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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.

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.

Estrogen receptor alpha (ER) is a nuclear transcription factor with a pivotal role in the function of the reproductive organs and of other organ systems. It constitutes a well-established therapeutic target for various small molecules. ER immunohistochemistry is used in surgical pathology for the distinction of tumors of the breast and gynecological organs. To comprehensively determine ER expression in normal and neoplastic tissues, a tissue microarray containing 18,560 samples from 149 different tumor types and subtypes as well as 608 samples of 76 different normal tissue types was analyzed by immunohistochemistry. ER positivity was found in 55 of 149 tumor categories, and 27 of these tumor categories contained at least one case with strong ER staining. ER positivity was most seen in various subtypes of breast cancer (78.9-95.7%) and in several different tumor categories of the ovary and the endometrium (8.5-75.9%). Other commonly ER positive tumor entities included leiomyoma (65.9%), neuroendocrine tumor of the appendix (43.8%), leiomyosarcoma (22.9%), Sertoli Leydig cell tumor of the ovary (1 of 3 positive), rhabdomyosarcoma (16.7%), angiomyolipoma (10%), colorectal neuroendocrine carcinoma (9.1%), myoepithelial carcinoma of the salivary gland (8.3%), granulosa cell tumor of the ovary (8.3%), epithelial-myoepithelial carcinoma of the salivary gland (7.7%), squamous cell carcinomas of various sites of origin (0.8-6.7%), testicular teratoma (5.3%), Brenner tumor of the ovary (5.1%), Ewing sarcoma (5%), adenocarcinoma NOS of the salivary gland (4.8%), papillary thyroid carcinoma (3.3%), sarcoma, not otherwise specified (NOS) (2.8%), paraganglioma (2.6%), non-invasive papillary urothelial carcinoma, pTa G3 (2.4%), pulmonary adenocarcinoma (2.1%), neuroendocrine tumor of the ileum (2.1%), and adenocarcinoma of the prostate, Gleason 5+5 (1.2%). Among 1,411 evaluable invasive breast carcinomas of no special type (NST), absent or low ER immunostaining was significantly linked to high BRE grade (p Citation Format: Florian Viehweger, David Dum, Anne Menz, Ria Uhlig, Andrea Hinsch, Doris Hoeflmayer, Christoph Fraune, Christian Bernreuther, Patrick Lebok, Soeren Weidemann, Guido Sauter, Maximilian Lennartz, Frank Jacobsen, Till Sebastian Clauditz, Till Krech, Andreas H Marx, Ronald Simon, Stefan Steurer, Sarah Minner, Eike Burandt, Natalia Gorbokon. Patterns of estrogen receptor expression across 18,500 tumor samples obtained from 149 cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2164.

Background: Automated prognosis marker assessment in prostate and breast cancer using immunohistochemistry is currently hampered by the lack of a reliable differentiation between benign and malignant glands. To evaluate the patient’s risk in routine clinical practice in prostate cancer prognosis parameters such as the Gleason grading, that are accompanied by a high interobserver variability are used. In breast cancer multi-gene panels are used that are influenced by fluctuating tumor purity. A reproducible prognostic evaluation is lacking in both tumor entities. Design: To enable automated prognosis marker quantification, we have developed and validated a framework for automated prostate and breast cancer detection that comprises three different artificial intelligence analysis steps and an algorithm for cell-distance analysis of multiplex fluorescence immunohistochemistry. Pan-cytokeratin (panCK) antibodies were used to detect epithelial cells and antibodies directed against Myosin and p63 were used to identify basal cells. Results: The optimal distance between Myosin+ and p63+ basal cells and benign panCK+ cells was identified as 25 µm in breast and 23 µm in prostate cancer and used to exclude benign glands from the analysis combined with several deep learning-based algorithms. Our framework discriminated normal glands from malignant glands with an AUC of 0.96 in breast and 0.98 % in prostate cancer. The approach for automated prostate and breast cancer detection, by excluding benign gland from the analysis, improved the predictive performance of prognosis markers significantly (p Conclusion: Automated prostate and breast cancer identification enables fully automated prognosis marker assessment in routine clinical practice using deep learning and multiplex fluorescence immunohistochemistry. BLEACH&STAIN as well as other multiplex fluorescence immunohistochemistry approaches that enable the simultaneous analysis of 20+ biomarkers can be used to established prognosis panels that can be now assessed in an automated manner. Citation Format: Tim Mandelkow, Gisa Mehring, Elena Bady, Maximilian Lennartz, Christoph Fraune, Frank Jacobsen, Natalia Gorbokon, Till Krech, Eike Burandt, Anne Menz, Ria Uhlig, Till S. Clauditz, Guido Sauter, Markus Graefen, Sarah Minner, Niclas C. Blessin. The combination of artificial intelligence and BLEACH&STAIN multiplex fluorescence immunohistochemistry facilitates automated prostate and breast cancer detection. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5438.

Background: Kallikrein-related peptidase 7 (KLK7) is a chymotrypsin-like serine protease which is essential for the desquamation of corneocytes and thus plays a pivotal role in maintaining skin homeostasis. In cancer, KLK7 overexpression was suggested to represent a route for metastasis through cleavage of cell junction and extracellular matrix proteins of cancer cells. Methods: To comprehensively determine KLK7 protein expression in normal and neoplastic tissues, a tissue microarray containing 13,447 samples from 147 different tumor types and subtypes as well as 608 samples of 76 different normal tissue types was analyzed by immunohistochemistry. Results: KLK7 positivity was found in 64 of 147 tumor categories, including 17 tumor categories with at least one strongly positive case. The highest rate of KLK7 positivity was found in squamous cell carcinomas from various sites of origin (positive in 18.1%-63.8%), ovarian and endometrium cancers (4.8%-56.2%), salivary gland tumors (4.8%-13.7%), bilio-pancreatic adenocarcinomas (20.0%-40.4%), and adenocarcinomas of the upper gastrointestinal tract (3.3%-12.5%). KLK7 positivity was linked to nodal metastasis (p=0.0005), blood vessel infiltration (p=0.0037), and lymph vessel infiltration (p Conclusions: These data provide a comprehensive overview on KLK7 expression in normal and neoplastic human tissues. The prognostic relevance of KLK7 expression and the possible role of KLK7 as a drug target need to be further investigated. Citation Format: Simon Kind, Carolina Castillo, Ria Uhlig, Natalia Gorbokon, Maximilian Lennartz, Sebastian Dwertmann Rico, Viktor Reiswich, Florian Viehweger, Martina Kluth, Claudia Hube-Magg, Christian Bernreuther, Franziska Büscheck, Till Clauditz, Christroph Fraune, Andrea Hinsch, Till Krech, Patrick Lebok, Stefan Steurer, Eike Burandt, Sarah Minner, Andreas Marx, Ronald Simon, Waldemar Wilczak, Guido Sauter, Anne Menz, Frank Jacobsen. KLK7 expression in human tumors: A tissue microarray study on 13,447 tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3302.