Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer.
Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer.
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DOI：
10.1186/s13058-018-1039-2
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发表时间：
2018-09-03
期刊：
影响因子：
--
通讯作者：
Li R
中科院分区：
文献类型：
--
作者：
Wu J;Li X;Teng X;Rubin DL;Napel S;Daniel BL;Li R
我们试图研究乳腺癌动态对比增强（DCE）磁共振成像（MRI）特征与肿瘤浸润淋巴细胞（TILs）之间的相关性，并研究MRI特征是否与TILs的分子标志物互补。在这项回顾性研究中，我们提取了17个计算DCE-MRI特征来表征癌症基因组图谱队列（n = 126）中的肿瘤和实质。在H& E染色的组织学全肿瘤切片上评估基质TIL的百分比。我们首先评估了个体影像学特征与TIL之间的相关性。多假设检验采用Benjamini-Hochberg方法，使用错误发现率（FDR）进行校正。其次，我们实施了LASSO（最小绝对收缩和选择算子）和线性回归嵌套十倍交叉验证，以开发TILs的成像特征。接下来，我们通过结合成像特征和分子特征建立了TILs的复合预测模型。最后，我们在一个独立的队列（I-SPY 1; n = 106）中测试了TIL模型的预后意义。肿瘤体积、信号增强比（SER）的簇状阴影、肿瘤周围背景增强（BPE）的平均SER和BPE的比例与TILs显著相关（P < 0.05和FDR < 0.2）。在分子和临床病理因素中，只有细胞溶解评分与TIL相关（ρ = 0.51; 95%CI，0.36-0.63; P = 1.6E-9）。线性组合五个特征的成像特征显示与TILs相关（ρ = 0.40; 95%CI，0.24-0.54; P = 4.2E-6）。结合成像特征和细胞溶解评分的复合模型改善了与TIL的相关性（ρ = 0.62; 95% CI，0.50-0.72; P = 9.7E-15）。复合模型成功区分了低与高、中间与高以及低与中间TIL组，AUC分别为0.94、0.76和0.79。在验证期间（I-SPY 1），来自成像特征的预测TIL将患者分为两组，具有不同的无复发生存期（RFS），三阴性乳腺癌（TNBC）中的对数秩P = 0.042。复合模型进一步改善了具有不同RFS的患者的分层（对数秩P = 0.0008），其中没有/最小TIL的TNBC预后较差。肿瘤和实质的特定MRI特征与乳腺癌中的TILs相关，并且成像可以在TILs的评价中发挥重要作用，在模棱两可的病例或易于发生采样偏倚的情况下提供关键的补充信息。本文的在线版本（10.1186/s13058-018-1039-2）包含补充材料，可供授权用户使用。
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. The online version of this article (10.1186/s13058-018-1039-2) contains supplementary material, which is available to authorized users.
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影响因子：
3.8
作者：
Esserman, Laura J.;Berry, Donald A.;Cheang, Maggie C. U.;Yau, Christina;Perou, Charles M.;Carey, Lisa;DeMichele, Angela;Gray, Joe W.;Conway-Dorsey, Kathleen;Lenburg, Marc E.;Buxton, Meredith B.;Davis, Sarah E.;van't Veer, Laura J.;Hudis, Clifford;Chin, Koei;Wolf, Denise;Krontiras, Helen;Montgomery, Leslie;Tripathy, Debu;Lehman, Constance;Liu, Minetta C.;Olopade, Olufunmilayo I.;Rugo, Hope S.;Carpenter, John T.;Livasy, Chad;Dressler, Lynn;Chhieng, David;Singh, Baljit;Mies, Carolyn;Rabban, Joseph;Chen, Yunni-Yi;Giri, Dilip;Au, Alfred;Hylton, Nola
通讯作者：
Hylton, Nola
DOI：
10.1186/s13058-017-0846-1
发表时间：
2017-05-18
期刊：
Breast cancer research : BCR
影响因子：
--
作者：
Braman NM;Etesami M;Prasanna P;Dubchuk C;Gilmore H;Tiwari P;Plecha D;Madabhushi A
通讯作者：
Madabhushi A
影响因子：
5
作者：
Hattangadi, Jona;Park, Catherine;Hylton, Nola
通讯作者：
Hylton, Nola
影响因子：
4.6
作者：
Khan, Adnan Mujahid;Yuan, Yinyin
通讯作者：
Yuan, Yinyin
影响因子：
45.3
作者：
Adams, Sylvia;Gray, Robert J.;Badve, Sunil S.
通讯作者：
Badve, Sunil S.