Semi-automated validation and quantification of CTLA-4 in 90 different tumor entities using multiple antibodies and artificial intelligence
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Semi-automated validation and quantification of CTLA-4 in 90 different tumor entities using multiple antibodies and artificial intelligence. / Dum, David; Henke, Tjark L C; Mandelkow, Tim; Yang, Cheng; Bady, Elena; Raedler, Jonas B; Simon, Ronald; Sauter, Guido; Lennartz, Maximilian; Büscheck, Franziska; Luebke, Andreas M; Menz, Anne; Hinsch, Andrea; Höflmayer, Doris; Weidemann, Sören; Fraune, Christoph; Möller, Katharina; Lebok, Patrick; Uhlig, Ria; Bernreuther, Christian; Jacobsen, Frank; Clauditz, Till S; Wilczak, Waldemar; Minner, Sarah; Burandt, Eike; Steurer, Stefan; Blessin, Niclas C.
In: LAB INVEST, Vol. 102, No. 6, 06.2022, p. 650-657.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Semi-automated validation and quantification of CTLA-4 in 90 different tumor entities using multiple antibodies and artificial intelligence
AU - Dum, David
AU - Henke, Tjark L C
AU - Mandelkow, Tim
AU - Yang, Cheng
AU - Bady, Elena
AU - Raedler, Jonas B
AU - Simon, Ronald
AU - Sauter, Guido
AU - Lennartz, Maximilian
AU - Büscheck, Franziska
AU - Luebke, Andreas M
AU - Menz, Anne
AU - Hinsch, Andrea
AU - Höflmayer, Doris
AU - Weidemann, Sören
AU - Fraune, Christoph
AU - Möller, Katharina
AU - Lebok, Patrick
AU - Uhlig, Ria
AU - Bernreuther, Christian
AU - Jacobsen, Frank
AU - Clauditz, Till S
AU - Wilczak, Waldemar
AU - Minner, Sarah
AU - Burandt, Eike
AU - Steurer, Stefan
AU - Blessin, Niclas C
N1 - © 2022. The Author(s).
PY - 2022/6
Y1 - 2022/6
N2 - 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+ cells between different tumor entities. To quantify CTLA-4+ cells, 4582 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. 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). 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). 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.
AB - 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+ cells between different tumor entities. To quantify CTLA-4+ cells, 4582 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. 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). 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). 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.
U2 - 10.1038/s41374-022-00728-4
DO - 10.1038/s41374-022-00728-4
M3 - SCORING: Journal article
C2 - 35091676
VL - 102
SP - 650
EP - 657
JO - LAB INVEST
JF - LAB INVEST
SN - 0023-6837
IS - 6
ER -