Pathway-based personalized analysis of breast cancer expression data

Abstract

Introduction: Most analyses of high throughput cancer data represent tumors by "atomistic" single-gene properties. Pathifier, a recently introduced method, characterizes a tumor in terms of "coarse grained" pathway-based variables.

Methods: We applied Pathifier to study a very large dataset of 2000 breast cancer samples and 144 normal tissues. Pathifier uses known gene assignments to pathways and biological processes to calculate for each pathway and tumor a Pathway Deregulation Score (PDS). Individual samples are represented in terms of their PDSs calculated for several hundred pathways, and the samples of the data set are analyzed and stratified on the basis of their profiles over these "coarse grained", biologically meaningful variables.

Results: We identified nine tumor subtypes; a new subclass (comprising about 7% of the samples) exhibits high deregulation in 38 PKA pathways, induced by overexpression of the gene PRKACB. Another interesting finding is that basal tumors break into two subclasses, with low and high deregulation of a cluster of immune system pathways. High deregulation corresponds to higher concentrations of Tumor Infiltrating Lymphocytes, and the patients of this basal subtype have better prognosis. The analysis used 1000 "discovery set" tumors; our results were highly reproducible on 1000 independent "validation" samples.

Conclusions: The coarse-grained variables that represent pathway deregulation provide a basis for relevant, novel and robust findings for breast cancer. Our analysis indicates that in breast cancer reliable prognostic signatures are most likely to be obtained by treating separately different subgroups of the patients.

Keywords: Breast cancer; PKA pathways; Pathway-based analysis.

Figure 1

Principal curves of selected pathways in the METABRIC dataset. The principal curve going through the cloud of points (in black) which are representing samples in the METABRIC discovery + normals set (A–C) or the validation + normals set (D). The data points and the principal curve are projected onto the three leading principal components. The samples (colored according to their PAM50 classification) are projected onto the curve. A. Principal curve of the BioCarta P53 pathway in the discovery dataset. B. Principal curve of the KEGG “Pathways in Cancer” gene set in the discovery dataset. C. Principal curve of the Biocarta CBL Pathway in the discovery dataset. D. Principal curve of the BioCarta P53 pathway in the validation dataset. The curve and points are plotted based on the scores derived using the recalculation method.

Figure 2

Clustering of Pathway Deregulation Scores matrix for the discovery dataset. Every row in the matrix corresponds to one of 552 pathways; every column to one of 1141 samples (either from the discovery set or a normal tissue sample). Each row is z‐score normalized. The color‐bars in the bottom indicate the PAM50 subtype of the sample (top), the ER marker status as measured by immunohistochemistry (middle), and the cluster it was assigned to (bottom).

Figure 3

Summary of the clustered PDS for the METABRIC datasets: A. Discovery dataset B. Validation dataset. Each row corresponds to a pathway cluster and each column – to a sample cluster, displaying the mean deregulation value for the samples and pathways that belong each pair of clusters.

Figure 4

Kaplan–Meier plots of overall survival across the PDS clusters, A. of the discovery dataset. Each PDS cluster is plotted separately. The two clusters of Basal tumors (7 and 8, bold) have significantly different survival (log rank p‐value = 0.013). B. for two extreme groups of validation patients with Basal tumors. Each group contains 70 patients. The groups are significantly different in terms of survival (log rank p‐value = 0.0565).

Figure 5

Gene expression of PRKACB as measured using microarrays and PCR. Twenty‐two tumor samples had gene expression measurements of both Illumina microarrays and RT‐qPCR (log base 2 of intensity of both measurements). Two different probes (F1R1, F1R2) were used in the PCR analysis; both are plotted against the microarray‐based data. Two points that represent the same sample have the same color. The top/rightmost 5 pairs of points of the plot (marked in red) are the 5 samples belonging to PDS Cluster 3.