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Whole exome sequencing of pediatric leukemia reveals a novel InDel within FLT-3 gene in AML patient from Mizo tribal population, Northeast India



Leukemia is the most common type of cancer in pediatrics. Genomic mutations contribute towards the molecular mechanism of disease progression and also helps in diagnosis and prognosis. This is the first scientific mutational exploration in whole exome of pediatric leukemia patients from a cancer prone endogamous Mizo tribal population, Northeast India.


Three non-synonymous exonic variants in NOTCH1 (p.V1699E), MUTYH (p.G143E) and PTPN11 (p.S502P) were found to be pathogenic. A novel in-frame insertion-deletion within the juxtamembrane domain of FLT3 (p.Tyr589_Tyr591delinsTrpAlaGlyAsp) was also observed.


These unique variants could have a potential mutational significance and these could be candidate genes in elucidating the possibility of predisposition to cancers within the population. This study merits further investigation for its role in diagnosis and prognosis and also suggests the need for population wide screening to identify unique mutations that might play a key role towards precision medicine.

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Leukemia is the most common type of childhood cancer and the incidence is estimated to be 3.1 per 100,000 cases worldwide [1]. Leukemia can be broadly classified according to the type of hematopoietic lineage that turns cancerous as lymphoid or myeloid leukemia and by the progressiveness of the disease as acute or chronic. Previously, the causal root factor for leukemia was thought to be chromosomal translocation [2], however, there are reports that indicate that this translocation alone is not adequate for leukemiogenesis and are even observed during pregnancy [2,3,4]. Moreover, the translocation does not define the progressiveness of ALL patients [5, 6].

Apart from the chromosomal translocation, studies on nuclear mutational pattern revealed a crucial event in the Acute Myeloid Leukemia (AML) pathogenesis and its clinical significance [7, 8]. The two-hit model of leukemiogenesis captures the key events in the genomic alteration, where the two classes of mutations: one in the genes responsible for growth or survival and the other in the genes responsible for differentiation leading to self-renewability were proposed for leukemiogenesis [9]. Identifying a specific gene mutation in leukemia plays a vital role in its diagnosis, prognosis and also in predicting the disease-free survival rate and recurrence [10].

Next Generation Sequencing (NGS) approach such as Whole Exome Sequencing (WES) has been used in identifying the mutational profiles of different cancers and its subtypes. The mutational profiles of pediatric leukemia have also been studied in different ethnic groups revealing recurrent mutational hotspots, driver genes and variants involved in different pathways: RTK/RAS signaling and its downstream MAPK/ERK signaling, PI3K/AKT and MTOR, JAK/STAT signaling, Notch signaling, WNT/β-catenin, CXCL12, NF-κB, Metabolic and other pathways, including p53 [11,12,13,14]. The class of genes that are frequently mutated includes lymphoid/myeloid differentiation, transcription factors, epigenetic regulators, signal transduction, apoptotic regulators [15, 16]. FLT-3 variants within a particular hotspot region have been reported to be different across different ethnic groups and various types of indels and internal tandem duplication have also been reported [17]. Hence, it is very much essential to study unexplored ethnic groups with high incidences of cancers.

Here, we report whole exome sequencing of pediatric leukemic patients as the first scientific report from Mizo endogamous tribal population, Northeast India wherein the state has the highest incidences of various Cancers in the country [18]. We hypothesize that the high incidence of cancer rate in the population might be a result of unique mutations that are present within the coding regions of the genome. To understand the germline mutations in the population as well as to capture the variants that may be directly responsible for the disease, the present study is a pilot approach to explore the pediatric patient samples.


Whole exome analysis of pediatric leukemia patients identified 46 non-synonymous exonic variants with allele frequency ≤ 0.05, out of which 16 variants have been reported in ClinVar (Table 1). However, only MUTYH variant (p.G143E; dbSNP id: rs730881833) present in AML-M1 patient was reported as likely pathogenic for MUTYH associated Polyposis and Hereditary Cancer Predisposition Syndrome in ClinVar. Non-synonymous exonic gene variants that are not present in ClinVar are listed in Table 2. NOTCH1 variant (p.V1699E) in one patient (AML-M1) was not reported in any database and predicted as pathogenic by 7 different prediction tools using VarSome [19]. PTPN11 variant (p.S502P) present in one patient (AML-M1) was identified which was also not present in ClinVar. Sanger Validation of point mutation observed in this study are shown in Supplementary Figs. 1, 2 and 3.

Table 1 Non-synonymous exonic variants that matched with ClinVar with their clinical significance and disease associated
Table 2 Non-synonymous exonic variants not matched in CIViC and ClinVar with their OMIM phenotype and pathogenicity prediction

Identification of novel FLT3 InDel in PTPN11 mutation positive patient

Our study observed two tyrosine amino acid (in 589, 591 position) and phenylalanine (590 position) to be deleted and an in-frame insertion consistent with ITD region [17], four amino acids are inserted [tryptophan (W), alanine (A), glycine (G), aspartic acid (D)- (p.Tyr589_Tyr591delinsTrpAlaGlyAsp)] (Fig. 1). along with PTPN11 p.S502P from the same patient. NGS based evidence of the indel and its Sanger validation is given in Supplementary Figures (Supplementary Figs. 4 and 5).

Fig. 1
figure 1

Novel InDel in FLT-3 identified in AML-M1. A Wildtype FLT-3 (exon 14) depicting the genomic DNA with amino acid it encodes and the position. Bases in lower script indicates the deleted bases (ttctac) in the Mutant type. B Mutant FLT-3 depicting the genomic DNA with amino acid it encodes and the position. * Indicates the position of insertion and bases in lower script (gggcggggg) are the inserted bases


Whole exome analysis performed in the germline genomic mutational screening in pediatric leukemia patients showed important heterozygous variants and not in the corresponding mother samples suggesting that it could be a de novo germline mutation or is inherited from the father. The exception was for two homozygous variants, BCL10: p.A5S and ASXL: p.G652 which were reported as benign in ClinVar for immunodeficiency syndrome and C-like syndrome, respectively. Unreported variants were observed in this study which could be population specific variant.

MUTYH encodes an enzyme DNA glycosylase that functions in base excision repair when there is DNA damage from oxidation. MUYTH variants are also found in different types of cancers like gastric cancers [20], pediatric high grade midline gliomas patients [21] and in pediatric leukemia [22, 23]. However, a previously unreported variant G143E was found in a two years old girl with AML-M1 subtype with a family history of gastric cancer, but the mother did not carry the same mutation. Nonetheless, as the variant was predicted as pathogenic by three predicting softwares, as well as categorized as MUTYH Associated Polyposis (MAP) and Hereditary Cancer Predisposing Syndrome in ClinVar, the variant might confer loss of the protein function.

NOTCH1 encodes a transmembrane receptor protein that is required in the differentiation and maturation process and is activated during early embryo or in hematopoiesis [24, 25] Mutations in the PEST and heterodimer domains within NOTCH1 are found in 50% of T-cell-ALL patients [26]. Mutations in the gene are likely in ALL patients where its role is poorly understood in myeloid malignancies. This may be because activation of the Notch pathway varies between different cell types [27] Fu et al. [28] first reported the NOTCH1 mutation and even suggested that NOTCH1 mutations are rare events in AML patients. Study reported that in vivo activation of NOTCH1 by its ligands arrest AML growth while inhibition confers proliferation [29]. This suggested that NOTCH1 plays a role as tumour suppressor in AML, furthermore, a novel pathway that activates NOTCH1 for inhibiting cell growth was identified [30]. The mutation observed in this study as predicted by the prediction softwares (SIFT, PolyPhen2 and Mutation Taster) was deleterious suggesting that NOTCH1 p.V1699E mutation might confer loss of function and its ability to suppress tumour might be lost. From the aforementioned studies, inactivation or loss of function aids in cell proliferation suggesting that the patient in this study with AML-M1 subtype might have a proliferative advantage as extensive expression of NOTCH1 especially in M1 and M0 – AML patients with simultaneous expression of CD7 which is a marker for immaturity was observed that reflects in a poor overall survival rate [31].

FLT3 mutations can be classified into point mutations in the Tyrosine Kinase Domain (TKD) and Internal Tandem Duplications (ITD) in the juxtamembrane domain with each accounting for 5 and 25% of patients with AML, respectively. Both these types of mutations resulted in constitutive activation of the gene where the autoinhibitory mechanism is disrupted in the case of ITD and turns to ligand independent FLT3 thereby promoting cell proliferation. Similarly, point mutations in the TKD are in the activation loop that stabilize the active kinase conformation resulting in constitutive activation of its kinase activity [32]. It was also highlighted that approximately 30% of ITDs insert in the TKD1 and not in the JMD [33]. It was observed that 77 pediatric AML patients out of 630 tested positive for ITD out of which 59 had a single duplication and the rest 18 had 2 or 3 ITD’s [17]. Chow et al. [34] also showed that in 569 consecutive adult AML patients 126 (22.1%) harbored FLT3-ITDs. FLT3 mutations occurred in about 35–45% of AML patients with normal karyotype [35]. Consistently, these FLT3-ITD are in-frame mutations with varying size that ranges from 3 to > 1000 nucleotides [36].

Different types of FLT3-ITD within a hotspot region have also been reported [35,36,37]. The InDel found in this study have not been reported earlier. However, the site of duplication observed in this study is fairly consistent with other duplication site which is in the juxtamembrane domain, amino acid 591–599 [17, 34]. This study identified an insertion deletion mutation, where amino acids YFY (positions 589, 590 and 591) are deleted and 4 amino acids (WAGD) are inserted. Y589 and Y591 were reported to be the STAT5 docking site [38] where it activates and expresses an antiapoptotic protein called BCL-xL [39]. Though FLT3-ITD was reported to be a driver mutation in AML patients’ initiation of leukemia by FLT-3 through STAT pathway might not be the case for this patient. However, evading cell death is not the only property of cancers, as acquiring a proliferative advantage is also one of the natures of cancerous cells as proposed in the “two hit model” [9]. The proliferative advantage could be attained for this patient as the tyrosine residue at position 599 in FLT-3 is still intact and this residue was reported to be the interacting site of FLT-3 with PTPN11. They also showed that the absence of tyrosine residue (Y > F mutant) showed enhanced Erk activation and acquired proliferation and survival advantages when compared with WT-FLT-3 [40]. This could be a potential pathway for its initiation as hyperactive PTPN11 deregulates the RAS pathway, thereby contributing to its growth [41, 42]. This indel mutation generates a protein with one amino acid longer than the wild type. Length mutation of FLT-3 – ITD either by elongation or shortening of the juxtamembrane domain results in gain-of-function and could transform 32D cells, irrespective of the tyrosine residues [43, 44].

Mutations in PTPN11 are found commonly in JMML patients without RAS and NF1 mutation and are involved in leukemiogenesis by negative regulation of the RAS pathway by conferring growth advantage [45]. Most of the mutations reported in PTPN11 are within the domain N-terminal src-homology-2 (N-SH2) and protein tyrosine phosphatase (PTP) domain. The change of serine to proline results in the loss of S502 – E76 H-bond that is required for its auto-inhibition and thus acquiring an open conformation exposing the catalytic site leading to an increase by 8-fold turnover value of S502P when compared to wild type PTPN11 in their basal activity [46]. Consistent with other findings, GND4261 has a mutation in PTP domain (p.S502P) with no RAS mutation but positive for FLT-3 mutants. PTPN11 mutation was found to be seen more among boys [47], but in the present study, the mutation was found in a girl child. In contrast to adult AML patients, where there is no association observed between the two gene mutations, PTPN11 and FLT-3-ITD [47]. However, the sample size is small to define a true association for this population.


There are four different amino acid changes in the same position of the PTPN11 (p.S502A, p.S502T, p.S502P, p.S502L) that are reported in ClinVar. A change from serine to alanine was interpreted as pathogenic with clinical conditions like Rasopathy and Noonan Syndrome [48], a change from serine to threonine was interpreted as pathogenic with clinical conditions like Noonan Syndrome 1and Juvenile Myelomonocytic Leukemia [49] and a change from serine to leucine was interpreted as pathogenic with clinical conditions like Noonan Syndrome 1 and Juvenile Myelomonocytic Leukemia [50]. Even though, a change of serine to proline in the same position was reported in few studies in AML and Myelodysplastic Syndrome (MDS) [51], there is no record of the variant’s pathogenicity in its clinical conditions in ClinVar. However, as the other three changes p.S502A, p.S502T, and p.S502L are interpreted as pathogenic, the chance of p.S502P becoming pathogenic is also greatly increased. Additionally, the amino acid residues that are close by (p.R498W/L, p.R501K, p.G503R/V/A/E, p.M504V, p.Q506P, p.T507K) are also reported for Noonan Syndrome in Human Gene Mutation Database (HGMD) [52] which suggest the functional importance of this region.

The two mutations, NOTCH1 (p.V1699E), and FLT-3 (p.Tyr589_Tyr591delinsTrpAlaGlyAsp) observed in this study have not been reported and the frequencies are unknown as well. IndiGenomes is a database that had over 1000 healthy Indian genomes where Mizo tribal population are also included in the study [53]. South Asian Genomes and Exomes (SAGE) database consists of 1213 genomes and exome data sets from South Asians comprising 154 million genetic variants [54]. The variants found in our study were not present in the IndiGenomes and SAGE database suggesting that these variants observed might be a disease specific polymorphism for the region. As the sample size of this study is small, stressing the importance of these variants in the population might not be appropriate. However, these findings could be a potential mutational uniqueness towards the population that merits further investigation.

Materials and methods

Sample collection

All pediatric leukemia patients totaling to eleven children between 2 and 16 years (median age = 11, 3 girls and 8 boys) who are diagnosed with leukemia and undergoing treatment at Mizoram State Cancer Institute, Aizawl, Mizoram, Northeast India from January–July 2018 were included in this study (Supplementary Table 1). After obtaining informed consent from the parents, 2 ml of peripheral blood was drawn from the patients. Blood sample was also collected from four mothers who are willing to participate. Peripheral blood was collected in EDTA coated vials and stored in -20 °C for DNA isolation.

DNA isolation and whole exome sequencing

DNA was isolated from whole blood by using QIAamp DNA Mini Kit (CA, USA) as per the manufacturer’s protocol with some modifications. The quality of isolated DNA was checked using Nanodrop (NanoDrop™ 1000 Spectrophotometer, Thermofisher) at optical density (OD) 260 nm. The purity of the isolated DNA was checked by measuring OD at 260/280 for protein contamination as well as 260/230 for RNA contamination. The quality of the isolated DNA was also checked by 0.8% Agarose Gel Electrophoresis. After the required concentration of 100 ng for library preparation was obtained, DNA library was prepared by using Illumina v4 TruSeq Exome library prep as per the manufacturer’s protocol. The sequencing and data analysis was carried out at CSIR- IGIB, New Delhi.

WES data analysis

Whole Exome Sequencing was performed using Illumina HiSeq 2500 and generated approximately 52.2 million reads that passed Quality Control (QC) with 52.1 million reads (99.97%) aligned to the reference genome (hg19) per sample (Supplementary Table S2). GATK haplotype caller was used for calling germline variants from the generated BAM files [55]. The VCF file was annotated using ANNOVAR [56].

Prioritization of variants

The quality of the raw read fastq files were checked twice before and after trimming the adapter sequence and the low-quality reads by Trimmomatic software [57] and FastQC [58]. Processed fastq files were mapped on human reference genome (hg19) using BWA-MEM [59]. Variant calling was done using GATK haplotype caller [55] and the vcf file was annotated using ANNOVAR [56]. Prioritizations of variants found in the whole exome data are shown in Fig. 2. The number of variants after every filtering step is given in Supplementary Table S3. From the annotated variants: the first filtering step (F1) variants that are non-synonymous and exonic were selected, the second filter (F2) selected variants that have allele frequency ≤ 0.05, and the third filtering step (F3) selected variants that are predicted as deleterious by any two of the predicting software (SIFT, PolyPhen2 or Mutation Taster) [60,61,62] for further analysis. Frequently mutated genes which are reported in leukemia patients were listed out after performing data mining through literature survey as well as which are catalogued in databases (Supplementary Table S4). F2 and F3 were then matched with the list of frequently mutated genes in leukemia (F4). The observed variants were interpreted using CIViC [63] and ClinVar database [64], while variants not present in CIViC and ClinVar were interpreted using dbSNP [65] and OMIM database [66]. The allele frequency was also compared using databases like ExAc [67], gnoMAD [68], ESP6500 (, 1000genomes [69], IndiGenomes [53] and SAGE [54].

Fig. 2
figure 2

Prioritization of variants for whole exome data. F1 to F4: Filter’s applied. 1: Raw VCF file annotated using ANNOVAR; 2: Selection of non-Synonymous exonic variants from the annotated variants; 3: Selection of variants having allele frequency lower than 0.05; 4: Selection of variants that are predicted as deleterious in any of the two-predicting software (SIFT, PolyPhen2, Mutation Taster); 5: Matching with frequently mutated genes associate with leukemia; 6: Matching with CIViC and ClinVar database; 7: Interpreting using OMIM database

Availability of data and materials

Alignment files (.bam) that support the findings of this study have been deposited in SRA with the accession codes PRJNA774922.



Acute Lymphoblastic Leukemia


Acute Myeloid Leukemia


ASXL Transcriptional Regulator 1


BCL10 immune signalling adaptor


Clinical Interpretation of Variants in Cancer


Chronic Myeloid Leukemia


Extracellular Signal Regulated Kinase


Exome Aggregation Consortium


Fms Related Receptor Tyrosine Kinase 3


Genome Analysis Toolkit


Human Gene Mutation Database


Juvenile Chronic Myelogenous Leukemia


MUTYH Associated Polyposis


Myeloid Lymphoid Leukemia


MutY DNA Glycosylase


Next Generation Sequencing


Neurogenic locus notch homolog protein 1


Online Mendelian Inheritance in Man


Proline (P), glutamic acid (E), serine (S), and threonine (T)


Protein Tyrosine Phosphatase


Protein Tyrosine Phosphatase Non-Receptor Type 11


Quality Control


South Asian Genomes and Exomes


Sorting Intolerant From Tolerant


Signal Transducer and Activator of Transcription proteins


Variant Calling File


Whole Exome Sequencing


Wildtype-Fms Related Receptor Tyrosine Kinase 3


  1. World Health Organization International Agency for Research on Cancer (IARC). GLOBOCAN 2012: estimated cancer incidence, mortality and prevalence worldwide in 2012.

  2. Wiemels J. Chromosomal translocations in childhood leukemia: natural history, mechanisms, and epidemiology. J Natl Cancer Inst Monogr. 2008;39:87–90.

    CAS  Google Scholar 

  3. Montes R, Ayllón V, Gutierrez-Aranda I, et al. Enforced expression of MLL-AF4 fusion in cord blood CD34+ cells enhances the hematopoietic repopulating cell function and clonogenic potential but is not sufficient to initiate leukemia. Blood. 2011;117(18):4746–58.

    CAS  PubMed  Google Scholar 

  4. McHale CM, Wiemels JL, Zhang L, et al. Prenatal origin of childhood acute myeloid leukemias harboring chromosomal rearrangements t(15;17) and inv(16). Blood. 2003;101(11):4640–1.

    CAS  PubMed  Google Scholar 

  5. Pui CH, Frankel LS, Carroll AJ, et al. Clinical characteristics and treatment outcome of childhood acute lymphoblastic leukemia with the t(4;11)(q21;q23): a collaborative study of 40 cases. Blood. 1991;77(3):440–7.

    CAS  PubMed  Google Scholar 

  6. Pui CH, Raimondi SC, Srivastava DK, et al. Prognostic factors in infants with acute myeloid leukemia. Leukemia. 2000;14(4):684–7.

    CAS  PubMed  Google Scholar 

  7. Boissel N, Leroy H, Brethon B, et al. Incidence and prognostic impact of c-kit, FLT3, and Ras gene mutations in core binding factor acute myeloid leukemia (CBF-AML). Leukemia. 2006;20(6):965–70.

    CAS  PubMed  Google Scholar 

  8. Boissel N, Renneville A, Biggio V, et al. Prevalence, clinical profile, and prognosis of NPM mutations in AML with normal karyotype. Blood. 2005;106(10):3618–20.

    CAS  PubMed  Google Scholar 

  9. Kelly LM, Gilliland DG. Genetics of myeloid leukemias. Annu Rev Genomics Hum Genet. 2002;3:179–98.

    CAS  PubMed  Google Scholar 

  10. Renneville A, Roumier C, Biggio V, et al. Cooperating gene mutations in acute myeloid leukemia: a review of the literature. Leukemia. 2008;22(5):915–31.

    CAS  PubMed  Google Scholar 

  11. Bonaccorso P, Nellina A, Valeria I, et al. Molecular pathways in childhood acute lymphoblastic leukemia: from the bench to the bedside. J Pediatr Biochem. 2016;5(4):146–56.

    Google Scholar 

  12. Farrar JE, Schuback HL, Ries RE, et al. Genomic profiling of pediatric acute myeloid leukemia reveals a changing mutational landscape from disease diagnosis to relapse. Cancer Res. 2016;76(8):2197–205.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhang J, Mullighan CG, Harvey RC, et al. Key pathways are frequently mutated in high-risk childhood acute lymphoblastic leukemia: a report from the Children's oncology group. Blood. 2011;118(11):3080–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Mirabilii S, Ricciardi MR, Allegretti M, et al. Targeting metabolic pathways for leukemia treatment. Blood. 2012;120(21):1371.

    Google Scholar 

  15. Bolouri H, Farrar J, Triche T, et al. The molecular landscape of pediatric acute myeloid leukemia reveals recurrent structural alterations and age-specific mutational interactions. Nat Med. 2018;24:103–12.

    CAS  PubMed  Google Scholar 

  16. Ding LW, Sun QY, Tan KT, et al. Mutational landscape of pediatric acute lymphoblastic leukemia [published correction appears in Cancer res. 2017 Apr 15;77(8):2174]. Cancer Res. 2017;77(2):390–400.

    CAS  PubMed  Google Scholar 

  17. Meshinchi S, Stirewalt DL, Alonzo TA, et al. Structural and numerical variation of FLT3/ITD in pediatric AML. Blood. 2008;111(10):4930–3.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Mathur P, Sathishkumar K, Chaturvedi M, et al. Cancer statistics, 2020: report from National Cancer Registry Programme, India. JCO Glob Oncol. 2020;6:1063–75.

    PubMed  Google Scholar 

  19. Kopanos C, Tsiolkas V, Kouris A, et al. VarSome: the human genomic variant search engine. Bioinformatics. 2019;35(11):1978–80.

    CAS  PubMed  Google Scholar 

  20. Kim CJ, Cho YG, Park CH, et al. Genetic alterations of the MYH gene in gastric cancer. Oncogene. 2004;23(40):6820–2.

    CAS  PubMed  Google Scholar 

  21. Kline CN, Joseph NM, Grenert JP, et al. Inactivating MUTYH germline mutations in pediatric patients with high-grade midline gliomas. Neuro-Oncology. 2016;18(5):752–3.

    PubMed  PubMed Central  Google Scholar 

  22. Stanczyk M, Sliwinski T, Cuchra M, et al. The association of polymorphisms in DNA base excision repair genes XRCC1, OGG1 and MUTYH with the risk of childhood acute lymphoblastic leukemia. Mol Biol Rep. 2011;38(1):445–51.

    CAS  PubMed  Google Scholar 

  23. Akyerli CB, Ozbek U, Aydin-Sayitoğlu M, Sirma S, Ozçelik T. Analysis of MYH Tyr165Cys and Gly382Asp variants in childhood leukemias. J Cancer Res Clin Oncol. 2003;129(10):604–5.

    PubMed  Google Scholar 

  24. Kojika S, Griffin JD. Notch receptors and hematopoiesis. Exp Hematol. 2001;29(9):1041–52.

    CAS  PubMed  Google Scholar 

  25. Schroeder T, Kohlhof H, Rieber N, Just U. Notch signaling induces multilineage myeloid differentiation and up-regulates PU.1 expression. J Immunol. 2003;170(11):5538–48.

    CAS  PubMed  Google Scholar 

  26. Weng AP, Ferrando AA, Lee W, et al. Activating mutations of NOTCH1 in human T cell acute lymphoblastic leukemia. Science. 2004;306(5694):269–71.

    CAS  PubMed  Google Scholar 

  27. Baldi A, De Falco M, De Luca L, et al. Characterization of tissue specific expression of Notch-1 in human tissues. Biol Cell. 2004;96(4):303–11.

    CAS  PubMed  Google Scholar 

  28. Fu L, Kogoshi H, Nara N, Tohda S. NOTCH1 mutations are rare in acute myeloid leukemia. Leuk Lymphoma. 2006;47(11):2400–3.

    CAS  PubMed  Google Scholar 

  29. Kannan S, Sutphin RM, Hall MG, et al. Notch activation inhibits AML growth and survival: a potential therapeutic approach. J Exp Med. 2013;210(2):321–37.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Lobry C, Ntziachristos P, Ndiaye-Lobry D, et al. Notch pathway activation targets AML-initiating cell homeostasis and differentiation. J Exp Med. 2013;210(2):301–19.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Sliwa T, Awsa S, Vesely M, et al. Hyperexpression of NOTCH-1 is found in immature acute myeloid leukemia. Int J Clin Exp Pathol. 2014;7(3):882–9 Published 2014 Feb 15.

    PubMed  PubMed Central  Google Scholar 

  32. Gilliland DG, Griffin JD. The roles of FLT3 in hematopoiesis and leukemia. Blood. 2002;100(5):1532–42.

    CAS  PubMed  Google Scholar 

  33. Bretenbuecher F, Shnittger S, Grundler R, et al. Identification of a novel type of ITD mutations located in nonjuxtamembrane domains of the FLT3 tyrosine kinase receptor. Blood. 2008;113:4074–7.

    Google Scholar 

  34. Chou WC, Hou HA, Liu CY, et al. Sensitive measurement of quantity dynamics of FLT3 internal tandem duplication at early time points provides prognostic information. Ann Oncol. 2011;22(3):696–704.

    Article  PubMed  Google Scholar 

  35. Blau O, Berenstein R, Sindram A, Blau IW. Molecular analysis of different FLT3-ITD mutations in acute myeloid leukemia. Leuk Lymphoma. 2013;54(1):145–52.

    CAS  PubMed  Google Scholar 

  36. Schnittger S, Bacher U, Haferlach C, Alpermann T, Kern W, Haferlach T. Diversity of the juxtamembrane and TKD1 mutations (exons 13-15) in the FLT3 gene with regards to mutant load, sequence, length, localization, and correlation with biological data. Genes Chromosomes Cancer. 2012;51(10):910–24.

    CAS  PubMed  Google Scholar 

  37. Kiyoi H, Naoe T, Yokota S, et al. Internal tandem duplication of FLT3 associated with leukocytosis in acute promyelocytic leukemia. Leukemia study Group of the Ministry of Health and Welfare (Kohseisho). Leukemia. 1997;11(9):1447–52.

    CAS  PubMed  Google Scholar 

  38. Rocnik JL, Okabe R, Yu JC, et al. Roles of tyrosine 589 and 591 in STAT5 activation and transformation mediated by FLT3-ITD. Blood. 2006;108(4):1339–45.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Irish JM, Anensen N, Hovland R, et al. Flt3 Y591 duplication and Bcl-2 overexpression are detected in acute myeloid leukemia cells with high levels of phosphorylated wild-type p53. Blood. 2007;109(6):2589–96.

    CAS  PubMed  Google Scholar 

  40. Heiss E, Masson K, Sundberg C, et al. Identification of Y589 and Y599 in the juxtamembrane domain of Flt3 as ligand-induced autophosphorylation sites involved in binding of Src family kinases and the protein tyrosine phosphatase SHP2. Blood. 2006;108(5):1542–50.

    CAS  PubMed  Google Scholar 

  41. Loh ML, Reynolds MG, Vattikuti S, et al. PTPN11 mutations in pediatric patients with acute myeloid leukemia: results from the Children's Cancer group. Leukemia. 2004;18(11):1831–4.

    CAS  PubMed  Google Scholar 

  42. Loh ML, Vattikuti S, Schubbert S, et al. Mutations in PTPN11 implicate the SHP-2 phosphatase in leukemogenesis. Blood. 2004;103(6):2325–31.

    CAS  PubMed  Google Scholar 

  43. Kiyoi H, Towatari M, Yokota S, et al. Internal tandem duplication of the FLT3 gene is a novel modality of elongation mutation which causes constitutive activation of the product. Leukemia. 1998;12(9):1333–7.

    CAS  PubMed  Google Scholar 

  44. Kiyoi H, Ohno R, Ueda R, et al. Mechanism of constitutive activation of FLT3 with internal tandem duplication in the juxtamembrane domain. Oncogene. 2002;21(16):2555–63.

    CAS  PubMed  Google Scholar 

  45. Tartaglia M, Martinelli S, Cazzaniga G, et al. Genetic evidence for lineage-related and differentiation stage-related contribution of somatic PTPN11 mutations to leukemogenesis in childhood acute leukemia. Blood. 2004;104(2):307–13.

    CAS  PubMed  Google Scholar 

  46. LaRochelle JR, Fodor M, Xu X, et al. Structural and functional consequences of three Cancer-associated mutations of the oncogenic phosphatase SHP2. Biochemistry. 2016;55(15):2269–77.

    CAS  PubMed  Google Scholar 

  47. Kratz CP, Niemeyer CM, Castleberry RP, et al. The mutational spectrum of PTPN11 in juvenile myelomonocytic leukemia and Noonan syndrome/myeloproliferative disease. Blood. 2005;106(6):2183–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. National Center for Biotechnology Information. ClinVar; [VCV000040556.3], Accessed June 10, 2021. PTPN11 S502A.

  49. National Center for Biotechnology Information. ClinVar; [VCV000013332.6], Accessed June 10, 2021. PTPN11 S502T.

  50. National Center for Biotechnology Information. ClinVar; [VCV000040557.6], Accessed June 11, 2021.

  51. Aoki Y, Niihori T, Narumi Y, Kure S, Matsubara Y. The RAS/MAPK syndromes: novel roles of the RAS pathway in human genetic disorders. Hum Mutat. 2008;29(8):992–1006.

    CAS  PubMed  Google Scholar 

  52. Stenson PD, Mort M, Ball EV, et al. The human gene mutation database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum Genet. 2014;133:1–9.

    CAS  PubMed  Google Scholar 

  53. Jain A, Bhoyar RC, Pandhare K, et al. IndiGenomes: a comprehensive resource of genetic variants from over 1000 Indian genomes. Nucleic Acids Res. 2021;49(D1):D1225–32.

    CAS  PubMed  Google Scholar 

  54. Hariprakash JM, Vellarikkal SK, Verma A, et al. SAGE: a comprehensive resource of genetic variants integrating South Asian whole genomes and exomes. Database (Oxford). 2018:1–10 Published 2018 Jan 1.

  55. McKenna A, Hanna M, Banks E, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164.

    PubMed  PubMed Central  Google Scholar 

  57. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014:btu170.

  58. Andrews S. (2010). FastQC: a quality control tool for high throughput sequence data. Availableonline at:

    Google Scholar 

  59. Li H (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:1303.3997v1 [q-bio.GN].

  60. Ng PC, Henikoff S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003;31(13):3812–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet. 2013; Chapter7:Unit7.20.

  62. Schwarz JM, Cooper DN, Schuelke M, Seelow D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods. 2014;11(4):361–2.

    CAS  PubMed  Google Scholar 

  63. Griffith M, Spies NC, Krysiak K, et al. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat Genet. 2017;49(2):170–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Landrum MJ, Lee JM, Benson M, Brown G, Chao C, Chitipiralla S, et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 2016;44(D1):D862–8.

    CAS  PubMed  Google Scholar 

  65. Sherry ST, Ward M, Sirotkin K. dbSNP-database for single nucleotide polymorphisms and other classes of minor genetic variation. Genome Res. 1999;9(8):677–9.

    CAS  PubMed  Google Scholar 

  66. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian inheritance in man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005;33(Database issue):D514–7.

    CAS  PubMed  Google Scholar 

  67. Karczewski KJ, Weisburd B, Thomas B, Solomonson M. The ExAC browser: displaying reference data information from over 60 000 exomes. Nucleic Acids Res. 2017;45(D1):D840–5.

    CAS  PubMed  Google Scholar 

  68. Karczewski KJ, Francioli LC, Tiao G, et al. The mutational constraint spectrum quantified from variation in 141,456 humans [published correction appears in nature. 2021 Feb;590(7846):E53]. Nature. 2020;581(7809):434–43.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 1000 Genomes Project Consortium, Auton A, Brooks LD, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74.

    Google Scholar 

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The authors acknowledge the research scholars from Department of Biotechnology, Mizoram University and research scholars from SSB and VS lab of CSIR-IGIB, New Delhi. The authors also thank Mr. David K Zorinsanga, Department of Biotechnology, Mizoram University for his help during the work.


The authors would like to acknowledge GUaRDIAN program, CSIR-Institute of Genomics and Integrative Biology, New Delhi for the support. The work was supported by Department of Science and Technology, New Delhi sponsored Technology enabling Center, Mizoram University.

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NSK, JLP, DL conceptualized and designed the work. JLP, DL and AV performed sampling. AV did the literature search and experimental studies. SS, VK performed whole exome sequencing and data acquisition. SS, VS and AV performed preliminary data analysis. AV and NSK carried out data analysis using variants. All the authors contributed in manuscript preparation, manuscript editing and manuscript review.

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Correspondence to Nachimuthu Senthil Kumar.

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All the participants in this study gave their voluntary consent to publish.

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Vanlallawma, A., Lallawmzuali, D., Pautu, J.L. et al. Whole exome sequencing of pediatric leukemia reveals a novel InDel within FLT-3 gene in AML patient from Mizo tribal population, Northeast India. BMC Genom Data 23, 23 (2022).

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  • Pediatric leukemia
  • Exome sequencing
  • FLT3
  • PTPN11
  • Non-synonymous
  • Mizoram