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Multiplex fluorescence IHC (mfIHC) approaches were yet either limited to six markers or limited to a small tissue size that hampers translational studies on large tissue microarray cohorts. Here we have developed a BLEACH&STAIN mfIHC method that enabled the simultaneous analysis of 15 biomarkers (PD-L1, PD-1, CTLA-4, panCK, CD68, CD163, CD11c, iNOS, CD3, CD8, CD4, FOXP3, CD20, Ki67, and CD31) in 3,098 tumor samples from 44 different carcinoma entities within one week. To facilitate automated immune checkpoint quantification on tumor and immune cells and study its spatial interplay an artificial intelligence–based framework incorporating 17 different deep-learning systems was established. Unsupervised clustering showed that the three PD-L1 phenotypes (PD-L1+ tumor and immune cells, PD-L1+ immune cells, PD-L1−) were either inflamed or noninflamed. In inflamed PD-L1+patients, spatial analysis revealed that an elevated level of intratumoral M2 macrophages as well as CD11c+ dendritic cell (DC) infiltration (P < 0.001 each) was associated with a high CD3+ CD4± CD8± FOXP3± T-cell exclusion and a high PD-1 expression on T cells (P < 0.001 each). In breast cancer, the PD-L1 fluorescence intensity on tumor cells showed a significantly higher predictive performance for overall survival (OS; AUC, 0.72, P < 0.001) compared with the commonly used percentage of PD-L1+ tumor cells (AUC, 0.54). In conclusion, our deep-learning–based BLEACH&STAIN framework facilitates rapid and comprehensive assessment of more than 60 spatially orchestrated immune cell subpopulations and its prognostic relevance. Implications: The development of an easy-to-use high-throughput 15+1 multiplex fluorescence approach facilitates the in-depth understanding of the immune tumor microenvironment (TME) and enables to study the prognostic relevance of more than 130 immune cell subpopulations.

CTLA-4 is an inhibitory immune checkpoint receptor and a negative regulator of anti-tumor T-cell function. This study is aimed for a comparative analysis of CTLA-4

Multiplex fluorescence IHC (mfIHC) approaches were yet either limited to six markers or limited to a small tissue size that hampers translational studies on large tissue microarray cohorts. Here we have developed a BLEACH&STAIN mfIHC method that enabled the simultaneous analysis of 15 biomarkers (PD-L1, PD-1, CTLA-4, panCK, CD68, CD163, CD11c, iNOS, CD3, CD8, CD4, FOXP3, CD20, Ki67, and CD31) in 3,098 tumor samples from 44 different carcinoma entities within one week. To facilitate automated immune checkpoint quantification on tumor and immune cells and study its spatial interplay an artificial intelligence–based framework incorporating 17 different deep-learning systems was established. Unsupervised clustering showed that the three PD-L1 phenotypes (PD-L1+ tumor and immune cells, PD-L1+ immune cells, PD-L1−) were either inflamed or noninflamed. In inflamed PD-L1+patients, spatial analysis revealed that an elevated level of intratumoral M2 macrophages as well as CD11c+ dendritic cell (DC) infiltration (P < 0.001 each) was associated with a high CD3+ CD4± CD8± FOXP3± T-cell exclusion and a high PD-1 expression on T cells (P < 0.001 each). In breast cancer, the PD-L1 fluorescence intensity on tumor cells showed a significantly higher predictive performance for overall survival (OS; AUC, 0.72, P < 0.001) compared with the commonly used percentage of PD-L1+ tumor cells (AUC, 0.54). In conclusion, our deep-learning–based BLEACH&STAIN framework facilitates rapid and comprehensive assessment of more than 60 spatially orchestrated immune cell subpopulations and its prognostic relevance.Implications:The development of an easy-to-use high-throughput 15+1 multiplex fluorescence approach facilitates the in-depth understanding of the immune tumor microenvironment (TME) and enables to study the prognostic relevance of more than 130 immune cell subpopulations.

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.

Purpose Expansion of CD8+ cytotoxic Tlymphocytes is a prerequisite for anti-cancer immune activity and has gained interest in the era of immune checkpoint therapy. Methods To understand the CD8+ T cell dynamics in the tumor microenvironment, we used multiplex fluorescence immunohistochemistry to quantitate CD8+ proliferation (Ki67 co-expression) in tissue microarrays from 1107 colorectal, 642 renal cell, 1066 breast, 375 ovarian, 451 pancreatic and 347 gastric cancer samples. Results The density and the percentage of proliferating (Ki67+) CD8+ T cells were both highly variable between tumor types as well as between patients with the same tumor type. Elevated density and percentage of proliferating CD8+ cytotoxic T cells were significantly associated with favorable tumor parameters such as low tumor stage, negative nodal stage (p ≤ 0.0041 each), prolonged overall survival (p ≤ 0.0028 each) and an inflamed immune phenotype (p = 0.0025) in colorectal cancer and, in contrast, linked to high tumor stage, advanced ISUP/Fuhrman/Thoenes grading (each p ≤ 0.003), shorter overall survival (p ≤ 0.0330 each) and an immune inflamed phenotype (p = 0.0094) in renal cell cancer. In breast, ovarian, pancreatic and gastric cancer the role of (Ki67+)CD8+ Tcells was not linked to clinicopathological data. Conclusion Our data demonstrate a tumor type dependent prognostic impact of proliferating (Ki67+)CD8+ Tcells and an inverse impact in colorectal and renal cell cancer.

Purpose Expansion of CD8+ cytotoxic Tlymphocytes is a prerequisite for anti-cancer immune activity and has gained interest in the era of immune checkpoint therapy. Methods To understand the CD8+ T cell dynamics in the tumor microenvironment, we used multiplex fluorescence immunohistochemistry to quantitate CD8+ proliferation (Ki67 co-expression) in tissue microarrays from 1107 colorectal, 642 renal cell, 1066 breast, 375 ovarian, 451 pancreatic and 347 gastric cancer samples. Results The density and the percentage of proliferating (Ki67+) CD8+ T cells were both highly variable between tumor types as well as between patients with the same tumor type. Elevated density and percentage of proliferating CD8+ cytotoxic T cells were significantly associated with favorable tumor parameters such as low tumor stage, negative nodal stage (p ≤ 0.0041 each), prolonged overall survival (p ≤ 0.0028 each) and an inflamed immune phenotype (p = 0.0025) in colorectal cancer and, in contrast, linked to high tumor stage, advanced ISUP/Fuhrman/Thoenes grading (each p ≤ 0.003), shorter overall survival (p ≤ 0.0330 each) and an immune inflamed phenotype (p = 0.0094) in renal cell cancer. In breast, ovarian, pancreatic and gastric cancer the role of (Ki67+)CD8+ Tcells was not linked to clinicopathological data. Conclusion Our data demonstrate a tumor type dependent prognostic impact of proliferating (Ki67+)CD8+ Tcells and an inverse impact in colorectal and renal cell cancer. Supplementary Information The online version contains supplementary material available at 10.1007/s13402-021-00601-4.

Introduction: CTLA-4 is an inhibitory immune checkpoint receptor and a negative regulator of anti-tumor T-cell function. This study aimed at a comparative analysis of CTLA-4+ cells between different tumor entities. Methods: To quantify CTLA-4+ cells, 4,582 tumor samples from 90 different tumor entities as well as 608 samples of 76 different normal tissue types were analyzed by immunohistochemistry in a tissue microarray format. Two different antibody clones (MSVA-152R and CAL49) were validated and quantified using a deep learning framework for automated exclusion of unspecific immunostaining. Results: Comparing both CTLA-4 antibodies revealed a clone dependent cytoplasmic cross-reactivity in adrenal cortical adenoma (63%) for MSVA-152R and in pheochromocytoma (67%) as well as hepatocellular carcinoma (36%) for CAL49. After automated exclusion of non-specific staining reaction (3.6%), a strong correlation was observed for the densities of CTLA-4+ lymphocytes obtained by both antibodies (r=0.87; p Conclusion: Marked differences exist in the number of CTLA-4+ lymphocytes between tumors. Analyzing two independent antibodies by a deep learning framework identifies clone-specific cross-reactivity and facilitates automated quantification of target proteins such as CTLA-4. Citation Format: David Dum, Tjark L. C. Henke, Tim Mandelkow, Elena Bady, Jonas B. Raedler, Ronald Simon, Guido Sauter, Maximilian Lennartz, Waldemar Wilczak, Eike Burandt, Stefan Steurer, Niclas C. Blessin. Semi-automated validation and quantification of CTLA-4 in 90 different Tumor entities using multiple antibodies and artificial intelligence [abstract]. In: Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; 2021 Oct 5-6. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(1 Suppl):Abstract nr P069.

Introduction: The quantification of PD-L1 (programmed cell death ligand 1) has been used to predict patient's survival, to characterize the tumor immune microenvironment, and to predict response to immune checkpoint therapies. However, a framework to assess the PD-L1 status with a high interobserver reproducibility on tumor cells and different types of immune cells has yet to be established. Methods: To study the impact of PD-L1 expression on the tumor immune microenvironment and patient outcome, a framework for fully automated PD-L1 quantification on tumor cells and immune cells was established and validated. Automated PD-L1 quantification was facilitated by incorporating three different deep learning steps for the analysis of more than 80 different neoplasms from more than 10'000 tumor specimens using a bleach & stain 15-marker multiplex fluorescence immunohistochemistry panel (i.e., PD-L1, PD-1, CTLA-4, panCK, CD68, CD163, CD11c, iNOS, CD3, CD8, CD4, FOXP3, CD20, Ki67, CD31). Clinicopathological parameter were available for more than 30 tumor entities and overall survival data were available for 1517 breast cancer specimens. Results: Comparing the automated deep-learning based PD-L1 quantification with conventional brightfield PD-L1 data revealed a high concordance in tumor cells (p Conclusion: In conclusion, multiplex immunofluorescence PD-L1 assessment provides cutoff-free/continuous PD-L1 data which are superior to the conventional percentage of PD-L1+ tumor cells and of high prognostic relevance. The combined analysis of spatial PD-L1/PD-1 data and more than 20 different immune cell subtypes of the immune tumor microenvironment revealed distinct PD-L1 immune phenotypes. Citation Format: Niclas C. Blessin, Elena Bady, Tim Mandelkow, Cheng Yang, Jonas B. Raedler, Ronald Simon, Christoph Fraune, Maximilian Lennartz, Sarah Minner, Eike Burandt, Doris Höflmayer, Guido Sauter, Katharina Möller, Sören A. Weidemann. Automated cell type specific PD-L1 quantification by artificial intelligence using high throughput bleach & stain 15-marker multiplex fluorescence immunohistochemistry in human cancers [abstract]. In: Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; 2021 Oct 5-6. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(1 Suppl):Abstract nr P068.

Immune checkpoint inhibitor (CPI) therapies targeting the PD-1/PD-L1 pathway have shown remarkable results in a rising number of different tumor entities. The likelihood of a positive response rate to CPIs depends - amongst other factors - on the PD-L1 expression level in tumor cells and tumor infiltrating immune cells. PD-L1 expression on different cell types is known to impact both the function and the composition of immune cells in the tumor microenvironment. However, this process is not fully understood. To study the impact of PD-L1 expression on the intratumoral immune environment, a multiplex fluorescence immunohistochemistry approach was used enabling the simultaneous analysis of 15 different antibodies on a set of tissue microarrays containing samples from more than 10000 neoplasms from more than 100 different tumor types and subtypes. Image analysis was performed using a U-net deep learning algorithm for cell detection and the “R” software package for statistical analysis. PD-L1 expression was measured on tumor cells (AE1/AE3+), subsets of macrophages (CD68+CD163+/CD68+iNOS+) and dendritic cells (CD11c+). To address the complexity of the tumor microenvironment, PD-1 expression was measured on B-cells (CD20+) and T-cell subsets, including cytotoxic T-cells (CD3+CD8+), T-helper cells (CD3+CD4+), regulatory T-cells (CD3+CD4+FOXP3+) and their memory (CD45RO+), activated (CD7-) or expanding (ki67+) phenotypes. Although PD-L1 expression varied significantly between cell types, patients and different tumor entities, the number of PD-1+ T-cells was consistently linked to PD-L1 expression. Combined analysis of cell densities, expression patterns, intensity measurements, interaction and distance analysis between immune cells and tumor cells revealed distinct changes in the immune cell infiltration pattern linked to PD-L1 expression. Previously uncharacterized immune cell-composition dynamics in clustered tumor phenotypes, according to the PD-L1 expression, were detected. This included significant associations between PD-L1+ tumor cells and PD-L1+ antigen-presenting cells and various PD-1+ T-cell subsets. In conclusion, deep profiling of 15 biomarkers in 10 000 cancers revealed complex differences in the composition of tumor infiltrating immune cells depending on the PD-L1 expression level of tumor cells and the mononuclear phagocyte system. Citation Format: Niclas Christian Blessin, Cheng Yang, Jonas Raedler, Julia Ebner, Tim Mandelkow, Ronald Simon, Christoph Fraune, Maximilian Lennartz, Andreas M. Luebke, Sarah Minner, Eike Burandt, Doris Höflmayer, Guido Sauter, Katharina Möller, Sören A Weidemann. Deep profiling of the PD-1/PD-L1 pathway in 10000 cancers revealed changes in the immune cell composition between cancer entities [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2773.