B. Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.
Institute of Pathology, Tel-Aviv Sourasky Medical Center, Dafna 5, 6492601, Tel Aviv, Israel.
Breast Cancer Res Treat. 2022 Jul;194(2):297-305. doi: 10.1007/s10549-022-06630-3. Epub 2022 May 27.
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.
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.
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).
Our model can be used to estimate the risk of PD-L1 misclassification in biopsies, with potential implications for treatment decisions.
三阴性乳腺癌（TNBC）患者的分层治疗基于肿瘤活检中 PD-L1 的表达。本研究旨在评估 PD-L1 分类错误的风险。
我们对 10 例 TNBC 手术标本进行了高分辨率分析。首先，我们通过手动分割和测量 6666 个 PD-L1 免疫组化染色阳性的微观簇，确定 PD-L1 表达模式分布。然后，根据这些结果，我们生成了一个计算机模型，以计算阳性 PD-L1 分数、聚集大小和 PD-L1 阳性细胞分布对诊断准确性的影响。
我们的基于计算机的模型显示，较大的 PD-L1 阳性细胞聚集和较小的活检大小与更高的假结果分数相关（分别为 P<0.001，P<0.001）。此外，当 PD-L1 表达分数接近截止值时，我们的模型显示出显著增加的错误率（PD-L1 阳性率为 0.5-1.5%时，错误率为 12.1%、0.84%和 0.65%，阳性率为≤0.5%和阳性率为≥1.5%时，错误率分别为 0.01%和 0.01%，P<0.0001）。有趣的是，假阳性结果明显高于假阴性结果（0.51-22.62%，平均为 6.31%，而假阳性和假阴性结果的平均分别为 0.11-11.36%和 1.58%，P<0.05）。此外，与同质肿瘤相比，同一肿瘤中具有不同聚集大小的异质肿瘤与假结果率增加相关（P<0.001）。
我们的模型可用于估计活检中 PD-L1 分类错误的风险，对治疗决策具有潜在影响。
