ArticleBreast cancer research and treatment2022
The risk of PD-L1 expression misclassification in triple-negative breast cancer.
Article in Breast cancer research and treatment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- CSF1R Inhibition with Chemotherapy Relieves Systemic Immune Suppression in Patients with Metastatic Triple-Negative Breast Cancer and Boosts Anti-PD-1 Efficacy in Transgenic Mammary Tumors.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Trial
- Early first-line systemic anticancer therapeutic attrition after metastatic recurrence in triple-negative breast cancer following neoadjuvant chemo-immunotherapy: a multicenter real-world study.Breast cancer research and treatment · 2026Article
- Programmed Death-Ligand 1 Expression in Triple-Negative Breast Cancer: Insights from a Mexican Cohort.Cancers · 2026Article
- Diagnostic biopsy does not accurately reflect the PD-L1 expression in triple-negative breast cancer.Clinical and experimental medicine · 2023Article
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Authors and funding
6 authors.
Funding
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Abstract
purposeStratification 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.
methodsWe 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.
resultsOur 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).
conclusionOur model can be used to estimate the risk of PD-L1 misclassification in biopsies, with potential implications for treatment decisions.
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