High level of EZH2 expression is linked to high density of CD8-positive T-lymphocytes and an aggressive phenotype in renal cell carcinoma

Purpose!#!Enhancer of zeste homolog 2 (EZH2), the catalytic part of the Polycomb repressive complex 2 (PRC2), has a prognostic role in renal cell carcinoma (RCC) and was recently shown to modulate the immune response by reducing tumor cell immunogenicity.!##!Methods!#!To investigate whether the prognostic role of EZH2 might be driven by a modified immune environment, more than 1800 RCCs were analyzed in a tissue microarray for EZH2 expression and CD8 positive lymphocytes were quantitated by automated digital imaging.!##!Results!#!EZH2 positivity was found in 75.2% of 1603 interpretable tumors. In clear cell RCC, high EZH2 expression was significantly linked to high ISUP, Furmann, and Thoenes grade (p < 0.0001 each), advanced stage (p < 0.0001), nodal (p = 0.0190) and distant metastasis (p < 0.0001) as well as shortened overall (p < 0.0027) and recurrence free survival (p < 0.0001). The density of CD8+ cells varied from 0 to 5048 cells/mm!##!Conclusion!#!Our data support a striking prognostic role of both EZH2 expression and the density of CD8+ cells in RCC. The tight relationship of EZH2 expression and CD8+ cell counts in RCC is consistent with models suggesting that EZH2 overexpression can be caused by high lymphocyte content in certain tumor types. Such a mechanism could explain the unique finding of high lymphocyte counts driving poor prognosis in RCC patients

T-Cell Density at the Invasive Margin and Immune Phenotypes Predict Outcome in Vulvar Squamous Cell Cancer

Background: Although quantification of tumor infiltrating lymphocytes (TILs) has become of increasing interest in immuno-oncology, only little is known about TILs infiltration in the tumor microenvironment and its predictive value in vulvar cancer. Methods: Immunohistochemistry and automated digital image analysis was applied to measure the densities of CD3+ (DAKO, #IR503) and CD8+ (DAKO, #IR623) TILs at the invasive margin and in the center of 530 vulvar squamous cell cancers. Results: An elevated density of CD3+ T-cell at the invasive margin was significantly associated with low tumor stage (p = 0.0012) and prolonged survival (overall survival [OS] p = 0.0027, progression free survival [PFS] p = 0.024) and was independent from tumor stage, nodal stage, grade, and HPV-status in multivariate analysis (p + cells in the center of the tumor was weaker compared to the invasive margin (OS p = 0.046, PFS p = 0.031) and lacking for CD8+ T-cell densities at any location (p ≥ 0.14 each). Unsupervised clustering of CD3+ and CD8+ T-cell densities identified three major subgroups corresponding to the immune desert (137 patients), immune excluded (220 patients) and immune inflamed phenotypes (133 patients). Survival analysis revealed a particular poor prognosis for the immune desert phenotype for OS (p = 0.0071) and PFS (p = 0.0027). Conclusion: Our data demonstrate a high prognostic value of CD3+ T-cells at the invasive margin and immune phenotypes in vulvar squamous cell cancer

Loss of cytoplasmic survivin expression is an independent predictor of poor prognosis in radically operated prostate cancer patients

Abstract Survivin is an inhibitor of apoptosis. Aberrant survivin expression occurs in malignant tumors and has often been linked to unfavorable patient outcome. Here we analyzed 12 432 prostate cancers by immunohistochemistry. Survivin immunostaining was regularly expressed at high levels in normal prostate epithelium but expression was often reduced in prostate cancers. Among 9492 evaluable prostate cancers, 9% expressed survivin strongly, 19% moderately, 28% weakly, and 44% lacked it. Loss of cytoplasmic survivin was seen in advanced tumor stage, higher Gleason score, preoperative PSA levels, and Ki‐67 labeling index, and associated with earlier PSA recurrence (P < .0001). Survivin loss was significantly more common in cancers carrying TMPRSS2:ERG fusions (61% survivin negative) than in ERG wild‐type cancers (32% survivin negative; P < .0001). Multivariate analysis revealed that reduced cytoplasmic survivin expression predicted poor prognosis independent from Gleason score, pT, pN, and serum PSA level. This was valid for ERG‐positive and ERG‐negative cancers. Survivin expression loss even retained its prognostic impact in 1020 PTEN deleted cancers, a group that is already characterized by dismal patient prognosis. In conclusion, reduced survivin expression is associated with more aggressive tumors and inferior prognosis in prostate cancer

Patterns of TIGIT Expression in Lymphatic Tissue, Inflammation, and Cancer

TIGIT is an inhibitory immune checkpoint receptor and a putative target for novel immune therapies. Here, we analysed two different types of tissue microarrays of healthy lymphatic and various inflamed tissues, colorectal and lung cancers, as well as >1700 tumour samples from 86 different tumour entities for TIGIT and/or PD-1 by bright field and/or multiplex fluorescence immunohistochemistry. TIGIT was detected in CD8+ cytotoxic T cells, CD4+ T helper cells, FOXP3+ regulatory T cells, and NK cells, but not in CD11c+ dendritic cells, CD68+ macrophages, and CD20+ B lymphocytes. TIGIT expression paralleled that of PD-1. More than 70% of TIGIT+ cells were PD-1+, and more than 90% of the PD-1+ cells were TIGIT+. Expression varied between different tissue compartments. TIGIT expression in tonsil gradually increased from the interfollicular area over the marginal/mantle zone to the germinal centre in all T cell subtypes. In inflammatory diseases, the strongest expression of TIGIT/PD-1 was found in Hashimoto thyroiditis. TIGIT+ lymphocytes were seen in all 86 different tumour entities with considerable high variability of TIGIT positivity within and between different cancer entities. Particularly, high densities of TIGIT+ lymphocytes were, for example, seen in squamous cell cancers of various origins. In summary, the variable expression levels of TIGIT and PD-1 in cell types and tissue compartments illustrate the high complexity of immune microenvironments. The high frequency of TIGIT (and PD-1) expressing lymphocytes in cancers highlights considerable opportunities for cotargeting with checkpoint inhibitors

Expression of the immune checkpoint receptor TIGIT in Hodgkin’s lymphoma

Abstract Hodgkin’s lymphoma (HL) is characterized by a high background of inflammatory cells which play an important role for the pathogenesis of the disease. T cell immunoreceptor with Ig and ITIM domains (TIGIT) is an inhibitory immune checkpoint receptor and a putative target for novel immunotherapies. To study patterns of TIGIT expression in the T cell background surrounding malignant cells including Hodgkin cells, Reed-Sternberg cells and histiocytic cells, a microenvironment (ME) tissue microarray (TMA) was constructed from tissue punches measuring 2 mm in diameter obtained from formalin-fixed tissue samples of Hodgkin’s lymphoma lymph nodes (n = 40) and normal human tonsil (n = 2). The ME-TMA was stained by brightfield and fluorescence multiplex immunohistochemistry (IHC) to evaluate expression levels of TIGIT and PD-1 as well as standard lymphocyte markers (CD3, CD8, CD4, FOXP3) in the lymphocytic background. All analyzed cases of HL contained 9–99% (median: 86%) of TIGIT+ lymphoid cells. In general, TIGIT localized to the same cells as PD-1. Strikingly, expression levels of TIGIT and PD-1 were highly variable among the analyzed samples. Highest levels of TIGIT and PD-1 were found in one sample of nodular lymphocytic-predominant HL (NLPHL). In conclusion, TIGIT expression is highly variable between patients with Hodgkin’s lymphoma. Our results encourage further studies evaluating the role of TIGIT as a target for immunotherapies in Hodgkin’s lymphoma

Supplementary Figures 1 - 12, Supplementary Tables 1-5 from BLEACH&STAIN 15-marker Multiplexed Imaging in 3,098 Human Carcinomas Reveals Six Major PD-L1–driven Immune Phenotypes with Distinct Spatial Orchestration

Figure S1a: Representative images of the BLEACH&STAIN 15+1 staining. Figure S1b: BLEACH&STAIN 15+1 validation. Figure S2: Deep learning framework for automated cell type identification. Figure S3: Performance of a Deep Learning-based framework for cell type identification compared to manual set thresholds. Figure S4: Comparison between BLEACH&STAIN and manual PD-L1 assessment. Figure S5: Immune phenotype identification. Figure S6: Association between the immune phenotypes and the individual immune cell parameters. Figure S7: Representative image of the 6 major immune phenotypes of Figure 2 Figure S8a: Association between PD-L1 phenotypes and clinico-histopathological parameters. Figure S8b: Distribution of six PD-L1 phenotypes across 44 carcinoma entities. Figure S9: Spatial orchestration of immune cells across the PD-L1 immune phenotypes. Figure S10: Representative image of the spatial orchestration shown in Figure 3 Figure S11: Composition of immune cells across the three PD-L1 phenotypes Figure S12: Prognosis analysis of spatial immune parameters in breast cancer for overall 5-years survival after surgery. Table S1: TMA cohort of the PD-L1 15+1 study. Table S2: List of the used antibodies, antigen retrieval (AR), dilutions, and Opal dyes for multiplex fluorescence immunohistochemistry. Table S3: List of 132 immune cell (sub)population. Table S4: Associations between spatial parameters and PD-L1 phenotypes were independent from the used threshold. Table S5: Immune landscape of 3098 human carcinoma samples.</p