P02.06 Semi-automated validation and quantification of CTLA-4 in 90 different Tumor entities using multiple antibodies and artificial intelligence

BackgroundCTLA-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.Materials and MethodsTo 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.ResultsComparing both CTLA-4 antibodies revealed a clone dependent unspecific staining pattern 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<0.0001). The mean density of CTLA-4+ cells was 674±1482 cells/mm2 and ranged from 71±175 cells/mm2 in leiomyoma to 5916±3826 cells/mm2 in Hodgkin’s lymphoma. Within epithelial tumors, the density of CTLA-4+ lymphocytes were higher in squamous cell (421±467 cells/mm2) and urothelial carcinomas (419±347 cells/mm2) than in adenocarcinomas (269±375 cells/mm2) and renal cell neoplasms (256±269 cells/mm2). A high CTLA-4+ cell density was linked to low pT category (p<0.0001), absent lymph node metastases (p=0.0354), and PD-L1 expression in tumor cells or inflammatory cells (p<0.0001 each). A high CTLA-4/CD3-ratio was linked to absent lymph node metastases (p=0.0295) and to PD-L1 positivity on immune cells (p<0.0026).ConclusionsMarked differences exist in the number of CTLA-4+ lymphocytes between tumors. Analyzing two independent antibodies by a deep learning framework can facilitate automated quantification of immunohistochemically analyzed target proteins such as CTLA-4.Disclosure InformationD. Dum: None. T.L.C. Henke: None. T. Mandelkow: None. E. Bady: None. R. Simon: None. G. Sauter: None. S. Steuerer: None. W. Wilczak: None. E. Burandt: None. J. Raedler: None. M. Lennartz: None. N.C. Blessin: None.

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Semi-automated validation and quantification of CTLA-4 in 90 different Tumor entities using multiple antibodies and artificial intelligence

American Journal of Clinical Pathology ◽

10.1093/ajcp/aqab191.293 ◽

2021 ◽

Vol 156 (Supplement_1) ◽

pp. S137-S138

Author(s):

D Dum ◽

N C Blessin ◽

T L Henke ◽

T Mandelkow ◽

E Bady ◽

...

Keyword(s):

Deep Learning ◽

Lymph Node ◽

Lymph Node Metastases ◽

Cell Function ◽

Negative Regulator ◽

Staining Reaction ◽

Specific Staining ◽

Learning Framework ◽

Node Metastases ◽

Tumor Entities

Abstract Introduction/Objective 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+ entities. cells between different tumor Methods/Case Report 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 (if a Case Study enter NA) Results: Comparing both CTLA-4 antibodies revealed a clone dependent unspecific staining pattern 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<0.0001). The mean density of CTLA-4+cells was 674±1482 cells/ mm2 and ranged from 71±175 cells/mm2 in leiomyoma to 5916±3826 cells/mm2 in Hodgkin’s lymphoma. Within epithelial tumors, the density of CTLA-4+ lymphocytes were higher in squamous cell (421±467 cells/ mm2) and urothelial carcinomas (419±347 cells/ mm2) than in adenocarcinomas (269±375 cells/ mm2) and renal cell neoplasms (256±269 cells/ mm2). A high CTLA-4+ cell density was linked to low pT category (p<0.0001), absent lymph node metastases (p=0.0354), and PD-L1 expression in tumor cells or inflammatory cells (p<0.0001 each). A high CTLA-4/CD3-ratio was linked to absent lymph node metastases (p=0.0295) and to PD-L1 positivity on immune cells (p<0.0026). Conclusion Marked differences exist in the number of CTLA-4+ lymphocytes between tumors. Analyzing two independent antibodies by a deep learning framework can facilitate automated quantification of immunohistochemically analyzed target proteins such as CTLA-4.

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