TY - JOUR
T1 - Computer-assisted three-dimensional quantitation of programmed death-ligand 1 in non-small cell lung cancer using tissue clearing technology
AU - Lin, Yen Yu
AU - Wang, Lei Chi
AU - Hsieh, Yu Han
AU - Hung, Yu Ling
AU - Chen, Yung An
AU - Lin, Yu Chieh
AU - Lin, Yen Yin
AU - Chou, Teh Ying
N1 - Funding Information:
This work was supported by an industry-academia collaboration grant from JelloX Biotech Inc. (Grant Number R1900500), the Brain Research Center from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE); Ministry of Science and Technology (MOST) in Taiwan, and the “Cancer Progression Research Center, National Yang-Ming University” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE); Ministry of Health and Welfare, Taiwan [MOHW108-TDU-B-211–124019, MOHW109-TDU-B-211–134019].
Funding Information:
This work was supported by an industry-academia collaboration grant from JelloX Biotech Inc. Y.H.H., Y.L.H., Y.A.C., Y.C.L. and Yen-Yin Lin are employees of JelloX Biotech Inc.
Funding Information:
All samples for this study were obtained from the Biobank of Taipei Veterans General Hospital. The authors would like to acknowledge the support by the Biobank of Taipei Veterans General Hospital.
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - Immune checkpoint blockade therapy has revolutionized non-small cell lung cancer treatment. However, not all patients respond to this therapy. Assessing the tumor expression of immune checkpoint molecules, including programmed death-ligand 1 (PD-L1), is the current standard in predicting treatment response. However, the correlation between PD-L1 expression and anti-PD-1/PD-L1 treatment response is not perfect. This is partly caused by tumor heterogeneity and the common practice of assessing PD-L1 expression based on limited biopsy material. To overcome this problem, we developed a novel method that can make formalin-fixed, paraffin-embedded tissue translucent, allowing three-dimensional (3D) imaging. Our protocol can process tissues up to 150 μm in thickness, allowing anti-PD-L1 staining of the entire tissue and producing high resolution 3D images. Compared to a traditional 4 μm section, our 3D image provides 30 times more coverage of the specimen, assessing PD-L1 expression of approximately 10 times more cells. We further developed a computer-assisted PD-L1 quantitation method to analyze these images, and we found marked variation of PD-L1 expression in 3D. In 5 of 33 needle-biopsy-sized specimens (15.2%), the PD-L1 tumor proportion score (TPS) varied by greater than 10% at different depth levels. In 14 cases (42.4%), the TPS at different depth levels fell into different categories (< 1%, 1–49%, or ≥ 50%), which can potentially influence treatment decisions. Importantly, our technology permits recovery of the processed tissue for subsequent analysis, including histology examination, immunohistochemistry, and mutation analysis. In conclusion, our novel method has the potential to increase the accuracy of tumor PD-L1 expression assessment and enable precise deployment of cancer immunotherapy.
AB - Immune checkpoint blockade therapy has revolutionized non-small cell lung cancer treatment. However, not all patients respond to this therapy. Assessing the tumor expression of immune checkpoint molecules, including programmed death-ligand 1 (PD-L1), is the current standard in predicting treatment response. However, the correlation between PD-L1 expression and anti-PD-1/PD-L1 treatment response is not perfect. This is partly caused by tumor heterogeneity and the common practice of assessing PD-L1 expression based on limited biopsy material. To overcome this problem, we developed a novel method that can make formalin-fixed, paraffin-embedded tissue translucent, allowing three-dimensional (3D) imaging. Our protocol can process tissues up to 150 μm in thickness, allowing anti-PD-L1 staining of the entire tissue and producing high resolution 3D images. Compared to a traditional 4 μm section, our 3D image provides 30 times more coverage of the specimen, assessing PD-L1 expression of approximately 10 times more cells. We further developed a computer-assisted PD-L1 quantitation method to analyze these images, and we found marked variation of PD-L1 expression in 3D. In 5 of 33 needle-biopsy-sized specimens (15.2%), the PD-L1 tumor proportion score (TPS) varied by greater than 10% at different depth levels. In 14 cases (42.4%), the TPS at different depth levels fell into different categories (< 1%, 1–49%, or ≥ 50%), which can potentially influence treatment decisions. Importantly, our technology permits recovery of the processed tissue for subsequent analysis, including histology examination, immunohistochemistry, and mutation analysis. In conclusion, our novel method has the potential to increase the accuracy of tumor PD-L1 expression assessment and enable precise deployment of cancer immunotherapy.
KW - 3D imaging
KW - Artificial intelligence
KW - Immunotherapy
KW - Non-small cell lung cancer
KW - PD-L1
KW - Tissue clearing
UR - http://www.scopus.com/inward/record.url?scp=85126705808&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85126705808&partnerID=8YFLogxK
U2 - 10.1186/s12967-022-03335-5
DO - 10.1186/s12967-022-03335-5
M3 - Article
C2 - 35296339
AN - SCOPUS:85126705808
SN - 1479-5876
VL - 20
JO - Journal of Translational Medicine
JF - Journal of Translational Medicine
IS - 1
M1 - 131
ER -