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

Area of Science:

• Immunology
• Oncology
• Computational Pathology

Background:

• Cytotoxic T-Lymphocyte Associated protein 4 (CTLA-4) is a key inhibitory immune checkpoint receptor regulating anti-tumor T-cell function.
• Understanding CTLA-4 expression patterns in various tumor types is crucial for immunotherapy development.

Purpose of the Study:

• To conduct a comparative analysis of CTLA-4 expressing cells across a wide spectrum of tumor entities.
• To validate and compare two distinct antibody clones for CTLA-4 quantification.
• To assess the correlation of CTLA-4+ cell density with clinicopathological features and PD-L1 expression.

Main Methods:

• Immunohistochemistry on a large tissue microarray cohort (4582 tumor samples, 90 entities; 608 normal samples, 76 entities).
• Validation of two CTLA-4 antibody clones (MSVA-152R and CAL49).
• Deep learning framework for automated exclusion of non-specific immunostaining and quantification of CTLA-4+ cells.

Main Results:

• Automated exclusion of non-specific staining (3.6%) enabled reliable quantification.
• Strong correlation (r=0.87, p<0.0001) between CTLA-4+ lymphocyte densities obtained by both antibodies.
• High CTLA-4+ cell density associated with lower pT category, absence of lymph node metastases, and PD-L1 expression (p<0.0001).
• High CTLA-4/CD3 ratio linked to absent lymph node metastases and PD-L1 positivity on immune cells.

Conclusions:

• Significant variations in CTLA-4+ lymphocyte counts exist among different tumor types.
• Deep learning facilitates automated and reliable quantification of CTLA-4 expression using multiple antibodies.
• CTLA-4 expression patterns provide valuable prognostic information and correlate with PD-L1 status.