A transcriptional and metabolic signature of primary aneuploidy is present in chromosomally unstable cancer cells and informs clinical prognosis

Abstract

Aneuploidy is invariably associated with poor proliferation of primary cells, but the specific contributions of abnormal karyotypes to cancer, a disease characterized by aneuploidy and dysregulated proliferation, remain unclear. In this study, I demonstrate that the transcriptional alterations caused by aneuploidy in primary cells are also present in chromosomally unstable cancer cell lines, but the same alterations are not common to all aneuploid cancers. Chromosomally unstable cancer lines and aneuploid primary cells also share an increase in glycolytic and TCA cycle flux. The biological response to aneuploidy is associated with cellular stress and slow proliferation, and a 70-gene signature derived from primary aneuploid cells was defined as a strong predictor of increased survival in several cancers. Inversely, a transcriptional signature derived from clonal aneuploidy in tumors correlated with high mitotic activity and poor prognosis. Together, these findings suggested that there are two types of aneuploidy in cancer: one is clonal aneuploidy, which is selected during tumor evolution and associated with robust growth, and the other is subclonal aneuploidy caused by chromosomal instability (CIN). Subclonal aneuploidy more closely resembles the stressed state of primary aneuploid cells, yet CIN is not benign; a subset of genes upregulated in high-CIN cancers predict aggressive disease in human patients in a proliferation-independent manner.

Figure 1

Transcriptional similarities between trisomic MEFs and chromosomally-unstable cancer cell lines. (A and B). Representative examples of genes that are negatively (MCM6) or positively (CALU) correlated with karyotype heterogeneity in the NCI60 panel. Each square represents the expression level of the indicated gene in one cell line, and a linear regression (gray line) is plotted against the data. (C) Gene Ontology categories that are enriched among genes negatively or positively correlated with aneuploidy in MEFs (black bars) or karyotype heterogeneity in the NCI60 panel (white bars). Complete GO term lists are listed in Tables S1 and S2. (D) Genes that are negatively correlated with aneuploidy in MEFs are significantly enriched for those that are also negatively correlated with karyotype heterogeneity in the NCI60 panel. The black bar depicts the percent of genes that are negatively correlated with aneuploidy in MEFs. White bars represent the percent of genes negatively correlated with aneuploidy in MEFs that are also positively correlated with karyotype heterogeneity in the NCI60 panel. Hashed bars represent the percent of genes negatively correlated with aneuploidy that are also negatively correlated with karyotype heterogeneity. Asterisks represent a degree of overlap that is greater than or less than the overlap expected by chance (p<.05; hypergeometric test). (E) Same analysis as in (D) for genes positively correlated with aneuploidy in MEFs.

Figure 2

Transcriptional similarities between chromosomally-unstable cancer cell lines and cancer cell lines with slow doubling times. (A) Karyotype heterogeneity and doubling time are significantly correlated in the NCI60 panel. A linear regression (gray line) is plotted against the data. (B) Gene Ontology categories that are enriched among genes negatively or positively correlated with doubling time (black bars) or karyotype heterogeneity (white bars) in the NCI60 panel. Complete GO term lists are listed in Tables S2 and S9.(C) Spearman rank correlations for each gene expression vector and karyotype heterogeneity (X axis) or doubling time (Y axis) were plotted. Genes that display a stronger positive correlation with karyotype heterogeneity are displayed in blue, while genes that display a stronger positive correlation with doubling time are displayed in red.

Figure 3

Metabolic similarities between trisomic MEFs and chromosomally-unstable cancer cell lines. (A) Glucose and glutamine consumption tend to increase, and glutamate and lactate production tend to increase, with karyotype heterogeneity in the NCI60 panel. Gray lines represent linear regressions plotted against the data. (B) Comparison between the metabolic profiles of chromosomally-unstable and slow-growing cancer lines. Metabolites whose consumption or production increase with doubling time (white bars) tend to show similar but smaller trends than when those metabolites are correlated with karyotype heterogeneity (black bars; p<.01 for both produced and consumed metabolities, paired t-test).

Figure 4

Stratification of patient risk based on proliferation and aneuploidy gene signatures. Patients from each clinical cohort were divided into below-mean and above-mean subsets for each gene signature, and significant differences in survival time were identified using a logrank test. Representative Kaplan-Meier curves for GSE14520 and GSE8894 are displayed. In general, PCNA25, CIN70, and TRI70 were able to classify the same patient cohorts, while HET70 classified some cohorts in which proliferation-related markers were not prognostic. The X axes indicate survival time in months. Complete results are presented in Table S18 and Figure S6.