Clustering of Cardiac Transcriptome Profiles Reveals Unique: Subgroups of Dilated Cardiomyopathy Patients

Abstract

Dilated cardiomyopathy is a heterogeneous disease characterized by multiple genetic and environmental etiologies. The majority of patients are treated the same despite these differences. The cardiac transcriptome provides information on the patient's pathophysiology, which allows targeted therapy. Using clustering techniques on data from the genotype, phenotype, and cardiac transcriptome of patients with early- and end-stage dilated cardiomyopathy, more homogeneous patient subgroups are identified based on shared underlying pathophysiology. Distinct patient subgroups are identified based on differences in protein quality control, cardiac metabolism, cardiomyocyte function, and inflammatory pathways. The identified pathways have the potential to guide future treatment and individualize patient care.

Keywords: clustering; dilated cardiomyopathy; genetics; transcriptomics.

Graphical abstract

Figure 1

Clustering of Patients Based on Transcriptomic Profile (A) Dispersion plot of RNA sequencing data. The top 2,000 transcripts were selected as input for the clustering analysis based on expression in the number of samples, base mean reads, and the dispersion of the reads among samples. (B) Uniform manifold approximation and projection (UMAP) plot identified 4 clusters. (C) The association between genotypes and RNA-seq clusters. (D) UMAP plot of samples labeled with genotype.

Figure 2

Heatmap of Significant Differentially Expressed Genes per Cluster in Association With Genotype and Phenotype The enriched biological process GO items in each gene group were summarized using biological blocks identified in the clusters. Samples were annotated to genotypes and important clinical parameters.

Figure 3

Top GO Items of Each Cluster Grouped to Biological Functions Results are displayed per cluster and the comparison between the 2 superclusters.

Figure 4

Association Among Phenotype, Genotype, and Clusters Based on Cardiac Transcriptome (A) Over-representation analysis showing the most distinct clinical variables per cluster (v-test). Only significant variables with P values <0.05 are listed. (B) Details of significant variables by grouped bar plot, or violin plot + box plot for categorical variables and quantitative variables, respectively.

Figure 5

Event-Free Survival Stratified by Transcriptomic Cluster Kaplan-Meier curve for the combined outcome of life-threatening arrhythmias, cardiovascular death, heart transplantation, or heart-failure hospitalization stratified by transcriptomic cluster.

Figure 6

Clustering of Patients With End-Stage Dilated Cardiomyopathy Based on Transcriptomic Profile (A) UMAP plot identified 3 clusters. (B) UMAP plot of samples labeled by sex. (C) Heatmap of significant differentially expressed genes per cluster in association with phenotypic characteristics below. The enriched biological process GO items in each gene group were summarized using biological blocks identified in the clusters on the left. Abbreviation as in Figure 1.

Figure 7

Integration of the Cardiac Transcriptomic Profile in Association With the Genotype and Phenotype The cardiac transcriptome is unique per individual patient and is influenced by the genotype and phenotype of the patient. The transcriptome reflects the activity of (patho)physiological mechanisms, which possibly is reflected in biomarkers in the blood. Such biomarker profile could be used to help guide the individual patient's treatment in selecting the right medication and timing it at the best moment in the disease stage.