Altered interactions between unicellular and multicellular genes drive hallmarks of transformation in a diverse range of solid tumors

Abstract

Tumors of distinct tissues of origin and genetic makeup display common hallmark cellular phenotypes, including sustained proliferation, suppression of cell death, and altered metabolism. These phenotypic commonalities have been proposed to stem from disruption of conserved regulatory mechanisms evolved during the transition to multicellularity to control fundamental cellular processes such as growth and replication. Dating the evolutionary emergence of human genes through phylostratigraphy uncovered close association between gene age and expression level in RNA sequencing data from The Cancer Genome Atlas for seven solid cancers. Genes conserved with unicellular organisms were strongly up-regulated, whereas genes of metazoan origin were primarily inactivated. These patterns were most consistent for processes known to be important in cancer, implicating both selection and active regulation during malignant transformation. The coordinated expression of strongly interacting multicellularity and unicellularity processes was lost in tumors. This separation of unicellular and multicellular functions appeared to be mediated by 12 highly connected genes, marking them as important general drivers of tumorigenesis. Our findings suggest common principles closely tied to the evolutionary history of genes underlie convergent changes at the cellular process level across a range of solid cancers. We propose altered activity of genes at the interfaces between multicellular and unicellular regions of human gene regulatory networks activate primitive transcriptional programs, driving common hallmark features of cancer. Manipulation of cross-talk between biological processes of different evolutionary origins may thus present powerful and broadly applicable treatment strategies for cancer.

Keywords: atavism; cancer; evolution; systems biology; transcriptomics.

Fig. 1.

Overexpression of genes that date back to UC ancestors are preferentially expressed in tumors. (A) TAI of tumor and normal samples, by subtype. A lower TAI corresponds to higher expression of genes from earlier phylostrata. Tumors have lower TAI scores (more ancient transcriptomes) than normal samples (Wilcoxon tests: ***P < 0.01). (B) Percentage of transcriptome composed of UC genes increases in all tumor subtypes. Shaded areas: median percentages across samples. (C) Difference in proportion of transcriptome composed of genes from each phylostratum in tumors vs. normal samples, by subtype. (D) TAI decreases as degree of differentiation increases as measured by Gleason score (Jonckheere–Terpstra test P value = 2.79 × 10⁻¹⁶). (E) Negative correlation between the proliferation marker, MKI67, and the TAI (Spearman correlation = −0.537, P value = 1.1 × 10⁻⁷) in prostate tumors.

Fig. 2.

Effect of the expression patterns of UC and MC genes on cellular processes. (A) Expression patterns of cellular processes. (A, Right) LogFC of processes, showing up-regulation of a subset of UC processes and widespread down-regulation of MC processes across tumors. (A, Left) Median logFC of UC and MC components of processes. The logFCs of UC components are more positive or not significantly different from those of MC components, indicating UC genes push processes toward activation. Bars: range in tumors. Triangles: UC component greater (Wilcoxon test P value <0.05) than MC component. (B) Difference in the absolute logFC of UC and MC components of GOslims (y axis) vs. overall logFC for the GOslim in tumors vs. normal samples (x axis). Up-regulated GOslims are driven by UC genes, whereas down-regulated ones are driven by MC genes. Points: median logFC across tumors. Error bars: range in tumors. Linear model P value = 1.9 × 10⁻¹⁰. (C) Response to stress GOterm tree. UC stress-response programs tend to be up-regulated in tumors (55%), whereas recently acquired ones are down-regulated (69%). Node size is proportional to the number of genes annotated with the GOterm.

Fig. 3.

Patterns of coexpression between highly interconnected cellular processes are disrupted in tumors. (A) Transcriptional network of cellular processes in tumors, displaying the median correlation (edge brightness) between processes across the seven tumor types. (B) Number of positive and negative interactions between processes. Dots correspond to results from each tumor type. (C) Median correlation in expression between cellular processes in tumors and normal samples. (D) Variance in the correlation of expression between cellular processes, showing decreased variability in tumors with respect to normal samples.