Individualized discovery of rare cancer drivers in global network context
Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become 'hard currency' of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of global interaction network and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations' co-occurrence in each tumour genome - as an alternative to summarizing information over the whole cancer patient cohorts. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to ten largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. This revealed driver genes with individually sparse mutation patterns that would not be detectable by other computational methods and related to cancer biology domains poorly covered by previous analyses. In particular, recurrent mutations of collagen, laminin, and integrin genes were observed in the adenocarcinoma and glioblastoma cancers. Considering constellation patterns of candidate drivers in individual cancer genomes opens a novel avenue for personalized cancer medicine.
All data generated or analysed during this study are included in the manuscript and supporting files.
Article and author information
- Iurii Petrov
- Andrey Alexeyenko
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- C Daniela Robles-Espinoza, International Laboratory for Human Genome Research, Mexico
- Received: September 17, 2021
- Preprint posted: October 5, 2021 (view preprint)
- Accepted: May 20, 2022
- Accepted Manuscript published: May 20, 2022 (version 1)
- Version of Record published: June 1, 2022 (version 2)
© 2022, Petrov & Alexeyenko
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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