Diagnostic and prognostic implications of ribosomal protein transcript expression patterns in human cancers

Abstract

Background: Ribosomes, the organelles responsible for the translation of mRNA, are comprised of four rRNAs and ~ 80 ribosomal proteins (RPs). Although canonically assumed to be maintained in equivalent proportions, some RPs have been shown to possess differential expression across tissue types. Dysregulation of RP expression occurs in a variety of human diseases, notably in many cancers, and altered expression of some RPs correlates with different tumor phenotypes and patient survival. Little work has been done, however, to characterize overall patterns of RP transcript (RPT) expression in human cancers.

Methods: To investigate the impact of global RPT expression patterns on tumor phenotypes, we analyzed RPT expression of ~ 10,000 human tumors and over 700 normal tissues from The Cancer Genome Atlas (TCGA) using t-distributed stochastic neighbor embedding (t-SNE). Clusters of tumors identified by t-SNE were then analyzed with chi-squared and t-tests to compare phenotypic data, ANOVA to compare individual RPT expression, and Kaplan-Meier curves to assess survival differences.

Results: Normal tissues and cancers possess distinct and readily discernible RPT expression patterns that are independent of their absolute levels of expression. In tumors, RPT patterning is distinct from that of normal tissues, identifies heretofore unrecognized tumor subtypes, and in many cases correlates with molecular, pathological, and clinical features, including survival.

Conclusions: RPT expression patterns are both tissue-specific and tumor-specific. These could be used as a powerful and novel method of tumor classification, offering a potential clinical tool for prognosis and therapeutic stratification.

Keywords: 5q- syndrome; Diamond-Blackfan anemia; Ribosomopathy; Shwachman-diamond syndrome; t-SNE.

Fig. 1

t-SNE better identifies clusters of RPT expression than PCA. a. Relative expression of RPTs in normal tissues from five cohorts was analyzed with PCA. In both methods, clustering occurs when samples possess similar underlying patterns of variation. t-SNE provides more distinct clusters that better associate with tissue of origin, indicating that normal tissues have distinct patterns of RPT expression. Axes are not labeled with t-SNE, as points are not mapped linearly and axes are not directly interpretable. b. Similar analyses in tumors. PCA clusters are poorly defined and do not correlate strongly with tumor type. t-SNE clusters are distinct and strongly associate with cancer type, indicating that tumors possess unique patterns of RPT expression based on their tissue of origin. c. Combined t-SNE analysis of RPT expression in normal tissue and tumor samples. Normal tissues and tumors cluster together but can be distinguished from one another, indicating that the latter retain a pattern of RPT expression resembling that of the normal tissue from which they originated. d. Many single cancer cohorts demonstrate sub-clustering by t-SNE. Clustering of six cohorts are provided as examples here. The number of clusters found in each cohort is listed in Additional file 1: Table S1. e. 3D area map of RPT relative expression in tumors from two cancer cohorts, sorted by cluster. The x-axis represents individual tumors, the z-axis represents individual RPTs, and the y-axis represents deviation from the mean relative expression. Cluster 2 of prostate cancer and Cluster 3 of HCC are both comprised of tumors with high relative expression of RPL8 and low RPL3. See Additional file 1: Figure S1, S2, and S5 for additional t-SNE plots of tumors and normal tissues. Perplexity settings for t-SNE analyses are designated in each plot by “P:”. For all analyses, learning rate (epsilon) = 10 and iterations = 5000

Fig. 2

Volcano plots of relative RPT expression in tumor clusters in twelve cancer cohorts. Relative expression of RPTs was compared between tumor clusters in each included cancer cohort with ANOVA tests. The negative log of the ANOVA P-value for each RPT is displayed on the y-axis and the difference in relative expression across tumor clusters is displayed on the x-axis. RPTs near the top of the graphs are most significantly differentially expressed between tumor clusters. Note that nearly every RPT in virtually all cancer cohorts falls above –log(P) of 2, corresponding to P <  0.01 and indicating that tumor clusters have significantly distinct expression of virtually all RPTs. For each cohort, the number of samples in each cluster are shown under the label “n”. Additional volcano plots of seven other cancer cohorts are continued in Fig. 3

Fig. 3

Volcano plots of relative RPT expression in tumor clusters associated with survival. a. Volcano plots comparing RPT relative expression between tumor clusters were generated, as in Fig. 2, for the remaining seven cancer cohorts which possessed tumor sub-clustering by t-SNE. Note that for the sake of clarity, clusters 5 and 6 are excluded from the LUNG cohort plot. These clusters correlated near perfectly with amplification and highly significant up-regulation of RPS3 and RPS16, respectively (Table 2). b. Patient survival by t-SNE cluster. Of the 19 cancer cohorts with sub-clustering of RPT expression patterns by t-SNE, seven possessed clusters that correlated with survival. Significance was determined with log-rank and Wilcoxon rank sum tests where appropriate, using all survival data available, including any data points beyond what are displayed in the survival curves