BMC Cancer (Jan 2024)

A novel computer-assisted tool for 3D imaging of programmed death-ligand 1 expression in immunofluorescence-stained and optically cleared breast cancer specimens

  • Yi-Hsuan Lee,
  • Chung-Yen Huang,
  • Yu-Han Hsieh,
  • Chia-Hung Yang,
  • Yu-Ling Hung,
  • Yung-An Chen,
  • Yu-Chieh Lin,
  • Ching-Hung Lin,
  • Jih-Hsiang Lee,
  • Ming-Yang Wang,
  • Wen-Hung Kuo,
  • Yen-Yin Lin,
  • Yen-Shen Lu

DOI
https://doi.org/10.1186/s12885-023-11748-8
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 11

Abstract

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Abstract Background Programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1) are the two most common immune checkpoints targeted in triple-negative breast cancer (BC). Refining patient selection for immunotherapy is non-trivial and finding an appropriate digital pathology framework for spatial analysis of theranostic biomarkers for PD-1/PD-L1 inhibitors remains an unmet clinical need. Methods We describe a novel computer-assisted tool for three-dimensional (3D) imaging of PD-L1 expression in immunofluorescence-stained and optically cleared BC specimens (n = 20). The proposed 3D framework appeared to be feasible and showed a high overall agreement with traditional, clinical-grade two-dimensional (2D) staining techniques. Additionally, the results obtained for automated immune cell detection and analysis of PD-L1 expression were satisfactory. Results The spatial distribution of PD-L1 expression was heterogeneous across various BC tissue layers in the 3D space. Notably, there were six cases (30%) wherein PD-L1 expression levels along different layers crossed the 1% threshold for admitting patients to PD-1/PD-L1 inhibitors. The average PD-L1 expression in 3D space was different from that of traditional immunohistochemistry (IHC) in eight cases (40%). Pending further standardization and optimization, we expect that our technology will become a valuable addition for assessing PD-L1 expression in patients with BC. Conclusion Via a single round of immunofluorescence imaging, our approach may provide a considerable improvement in patient stratification for cancer immunotherapy as compared with standard techniques.

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