Shani Ben Dori1, Asaf Aizic2, Edmond Sabo3, Dov Hershkovitz4. 1. B. Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Israel. 2. Institute of Pathology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel. 3. B. Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Israel; Institute of Pathology, Carmel Medical Center, Haifa, Israel. 4. Institute of Pathology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel. Electronic address: dovh@tlvmc.gov.il.
Abstract
BACKGROUND: Intra-tumor heterogeneity for PD-L1 expression in non-small cell lung cancer (NSCLC) might lead to inaccurate stratification of patients to immunotherapy. The purpose of this research was to quantitate the effect of different factors on the risk of inaccurate diagnosis of PD-L1 expression. METHODS: MATLAB software was used to model tumor with a different fraction, distribution and clustering of PD-L1 protein expression and their effect on false positive and negative diagnosis in subsets of the modeled tumor (representing biopsies). Additionally, we evaluated the agreement between PD-L1 status in random segments and whole slides of PD-L1 stained clinical NSCLC cases. RESULTS: Our computer-based model showed a significant increase in error rate when the fraction of PD-L1 positive cells was closer to the cut-off value (error rate of 33.33 %, 0.45 % and 0.74 % for PD-L1 positivity in 40-60%, ≤20 % and ≥80 % of tumor cells, respectively, P < 0.0001). In addition, biopsy size showed negative correlation with error rate (P < 0.0001) and larger clusters of PD-L1 positive cells were associated with higher error rate (P < 0.0001). Analysis of the clinical samples supported those of the computer-based model with higher error rate in cases with positive PD-L1 expression closer to the cutoff value. Based on our computerized model and clinical analysis, we developed a model to predict error rate based on biopsy size and the fraction of PD-L1 positive cells in the biopsy. CONCLUSION: Analysis of small biopsies for PD-L1 expression might be associated with significant error rate. The model presented can be used to identify cases with increased risk for error in whom interpretation of the test results should be made with caution.
BACKGROUND: Intra-tumor heterogeneity for PD-L1 expression in non-small cell lung cancer (NSCLC) might lead to inaccurate stratification of patients to immunotherapy. The purpose of this research was to quantitate the effect of different factors on the risk of inaccurate diagnosis of PD-L1 expression. METHODS: MATLAB software was used to model tumor with a different fraction, distribution and clustering of PD-L1 protein expression and their effect on false positive and negative diagnosis in subsets of the modeled tumor (representing biopsies). Additionally, we evaluated the agreement between PD-L1 status in random segments and whole slides of PD-L1 stained clinical NSCLC cases. RESULTS: Our computer-based model showed a significant increase in error rate when the fraction of PD-L1 positive cells was closer to the cut-off value (error rate of 33.33 %, 0.45 % and 0.74 % for PD-L1 positivity in 40-60%, ≤20 % and ≥80 % of tumor cells, respectively, P < 0.0001). In addition, biopsy size showed negative correlation with error rate (P < 0.0001) and larger clusters of PD-L1 positive cells were associated with higher error rate (P < 0.0001). Analysis of the clinical samples supported those of the computer-based model with higher error rate in cases with positive PD-L1 expression closer to the cutoff value. Based on our computerized model and clinical analysis, we developed a model to predict error rate based on biopsy size and the fraction of PD-L1 positive cells in the biopsy. CONCLUSION: Analysis of small biopsies for PD-L1 expression might be associated with significant error rate. The model presented can be used to identify cases with increased risk for error in whom interpretation of the test results should be made with caution.
Authors: Deborah Blythe Doroshow; Sheena Bhalla; Mary Beth Beasley; Lynette M Sholl; Keith M Kerr; Sacha Gnjatic; Ignacio I Wistuba; David L Rimm; Ming Sound Tsao; Fred R Hirsch Journal: Nat Rev Clin Oncol Date: 2021-02-12 Impact factor: 66.675
Authors: Shani Ben Dori; Asaf Aizic; Asia Zubkov; Shlomo Tsuriel; Edmond Sabo; Dov Hershkovitz Journal: Breast Cancer Res Treat Date: 2022-05-27 Impact factor: 4.624