Computational characterisation of cancer molecular profiles derived using next generation sequencing

Abstract

Our current understanding of cancer genetics is grounded on the principle that cancer arises from a clone that has accumulated the requisite somatically acquired genetic aberrations, leading to the malignant transformation. It also results in aberrent of gene and protein expression. Next generation sequencing (NGS) or deep sequencing platforms are being used to create large catalogues of changes in copy numbers, mutations, structural variations, gene fusions, gene expression, and other types of information for cancer patients. However, inferring different types of biological changes from raw reads generated using the sequencing experiments is algorithmically and computationally challenging. In this article, we outline common steps for the quality control and processing of NGS data. We highlight the importance of accurate and application-specific alignment of these reads and the methodological steps and challenges in obtaining different types of information. We comment on the importance of integrating these data and building infrastructure to analyse it. We also provide exhaustive lists of available software to obtain information and point the readers to articles comparing software for deeper insight in specialised areas. We hope that the article will guide readers in choosing the right tools for analysing oncogenomic datasets.

Molecular profiling of cancer genomes

Over the years, individual laboratories and large-scale projects such as TCGA and ICGC have discovered that cancer is a heterogeneous disease with lots of variability within a single tumour type or even within a single tumour [1–4]. Nonetheless, much of our current understanding of cancer genetics is grounded on the principle that cancer arises from a clone that has accumulated the requisite somatically acquired genetic aberrations leading to the malignant transformation [5]. Characterising individual tumours or cohorts at the molecular level has helped in identifying common and type specific cancer vulnerabilities as well as recording the individual history of tumours [4, 6–8]. This has enabled the creation of drugs that target these molecular vulnerabilities and provide tailored treatments for patients, improving therapy efficacy and minimising its side effects [9, 10]. For example, Imatinib specifically targets the BCR-Abl fusion tyrosine kinase that exists only in the cells of chronic myelogenous leukaemia and other tumours but not in healthy cells [11]. Similarly, Herceptin is a monoclonal antibody that is used to target HER2 positive breast tumours [12].

At present, the TCGA and other large-scale projects characterise tumours with microarray and next generation sequencing (NGS) platforms to obtain a different type of genetic information at the whole genome level [6–8, 13–15]. The microarray platform had been, and is currently being, used to identify gene and microRNA expression, alternative splicing, copy number alterations, DNA methylation, and identification of protein-DNA and protein-RNA interactions [16]. Next generation sequencing platforms are now replacing the microarray platforms for obtaining these data. Moreover, sequence reads from whole exome sequencing, along with DNA and RNA sequencing, also allow detection of mutations and gene fusions for coding and non-coding regions of the genome [17]. While conceptually similar in experiment design, the sequence read information generated using NGS platforms has very different statistical properties to intensity-based information acquired from microarray platforms [18].

Multiple articles have reviewed protocols to generate microarray profiles and their statistical analysis to extract meaningful information [19–22]. While relatively new, a vast amount literature describing statistical methodologies to analyse NGS data already exists [23–28]. So, in this article we focus only on the methodologies to extract meaningful information from NGS reads. Moreover, we will discuss only those data types that are generated and analysed by TCGA. Furthermore, for each data type, we only describe the main steps to obtain this information and point the readers to an exhaustive list of methodologies/software and articles for deeper insight. Also, we do not provide any comments on comparison of these methods but rather point to articles comparing different methodologies and identifying their strengths and errors [29–32].

Pre-processing and Quality Control of NGS data

To date, several next-generation sequencing platforms are available, including the Illumina Genome Analyser, which is being used extensively TCGA by consortium for tumour profiling. Each platform has its own method for generating sequencing reads from samples. But in every case, the sequence reads obtained using these platforms are short – typically from 36 to several hundred nucleotides. Furthermore the sequencing run can be single-end or paired-end, meaning the reads are sequenced in one or two directions (from 3’ and 5’ ends). The first tasks in any NGS computational pipeline are: performing primary data acquisition, determining base calls and confidence scores from the fluorescent signals of the sequencer, and converting them to FASTQ files containing the raw sequence reads and per base quality scores. When multiple samples are pooled in one lane using sample-specific index/barcode adapters, the FASTQ should be demultiplexed and reorganised based on index information, and the adapters ought to be trimmed [33].

Quality control is a very important part of the data preparation (Table 1). There are several kinds of sequencing artefacts that could have a serious negative impact on downstream analyses. The artefacts commonly exist in raw reads, regardless of the sequencing platform. Firstly, sequences may be contaminated with adapters on their 5′- or 3′ ends that were added as part of the sequencing protocol. Secondly, base quality and sequence complexity vary both within and between reads. The qualities of bases on most sequencing platforms will degrade as the run progresses, so it is common to see the quality of base calls falling towards the end of the read. It is desirable to remove or trim such sequences with appropriate thresholds. Additionally, NGS reads can be highly redundant with the same sequence being represented in large numbers, so it is important to reduce these PCR amplification artefacts. The contamination in the sequencing dataset can also be caused by laboratory factors such as sample preparation, library construction, and other steps of the experiment. Moreover, samples may contain DNA/RNA from other sources including viruses, which are hard to avoid during the sample preparation process. Finally, general statistical methods like sample clustering and principal component analysis (PCA), and outlier detection can be used for assessment of overall quality and sample comparison according to experiment design.

Table 1.

Software for primary quality control of NGS data

Method name	Year published	PMID	Data type	Platform	Statistical method	Input requirements
BIGpre	2011	22289480	Illumina, 454	Perl	Correlation between forward and reverse reads, trimming low-quality reads	FASTQ
FastQC	2010	–	any	Java	Sequence length, quality, k-mers presence reports	FASTQ, SAM/BAM
HTQC	2013	23363224	Illumina	C++	Tail trimming, filter by quality/length/tile	FASTQ
QC-Chain	2013	23565205	any	C++	Quality assessment, trimming, filtering unknown contamination	FASTQ
Qualimap	2012	22914218	any	Java, R	Alignment biases detection, sample comparison	SAM/BAM
PRINSEQ	2011	21278185	any	Perl	Sequence complexity, duplicates, occurrence of Ns and poly-A/T tails, tag sequences reports	FASTQ, FASTA
PIQA	2009	19602525	Illumina	R	Assess the clusters density per tile, base-calls proportions per tile/cycle	FASTQ
FastUniq	2012	23284954	any	C++	De novo PCR duplicates removal for paired short reads	FASTQ

Aligning short reads to the reference genome

Accurate alignment of short sequence reads generated using NGS platforms to a repeat masked reference genome is the first step in obtaining biological information from NGS data. Since, the numbers of reads generated in any given NGS experiment are very large (typically in millions), many efficient algorithms have been developed to deal with the alignment process. It is important to note that different read mapping procedures are necessary depending on the needs of downstream analysis, and alignment accuracy has a high impact on the interpretation of the data. We comment on that in sections to follow. Most applications aim to identify uniquely mapped reads - matching to a single “best” genomic position. The non-uniquely mapped reads are filtered using an upper boundary for the number of reported mappings.

Most short read alignment algorithms use auxiliary data structures (also called indexes) for the reads or reference sequence. The main indexing methods are based on hash tables, prefix/suffix trees, or merge sorting methods (Table 2). Such representation of the entire human genome takes only a few GB of memory and enables exact matches to be found in a short time. Burrow-Wheeler transform and FM-index-based algorithms give better results for reads from repeated regions, but there is no efficient general method for handling errors in the reads for this category. Some hybrid solutions have been proposed, e.g. Stampy (see Table 2). These enhancements result in higher sensitivity and smaller memory requirements of mapping tools. Reported mapping positions are particularly useful as they prevent the result list being blown up by reads mapping to highly repetitive regions. It is important to note that in the case of paired-end sequencing the paired reads need to be mapped to identical genomic positions to be considered multi-mapping reads. Data from Illumina's machine has few substitution errors per read and virtually no insertion or deletion (INDELs) errors [34]. Thus, it can be mapped efficiently by, for example, Bowtie [35], and then its junction-mapping extension can be done by Tophat [36], which can handle up to three mismatches per sequence and no INDELs.

Table 2.

Software for mapping sequence reads to genome

Method name	Year published	PMID	Data type	Platform	Statistical method	Input requirements
BFAST	2009	19907642	RNA	C	Based on creating flexible, efficient whole genome indexes to rapidly map reads to candidate alignment locations, with arbitrary multiple independent indexes allowed to achieve robustness against read errors and sequence variants. Final local alignment uses a Smith-Waterman method, with gaps to support the detection of small INDELs	FASTQ
Bowtie	2009	19261174	RNA	C++ (SeqAn library)	Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches	FASTQ, FASTA
BWA	2009	19451168	RNA	C	Backward search with Burrows-Wheeler Transform (BWT), allowing mismatches and gaps.	FASTQ
BWA-PSSM	2014	24717095	RNA	C	Probabilistic adaptable alignment based on the use of position specific scoring matrices (PSSM) and BWT	FASTQ
CUSHAW2		22576173	RNA	C++	Uses Burrows-Wheeler transform (BWT), the Ferragina-Manzini index and CUDA parallel programming model for GPUs. Supports only ungapped alignment	FASTQ
DistMap	2013	24009693	RNA	Perl, Java	Wrapper for many aligners, based on MapReduc API for parallel processing. Currently not handling spliced alignments	FASTQ
MAQ	2008	18714091	RNA	C++, Perl	Based on Smith-Waterman gapped alignment and Bayesian statistical model that incorporates the mapping qualities and error probabilities	FASTQ
MOSAIK	2014	24599324	RNA	C++	Uses hash clustering strategy coupled with the Smith-Waterman algorithm. Detects mismatches, short insertions and deletions	FASTQ
PASS	2009	19218350	RNA	C++	Based on precomputed score tables (PST) calculated with the Needleman and Wunsch algorithm	FASTQ
RMAP	2009	19736251	RNA	C++	Uses multiple filtration (Pevzner and Waterman) and approximate pattern matching. Incorporates the use of quality scores directly into the mapping process	FASTQ, FASTA
SOAPaligner/SOAP2	2009	19497933	RNA	C	Based on Burrows Wheeler Transformation (BWT) compression index	FASTQ
Stampy	2011	20980556	RNA	Python	Hybrid probabilistic model for mapping quality (measured by Phred score)	FASTQ
ZOOM	2008	18684737	RNA		Custom filtering model	FASTQ

Transcriptome sequencing produces reads from transcribed sequences with introns and intergenic regions excluded. Standard alignment algorithms, which handle mismatches and gaps, generally do not handle mapping reads spanning across exons. Tools for identifying novel splice junctions usually use standard algorithms in the first step and then derive exon positions, e.g. from clustering of mapped reads or reads mapped into introns at their last few bases. Even in de novo assembly, in some parallel algorithms, if the location of each individual read is not tracked the reads may still need to be aligned back to the assembly. Therefore, sequence mapping is essential to almost all NGS techniques. Quantitation of microRNA expression requires similar steps but reads are mapped to the mature and precursor sequences of known miRNAs collected in microRNA databases. Prediction of secondary structure and genomic cluster analysis is useful [37].

Expression quantitation and identification of differential expression

The expression level of each mRNA is measured by the number of sequenced fragments that map to the transcript (or counts and its derivatives), which is expected to correlate directly with its abundance level. Counts usually refer to the number of reads that align to a particular genomic feature. Like gene counts, any other targets may be quantified, including exons, transcripts, and miRNAs. Counts are heavily dependent on RNA sequencing depth and the effective length of the feature. Therefore, counts need to be adjusted for feature length to make the expression comparable. Effective gene counts are adjusted for the amount of bias in the experiment. Counts per million (CPM) mapped reads are counts scaled by the number of sequenced fragments multiplied by one million. CPM's length-normalised analogues are reads per kilobase per million (RPKM) and fragment per kilobase per million (FPKM). RPKM and FPKM are identical for single-end sequencing but differ for the paired-end sequencing. Calculating length-normalised measures makes them comparable within a sample. The RSEM package computes maximum likelihood abundance estimates using the Expectation-Maximisation algorithm and effectively takes care of multi-mapping reads. The RSEM representation is a current standard for reporting expression by Firehose GDAC pipeline.

A deficiency of the RPKM/FPKM approach is that the proportional representation of each gene is dependent on the expression levels of all other genes. Often a small fraction of genes account for large proportions of the sequenced reads, and small expression changes in these highly expressed genes will skew the counts of lowly expressed genes under this scheme. This can result in deduction of erroneous differential expression. Therefore, methods for calculating differential expression require counts to begin with. Thus RNA-Seq non-negative counts follow discrete distribution as opposed to the intensities recorded from microarrays, which are treated as continuous measurements and commonly assumed to follow a log-normal distribution. For RNA-Seq data Poisson distribution and Negative Binomial (NB) distribution are the two most commonly used models [38–42] (Table 3). Other distributions, such as beta-binomial [43], have also been proposed.

Table 3.

Software for RNA-Seq data analysis

Method name	Year published	PMID	Data type	Platform	Statistical method	Input requirements
Aldex	2013	23843979	RNA-seq	R	ANOVA, Dirichlet distribution	Raw counts
ALDEx2	2014	24910773	RNA-seq	R	Dirichlet distribution, glm	Raw counts
ASC	2010	21080965	RNA-seq	R	Empirical Bayes,	Raw counts
baySeq	2010	20698981	RNA-seq	R	Empirical Bayes	Raw counts
BBSeq	2011	21810900	RNA-seq	R	Beta-Binomial, linear model	Raw counts
CEDER	2012	22641709	RNA-seq	R	Negative binomial	Raw counts
campcodeR (uses edgeR & DESeq)	2014	24813215	RNA-seq	R	Empirical Bayes	Raw counts
COV2HTML (not working)	2014	24512253	RNA-seq	Web site
CPTRA	2009	19811681	RNA-seq	Python	???	Long-read sequence w/ annotation, short-read sequence tag // fasta, fastq
CQN	2012	22285995	RNA-seq	R	Conditional quantile normalisation, robust generalised regression	Raw counts
Cuffdiff	2013	23222703	RNA-seq	Standalone	Beta negative binomial distribution
DegPack	2013	24981075	RNA-seq	Web site	Non-parametric (ranks)	Raw counts
DEGseq	2010	19855105	RNA-seq	R	MA-plot-based (?)	Raw counts
DER Finder	2014	24398039	RNA-seq	R	Hidden Markov Model	Raw counts
DESeq	2010	20979621	RNA-seq	R	Negative binomial distribution	Raw counts
DEXUS	2013	24049071	RNA-seq	R	Expectation-maximisation algorithm, Bayes	Raw counts
EBSeq	2013	23428641	RNA-seq	R	Empirical Bayes	Raw counts
EDASeq (users edgeR & DESeq)	2011	22177264	RNA-seq	R	Empirical Bayes	Raw counts
edgeR	2012	22287627	RNA-seq	R	Empirical Bayes, glm	Raw counts
edgeR-robust	2014	24753412	RNA-seq	R	Weights, empirical Bayes	Raw counts
GExposer					Machine learning algorithm
iFad	2012	22581178	RNA-seq	R	Bayesian sparse factor model	Raw counts
MRFSEQ (uses DESeq)	2013	23793751	RNA-seq	Standalone	Markov random field model	Raw counts, co-expression database
Myrna	2010	20701754	RNA-seq	Cloud-computing, Bowtie, R
NOISeq	2011	21903743	RNA-seq	R	Non-parametric	Raw counts
NPEBseq	2013	23981227	RNA-seq	R	Non-parametric Bayesian	Raw counts
pairedBayes			RNA-seq	R	Empirical Bayes	Raw counts
PoissonSeq	2012	22003245	RNA-seq	R	Poisson goodness-of-fit	Raw counts
QuasiSeq	2012	23104842	RNA-seq	R	Quasi-Poisson, quasi-negative binomial	Raw counts
RNASeqGUI (uses edgeR, DESeq, NoiSeq, BaySeq)	2014	24812338	RNA-seq	R	Empirical Bayes, negative binomial	Raw counts
SAMSeq	2013	22127579	RNA-seq	Standalone	Non-parametric	Raw counts
ShrinkBaye	2014	24766777	RNA-seq	R	Negative binomial, Poisson-Gaussian, Bayesian GLM	Raw counts
sSeq	2013	23589650	RNA-seq	R	Negative Binomial	Raw Counts
TCC (uses edgeR, DESeq, DESeq2)	2013	23837715	RNA-seq	R	Negative Binomial, Empirical Bayes	Raw counts
tRanslatome	2013	24222209	RNA-seq	R	Rank Product, t-test, SAM, limma, ANOTA, DESeq, edgeR	Raw counts
TSPM.R
tweeDEseq	2013	23965047	RNA-seq	R	Poisson-Tweedie distributions	Raw counts

The Poisson distribution has the advantage of simplicity and has only one parameter, but it constrains the variance of the modelled variable to be equal to the mean. The Negative Binomial distribution has two parameters, encoding the mean and the dispersion, and hence allows modelling of more general mean-variance relationships. For RNA-seq, it has been suggested that the Poisson distribution is well suited for analysis of technical replicates, whereas the higher variability between biological replicates necessitates a distribution incorporating overdispersion, such as Negative Binomial [28, 44, 45]. Analogous to microarray data analysis, it is clear that borrowing the variance from other genes help to better estimate the variation in read counts for a gene and condition. This overcomes a common problem with an underestimation of variance when based on a low number of observations. The most commonly used parametric methods include EdgeR, DESeq, and baySeq and use negative binomial distribution. Other methods such as Cuffdiff2 uses a beta-negative binomial distribution, which is a combination of beta and negative binomial distribution. Non-parametric methods like SAM-Seq also work relatively well on the count data.

Identification of alternative splicing from transcriptomic reads

A widely recognised source of proteome diversity in eukaryotic species is expression of multiple distinct mRNA transcripts from a single gene locus by alternative transcript initiation, alternative splicing [47] (Table 4), and alternative polyadenylation [48]. If RNA-Seq reads span exon junctions, parts of reads will map to two different exons. This allows inference of alternative splicing. However, such a read structure will pose problems to standard aligners that map reads contiguously to the reference. Splice sites can be detected initially by identifying reads that span exon junctions. Split-read aligners such as TopHat, methods that identify minimal match on either side of exon junction, and genomic short-read nucleotide alignment are used to identify alternative splicing. Most methods utilise a database of expression and alternative expression sequence ‘features’. These and other strategies that perform de-novo assemblies present a number of computational challenges because of computation time and the depth of sequencing, which results in few junction-spanning reads. This quantitation of alternatively spliced transcripts needs to be followed by identification of differential expression of these transcripts between samples.

Table 4.

Alternative splicing algorithm

Method name	Year published	PMID	Data type	Platform	Statistical method	Input requirements
ABMapper	2011	21169377	RNA	C++, Perl	Fast suffix-array algorithm and a dual-seed strategy for spliced alignment	FASTA, FASTQ
ERANGE	2008	18516045	RNA	Python	Splice junctions identification relies on reference genome exon positions	FASTQ
GEM Mapper	2012	23103880	RNA	C, Objective Caml	Based on Burrows-Wheeler Transform and custom mapping algorithms. Uses custom mappability concept	FASTQ
MapSplice	2010	20802226	RNA	C++, Python	Algorithm not dependent on splice site features or intron length; consequently, it can detect novel canonical as well as non-canonical splices. This method has tag alignment phase and splice inference phase	FASTQ
PALMapper	2010	21154708	RNA	Web/ Galaxy	Combines GenomeMapper (based on BWT and k-mer indexes) read mapper with the spliced aligner QPALMA	FASTQ
QPALMA	2008	18689821	RNA	C++, Python	SVM-based splice site predictor with the so-called ‘weighted degree’ kernel. Alignment based on extended BWT	FASTQ
SpliceMap	2010	20371516	RNA	C++, Python	Cannoical GT-AG splice sites identification using half-read mapping	FASTQ
SplitSeek	2010	20236510	RNA		Candidate junction reads generation in intermediate BEDPE format feasible for paired-end sequences	FASTQ
Subread	2013	23558742	RNA	R	Seed-and-vote - new multi-seed alignment strategy for overlapping seeds from each read (subreads)	FASTQ
TopHat	2009	19289445	RNA	C++	Canonical GT–AG splice sites identification	FASTQ

Apart from detection and quantitation of expression and alternative splicing, RNAseq has the capacity to identify RNA editing events [49–51], allele-specific expression (ASE) [52, 53], quantify noncoding RNAs [54, 55], and detect exogenous RNA [56, 57], single-nucleotide polymorphisms (SNPs), somatic mutations [8, 58], and structural variations.

Detection of somatic copy number alterations and structural variants

Genomic alterations accumulate in tumours during cancer development [59]. In addition to point-mutations, inversions, and translocations, somatic copy-number alterations (SCNAs) are ubiquitous in cancer [60, 61], and several recurrent SCNAs are associated with particular cancer types [62], tumour aggressiveness [63], and patient prognosis [64]. Reliable detection of SCNAs can lead to identification of cancer driver genes [65] and development of new therapeutic approaches [66–68]. Deep sequencing [69] and exome-based sequencing efforts are now replacing microarray-based array CGH (aCGH) [70] and single nucleotide polymorphism arrays (SNP arrays) [23, 71]. Moreever, similarly to microarray-based studies, the inference about cancer related copy number alterations requires comparison of paired samples – normal and tumour.

The general workflow to detect SCNAs from sequencing data consists of three main steps: (i) raw copy-number inference in the local genome region by either calculating read counts or depth of coverage ratio between tumour and control samples, (ii) the raw copy-number profiles segmentation to find change-points in the raw copy-number signal and divide the chromosomes accordingly into segments with similar copy-numbers, and (iii) classification of different segments into gains or losses. The first step is essentially based on understanding variations in depth of coverage (DOC) of aligned sequence reads against the reference genome [69, 72, 73]. Deviation from the background in DOC may signify the presence of a copy number variation (CNV). The last two steps for obtaining copy number alteration are not specific to the sequencing data.

Multiple methods exist for identification of structural variations using whole genome sequencing. Methods include identification of atypical alignment patterns of sequence reads against the reference genome, which reflect gaps in the sequence alignment [74]. In paired-end read mapping, the sequenced ends of a short DNA fragment are aligned against the reference genome. The mean insert size of the fragment is compared with the reference genome distance between aligned fragment ends to deduce the presence of deletions or insertions [74–76]. This detection requires high alignment accuracy and underscores its importance.

Although coding regions comprise only ∼1% of the genome, they are enriched for causal variation, making exome-based studies valuable, manageable, and cost-effective. Whole exome sequencing (WES) data have been used effectively for the identification of small INDELs, usually of a size < 50 bp, within exon targets that are typically sized between 200 and 300 bp. The approaches discussed above, while appropriate for (deeply sequenced) DNA-sequencing data, are less effective for exome sequencing and detecting CNV, as the CNV's breakpoints are likely to lie outside the targeted exons [77]. Detecting structural and copy number variations from RNA-Seq data presents similar challenges.

Identification of cancer driver mutations and their functional impact

Cancer is abundantly composed of somatic mutations accumulating in the genome over an individual's lifetime, only a fraction of which drive cancer progression. Mutations can be identified from DNA-seq, RNA-seq, and Exome sequencing data [78–80] (Table 5). The most basic way of detecting somatic mutations from NGS reads is to identify mismatch/gaps in the alignment of the read with the reference. However, large datasets possess sequencing errors: random mutations that occur during cell division and single nucleotide polymorphisms that differ from reference assembly. This makes identification of cancer driver mutations a challenging issue [81]. Moreover, intra-tumour heterogeneity also hinders the identification of all types of somatic mutations [82]. Several methods for detecting somatic mutations are currently in use, such as MuTect [83], Strelka [84], and VarScan 2 [85] for SNV detection or BIC-Seq [86], APOLLOH [87], CoNIEFER [88], BreakDancer [89], and Meerkat [87] for CNA or SV detection. Most methods for somatic mutation detection take into account only part of the possible source of errors; therefore, running different methods simultaneously is advisable.

Table 5.

Methods for finding mutations

Method name	Year published	PMID	Data type	Platform	Statistical method	Input requirements
ActiveDriver	2013	23340843	DNA-seq	R	Generalized linear regression	FASTA, TAB
CancerMutationAnalysis	2014	24233780	DNA-seq	R	Empirical Bayes	Non-standard tables
CanDrA	2013	24205039	DNA-seq	Perl	U-Mann Whitney, AUC (area under curve)	Non-standard tables
CanPredict	2007	17537827	DNA-seq	Web site	SIFT, Pfam-based logR.E-value metric, GOSS	FASTA
CAROL	2012	22261837	DNA-seq	R	SIFT, PolyPhen-2	Tab-delimited, FASTA
CHASM/SNV-Box	2009	19654296	DNA-seq	Standalone	CHASM, SNV-Box	dnSNP r#, Pubmed ID, VCF, bed,
CRAVAT	2013	23325621	DNA-seq	Web site	CHASM, SnvGet	Non-standard tables
DDIG-in	2013	23497682	DNA-seq	Web site	Support vector machine-based method	Non-standard tables
DMI	2012	23044540	DNA-seq	Standalone	Machine learning, discrimination index	Text file
DrGaP	2013	23954162	DNA-seq	Standalone	Chi-square distribution	Non-standard tables
e-Driver	2014	25064568	DNA-seq	Perl	Binomial distribution	Non-standard tables
eXtasy	2013	24076761	DNA-seq	Web site	Variant impact prediction, haploinsufficiency prediction, phenotype-specific gene prioritisation	VCF
FATHMM	2013	23620363	DNA-seq	Web site	Hidden Markov Model	Annotated VCF
InVEx	2012	22817889	DNA-seq	Python	Permutation-based	Non-standard tables, power FASTA
MuSIC	2012	22759861	DNA-seq	Standalone	Fisher p-value, likelihood ratio test, convolution test (summarised log statistic of joint binomial point probability)	BAM, SNV, MAF
MutSig	2013	23770567	DNA-seq	Standalone	MutSigCV (Background mutation rate)
nsSNPAnalyzer	2005	15980516	DNA-seq	Web site	Machine learning (random forest)	FASTA, SNP
Oncodrive-fm	2012	22904074	DNA-seq	Standalone	SIFT, PolyPhen2, MutationAssessor	TDM, TSV
OncodriveCLUST	2013	23884480	DNA-seq	Python	Clustering	Non-standard tables
PANTHER	2013	23193289	DNA-seq	Web site	subPSEC	FASTA
PhD-SNP	2006	16895930	DNA-seq	Web site	Sequence and Profile-Based	FASTA
PROVEAN	2012	23056405	DNA-seq	Standalone	Alignment-Based	Non-standard tables
transFIC	2012	23181723	DNA-seq	Web site	SIFT, PolyPhen2, MutationAssessor	File w/ chromosome/protein coordinates (hg19)

The most basic task for mutation analysis in cancer is the distinction between driver and passenger mutations. To help filter a subset of driver mutations from the long list of detected somatic and passenger mutations, three major computational predictive approaches utilising different statistical tests can be applied [90–92]:

(1) Identification of recurrent somatic mutations is based on the idea of clonal evolution of tumour cell populations. To predict genes with recurrent single-mutations in a cohort of cancer patients, several statistical methodologies including MutSigCV [3], MuSiC [93], and DrGaP [94] are available. These methods are based on the determination of the probability of the observed number of mutations in a gene to the expected background mutation rate, the BMR (probability of observed passenger mutation) across a cohort of patients. As opposed to mutations, there is no accurate model established to identify genes with recurrent copy number aberrations (CNAs); therefore, methods are based on a non-parametric approach, e.g. GISTIC2 [95], CMDS [96], and ADMIRE [97].

(2) Prediction of the functional impact of individual mutations is based on the utilisation of additional information about protein sequence and/or structure and evolutionary conservation of the protein encoded by the mutated gene. Methods like SIFT [98], Polyphen-2 [99], and MutationAssesor [100] predict the functional impact (deleteriousness) of missense mutations. CHASM utilises random forest classification to identify driver and passenger somatic missense mutations, based on a training set of labelled positive (driver) and negative (passenger) examples [101]. Furthermore, clusters of non-synonymous mutations across patients, typically to detect ‘activating’ mutations, NMC [102] and the Invex [103] method can be applied. Moreover, the iPAC method is able to search for clusters of mutations, but in the context of crystal structures of proteins [104].

(3) Identification of recurrent combinations of mutations is based on assessment of combinations of mutations enriched in known pathways (e.g. GSEA [105], Path-Scan [106], Patient-oriented gene sets [107]), interaction networks (NetBox [108], HotNet [109], MEMo [110]), or de novo defined sets (Dendrix [109], Muti-Dendrix [111] or RME [112]), enabling the discovery of novel combinations of mutated genes in cancer.

Identification of gene fusions

Gene fusions appear as a result of chromosomal rearrangements, such as deletion, insertion, inversion, or translocation. A fused gene is expressed as a hybrid entity encoding sequences of two distinct genes. Tumorigenic fusions successfully evade the gene regulation that its constituents are subjected to. Multiple cancer-related gene fusions have been identified, including prototypic BCR-ABL [113], EML4-ALK [114], TMPRSS2-ERG [115], KIF5B-RET [116], and others [7, 13, 14, 117]. Such alterations may serve as a good cancer biomarker or therapeutic target [120, 121]. Whole genome [75, 119] and transcriptome sequencing [120, 121] profiles can be used to identify fusions.

Transcriptome sequencing is proven to be superior over WGS and therefore is more commonly used. This is due to the fact that RNA-seq covers only transcribed sequences, which constitute a small percentage of the genome, thus reducing the cost, time, and resources needed for full analysis. Furthermore, RNA-seq provides information on the transcriptionally active fusion genes and their splicing variants. However, it also harbours certain limitations, including lack of information regarding non-transcribed regions and dependence on the heterogeneity in the expression levels between various cell types [122]. Over the last few years several software packages (Table 6) have been developed for the detection of gene fusions and/or structural variants (SV) that cause gene fusions. The majority of the software utilises RNA-seq data as an input. However, other tools use WGS data or both to increase the likelihood of detection of true fusion.

Table 6.

Identifying gene fusions

Method name	Year published	PMID	Data type	Platform	Statistical method	Input requirements
BreakFusion	2012	22563071	RNA-seq	C++, Perl	A computational pipeline for identifying gene fusions from RNA-seq data	BAM
BreakTrans	2013	23972288	RNA-seq	Perl	Uncovering the genomic architecture of gene fusions	Tab-delimited text files
chimerascan	2011	21840877	RNA-seq	Python	Identifying chimeric transcription in sequencing data	FASTQ
comrad	2011	21478487	RNA-seq, DNA-seq	C++	Discovery of gene fusions using paired end RNA-Seq and WGSS.	FASTQ
deFuse	2011	21625565	RNA-seq	C++	Detecting gene fusions from paired-end RNA-seq	FASTQ
FusionAnalyser	2012	22570408	RNA-seq	C#	Detecting gene fusions from paired-end RNA-Seq data	SAM/BAM
FusionHunter	2011	21546395	RNA-seq	Perl	Detecting gene fusions from paired-end RNA-Seq data	FASTQ
FusionMap	2011	21593131	RNA-seq, DNA-seq	C#	Detecting gene fusions from single- and paired-end RNA-Seq and DNA-seq data	FASTQ/BAM with unmapped reads
FusionSeq	2010	20964841	RNA-seq	C	A modular framework for finding gene fusions by analysing Paired-End RNA-Sequencing data	MRF, SAM
ShortFuse	2011	21330288	RNA-seq	C++, Python	Detecting gene fusions from paired-end RNA-Seq data	FASTQ
SnowShoes-FTD	2011	21622959	RNA-seq	Perl	Detecting gene fusions from paired-end RNA-Seq data	FASTQ
SOAPfusion	2013	24123671	RNA-seq	Perl	Detecting gene fusions from paired-end RNA-Seq data	FASTQ
TopHat-Fusion	2011	21835007	RNA-seq	C++	Detecting gene fusions from single- and paired-end RNA-Seq data	FASTQ

The most common analysis steps for identifying gene fusions are as follows: (i) alignment and filtering, (ii) detection of fusion junctions in candidate genes, and (iii) fragment assembly and selection of putative fusions [122]. Apart from mapping to the current reference genome available, RNA-seq reads are additionally mapped to annotated transcriptome libraries (e.g. RefSeq). The most commonly used mapping tool is Bowtie, due to its speed and high efficiency. The reads that map appropriately to the reference genome are filtered out from further analysis. The unmapped or discordantly mapped reads are fusion candidates that might be further passed through additional filters (e.g. ribosomal filter, repetitive region filter, short distance filter, etc.) to eliminate potential false negatives. Next, the reads remaining after filtration are divided into smaller fragments (so-called “split reads”) with even or pre-defined length, and both terminal parts are independently aligned to the reference genome. If they map to two different genes, they are further subjected to detection of fusion junction. The sequences of both genes are put together according to the fusion boundary, and the whole read is re-aligned to the candidate fusion gene to call supporting reads essential for the final selection of fusion. Another approach for the detection of fusion intersection is grouping discordantly mapped reads (“spanning reads”) according to the same breakpoints. Detection of fusion junction from such groups fuels prediction of the putative fusion transcript. Subsequently, the reads are re-aligned to predicted sequences and the predictions with the highest mapping scores aid identification of candidate fusion genes. The final selection of the fusion genes depends on several parameters including the number of supporting reads, quality of the alignment, and sequencing coverage [122, 123].

Estimating sample purity

Most genomics and expression-profiling studies including TCGA use a mixture of different clonal populations of tumour cells, which is often contaminated with stromal and immune cells. Indeed, many common tumours, such as pancreatic tumours, are intensively infiltrated by stroma [124] making it difficult to obtain homogenous material for genomic studies. Furthermore, epithelial cells are also often found in tumour samples, as they are at the interior surface of blood vessels necessary for providing nutrients for cancer cells. Methods like laser capture micro-dissection are rarely used in RNA-studies requiring stable material [125]. Estimating purity and clonality of a tumour sample containing a mixed population of cells requires accurate measurement of the proportion of tumour and stromal cell samples. Over the years several different methods (Table 7) have been developed to deconvolute genomic and transcriptomic data obtained from mixed-cell populations. Software packages based on these methods provide powerful tools for estimation of tumour heterogeneity and purity and in consequence identification of likely early driver events during tumorigenesis [126].

Table 7.

Available software for purity estimation

Method name	Year published	PMID	Data type (technique)	Platform	Statistical method	Input requirements
Dsection	2010	20631160	RNA (Microarray)	Web-based and MATLAB	Bayesian model	Expression and proportion data required
csSAM	2010	20208531	RNA (Microarray)	-	Linear regression-based model	Expression profile of mixed tissue samples
mixture_estimation.R	2010	20202973	RNA (Microarray)	R based	Variation of electronic subtraction method	Expression profile of mixed tissue samples
ASCAT	2010	20837533	DNA (Microarray)	R based	Analytical optimisation method	SNP array data with Log R and B-Allele frequency information
PERT	2012	23284283	RNA (Microarray)	Octave	Perturbation model	Expression data from mixed cell type and expression profile of each homogeneous cell type
ABSOLUTE	2012	22544022	DNA (Microarray and HTS)	R based	Gaussian mixture model	Copy number data in segmentation file
JointSNVMixl, JointSNVMix2	2012	22285562	DNA (HTS)	Python	Probabilistic graphical model	Sequence data from tumour/normal pairs
CNAnorm	2012	22039209	DNA (HTS)	R based	Analytical optimization method	Sequencing data of tumour and normal samples in bam format
DeconRNASeq	2013	23428642	RNA (Microarray and HTS)	R based	Globally optimised nonnegative decomposition algorithm	Expression data from multiple tissue, signature of individual tissue and proportion data required
TEMT	2013	23735186	RNA (HTS)	Python	Probabilistic model including position and sequence-specific biases	Required RNA-seq sequencing data from pure tissue and mixed tissue
ESTIMATE	2013	24113773	RNA (HTS)	R based	Gene signature (ssGSEA) based model	Expression data in Gene Set Enrichment Analysis (GSEA) gct format
THetA	2013	23895164	DNA (HTS)	Python	Explicit probabilistic model	Copy number data in interval count file format
ExPANdS	2013	24177718	DNA (HTS)	R based and MATLAB	Probability distributions model	Somatic mutations and copy number data required
Virmid	2013	23987214	DNA (HTS)	Java based	Probabilistic model and maximum likelihood estimator	Disease and normal sequencing data in bam format
MuTect	2013	23396013	DNA (HTS)	Java based	Bayesian model	Tumour and normal sequencing data
TrAp	2013	23892400	DNA (HTS)	Java based	Linear mixture model with evolutionary framework	Tumour karyotypes and somatic hypermutation datasets
Seo et al.	2013	23650637	RNA (Microarray)	–	Linear mixture model	Disease-associated variants and expression of heterogeneous normal tissue

Comparative and integrative analysis of tumour samples

One of the major achievements of the TCGA project is the generation of different types of data from the same sample for a large number of tumours. This data generation is followed by uniform data processing and correlation with clinical information by Firehose and other analysis pipelines at various genome data analysis centres. The availability of such paired data allows detection of functional impact on genomic lesions (e.g. mutations, copy numbers, and gene fusions) on gene/miRNA (using RNA-Seq) and protein expression (using RPPA arrays) and pathway levels while reducing errors due to individual patient variation. Another example is the utilisation of multiple data types to identify integrated subtypes for a given tumour type using the iCLUSTER method [13, 14, 127]. Other approaches include integration of pathway information (e.g. PARADIGM & Paradigm-shift) and regulatory network information (e.g. GEMINI – [128]).

Comparative analysis of multiple tumour types increases the statistical power to detect common events that drive tumorigenesis and repurpose the therapy. For example, ERBB2-HER2 is mutated and/or amplified in subsets of glioblastoma, gastric, serous endometrial, bladder, and lung cancers. The result, at least in some cases, is responsiveness to HER2-targeted therapy, analogous to that previously observed for HER2-amplified breast cancer. There are more examples that underscore the importance of such comparative analysis [4].

Future of cancer profile analysis

As we are entering the era of $1000 genome sequencing, tumour profiles are being sequenced routinely. Moreover, tumour catalogues and pre-clinical models [129, 130] have similar types of information available, with or without drug treatments. Integration of such datasets can speed up pre-clinical drug development and repurposing of available drugs. Tumour profiling by sequencing is also expected to enter both the pre-clinical and clinical setting for standardised testing as well as personalisation of medicine. However, the sequencing data fits the definition of “big data”, and a reliable computational infrastructure for storage, processing, analysis, and visualisation [131, 132] is required to make most of this avalanche of information [133]. Indeed, ambitious efforts like the cancer moonshot program and APOLLO launched by the UT MD Anderson Cancer Centre, aim to combine big data warehousing with IBM WATSON based cognitive and adaptive learning to reduce cancer mortality for several tumour types, will fully realise the power of tumour profiling.

Authors declare no conflict of interest.

The authors thank Hubert Świerczyński, Wojciech Pieklik, Juliusz Pukacki, and Dr Cezary Mazurek from the Poznan Supercomputing and Networking Centre affiliated to the Institute of Bioorganic Chemistry of the Polish Academy of Sciences for their help in preparation of the tables. This work was supported by the Foundation for Polish Science Welcome program grant No: 2010-3/3 to Maciej Wiznerowicz and UT MD Anderson Cancer Center intramural grants.