A novel approach to triple-negative breast cancer molecular classification reveals a luminal immune-positive subgroup with good prognoses

Abstract

Triple-negative breast cancer is a heterogeneous disease characterized by a lack of hormonal receptors and HER2 overexpression. It is the only breast cancer subgroup that does not benefit from targeted therapies, and its prognosis is poor. Several studies have developed specific molecular classifications for triple-negative breast cancer. However, these molecular subtypes have had little impact in the clinical setting. Gene expression data and clinical information from 494 triple-negative breast tumors were obtained from public databases. First, a probabilistic graphical model approach to associate gene expression profiles was performed. Then, sparse k-means was used to establish a new molecular classification. Results were then verified in a second database including 153 triple-negative breast tumors treated with neoadjuvant chemotherapy. Clinical and gene expression data from 494 triple-negative breast tumors were analyzed. Tumors in the dataset were divided into four subgroups (luminal-androgen receptor expressing, basal, claudin-low and claudin-high), using the cancer stem cell hypothesis as reference. These four subgroups were defined and characterized through hierarchical clustering and probabilistic graphical models and compared with previously defined classifications. In addition, two subgroups related to immune activity were defined. This immune activity showed prognostic value in the whole cohort and in the luminal subgroup. The claudin-high subgroup showed poor response to neoadjuvant chemotherapy. Through a novel analytical approach we proved that there are at least two independent sources of biological information: cellular and immune. Thus, we developed two different and overlapping triple-negative breast cancer classifications and showed that the luminal immune-positive subgroup had better prognoses than the luminal immune-negative. Finally, this work paves the way for using the defined classifications as predictive features in the neoadjuvant scenario.

Figure 1

PGM resulting network; each functional node is encoded from 0 to 26. Each box (node) represents one gene, and lines (edges) connect genes with related expression. Functional nodes are represented by the same color, and metanodes are presented the same color palette, with basal nodes in red, luminal nodes in blue and immune nodes in green.

Figure 2

PGM represents the resulting network in which each functional node is encoded from 0 to 26, each box (node) represents one gene and lines (edges) connect genes with related expression. Genes from Rody’s metagenes are represented by different colors.

Figure 3

Workflow from the sparse k-means groups in each metanode to the final cellular classification.

Figure 4

Kaplan-Meier survival curves represent the survival rate of immune-positive and immune-negative tumors in the whole cohort (A) and in the four cellular subgroups (B).

Figure 5

Various molecular classifications compared with the cellular classification. From top to bottom, cellular, PAM50 + CLDN-low, Lehmann 2016 TNBC4 type, immune and Burstein’s classifications are presented.

Figure 6

Kaplan-Meier survival curves represent the survival rate of immune-positive and immune-negative tumors in the TNBC4-type subgroups.

Figure 7

Kaplan-Meier survival curves represent the survival rate of immune-positive and immune-negative tumors in the PAM50 + CLDN-low subgroups.

Figure 8

Kaplan-Meier survival curves represent the survival rate of immune-positive and immune-negative tumors in the Burstein’s subgroups.

Figure 9

Kaplan–Meier survival curves represent the distant relapse-free survival rate of the cellular and the TNBC4-type subgroups in the GSE25066 series.