Abstract

Background

   Despite targeted sequencing have identified several mutations for
   leukemia, there is still a limit of mutation screening for Chinese
   leukemia. Here, we used targeted next‐generation sequencing for testing
   the mutation patterns of Chinese leukemia patients.

Methods

   We performed targeted sequencing of 504 tumor‐related genes in 109
   leukemia samples to identify single‐nucleotide variants (SNVs) and
   insertions and deletions (INDELs). Pathogenic variants were assessed
   based on the American College of Medical Genetics and Genomics (ACMG)
   guidelines. The functional impact of pathogenic genes was explored
   through gene ontology (GO), pathway analysis, and protein–protein
   interaction network in silico.

Results

   We identified a total of 4,655 SNVs and 614 INDELs in 419 genes, in
   which PDE4DIP, NOTCH2, FANCA, BCR, and ROS1 emerged as the highly
   mutated genes. Of note, we were the first to demonstrate an association
   of PDE4DIP mutation and leukemia. Based on ACMG guidelines, 39
   pathogenic and likely pathogenic mutations in 27 genes were found. GO
   annotation showed that the biological process including gland
   development, leukocyte differentiation, respiratory system development,
   myeloid leukocyte differentiation, mesenchymal to epithelial
   transition, and so on were involved.

Conclusion

   Our study provided a map of gene mutations in Chinese patients with
   leukemia and gave insights into the molecular pathogenesis of leukemia.

   Keywords: gene ontology, INDELs, Leukemia, pathway analysis, SNVs
     __________________________________________________________________

   We identified a total of 4,655 single‐nucleotide variants and 614
   insertions and deletions in 419 genes, in which PDE4DIP, NOTCH2, FANCA,
   BCR and ROS1 emerged as the highly mutated genes. Based on American
   College of Medical Genetics and Genomics guidelines, 39 pathogenic and
   likely pathogenic mutations in 27 genes were found. Our study provided
   a map of gene mutations in Chinese patients with leukemia and gave
   insights into the molecular pathogenesis of leukemia.

   graphic file with name MGG3-8-e1369-g006.jpg

1. INTRODUCTION

   Leukemia is malignant disorders of the blood and bone marrow
   (Musharraf, Siddiqui, Shamsi, Choudhary, & Rahman, [40]2016). In China,
   there were 75,300 new cases of leukemia and 53,400 death of leukemia in
   2015 (Chen et al., [41]2016). The incidence and mortality of leukemia
   in males is higher than those in females, and myeloid leukemia has
   significantly higher levels of incidence and mortality than lymphoid
   leukemia (Liu, Zhao, Chen, & Chen, [42]2013). Acute myeloid leukemia
   (AML), acute lymphoblastic leukemia (ALL), acute promyelocytic leukemia
   (APL), chronic myeloid leukemia (CML), and chronic lymphocytic leukemia
   (CLL) are the common types of leukemia (Juliusson & Hough, [43]2016).
   Acute leukemia is composed of primary undifferentiated cells; while
   chronic leukemia, the malignant cells are more differentiated. Exposure
   to environmental radiation and solvents were reported as predisposing
   factors for leukemia (Schuz & Erdmann, [44]2016). However, the direct
   cause of leukemia has not been found. Recently, mutation profiling of
   genes provides prognostic prediction and treatment guidance for
   patients with leukemia (Itzykson et al., [45]2013; Shin
   et al., [46]2016). Reports on mutational patterns of Chinese leukemia
   patients are limited.

   The application of next‐generation sequencing (NGS) technique can
   better achieve testing for a larger group of mutational markers. NGS is
   a massively parallel high‐throughput DNA sequencing approach
   (Metzker, [47]2010). The major advantages of this approach are that
   simultaneously screen a large number of genes and samples using very
   low amount of nucleic acids, and have high sensitivity for mutation
   detection (Meldrum, Doyle, & Tothill, [48]2011). Types of NGS include
   whole‐genome sequencing, whole‐exome sequencing, whole‐transcriptome
   sequencing, and targeted regions sequencing (Ross & Cronin, [49]2011).
   Targeted regions sequencing for multiple specific genomic regions have
   been widely employed in many fields to identify genetic variants
   related to disease pathogenesis and prognosis (Mansouri
   et al., [50]2014).

   Despite targeted sequencing have identified several mutations for
   leukemia, there is still a limit of mutation screening for Chinese
   leukemia. Here, we performed targeted regions sequencing containing
   these 504 genes in 109 patients with leukemia among Chinese Han
   population to explore the genetic basis of Chinese leukemia patients.

2. MATERIALS AND METHODS

2.1. Study population

   A total of 109 patients diagnosed with leukemia at Hainan General
   Hospital were enrolled. All of the patients were genetically unrelated
   ethnic Han Chinese. Patients with leukemia were diagnosed according to
   2016 WHO classification criteria (Sabattini, Bacci, Sagramoso, &
   Pileri, [51]2010). The samples were confirmed by bone marrow
   microscopy, flow immunophenotyping, chromosome screening, and fusion
   gene detection. Our study was approved by the ethical review board of
   Hainan General Hospital, and complied with the Declaration of Helsinki.
   Informed written consent was obtained from all patients enrolled in
   this study.

2.2. DNA extraction and sequencing

   Peripheral blood (5 ml) was collected from each subject into
   EDTA‐coated vacutainer tubes. Genomic DNA was isolated using a
   commercially available DNA extraction kit (GoldMag Co. Ltd.), and then
   quantified using a NanoDrop 2000 Spectrophotometer (NanoDrop
   Technologies).

   The sample prepared using a Truseq DNA Sample preparation Kit
   (Illumina) following the standard protocol. Agilent SureDesign website
   ([52]https://earray.chem.agilent.com/‐suredesign/home.htm) was used to
   design capture oligos for 504 cancer‐related genes. Paired‐end
   libraries were prepared following the Illumina protocol. Hybridization
   reactions were performed on AB 2720 Thermal Cycler (Life Technologies
   Corporation). The hybridization mixture was captured using magnetic
   beads (Invitrogen) and Agilent Custom Sureselect Enrichment Kit
   according to the manufacturer's instructions. Sequencing (2 × 150 bp
   reads) was carried out on Illumina HiSeq2500 platform (Illumina).

   Sequencing reads were aligned to the human reference genome (UCSC
   Genome Browser hg19, [53]http://genome.ucsc.edu/) using the
   Burrows‐Wheeler Aligner (Li & Durbin, [54]2010). Picard software
   ([55]https://github.com/broadinstitute/picard) was employed to remove
   duplicate PCR reads and evaluate the quality of variants by attaining
   effective reads, effective base, and average coverage depth. Sequencing
   quality controls were as follows: (a) Q20 and Q30 more than 90% and
   80%, respectively; (b) coverage of target regions more than 99%; and
   mapping rate no less than 95%. single‐nucleotide variant (SNV) calling
   was performed using GATK and Varscan programs (Koboldt
   et al., [56]2012; McKenna et al., [57]2010). Variants were annotated
   using ANNOVAR ([58]http://annovar.openbioinformatics.org/en/latest/).

2.3. In silico analysis

   Pathogenic or likely pathogenic SNVs was assessed by the 1000 Genomes
   Project, Sorting Tolerant From Intolerant (SIFT), PolyPhen,
   MutationTaster, and Combined Annotation Dependent Depletion (CADD)
   based on the American College of Medical Genetics and Genomics (ACMG)
   guidelines (Richards et al., [59]2015). Pathway enrichment analysis of
   gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes
   (KEGG) for the candidate pathogenic genes was carried out using R
   clusterprofiler software package
   ([60]http://bioconductor.org/packages/release/bioc/html/clusterProfiler
   .html). GO terminology was annotated on the following three aspects:
   cellular component (GO‐CC), molecular function (GO‐MF), and biological
   process (GO‐BP). Pathway enrichment based on KEGG pathway database
   ([61]https://www.kegg.jp/kegg/) was applied for pathway annotation. GO
   terms and KEGG pathway with p < .05 were considered to be significant.
   Protein–protein interaction (PPI) network was predicted by STRING
   online software ([62]https://string‐db.org/). GEPIA
   ([63]http://gepia.cancer‐pku.cn/) database was used to evaluate the
   expression and prognostic of candidate genes in leukemia.

3. RESULTS

3.1. Clinical characteristics of patients with leukemia

   A total of 109 patients with leukemia were identified. The median age
   of the studied cohort was 39.83 years (range: 11–77 years), included 58
   (53.2%) males and 51 (46.8%) females (Table [64]1). Among these, 30
   patients diagnosed as AML, 19 patients as ALL, 24 patients as APL, 28
   patients as CML, and 8 patients as CLL.

Table 1.

   Characteristics of patients with leukemia
   Variable Total (n = 109) AML (n = 30) ALL (n = 19) APL (n = 24) CML
   (n = 28) CLL (n = 8)
   Age, years 39.8 (11–77) 29.5 (12–74) 36 (13–73) 41 (16–62) 42 (11–75)
   60.5 (51–77)
   Gender
   Male, n 58 13 10 11 18 6
   Female, n 51 17 9 13 10 2
   SNVs and INDELs
   SNVs, n 4,655 2,435 1,870 2,570 2,701 2,661
   Indels, n 614 359 309 388 408 393
   Blast (%, IQR) 16.00 (2.50–47.50) 36.50 (26.25–59.00) 47.50
   (14.00–80.00) 2.25 (1.50–2.88) 1.25 (1.00–4.50) 3.00 (2.13–6.75)
   Lymphocytes population (%, IQR) 16.00 (6.65–41.42) 13.60 (8.18–18.43)
   23.90 (14.90–44.30) 31.86 (14.28–43.65) 6.90 (1.95–23.4) 79.25
   (64.00–86.18)
   BCR‐ABL1 transcript positive (n) 24
   [65]Open in a new tab

   Abbreviations: ALL, acute lymphoblastic leukemia; AML, acute myeloid
   leukemia; APL, acute promyelocytic leukemia; CLL, chronic lymphocytic
   leukemia; CML, chronic myeloid leukemia; INDELs, insertions and
   deletions; IQR, interquartile range; SNV, single‐nucleotide variants.

3.2. Spectrum of gene variants

   The targeted capture deep sequencing of 504 tumor‐related genes in 109
   leukemia samples revealed 4,655 SNVs and 614 insertions and deletions
   (INDELs) in 419 genes (Table [66]S1). There were 208 genes with greater
   than 10 mutations, 78 genes with greater than 20 mutations. Top 50
   mutated genes across the leukemia patients were described in
   Figure [67]1. Among them, the most commonly mutated gene was PDE4DIP
   (128), followed by NOTCH2 (59), FANCA (55), BCR (53), ROS1 (51), NACA
   (47), KDM5A (44), CLTCL1 (43), AKAP9 (43), MYH11 (42), PCM1 (42),
   NOTCH1 (41), COL1A1 (40), and so on. According to GEPIA database,
   PDE4DIP was down‐regulated in AML compared with normal samples
   (p < .01), and high expression of PDE4DIP was associated with poor AML
   prognosis (HR = 2.3, logrank p = .0027, Figure [68]S1). STRING database
   was used to predict the potential interacting protein with PDE4DIP, as
   Figure [69]S2 and Table [70]S2. The results showed that PDE4DIP protein
   might interact with PDE4D, PRKAR2A, and AKAP9, which can bind to cAMP
   or protein kinase A (PKA), suggesting that PDE4DIP may participate in
   cAMP/PKA signaling pathway.

Figure 1.

   Figure 1
   [71]Open in a new tab

   Landscape of mutations in 109 leukemia patients (top 50 mutated genes).
   The number of SNVs and INDELs for each gene is shown (left). The number
   of SNVs and INDELs for each patient is shown (top). Each vertical
   column represented an individual. Shaded bars indicate the number of
   mutations in genes. INDELs, insertions and deletions; SNVs,
   single‐nucleotide variants

   We analyzed single‐nucleotide changes of these detected SNVs and found
   that transitions as C/G>T/A and A/T>G/C were more prevalent than
   transversions including C/G>G/C, C/G>A/T, A/T>C/G, and A>T/T>A in all
   leukemia patients, as shown in Figure [72]2a. In addition, we also
   analyzed the regions of these SNVs. Among these variants
   (Figure [73]2b), exonic variants (54.55%) were the most frequent,
   followed in order by intronic variants (34.92%), splicing variants
   (6.14%), and UTR variants (4.39%). Additionally, we detected the
   genetic effects of these variants in exonic region, with missense
   variants, synonymous variants, stopgain/loss variants, and unknown
   variants.

Figure 2.

   Figure 2
   [74]Open in a new tab

   Patterns of somatic SNVs by targeted next‐generation sequencing. (a)
   The percentages of distinct transitions and transversions of SNVs. (b)
   Proportions of SNVs types according to their regions in the gene. SNVs,
   single‐nucleotide variants

   There were 2,435 SNVs and 359 INDELs in AML, 1,870 SNVs and 309 INDELs
   in ALL, 2,570 SNVs and 388 INDELs in APL, 2,701 SNVs and 408 INDELs in
   CML, and 2,661 SNVs and 393 INDELs in CLL. We then compared the
   frequency of mutations and INDELs in five subgroups of leukemia.
   Mutations or INDELs in 50 genes (PDE4DIP, NOTCH2, FANCA, BCR, CLTCL1,
   MYH11, NOTCH1, COL1A1, CAMTA1, ALK, NIN, CIITA, CARD11, RECQL4, USP6,
   NF1, NUP214, KIAA1549, MYH9, FLT3, NSD1, PCSK7, XPC, PMS2, SETBP1,
   SRGAP3, ASXL1, PDGFRA, ATIC, RALGDS, PRDM16, TSHR, FGFR2, EGFR, CDH11,
   DNM2, SYK, MUTYH, BCOR, FNBP1, RAC1, IL7R, CNOT3, RNF43, TP53, KCNJ5,
   MEN1, SH2B3, SRSF2, CREB3L1) were shared across all patients with
   leukemia. The different pathological classification of leukemia might
   have distinct patterns of variants and INDELs. Table [75]S3 showed the
   top 50 significantly different genes. In ALL, the mutation frequencies
   of XPA, CDK4, and BCL11B were higher than other subgroups, but the
   frequencies of SMARCE1 and FEV were lower. The high prevalence of GNAQ,
   ELF4, HOXD13, and COX6C mutations in patients with APL were noteworthy,
   SMARCE1 variant predominated in patients with CML, variant of PER1 and
   ETV6 had higher frequencies in patients with AML. The variants of
   PIK3CA, CHCHD7, FEV, and PHF6 variants were significantly enriched in
   CLL. These data suggested a strong functional role of these genes in
   the different types of leukemia.

3.3. The filtrate pathogenic and likely pathogenic genes

   Based on ACMG guideline, we identified 39 pathogenic and likely
   pathogenic mutations in 27 genes including SNVs or INDELs in exonic and
   splicing regions among 32 leukemia patients (Figure [76]3 and
   Table [77]2). In AML, 21 pathogenic or likely pathogenic germline
   mutations in 17 genes were identified, of which 10 mutant genes only in
   AML patients. ALL patients had six pathogenic or likely pathogenic
   germline mutations, especially PBRM1 (c.2819_2829del, p.L940fs) and
   SUZ12 (c.1716_1717insG, p.L572fs). There were six pathogenic or likely
   pathogenic germline mutations among APL patients, of which PAX8
   (c.G201T, p.E67D) only in APL patients. Two pathogenic mutations in
   ALDH2 (rs540073928, p.A175D) and FBXW7 (rs866987936, p.R361Q) were
   found in CLL patients. Among CML patients, seven pathogenic or likely
   pathogenic germline mutations were also detected, of which five mutant
   genes just in CML patients. These results hinted us that these
   mutations might play an important role in the pathogenesis of the
   different leukemia subgroup. The pathologic mutations for leukemia
   patients were shown in Table [78]S4.

Figure 3.

   Figure 3
   [79]Open in a new tab

   Overview of the distribution of the 27 pathogenic and likely pathogenic
   gene according to ACMG guidelines in five subgroup of leukemia. ACMG,
   American College of Medical Genetics and Genomics

Table 2.

   Summary of ACMG Likely Pathogenic mutations
   Gene Leukemia Priority Chr: Position SNV/Indel REF ALT Region
   Transcript ID Exonic Function Nucleotide change AA change
   ALDH2 AML, CLL H 12:112228350 rs540073928 C A exonic [80]NM_001204889
   missense SNV c.C524A p.A175D
   ASXL1 AML H 20:31021634 . C CA exonic [81]NM_015338 frameshift
   insertion c.1633_1634insA p.R545fs
   ATIC AML H 2:216182882 rs575560797 A T exonic [82]NM_004044 missense
   SNV c.A149T p.D50V
   CANT1 AML H 17:76993297 . CA C exonic [83]NM_001159772 frameshift
   deletion c.407delT p.L136fs
   CBL AML H 11:119155730 . C CCGCGCTTTCTT exonic [84]NM_005188 frameshift
   insertion c.1483_1484insCGCGCTTTCTT p.P495fs
   CBL CML H 11:119148892 rs387906666 A G exonic [85]NM_005188 missense
   SNV c.A1112G p.Y371C
   CEBPA AML H 19:33793130 . AT A exonic NM_00128743 frameshift deletion
   c.295delA p.I99fs
   CEBPA AML H 19:33793153 . GCAGATGCCGCC G exonic [86]NM_001287435
   frameshift deletion c.262_272del p.G88fs
   CEBPA APL H 19:33793092 . A ACT exonic [87]NM_001287435 frameshift
   insertion c.333_334insAG p.F112fs
   FANCG CML H 9:35077398 rs376732298 T TG splicing [88]NM_004629 .
   FBXW7 ALL H 4:153249385 rs867384286 G A exonic [89]NM_018315 missense
   SNV c.C1039T p.R347C
   FBXW7 CLL H 4:153247366 rs866987936 C T exonic [90]NM_018315 missense
   SNV c.G1082A p.R361Q
   FLT3 AML, APL H 13:28592642 rs121913488 C A exonic [91]NM_004119
   missense SNV c.G2503T p.D835Y
   FLT3 AML H 13:28592640 rs121913487 A C exonic [92]NM_004119 missense
   SNV c.T2505G p.D835E
   FOXP1 CML H 3:71037162 . TG T exonic [93]NM_001244810 frameshift
   deletion c.828delC p.P276fs
   IDH1 AML H 2:209113113 rs121913499 G C exonic [94]NM_001282387 missense
   SNV c.C394G p.R132G
   KIAA1549 AML H 7:138602332 . TGA T exonic [95]NM_020910 frameshift
   deletion c.2038_2039del p.S680fs
   KRAS AML H 12:25398281 rs112445441 C T exonic [96]NM_004985 missense
   SNV c.G38A p.G13D
   MYH9 AML H 22:36715582 rs372016779 T C splicing [97]NM_002473 .
   NFIB CML H 9:14398522 . C A splicing [98]NM_001190738 .
   NRAS AML H 1:115258744 rs121434596 C A exonic [99]NM_002524 missense
   SNV c.G38T p.G13V
   NRAS AML H 1:115256529 rs11554290 T G exonic [100]NM_002524 missense
   SNV c.A182C p.Q61P
   NRAS ALL H 1:115258747 rs121913237 C T exonic [101]NM_002524 missense
   SNV c.G35A p.G12D
   PAX5 AML H 9:36966685 . AGCGAGTG A exonic [102]NM_001280552 frameshift
   deletion c.310_316del p.H104fs
   PAX8 APL H 2:114002192 . C A exonic [103]NM_013953 missense SNV c.G201T
   p.E67D
   PBRM1 ALL H 3:52623221 . GTAAGCCTGAGA G exonic [104]NM_018313
   frameshift deletion c.2819_2829del p.L940fs
   RUNX1 CML H 21:36171607 . G A exonic [105]NM_001001890 stopgain c.C877T
   p.R293X
   SETBP1 CML H 18:42531913 rs267607040 G A exonic [106]NM_015559 missense
   SNV c.G2608A p.G870S
   SETD2 AML H 3:47155452 . C CCGGTCCAA exonic [107]NM_014159 frameshift
   insertion c.4628_4629insTTGGACCG p.R1543fs
   SMO AML H 7:128846423 . T C exonic [108]NM_005631 missense SNV c.T1259C
   p.I420T
   SMO ALL H 7:128848655 . G C exonic [109]NM_005631 missense SNV c.G1320C
   p.K440N
   SUZ12 ALL H 17:30322703 . A AG exonic [110]NM_015355 frameshift
   insertion c.1716_1717insG p.L572fs
   TFRC ALL, APL H 3:195791279 rs184956956 C T exonic [111]NM_003234
   missense SNV c.G1219A p.A407T
   TFRC CML H 3:195785460 rs772017482 T C exonic [112]NM_003234 missense
   SNV c.A1580G p.N527S
   TP53 AML H 17:7578406 rs28934578 C T exonic [113]NM_001126115 missense
   SNV c.G128A p.R43H
   WT1 APL H 11:32413557 . G T exonic [114]NM_024426 missense SNV c.C1342A
   p.H448N
   WT1 APL H 11:32417920 . G GAGTCGGGGCTACTCCAGGC exonic [115]NM_024426
   frameshift insertion c.1080_1081insGCCTGGAGTAGCCCCGACT p.L361fs
   WT1 AML H 11:32417909 . C CGACA exonic [116]NM_024426 frameshift
   insertion c.1091_1092insTGTC p.S364fs
   WT1 AML H 11:32417942 . A AG exonic [117]NM_024426 frameshift insertion
   c.1058_1059insC p.R353fs
   [118]Open in a new tab

   Abbreviations: ACMG, American College of Medical Genetics and Genomics;
   ALL, acute lymphoblastic leukemia; ALT, alter; AML, acute myeloid
   leukemia; APL, acute promyelocytic leukemia; CLL, chronic lymphocytic
   leukemia; CML, chronic myeloid leukemia; INDELs, insertions and
   deletions; REF, reference; SNV, single nucleotide variants.

3.4. GO annotation for pathogenic and likely pathogenic genes

   Gene ontology annotation and pathway analyses were conducted for 27
   pathogenic and likely pathogenic genes. The possible BP of these
   overlapping genes were related to the gland development, leukocyte
   differentiation, respiratory system development, myeloid leukocyte
   differentiation, mesenchymal to epithelial transition, lung
   development, skeletal system development, positive regulation of
   mesonephros development, respiratory tube development, vasculogenesis,
   and so on (Figure [119]4a). The results of GO‐CC annotation suggested
   that these genes were involved in CC including RNA polymerase II
   transcription factor complex, nuclear transcription factor complex,
   tertiary granule, PcG protein complex, tertiary granule lumen, caveola,
   transcription factor complex, membrane raft, membrane microdomain, and
   plasma membrane raft (Figure [120]4b). Moreover, the MF of these
   pathogenic and likely pathogenic genes were mainly correlated with the
   DNA‐binding transcription activator activity, RNA polymerase
   II‐specific, RNA polymerase II proximal promoter sequence‐specific DNA
   binding, proximal promoter sequence‐specific DNA binding, protein
   self‐association, cadherin binding, nuclear hormone receptor binding,
   histone‐lysine N‐methyltransferase activity, protein phosphorylated
   amino acid binding, hormone receptor binding, promoter‐specific
   chromatin binding (Figure [121]4c).

Figure 4.

   Figure 4
   [122]Open in a new tab

   Top 10 enrichment scores in GO enrichment analysis for 27 pathogenic
   and likely pathogenic genes. (a) Biological process of pathogenic and
   likely pathogenic genes; (b) Cellular component of pathogenic and
   likely pathogenic genes; (c) Molecular function of pathogenic and
   likely pathogenic genes. GO, gene ontology

3.5. Pathway analysis and prediction of PPI

   Further enrichment analysis based on the KEGG database showed that
   these pathogenic and likely pathogenic genes were highly enriched in
   leukemia and cancer‐related pathways, as shown in Table [123]3. The
   pathways included (a) AML and CML; (b) cancer‐related pathways, such as
   transcriptional misregulation, central carbon metabolism, and
   proteoglycans; (c) various cancers including thyroid cancer, bladder
   cancer, and endometrial cancer.

Table 3.

   Annotation of pathways for pathogenic and likely pathogenic genes
   ID Description Gene Ratio Bg Ratio P value Q value Gene ID Count
   hsa05221 Acute myeloid leukemia 5/21 57/7010 5.48 × 10^−7 2.83 × 10^−5
   CEBPA/FLT3/KRAS/NRAS/RUNX1 5
   hsa05202 Transcriptional misregulation in cancer 7/21 180/7010
   5.62 × 10^−7 2.83 × 10^−5 CEBPA/FLT3/PAX5/PAX8/RUNX1/TP53/WT1 7
   hsa05200 Pathways in cancer 9/21 397/7010 8.76 × 10^−7 2.83 × 10^−5
   CBL/CEBPA/FLT3/KRAS/NRAS/PAX8/RUNX1/SMO/TP53 9
   hsa05230 Central carbon metabolism in cancer 5/21 67/7010 1.24 × 10^−6
   2.83 × 10^−5 FLT3/IDH1/KRAS/NRAS/TP53 5
   hsa05216 Thyroid cancer 4/21 29/7010 1.35 × 10^−6 2.83 × 10^−5
   KRAS/NRAS/PAX8/TP53 4
   hsa05220 Chronic myeloid leukemia 5/21 73/7010 1.91 × 10^−6
   3.33 × 10^−5 CBL/KRAS/NRAS/RUNX1/TP53 5
   hsa05219 Bladder cancer 3/21 41/7010 0.00023 0.003444 KRAS/NRAS/TP53 3
   hsa05205 Proteoglycans in cancer 5/21 203/7010 0.00027 0.003546
   CBL/KRAS/NRAS/SMO/TP53 5
   hsa05213 Endometrial cancer 3/21 52/7010 0.000466 0.005437
   KRAS/NRAS/TP53 3
   hsa05223 Non‐small cell lung cancer 3/21 56/7010 0.00058 0.00609
   KRAS/NRAS/TP53 3
   hsa05214 Glioma 3/21 65/7010 0.000898 0.008575 KRAS/NRAS/TP53 3
   hsa05218 Melanoma 3/21 71/7010 0.001162 0.010167 KRAS/NRAS/TP53 3
   hsa04012 ErbB signaling pathway 3/21 87/7010 0.002089 0.016725
   CBL/KRAS/NRAS 3
   hsa05215 Prostate cancer 3/21 89/7010 0.00223 0.016725 KRAS/NRAS/TP53 3
   hsa04211 Longevity regulating pathway—mammal 3/21 94/7010 0.002607
   0.018248 KRAS/NRAS/TP53 3
   hsa04660 T cell receptor signaling pathway 3/21 104/7010 0.003474
   0.022799 CBL/KRAS/NRAS 3
   hsa04919 Thyroid hormone signaling pathway 3/21 118/7010 0.004957
   0.028711 KRAS/NRAS/TP53 3
   hsa04722 Neurotrophin signaling pathway 3/21 120/7010 0.005195 0.028711
   KRAS/NRAS/TP53 3
   hsa04071 Sphingolipid signaling pathway 3/21 120/7010 0.005195 0.028711
   KRAS/NRAS/TP53 3
   hsa05160 Hepatitis C 3/21 133/7010 0.006916 0.036307 KRAS/NRAS/TP53 3
   hsa04530 Tight junction 3/21 139/7010 0.007812 0.036373 KRAS/MYH9/NRAS
   3
   hsa04910 Insulin signaling pathway 3/21 139/7010 0.007812 0.036373
   CBL/KRAS/NRAS 3
   hsa04210 Apoptosis 3/21 140/7010 0.007967 0.036373 KRAS/NRAS/TP53 3
   hsa05161 Hepatitis B 3/21 146/7010 0.008941 0.039115 KRAS/NRAS/TP53 3
   [124]Open in a new tab

   STRING database was used to construct the PPI network for 27 pathogenic
   and likely pathogenic genes. String map displayed these genes
   containing 27 nodes and 88 edges, in which nodes representing proteins
   and edges depicting associated interactions (Figure [125]5). PPI
   analysis showed that CEBPA, FLT3, PAX5, PAX8, RUNX1, TP53, and WT1
   genes located in network hub appeared in Transcriptional misregulation
   in cancer.

Figure 5.

   Figure 5
   [126]Open in a new tab

   PPI network for 27 pathogenic and likely pathogenic genes. PPI,
   protein–protein interaction

4. DISCUSSION

   In this study, we performed a targeted capture deep sequencing of 504
   tumor‐related genes in 109 leukemia samples. We identified a total of
   4,655 SNVs and 614 INDELs in 419 genes, in which PDE4DIP, NOTCH2,
   FANCA, BCR, ROS1, NACA, KDM5A, CLTCL1, AKAP9, MYH11, PCM1, NOTCH1,
   COL1A1 had more than 40 mutations. We compared the frequency of
   mutations and INDELs in five subgroup of leukemia (ALL, APL, AML, CLL,
   and CML). Moreover, ACMG pathogenic analysis identified 27 pathogenic
   and likely pathogenic genes. GO enrichment analysis, pathway analysis,
   and PPI network were performed on the pathogenic and likely pathogenic
   genes. Our results might provide some molecular data on mutations in
   leukemia to map the genetic variations of Chinese patients with
   leukemia.

   Mutational analysis was used to map the genetic variants in leukemia,
   and found that the highest mutations occurred in PDE4DIP, followed by
   NOTCH2, FANCA, BCR, and ROS1 in almost all leukemia patients.
   Phosphodiesterase 4D interacting protein (PDE4DIP) anchored PDE4D at
   the centrosome‐Golgi cell region, which was involved in signal
   transduction and hydrolyze cGMP and cAMP to energize several reactions
   in the cell, including related to immune cell activation, hormone
   secretion, smooth vascular muscle action, and platelet aggregation
   (Shapshak, [127]2012). The protein is found to interact with a
   phosphodiesterase superfamily protein member (Vinayagam
   et al., [128]2011). The PDE4DIP mutations have also been previously
   identified in various cancers including lung cancer, medullary thyroid
   cancer, and ovarian cancer (Chang et al., [129]2018; Er
   et al., [130]2016; Y. Li et al., [131]2018), but not been reported in
   leukemia previously. Our study first reported that PDE4DIP mutations
   (128) in leukemia patients, suggesting PDE4DIP would be involved in
   pathogenesis of leukemia. Moreover, we found that PDE4DIP was
   downregulated in AML base on TCGA database, and the high expression was
   associated with poor AML prognosis. These suggested that PDE4DIP might
   be a tumor suppressor gene in leukemia. Results of STRING database
   showed that PDE4DIP protein might interact with PDE4D, PRKAR2A, and
   AKAP9, which can bind to cAMP or PKA, suggesting that PDE4DIP may
   participate in cAMP/PKA signaling pathway. In addition, PDE4DIP, or
   myomegalin, is a dual‐specificity AKAP known to colocalize with AKAP9
   and PKA at the centrosome, which could play important roles in the
   localization and function of the AKAP/PKA complex in microtubule
   dynamics (Schmoker et al., [132]2018). The cAMP‐dependent PKA signaling
   pathway was reported to involve in many fundamental cellular processes
   in leukemia, including migration and proliferation (Murray &
   Insel, [133]2013; Xu et al., [134]2016). These studies hinted that
   PDE4DIP might play a role in leukemia by participating in the cAMP/PKA
   signaling pathway, but more convincing studies were needed to validate.
   Previously, NOTCH2, FANCA, BCR, and ROS1 have been described to play an
   important role in leukemia. For example, Notch2 controlled
   nonautonomous Wnt‐signaling in CLL CLL (Mangolini, Gotte, &
   Moore, [135]2018). FANCA dysfunction might promote cytogenetic
   instability in adult acute myelogenous leukemia (Lensch
   et al., [136]2003). BCR‐ABL1 fusion genes were leukemogenic, causing
   CML or ALL (Baccarani et al., [137]2019). ROS1 revealed a central
   oncogenic role in CMML, which might represent a molecular target
   (Cilloni et al., [138]2013). In our study, we found NOTCH2, FANCA, BCR,
   and ROS1 were significantly mutated in 109 Chinese patients with
   leukemia.

   We used a methodology based on ACMG variant classification guidelines
   in 419 mutation genes, and identified 39 pathogenic and likely
   pathogenic mutations in 27 genes, which might be the cause of leukemia
   pathogenesis in these individuals. Particularly, there were 21
   pathogenic or likely pathogenic gene in AML patients and 7 genes in CML
   patients, indicates that the pathogenesis of AML and CML might be more
   complicated. PBRM1 (c.2819_2829del, p.L940fs) and SUZ12
   (c.1716_1717insG, p.L572fs) were only identified in ALL patients, ALDH2
   (rs540073928, p.A175D) and FBXW7 (rs866987936, p.R361Q) only in CLL
   patients, and CANT1 (c.407delT, p.L136fs) and PAX8 (c.G201T, p.E67D)
   only in APL patients. These results suggested that the pathogenesis of
   different leukemia subgroups might be different. In addition, there
   were no studies of CANT1, KIAA1549, and NFIB on leukemia in previous
   literatures; therefore, the association between these genes and
   leukemia should be further investigated.

   Subsequently, GO and KEGG analysis were performed to further understand
   the role of pathogenic mutation genes. GO annotation showed that the BP
   including gland development, leukocyte differentiation, respiratory
   system development, myeloid leukocyte differentiation, mesenchymal to
   epithelial transition, and so on were involved, which might provide
   further insight into the occurrence and development of leukemia.
   Moreover, the enriched KEGG pathway was found to be involved in
   leukemia (hsa05221 and hsa05220) and cancer‐related pathways (hsa05200,
   hsa05202, and hsa05230), which was consistent with findings from other
   previous studies on the pathogenesis of leukemia (McClure
   et al., [139]2018; de Noronha, Mitne‐Neto, & Chauffaille, [140]2017).
   Seven key genes (CEBPA, FLT3, PAX5, PAX8, RUNX1, TP53, and WT1) were
   obtained from PPIs network, most of which were reported to play a
   critical role in carcinogenesis and tumor progression (Junk
   et al., [141]2019; Rhodes, Vallikkannu, & Jayalakshmi, [142]2017;
   Slattery, Herrick, & Mullany, [143]2017).

   Inevitably, this study has several drawbacks. First, this is a
   single‐center study, and a multi‐institutional large study will be
   necessary to verify the results. Second, due to insufficient data of
   leukemia patients, we could not evaluate the correlation of the
   mutations and clinical and prognostic data of leukemia. Third, the
   potential function and pathways were only predicted by bioinformatics
   and needed experimental verification.

5. CONCLUSION

   Taken together, our study provided a map of gene mutations in Chinese
   patients with leukemia and enriched an understanding of the
   pathogenesis of leukemia. Of note, we are the first to demonstrate an
   association between PDE4DIP mutation and leukemia. Furthermore, we
   screened some pathogenic genes based on ACMG guidelines and performed
   GO analysis, pathway analysis, and PPI network. However, further
   investigations with larger cohorts and experimental research are
   warranted to further explore the potential mechanisms.

CONFLICTS OF INTEREST

   The authors declare that there are no conflicts of interest.

AUTHOR CONTRIBUTIONS

   The work presented here was carried out in collaboration between all
   authors. Hongxia Yao and Congming Wu carried out the molecular genetic
   studies and drafted the manuscript. Yueqing Chen, Li Guo, and Wenting
   Chen designed the methods and experiments, performed the statistical
   analyses and interpreted the results. Yanping Pan, Xiangjun Fu, and
   Guyun Wang collected clinical samples and information about patients.
   Hongxia Yao and Yipeng Ding conceived of the study, worked on
   associated data collection and their interpretation, participated in
   the design and coordination of the study, and funded the study. All
   authors read and approved the final manuscript.

Supporting information

   Fig S1
   [144]Click here for additional data file.^ (14.2MB, tif)

   Fig S2
   [145]Click here for additional data file.^ (5.3MB, tif)

   Table S1‐S4
   [146]Click here for additional data file.^ (30.2KB, docx)

ACKNOWLEDGMENTS