Methods for cancer driver gene identification

The current version of the intOGen pipeline uses six cancer driver identification methods (hereinafter DIMs) to identify cancer driver genes from somatic point mutations: dNdScv and cBaSE, which test for mutation count bias in genes while correcting for regional genomic covariates, mutational processes and coding consequence type; OncodriveCLUSTL, which tests for significant clustering of mutations in the protein sequence; smRegions, which tests for enrichment of mutations in protein functional domains; HotMAPS, which tests for significant clustering of mutations in the 3D protein structure; and OncodriveFML, which tests for functional impact bias of the observed mutations. Next, we briefly describe the rationale and the configuration used to run each DIM.

dNdScv

dNdScv 1 asserts gene-specific positive selection by inferring the ratio of non-synonymous to synonymous substitutions (dN/dS) in the coding region of each gene. The method resorts to a Poisson-based hierarchical count model that can correct for: i) the mutational processes operative in the cohort determined by the mutational profile of single-nucleotide substitutions with its flanking nucleotides; ii) the regional variability of the background mutation rate explained by histone modifications – it incorporates information about 10 histone marks from 69 cell lines obtained in ENCODE project 2; iii) the abundance of sites per coding consequence type across in the coding region of each gene.

We downloaded (release date 2018/10/12) and built a new reference database based on the list canonical transcripts defined by VEP.92 (GRCh38). We then used this reference database to run dNdScv on all datasets of somatic mutations using the default setting of the method.

OncodriveFML

OncodriveFML 3 is a tool that aims to detect genes under positive selection by analysing the functional impact bias of the observed somatic mutations. Briefly, OncodriveFML consists of three steps: in the first step, it computes the average Functional Impact (FI) score (in our pipeline we used CADD v1.4) of coding somatic mutations observed in gene of interest across a cohort of tumor samples. In the next step, sets of mutations of the same size as the number of mutations observed in the gene of interest are randomly sampled following trinucleotide context conditional probabilities consistent with the relative frequencies of the mutational profile of the cohort. This sampling is repeated N times (with N = \(10^6\) in our configuration) to generate expected average scores across all genes of interest. Finally, it compares the observed average FI score with the expected from the simulations in the form of an empirical p-value. The p-values are then adjusted with a multiple testing correction using the Benjamini–Hochberg (FDR).

OncodriveCLUSTL

OncodriveCLUSTL is a sequence-based clustering algorithm to detect significant linear clustering bias of the observed somatic mutations 4. Briefly, OncodriveCLUSTL first maps somatic single nucleotide variants (SNVs) observed in a cohort to the genomic element under study. After smoothing the mutation count per position along its genomic sequence using a Tukey kernel-based density function, clusters are identified and scored taking into account the number and distribution of mutations observed. A score for each genomic element is obtained by adding up the scores of its clusters. To estimate the significance of the observed clustering signals, mutations are locally randomized using tri- or penta-nucleotide context conditional probabilities consistent with the relative frequencies of the mutational profile of the cohort.

For this analysis, OncodriveCLUSTL version 1.1.1 was run for the set of defined canonical transcripts bearing 2 or more SNVs mapping the mutations file. Tuckey-based smoothing was conducted with 11bp windows. The different consecutive coding sequences contained on each transcript were concatenated to allow the algorithm to detect clusters of 2 or more SNVs expanding two exons in a transcript. Simulations were carried out using pre-computed mutational profiles. All cohorts were run using tri-nucleotide context SNVs profiles except for cutaneous melanomas, where penta-nucleotide profiles were calculated. Default randomization windows of 31bp were not allowed to expand beyond the coding sequence boundaries (e.g., windows overlapping part of an exon and an intron were shifted to fit inside the exon). A total number of N = 1,000 simulations per transcript were performed.

cBaSE

cBaSE 5 asserts gene-specific positive and negative selection by measuring mutation count bias with Poisson-based hierarchical models. The method allows six different models based on distinct prior alternatives for the distribution of the regional mutation rate. As in the case of dNdScv, the method allows for correction by i) the mutational processes operative in the tumor, with either tri- or penta- nucleotide context; ii) the site count per consequence type per gene; iii) regional variability of the neutral mutation rate.

We run a modified version of the cBaSE script to fit the specific needs of our pipeline. The main modification was adding a rule to automatically select a regional mutation rate prior distribution. Based on the total mutation burden in the dataset, the method runs either an inverse-gamma (mutation count < 12,000), an exponential-inverse-gamma mixture (12,000 < mutation count < 65,000) or a gamma-inverse-gamma mixture (mutation count > 65,000) as mutation rate prior distributions – after communication with Donate Weghorn, cBaSE’s first author). We also skip the negative selection analysis part, as it is not needed for downstream analyses.

Mutpanning

Mutpanning 9 resorts to a mixture signal of positive selection based on two components: i) the mutational recurrence realized as a Poisson-based count model reminiscent to the models implemented at dNdScv or cBaSE; ii) a measure of deviance from the characteristic tri-nucleotide contexts observed in neutral mutagenesis; specifically, an account of the likelihood that a prescribed count of non-synonymous mutations occur in their observed given a context-dependent mutational likelihood attributable to the neutral mutagenesis.

HotMaps3D

HotMAPS 6 asserts gene-specific positive selection by measuring the spatial clustering of mutations in the 3D structure of the protein. The original HotMAPS method assumes that all amino-acid substitutions in a protein structure are equally likely. We employed HotMAPS-1.1.3 and modified it to incorporate a background model that more accurately represents the mutational processes operative in the cohort.

We implemented a modified version of the method where the trinucleotide context probability of mutation is compatible with the mutational processes operative in the cohort. Briefly, for each analyzed protein structure harbouring missense mutations, the same number of simulated mutations were randomly generated within the protein structure considering the precomputed mutation frequencies per tri-nucleotide in the cohort. This randomization was performed N times (N = \(10^5\) in our configuration) thereby leading to a background with which to compare the observed mutational data. The rest of HotMAPS algorithm was not modified.

We downloaded the pre-computed mapping of GRCh37 coordinates into structure residues from the Protein Data Bank (PDB) (http://karchinlab.org/data/HotMAPS/mupit_modbase.sql.gz). We also downloaded (on 2019/09/20) all protein structures from the PDB alongside all human protein 3D models from Modeller (ftp://salilab.org/databases/modbase/projects/genomes/H_sapiens/2013/H_sapiens_2013.tar.xz). and (ftp://salilab.org/databases/modbase/projects/genomes/H_sapiens/2013/ModBase_H_sapiens_2013_refseq.tar.xz). We then annotated the structures following the steps described in HotMAPS tutorial (https://github.com/KarchinLab/HotMAPS/wiki/Tutorial-(Exome-scale)).

Since HotMAPS configuration files are pre-built in GRCh37 coordinates and our pipeline is designed to run using GRCh38, for each input cohort, we first converted input somatic mutations to GRCh37, executed the HotMAPS algorithm and transformed the output to coordinates to GRCh38. All conversions were done using the PyLiftover tool.

smRegions

smRegions 7 is a method developed to detect linear enrichment of somatic mutations in user-defined regions of interest. Briefly, smRegions first counts the number of non-synonymous mutations overlapping with a Pfam domain in a particular protein. Next, these non-synonymous variants are randomized N times (N = 1,000 in our configuration) along the nucleotide sequence of the gene, following the trinucleotide context probability derived from precomputed mutation frequencies per tri-nucleotide in the cohort. The observed and average number of simulated mutations in the Pfam domain and outside of it are compared using a G-test of goodness-of-fit, from which the smRegions p-value is derived. We discarded those domains with a number of observed mutations lower than the average from the randomizations. The p-values were adjusted with a multiple testing correction using the Benjamini–Hochberg procedure. Therefore, we confined the analysis to Pfam domains with a number of observed mutations higher or equal than the mean simulated number of mutations in the re-sampling.

To create the database of genomic coordinates of Pfam domains we followed the next steps: i) we gathered the first and last amino acid positions of all Pfam domains for canonical transcripts (VEP.92) from BioMart; ii) for each Pfam domain we mapped the first and last amino acid positions into genomic coordinates using TransVar –using GRCh38 as reference genome–; iii) we discarded Pfam domains failing to map either the first or last amino acid positions into genomic coordinates.

smRegions was conceptually inspired by e-driver 8, although significant enhancements were introduced. Particularly, i) our background model accounts for the observed tri-nucleotide frequencies rather than assuming that all mutations are equally likely; ii) the statistical test is more conservative; iii) Pfam domains are part of the required input and can be easily updated by downloading the last Pfam release iv) the method can be configured to any other setting that aims to detect genes possibility selected by enrichment of mutations in pre-defined gene regions.

1

Martincorena, I. et al. Universal Patterns of Selection in Cancer and Somatic Tissues. Cell 171, 1029-1041.e21 (2017). doi: 10.1016/j.cell.2017.09.042

2

Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature volume 518, pages 317–330 (19 February 2015). doi: 10.1038/nature14248

3

Loris Mularoni, et al. OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations . Genome Biology (2016)

4

Claudia Arnedo-Pac, et al. OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers. 2019 Jun 22. Bioinformatics. pii: btz501. doi: 10.1093/bioinformatics/btz501 .

5

Weghorn, et al. D. & Sunyaev, S. Bayesian inference of negative and positive selection in human cancers. Nature Genetics 49, 1785–1788 (2017). doi: 10.1038/ng.3987

6

Tokheim C, et al. Exome-scale discovery of hotspot mutation regions in human cancer using 3D protein structure. Cancer research. 2016a;76:3719–3731. doi: 10.1158/0008-5472.CAN-15-3190

7

Francisco Martínez-Jiménez, et al. Disruption of ubiquitin mediated proteolysis is a widespread mechanism of tumorigenesis. bioRxiv 2019. doi: https://doi.org/10.1101/507764

8

Porta-Pardo E, et al. e-Driver: a novel method to identify protein regions driving cancer. Bioinformatics. 2014;30(21):3109–3114. doi:10.1093/bioinformatics/btu499

9

Dietlein, F., Weghorn, D., Taylor-Weiner, A. et al. Identification of cancer driver genes based on nucleotide context. Nat Genet (2020). https://doi.org/10.1038/s41588-019-0572-y