Resistance to immune checkpoint therapies by tumour-induced T-cell desertification and exclusion: key mechanisms, prognostication and new therapeutic opportunities.

Immune checkpoint therapies (ICT) can reinvigorate the effector functions of anti-tumour T cells, improving cancer patient outcomes. Anti-tumour T cells are initially formed during their first contact (priming) with tumour antigens by antigen-presenting cells (APCs). Unfortunately, many patients are refractory to ICT because their tumours are considered to be 'cold' tumours-i.e., they do not allow the generation of T cells (so-called 'desert' tumours) or the infiltration of existing anti-tumour T cells (T-cell-excluded tumours). Desert tumours disturb antigen processing and priming of T cells by targeting APCs with suppressive tumour factors derived from their genetic instabilities. In contrast, T-cell-excluded tumours are characterised by blocking effective anti-tumour T lymphocytes infiltrating cancer masses by obstacles, such as fibrosis and tumour-cell-induced immunosuppression. This review delves into critical mechanisms by which cancer cells induce T-cell 'desertification' and 'exclusion' in ICT refractory tumours. Filling the gaps in our knowledge regarding these pro-tumoral mechanisms will aid researchers in developing novel class immunotherapies that aim at restoring T-cell generation with more efficient priming by APCs and leukocyte tumour trafficking. Such developments are expected to unleash the clinical benefit of ICT in refractory patients.

Epigenetic control of CD1D expression as a mechanism of resistance to immune checkpoint therapy in poorly immunogenic melanomas.

Immune Checkpoint Therapies (ICT) have revolutionized the treatment of metastatic melanoma. However, only a subset of patients reaches complete responses. Deficient ß2-microglobulin (ß2M) expression impacts antigen presentation to T cells, leading to ICT resistance. Here, we investigate alternative ß2M-correlated biomarkers that associate with ICT resistance. We shortlisted immune biomarkers interacting with human ß2M using the STRING database. Next, we profiled the transcriptomic expression of these biomarkers in association with clinical and survival outcomes in the melanoma GDC-TCGA-SKCM dataset and a collection of publicly available metastatic melanoma cohorts treated with ICT (anti-PD1). Epigenetic control of identified biomarkers was interrogated using the Illumina Human Methylation 450 dataset from the melanoma GDC-TCGA-SKCM study. We show that ß2M associates with CD1d, CD1b, and FCGRT at the protein level. Co-expression and correlation profile of B2M with CD1D, CD1B, and FCGRT dissociates in melanoma patients following B2M expression loss. Lower CD1D expression is typically found in patients with poor survival outcomes from the GDC-TCGA-SKCM dataset, in patients not responding to anti-PD1 immunotherapies, and in a resistant anti-PD1 pre-clinical model. Immune cell abundance study reveals that B2M and CD1D are both enriched in tumor cells and dendritic cells from patients responding to anti-PD1 immunotherapies. These patients also show increased levels of natural killer T (NKT) cell signatures in the tumor microenvironment (TME). Methylation reactions in the TME of melanoma impact the expression of B2M and SPI1, which controls CD1D expression. These findings suggest that epigenetic changes in the TME of melanoma may impact ß2M and CD1d-mediated functions, such as antigen presentation for T cells and NKT cells. Our hypothesis is grounded in comprehensive bioinformatic analyses of a large transcriptomic dataset from four clinical cohorts and mouse models. It will benefit from further development using well-established functional immune assays to support understanding the molecular processes leading to epigenetic control of ß2M and CD1d. This research line may lead to the rational development of new combinatorial treatments for metastatic melanoma patients that poorly respond to ICT.

PreservCyt Is an Optimal Fixative that Permits Cytologic and Molecular Analyses of Vitreoretinal Lymphoma Biopsies.

Purpose: Vitreoretinal lymphoma (VRL) is a potentially fatal intraocular malignancy. Diagnosis is hampered by poor preservation of morphology and DNA/RNA integrity, which precludes adjunctive molecular analysis. We aimed to determine the optimum fixative protocol for VRL biopsies that permits cytology, IHC/flow cytometry and molecular analyses.Methods: Six fixatives were compared on cultured Pfeiffer cells used as a cellular model. Cells were fixed and evaluated on cellular morphology, antibody staining, DNA/RNA amount and integrity. VRL clinical cases were used as validation and proof-of-concept.Results: PreservCyt was the best fixative for preserving cellular morphology and high-quality RNA/DNA from vitreous fluid biopsies. Cells from clinical VRL cases fixed with PreservCyt showed adequate cellular morphology and IHC positivity. Sufficient DNA was obtained for IgH clonality and MYD88 mutation detection using remnant cytological fluid.Conclusions: PreservCyt maintains good morphology and RNA/DNA integrity suggesting that it is a suitable fixative for VRL diagnosis and molecular analysis.

Single B-Cell Genomic Analyses Differentiate Vitreoretinal Lymphoma from Chronic Inflammation.

PURPOSE: To test whether analyzing DEPArray (Menarini Silicon Biosystems) isolated single B cells from the vitreous fluid can reveal crucial genomic and clinicopathological features to distinguish patients with vitreoretinal lymphoma (VRL) from those with chronic inflammation using immunoglobulin heavy chain (IGH), disease biomarker myeloid differentiation primary response 88 (MYD88)L265P mutation, and copy number profiling. DESIGN: A single-center, retrospective study. PARTICIPANTS: Remnant vitreous biopsies from 7 patients with VRL and 4 patients with chronic inflammation were acquired for molecular analysis. METHODS: Vitreous fluid samples were prefixed in PreservCyt (Hologic) and underwent cytologic analysis and immunohistochemistry examination. Single cells were isolated using the DEPArray NxT system, followed by downstream genomic analysis. MAIN OUTCOME MEASURES: The frequencies of the dominant IGH and MYD88L265P mutation and the genome-wide copy number aberration (CNA) profiles of individual vitreous-isolated B cells were characterized. RESULTS: An average of 10 to 13 vitreous B cells were used in the single-cell IGH and MYD88 analyses. Higher frequencies of dominant IGH (88.8% ± 13.2%) and MYD88L265P mutations (35.0% ± 31.3%) were detected in patients with VRL than in patients with chronic inflammation (65.9% ± 13.4% and 1.5% ± 2.6% for IGH and MYD88L265P, respectively). In a cytology-proven VRL case, all 15 vitreous isolated B cells were derived from the same clone with 100% paired IGH: immunoglobulin light chain (IGK) sequences. Genome-wide copy number profiling revealed a high degree of similarity between B cells from the same patient with VRL, with extensive gains and losses at the same areas across the whole genome. In addition, 14 of 15 B cells showed a BCL2/JH t(14;18) translocation, confirming cellular malignancy with a clonal origin. Clustering analysis of the copy number profiles revealed that malignant B cells derived from different patients with VRL had no common genome-wide signatures. CONCLUSIONS: Single B-cell genomic characterization of the IGH, MYD88L265P mutation, and copy number profile enables VRL diagnosis. Because our study involved only a small cohort, these meaningful proof-of-concept data now warrant further investigation in a larger patient cohort.

Cytologic and Molecular Diagnostics for Vitreoretinal Lymphoma: Current Approaches and Emerging Single-Cell Analyses.

Vitreoretinal lymphoma (VRL) is a rare ocular malignancy that manifests as diffuse large B-cell lymphoma. Early and accurate diagnosis is essential to prevent mistreatment and to reduce the high morbidity and mortality associated with VRL. The disease can be diagnosed using various methods, including cytology, immunohistochemistry, cytokine analysis, flow cytometry, and molecular analysis of bulk vitreous aspirates. Despite these options, VRL diagnosis remains challenging, as samples are often confounded by low cellularity, the presence of debris and non-target immunoreactive cells, and poor cytological preservation. As such, VRL diagnostic accuracy is limited by both false-positive and false-negative outcomes. Missed or inappropriate diagnosis may cause delays in treatment, which can have life-threatening consequences for patients with VRL. In this review, we summarize current knowledge and the diagnostic modalities used for VRL diagnosis. We also highlight several emerging molecular techniques, including high-resolution single cell-based analyses, which may enable more comprehensive and precise VRL diagnoses.

Applying Single-Cell Technology in Uveal Melanomas: Current Trends and Perspectives for Improving Uveal Melanoma Metastasis Surveillance and Tumor Profiling.

Uveal melanoma (UM) is the most common primary adult intraocular malignancy. This rare but devastating cancer causes vision loss and confers a poor survival rate due to distant metastases. Identifying clinical and molecular features that portend a metastatic risk is an important part of UM workup and prognostication. Current UM prognostication tools are based on determining the tumor size, gene expression profile, and chromosomal rearrangements. Although we can predict the risk of metastasis fairly accurately, we cannot obtain preclinical evidence of metastasis or identify biomarkers that might form the basis of targeted therapy. These gaps in UM research might be addressed by single-cell research. Indeed, single-cell technologies are being increasingly used to identify circulating tumor cells and profile transcriptomic signatures in single, drug-resistant tumor cells. Such advances have led to the identification of suitable biomarkers for targeted treatment. Here, we review the approaches used in cutaneous melanomas and other cancers to isolate single cells and profile them at the transcriptomic and/or genomic level. We discuss how these approaches might enhance our current approach to UM management and review the emerging data from single-cell analyses in UM.