The risk of PD-L1 expression misclassification in triple-negative breast cancer.

PURPOSE: Stratification of patients with triple-negative breast cancer (TNBC) for anti-PD-L1 therapy is based on PD-L1 expression in tumor biopsies. This study sought to evaluate the risk of PD-L1 misclassification. METHODS: We conducted a high-resolution analysis on ten surgical specimens of TNBC. First, we determined PD-L1 expression pattern distribution via manual segmentation and measurement of 6666 microscopic clusters of positive PD-L1 immunohistochemical staining. Then, based on these results, we generated a computer model to calculate the effect of the positive PD-L1 fraction, aggregate size, and distribution of PD-L1 positive cells on the diagnostic accuracy. RESULTS: Our computer-based model showed that larger aggregates of PD-L1 positive cells and smaller biopsy size were associated with higher fraction of false results (P < 0.001, P < 0.001, respectively). Additionally, our model showed a significant increase in error rate when the fraction of PD-L1 expression was close to the cut-off (error rate of 12.1%, 0.84%, and 0.65% for PD-L1 positivity of 0.5-1.5%, ≤ 0.5% ,and ≥ 1.5%, respectively, P < 0.0001). Interestingly, false positive results were significantly higher than false negative results (0.51-22.62%, with an average of 6.31% versus 0.11-11.36% with an average of 1.58% for false positive and false negative results, respectively, P < 0.05). Furthermore, heterogeneous tumors with different aggregate sizes in the same tumor, were associated with increased rate of false results in comparison to homogenous tumors (P < 0.001). CONCLUSION: Our model can be used to estimate the risk of PD-L1 misclassification in biopsies, with potential implications for treatment decisions.

Spatial heterogeneity of PD-L1 expression and the risk for misclassification of PD-L1 immunohistochemistry in non-small cell lung cancer.

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.