Earle A. Chiles Research Institute, 4805 N.E. Glisan St., North Tower, Suite 2N87, Portland, OR, 97213, USA.
Providence Cancer Institute, Portland, OR, USA.
Breast Cancer Res. 2021 Jan 7;23(1):2. doi: 10.1186/s13058-020-01378-4.
The H&E stromal tumor-infiltrating lymphocyte (sTIL) score and programmed death ligand 1 (PD-L1) SP142 immunohistochemistry assay are prognostic and predictive in early-stage breast cancer, but are operator-dependent and may have insufficient precision to characterize dynamic changes in sTILs/PD-L1 in the context of clinical research. We illustrate how multiplex immunofluorescence (mIF) combined with statistical modeling can be used to precisely estimate dynamic changes in sTIL score, PD-L1 expression, and other immune variables from a single paraffin-embedded slide, thus enabling comprehensive characterization of activity of novel immunotherapy agents.
Serial tissue was obtained from a recent clinical trial evaluating loco-regional cytokine delivery as a strategy to promote immune cell infiltration and activation in breast tumors. Pre-treatment biopsies and post-treatment tumor resections were analyzed by mIF (PerkinElmer Vectra) using an antibody panel that characterized tumor cells (cytokeratin-positive), immune cells (CD3, CD8, CD163, FoxP3), and PD-L1 expression. mIF estimates of sTIL score and PD-L1 expression were compared to the H&E/SP142 clinical assays. Hierarchical linear modeling was utilized to compare pre- and post-treatment immune cell expression, account for correlation of time-dependent measurement, variation across high-powered magnification views within each subject, and variation between subjects. Simulation methods (Monte Carlo, bootstrapping) were used to evaluate the impact of model and tissue sample size on statistical power.
mIF estimates of sTIL and PD-L1 expression were strongly correlated with their respective clinical assays (p < .001). Hierarchical linear modeling resulted in more precise estimates of treatment-related increases in sTIL, PD-L1, and other metrics such as CD8+ tumor nest infiltration. Statistical precision was dependent on adequate tissue sampling, with at least 15 high-powered fields recommended per specimen. Compared to conventional t-testing of means, hierarchical linear modeling was associated with substantial reductions in enrollment size required (n = 25➔n = 13) to detect the observed increases in sTIL/PD-L1.
mIF is useful for quantifying treatment-related dynamic changes in sTILs/PD-L1 and is concordant with clinical assays, but with greater precision. Hierarchical linear modeling can mitigate the effects of intratumoral heterogeneity on immune cell count estimations, allowing for more efficient detection of treatment-related pharmocodynamic effects in the context of clinical trials.
NCT02950259 .
在早期乳腺癌中，苏木精和伊红（H&E）基质肿瘤浸润淋巴细胞（sTIL）评分和程序性死亡配体 1（PD-L1）SP142 免疫组化检测具有预后和预测价值，但依赖于操作者，并且可能不足以精确描述临床研究中 sTIL/PD-L1 的动态变化。我们举例说明了如何结合统计建模使用多重免疫荧光（mIF）来从单个石蜡包埋切片中精确估计 sTIL 评分、PD-L1 表达和其他免疫变量的动态变化，从而能够全面描述新型免疫治疗药物的活性。
最近一项临床试验评估了局部细胞因子递送作为促进乳腺癌肿瘤免疫细胞浸润和激活的策略，从该试验中获得了连续的组织标本。使用标记肿瘤细胞（细胞角蛋白阳性）、免疫细胞（CD3、CD8、CD163、FoxP3）和 PD-L1 表达的 mIF（PerkinElmer Vectra）抗体面板对预处理活检和治疗后肿瘤切除标本进行分析。mIF 估计的 sTIL 评分和 PD-L1 表达与 H&E/SP142 临床检测进行了比较。利用分层线性模型比较了预处理和治疗后免疫细胞的表达，解释了时间依赖性测量的相关性、每个受试者内高倍放大视野之间的变异性以及受试者之间的变异性。模拟方法（蒙特卡罗、自举法）用于评估模型和组织样本量对统计功效的影响。
mIF 估计的 sTIL 和 PD-L1 表达与各自的临床检测具有很强的相关性（p<0.001）。分层线性模型导致 sTIL、PD-L1 和其他指标（如 CD8+肿瘤巢浸润）的治疗相关增加的估计更为精确。统计精度取决于足够的组织采样，建议每个标本至少有 15 个高倍视野。与传统的均值 t 检验相比，分层线性模型与检测观察到的 sTIL/PD-L1 增加所需的入组人数显著减少（n=25➔n=13）。
mIF 可用于定量评估 sTILs/PD-L1 的治疗相关动态变化，与临床检测结果一致，但具有更高的精度。分层线性模型可以减轻肿瘤内异质性对免疫细胞计数估计的影响，从而在临床试验中更有效地检测治疗相关的药效学效应。
NCT02950259。
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