles within the omental fat or omental fibrous tissue surrounding the areas of an invading tumor in 13/75 female patients with MPeM (32). This morphological change is akin to a TLS. Recently, Benzerdjeb et al (33) reported there were numerous lymphoid aggregates with or without germinal centers in 52/138 cases with epithelioid MPeM. Another study demonstrated that TLS were present in MPeMs (32). Additionally, the study reported that neoadjuvant chemotherapy could induce the formation of TLS; however, the study did not report on the relationship between the presence of TLS and the response to ICB therapy.
TLS are an important component of the TME and serve a critical role in regulating tumor-specific immune responses (24,32). A growing body of evidence has suggested that TLS can serve as a predictive biomarker for the response to ICB treatment in certain types of solid tumors. The presence of TLS in pre-treatment biopsies of melanoma, renal cell carcinoma, soft tissue sarcoma and urothelial carcinoma has been reported to be associated with the response to anti-PD-1 or anti-PD-1 + anti-CTLA-4 treatment (20,24,34–36). These studies highlighted that there was a higher density of TLS in pre-treatment tumor tissues of responder patients as compared to non-responder patients. Furthermore, in a randomized phase II study, patients with MPM were treated using a single cycle of durvalumab + tremelimumab or durvalumab alone in a neoadjuvant setting (37). There was a marked increase in TLS density following ICB combination therapy, a greater increase in TLS formation in tumors that had partial remission (PR), and a higher pre-treatment TLS density associated with PR. The study also reported that pre-treatment TLS was more closely associated with the efficacy of ICB treatment, and TLS formation could be induced following ICB treatment just as with chemotherapy (33,37). This evidence suggests that pre-treatment TLS may fully represent the initial tumor status and have a greater impact on the response to ICB therapy. Thus, the presence of TLS pre-treatment is more accurate in predicting the response to ICB treatment, as it implicates that the tumor and the host under the influence of the tumor may already be generating an antitumor immune response, which is potentially enhanced by ICB therapy and chemotherapy.
The findings from the limited body of studies indicate that TLS are present in MPeM and the presence of TLS pre-treatment has potential as a biomarker for predicting the efficacy of ICB therapy. Notably, TLS is a readily testable and acquirable indicator using immunohistochemistry in the clinic (23). However, there are no studies on the predictive role of TLS for ICB treatment in MPeM, to the best of our knowledge. Additionally, the underlying mechanism of TLS formation is not clear, and this may be critical for understanding the predictive value of TLS status in determining the efficacy of ICB therapy. Therefore, additional studies are required to verify the relationship between the TLS and ICB treatment in MPeM.
Immune checkpoints and their ligands
PD-L1
PD-L1 expression is the most commonly used biomarker for predicting the benefits of anti-PD-1/anti-PD-L1 treatment, and it has been reported that PD-L1 is expressed in mesotheliomas (38–40). A study by Chapel et al (38) used Dako PD-L1 IHC 22C3 pharmDx and Dako PD-L1 IHC 28–8 pharmDx (Agilent Technologies, Inc.) to detect PD-L1 expression in mesothelioma, with a 22 and 27% positive tumor proportion score (TPS; cutoff ≥1%), respectively. The proportion of cases with positive PD-L1 expression was notably higher among MPeMs compared with that in MPMs (22C3 assay: MPM, 18% and MPeM 54%; 28-8 assay: MPM, 24% and MPeM, 54%). In another study, the positive combined positive score (CPS) and positive TPS (both cutoff ≥1%; 22C3 assay) were 76 and 43% among MPeMs, respectively (39). More aggressive biphasic/sarcomatoid MPeMs had a higher CPS and TPS. However, a study by Pezzuto et al (40) reported that there were only 2% positive TPS (cutoff ≥1%; 22C3 assay) cases among 43 patients with MPeM. It was hypothesized that this discrepancy was attributed to the small sample size of non-epithelioid cases, which are more commonly positive for PD-L1.
The role of PD-L1 in predicting the response to ICB-based therapy for MPeM is also contested. In a phase II trial, 56 patients with MPM and eight patients with MPeM were treated with pembrolizumab alone (41). The ORR was 20 and 12.5%, respectively. Notably, PD-L1 expression was not associated with ORR. Moreover, Raghav et al (8) reported 20 patients with MPeM treated with atezolizumab + bevacizumab. The ORR was 40% and responses were reported in both PD-L1-positive and -negative cases. Given these contradictory results, there is no consensus on identifying patients with MPeM who would benefit from anti-PD-1/anti-PD-L1 treatment based solely on PD-L1 expression. Therefore, there is no solid evidence to support PD-L1 expression as a biomarker for predicting the efficacy of anti-PD-1/anti-PD-L1 treatment for MPeM. It is necessary to determine PD-L1 expression status and the association between PD-L1 expression and the efficacy of anti-PD-1/anti-PD-L1 treatment in patients with MPeM. Additionally, there is a need to optimize other histological and molecular characteristics as biomarkers to guide ICB-based therapy for MPeM.
V-domain Ig suppressor of T cell activation (VISTA)
VISTA is a negative checkpoint regulator, which is expressed in most hematopoietic cells. In addition, VISTA is expressed in numerous types of tumor cells (42–45). Several studies have reported notable upregulated expression of VISTA in immunohistochemical analysis of malignant mesothelioma tissues and in the normal mesothelium, as well as higher VISTA expression levels in the epithelioid subtype (46–50).
Chung et al (49) reported no significant difference in VISTA expression between MPMs and MPeMs using immunohistochemistry. However, Offin et al (50) reported that, in 50 patients with MPeM, up to 89% exhibited positive VISTA expression. In addition, two studies reported that anti-PD-1 and anti-CTLA-4 treatment resulted in upregulated VISTA expression in the TME, resulting in no benefit from or resistance to anti-PD-1 and anti-CTLA-4 treatment (51,52). Gao et al (51) evaluated the expression of PD-L1 and VISTA using immunohistochemistry in the tumors of pre- and post-treatment patients with prostate cancer who did not benefit from ipilimumab treatment. They found increased PD-L1 and VISTA expression and a higher frequency of upregulated VISTA expression in dependent subsets of T cells and macrophages in the samples of post-ipilimumab treatment. They hypothesized that VISTA was a compensatory inhibitory pathway following ipilimumab treatment. Kakavand et al (52) reported that 8/12 patients with melanoma with acquired resistance to anti-PD-1 treatment had a notably increased frequency of VISTA-positive lymphocytes. They hypothesized that VISTA represented an important mechanism of acquired resistance in patients with melanoma treated with anti-PD-1 treatment. These outcomes suggest that VISTA expression in MPeMs may be involved in a mechanism that underlies the inefficacy of anti-PD-1 and anti-CTLA-4 treatment. Thus, ICB treatment targeting VISTA may be a potential therapeutic strategy for the management of MPeM, particularly in patients with an epithelioid subtype (53,54).
VISTA serves a role in regulating the steady state of both lymphoid and myeloid cells involved in the immune system (53–56). Liu et al (57) reported that VISTA/PD-1 double deficient knockout (KO) mice exhibited notably higher levels of chronic inflammation and activation of T cells than the single KO mice. This suggests that VISTA had a nonredundant role, distinct from the PD-1/PD-L1 pathway. Moreover, an ex vivo study supported the aforementioned result, in which pro-inflammatory factors were upregulated and anti-inflammatory factors were downregulated when T cells were co-cultured with MCF7 cells with VISTA and CTLA-4 expression knocked down. Combined knockdown of VISTA and CTLA-4 inhibited MCF7 breast cancer cell development (58). In a study of liver cancer using a mouse model, symptoms and tumor growth were reduced using anti-VISTA monoclonal antibodies (mAbs), resulting in a reduction in mouse mortality (59). In the study of CT26 colon cancer cells and less immunogenic B16BL6 melanoma mice models, the combinatorial treatment of VISTA and PD-L1 mAbs led to suppressed tumor growth and tumor regression and conferred a survival advantage compared with VISTA mAb alone or PD-L1 mAb alone, respectively (57). Treatment with the anti-VISTA antibody KVA12123 mediated strong antitumor activity and showed enhanced efficacy in combination with anti-PD-1 treatment in several tumor models, including bladder cancer, colon cancer, lymphoma and melanoma (60). These results suggest that anti-VISTA treatment with anti-PD-1/PD-L1 or anti-CTLA-4 treatment may contribute to the acquisition of an immuno-active TME, enhancing antitumor responses and improving efficacy.
Given the high frequency of upregulated VISTA expression in MPeM and the marked benefits from VISTA blockade treatment in preclinical models, VISTA blockade, particularly combined with anti-PD-1/PD-L1 or anti-CTLA-4 treatment, is a promising approach for management of MPeM. The small molecule immune checkpoint inhibitor CA-170, which targets both PD-L1 and VISTA, has been reported to exhibit antitumor efficacy preclinically (61). There is currently an ongoing phase I study in which patients with tumors with high levels of VISTA expression, including malignant mesothelioma, are being treated with CA-170 (62). Therefore VISTA, particularly when combined with PD-L1, may serve as a suitable biomarker and therapeutic target to improve the outcomes in patients receiving anti-PD-1/PD-L1 or anti-CTLA-4 treatment.
LAG-3
In addition to PD-L1 and VISTA, LAG-3 may also serve as a potential biomarker for guiding ICB-based therapy for MPeM. LAG-3 is expressed on the membranes of immune cells, including T cells, and it negatively regulates T-cell proliferation and effector T-cell function (63). Upregulated LAG-3 mRNA expression was detected in malignant mesothelioma under specific settings. In a comprehensive analysis of 19 cases of treatment-naive MPeM in 18 patients, high levels of LAG-3 mRNA expression were detected in MPeM with BRCA1-associated protein-1 (BAP1) haploinsufficiency (64). In another analysis of MPM datasets from The Cancer Genome Atlas (TCGA; n=86) and Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (n=61), the mRNA expression levels of LAG-3 and VISTA were markedly higher in patients with tumors with BAP1 mutations [not accompanied with neurofibromatosis type 2 (NF2) and cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) mutations] than tumors with NF2 or CDKN2A/B mutation alone, or BAP1 accompanied with NF2 and CDKN2A/B mutations (65).
Preclinical experiments have indicated that LAG-3 and PD-1 may have a synergistic effect in inhibiting T-cell activation and promoting tumor immune evasion. Simultaneous blockade of both receptors has a more potent immune response compared with blocking either receptor alone (66,67). In the AB1-HA BALB/cJ mesothelioma mice model, a delay in tumor growth and a notable improvement in survival were observed for anti-PD-L1 and anti-PD-L1 + anti-LAG-3 treatment when compared with the PBS control group. The combination of PD-L1 and LAG-3 blockade differed more from the PBS control than the anti-PD-L1 monotherapy, suggesting that anti-LAG-3 treatment had an additional effect (68). Notably, anti-LAG-3-based treatment has been used in the clinic. The US Food and Drug Administration approved the combination of relatlimab (anti-LAG-3 mAb) and nivolumab (anti-PD-1 mAb) for the treatment of unresectable or metastatic melanoma in 2022 (69). The approval was based on a phase II/III randomized trial, in which treatment with relatlimab + nivolumab was associated with a 47.7% 12-month progression-free survival (PFS) in patients with melanoma, compared with 36% in those who underwent nivolumab monotherapy. In a prespecified exploratory analysis, median PFS estimates were markedly longer for patients with a LAG-3 expression of ≥1% than those with a LAG-3 expression of <1% (63).
Similar to VISTA, LAG-3 blockade, particularly when combined with anti-PD-1/PD-L1 treatment, is promising for MPeM. Therefore LAG-3, particularly when combined with PD-L1, may serve as a biomarker to guide the blockade of VISTA + PD-1 treatment. Additionally, it is common that drugs approved for common tumors are also used to treat rarer tumors clinically (70–72). The potential use of relatlimab provides a potential opportunity for the treatment of MPeM.
Fusion gene neoantigen burden
Neoantigens are tumor-specific antigens that can stimulate an antitumor immune response (73). An increasing number of studies have reported that high clonal neoantigen burden (present in all tumor cells) and low neoantigen intratumor heterogeneity are associated with the efficacy of ICB treatment (73–76). Previously, it was hypothesized that single-nucleotide variants (SNVs) and insertions or deletions (indels) were the most common source of tumor neoantigen acquisition. At present, gene fusions are also suggested to be a notable means of acquisition of tumor neoantigens with higher immunogenic potential (77–79). Analysis of whole genomes from 2,528 tumors revealed that gene fusions from genomic rearrangement notably contributed to neoantigen formation in both quantity and quality (77). By comparing TCGA fusion candidate neoantigens with TCGA SNVs and indel candidate neoantigens, it was reported that gene fusions generated six-fold more candidate neoantigens and 11-fold more specific candidate neoantigens (78).
Chromosomal rearrangements are the source of gene fusions and they are common in malignant mesotheliomas (80–83). Yoshikawa et al (84) reported multiple noncontiguous minute deletions on chromosome 3p21 in malignant mesotheliomas, where the BAP1 gene is located. This genetic change was attributed to chromothripsis, which is a mutational phenomenon in which the rapid accrual of hundreds of rearrangements occurs (80). Oey et al (81) reported that 8/9 tested mesotheliomas had complex rearranged genomes with evidence of chromothripsis and chromoplexy, the latter representing a series of linked translocations or weaving of chromosomal fragments (80). In the study by Bueno et al (82), gene fusions from chromosomal rearrangements and splice alterations were frequent mechanisms for the inactivation of the driver genes of malignant mesothelioma, such as NF2, BAP1 and SET domain containing 2. Neoantigen prediction analysis revealed that neoantigens formed from gene fusions. Mansfield et al (83) reported that there were 1,535 chromosomal rearrangements by Mate-pair sequencing in 22 treatment-naive cases of malignant mesothelioma specimens and several of these abnormalities were obtained due to chromothripsis and chromoplexy. The study also demonstrated that gene fusions from chromosomal rearrangements could result in neoantigen formation. Together, these results demonstrated that chromosomal rearrangements were common in malignant mesotheliomas, in accordance with the complex structural and numerical abnormalities of the karyotype reported in other studies (85,86). Malignant mesotheliomas may exhibit marked neoantigen formation from rearrangement-related gene fusions. However, chromosomal rearrangements and rearrangement-related gene fusions were notably underestimated due to the limitations of previous detection techniques and approaches, such as standard next-generation sequencing technology (80,82).
Fusion gene neoantigens tend to have higher immunogenic potential and trigger more effective adaptive immune responses. Analysis of whole genomes from 2,528 tumors observed the derived neoantigens from genomic rearrangements, especially the clonal neoantigens, were extensively immuno-edited, which suggested their immunogenic potential (77). A comprehensive analysis of tumor neoantigens reported that candidate neoantigens with the highest immunogenic potential were produced by fusion genes in 32.2% of cases in a TCGA dataset (78). By analyzing 522 cases of head and neck squamous cell carcinoma with a low TMB, it was reported that fusion-associated neoantigens were able to stimulate a more potent T cell response compared with missense mutation-associated neoantigens (79). Fusion gene neoantigens with the immunogenic potential to elicit an adaptive immune response were also observed in mesothelioma. Mansfield et al (83) estimated that 1,535 chromosomal rearrangements in malignant mesothelioma specimens resulted in the expression of 179 novel peptides. A number of the 179 candidate neoantigens could bind to patient-specific human leukocyte antigen molecules and improve the expansion of tumor-infiltrating T cells in the TME (83). These results revealed that neoantigens from chromosomal rearrangement-related gene fusions in malignant mesotheliomas had the potential to elicit more effective anti-neoplasm immune responses.
Conventionally, TMB is used to estimate the neoantigen burden and has been shown to predict the efficacy of anti-PD-1/anti-PD-L1 treatment for several tumors (87–90). However, malignant mesotheliomas, including MPeM, are commonly viewed as tumors with a low TMB. This is attributed to the disadvantages of conventional detection techniques and approaches (84). Notably, Kosari et al (91) used the burden of tumor junction from chromosomal rearrangements as a surrogate biomarker for determining the fusion gene neoantigen burden. It was reported that tumor junction burden was associated with improved survival outcomes in patients with mesothelioma treated with ICB in the presence of antigen processing and presentation gene set expression. Moreover, it was revealed that antigen processing and presentation were essential for the role of tumor junction burden. Another study also emphasized that a high burden of neopeptides could successfully predict the response to PD-1 inhibitors in patients with advanced MPM or melanoma when accompanied by upregulated expression of major histocompatibility complex proteins specific for the neopeptides (92). These studies suggest that fusion gene neoantigen burden could be used instead of TMB and that it serves a predictive role in ICB treatment in mesothelioma when antigen processing and presentation are taken into account.
Together, the available studies suggest that malignant mesotheliomas exhibit a higher degree of neoantigen formation, and this can be attributed to chromosomal rearrangement-related gene fusions. When neoantigens are processed and presented appropriately, they have a notable immunogenic potential to affect effector cells in the TME, and the burden of these neoantigens has potential as a biomarker for predicting the response to ICB treatment for patients with MPeM. However, prospective studies are required to validate these findings.
BAP1 haploinsufficiency
BAP1 is a tumor suppressor gene and BAP1 inactivation is a key driver event in malignant mesotheliomas. In a single-center study including 244 cases with MPeM, 55% of the cases were BAP1-negative based on immunohistochemistry analysis (93). In another study, loss of BAP1 nuclear expression and BAP1 heterozygous inactivation was observed in 57 and 13%, respectively, of the 46 cases with MPeM (94). These studies demonstrate that loss of BAP1 function is common in MPeM.
Shrestha et al (64) performed integrative multi-omics analyses and reported that BAP1 haploinsufficiency [homozygous or heterozygous loss of BAP1 (BAP1del)] resulted in a distinct molecular subtype of MPeM with strong cytokine signaling activity and innate immune system activity. Further analysis demonstrated that there was increased infiltration of T cells in the TME of BAP1del MPeM. Additionally, the mRNA expression levels of immune checkpoint receptors (such as PD-1, CD80, CTLA-4, LAG-3 and inducible co-stimulator) were increased in BAP1del MPeM. The aforementioned signature of BAP1del MPeM was also reported in certain other solid tumors. In a comprehensive analysis of data of MPM, the tumors with alterations in only BAP1 (not accompanied by NF2 or CDKN2A/B mutations) had a distinct expression of inflammatory microenvironment genes, including the activation of interferon signaling and interferon regulatory factor (IRF) transcription factors. The mRNA expression levels of the immune checkpoint receptors LAG-3 and VISTA, were also upregulated (65). In two independent uveal melanoma cohorts, the lack of BAP1 expression detected using immunohistochemistry was associated with increased T cell infiltration into the TME (95). The inflammatory TME formation in BAP1del MPeMs could be due to the fact that the majority of BAP1 mutations in MPeM are frameshift indels and chromosomal rearrangements, both of which are associated with neoantigen formation (77,79,96). Furthermore, as previously discussed, tumor neoantigens are critical for T cell expansion and activation. Additionally, BAP1-inactivation notably increases IRF8 activity, which is involved in DC differentiation. DCs with antigen-presenting function are highly effective at stimulating cytotoxic T cells in the TME (46). These outcomes suggest that insufficient BAP1 function is associated with the formation of an inflammatory TME in MPeM. Additionally, negative immune checkpoint receptors beyond PD-1 are highly expressed, including LAG-3 and VISTA (64,65).
Based on the aforementioned findings, it is reasonable to treat BAP1del MPeM with ICB-based therapy, such as anti-PD-1/anti-PD-L1, anti-LAG-3 and anti-VISTA treatment. BAP1 haploinsufficiency may serve as a biomarker for predicting the response to ICB therapy. In a case report, a patient with MPeM with loss of nuclear expression of BAP1 was treated with nivolumab + ipilimumab as the first-line therapy, and they achieved PR after 8 months (97). There is BAP1 inactivation in 70% of patients with MPeM (57% loss of BAP1 nuclear expression and 13% BAP1 heterozygous inactivation), but only 19% of patients benefit from anti-PD-1/anti-PD-L1 treatment alone and 40% of patients benefit from anti-PD-1/anti-PD-L1 + anti-CTLA-4 and anti-PD-1/anti-PD-L1 + anti-angiogenesis treatment as ≥second-line therapy (7,8,94). It is unclear whether the response rate may be higher when ICB-based therapy is used as a first-line therapy, or when patients are treated with anti-PD-1/anti-PD-L1 + anti-CTLA-4, anti-LAG-3 or anti-VISTA treatment.
Therefore, BAP1 haploinsufficiency alone may not be adequate for predicting the response to ICB-based therapy. In a retrospective analysis of the JAVELIN Renal 101 and checkmate-009/010/025 trials, the BAP1 score was developed using the top 20 BAP1 mutation-associated differentially expressed genes. The BAP1-score was a significant predictor of the immune microenvironment and the clinical benefits of ICB-based therapies in patients with advanced clear cell renal cell carcinoma (98). Therefore, BAP1 haploinsufficiency-based biomarkers may be superior to BAP1 haploinsufficiency alone. The value of BAP1 haploinsufficiency or BAP1 haploinsufficiency-based biomarkers both warrant further investigation.
Transcriptome-based biomarkers
It has been reported that certain tumor transcriptomic changes can be used as biomarkers to guide tumor therapy (99–102). However, the predictive roles of these biomarkers are limited to a highly specific clinical context and treatments. Lee et al (103) developed a uniform systematic approach, SynthEtic LEthality and rescue-mediated precision onCology via the Transcriptome (SELECT), which does not train any model parameters by looking at the test data. Using SELECT, the best drug was selected for a given patient based on the tumor pre-treatment transcriptome data. Unlike commonly matching drugs based on the transcriptome-based expression of their targets, SELECT focused on identifying and utilizing the genetic interaction (GI) of drug targets as the biologically testable biomarkers for the prediction of a therapeutic response. The study analyzed data obtained from TCGA and found notable pan-cancer synthetic rescue (SR) interaction partners of PD-1/PD-L1 and CTLA-4. SR interaction is described as the inactivation of one gene reducing cell viability, but the alteration of another gene's activity-rescuing viability. The study defined SR scores, where tumors with higher SR scores were predicted to respond better to ICB treatment (anti-PD-1/anti-PD-L1 and anti-CTLA-4 treatment). Higher SR scores were associated with better responses to ICB treatment based on an analysis of 21 immune checkpoint therapy datasets. Subsequently, Nair et al (104) used the same parameters and procedure described by Lee et al (103) to predict the response to anti-PD-1 treatment in an National Cancer Institute mesothelioma patient cohort, which included an equal proportion of patients with pleural and peritoneal mesothelioma. SR scores were able to accurately predict the response to anti-PD-1 treatment in these patients with mesothelioma.
ENLIGHT was designed and developed on the basis of SELECT, which is a transcriptomics-based computational approach that identifes clinically relevant genetic interactions and uses them to predict responses to a variety of therapies in multiple cancer types. ENLIGHT-matching scores (EMS) could be used to predict the efficacy of immunotherapies (including ICBs and mAbs) more accurately than targeted small molecules. EMS was comparable with other ICB-specific biomarkers, including the proliferation signature, cytolytic index and interferon (IFN)-γ signature. The combination of EMS and IFN-γ was more accurate than either of them alone or the other combinations in the ICB cohorts (105). Similar to SELECT, ENLIGHT did not require training on previous treatment response data, which is useful for rare tumors, such as MPeM (105).
Furthermore, Hoang et al (106) recently established the ENLIGHT-Deep Pathology for Transcriptomics (DeepPT) approach, which is DeepPT combined with ENLIGHT DeepPT is a deep-learning framework that predicts genome-wide tumor mRNA expression levels from hematoxylin and eosin slides. The predicted genome-wide tumor mRNA expression serves as an input for ENLIGHT to generate an EMS to predict the response of a patient to therapy. Patients with lung, cervical and head and neck cancers received an ENLIGHT-matched therapy of bintrafusp alfa, a bi-specific antibody targeting TGF-β and PD-L1, and they exhibited a more favorable odds ratio of response than expected by chance (however, the data is not yet peer-reviewed). ENLIGHT-DeepPT may be a promising approach to guide immunotherapy, whilst also making transcriptome-based biomarkers more feasible in the clinical practice.
Therefore, transcriptome-based biomarkers are promising, as they seem to provide more comprehensive and valuable information. Nevertheless, the veracity of the SR-score, EMS and ENLIGHT-DeepPT require further testing in carefully controlled prospective clinical trials.
Conclusions and future directions
ICB-based therapy has rapidly emerged as a principal therapeutic modality for numerous solid tumors owing to its efficacy. Notably, the efficacy of ICB-based therapy depends on multiple factors, from tumor-specific neoantigen formation to neoantigen processing and presentation, then to tumor cell interactions with effector cells and the TME, among other factors. Regarding ICB-based therapy, the goal of anti-PD-1/anti-PD-L1 therapy is to reactivate cytotoxic T cells within the TME against tumor cells. Therefore, it is inevitable that the majority of tumors show primary or secondary resistance. Thus, it is critical to stratify patients with MPeM and develop novel therapeutic strategies targeting the other factors underlying the pathogenesis of MPeM.
At present, there have been several exploratory studies regarding MPeM treatment, primarily targeting the components beyond PD-1/PD-L1 within the TME, or targeting abnormal molecular and signaling pathway alterations within tumor cells. The former includes targeting mast cells to restore/promote T cell infiltration within the TME, chemokines and cytokines for the modulation of TLS neogenesis, and vascular endothelial growth factor/vascular endothelial growth factor receptor to inhibit neovascularization, among other approaches (47,107–109). The latter includes targeting the PI3K/mTOR pathway, Hippo-Yes-associated protein pathway, BAP1-/BRCA1-deficiency, anaplastic lymphoma kinase rearrangements, focal adhesion kinase phosphorylation, fibroblast growth factor receptor inhibition and histone deacetylase inhibitor inhibition, among other targets (110–116). Although these therapeutic approaches have shown antitumor efficacy to a certain degree, further studies are still required. Moreover, given the complicated alterations that can occur within tumor cells themselves and the TME, combination treatment seems to be the most promising approach (8).
The aim of the present review was to highlight predictive biomarkers to guide ICB-based therapy (Fig. 1). The aforementioned biomarkers only reflect a small portion of the tumor-associated alterations that likely occur in MPeM. Therefore, combining multiple parameters to develop predictive biomarker profiles is a more common-sense approach. This principle is also applicable to other therapeutic strategies, including combination treatment. However, this requires not only a deep understanding of the alterations of tumors themselves and the TME, but also the development of appropriate tools to analyze these alterations. In this regard, artificial intelligence-based approaches may be a valuable tool (117,118).
In conclusion, a wider range of appropriate biomarkers in MPeM pathogenesis are also required to guide ICB-based therapy and other novel therapeutic strategies.
Acknowledgements
Not applicable.
Funding
Funding: No funding was received.
Availability of data and materials
Not applicable.
Authors' contributions
CW performed the review. CW, YZ and WL contributed to the writing and editing of the manuscript. All authors have read and approved the final manuscript and have full access to all the data in the study and take responsibility for the integrity and security of the data. Data authentication is not applicable.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
References
Kusamura S, Kepenekian V, Villeneuve L, Lurvink RJ, Govaerts K, De Hingh IHJT, Moran BJ, Van der Speeten K, Deraco M and Glehen O; PSOGI: Peritoneal mesothelioma: PSOGI/EURACAN clinical practice guidelines for diagnosis, treatment and follow-up. Eur J Surg Oncol. 47:36–59. 2021. View Article : Google Scholar | |
Greenbaum A and Alexander HR: Peritoneal mesothelioma. Transl Lung Cancer Res. 9 (Suppl 1):S120–S132. 2020. View Article : Google Scholar | |
Rui R, Zhou L and He S: Cancer immunotherapies: Advances and bottlenecks. Front Immunol. 14:12124762023. View Article : Google Scholar | |
Scherpereel A, Mazieres J, Greillier L, Lantuejoul S, Dô P, Bylicki O, Monnet I, Corre R, Audigier-Valette C, Locatelli-Sanchez M, et al: Nivolumab or nivolumab plus ipilimumab in patients with relapsed malignant pleural mesothelioma (IFCT-1501 MAPS2): A multicentre, open-label, randomised, non-comparative, phase 2 trial. Lancet Oncol. 20:239–253. 2019. View Article : Google Scholar | |
Disselhorst MJ, Quispel-Janssen J, Lalezari F, Monkhorst K, de Vries JF, van der Noort V, Harms E, Burgers S and Baas P: Ipilimumab and nivolumab in the treatment of recurrent malignant pleural mesothelioma (INITIATE): Results of a prospective, single-arm, phase 2 trial. Lancet Respir Med. 7:260–270. 2019. View Article : Google Scholar | |
Baas P, Scherpereel A, Nowak AK, Fujimoto N, Peters S, Tsao AS, Mansfield AS, Popat S, Jahan T, Antonia S, et al: First-line nivolumab plus ipilimumab in unresectable malignant pleural mesothelioma (CheckMate 743): A multicentre, randomised, open-label, phase 3 trial. Lancet. 397:375–386. 2021. View Article : Google Scholar | |
Raghav K, Liu S, Overman M, Morani A, Willette A, Fournier K and Varadhachary G: Clinical efficacy of immune checkpoint inhibitors in patients with advanced malignant peritoneal mesothelioma. JAMA Netw Open. 4:e21199342021. View Article : Google Scholar | |
Raghav K, Liu S, Overman MJ, Willett AF, Knafl M, Fu SC, Malpica A, Prasad S, Royal RE, Scally CP, et al: Efficacy, safety, and biomarker analysis of combined PD-L1 (Atezolizumab) and VEGF (Bevacizumab) blockade in advanced malignant peritoneal mesothelioma. Cancer Discov. 11:2738–2747. 2021. View Article : Google Scholar | |
Herbst RS, Soria JC, Kowanetz M, Fine GD, Hamid O, Gordon MS, Sosman JA, McDermott DF, Powderly JD, Gettinger SN, et al: Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature. 515:563–567. 2014. View Article : Google Scholar | |
Marabelle A, Fakih M, Lopez J, Shah M, Shapira-Frommer R, Nakagawa K, Chung HC, Kindler HL, Lopez-Martin JA, Miller WH Jr, et al: Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: Prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study. Lancet Oncol. 21:1353–1365. 2020. View Article : Google Scholar | |
Sepulveda AR, Hamilton SR, Allegra CJ, Grody W, Cushman-Vokoun AM, Funkhouser WK, Kopetz SE, Lieu C, Lindor NM, Minsky BD, et al: Molecular biomarkers for the evaluation of colorectal cancer: Guideline From the American Society for Clinical Pathology, College of American Pathologists, Association for Molecular Pathology, and American Society of Clinical Oncology. J Clin Oncol. 35:1453–1486. 2017. View Article : Google Scholar | |
Lu S, Stein JE, Rimm DL, Wang DW, Bell JM, Johnson DB, Sosman JA, Schalper KA, Anders RA, Wang H, et al: Comparison of biomarker modalities for predicting response to PD-1/PD-L1 checkpoint blockade: A systematic review and meta-analysis. JAMA Oncol. 5:1195–1204. 2019. View Article : Google Scholar | |
André T, Shiu KK, Kim TW, Jensen BV, Jensen LH, Punt C, Smith D, Garcia-Carbonero R, Benavides M, Gibbs P, et al: Pembrolizumab in microsatellite-instability-high advanced colorectal cancer. N Engl J Med. 383:2207–2218. 2020. View Article : Google Scholar | |
Bonneville R, Krook MA, Kautto EA, Miya J, Wing MR, Chen HZ, Reeser JW, Yu L and Roychowdhury S: Landscape of microsatellite instability across 39 cancer types. JCO Precis Oncol. 2017.PO.17.00073. 2017. View Article : Google Scholar | |
Cedrés S, Ponce-Aix S, Iranzo P, Callejo A, Pardo N, Navarro A, Martinez-Marti A, Gómez-Abecia S, Zucchiatti AC, Sansano I, et al: Analysis of mismatch repair (MMR) proteins expression in a series of malignant pleural mesothelioma (MPM) patients. Clin Transl Oncol. 22:1390–1398. 2020. View Article : Google Scholar | |
Zhang Z, Liu S, Zhang B, Qiao L and Zhang Y and Zhang Y: T cell dysfunction and exhaustion in cancer. Front Cell Dev Biol. 8:172020. View Article : Google Scholar | |
Ott PA, Bang YJ, Piha-Paul SA, Razak ARA, Bennouna J, Soria JC, Rugo HS, Cohen RB, O'Neil BH, Mehnert JM, et al: T-Cell-Inflamed gene-expression profile, programmed death ligand 1 expression, and tumor mutational burden predict efficacy in patients treated with pembrolizumab across 20 cancers: KEYNOTE-028. J Clin Oncol. 37:318–327. 2019. View Article : Google Scholar | |
Hiltbrunner S, Mannarino L, Kirschner MB, Opitz I, Rigutto A, Laure A, Lia M, Nozza P, Maconi A, Marchini S, et al: Tumor immune microenvironment and genetic alterations in mesothelioma. Front Oncol. 11:6600392021. View Article : Google Scholar | |
Bentham R, Litchfield K, Watkins TBK, Lim EL, Rosenthal R, Martínez-Ruiz C, Hiley CT, Bakir MA, Salgado R, Moore DA, et al: Using DNA sequencing data to quantify T cell fraction and therapy response. Nature. 597:555–560. 2021. View Article : Google Scholar | |
Petitprez F, de Reyniès A, Keung EZ, Chen TW, Sun CM, Calderaro J, Jeng YM, Hsiao LP, Lacroix L, Bougoüin A, et al: B cells are associated with survival and immunotherapy response in sarcoma. Nature. 577:556–560. 2020. View Article : Google Scholar | |
Khanal S, Wieland A and Gunderson AJ: Mechanisms of tertiary lymphoid structure formation: Cooperation between inflammation and antigenicity. Front Immunol. 14:12676542023. View Article : Google Scholar | |
Trüb M and Zippelius A: Tertiary lymphoid structures as a predictive biomarker of response to cancer immunotherapies. Front Immunol. 12:6745652021. View Article : Google Scholar | |
Jacquelot N, Tellier J, Nutt SL and Belz GT: Tertiary lymphoid structures and B lymphocytes in cancer prognosis and response to immunotherapies. Oncoimmunology. 10:19005082021. View Article : Google Scholar | |
Schumacher TN and Thommen DS: Tertiary lymphoid structures in cancer. Science. 375:eabf94192022. View Article : Google Scholar | |
Schweiger T, Berghoff AS, Glogner C, Glueck O, Rajky O, Traxler D, Birner P, Preusser M, Klepetko W and Hoetzenecker K: Tumor-infiltrating lymphocyte subsets and tertiary lymphoid structures in pulmonary metastases from colorectal cancer. Clin Exp Metastasis. 33:727–739. 2016. View Article : Google Scholar | |
Siliņa K, Soltermann A, Attar FM, Casanova R, Uckeley ZM, Thut H, Wandres M, Isajevs S, Cheng P, Curioni-Fontecedro A, et al: Germinal centers determine the prognostic relevance of tertiary lymphoid structures and are impaired by corticosteroids in lung squamous cell carcinoma. Cancer Res. 78:1308–1320. 2018. View Article : Google Scholar | |
Calderaro J, Petitprez F, Becht E, Laurent A, Hirsch TZ, Rousseau B, Luciani A, Amaddeo G, Derman J, Charpy C, et al: Intra-tumoral tertiary lymphoid structures are associated with a low risk of early recurrence of hepatocellular carcinoma. J Hepatol. 70:58–65. 2019. View Article : Google Scholar | |
Sofopoulos M, Fortis SP, Vaxevanis CK, Sotiriadou NN, Arnogiannaki N, Ardavanis A, Vlachodimitropoulos D, Perez SA and Baxevanis CN: The prognostic significance of peritumoral tertiary lymphoid structures in breast cancer. Cancer Immunol Immunother. 68:1733–1745. 2019. View Article : Google Scholar | |
He W, Zhang D, Liu H, Chen T, Xie J, Peng L, Zheng X, Xu B, Li Q and Jiang J: The high level of tertiary lymphoid structure is correlated with superior survival in patients with advanced gastric cancer. Front Oncol. 10:9802020. View Article : Google Scholar | |
Lin Q, Tao P, Wang J, Ma L, Jiang Q, Li J, Zhang G, Liu J, Zhang Y, Hou Y, et al: Tumor-associated tertiary lymphoid structure predicts postoperative outcomes in patients with primary gastrointestinal stromal tumors. Oncoimmunology. 9:17473392020. View Article : Google Scholar | |
Magara T, Nakamura M, Nojiri Y, Yoshimitsu M, Kano S, Matsubara A, Kato H and Morita A: Tertiary lymphoid structures correlate with better prognosis in cutaneous angiosarcoma. J Dermatol Sci. 103:57–59. 2021. View Article : Google Scholar | |
Baker PM, Clement PB and Young RH: Malignant peritoneal mesothelioma in women: A study of 75 cases with emphasis on their morphologic spectrum and differential diagnosis. Am J Clin Pathol. 123:724–737. 2005. View Article : Google Scholar | |
Benzerdjeb N, Dartigues P, Kepenekian V, Valmary-Degano S, Mery E, Avérous G, Chevallier A, Laverriere MH, Villa I, Harou O, et al: Tertiary lymphoid structures in epithelioid malignant peritoneal mesothelioma are associated with neoadjuvant chemotherapy, but not with prognosis. Virchows Arch. 479:765–772. 2021. View Article : Google Scholar | |
Gao J, Navai N, Alhalabi O, Siefker-Radtke A, Campbell MT, Tidwell RS, Guo CC, Kamat AM, Matin SF, Araujo JC, et al: Neoadjuvant PD-L1 plus CTLA-4 blockade in patients with cisplatin-ineligible operable high-risk urothelial carcinoma. Nat Med. 26:1845–1851. 2020. View Article : Google Scholar | |
Helmink BA, Reddy SM, Gao J, Zhang S, Basar R, Thakur R, Yizhak K, Sade-Feldman M, Blando J, Han G, et al: B cells and tertiary lymphoid structures promote immunotherapy response. Nature. 577:549–555. 2020. View Article : Google Scholar | |
Cabrita R, Lauss M, Sanna A, Donia M, Skaarup Larsen M, Mitra S, Johansson I, Phung B, Harbst K, Vallon-Christersson J, et al: Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature. 577:561–565. 2020. View Article : Google Scholar | |
Lee HS, Jang HJ, Ramineni M, Wang DY, Ramos D, Choi JM, Splawn T, Espinoza M, Almarez M, Hosey L, et al: A phase II window of opportunity study of neoadjuvant PD-L1 versus PD-L1 plus CTLA-4 blockade for patients with malignant pleural mesothelioma. Clin Cancer Res. 29:548–559. 2023. View Article : Google Scholar | |
Chapel DB, Stewart R, Furtado LV, Husain AN, Krausz T and Deftereos G: Tumor PD-L1 expression in malignant pleural and peritoneal mesothelioma by Dako PD-L1 22C3 pharmDx and Dako PD-L1 28-8 pharmDx assays. Hum Pathol. 87:11–17. 2019. View Article : Google Scholar | |
Gazivoda VP, Kangas-Dick AW, Greenbaum AA, Roshal J, Chen C, Moore DF, Langan RC, Kennedy TJ, Minerowicz C and Alexander HR: Expression of PD-L1 in patients with malignant peritoneal mesothelioma: A pilot study. J Surg Res. 277:131–137. 2022. View Article : Google Scholar | |
Pezzuto F, Vimercati L, Fortarezza F, Marzullo A, Pennella A, Cavone D, Punzi A, Caporusso C, d'Amati A, Lettini T and Serio G: Evaluation of prognostic histological parameters proposed for pleural mesothelioma in diffuse malignant peritoneal mesothelioma. A short report. Diagn Pathol. 16:642021. View Article : Google Scholar | |
Desai A, Karrison T, Rose B, Tan Y, Hill B, Pemberton E, Straus C, Seiwert T and Kindler HL: OA08.03 phase II Trial of pembrolizumab (NCT02399371) in previously-treated malignant mesothelioma (MM): Final analysis. J Thorac Oncol. 13:S3392018. View Article : Google Scholar | |
Zong L, Mo S, Yu S, Zhou Y, Zhang M, Chen J and Xiang Y: Expression of the immune checkpoint VISTA in breast cancer. Cancer Immunol Immunother. 69:1437–1446. 2020. View Article : Google Scholar | |
Zong L, Zhou Y, Zhang M, Chen J and Xiang Y: VISTA expression is associated with a favorable prognosis in patients with high-grade serous ovarian cancer. Cancer Immunol Immunother. 69:33–42. 2020. View Article : Google Scholar | |
Xie S, Huang J, Qiao Q, Zang W, Hong S, Tan H, Dong C, Yang Z and Ni L: Expression of the inhibitory B7 family molecule VISTA in human colorectal carcinoma tumors. Cancer Immunol Immunother. 67:1685–1694. 2018. View Article : Google Scholar | |
Noelle RJ, Lines JL, Lewis LD, Martell RE, Guillaudeux T, Lee SW, Mahoney KM, Vesely MD, Boyd-Kirkup J, Nambiar DK and Scott AM: Clinical and research updates on the VISTA immune checkpoint: Immuno-oncology themes and highlights. Front Oncol. 13:12250812023. View Article : Google Scholar | |
Hmeljak J, Sanchez-Vega F, Hoadley KA, Shih J, Stewart C, Heiman D, Tarpey P, Danilova L, Drill E, Gibb EA, et al: Integrative molecular characterization of malignant pleural mesothelioma. Cancer Discov. 8:1548–1565. 2018. View Article : Google Scholar | |
Alcala N, Mangiante L, Le-Stang N, Gustafson CE, Boyault S, Damiola F, Alcala K, Brevet M, Thivolet-Bejui F, Blanc-Fournier C, et al: Redefining malignant pleural mesothelioma types as a continuum uncovers immune-vascular interactions. EBioMedicine. 48:191–202. 2019. View Article : Google Scholar | |
Muller S, Victoria Lai W, Adusumilli PS, Desmeules P, Frosina D, Jungbluth A, Ni A, Eguchi T, Travis WD, Ladanyi M, et al: V-domain Ig-containing suppressor of T-cell activation (VISTA), a potentially targetable immune checkpoint molecule, is highly expressed in epithelioid malignant pleural mesothelioma. Mod Pathol. 33:303–311. 2020. View Article : Google Scholar | |
Chung YS, Kim M, Cha YJ, Kim KA and Shim HS: Expression of V-set immunoregulatory receptor in malignant mesothelioma. Mod Pathol. 33:263–270. 2020. View Article : Google Scholar | |
Offin M, Yang SR, Egger J, Jayakumaran G, Spencer RS, Lopardo J, Nash GM, Cercek A, Travis WD, Kris MG, et al: Molecular characterization of peritoneal mesotheliomas. J Thorac Oncol. 17:455–460. 2022. View Article : Google Scholar | |
Gao J, Ward JF, Pettaway CA, Shi LZ, Subudhi SK, Vence LM, Zhao H, Chen J, Chen H, Efstathiou E, et al: VISTA is an inhibitory immune checkpoint that is increased after ipilimumab therapy in patients with prostate cancer. Nat Med. 23:551–555. 2017. View Article : Google Scholar | |
Kakavand H, Jackett LA, Menzies AM, Gide TN, Carlino MS, Saw RPM, Thompson JF, Wilmott JS, Long GV and Scolyer RA: Negative immune checkpoint regulation by VISTA: A mechanism of acquired resistance to anti-PD-1 therapy in metastatic melanoma patients. Mod Pathol. 30:1666–1676. 2017. View Article : Google Scholar | |
ElTanbouly MA, Zhao Y, Nowak E, Li J, Schaafsma E, Le Mercier I, Ceeraz S, Lines JL, Peng C, Carriere C, et al: VISTA is a checkpoint regulator for naïve T cell quiescence and peripheral tolerance. Science. 367:eaay05242020. View Article : Google Scholar | |
Shekari N, Shanehbandi D, Kazemi T, Zarredar H, Baradaran B and Jalali SA: VISTA and its ligands: The next generation of promising therapeutic targets in immunotherapy. Cancer Cell Int. 23:2652023. View Article : Google Scholar | |
ElTanbouly MA, Schaafsma E, Noelle RJ and Lines JL: VISTA: Coming of age as a multi-lineage immune checkpoint. Clin Exp Immunol. 200:120–130. 2020. View Article : Google Scholar | |
ElTanbouly MA, Croteau W, Noelle RJ and Lines JL: VISTA: A novel immunotherapy target for normalizing innate and adaptive immunity. Semin Immunol. 42:1013082019. View Article : Google Scholar | |
Liu J, Yuan Y, Chen W, Putra J, Suriawinata AA, Schenk AD, Miller HE, Guleria I, Barth RJ, Huang YH, et al: Immune-checkpoint proteins VISTA and PD-1 nonredundantly regulate murine T-cell responses. Proc Natl Acad Sci USA. 112:6682–6687. 2015. View Article : Google Scholar | |
Hosseinkhani N, Hemmat N, Baghbani E, Baghbanzadeh A, Kazemi T, Mokhtarzadeh A, Jafarlou M, Amin Doustvandi M and Baradaran B: Dual silencing of tumor-intrinsic VISTA and CTLA-4 stimulates T-cell mediated immune responses and inhibits MCF7 breast cancer development. Gene. 896:1480432024. View Article : Google Scholar | |
Lei CJ, Wang B, Long ZX, Ren H, Pan QY and Li Y: Investigation of PD-1H in DEN-induced mouse liver cancer model. Eur Rev Med Pharmacol Sci. 22:5194–5199. 2018. | |
Iadonato S, Ovechkina Y, Lustig K, Cross J, Eyde N, Frazier E, Kabi N, Katz C, Lance R, Peckham D, et al: A highly potent anti-VISTA antibody KVA12123-a new immune checkpoint inhibitor and a promising therapy against poorly immunogenic tumors. Front Immunol. 14:13116582023. View Article : Google Scholar | |
Sasikumar PG, Sudarshan NS, Adurthi S, Ramachandra RK, Samiulla DS, Lakshminarasimhan A, Ramanathan A, Chandrasekhar T, Dhudashiya AA, Talapati SR, et al: PD-1 derived CA-170 is an oral immune checkpoint inhibitor that exhibits preclinical anti-tumor efficacy. Commun Biol. 4:6992021. View Article : Google Scholar | |
Curis Inc: A Study of CA-170 (Oral PD-L1, PD-L2 and VISTA Checkpoint Antagonist) in Patients With Advanced Tumors and Lymphomas. 2016. https://ClinicalTrials.gov/show/NCT02812875 | |
Tawbi HA, Schadendorf D, Lipson EJ, Ascierto PA, Matamala L, Castillo Gutiérrez E, Rutkowski P, Gogas HJ, Lao CD, De Menezes JJ, et al: Relatlimab and nivolumab versus nivolumab in untreated advanced melanoma. N Engl J Med. 386:24–34. 2022. View Article : Google Scholar | |
Shrestha R, Nabavi N, Lin YY, Mo F, Anderson S, Volik S, Adomat HH, Lin D, Xue H, Dong X, et al: BAP1 haploinsufficiency predicts a distinct immunogenic class of malignant peritoneal mesothelioma. Genome Med. 11:82019. View Article : Google Scholar | |
Osmanbeyoglu HU, Palmer D, Sagan A, Sementino E, Becich MJ and Testa JR: Isolated BAP1 genomic alteration in malignant pleural mesothelioma predicts distinct immunogenicity with implications for immunotherapeutic response. Cancers (Basel). 14:56262022. View Article : Google Scholar | |
Zelba H, Bedke J, Hennenlotter J, Mostböck S, Zettl M, Zichner T, Chandran A, Stenzl A, Rammensee HG and Gouttefangeas C: PD-1 and LAG-3 dominate checkpoint receptor-mediated T-cell inhibition in renal cell carcinoma. Cancer Immunol Res. 7:1891–1899. 2019. View Article : Google Scholar | |
Woo SR, Turnis ME, Goldberg MV, Bankoti J, Selby M, Nirschl CJ, Bettini ML, Gravano DM, Vogel P, Liu CL, et al: Immune inhibitory molecules LAG-3 and PD-1 synergistically regulate T-cell function to promote tumoral immune escape. Cancer Res. 72:917–927. 2012. View Article : Google Scholar | |
Marcq E, Van Audenaerde JRM, De Waele J, Merlin C, Pauwels P, van Meerbeeck JP, Fisher SA and Smits ELJ: The Search for an Interesting Partner to Combine with PD-L1 Blockade in Mesothelioma: Focus on TIM-3 and LAG-3. Cancers (Basel). 13:2822021. View Article : Google Scholar | |
Aggarwal V, Workman CJ and Vignali DAA: LAG-3 as the third checkpoint inhibitor. Nat Immunol. 24:1415–1422. 2023. View Article : Google Scholar | |
Feng L, Gao X, Jiao Z, Wang Z and Min F: BTK inhibitor combined with anti-PD-1 monoclonal antibody for the treatment of CD20-negative primary central nervous system lymphoma: A case report. Oncol Lett. 25:482022. View Article : Google Scholar | |
Maccaroni E, Lunerti V, Agostinelli V, Giampieri R, Zepponi L, Pagliacci A and Berardi R: New insights into hormonal therapies in uterine sarcomas. Cancers (Basel). 14:9212022. View Article : Google Scholar | |
Pan C, Yu T, Han L, Hao D, Yang M, Li L, Chu L and Ni Q: Surufatinib combined camrelizumab as a valuable third-line rescue therapy for a patient with extensive-stage for small-cell lung cancer: A case report and literature review. Anticancer Drugs. 35:271–276. 2024. View Article : Google Scholar | |
Peri A, Salomon N, Wolf Y, Kreiter S, Diken M and Samuels Y: The landscape of T cell antigens for cancer immunotherapy. Nat Cancer. 4:937–954. 2023. View Article : Google Scholar | |
McGranahan N, Furness AJ, Rosenthal R, Ramskov S, Lyngaa R, Saini SK, Jamal-Hanjani M, Wilson GA, Birkbak NJ, Hiley CT, et al: Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science. 351:1463–1469. 2016. View Article : Google Scholar | |
Anzar I, Malone B, Samarakoon P, Vardaxis I, Simovski B, Fontenelle H, Meza-Zepeda LA, Stratford R, Keung EZ, Burgess M, et al: The interplay between neoantigens and immune cells in sarcomas treated with checkpoint inhibition. Front Immunol. 14:12264452023. View Article : Google Scholar | |
Nguyen KB, Roerden M, Copeland CJ, Backlund CM, Klop-Packel NG, Remba T, Kim B, Singh NK, Birnbaum ME, Irvine DJ and Spranger S: Decoupled neoantigen cross-presentation by dendritic cells limits anti-tumor immunity against tumors with heterogeneous neoantigen expression. Elife. 12:e852632023. View Article : Google Scholar | |
Shi Y, Jing B and Xi R: Comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumors. Genome Biol. 24:1692023. View Article : Google Scholar | |
Wei Z, Zhou C, Zhang Z, Guan M, Zhang C, Liu Z and Liu Q: The landscape of tumor fusion neoantigens: A pan-cancer analysis. iScience. 21:249–260. 2019. View Article : Google Scholar | |
Yang W, Lee KW, Srivastava RM, Kuo F, Krishna C, Chowell D, Makarov V, Hoen D, Dalin MG, Wexler L, et al: Immunogenic neoantigens derived from gene fusions stimulate T cell responses. Nat Med. 25:767–775. 2019. View Article : Google Scholar | |
Cortés-Ciriano I, Lee JJ, Xi R, Jain D, Jung YL, Yang L, Gordenin D, Klimczak LJ, Zhang CZ, Pellman DS, et al: Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing. Nat Genet. 52:331–341. 2020. View Article : Google Scholar | |
Oey H, Daniels M, Relan V, Chee TM, Davidson MR, Yang IA, Ellis JJ, Fong KM, Krause L and Bowman RV: Whole-genome sequencing of human malignant mesothelioma tumours and cell lines. Carcinogenesis. 40:724–734. 2019. View Article : Google Scholar | |
Bueno R, Stawiski EW, Goldstein LD, Durinck S, De Rienzo A, Modrusan Z, Gnad F, Nguyen TT, Jaiswal BS, Chirieac LR, et al: Comprehensive genomic analysis of malignant pleural mesothelioma identifies recurrent mutations, gene fusions and splicing alterations. Nat Genet. 48:407–416. 2016. View Article : Google Scholar | |
Mansfield AS, Peikert T, Smadbeck JB, Udell JBM, Garcia-Rivera E, Elsbernd L, Erskine CL, Van Keulen VP, Kosari F, Murphy SJ, et al: Neoantigenic potential of complex chromosomal rearrangements in mesothelioma. J Thorac Oncol. 14:276–287. 2019. View Article : Google Scholar | |
Yoshikawa Y, Emi M, Hashimoto-Tamaoki T, Ohmuraya M, Sato A, Tsujimura T, Hasegawa S, Nakano T, Nasu M, Pastorino S, et al: High-density array-CGH with targeted NGS unmask multiple noncontiguous minute deletions on chromosome 3p21 in mesothelioma. Proc Natl Acad Sci USA. 113:13432–13437. 2016. View Article : Google Scholar | |
Relan V, Morrison L, Parsonson K, Clarke BE, Duhig EE, Windsor MN, Matar KS, Naidoo R, Passmore L, McCaul E, et al: Phenotypes and karyotypes of human malignant mesothelioma cell lines. PLoS One. 8:e581322013. View Article : Google Scholar | |
Panagopoulos I, Andersen K, Brunetti M, Gorunova L, Davidson B, Lund-Iversen M, Micci F and Heim S: Genetic pathways in peritoneal mesothelioma tumorigenesis. Cancer Genomics Proteomics. 20:363–374. 2023. View Article : Google Scholar | |
Verdegaal EM, de Miranda NF, Visser M, Harryvan T, van Buuren MM, Andersen RS, Hadrup SR, van der Minne CE, Schotte R, Spits H, et al: Neoantigen landscape dynamics during human melanoma-T cell interactions. Nature. 536:91–95. 2016. View Article : Google Scholar | |
Samstein RM, Lee CH, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, Barron DA, Zehir A, Jordan EJ, Omuro A, et al: Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet. 51:202–206. 2019. View Article : Google Scholar | |
Ren Y, Cherukuri Y, Wickland DP, Sarangi V, Tian S, Carter JM, Mansfield AS, Block MS, Sherman ME, Knutson KL, et al: HLA class-I and class-II restricted neoantigen loads predict overall survival in breast cancer. Oncoimmunology. 9:17449472020. View Article : Google Scholar | |
Zou XL, Li XB, Ke H, Zhang GY, Tang Q, Yuan J, Zhou CJ, Zhang JL, Zhang R and Chen WY: Prognostic value of neoantigen load in immune checkpoint inhibitor therapy for cancer. Front Immunol. 12:6890762021. View Article : Google Scholar | |
Kosari F, Disselhorst M, Yin J, Peikert T, Udell J, Johnson S, Smadbeck J, Murphy S, McCune A, Karagouga G, et al: Tumor junction burden and antigen presentation as predictors of survival in mesothelioma treated with immune checkpoint inhibitors. J Thorac Oncol. 17:446–454. 2022. View Article : Google Scholar | |
Lee HS, Jang HJ, Choi JM, Zhang J, de Rosen VL, Wheeler TM, Lee JS, Tu T, Jindra PT, Kerman RH, et al: Comprehensive immunoproteogenomic analyses of malignant pleural mesothelioma. JCI Insight. 3:e985752018. View Article : Google Scholar | |
Tandon RT, Jimenez-Cortez Y, Taub R and Borczuk AC: Immunohistochemistry in peritoneal mesothelioma: A single-center experience of 244 cases. Arch Pathol Lab Med. 142:236–242. 2018. View Article : Google Scholar | |
Leblay N, Leprêtre F, Le Stang N, Gautier-Stein A, Villeneuve L, Isaac S, Maillet D, Galateau-Sallé F, Villenet C, Sebda S, et al: BAP1 is altered by copy number loss, mutation, and/or loss of protein expression in more than 70% of malignant peritoneal mesotheliomas. J Thorac Oncol. 12:724–733. 2017. View Article : Google Scholar | |
Gezgin G, Dogrusöz M, van Essen TH, Kroes WGM, Luyten GPM, van der Velden PA, Walter V, Verdijk RM, van Hall T, van der Burg SH and Jager MJ: Genetic evolution of uveal melanoma guides the development of an inflammatory microenvironment. Cancer Immunol Immunother. 66:903–912. 2017. View Article : Google Scholar | |
Lai J, Zhou Z, Tang XJ, Gao ZB, Zhou J and Chen SQ: A tumor-specific neo-antigen caused by a frameshift mutation in BAP1 is a potential personalized biomarker in malignant peritoneal mesothelioma. Int J Mol Sci. 17:7392016. View Article : Google Scholar | |
Rizzolo A, Ah-Lan KC, Nu TNT and Alcindor T: Response to Ipilimumab and nivolumab in a patient with malignant peritoneal mesothelioma. Clin Colorectal Cancer. 21:371–374. 2022. View Article : Google Scholar | |
Liu K, Huang Y, Xu Y, Wang G, Cai S, Zhang X and Shi T: BAP1-related signature predicts benefits from immunotherapy over VEGFR/mTOR inhibitors in ccRCC: A retrospective analysis of JAVELIN Renal 101 and checkmate-009/010/025 trials. Cancer Immunol Immunother. 72:2557–2572. 2023. View Article : Google Scholar | |
Topalian SL, Taube JM, Anders RA and Pardoll DM: Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat Rev Cancer. 16:275–287. 2016. View Article : Google Scholar | |
Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, Albright A, Cheng JD, Kang SP, Shankaran V, et al: IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest. 127:2930–2940. 2017. View Article : Google Scholar | |
Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, Li Z, Traugh N, Bu X, Li B, et al: Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 24:1550–1558. 2018. View Article : Google Scholar | |
Cui C, Xu C, Yang W, Chi Z, Sheng X, Si L, Xie Y, Yu J, Wang S, Yu R, et al: Ratio of the interferon-γ signature to the immunosuppression signature predicts anti-PD-1 therapy response in melanoma. NPJ Genom Med. 6:72021. View Article : Google Scholar | |
Lee JS, Nair NU, Dinstag G, Chapman L, Chung Y, Wang K, Sinha S, Cha H, Kim D, Schperberg AV, et al: Synthetic lethality-mediated precision oncology via the tumor transcriptome. Cell. 184:2487–2502.e13. 2021. View Article : Google Scholar | |
Nair NU, Jiang Q, Wei JS, Misra VA, Morrow B, Kesserwan C, Hermida LC, Lee JS, Mian I, Zhang J, et al: Genomic and transcriptomic analyses identify a prognostic gene signature and predict response to therapy in pleural and peritoneal mesothelioma. Cell Rep Med. 4:1009382023. View Article : Google Scholar | |
Dinstag G, Shulman ED, Elis E, Ben-Zvi DS, Tirosh O, Maimon E, Meilijson I, Elalouf E, Temkin B, Vitkovsky P, et al: Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome. Med. 4:15–30.e8. 2023. View Article : Google Scholar | |
Hoang DT, Dinstag G, Shulman ED, Hermida LC, Ben-Zvi DS, Elis E, Caley K, Sammut SJ, Sinha S, Sinha N, et al: A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics. Nat Cancer. Jul 3–2024.(Epub ahead of print). View Article : Google Scholar | |
Panagi M, Mpekris F, Voutouri C, Hadjigeorgiou AG, Symeonidou C, Porfyriou E, Michael C, Stylianou A, Martin JD, Cabral H, et al: Stabilizing tumor-resident mast cells restores T-Cell infiltration and sensitizes sarcomas to PD-L1 inhibition. Clin Cancer Res. 30:2582–2597. 2024. View Article : Google Scholar | |
Sautès-Fridman C, Lawand M, Giraldo NA, Kaplon H, Germain C, Fridman WH and Dieu-Nosjean MC: Tertiary lymphoid structures in cancers: Prognostic value, regulation, and manipulation for therapeutic intervention. Front Immunol. 7:4072016. View Article : Google Scholar | |
Yang ZR, Su YD, Ma R, Wu HL and Li Y: Efficacy and adverse events of apatinib salvage treatment for refractory diffuse malignant peritoneal mesothelioma: A pilot study. Front Oncol. 12:8118002022. View Article : Google Scholar | |
Zauderer MG, Alley EW, Bendell J, Capelletto E, Bauer TM, Callies S, Szpurka AM, Kang S, Willard MD, Wacheck V and Varghese AM: Phase 1 cohort expansion study of LY3023414, a dual PI3K/mTOR inhibitor, in patients with advanced mesothelioma. Invest New Drugs. 39:1081–1088. 2021. View Article : Google Scholar | |
Yang H, Hall SRR, Sun B, Zhao L, Gao Y, Schmid RA, Tan ST, Peng RW and Yao F: NF2 and Canonical Hippo-YAP pathway define distinct tumor subsets characterized by different immune deficiency and treatment implications in human pleural mesothelioma. Cancers (Basel). 13:15612021. View Article : Google Scholar | |
Fennell DA, King A, Mohammed S, Branson A, Brookes C, Darlison L, Dawson AG, Gaba A, Hutka M, Morgan B, et al: Rucaparib in patients with BAP1-deficient or BRCA1-deficient mesothelioma (MiST1): An open-label, single-arm, phase 2a clinical trial. Lancet Respir Med. 9:593–600. 2021. View Article : Google Scholar | |
Hung YP, Dong F, Watkins JC, Nardi V, Bueno R, Dal Cin P, Godleski JJ, Crum CP and Chirieac LR: Identification of ALK rearrangements in malignant peritoneal mesothelioma. JAMA Oncol. 4:235–238. 2018. View Article : Google Scholar | |
Li Petri G, Pecoraro C, Randazzo O, Zoppi S, Cascioferro SM, Parrino B, Carbone D, El Hassouni B, Cavazzoni A, Zaffaroni N, et al: New Imidazo[2,1-b][1,3,4]Thiadiazole derivatives inhibit FAK phosphorylation and potentiate the antiproliferative effects of gemcitabine through modulation of the human equilibrative nucleoside transporter-1 in peritoneal mesothelioma. Anticancer Res. 40:4913–4919. 2020. View Article : Google Scholar | |
Quispel-Janssen JM, Badhai J, Schunselaar L, Price S, Brammeld J, Iorio F, Kolluri K, Garnett M, Berns A, Baas P, et al: Comprehensive pharmacogenomic profiling of malignant pleural mesothelioma identifies a subgroup sensitive to FGFR inhibition. Clin Cancer Res. 24:84–94. 2018. View Article : Google Scholar | |
Bensaid D, Blondy T, Deshayes S, Dehame V, Bertrand P, Grégoire M, Errami M and Blanquart C: Assessment of new HDAC inhibitors for immunotherapy of malignant pleural mesothelioma. Clin Epigenetics. 10:792018. View Article : Google Scholar | |
McGale JP, Chen DL, Trebeschi S, Farwell MD, Wu AM, Cutler CS, Schwartz LH and Dercle L: Artificial intelligence in immunotherapy PET/SPECT imaging. Eur Radiol. Feb 15–2024.(Epub ahead of print). View Article : Google Scholar | |
Addala V, Newell F, Pearson JV, Redwood A, Robinson BW, Creaney J and Waddell N: Computational immunogenomic approaches to predict response to cancer immunotherapies. Nat Rev Clin Oncol. 21:28–46. 2024. View Article : Google Scholar |