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Introduction

Acute myeloid leukemia (AML) is the most common acute leukemia in adults and one of the most common hematological malignancies, with strong biological heterogeneity and clinical heterogeneity (1). It accounted for ~20% of acute leukemia cases in children worldwide in 2008 (1). Due to chromosomal abnormalities and genetic mutations, the normal hematopoietic system undergoes malignant transformation at different stages, which causes blockade of blood cell differentiation and results in different subtypes of AML in the process of directional myeloid differentiation (2). An important pathogenic factor of AML is chromosomal abnormality, including translocation, inversion, deletion and tandem duplication (3). There are varieties of recurrent genetic abnormalities, such as t (8:21), inv (16) and t (15:17) (4). In addition to large chromosomal abnormalities, genetic mutation is another feature for AML (5). With the development and widespread application of high-throughput sequencing technologies, leukemia-associated mutant genes have been identified with diagnostic and therapeutic values in AML. A few gene mutations, including Fms related receptor tyrosine kinase 3 (FLT3), nucleophosmin 1, KIT proto-oncogene, receptor tyrosine kinase, CCAAT enhancer binding protein α, tet methylcytosine dioxygenase 2, and DNA methyltransferase 3 α have been associated with AML (6,7). At the individual level, more previously unknown gene mutations associated with AML should be identified. The present study reported two AML cases with kinship (a father-son pair) in which genome-wide sequencing, whole-exome sequencing (WES) and transcriptomic analysis was performed, and some novel mutant genes and sites were identified.

Materials and methods

Patients

A father-son pair were both diagnosed with AML at The General Hospital of Western Theater Command (Chengdu, China). The son was diagnosed in April 2014 while the father was diagnosed in May 2016. The father was aged 66 and the son aged 44 years. According to National Comprehensive Cancer Network Guideline for AML (8), both patients were diagnosed with AML and vein blood samples were collected, and DNAs were extracted for whole-genome sequencing (WGS) or WES using the phenol-chloroform method. The Hospital Ethics Committee approved the study and the two patients signed informed consent before donating samples. For transcriptome sequencing, the RNA sample was extracted from the blood of the son.

WGS

The DNA sample from the father was used for genome-wide sequencing. DNA was extracted from peripheral white blood cells using a DNA extraction kit (Qiagen GmbH) according to the manufacturer's instructions. A total of 2 µl purified DNA was used to measure the quality and quantity of the DNA (ng/µl) (A260/A280) using an UV visible spectrophotometer (BioTek Instruments, Inc.). Finally, 20 ng/µl DNA samples were stored at 20°C, until further use. DNA degradation and the molecular weight was analyzed using 0.2% agarose gel electrophoresis (loading 5 µl extracted DNA). DNA libraries were prepared following the manufacturer's protocol (Illumina DNA Prep with Enrichment; cat. no. 20025523; Illumina, Inc.). Briefly, for each sample, DNA was firstly sheared into fragments of ~350 bp. The fragments were then end-repaired, A-tailed, ligated to paired-end adaptors and PCR amplified (according to the manufacturer's instructions) for library construction. The final library concentration used was 6.2 pM. The resulting DNA libraries were subjected to 150 bp pair-end sequencing on the Illumina HiSequencing PE150 platform (Illumina, Inc.). The reference genome was downloaded from the University of California, Santa Cruz (UCSC) database (GRCh38/hg38; http://genome.ucsc.edu). Low-quality reads were discarded using the PRINSEQ software (v0.20.4) and the resulting clean data were aligned to the human reference sequence (hg19) with the Burrow-Wheeler Aligner (BWA) software (0.7.12-r1044) (9). Duplicate reads were removed using the Picard software (http://sourceforge.net/projects/picard/). The aligned reads were sorted with the Genome Analysis Toolkit (GATK) software (v2.8.1; http://github.com/RRafiee/GenomeAnalysisToolkit). SIFT (sift.jcvi.org) and PolyPhen2 (genetics.bwh.harvaed.edu) software were used to predict the effects of amino acid mutations on protein structure and function. All variants were annotated with ANNOVAR (10).

WES

The DNA sample from the son was used for WES. The protocol of DNA extraction was identical to that of the father's sample. DNA was randomly digested into 150–200 bp fragments using a Bioruptor Pico ultrasound. Fragmented DNA was end-repaired, A-tailed and then connected. Sample labeling and enrichment of DNA were conducted using PCR amplification according to the manufacturer's instructions. The DNA library with the specific index was subjected to liquid phase hybridization with the biotin-labeled RNA probe, and the target gene exon was obtained using the streptavidin-labeled magnetic beads. Then, target exon genes were enriched by PCR amplification. Sequencing was performed using an Ilumina platform (AmpliSeq™ Focus Panel for Illumina; cat. no. 20019164; 10 ng) and the HiSeq 3000/4000 PE (150 paired-end) Cluster kit (both from Illumina, Inc.), according to the manufacturer's instrustions. Clean reads were mapped to the reference genome, GRCh37 using BWA. After removing duplications, single nucleotide polymorphisms, and insertions and deletions (Indel) were assigned and annotated using the (GATK) based on dbSNP build 150.

Transcriptome sequencing

The total RNA from blood samples of the son was extracted using TRIzol® reagent (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions. RNA purity was determined using a NanoDrop 2000 spectrophotometer (NanoDrop Technologies; Thermo Fisher Scientific, Inc.), and RNA integrity was verified using 1.5% agarose gels. Magnetic beads conjugated with oligo (dT) were used to isolate mRNA, which was fragmented into short lengths of ~200 bp using an RNA Fragmentation kit (Illumina, Inc.). The short RNA fragments were used as templates for first-strand DNA synthesis with random primers according to the manufacturer's instruction. Next, second-strand DNA was generated, purified using magnetic beads, end repaired, and a single adenine (A) nucleotide was added to the 3′ ends. DNA degradation and the molecular weight were analyzed using 0.2% agarose gel electrophoresis (loading 5 µl extracted DNA). RNA-Sequencing (RNA-Seq) libraries were prepared using the VAHTS Total RNA-seq (H/M/R) Library Prep kit for Illumina (Vazyme Biotech Co., Ltd.) following the manufacturer's instructions. The raw reads were processed to remove the adapter and primer sequences using SeqPrep software (v1.0; http://github.com/jstjohn/SeqPrep). Low-quality bases at the 3′ ends were deleted using Sickle software (v1.33; http://github.com/najoshi/sickle). Fragments <30 bp in length were excluded from further analyses and reads containing N >10% were removed. The obtained high-quality sequencing sequence after quality control was aligned with the designated Ensembl genome using TopHat (http://tophat.cbcb.umd.edu/). Saturation, duplicate reads and coverage analysis were conducted software from Majorbio, including SeQC-2.3.2 (http://code.google.com/p/rseqc/) and RSeQC-2.3.2 (http://code.google.com/p/rseqc/). Predictionof novel transcripts in annotated genomes was performed using Cufflink (http://cufflinks.cbcb.umd.edu/) (11). To identify the known long non-coding (lnc)RNAs, the predicted novel transcript was aligned with a known lncRNA using collection of databases, such as NONCODE (www.noncode.org), Ensembl (ensembl.org), NCBI (http://www.ncbi.nlm.nih.gov/), and UCSC (genome.ucsc.edu), LncRNAdb (http://www.lncrnadb.org/), GENCODE (https://www.gencodegenes.org/) and LncRNA Disease. The gene expression levels and differentially expressed genes (DEGs) were estimated using cufflinks (http://cole-trapnell-lab.github.io/cufflinks/) (12–14). The default parameters for DEGs were false discovery rate (FDR) ≤0.05 and log2 fold-change (FC) ≥1 or ≤-1.

Gene Ontology (GO) enrichment analysis was performed using the software Goatools (https://github.com/tanghaibao/GOatools). The P-values were corrected using four multiple test methods (Bonferroni's, Holm, Sidak and FDR) to control the calculated false positive rate. When the corrected P-value (or P-FDR) ≤0.05, it was considered significant (15–17). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using KOBAS (http://kobas.cbi.pku.edu.cn/home.do). A corrected P≤0.05 was considered to indicate a significant difference (18). Samtools (http://samtools.sourceforge.net/) and VarScan version 2.2.7 (http://varscan.sourceforge.net/) software were used to find candidate SNPs.

Results

WGS results

The WGS results for the father's blood sample were as follows. A total of 638,386,172 raw reads were generated as shown in Table I, achieving WGS mean depth of 25. The coverage of the whole genome was 96.9%. All properly pair-end mapped sequences were used for subsequent variants detection. A total of 3,897,164 SNPs and 780,834 indels were identified (Fig. 1). The distribution of each SNP locus was analyzed (Fig. 2A) (Table II). The results showed that C>T and G>A were the most common variations. The distribution of indel lengths is presented in Fig. 2B. All SNP and indel variants were annotated using the ANNOVAR program in conjunction with the reference gene annotation information of the UCSC Genome Browser. A total of 24,150 SNPs located in the exon region were probed and 727 indel variants in the exon region were found. The distribution of SNPs and indel variants in gene functional regions shown in Table II.

Figure 1. Distribution of SNPs and indels in different chromosomes. (A) SNP base distribution. (B) Indel base distribution. SNP, single nucleotide polymorphism; indel; insertion deletion.

Figure 2. SNP types and distribution of indel lengths. (A) SNP types and (B) distribution of indel lengths. SNP, single nucleotide polymorphism; indel; insertion deletion.

Table I. Summary of the WGS and WES.

Table I.

Summary of the WGS and WES.

Index	WGS	WES
Raw reads	638,386,172	638.39a
Raw data	96,396,311,972	96,396.31b
Clean reads	91,827,913,399	609.51a
Mean depth, x	25	28.61
Coverage, %	96.90	99.91

a M.

b Mb. WGS, whole-genome sequencing; WES, whole-exome sequencing.

Table II. Acute myeloid leukemia-related mutation genes in the exon region from father's whole-genome sequencing results.

Table II.

Acute myeloid leukemia-related mutation genes in the exon region from father's whole-genome sequencing results.

Chrom	Position	Refs.	Alt	Gene	Transcript	Exon region	Variant transcript	Variant protein
10	100219374	T	A	HPSE2	NM_001166245	10	c.A1400T	p.Y467F
					NM_001166244	11	c.A1562T	p.Y521F
					NM_021828	12	c.A1736T	p.Y579F
10	104934709	T	C	NT5C2	NM_001134373/NM_012229	2/3	c.A7G	p.T3A
10	5136651	C	G	AKR1C3	NM_001253909/NM_003739	1	c.C15G	p.H5Q
10	5139685	G	A	AKR1C3	NM_001253908/NM_001253909/NM_003739	3	c.G312A	p.K104K
10	54531235	C	T	MBL2	NM_000242	1	c.G161A	p.G54D
10	70405855	A	G	TET1	NM_030625	4	c.A3369G	p.I1123M
10	88422116	C	T	OPN4	NM_033282	4	c.C1181T	p.T394I
					NM_001030015	9	c.C1214T	p.T405I
10	89623716	G	A	PTEN	NM_001304717	1	c.G10A	p.G4R
10	89623901	G	C	PTEN	NM_001304717	2	c.G194C	p.C65S
10	89624218	C	G	PTEN	NM_001304717	2	c.C511G	p.L171V
11	102595492	G	A	MMP8	NM_002424	1	c.C95T	p.T32I
					NM_002424	2	c.A259G	p.K87E
					NM_001304441/NM_001304442	3	c.A190G	p.K64E
11	35226155	A	G	CD44	NM_001001389	9	c.A1121G	p.K374R
					NM_000610	10	c.A1250G	p.K417R
11	35229673	T	C	CD44	NM_001001390	6	c.T689C	p.I230T
					NM_001001389	11	c.T1307C	p.I436T
					NM_000610	12	c.T1436C	p.I479T
13	28624294	G	A	FLT3	NM_004119	6	c.C680T	p.T227M

[i] Chrom, chromosome; ref, reference genome allele; Alt, alternate non-reference allele.

Regarding amino acid translation, there were 11,316 non-synonymous, 12 stop-loss, 12,033 synonymous, 92 stop-gain for SNPs, 63 frameshift insertions, 73 frameshift deletions, 242 non-frameshift insertions, 248 non-frameshift deletions, 4 stop-gain, 2 stop-loss for indel variants (Table III).

Table III. Summary of detected SNPs and indels in all sequencing samples.

Table III.

Summary of detected SNPs and indels in all sequencing samples.

A, SNPs
Index	WGS results	WES
Total SNPs in exon	24,150	22,656
Synonymous SNP	12,033	11,570
Non-synonymous SNP	11,316	10,504
Stop-gain	92	87
Stop-loss	12	12
B, Indels
Index	WGS	WES
Total indels in exon	727	620
Frameshift insertion	63	102
Frameshift deletion	73	117
Non-frameshift insertion	242	156
Non-frameshift deletion	248	190
Stop-gain	4	3
Stop-loss	2	1

[i] SNP, single nucleotide polymorphism; indel, insertion deletion; WGS, whole-genome sequencing; WES, whole-exome sequencing.

Non-synonymous SNPs can cause changes in the encoded amino acids. Together, 2,436 mutations were destructive to protein structure and function according to SIFT, and 1,154 mutations were probably harmful according to PolyPhen2. Comparing with 121 AML-related genes identified in previous AML studies, 14 genes were found in the father's exon region, including the following genes: HPSE2 (19), NT5C2 (20), AKR1C3 (21), MBL2 (22), ARID5B (23), TET1 (24), WT1 (25), CD44 (26) and FLT3 (27). However, only 10 of these had non-synonymous mutations changing encoded amino acids.

WES results

The WES results from the son achieved 96,396.31 Mb raw data, generating a mean depth of 28.61. The coverage rate was 99.91%. All properly pair-end mapped sequences were used for subsequent variants detection, as shown in Table II. Overall, 96,639 SNPs were identified, including 10,504 non-synonymous SNPs. For the types of base mutations in SNPs, C>T and G>A were the most commonly observed in this sample. The indel results showed 15,033 mutations, including 102 frameshift insertions, 117 frameshift deletions, 156 non-frameshift insertions, 190 non-frameshift deletions, three stop-gain and one stop-loss (Table III). Seven mutant genes were found in son's exon region compared with 121 AML-related genes, including FLT3, GATA2 (28,29), MCM7 (30), PTEN (31), and RUNX1T1 (32) (Table IV). Among them, six genes showed non-synonymous SNPs (Table IV).

Table IV. Acute myeloid leukemia-related mutation genes in the exon region from son's whole-exome sequencing results.

Table IV.

Acute myeloid leukemia-related mutation genes in the exon region from son's whole-exome sequencing results.

Chrom	Position	Refs.	Alt	Gene	Transcript	Exon region	Variant transcript	Variant protein
1	110882830	C	T	RBM15	NM_001201545/NM_022768	1	c.C803T	p.P268L
3	128202797	C	T	GATA2	NM_001145662/NM_032638	4	c.G923A	p.R308Q
7	99696316	G	A	MCM7	NM_001278595/NM_182776	5	c.C77T	p.P26L
					NM_005916	6	c.C605T	p.P202L
8	93107611	G	A	RUNX1T1	NM_001198634/NM_001198679	1	c.C85T	p.R29W
10	89623901	G	C	PTEN	NM_001304717	2	c.G194C	p.C65S
13	28609806	T	G	FLT3	NM_004119	12	c.A1423C	p.T475P
13	28674628	T	C	FLT3	NM_004119	1	c.A20G	p.D7G

[i] Chrom, chromosome; ref, reference genome allele; Alt, alternate non-reference allele.

Then the mutant genes in the exon region were compared between the father and son results, and some common ones were observed. Notably, two missense mutations in the FLT3 gene were identified. Internal tandem duplications in the FLT3 gene (FLT3-ITD) were the earliest described molecular alterations in AML (33). In the present results, the alterations of FLT3 were as follows. In WGS data from the father: Exon 6, c.C680T: p.T227M (NM_004119). In WES data from the son: Exon 1, c.A20G: p.D7G (NM_004119) and exon 12, c.A1423C: p.T475P (NM_004119). Another common gene was PTEN. As Tables III and IV show, in WGS data from the father, the following mutations were identified: c.G10A: p.G4R (NM_001304717) In exon 1, c.G194C: p.C65S(NM_001304717) in exon 2 and c.C511G: p.L171V (NM_001304717) in exon 2. In WES data from the son the c.G194C: p.C65S (NM_001304717) mutation was identified in exon 2.

Transcriptomic sequencing results

Transcriptomic sequencing generated 69,537,564 raw reads containing 10,430,634,600 nucleotides for the son. Following clean-up and quality filtering, 68,019,440 and 75,105,808 clean reads were obtained, with a Q20 percentage (proportion of nucleotides with quality value >20) >98.51%, which indicated the RNA sequencing results were of high quality and suitable for use in further analysis.

As Fig. 3 shows, there were 54 differentially expressed mRNAs from transcriptomic analysis, of which 31 were significantly upregulated in AML, and 23 were significantly downregulated. However, no common differentially expressed mRNAs between WGS and WES were identified. As aforementioned, FLT3 and PTEN might play an important role in AML development. Compared with patients with AML, expression of FLT3 was decreased (P=0.035; FDR, 0.919; log2FC, 3.17). A lower expression of PTEN were found in the patients with AML without statistical significance (P=0.4135; FDR, 0.993; log2FC, −0.71).

Figure 3. Differentially expressed mRNAs in the transcriptomic analysis.

GO annotations revealed that the aforementioned genes were enriched in ‘single-organism signaling’, ‘response to stimulus’, ‘biological regulation’ and ‘metabolic process’. The top 30 terms were all among the biological process class and are presented in Fig. 4. KEGG pathway analysis revealed 56 related pathways, while according to corrected P-value, only three pathways were significantly enriched: ‘Influenza A’, ‘hematopoietic cell lineage’ and ‘cell adhesion molecules’ (Fig. 5). Alternative splicing is an important mechanism for regulating gene expression and producing protein diversity, which can cause large differences in genes and proteins (12). There were 4,370 3′ end alternative splicing events, 2,575 3′ untranslated region (UTR) alternative splicing events, 4,927 5′ end alternative splicing events, 1,783 5′ UTR alternative splicing events, 2,468 exon spanning events, 336 intron retentions, and 2,761 other alternative splicing.

Figure 4. Top 30 terms of Gene Ontology functional enrichment in the transcriptomic analysis.

Figure 5. There were 3 pathways are significantly enriched in Kyoto Encyclopedia of Genes and Genomes pathway analysis based on transcriptomic analysis.

Discussion

Clinically, it is rare to identify AML cases similar to familial aggregation. In the present study, the common molecular mutation types of AML in the father were not consistent with those of his son. Therefore, it was hypothesized that the father had some molecular variation that had not been previously recognized, which led to the occurrence of AML to the father, and it could be stably and continuously expressed in his whole sexual maturity stage. The molecular variation was passed on to his son through genetic material and expressed at a certain developmental stage of the son, leading to the disease. Therefore, the current study performed genome-wide sequencing, WES and transcriptomic analyses of two patients with AML patients with kinship (a father-son pair) in order to investigate the profiling of AML development.

The father-son pair samples had a similar number of SNPs and indel variants in the exon region (24,150 SNPs and 727 indel variants in father sample; 22,656 SNPs and 620 indel variants in son sample), though different sequencing analysis technologies were used, following the decision of the patient. Their SNPs and indel variants were similar to previous studies of other types of blood cancer (e.g. chronic myelomonocytic leukemias) (7,34). However, transcriptomic analysis was also performed on the son sample and 10,072 SNPs in the exon region were found.

In total, >200 common exon mutant genes were identified in both the father and son, which suggested that AML may be the result of a combination of multiple genes. In these genes, an FLT3 mutation was identified in both father and son. They showed different variations in the exon region. However, different variations in FLT3 may cause the same result. FLT3-ITD has major clinical implications and is associated with adverse outcomes in recurrent somatic mutations in AML through multiple mechanisms (35). Previous studies revealed FLT3-ITD with different insertion sites spread over the whole stretch of exons 14–15 (36,37). However, the present study observed three mutations in exons 1, 6 and 12 and none of these were located in exons 14 and 15. These mutations may cause changes in protein structure or function, and the further study can investigate the role of these exons. FLT3 is known to be overexpressed in hematopoietic neoplasms (38). The current transcriptomic sequencing results also showed increased expression of FLT3 in AML. PTEN, a tumor suppressor gene (31), was another identified common gene. A missense mutation in the PTEN gene was reported in concurrent germ cell tumor (GCT)-associated AML (39). The mRNA and protein levels of PTEN in newly diagnosed patients with AML and patients with relapsed AML are significantly lower compared with those in the control and remission groups, which was supports the present findings (39).

Through high-throughput analysis, the mutation profiles of two patients with AML with a father-son relationship were obtained at the genetic level, and the son showed a changed mRNA profiles at the gene expression level. The study also identified some new mutation sites in known AML-associated genes, such as FLT3. The present case report may provide novel insight into the molecular events governing the pathogenesis of AML.

Acknowledgements

Not applicable.

Funding

No funding was received.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the NCBI-SRA repository (https://www.ncbi.nlm.nih.gov/sra/). The BioSample accession number is: SAMN16395638.

Authors' contributions

SHL and YCL designed the study and wrote the manuscript. SZ, RD, YD and FYF performed all the experiments. SHL, SZ and RD confirm the authenticity of all the raw data. All authors approved the final version of the manuscript.

Ethics approval and consent to participate

This study has been approved by The General Hospital of Western Theater Command (Ethics approval number: 2016KY82). Informed consent to participate was provided from both participants.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References