Bioinformatics Interpretation of Exome Sequencing: Blood Cancer

Article information

Genomics Inform. 2013;11(1):24-33
Publication date (electronic) : 2013 March 31
doi : https://doi.org/10.5808/GI.2013.11.1.24
1Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305-806, Korea.
2Department of Bioinformatics, University of Science and Technology, Daejeon 305-806, Korea.
Corresponding author: Tel: +82-42-879-8540, Fax: +82-42-879-8519, deepreds@kribb.re.kr

These two authors contributed equally to this work.

Received 2013 January 30; Revised 2013 February 20; Accepted 2013 February 22.

Abstract

We had analyzed 10 exome sequencing data and single nucleotide polymorphism chips for blood cancer provided by the PGM21 (The National Project for Personalized Genomic Medicine) Award program. We had removed sample G06 because the pair is not correct and G10 because of possible contamination. In-house software somatic copy-number and heterozygosity alteration estimation (SCHALE) was used to detect one loss of heterozygosity region in G05. We had discovered 27 functionally important mutations. Network and pathway analyses gave us clues that NPM1, GATA2, and CEBPA were major driver genes. By comparing with previous somatic mutation profiles, we had concluded that the provided data originated from acute myeloid leukemia. Protein structure modeling showed that somatic mutations in IDH2, RASGEF1B, and MSH4 can affect protein structures.

Introduction

Thanks to the next-generation sequencing (NGS) technology, genomics research area has been developed dramatically in recent years. Since 2008, many efforts have been made to find somatic mutations in cancer genomes; hundreds of samples by exome sequencing have been used to develop diagnosis and prognosis markers in various cancers in recent researches. Cheaper price has enabled us to get more exome sequencing data from hundreds of samples, even thousands of genomes.

By applying exome sequencing to cancer genomes, we can discover somatic mutations and somatic copy-number alterations (SCNAs) on a massive and genome-wide scale. Traditionally, array-based comparative genomic hybridization has been used to discover SCNA, but exome sequencing has many advantages, in that one can derive various results, such as somatic mutations, loss of heterozygosity (LOH), and SCNA. It is still under debate because bioinformatics methods could give different results depending on their parameters and software, but major driver genes of somatic mutations show a similar consensus.

We had used an in-house bioinformatics pipeline and databases for this research, especially "somatic copy-number and heterozygosity alteration estimation" (SCHALE) to find LOH and SCNA. Furthermore, driver genes were also inferred from network and pathway analyses. Protein structure prediction was used to predict putative structural changes by somatic mutations and also possibly their functional changes.

Methods

Dataset

Whole-exome sequencing data of tumor/normal paired samples from 10 individuals with blood cancer were obtained in FASTQ format from PGM21 (The National Project for Personalized Genomic Medicine). For 10 of the samples, the genotype information from single nucleotide poymorphism (SNP) chip was additionally obtained (but not the raw data).

Data processing and quality control (QC)

The QC of sequencing reads was performed with NGS QC Toolkit, and the high-quality reads were aligned to the human reference genome (hg19, http://genome.ucsc.edu) using BWA (http://bio-bwa.sourceforge.net/) [1] with default parameteres. Picard (http://picard.sourceforge.net) was used to remove PCR duplicates. Base quality recalibration and local realignment around indels were performed using Genome Analysis Toolkit (GATK, http://www.broadinstitute.org/gatk/). Because our previous study showed that the discordance of indel alignments had potential to generate false-positive somatic mutations, we performed the local realignment after the merging of alignments from same patients. Template lengths were calculated using the processed alignments, and the distributions for each samples were plotted to examine the qualities of sequencing libraries. Variant genotyping for each sample was performed with UnifiedGenotyper of GATK, and the variants were filtered as follows: mapping quality zero (MQ0) ≥ 4 && MQ0/depth of coverage (DP) > 0.1, variation quality (QUAL) < 200, quality by depth (QD) < 5, Fisher strand (FS) > 60, MQ < 40, DP < 10 (5 if no variant allele), genotype quality (GQ) < 13. The genomic annotation of variants was performed with custom-made scripts and refSeq information.

Identification of somatic mutations

We had developed a bioinformatics pipeline for this award program (Fig. 1). VarScan [2] was used to identify somatic mutations. The output of VarScan was filtered as follows: somatic p-value ≤ 0.05, normal allele depth ≤ 1, tumor allele depth ≥ 5, tumor total depth ≥ 10. The mutation set was annotated using a custom-made script with refSeq, SIFT (http://sift.jcvi.org/), MutationAssessor, phyloP46way (vertebrate), dbSNP 137 [3], in-house Korean variation database, etc. We selected the driver mutations that were not in dbSNP 137, excluding clinically associated variants (Flagged track in University of California Santa Cruz [UCSC] Genome Browser) and an in-house Korean variation database, and were deleterious missense, nonsense, read-through, splicesite, or coding indel mutations (NS/SS/I). The deleterious missense mutations were determined if at least 2 out of the following 3 were true: PhyloP score > 1.5, SIFT prediction = deleterious, MutationAssessor Func. Impact = high or medium.

Fig. 1

Bioinformatics pipeline for exome data and numbers of somatic mutation candidates for each filtering step. SNP, single nucleotide polymorphism; LOH, loss of heterozygosity; SCNA, somatic copy-number alterations; SCHALE, somatic copy-number and heterozygosity alteration estimation; NS/SS/I, nonsense, splice-site, or coding indel mutations.

The total numbers of candidates for each filtering step are summarized in Fig. 1. We found somatic mutations in samples G10, G08, and G09, but they were not NS/SS/I.

Comparison with genotypes from SNP chip

To compare with genotypes from SNP chip, the genotyping was performed using UnifiedGenotyper with the intervals, including all the coordinates of SNP chip and the option-output_mode EMIT_ALL_SITES. For each sample with genotype information from SNP chip, overall genotype concordance, non-reference sensitivity, and non-reference discrepancy rate were calculated according to the guide of GATK VariantEval using the genotypes with DP ≥ 5.

Genotype comparison of samples

The multi-sample SNP genotyping was performed with UnifiedGenotyper to compare the genotypes on the variant positions where all samples were able to be genotyped. The filtration scheme was the same as above. The distance between two samples was calculated as the sum of genotype difference values (0 if two genotypes are same, 1 if a genotype is 0/1 and another is 0/0 or 1/1, 2 if a genotype is 0/0 and another is 1/1). The distances were divided by the number of the variant positions. Heatmap and similarity matrix were used to visualize the distances.

Genome annotation

We used in-house genome annotation pipelines and various databaes, as summarized below.

  • Genomic annotation: refGene

  • Human Genome Variation Society (HGVS) nomenclature: nucleotide (non-coding, coding), protein

  • SIFT - tolerant amino acid substitutions

  • MutationAssessor

  • phyloP46way_vertebrate

  • GERP; GERP_element: rsScore

  • RepeatMasker

  • Tandem repeat definition

  • dbSNP 137; Common, Flagged, Mult

  • Korean variation database (54 Personal Genomes)

  • OMIM morbid genes

  • Oncogenes and tumor suppressors from Ariadne Pathway Studio

Detection of LOH and SCNA by SCHALE

Detection LOH and SCNA was performed with custom-made software SCHALE (Fig. 2). For the positions where the genotypes were heterozygous in normal samples, the values of LOH and SCNA were calculated. The values of LOH were calculated by the equation:

Fig. 2

Somatic copy-number and heterozygosity alteration estimation (SCHALE), developed for detection of loss of heterozygosity (LOH) and somatic copy-number alterations (SCNA). TMM, trimmed mean of M values; CBS, circular binary segmentation.

LOH = | RT - 0.5 | - | RN - 0.5 |, where RN is the count of reads supporting a non-reference allele divided by the total depth in the normal sample and RT is in tumor sample. The values of SCNA were calculated by the equation:

SCNA = log2(tumor depth/normal depth), where the depths were normalized using R library tweeDEseq. The values of LOH and SCNA were segmented together using R library DNAcopy with the parameters alpha = 0.01 and undo.SD = 2. The segments and values were plotted for each chromosome with the idiograms in R library SNPchip.

Network analysis

Ingenuity Pathway Analysis (IPA) was used to perform the network study of the identified somatically mutated genes and the genes in the LOH region.

Protein structure modeling

Before the structural modeling, the protein features related to the somatic mutations were found by BLAST on UniProt (10 most similar proteins, expectation value = 1e-30) and residue mapping. Homology modeling was performed using MODELLER (http://salilab.org/modeller) with the templates 1LWD for IDH2, 3KSY for RASGEF1B, and 2O8B for MSH4. Molecular dynamics simulation was performed with GROMACS (http://pubs.acs.org/doi/abs/10.1021/ct700301q). The structural images were drawn using PyMOL (http://www.pymol.org/).

Results

QC and results

We had used several methods to check the qualities of sequence reads. First, we used NGS QC Tools [4] to confirm sequence qualities (Supplementary Table 1). Overall, they had a very good quality, according to NGS QC Tools results. Template length (library size) was also evenly distributed for all samples (Supplementary Fig. 1).

Reference genome mapping

About 98% of reads were mapped onto the human reference genome (UCSC Genome Browser, hg19), summarized in Supplementary Table 2. After a duplicate removal step, 77.85% reads were retained; each sample had up to 25% duplicates. Eighty-nine percent of target areas covered at least 10 sequence reads, which is sufficient for discovering somatic mutations. We did not get target enrichment kit information from PGM21, so we had compared coverage information from several kits and concluded that SureSelect All Exon 50Mb was used for exome sequencing.

Genotyping by GATK and comparison with SNP chip

In order to check the genotyping parameters we had used, 10 SNP chip results were compared. Because SNP chip data were genotypes, we could not infer copy number changes; we could only calculate concordance between exome sequencing and SNP chip. We had used a separate bioinformatics pipeline for checking concordance, and GATK [5] was used mainly for genotyping of germline samples. We had discovered 18,500-19,200 coding SNPs. The number of SNPs from the 5' untranslated region (UTR) was close to that of the 3' UTR, so the target enrichment kit was not the latest 3' UTR extension version (Supplementary Table 3). In general, the 3' UTR is much larger than the 5' UTR; a new 3' UTR extension version of the exome kit will give at least a few times more SNPs than 5' UTR.

Two hundred fifty coding SNPs were retained after removal of dbSNP137 [6] and an in-house Korean database comprised of 54 normal individuals. Among them, we can focus on about 160 variants of NS/SS/I, which is believed to be most functionally important (Supplementary Table 4).

Overall concordance was at least 99.6%. In recent personal genome papers, they reported more than 99.95% concordance between whole-genome and SNP chips. Usually, one can achieve higher concordance if he uses strict parameters for their genotyping. Our purpose is to discover somatic mutations, not genotyping of normal germline, so we did not put much effort to get higher concordance values.

Especially, G10.N SNP chip data were totally different from the exome, because only 77% of SNPs were shared (Supplementary Table 5). As one can see in the table, 77% means they are different individuals (false positive for Award program). Distance and similarity matrix were calculated to see the differences and similarity (Supplementary Table 6, Supplementary Fig. 2). We had concluded that sample G06 has a different sample pair from different individuals. G10 also has only 92% similarity and showed a little similarity between G03 and G04. For such reasons, G10 was possibly contaminated and therefore rejected from our bioinformatics analysis.

In conclusion, we had used only 8 sample pairs for discovery of somatic mutations, LOH, and SCNA.

A list of somatic mutations

After removal of SNPs and indels from dbSNP137 [6] and an in-house Korean database, 27 somatic mutations (NS/SS/I) were discovered from 8 samples. Among them, NPM1 showed a TCTG internal tandem insertion. NPM1-ITD is known to be major driver gene in acute myeloid leukemia (AML) (Table 1, Supplementary Table 7). NPM1-ITD and GATA2 were recurrently found in two samples. We had removed all variants from dbSNP and an in-house Korean database that showed polymorphisms or normal variants, because they can be putatively functionally neutral, even if they are discovered as somatic mutations.

A list of functionally important somatic mutations in 8 samples

We had downloaded the COSMIC (http://www.sanger.ac.uk/genetics/CGP/cosmic/) database and compared with our somatic mutations. We found the same highly recurrent somatic mutation variants in four genes: NPM1, IDH2, CEBPA, and GATA2.

In 26 genes, we can easily see the NPM1, IDH2, GATA2, CEBPA genes-known to be highly mutated in AML [3]. Based on the recent somatic mutation profiles from exome sequencing, one can see the following trends of them. In solid tumors, somatic mutation in TP53 is high. But in melanoma, BRAF mutation is high. In hepatocellular carcinoma, CTNNB1 mutation is high. It means that somatic mutations can play a different role, depending on cancer. In AML, other than the well-known TP53 mutation, genes, such as FLT3, TET2, and NPM1, show somatic mutations and are the driver genes [7].

Discovery of LOH and SCAN by SCHALE

We had used SCHALE to discover LOH and SCNA. It appears that sample G06 has many LOHs and SCNAs, but an incorrect sample pair can give those results (Supplementary Fig. 3). We also discovered high LOH on chrX in G10 (Fig. 3).

Fig. 3

Changes in loss of heterozygosity and somatic copy-number alteration in sample G10.

We could not find any somatic mutations in G10. Previously, we had described that it could be contamination, but another possibility is that they are exomes from identical twins with different genders (we do not have any gender information from any samples).

Abnormal regions on chr1 in G05 showed LOH in the germline, but it is quite unnatural (Fig. 4). We are not describing much for this area until experimental validation is performed.

Fig. 4

Abnormal region on chr1 in G05. LOH, loss of heterozygosity; SNP, single nucleotide poymorphism; CNA, copy-number alteration.

We had found one LOH region on chr13 in G05 (Fig. 5). INTS6 and KIAA1704 are located at both ends and potentially could make a fusion gene (Fig. 6). Experimental validation is also required for this assumption. INTS6 is expressed in blood; many expressed sequence tags (ESTs) were found in INTS6 in UniGene (http://www.ncbi.nlm.nih.gov/UniGene). But, KIAA1704 has a small number of ESTs, so we could not infer expression levels from UniGene. There are 61 genes on chr13:45,578,409-52,035,344, and we had an additional set of network and pathway analyses, including 6 genes (MAST2, L1TD1, MSH4, GATA2, VDAC2, and PHF6) with somatic mutations.

Fig. 5

Loss of heterozygosity on chr13 in G05.

Fig. 6

Genes in a loss of heterozygosity region. Image adopted from University of California Santa Cruz (UCSC) Genome Browser, chr5:45,578,409-52,035,344.

The top network is "Cancer, Hematological Disease, Immunological Disease," comprised of GATA2 and MAST2. Many genes in LOH regions are included in this network. We can infer that those genes with somatic mutations and LOH can interact with each other (Fig. 7).

Fig. 7

Ingenity Pathway Analysis (IPA) from genes with loss of heterozygosity and somatic mutation (sample G05). Data were analyzed through the use of IPA (Ingenuity® Systems, http://www.ingenuity.com) [8].

Network and pathway analyses

Using 26 genes, we had used IPA (http://www.ingenuity.com/) [8] for network and pathway analyses. It can be summarized in two major networks (Table 2).

Two top networks inferred from Ingenuity Pathway Analysis

The top network is "Cancer, Hematologicial Disease, Cell Cycle," and it has NPM1, GATA2, and CEBPA genes (Figs. 8 and 9). These three genes showed a highly connected network and well-known major driver genes in leukemia.

Fig. 8

"Cancer, Hematological Disease, Cell Cycle" Network. Data were analyzed through the use of Ingenity Pathway Analysis (IPA) (Ingenuity® Systems, http://www.ingenuity.com) [8].

Fig. 9

Major interaction in top network. Data were analyzed through the use of Ingenity Pathway Analysis (IPA) (Ingenuity® Systems, http://www.ingenuity.com) [8].

The second network is related to cell death and survival (Fig. 10). IDH2 and PHF6 genes were located in this network. But overall, somatic mutation frequency in the top network is much higher than the second network above.

Fig. 10

"Cell Death and Survival, Cell Cycle, Cell-to-Cell Signaling and Interaction, Cellular Movement" Network. Data were analyzed through the use of Ingenity Pathway Analysis (IPA) (Ingenuity® Systems, http://www.ingenuity.com) [8].

IPA analysis reports are summarized in Supplementary Table 8.

Protein structure modeling

We have examined protein features related to residues substituted by the identified 27 somatic mutations and found the features of 19 mutations (Table 3). The mutation of CEBPA generates a duplication of an amino acid that affects the original helical secondary structure. The residue corresponding to the mutation of IDH2 has a function to bind with its substrate, so the substitutions of the residue decrease the binding affinity with the substrate. The protein region frame-shifted by the mutation of NPM1 is related to the function of nuclear localization, important for maintaining the structure. The structural modeling of RASGEF1B shows that the mutated residue Arg261 generates a new hydrogen bond with Asn303, affecting the stretch of neighbor helical structures. The protein structure of MSH4 shows that the salt bridge between Glu415 and Arg474 is essential for the folding and stability of the protein structure but is broken by the mutation (Table 4, Fig. 11).

Protein residues in which somatic mutation is located

Protein structure modeling

Fig. 11

Location of somatic mutation in protein structure. (A) IDH2. (B) RASGEF1B. (C) MSH4.

Discussion

We have analyzed 10 exome sequencing samples and SNP chip data using a variety of bioinformatics methods. According to the concordance results, comparing with SNP chip data, we concluded that the G10.N exome sample is not the correct pair. Comparing SNPs between normal and tumor pairs revealed that the normal-tumor pair of sample G06 was not matched, and the G10 exome showed abnormal LOH in chrX but had no somatic mutation. So, we had removed G06 and G10 for discovering somatic mutations.

After removing G06 and G10, with 8 paired-end samples, we discovered 27 functionally important somatic mutations with significance. They included well-known genes in AML, such as NPM1, IDH2, GATA2, and CEBPA. Especially, NPM1 and GATA2 were found in 2 out of 8 samples.

In sample G05, we found one abnormal region at chr1 and an LOH region at chr13. We could conclude that the LOH region could potentially make a fusion gene by INTS6 and KIAA1704, and we did further network analysis on that. The results of IPA analysis gave "Cell Death and Survival, Cell Cycle" network as the top network, and it includes GATA2 and MAST2 (somatic mutation).

We had done IPA analysis with 26 genes, giving the "Cancer, Hematological Disease, Cell Cycle Network" as the top network, and it includes NPM1, GATA2, and CEBPA, which are well-known driver genes in AML [3]. Since those 3 genes have many edges in the result, we could conclude that those genes are indirect evidence of driver genes.

The second network is related with "Cell Death and Survival," and it includes IDH2 and PHF6 genes, the same as G05's top network. So far, our conclusion is that 8 samples are related with the "Cancer, Hematological Disease, Cell Cycle Network" but sample G05 is related with the "Cell Death and Survival Network."

We had searched previous works of genome-wide somatic mutation discovery and concluded that 8 exome samples might be AML. Somatic mutation profiles of blood cancer are summarized in Table 5 [3, 9, 10]. But, we could not find any somatic mutations in the FLT3 or RAS gene. Chronic lymphocytic leukemia and Blast Crisis chronic myeloid leukemia did not have somatic mutations in NPM1 or CEBPA.

Somatic mutation profile of three leukemia studies

Seven out of 8 samples showed neither LOH nor SCNA, so we could conclude that those patients had a normal karyotype with no translocation or inversion. If possible, additional RNA-Seq experiments or another technique can provide enough evidence of karyotypes of those samples.

In CEBPA, IDH2, and NPM1, we saw somatic mutations located in helix regions. By using protein structure modeling, we found that IDH2, RASGEF1B, and MSH4 could have structural variations, affecting protein functions. Somatic mutations in IDH2 could break hydrogen bonds.

Acknowledgments

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (nos. 20110030770 and 20120009215), a grant from the Next-Generation BioGreen 21 Program (nos. PJ008019 and PJ008068), Rural Development Administration, and a grant from KRIBB Research Initiative Program, Republic of Korea.

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Supplementary materials

Supplementary data including eight tables and three figures can be found with this article online at http://www.genominfo.org/src/sm/gni-11-24-s001.pdf.

Article information Continued

Fig. 1

Bioinformatics pipeline for exome data and numbers of somatic mutation candidates for each filtering step. SNP, single nucleotide polymorphism; LOH, loss of heterozygosity; SCNA, somatic copy-number alterations; SCHALE, somatic copy-number and heterozygosity alteration estimation; NS/SS/I, nonsense, splice-site, or coding indel mutations.

Fig. 2

Somatic copy-number and heterozygosity alteration estimation (SCHALE), developed for detection of loss of heterozygosity (LOH) and somatic copy-number alterations (SCNA). TMM, trimmed mean of M values; CBS, circular binary segmentation.

Fig. 3

Changes in loss of heterozygosity and somatic copy-number alteration in sample G10.

Fig. 4

Abnormal region on chr1 in G05. LOH, loss of heterozygosity; SNP, single nucleotide poymorphism; CNA, copy-number alteration.

Fig. 5

Loss of heterozygosity on chr13 in G05.

Fig. 6

Genes in a loss of heterozygosity region. Image adopted from University of California Santa Cruz (UCSC) Genome Browser, chr5:45,578,409-52,035,344.

Fig. 7

Ingenity Pathway Analysis (IPA) from genes with loss of heterozygosity and somatic mutation (sample G05). Data were analyzed through the use of IPA (Ingenuity® Systems, http://www.ingenuity.com) [8].

Fig. 8

"Cancer, Hematological Disease, Cell Cycle" Network. Data were analyzed through the use of Ingenity Pathway Analysis (IPA) (Ingenuity® Systems, http://www.ingenuity.com) [8].

Fig. 9

Major interaction in top network. Data were analyzed through the use of Ingenity Pathway Analysis (IPA) (Ingenuity® Systems, http://www.ingenuity.com) [8].

Fig. 10

"Cell Death and Survival, Cell Cycle, Cell-to-Cell Signaling and Interaction, Cellular Movement" Network. Data were analyzed through the use of Ingenity Pathway Analysis (IPA) (Ingenuity® Systems, http://www.ingenuity.com) [8].

Fig. 11

Location of somatic mutation in protein structure. (A) IDH2. (B) RASGEF1B. (C) MSH4.

Table 1.

A list of functionally important somatic mutations in 8 samples

Patients Gene Mutation Nucleotide substitution Amino acid substitution PhyloP SIFT prediction Mutation Assessor Func. Impact COSMIC (occurrence)
G02, G07 NPM1 Frameshift c.773_776dupTCTG p.Trp259Cysfs*12 3.463 Yes (2,126)
c.860_863dupTCTG p.Trp288Cysfs*12
G02 ALDH6A1 Missense c.166T>A p.Trp56Arg 4.875 Deleterious Medium No
G02 IDH2 Missense c.419G>T p.Arg140Leu 4.024 Deleterious High Yes (24)
G02 MYH13 Missense c.926C>G p.Pro309Arg 5.335 Deleterious High p.P309T
G03 RPS6KA1 Missense c.2101A>G p.Thr701Ala 4.664 Deleterious Medium No
c.2128A>G p.Thr710Ala
G03 KCNH1 Missense c.856C>T p.Leu286Phe 4.083 Deleterious High No
G03 CACNB4 Missense c.1184C>T p.Ala395Val 5.993 Tolerated Medium No
c.1232C>T p.Ala411Val
c.1286C>T p.Ala429Val
G03 RAD21 Nonsense c.1238T>A p.Leu413* 5.044 No
G03 KSR2 Missense c.967C>T p.Pro323Ser 5.180 Tolerated Medium No
G04 CCT7 Frameshift c.1011C[4] p.His339Thrfs*45 5.138 No
c.1362C[4] p.His456Thrfs*45
c.1491C[4] p.His499Thrfs*45
c.1623C[4] p.His543Thrfs*45
G04 GATA2 Missense c.989G>T p.Arg330Leu 5.914 Deleterious High p.R330Q
G04 TRA2B Missense c.16C>Tc.316C>T p.Arg6Cys 2.951 Deleterious Medium No
p.Arg106Cys
G04 RBMXL2 Missense c.727T>A p.Tyr243Asn 3.247 Deleterious Medium No
G04 KRT9 Missense c.1004C>T p.Ala335Val 0.137 Deleterious Medium No
G04 CEBPA In-frame c.937_939dupAAG p.Lys313dup 5.464 Yes (46)
G05 MAST2 Missense c.2384G>A p.Arg795His 5.974 Deleterious Medium No
G05 L1TD1 Nonsense c.451G>T p.Glu151* No
G05 MSH4 Missense c.1243G>A p.Glu415Lys 3.441 Deleterious Low No
G05 GATA2 Missense c.1033T>G p.Leu345Val 2.421 Deleterious High Yes (16)
c.1075T>G p.Leu359Val
G05 VDAC2 Missense c.637C>A p.Leu213Ile 3.188 Tolerated Medium No
c.682C>A p.Leu228Ile
G05 PHF6 Splicing c.585+1G>A 5.145 No
c.588+1G>A
G07 RASGEF1B Missense c.781T>A p.Trp261Arg 4.968 Deleterious Low No
G07 FZD9 Missense c.1027G>A p.Gly343Arg 3.725 Deleterious Medium No
G07 ARHGEF5 Missense c.3956G>A p.Arg1319His 1.817 Deleterious High No
G07 TLL2 Missense c.2732A>C p.Asn911Thr 2.032 Tolerated Medium No
G07 IDH2 Missense c.419G>A p.Arg140Gln 4.024 Deleterious High Yes (1,462)
G07 ZNF317 Missense c.184G>T p.Val62Phe 1.154 Deleterious High p.V62V

Table 2.

Two top networks inferred from Ingenuity Pathway Analysis

ID Top networks
Associated network functions Score
1 Cancer, hematological disease, cell cycle 26
2 Cell death and survival, cell cycle, cell-to-cell signaling and interaction, cellular movement 26

Table 3.

Protein residues in which somatic mutation is located

Gene Amino acid substitution Uniprot features of corresponding residues in 10 similar proteins (expectation value = 1 e-30)
ALDH6A1 p.Trp56Arg Transit peptide (mitochondrion)
ARHGEF5 p.Arg1319His DH domain, required for RHOG activation and mediates interaction with EPHA2
CEBPA p.Lys313dup Helix
FZD9 p.Gly343Arg Cytoplasmic domain
GATA2 p.Leu359Val GATA-type 2 zinc finger region
IDH2 p.Arg140Leu, p.Arg140Gln Substrate binding, helix
KCNH1 p.Leu286Phe Cytoplasmic domain
KRT9 p.Ala335Val Coil 2, Rod, Necessary for interaction with PNN
MAST2 p.Arg795His AGC-kinase C-terminal domain
MYH13 p.Pro309Arg Myosin head-like domain
NPM1 p.Trp259Cysfs* 12, p.Trp288Cysfs* 12 Required for nucleolar localization, helix, mutagenesis site (destabilization of the structure)
RAD21 p.Leu413* Cleavage by ESPL1
RASGEF1B p.Trp261Arg Ras-GEF domain
RBMXL2 p.Tyr243Asn Arg/Gly/Pro-rich
RPS6KA1 p.Thr701 Ala, p.Thr710Ala Helix
TLL2 p.Asn911Thr Disulfide bond, CUB 5 domain
TRA2B p.Arg6Cys Arg/Ser-rich (RS1 domain)
VDAC2 p.Leu213Ile Transmembrane region (beta stranded)
ZNF317 p.Val62Phe KRAB domain

Table 4.

Protein structure modeling

Gene RefSeq protein Template PDB Template PDB title Coverage (%)
IDH2 NP_002159.2 1LWD Porcine mitochondrial NADP +-dependent isocitrate dehydrogenase complexed with Mn2+ and isocitrate 91.4
RASGEF1B NP_689758.1 3KSY Histone domain, DH-PH unit, and catalytic unit of the Ras activator Son of Sevenless 77.4
MSH4 NP_002431.2 2O8B Human MutSalpha (MSH2/MSH6) bound to ADP and a G T mispair 74.0
CACNB4 NP_001005747.1 1T3L Voltage-dependent calcium channel beta subunit functional core complexed with the alpha 1 interaction domain 63.0

PDB, Protein Data Bank.

Table 5.

Somatic mutation profile of three leukemia studies

AML CLL BC-CML
FLT3 p53 p16/INK4A
c-KIT TP53 p53
NRAS SF3B1 RB1
KRAS MYD88 RUNX1
AML1 FBXW7 ASXL1
C/EBPalpha NOTCH1 IKZF1
PU.1 ZMYM3 WT1
NPM1 DDX3X TET2
CEBPA MAPK1 IDH1
ASXL1 POT1 NRAS
DNMT3A CHD2 KRAS
IDH1 LRP1B CBL
IDH2 Fas (absent) TP53
MLL Bcl-2 (overexpression) BCR-ABL (translocation)
PHF6 ATM (germline and somatic mutation) EVI1 (overexpression)
TET2 AML1 (translocation)
WT1
ANKRD26
GATA2
RUNX1
TP53
PML-RARalpha (translocation)
AML1-ETO (translocation)
PLZF-RARalpha (translocation)

AML, acute myeloid leukemia; CLL, chronic lymphocytic leukemia; BC-CML, Blast Crisis chronic myeloid leukemia.