Department of Radiation Oncology, Stanford University School of Medicine, 1070 Arastradero Road, Stanford, CA, 94305, USA.
Department of Pathology, First Affiliated Hospital of Zhejiang University, Hangzhou, 310058, Zhejiang, China.
Breast Cancer Res. 2018 Sep 3;20(1):101. doi: 10.1186/s13058-018-1039-2.
We sought to investigate associations between dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) features and tumor-infiltrating lymphocytes (TILs) in breast cancer, as well as to study if MRI features are complementary to molecular markers of TILs.
In this retrospective study, we extracted 17 computational DCE-MRI features to characterize tumor and parenchyma in The Cancer Genome Atlas cohort (n = 126). The percentage of stromal TILs was evaluated on H&E-stained histological whole-tumor sections. We first evaluated associations between individual imaging features and TILs. Multiple-hypothesis testing was corrected by the Benjamini-Hochberg method using false discovery rate (FDR). Second, we implemented LASSO (least absolute shrinkage and selection operator) and linear regression nested with tenfold cross-validation to develop an imaging signature for TILs. Next, we built a composite prediction model for TILs by combining imaging signature with molecular features. Finally, we tested the prognostic significance of the TIL model in an independent cohort (I-SPY 1; n = 106).
Four imaging features were significantly associated with TILs (P < 0.05 and FDR < 0.2), including tumor volume, cluster shade of signal enhancement ratio (SER), mean SER of tumor-surrounding background parenchymal enhancement (BPE), and proportion of BPE. Among molecular and clinicopathological factors, only cytolytic score was correlated with TILs (ρ = 0.51; 95% CI, 0.36-0.63; P = 1.6E-9). An imaging signature that linearly combines five features showed correlation with TILs (ρ = 0.40; 95% CI, 0.24-0.54; P = 4.2E-6). A composite model combining the imaging signature and cytolytic score improved correlation with TILs (ρ = 0.62; 95% CI, 0.50-0.72; P = 9.7E-15). The composite model successfully distinguished low vs high, intermediate vs high, and low vs intermediate TIL groups, with AUCs of 0.94, 0.76, and 0.79, respectively. During validation (I-SPY 1), the predicted TILs from the imaging signature separated patients into two groups with distinct recurrence-free survival (RFS), with log-rank P = 0.042 among triple-negative breast cancer (TNBC). The composite model further improved stratification of patients with distinct RFS (log-rank P = 0.0008), where TNBC with no/minimal TILs had a worse prognosis.
Specific MRI features of tumor and parenchyma are associated with TILs in breast cancer, and imaging may play an important role in the evaluation of TILs by providing key complementary information in equivocal cases or situations that are prone to sampling bias.
本研究旨在探讨动态对比增强（DCE）磁共振成像（MRI）特征与乳腺癌肿瘤浸润淋巴细胞（TILs）之间的关联，并研究 MRI 特征是否与 TILs 的分子标志物互补。
本回顾性研究从癌症基因组图谱（TCGA）队列（n=126）中提取了 17 种计算 DCE-MRI 特征来描述肿瘤和实质。在 H&E 染色的全肿瘤切片上评估间质 TILs 的百分比。我们首先评估了个体影像学特征与 TILs 的相关性。采用错误发现率（FDR）对多重假设检验进行 Benjamini-Hochberg 校正。其次，我们采用 LASSO（最小绝对收缩和选择算子）和嵌套十折交叉验证的线性回归来开发 TILs 的影像学特征。接下来，我们通过结合影像学特征和分子特征构建了 TILs 的复合预测模型。最后，我们在独立队列（I-SPY 1；n=106）中测试了 TIL 模型的预后意义。
四个影像学特征与 TILs 显著相关（P<0.05， FDR<0.2），包括肿瘤体积、信号增强比值（SER）簇的色调、肿瘤周围背景实质增强（BPE）的平均 SER 和 BPE 比例。在分子和临床病理因素中，只有细胞毒性评分与 TILs 相关（ρ=0.51；95%CI，0.36-0.63；P=1.6E-9）。一个线性组合五个特征的影像学特征与 TILs 相关（ρ=0.40；95%CI，0.24-0.54；P=4.2E-6）。结合影像学特征和细胞毒性评分的复合模型改善了与 TILs 的相关性（ρ=0.62；95%CI，0.50-0.72；P=9.7E-15）。该复合模型成功区分了低 vs 高、中 vs 高和低 vs 中 TIL 组，AUC 分别为 0.94、0.76 和 0.79。在验证（I-SPY 1）中，来自影像学特征的预测 TILs 将患者分为两组，两组患者的无复发生存（RFS）有明显差异，三阴乳腺癌（TNBC）患者的对数秩 P=0.042。复合模型进一步改善了具有明显 RFS 的患者的分层（对数秩 P=0.0008），其中 TILs 无/最小的 TNBC 患者预后更差。
乳腺癌肿瘤和实质的特定 MRI 特征与 TILs 相关，影像学可能通过在有疑问的情况下或在容易发生取样偏倚的情况下提供关键的补充信息，在 TILs 的评估中发挥重要作用。
