Childhood cancers are a major cause of death among minors in Western countries. In recent years, next generation sequencing studies have led to the identification of genes that are frequently mutated in pediatric cancers. Mutations/alterations in one or a group of such genes are involved in cancer progression, and thus they can functionally contribute to the development of cancer via interaction with each other. It is possible to identify such interacting genes by looking for candidates who co-occur more often than expected using bioinformatical tools and statistical approaches. Such genes are referred to as co-occurring genes, whereas the genes whose presence is mutually exclusive of the other gene are called synthetic lethal.
Many tools and analysis are available to identify such interactions. However, they lack any biological explanation of their observations. Daub et al. have recently developed a robust pipeline that can identify such interactions with high statistical confidence.
Using the analysis pipeline, the researchers have analyzed over 2500 tumours from 23 cancer types. The analysis revealed multiple co-occurring and mutually exclusive gene pairs. Next, they have investigated the biological relevance of such gene pairs. Towards this, they used a combination of consulting experts in the field, extensive literature search, and a candidate reporting tool (R shiny web application for mutational information on each candidate pair).
The study has revealed 15 co-occurring and 27 mutually exclusive candidates. The biological explanation of these candidate gene interactions is cancer subtype, pathway epistasis, and cooperation, contributing to almost 29% of identified genes. The percentage of genes (7%) with synthetic lethality as an explanation was low.
These interaction maps are an important step towards identification and biological explanation of genetic interactome within childhood cancers. Also, the investigation underlines those future studies of such nature should not just provide the list of candidate gene pairs but complement their findings with biological explanations of their observations.
In conclusion, these findings provide a road map for exploring all interactions in pediatric tumors, and functional validation in appropriate model systems of the disease.
Daub JT, Amini S, Kersjes DJE, Ma X, Jäger N, Zhang J, et al. A systematic analysis of genetic interactions and their underlying biology in childhood cancer. Commun Biol [Internet]. 2021;4(1):1139. Available from: https://doi.org/10.1038/s42003-021-02647-4