Determinants of Response to Immune Checkpoint Blockade in Pleural Mesothelioma: Molecular, Immunological, and Clinical Perspectives

Abstract

Diffuse pleural mesothelioma (PM) is a rare thoracic malignancy with historically limited treatment options and poor outcomes. Despite the recent breakthrough of dual immune checkpoint blockade (ICB)-notably the combination of anti-PD-1 and anti-CTLA-4 therapies-clinical responses remain variable and overall survival gains modest. Consequently, there is an urgent need for multidimensional biomarkers and adaptive trial designs to unravel the complexity of PM immune biology. This review provides a comprehensive overview of current evidence on how histological subtypes (epithelioid vs. non-epithelioid) influence ICB efficacy, highlighting distinct genetic landscapes (e.g., BAP1, CDKN2A, NF2 mutations) and tumor microenvironment (TME) features, including immune infiltration patterns and PD-L1 or VISTA expression, that underlie differential responses. We further examine intrinsic tumor factors-such as mutational burden and checkpoint ligand expression-and extrinsic determinants, including immune cell composition, stromal architecture, patient immune status, and microbiota, as modulators of immunotherapy outcomes. We also discuss the rationale behind emerging strategies designed to enhance ICB efficacy, currently under clinical evaluation. These include combination regimens with chemotherapy, radiotherapy, surgery, epigenetic modulators, anti-angiogenic agents, and novel immunotherapies such as next-generation checkpoint inhibitors (LAG-3, VISTA), immune-suppressive cell-targeting agents, vaccines, cell-based therapies, and oncolytic viruses. Collectively, these advancements underscore the importance of integrating histological classification with molecular and microenvironmental profiling to refine patient selection and guide the development of combination strategies aimed at transforming "cold" mesotheliomas into "hot," immune-responsive tumors, thereby enhancing the efficacy of ICB.

Keywords: cell therapies; epigenetic modulators; immune infiltrate; immunotherapy; microbiota; oncolytic virus; pleural mesothelioma; predictive biomarkers; tumor microenvironment; vaccine.

Figure 1

Intrinsic (genetic) and extrinsic (microenvironmental) factors vary across histologic subtypes and influence response to immune checkpoint blockade (ICB) therapy. The tumor microenvironment (TME) of epithelioid pleural mesothelioma (PM) is typically immune-excluded, whereas non-epithelioid PM displays an inflamed but immunosuppressive phenotype. These distinctions provide a rationale for histology-tailored immunotherapeutic strategies. Th1 cell, T helper 1 cell; NK, Natural killer cell; TAM, tumor-associated macrophage; Treg, T regulatory cell; TGF-b1, Transforming growth factor-b1; VEGF, vascular endothelial growth factor; EC, endothelial cell; CAF, cancer-associated fibroblast. The figure was created with BioRender (www.BioRender.com, accessed on 12 November 2025).

Figure 2

Contribution of TMB and chromosomal alterations to neoantigen generation. (a) Although PM displays a relatively low TMB compared with other ICB-responsive cancers, BAP1 mutations are associated with both increased TMB and a more inflamed TME, suggesting enhanced sensitivity to ICB. In contrast, CDKN2A, MTAP, and NF2 alterations correlate with immunosuppressive or immune-excluded phenotypes, contributing to ICB resistance. (b) PM is characterized by extensive chromosomal rearrangements, including chromothripsis and chromoplexy, which can generate novel and potentially immunogenic neoantigens capable of driving tumor-specific T-cell responses and ICB sensitivity. The figure was created with BioRender (www.BioRender.com, accessed on 9 December 2025).

Figure 3

Inhibitory immune checkpoints as predictive biomarkers for ICB efficacy in PM. Current evidence indicates that PD-L1 expression on tumor cells or TAMs, as well as VISTA and TIM-3 expression on CD8⁺ T cells, have limited predictive value for ICB response in PM. VISTA expression on tumor cells and TAMs is generally associated with poor outcomes, whereas PD-1 expression on CD8⁺ T cells and TAMs correlates with improved response to ICB. Finally, LAG-3 has emerged as part of an exploratory four-gene inflammatory signature linked to better survival in patients treated with immunotherapy. The figure was created with BioRender (www.BioRender.com, accessed on 9 December 2025).

Figure 4

TME features influencing response to ICB in PM. The presence of TLS and a T-cell–inflamed phenotype, marked by abundant CD8⁺ T cells and reduced Th2-polarized CD4⁺ T cells, is associated with improved outcomes. In contrast, an immune-excluded TME—characterized by limited T-cell infiltration due to dense fibrotic stroma and chemokine misalignment—drives resistance to ICB. Additional resistance mechanisms include VEGF-mediated angiogenesis and heightened TGF-β signaling, which promote CAF expansion, M2 macrophage accumulation, increased Treg and MDSC levels, and impaired effector T-cell function. Collectively, these features shape the spatial and functional architecture of the TME, ultimately determining sensitivity or resistance to immunotherapy. The figure was created with BioRender (www.BioRender.com, accessed on 9 December 2025).

Figure 5

Patient immune status influences response to ICB. The composition of circulating immune cells can serve as an indicator of systemic immune fitness and overall inflammatory status, thereby affecting the efficacy of ICB. Elevated neutrophil-to-lymphocyte ratio (NLR) and higher eosinophil counts are associated with poorer outcomes. Emerging evidence also highlights the importance of microbial ecosystems across multiple anatomical sites in shaping disease progression and treatment response in PM. Specific gut bacterial genera correlate with more favorable TME features, including higher CD8⁺ and CD4⁺ TIL densities, reduced CD68⁺ TAM infiltration, and improved PFS. Local microbial communities (intra-tumoral and pleural) may similarly exert site-specific immunomodulatory effects with prognostic relevance. The figure was created with BioRender (www.BioRender.com, accessed on 9 December 2025).