Interpreting pathways to discover cancer driver genes with Moonlight

Abstract

Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions.

A molecular taxonomy of tumors independent of tissue-of-origin

“…This can only be explained as a result of other less common driver genes complementing the pathway disruption of the unique combination of driver genes that are disrupted in each tumor, and we speculate that many of these are relatively tissue-specific in terms of their sensitivity to mutation. Consistent with our observations, Colaprico et al (2020) showed how tissue-and cancer-specific driver genes and druggable targets of pathway moduli can be discovered by machine learning on integrated multi-omics datasets. Marrying these approaches in future work will greatly enhance our understanding of tumor biology.…”

“…One of the challenges in the field remains that beyond a couple hundred common driver genes and mutations, there may be thousands of moderate effect genes that occur at such low frequency-given tissue diversity-and low sample numbers that they are impossible to detect using positive selection theory. Some groups have attempted to address this challenge by applying machine learning to cancer data sets to discover groups of functionally related genes as they interact with larger pathways and networks (Kim and Kim 2018;Mourikis et al 2019;Colaprico et al 2020). These studies have proven very effective at highlighting fundamental disease phenotypes at the pathway level across cancers with different origins at the cellular and tissue level.…”

Ectopic Expression of Ankrd2 Affects Proliferation, Motility and Clonogenic Potential of Human Osteosarcoma Cells

“…The opposing effects exhibited by Ankrd2 in OS cells and in HNSCC [ 27 ] hint at a role of Ankrd2 as a “double-faced” cancer gene, a well-documented peculiarity of certain genes exhibiting oncogenic or tumor-suppressor behavior depending on the biological context [ 28 , 29 ]. Interestingly, a homolog of Ankrd2, Ankrd23 [ 1 ] was recently identified as a potential dual-role cancer driver gene acting as an oncogene in renal clear-cell carcinoma, and a tumor suppressor in bladder urothelial carcinoma [ 30 ].…”

PyInteraph2 and PyInKnife2 to analyze networks in protein structural ensembles

“…Proper reproducible protocols and solid tools accessible to the community are needed in the PSN field to reach the standards of recent open-access and collaborative initiatives for reproducibility and transparency in molecular modeling and simulations (PLUMED Consortium, 2019;Smith et al, 2020;Senapathi et al, 2020). These initiatives are also common in other areas of bioinformatics, such as cancer genomics (Siu et al, 2016;Colaprico et al, 2020Colaprico et al, , 2015Mounir et al, 2019;Terkelsen et al, 2020). In 2014, we developed PyInteraph (Tiberti et al, 2014)for the study of protein structure networks (PSNs) from structural ensembles, especially suited to work on trajectories from atomistic simulations such as Molecular Dynamics (MD).…”