Kinome expression profiling and prognosis of basal breast cancers

Abstract

Background: Basal breast cancers (BCs) represent ~15% of BCs. Although overall poor, prognosis is heterogeneous. Identification of good- versus poor-prognosis patients is difficult or impossible using the standard histoclinical features and the recently defined prognostic gene expression signatures (GES). Kinases are often activated or overexpressed in cancers, and constitute targets for successful therapies. We sought to define a prognostic model of basal BCs based on kinome expression profiling.

Methods: DNA microarray-based gene expression and histoclinical data of 2515 early BCs from thirteen datasets were collected. We searched for a kinome-based GES associated with disease-free survival (DFS) in basal BCs of the learning set using a metagene-based approach. The signature was then tested in basal tumors of the independent validation set.

Results: A total of 591 samples were basal. We identified a 28-kinase metagene associated with DFS in the learning set (N = 73). This metagene was associated with immune response and particularly cytotoxic T-cell response. On multivariate analysis, a metagene-based predictor outperformed the classical prognostic factors, both in the learning and the validation (N = 518) sets, independently of the lymphocyte infiltrate. In the validation set, patients whose tumors overexpressed the metagene had a 78% 5-year DFS versus 54% for other patients (p = 1.62E-4, log-rank test).

Conclusions: Based on kinome expression, we identified a predictor that separated basal BCs into two subgroups of different prognosis. Tumors associated with higher activation of cytotoxic tumor-infiltrative lymphocytes harbored a better prognosis. Such classification should help tailor the treatment and develop new therapies based on immune response manipulation.

Figure 1

Hierarchical clustering of basal breast cancer. (A) Unsupervised hierarchical clustering of 73 non-metastatic non-inflammatory basal BCs from IPC with 360 genes coding for kinase or kinase-interacting proteins overexpressed in basal tumors. Each row represents a gene and each column a sample. The expression level of each gene in each sample is relative to its median abundance across the samples and is depicted according to the color scale shown under the matrix. Red and green indicate expression levels respectively above and below the median. Relapses are indicated in the stripe under the dendrogram: white for no relapse during follow-up, and grey for relapse. Two tumor clusters (I and II) are delineated by the vertical green line. To the right, vertical colored bars indicate the three clusters identified by the QT clustering method: purple, immune-related cluster; green, biologically unspecific cluster; red, proliferation-related cluster. (B) Kaplan-Meier disease-free survival curves for cluster I patients (n = 24), and cluster II patients (n = 49).

Figure 2

Disease-free survival and basal subgroups in the learning set. Kaplan-Meier disease-free survival curves of basal BC patients in the IPC series according to the subgroups "Immune-High" (n = 25) and "Immune-Low" (n = 48).

Figure 3

Disease-free survival and basal subgroups in the validation set. Kaplan-Meier disease-free survival curves of basal BC patients in the independent validation series according to the subgroups "Immune-High" and "Immune-Low". (A) in all patients (95 versus 285 patients respectively), and (B) in patients having received no systemic adjuvant therapy (39 versus 148 patients respectively).