A Modeling Approach to Explain Mutually Exclusive and Co-Occurring Genetic Alterations in Bladder Tumorigenesis

Abstract

Relationships between genetic alterations, such as co-occurrence or mutual exclusivity, are often observed in cancer, where their understanding may provide new insights into etiology and clinical management. In this study, we combined statistical analyses and computational modeling to explain patterns of genetic alterations seen in 178 patients with bladder tumors (either muscle-invasive or non-muscle-invasive). A statistical analysis on frequently altered genes identified pair associations, including co-occurrence or mutual exclusivity. Focusing on genetic alterations of protein-coding genes involved in growth factor receptor signaling, cell cycle, and apoptosis entry, we complemented this analysis with a literature search to focus on nine pairs of genetic alterations of our dataset, with subsequent verification in three other datasets available publicly. To understand the reasons and contexts of these patterns of associations while accounting for the dynamics of associated signaling pathways, we built a logical model. This model was validated first on published mutant mice data, then used to study patterns and to draw conclusions on counter-intuitive observations, allowing one to formulate predictions about conditions where combining genetic alterations benefits tumorigenesis. For example, while CDKN2A homozygous deletions occur in a context of FGFR3-activating mutations, our model suggests that additional PIK3CA mutation or p21CIP deletion would greatly favor invasiveness. Furthermore, the model sheds light on the temporal orders of gene alterations, for example, showing how mutual exclusivity of FGFR3 and TP53 mutations is interpretable if FGFR3 is mutated first. Overall, our work shows how to predict combinations of the major gene alterations leading to invasiveness through two main progression pathways in bladder cancer.

A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence

“…A commonly used test for both co-occurrence and mutual exclusivity is Fisher’s exact test applied to a 2×2 contingency table [16–18]. The test is used to support co-occurrence when the number of tumors with alterations in both genes is significantly higher than expected by chance.…”

A pan‐cancer atlas of cancer hallmark‐associated candidate driver lncRNAs

“…Such functional redundancy of mutual exclusivity can be explained according to the clonal evolution hypothesis of tumor progression (Gillies et al, 2012). When damage of a driver gene is sufficient to disturb the activity of certain key pathways, other gene alterations with similar functional consequences will offer no further selective advantage on that clone; that is the selection pressure on these other alterations could be diminished or even nullified during tumor evolution (Ciriello et al, 2012;Remy et al, 2015). Therefore, mutual exclusivity is a common and informative phenomenon during cancerogenesis (Deng et al, 2017), which has important implications for the functional exploration of lncRNAs in cancer pathogenesis, and helps to uncover novel drivers and decipher their downstream functions.…”

Logic Modeling in Quantitative Systems Pharmacology

“…Also, the ErbB network was explored with a model built from prior knowledge and protein phosphorylation data to understand the drug resistance mechanisms of breast cancer cell lines 40. Logic modeling was applied to bladder cancer to investigate the effects of gene alterations leading to invasiveness 41. However, the use of logic modeling tools is not limited to cancer applications, and research in other diseases also benefit from these techniques.…”

“…The system of study might be a specific pathway, a cell line, or a disease, for example. The available data and publications related to the chosen system are usually a starting point for the following steps 41. In other cases, new experiments are designed and carried out to provide the necessary data for modeling 16.…”