Abstract
There is a growing need to develop novel therapeutics for targeted treatment of cancer. The prerequisite to success is the knowledge about which types of molecular alterations are predominantly driving tumorigenesis. To shed light onto this subject, we have utilized the largest database of human cancer mutations–TCGA PanCanAtlas, multiple established algorithms for cancer driver prediction (2020plus, CHASMplus, CompositeDriver, dNdScv, DriverNet, HotMAPS, OncodriveCLUSTL, OncodriveFML) and developed four novel computational pipelines: SNADRIF (Single Nucleotide Alteration DRIver Finder), GECNAV (Gene Expression-based Copy Number Alteration Validator), ANDRIF (ANeuploidy DRIver Finder) and PALDRIC (PAtient-Level DRIver Classifier). A unified workflow integrating all these pipelines, algorithms and datasets at cohort and patient levels was created. We have found that there are on average 12 driver events per tumour, of which 0.6 are single nucleotide alterations (SNAs) in oncogenes, 1.5 are amplifications of oncogenes, 1.2 are SNAs in tumour suppressors, 2.1 are deletions of tumour suppressors, 1.5 are driver chromosome losses, 1 is a driver chromosome gain, 2 are driver chromosome arm losses, and 1.5 are driver chromosome arm gains. The average number of driver events per tumour increases with age (from 7 to 15) and cancer stage (from 10 to 15) and varies strongly between cancer types (from 1 to 24). Patients with 1 and 7 driver events per tumour are the most frequent, and there are very few patients with more than 40 events. In tumours having only one driver event, this event is most often an SNA in an oncogene. However, with increasing number of driver events per tumour, the contribution of SNAs decreases, whereas the contribution of copy-number alterations and aneuploidy events increases.
Highlights
Driver events are the molecular and cellular events that drive cancer progression, often called driver mutations [1]
By analysing genomic and transcriptomic data from 10000 cancer patients through our custom-built computational pipelines and previously established third-party algorithms, we have found that half of all driver events in a patient’s tumour appear to be gains and losses of chromosomal arms and whole chromosomes
Patient-level analysis of driver events in TCGA PanCanAtlas cohorts for this article are available as Supporting Information
Summary
Driver events are the molecular and cellular events that drive cancer progression, often called driver mutations [1]. Most attention has been devoted to point mutations or single nucleotide alterations (SNAs), as most driver prediction algorithms work only with this class of driver events. It is known that cancer cells contain large numbers of deletions and amplifications (often called copy number alterations, or CNAs), translocations, inversions, full chromosome and chromosomal arm gains and losses (aneuploidy), as well as epigenetic modifications [3,4,5], but the driver potential of these alterations has been left almost unexplored due to the scarcity of driver prediction algorithms for these classes of events. We set the goal to predict, classify and quantify as many different classes of driver events as possible, using clear and straightforward principles, and developed custom computational pipelines for this purpose
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