Open Access

Spectrum of somatic mutations detected by targeted next-generation sequencing and their prognostic significance in adult patients with acute lymphoblastic leukemia

  • Juan Feng1, 2,
  • Yan Li1,
  • Yujiao Jia1,
  • Qiuyun Fang1,
  • Xiaoyuan Gong1,
  • Xiaobao Dong1,
  • Kun Ru1,
  • Qinghua Li1,
  • Xingli Zhao1,
  • Kaiqi Liu1,
  • Min Wang2,
  • Zheng Tian2,
  • Yannan Jia1, 2,
  • Ying Wang1,
  • Dong Lin1,
  • Hui Wei1, 2,
  • Kejing Tang2,
  • Yingchang Mi1, 2Email author and
  • Jianxiang Wang1, 2Email author
Contributed equally
Journal of Hematology & Oncology201710:61

DOI: 10.1186/s13045-017-0431-1

Received: 25 January 2017

Accepted: 23 February 2017

Published: 28 February 2017

Abstract

Target-specific next-generation sequencing technology was used to analyze 112 genes in adult patients with acute lymphoblastic leukemia (ALL). This sequencing mainly focused on the specific mutational hotspots. Among the 121 patients, 93 patients were B-ALL (76.9%), and 28 patients (23.1%) were T-ALL. Of the 121 patients, 110 (90.9%) harbored at least one mutation. The five most frequently mutated genes in T-ALL are NOTCH1, JAK3, FBXW7, FAT1, and NRAS. In B-ALL, FAT1, SF1, CRLF2, TET2, and PTPN1 have higher incidence of mutations. Gene mutations are different between Ph+ALL and PhALL patients. B-ALL patients with PTPN11 mutation and T-ALL patients with NOTCH1 and/or FBXW7 mutations showed better survival. But B-ALL with JAK1/JAK2 mutations showed worse survival. The results suggest that gene mutations exist in adult ALL patients universally, they are related with prognosis.

Keywords

Next-generation sequencing Somatic mutations Prognostic significance Acute lymphoblastic leukemia

To the editor

Acute lymphoblastic leukemia (ALL) represents one of the most common malignant diseases of childhood, accounts for about 15 ~ 25% of acute leukemia in adults [1]. Adult ALL is generally characterized by diverse biological features, evident clinical heterogeneity, and worse prognosis than pediatric ALL [2]. With the development of genetics in ALL, several new subtypes of ALL and a series of prognostic-related molecular markers are put forward [35].

In the recent years, with the application of next-generation sequencing (NGS) technology, genomics has been extensively developed in both pediatric and adult ALL patients [6]. Samples and clinical information were collected from 121 adult ALL patients (Additional file 1:Table S1) with informed consent (ethical approval serial number is KT2015001-EC-1). These patients were from the Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences. Target regions of 112 genes (Additional file 2: Table S2) were selected on the basis of known or suspected involvement in the pathogenesis of malignant hematologic disorder and were enriched and analyzed using a custom targeted NGS gene panel (Additional file 3). Then, the relationships between the mutations with higher incidence and the prognosis of ALL patients were analyzed (Additional file 4).

Of the 121 patients, a total of 110 patients (90.9%) harbored at least one gene mutation with a median of 2 (0–7) mutations per sample. Thirty-nine patients (32.2%) had more than 3 gene mutations (Additional file 5: Figure S1). Sixty genes were considered as possible pathogenic mutations when compared against multiple databases (Fig. 1a. The top 38 mutated genes were listed). The five most frequently mutated genes were FAT1, NOTCH1, SF1, CRLF2, and NRAS (mutated in >8% of the cases).
Fig. 1

Frequency of gene mutations and related signal pathways in ALL subtypes. a Frequency of the top 38 gene mutations in different ALL subtypes, which are shown in indicated colors. b Frequency of gene mutations involved in different functional pathways

In 28 T-ALL cases, the most common mutated gene was NOTCH1, with a mutation rate of 39.3% (n = 11), then JAK3, FBXW7, FAT1, NRAS, CREBBP, DNM2 (mutated in >10% of the cases) (Additional file 6: Figure S2A). In B-ALL, FAT1 was the most accepted mutated gene (10.75%), then SF1, CRLF2, TET2, PTPN11, NRAS, CREBBP, JAK2, DIS3, MPL, and KML2D (mutated in >5% of the cases) (Additional file 6: Figure S2B). In Ph+ B-ALL, FAT1, CRLF2, SF1, EP300, and CREBBP genes mutated at higher incidences (Additional file 6: Figure S2C). However, PTPN11, SF1, TET2, NRAS, JAK2, DIS3, and FAT1 gene mutations occurred popularly in PhB-ALL (Additional file 6: Figure S2D).

The main signaling pathways involved in this targeted NGS gene panel were transcription factor/regulator, Ras/protein phosphatase/MARK signaling pathway, JAK-STAT pathway, splicing and mRNA processing regulation, epigenetic modulators, and so on [79]. Frequencies of different signaling pathways involved are listed in Fig. 1b. Genes involved in these signaling pathways are listed in Additional file 7:Table S3.

In full cohort, the median overall survival (OS) was 34.88 (1.25–74.55) months, median relapse-free survival (RFS) was 30.85 (0–73.55) months and 3-year OS and RFS rates were 49%. In the full cohort, patients with PTPN11 mutation had a better prognosis compared with patients without PTPN11 mutation (p = 0.040, p = 0.047), and the patients with JAK2 mutation (7/117) had a worse prognosis compared with patients without JAK2 mutation (p = 0.031, p = 0.018) (Additional file 8: Figure S3). B-ALL patients with PTPN11 mutation (7/93) had a better OS and RFS compared with those without PTPN11 mutation (p = 0.041, p = 0.047) (Additional file 9: Figure S4).

In T-ALL, patients with NOTCH1 and/or FBXW7 mutations had a better OS and RFS than patients without these mutations (p = 0.035, p = 0.048) (Additional file 10: Figure S5).

Multivariate analysis of OS and RFS showed that the prognostic factors included JAK2 mutations (OS; p= 0.045, RFS; p = 0.021) in the total adult ALL patients cohort. JAK1 mutations (OS; p = 0.004, RFS; p = 0.005) and JAK2 mutations (OS; p = 0.049, RFS; p = 0.044) for PhB-ALL. The data was summarized in Table 1.
Table 1

Univariate and multivariate analysis for OS and RFS

 

OS

RFS

HR2

P value

HR2

P value

Univarite analysis (mut/all)

 PTPN11 mut; positive vs negative in full cohort (8/117)

0.043

0.040

0.044

0.047

  In B-ALL (7/92)

0.042

0.041

0.042

0.047

  In PhB-ALL (7/54)

0.035

0.030

0.037

0.047

 JAK2 Mut; positive vs negative in full cohort (7/117)

3.02

0.031

3.309

0.018

  In B-ALL (5/92)

3.033

0.061

3.145

0.051

  In PhB-ALL (5/54)

3.728

0.035

3.780

0.031

 JAK1 mut; positive vs negative in PhB-ALL (4/54)

6.808

0.001

6.562

0.001

 NOTCH1 and/or FBXW7 mut; positive vs negative

  In T-ALL (12/25)

0.204

0.035

0.223

0.048

Multivariate analysis

 PTPN11 mut; positive vs negative in full cohort

0.043

0.052

0.044

0.056

  In B-ALL

0.042

0.063

0.042

0.056

  In PhB-ALL

0.035

0.178

0.037

0.212

 JAK2 mut; positive vs negative in full cohort

3.02

0.045

3.309

0.021

  In PhB-ALL

3.728

0.049

3.780

0.044

 JAK1 mut; positive vs negative in PhB-ALL

6.808

0.004

6.562

0.005

 NOTCH1 and/or FBXW7 mut; positive vs negative

  In T-ALL

0.204

0.055

0.223

0.069

In summary, our study suggests that gene mutations exists in adult ALL patients universally, involving a variety of signaling pathways. The frequency and species are varied in different types of ALL. B-ALL patients were accompanied with PTPN11 mutation for good prognosis, while abnormal JAK family often indicates poor prognosis. In T-ALL, mutation of NOTCH1 and/or FBXW7 indicates good prognosis.

Abbreviations

ALL: 

Acute lymphoblastic leukemia

CR: 

Complete remission

NGS: 

Next-generation sequencing

OS: 

Overall survival

PCR: 

Polymerase chain reaction

RFS: 

Relapse-free survival

SNP: 

Single nucleotide polymorphisms

WBC: 

White blood cell

Declarations

Acknowledgements

The authors thank Drs. Tian Yuan and Qi Zhang of MD Anderson Cancer Center for their valuable suggestions.

Funding

This study was supported by the National Science & Technology Pillar Program (Grant no. 2014BAI09B12), Tianjin Major Research Program of Application Foundation and Advanced Technology (15JCZDJC36400), and Science and technology project of Tianjin (15ZXLCSY00010).

Availability of data and materials

Not applicable. All data used for conclusions are presented in the manuscript and figures.

Authors’ contributions

YM and JW were responsible for the conception and design of the study. JF, YL, YJ, and XD participated in the development of methodology of the study. JF, YL, YJ, QF, XG, QL, XZ, KL, ZT, and KT contributed to the acquisition of data (acquired and managed patients’ samples, provided facilities, etc.). JF, YL, KR, YJ, QF, XD, YJ, YW, and DL cooperated with the analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis). JF, YL, YM, JW, MW, and HW wrote, reviewed, and/or revised the manuscript. KR, QL, YJ, and XD contributed to administrative, technical, or material support (i.e., reporting or organizing data, constructing databases). YM supervised the study. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The study design was approved by Ethics Committee of Blood Diseases Hospital, Chinese Academy of Medical Sciences. The reference number is KT2015001-EC-1.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Institute of Hematology and Blood Disease Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
(2)
State Key Laboratory of Experimental Hematology

References

  1. Mullighan CG, Goorha S, Radtke I, Miller CB, Coustan-Smith E, Dalton JD, et al. Genome-wide analysis of genetic alterations in acute lymphoblastic leukaemia. Nature. 2007;446:758–64.View ArticlePubMedGoogle Scholar
  2. Mullighan CG, Miller CB, Radtke I, Phillips LA, Dalton J, Ma J, et al. BCR-ABL1 lymphoblastic leukaemia is characterized by the deletion of Ikaros. Nature. 2008;453:110–4.View ArticlePubMedGoogle Scholar
  3. Fransecky L, Neumann M, Heesch S, Schlee C, Ortiz-Tanchez J, et al. Silencing of GATA3 defines a novel stem cell-like subgroup of ETP-ALL. J Hematol Oncol. 2016;9(1):95.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Jain N, Lamb AV, O’Brien S, Ravandi F, Konopleva M, et al. Early T-cell precursor acute lymphoblastic leukemia/lymphoma (ETP-ALL/LBL) in adolescents and adults: a high-risk subtype. Blood. 2016;127(15):1863–9.View ArticlePubMedGoogle Scholar
  5. Xu N, Li YL, Li X, Zhou X, Cao R, et al. Correlation between deletion of the CDKN2 gene and tyrosine kinase inhibitor resistance in adult Philadelphia chromosome-positive acute lymphoblastic leukemia. J Hematol Oncol. 2016;9:40.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Roberts KG, Mullighan CG. Genomics in acute lymphoblastic leukaemia: insights and treatment implications. Nat Rev Clin Oncol. 2015;12:344–57.View ArticlePubMedGoogle Scholar
  7. Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, et al. Mutational landscape and significance across 12 major cancer types. Nature. 2013;502:333–9.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Neumann M, Vosberg S, Schlee C, Heesch S, Schwartz S, Gökbuget N, et al. Mutational spectrum of adult T-ALL. Oncotarget. 2015;6:2754–66.View ArticlePubMedGoogle Scholar
  9. Haferlach T, Nagata Y, Grossmann V, Okuno Y, Bacher U, Nagae G, et al. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia. 2014;28:241–7.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s). 2017

Advertisement