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01.12.2018 | Research article | Ausgabe 1/2018 Open Access

Breast Cancer Research 1/2018

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

Zeitschrift:
Breast Cancer Research > Ausgabe 1/2018
Autoren:
Jia Wu, Xuejie Li, Xiaodong Teng, Daniel L. Rubin, Sandy Napel, Bruce L. Daniel, Ruijiang Li
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s13058-018-1039-2) contains supplementary material, which is available to authorized users.

Abstract

Background

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.

Methods

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).

Results

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.

Conclusions

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.
Zusatzmaterial
Additional file 1: Figure S1. Flowcharts of detailed patient selection for both TCGA and I-SPY 1 trials in the proposed study. Figure S2. Pairwise Pearson’s correlation of 17 quantitative DCE-MRI features (see definition in Table  1). Figure S3. ROC curves corresponding to mutation burden, cytolytic activity, and proposed composite model for classification of low vs intermediate TIL groups. Figure S4. Predicted TIL values for I-SPY patients based on the composite model, stratified by ( a) three subtypes and ( b) recurrence status. (DOCX 600 kb)
Additional file 2: Table S1. Clinical and pathological characteristics for eligible patients in the I-SPY cohort. Table S2. Imaging features associated with tumor-infiltrating lymphocytes (TILs) with FDR < 0.2. Table S3. Mean and SD values for five quantitative imaging features. Table S4. Imaging features associated with nonsynonymous mutation burdens with FDR < 0.2. (DOCX 17 kb)
13058_2018_1039_MOESM2_ESM.docx
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