Integrated molecular profiles of invasive breast tumors and ductal carcinoma in situ (DCIS) reveal differential vascular and interleukin signaling

Abstract

We use an integrated approach to understand breast cancer heterogeneity by modeling mRNA, copy number alterations, microRNAs, and methylation in a pathway context utilizing the pathway recognition algorithm using data integration on genomic models (PARADIGM). We demonstrate that combining mRNA expression and DNA copy number classified the patients in groups that provide the best predictive value with respect to prognosis and identified key molecular and stromal signatures. A chronic inflammatory signature, which promotes the development and/or progression of various epithelial tumors, is uniformly present in all breast cancers. We further demonstrate that within the adaptive immune lineage, the strongest predictor of good outcome is the acquisition of a gene signature that favors a high T-helper 1 (Th1)/cytotoxic T-lymphocyte response at the expense of Th2-driven humoral immunity. Patients who have breast cancer with a basal HER2-negative molecular profile (PDGM2) are characterized by high expression of protumorigenic Th2/humoral-related genes (24-38%) and a low Th1/Th2 ratio. The luminal molecular subtypes are again differentiated by low or high FOXM1 and ERBB4 signaling. We show that the interleukin signaling profiles observed in invasive cancers are absent or weakly expressed in healthy tissue but already prominent in ductal carcinoma in situ, together with ECM and cell-cell adhesion regulating pathways. The most prominent difference between low and high mammographic density in healthy breast tissue by PARADIGM was that of STAT4 signaling. In conclusion, by means of a pathway-based modeling methodology (PARADIGM) integrating different layers of molecular data from whole-tumor samples, we demonstrate that we can stratify immune signatures that predict patient survival.

Fig. 1.

Distribution of identified PARADIGM clusters and survival in the discovery (MicMa) dataset. (A) Each bar represents the size of each cluster. (B) Survival curves of the MicMa PARADIGM clusters after mapping to the Chin dataset.

Fig. 2.

(A) High Th1/Th2 ratio distinguishes a good outcome in the PDGM clusters. The top 500 genes within each cluster (PDGM1–5) were classified based on their biological function. The percentage of immune and nonimmune genes in each cluster is shown within the discovery and validation datasets. (B) Immune genes identified in A were divided into functional groups: (i) Th1/CTL/NK cell, (ii) Th2/humoral immunity, and (iii) innate/inflammatory. The percentage of genes in each group, both within the discovery and validation datasets, is shown. The Th1/Th2 ratio for each dataset is also represented.

Fig. 3.

Normal to cancer. Heat maps of PARADIGM integrated pathway levels (IPLs) for each dataset. Normal breast, low mammographic density (A); normal breast, high mammographic density (B); DCIS (C); and invasive breast cancer (D; UPPSALA cohort). Each row shows the IPL of a gene or complex across all three cohorts. The colored bar across the top shows the MicMa-derived PARADIGM clusters, as in Fig. 1. Members of pathways of interest are labeled by their pathway. Red represents an activated IPL, and blue represents a deactivated IPL.

Fig. 4.

Validation datasets. Heat maps of PARADIGM integrated pathway levels (IPLs) for each dataset. Discovery dataset (A; MicMa), validation set 1 (B; Chin), and validation set 2 (C; UNC). Each row shows the IPL of a gene or complex across all three cohorts. The colored bar across the top shows the MicMa-derived PARADIGM clusters, as in Fig. 1. Members of pathways of interest are labeled by their pathway. Red represents an activated IPL, and blue represents a deactivated IPL. (D) Under each heat map, a silhouette plot illustrates the ratio between distance to centroid of belonging cluster vs. distance to all other members. (E) Survival curves.