Gene Coexpression Analyses Differentiate Networks Associated with Diverse Cancers Harboring TP53 Missense or Null Mutations

Abstract

In a variety of solid cancers, missense mutations in the well-established TP53 tumor suppressor gene may lead to the presence of a partially-functioning protein molecule, whereas mutations affecting the protein encoding reading frame, often referred to as null mutations, result in the absence of p53 protein. Both types of mutations have been observed in the same cancer type. As the resulting tumor biology may be quite different between these two groups, we used RNA-sequencing data from The Cancer Genome Atlas (TCGA) from four different cancers with poor prognosis, namely ovarian, breast, lung and skin cancers, to compare the patterns of coexpression of genes in tumors grouped according to their TP53 missense or null mutation status. We used Weighted Gene Coexpression Network analysis (WGCNA) and a new test statistic built on differences between groups in the measures of gene connectivity. For each cancer, our analysis identified a set of genes showing differential coexpression patterns between the TP53 missense- and null mutation-carrying groups that was robust to the choice of the tuning parameter in WGCNA. After comparing these sets of genes across the four cancers, one gene (KIR3DL2) consistently showed differential coexpression patterns between the null and missense groups. KIR3DL2 is known to play an important role in regulating the immune response, which is consistent with our observation that this gene's strongly-correlated partners implicated many immune-related pathways. Examining mutation-type-related changes in correlations between sets of genes may provide new insight into tumor biology.

An analytic approach for interpretable predictive models in high‐dimensional data in the presence of interactions with exposures

“…We then seek to identify nodes that are very different between environments. We determine differential coexpression using the absolute difference Oros Klein et al, 2016). We then use hierarchical clustering with average linkage on the derived difference matrix to identify these differentially coexpressed variables.…”

“…A comparison of gene expression levels in bone marrow from 327 children with acute leukemia found several differentially coexpressed genes in Philadelphiapositive leukemias compared to the cytogenetically normal group (Kostka & Spang, 2004). To give the third example, an analysis of RNA-sequencing data from The Cancer Genome Atlas (TCGA) revealed very different correlation patterns among sets of genes in tumors grouped according to their missense or null mutations in the TP53 tumor suppressor gene (Oros Klein et al, 2016). Therefore, in this paper, we pose the question whether clustering or dimension reduction that incorporates known covariate or exposure information can improve prediction models in HD genomic data settings.…”

Gene networks in cancer are biased by aneuploidies and sample impurities

“…in the Dialogue of Reverse Engineering and Assessment of Methods (DREAM) competitions 11 and many more comparisons on smaller scale [12][13][14][15][16][17][18] . They have provided many biological insights, and have been particularly useful to elucidate mechanisms of pathogenicity in human diseases such as cancer 1,2,[19][20][21][22][23][24] . However, there is a disconnect between evaluation in often relatively simple systems (synthetic networks or GRNs in E. coli and yeast) and their application to much more complex mammalian systems.…”

Network analysis allows to unravel breast cancer molecular features and to identify novel targets

“…Nevertheless, most published works are purely descriptive and do not exploit the wealth of information hidden in these networks for the definition and investigation of specific biological hypotheses. Additionally, the problem of differences in network structure across conditions has motivated the definition of differential co-expression analysis methods (Klein, Oualkacha, Lafond, & Bhatnagar, 2016;Yu, Zhao, Wang, Zhao, & Zhao, 2017;Zhu et al, 2017), but network structure and connectivity have never been compared across breast cancer subtypes. However, this investigation could drive the generation of new hypotheses about the molecular features conferring aggressiveness to the basal-like subtype.…”