PortalPublikationenSemi-automated validation and quantification of CTLA-4 in 90...

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

Standard

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, Jahrgang 102, Nr. 6, 06.2022, S. 650-657.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Dum, D, Henke, TLC, Mandelkow, T, Yang, C, Bady, E, Raedler, JB, Simon, R, Sauter, G, Lennartz, M, Büscheck, F, Luebke, AM, Menz, A, Hinsch, A, Höflmayer, D, Weidemann, S, Fraune, C, Möller, K, Lebok, P, Uhlig, R, Bernreuther, C, Jacobsen, F, Clauditz, TS, Wilczak, W, Minner, S, Burandt, E, Steurer, S & Blessin, NC 2022, 'Semi-automated validation and quantification of CTLA-4 in 90 different tumor entities using multiple antibodies and artificial intelligence', LAB INVEST, Jg. 102, Nr. 6, S. 650-657. https://doi.org/10.1038/s41374-022-00728-4

APA

Dum, D., Henke, T. L. C., Mandelkow, T., Yang, C., Bady, E., Raedler, J. B., Simon, R., Sauter, G., Lennartz, M., Büscheck, F., Luebke, A. M., Menz, A., Hinsch, A., Höflmayer, D., Weidemann, S., Fraune, C., Möller, K., Lebok, P., Uhlig, R., ... Blessin, N. C. (2022). Semi-automated validation and quantification of CTLA-4 in 90 different tumor entities using multiple antibodies and artificial intelligence. LAB INVEST, 102(6), 650-657. https://doi.org/10.1038/s41374-022-00728-4

Vancouver

Dum D, Henke TLC, Mandelkow T, Yang C, Bady E, Raedler JB et al. Semi-automated validation and quantification of CTLA-4 in 90 different tumor entities using multiple antibodies and artificial intelligence. LAB INVEST. 2022 Jun;102(6):650-657. https://doi.org/10.1038/s41374-022-00728-4

Bibtex

@article{cf5701c6f14449b49fc79195bb4d33f6,
title = "Semi-automated validation and quantification of CTLA-4 in 90 different tumor entities using multiple antibodies and artificial intelligence",
abstract = "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.",
author = "David Dum and Henke, {Tjark L C} and Tim Mandelkow and Cheng Yang and Elena Bady and Raedler, {Jonas B} and Ronald Simon and Guido Sauter and Maximilian Lennartz and Franziska B{\"u}scheck and Luebke, {Andreas M} and Anne Menz and Andrea Hinsch and Doris H{\"o}flmayer and S{\"o}ren Weidemann and Christoph Fraune and Katharina M{\"o}ller and Patrick Lebok and Ria Uhlig and Christian Bernreuther and Frank Jacobsen and Clauditz, {Till S} and Waldemar Wilczak and Sarah Minner and Eike Burandt and Stefan Steurer and Blessin, {Niclas C}",
note = "{\textcopyright} 2022. The Author(s).",
year = "2022",
month = jun,
doi = "10.1038/s41374-022-00728-4",
language = "English",
volume = "102",
pages = "650--657",
journal = "LAB INVEST",
issn = "0023-6837",
publisher = "NATURE PUBLISHING GROUP",
number = "6",

}

RIS

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 -