NIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis

Jing Zhang,Jason Liu,Donghoon Lee,Lucas Lochovsky,Patrick Mcgillivray,Caroline Yi,Mark Gerstein

doi:10.1186/s12859-020-03758-1

Jing Zhang, Jason Liu + Show 5 more

Open Access

https://doi.org/10.1186/s12859-020-03758-1

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Abstract

BackgroundIdentifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression.However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals. Moreover, it is known that this heterogeneity partially stems from genomic confounding factors, such as replication timing and chromatin organization. The increasing availability of cancer whole genome sequences and functional genomics data from the Encyclopedia of DNA Elements (ENCODE) may help address these issues.ResultsWe developed a negative binomial regression-based Integrative Method for mutation Burden analysiS (NIMBus). Our approach addresses the over-dispersion of mutation count statistics by (1) using a Gamma–Poisson mixture model to capture the mutation-rate heterogeneity across different individuals and (2) estimating regional background mutation rates by regressing the varying local mutation counts against genomic features extracted from ENCODE. We applied NIMBus to whole-genome cancer sequences from the PanCancer Analysis of Whole Genomes project (PCAWG) and other cohorts. It successfully identified well-known coding and noncoding drivers, such as TP53 and the TERT promoter. To further characterize the burdening of non-coding regions, we used NIMBus to screen transcription factor binding sites in promoter regions that intersect DNase I hypersensitive sites (DHSs). This analysis identified mutational hotspots that potentially disrupt gene regulatory networks in cancer. We also compare this method to other mutation burden analysis methods.ConclusionNIMBus is a powerful tool to identify mutational hotspots. The NIMBus software and results are available as an online resource at github.gersteinlab.org/nimbus.

Highlights

Identifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression.it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals
Local mutation rates are affected by genomic features such as endogenous DNA damage and chromatin organization
NIMBus addresses both of these concerns by utilizing a negative binomial regression and by adjusting the local background mutation rate based on the genomic context

Summary

Introduction

Identifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression.it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals. If a constant mutation rate is assumed, the positional level mutation counts often demonstrate larger than expected variance, known as overdispersion This assumption results in poor data fitting and can lead to numerous false positives [5], so it is necessary to introduce more sophisticated models to handle this mutation rate heterogeneity. Numerous genomic features have been reported to largely affect the mutation process [6,7,8,9,10,11,12], necessitating careful correction in burden analysis. These features include chromatin status and replication timing. Unified analysis of coding and noncoding regions is needed to give a thorough annotation of discovered hotspots

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