Comparison of gene expression patterns across 12 tumor types identifies a cancer supercluster characterized by TP53 mutations and cell cycle defects

Abstract

Transcriptional profile based subtypes of cancer are often viewed as identifying different diseases from the same tissue origin. Understanding the mechanisms driving the subtypes may be key in development of novel therapeutics but is challenged by lineage-specific expression signals. Using a t-test statistics approach we compared gene expression subtypes across twelve tumor types, which identified eight transcriptional superclusters characterized by commonly activated disease pathways and similarities in gene expression. One of the largest superclusters was determined by the upregulation of a proliferation signature, significant enrichment in TP53 mutations, genomic loss of CDKN2A (p16ARF), evidence of increased numbers of DNA double strand breaks and high expression of cyclin B1 protein. These correlations suggested that abrogation of the P53 mediated apoptosis response to DNA damage results in activation of cell cycle pathways and represents a common theme in cancer. A second consistent pattern, observed in nine of eleven solid tumor types, was a subtype related to an activated tumor-associated stroma. The similarity in transcriptional footprints across cancers suggested that tumor subtypes are commonly unified by a limited number of molecular themes.

Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

“…All three show a very high frequency of copy number changes (Figure 2C), and all are significantly enriched with amplifications of 3q26 and 8q24/cMYC and losses of chromosomes 4q, 5q, 8p, and 18q (Figure 2B). The COCA subtypes share features common to a pan-cancer cluster identified by a parallel analysis of the transcriptional profiles of these same tumors (Martinez et al, 2014), which was found to be associated with genomic loss of CDKN2A (p16ARF), increased numbers of DNA double strand breaks, high expression of cyclin B1, and upregulation of proliferation genes.…”

Identification of a multi-cancer gene expression biomarker for cancer clinical outcomes using a network-based algorithm

“…It has been proposed that molecular processes may be similar across cancer types141564. Consequently, a biomarker of clinical outcomes in a specific cancer type may be a good biomarker in a different cancer type.…”

“…Several cancer types have been divided into subtypes using TCGA data about gene expression8, mutations9, copy number alterations10, microRNA expression11, pseudogenes12, or even biological processes such as inflammation13. Nevertheless, specific subtypes across cancer types seem to share common gene expression properties such as correlations1415, stromal and immune signatures16, or mesenchymal signatures17. Clinically, better or alternative methods to identify cancer risk groups are always needed.…”

Network analysis revealed aurora kinase dysregulation in five gynecological types of cancer

“…Certain studies employing novel profiling techniques integrated with existing bioinformatics methods have revealed systemic-level properties emphasizing on networks that instigate the interconnection of genes, proteins and metabolites whose dynamic interactions generate a corresponding function (7) in various types of cancer (5,6,(8)(9)(10)(11). Previously, one study identified four transcriptional modules associated with the cell cycle and apoptosis in cervical, endometrial and vulvar cancer (1).…”

“…Studies regarding expression profiles and mutation rates have been conducted to identify common features among various cancer types, although few studies have identified common molecular mechanisms across diverse types of cancer (1,5,6). Certain studies employing novel profiling techniques integrated with existing bioinformatics methods have revealed systemic-level properties emphasizing on networks that instigate the interconnection of genes, proteins and metabolites whose dynamic interactions generate a corresponding function (7) in various types of cancer (5,6,(8)(9)(10)(11).…”