Heterogeneous Enhancement Patterns of Tumor-adjacent Parenchyma at MR Imaging Are Associated with Dysregulated Signaling Pathways and Poor Survival in Breast Cancer

Abstract

Purpose To identify the molecular basis of quantitative imaging characteristics of tumor-adjacent parenchyma at dynamic contrast material-enhanced magnetic resonance (MR) imaging and to evaluate their prognostic value in breast cancer. Materials and Methods In this institutional review board-approved, HIPAA-compliant study, 10 quantitative imaging features depicting tumor-adjacent parenchymal enhancement patterns were extracted and screened for prognostic features in a discovery cohort of 60 patients. By using data from The Cancer Genome Atlas (TCGA), a radiogenomic map for the tumor-adjacent parenchymal tissue was created and molecular pathways associated with prognostic parenchymal imaging features were identified. Furthermore, a multigene signature of the parenchymal imaging feature was built in a training cohort (n = 126), and its prognostic relevance was evaluated in two independent cohorts (n = 879 and 159). Results One image feature measuring heterogeneity (ie, information measure of correlation) was significantly associated with prognosis (false-discovery rate < 0.1), and at a cutoff of 0.57 stratified patients into two groups with different recurrence-free survival rates (log-rank P = .024). The tumor necrosis factor signaling pathway was identified as the top enriched pathway (hypergeometric P < .0001) among genes associated with the image feature. A 73-gene signature based on the tumor profiles in TCGA achieved good association with the tumor-adjacent parenchymal image feature (R² = 0.873), which stratified patients into groups regarding recurrence-free survival (log-rank P = .029) and overall survival (log-rank P = .042) in an independent TCGA cohort. The prognostic value was confirmed in another independent cohort (Gene Expression Omnibus GSE 1456), with log-rank P = .00058 for recurrence-free survival and log-rank P = .0026 for overall survival. Conclusion Heterogeneous enhancement patterns of tumor-adjacent parenchyma at MR imaging are associated with the tumor necrosis signaling pathway and poor survival in breast cancer. ^© RSNA, 2017 Online supplemental material is available for this article.

Figure 1a:

(a) Flowchart of the overall design for the radiogenomic study, which involves three key steps. (b) Venn diagram of the TCGA data composition and four subgroups. (c) The acquisition protocol for tumor and parenchymal RNA sequencing (RNA-Seq) data in TCGA. The tumor-adjacent parenchyma is defined as the red region, where the yellow line is the tumor boundary and the blue dotted line is the dilated tumor boundary of 2 cm. DCE = dynamic contrast enhanced, GEO = Gene Expression Omnibus, OS = overall survival.

Figure 1b:

(a) Flowchart of the overall design for the radiogenomic study, which involves three key steps. (b) Venn diagram of the TCGA data composition and four subgroups. (c) The acquisition protocol for tumor and parenchymal RNA sequencing (RNA-Seq) data in TCGA. The tumor-adjacent parenchyma is defined as the red region, where the yellow line is the tumor boundary and the blue dotted line is the dilated tumor boundary of 2 cm. DCE = dynamic contrast enhanced, GEO = Gene Expression Omnibus, OS = overall survival.

Figure 1c:

(a) Flowchart of the overall design for the radiogenomic study, which involves three key steps. (b) Venn diagram of the TCGA data composition and four subgroups. (c) The acquisition protocol for tumor and parenchymal RNA sequencing (RNA-Seq) data in TCGA. The tumor-adjacent parenchyma is defined as the red region, where the yellow line is the tumor boundary and the blue dotted line is the dilated tumor boundary of 2 cm. DCE = dynamic contrast enhanced, GEO = Gene Expression Omnibus, OS = overall survival.

Figure 2a:

Images for the prognostic imaging biomarker discovery cohort (n = 60). (a) Graph shows prognostic performance for each of 10 quantitative tumor-adjacent parenchymal features from dynamic contrast-enhanced MR imaging. (b) Kaplan-Meier curves of RFS with the significantly prognostic (FDR < 0.1) quantitative imaging feature (information measure of correlation). (c) Pearson correlation matrix for 10 tumor-adjacent parenchymal imaging features, where four of them are uncorrelated. (d) Pearson correlation matrix for four uncorrelated tumor-adjacent parenchymal features and three tumor imaging features.

Figure 2b:

Images for the prognostic imaging biomarker discovery cohort (n = 60). (a) Graph shows prognostic performance for each of 10 quantitative tumor-adjacent parenchymal features from dynamic contrast-enhanced MR imaging. (b) Kaplan-Meier curves of RFS with the significantly prognostic (FDR < 0.1) quantitative imaging feature (information measure of correlation). (c) Pearson correlation matrix for 10 tumor-adjacent parenchymal imaging features, where four of them are uncorrelated. (d) Pearson correlation matrix for four uncorrelated tumor-adjacent parenchymal features and three tumor imaging features.

Figure 2c:

Images for the prognostic imaging biomarker discovery cohort (n = 60). (a) Graph shows prognostic performance for each of 10 quantitative tumor-adjacent parenchymal features from dynamic contrast-enhanced MR imaging. (b) Kaplan-Meier curves of RFS with the significantly prognostic (FDR < 0.1) quantitative imaging feature (information measure of correlation). (c) Pearson correlation matrix for 10 tumor-adjacent parenchymal imaging features, where four of them are uncorrelated. (d) Pearson correlation matrix for four uncorrelated tumor-adjacent parenchymal features and three tumor imaging features.

Figure 2d:

Images for the prognostic imaging biomarker discovery cohort (n = 60). (a) Graph shows prognostic performance for each of 10 quantitative tumor-adjacent parenchymal features from dynamic contrast-enhanced MR imaging. (b) Kaplan-Meier curves of RFS with the significantly prognostic (FDR < 0.1) quantitative imaging feature (information measure of correlation). (c) Pearson correlation matrix for 10 tumor-adjacent parenchymal imaging features, where four of them are uncorrelated. (d) Pearson correlation matrix for four uncorrelated tumor-adjacent parenchymal features and three tumor imaging features.

Figure 3a:

Images for the radiogenomic discovery cohort. (a) Radiogenomic map of correlation between gene modules and quantitative imaging features, both extracted from the tumor-adjacent parenchymal tissue. (b) Dendrogram shows the hierarchic clustering of gene modules and the prognostic imaging feature (information measure of correlation).

Figure 3b:

Images for the radiogenomic discovery cohort. (a) Radiogenomic map of correlation between gene modules and quantitative imaging features, both extracted from the tumor-adjacent parenchymal tissue. (b) Dendrogram shows the hierarchic clustering of gene modules and the prognostic imaging feature (information measure of correlation).

Figure 4a:

Kaplan-Meier curves of (a) RFS and (b) overall survival for the independent TCGA testing cohort (subgroup 4).

Figure 4b:

Kaplan-Meier curves of (a) RFS and (b) overall survival for the independent TCGA testing cohort (subgroup 4).

Figure 5a:

Images for the GSE 1456 cohort. (a) The details of patient stratification based on hierarchic clustering of the 63 measured tumor genes in the 73-gene signature, as well as (b, c) Kaplan-Meier curves of RFS (b) and overall survival (c).

Figure 5b:

Images for the GSE 1456 cohort. (a) The details of patient stratification based on hierarchic clustering of the 63 measured tumor genes in the 73-gene signature, as well as (b, c) Kaplan-Meier curves of RFS (b) and overall survival (c).

Figure 5c:

Images for the GSE 1456 cohort. (a) The details of patient stratification based on hierarchic clustering of the 63 measured tumor genes in the 73-gene signature, as well as (b, c) Kaplan-Meier curves of RFS (b) and overall survival (c).