Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer

Abstract

BackgroundWe 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.MethodsIn 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).ResultsFour 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.ConclusionsSpecific 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.Electronic supplementary materialThe online version of this article (10.1186/s13058-018-1039-2) contains supplementary material, which is available to authorized users.

Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy forHER2 (ERBB2)–Positive Breast Cancer

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Correlation between magnetic resonance imaging and the level of tumor-infiltrating lymphocytes in patients with estrogen receptor-negative HER2-positive breast cancer

“…Wu et al. extracted 17 computational DCE-MRI features and showed significant association with TIL levels in breast cancer (30). In this study, four imaging features were significantly associated with TILS, including tumor volume, cluster shade of signal enhancement ratio (SER), mean SER of tumor-surrounding BPE, and proportion of BPE.…”

Radiogenomic signatures reveal multiscale intratumour heterogeneity associated with biological functions and survival in breast cancer

“…DCE-MRI data were normalized and resized 63 . Specifically, pixel values were normalized by the mean of the interquartile values of the gland tissue in the precontrast MR image, ensuring that the signals inside the tumour and from the parenchymal region were normalized comparably.…”