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Longitudinal whole-genome sequencing reveals the evolution of MPAL
Cancer Genetics 240 (2020) 59–65
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Longitudinal whole-genome sequencing reveals the evolution of MPAL Yu Zhang a,1, Zhijie Kang b,1, Dekang Lv a, Xuehong Zhang a, Yuwei Liao a, Yulong Li a, Ruimei Liu a, Peiying Li a, Mengying Tong c, Jichao Tian a, Yanyan Shao a, Chao Huang a, Dongcen Ge a, Jingkai Zhang a, Wanting Bai a, Yichen Wang a, Quentin Liu a,∗, Zhiguang Li a,∗, Jinsong Yan b,∗∗ a b c
Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, Liaoning, China The Second Hospital of Dalian Medical University, Dalian, Liaoning, China The First Hospital of Dalian Medical University, Dalian, Liaoning, China
a r t i c l e
i n f o
Article history: Received 6 June 2019 Revised 21 October 2019 Accepted 21 November 2019
Keywords: Longitudinal WGS MPAL Evolution Genomic variation
a b s t r a c t Purpose: Mixed phenotype acute leukemia (MPAL) is a rare subtype of acute leukemia and its progressive genomic basis over time remains unclear. We aimed to investigate the longitudinal genomic evolution of MPAL from diagnosis to relapse. Methods: We performed whole genome sequencing (WGS) on bone marrow (BM) samples obtained at the four stages of this disease in a male patient with Philadelphia chromosome positive (Ph+) MPAL, including primary, complete cytogenetic remission (CCR), complete molecular remission (CMR), and relapse stage during the 3 year follow-up period. Results: 156 single-nucleotide variants (SNVs) and indels were detected, which exhibited distinctive evolutionary behaviors. Seventeen mutations disappeared quickly upon DCTER treatment and never came back. Seven mutations, although disappeared initially, reoccurred with the withdrawal of TKI treatment. Notably, ten mutations emerged in spite of the active DCTER chemotherapy. Moreover, copy number loss played critical roles in monitoring MPAL progression, displaying 7, 0, 0, and 383 losses at the stages of primary, CCR, CMR, and relapse respectively. Conclusion: This longitudinal genomic investigation of the Ph+ MPAL patient established one MPAL evolution model in which the primary tumor acquired additional variations leading to tumor relapse. Moreover, the event of copy number loss remained a valuable hallmark in the progression of MPAL. © 2019 Elsevier Inc. All rights reserved.
Introduction Mixed phenotype acute leukemia (MPAL) is a recently defined disease entity, characterized by the blast cells of multilineage origin or blasts expressing markers specific to several lineages [1]. MPAL is rare and comprised 2–5% of all acute leukemia [2]. The WHO definition of MPAL is based on the expression of strictly specific immunological markers, which principally detected by either flow cytometry (FCM) or cytochemistry and/or clear evidence of monocytic differentiation [3]. Generally, patients with MPAL are
∗
Corresponding authors. Corresponding author at: Department of Hematology, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China. E-mail addresses: [email protected] (Q. Liu), [email protected] (Z. Li), [email protected] (J. Yan). 1 Yu Zhang and Zhijie Kang contributed equally for this work. ∗∗
https://doi.org/10.1016/j.cancergen.2019.11.007 2210-7762/© 2019 Elsevier Inc. All rights reserved.
considered to have poor outcomes. The analysis of SEER registry data demonstrated that the risk of death of a patient with MPAL was increased by 59% and 26% when compared with the patients with ALL or AML, respectively [4]. Patients with MPAL often present with abnormal karyotypes or complex chromosomal abnormalities, which may account for the mixed phenotype and distinct characteristics of MPAL. Two major retrospective analyses of MPAL patients revealed that 64% (59/92) and 87% (66/76) patients had an abnormal karyotype [5,6]. In the 2016 revision of WHO, two types of MPAL were classified based on their genetic variation: MPALs with BCR-ABL1 or MLL rearrangements [7]. Genome-wide studies of clinical samples using nextgeneration sequencing (NGS) have improved our understanding of the molecular mechanisms of MPAL [8–10]. Although there were no hallmarks applying in clinical for MPAL patients, mutations of DNMT3A were identified in 33% of MPAL patients [11]. Besides mutations, copy number variations (CNVs) have been reported to be of
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Fig. 1. The case history of a patient with MPAL. Four bone marrow samples of the Ph+ MPAL patient were collected at different time points of primary, CCR, CMR and relapse, respectively, and were analyzed by WGS. OEC was collected at CCR as control. CCR: complete cytogenetic remission, CMR: complete molecular remission. VDCLP and DCTER were used for the chemotherapy treatment and tyrosine kinase inhibitor (TKI) Dasatinib was used for induction therapy (Supplementary S1).
independent prognostic relevance of AML [12], and structural variations (SVs), such as rearrangement of ZNF384 and biallelic WT1, are common in B/M MPAL and T/M MPAL respectively [8]. Although these recent genomic findings have provided the features of MPAL genome at a single time point, the genetic aberrations that drive MPAL progression remain unclear. To investigate the genetic changes associated with MPAL relapse, and to determine whether clonal evolution contributes to relapse, we performed whole-genome sequencing of a B/Myeloid (Ph+) MPAL patient at four time points throughout the disease course, and made in-depth analysis about the dynamic variations of single-nucleotide variants (SNVs), indels, CNVs and SVs. Materials and methods Patient samples Patient samples were acquired with patient consent in accordance with the Ethics Committee of Dalian Medical University (Dalian, Liaoning, China). Bone marrow (BM) samples of the Ph+ MPAL patient were collected at primary diagnosis (Primary), cytogenetic remission (CCR), complete molecular remission (CMR) and relapse respectively. Oral epithelial cell (OEC) was obtained at the time of CCR for use as control. Whole-genome sequencing BM and OEC total DNA was isolated using the TIANGEN DNA/RNA isolation kit (cat#DP422). DNA concentration and purity were measured with Qubit 2.0 Fluorometer (Life Technologies) and Bioanalyzer 2100 (Agilent Technologies). Libraries were prepared according to the protocol of the NEBNext® UltraTM Directional DNA Library Prep Kit for Illumina. Massively parallel DNA sequencing (DNA-seq) was performed on the HiSeq 2500 platform with paired-end 150-bp read-length in Novogene Company (Beijing, China). Sequencing reads were aligned to NCBI human reference genome (hg19) using BWA (v0.5.9) [13] with default parameters. Picard (v1.54) (http://picard.sourceforge.net/) was used to mark duplicates and followed by Genome Analysis Toolkit (v1.0.6076, GATK IndelRealigner) [14] to improve alignment accuracy. The average depth of each sample was 30x. Single-nucleotide variants Somatic SNVs and indels were detected by MuTect [15] and by strelka [16] with default parameters, respectively. Both MuTect and Strelka apply a Bayesian approach to detect somatic variations from matched normal and tumor samples. All high confidence SNVs and indels were filtered by the following rules: 1) depth of the SNV/indel site is more than 10; 2) variant allele frequency (VAF) of the SNVs/indels is more than 10%; 3) SNV/indel is falling in exonic region or recorded in cosmic database. 156 SNVs/indels were obtained and these variants were clustered by PyClone[17]. PyClone uses a Bayesian clustering method to estimate the cellular
prevalence and accounts for allelic imbalances. The program used from PyClone was ‘run_analysis_pipeline’ with default parameters with 10,0 0 0 iterations. Copy number and structural variations CNVs were detected in a read-depth based approach using CNVSeq [18] with a window size of 50 0 0 bp and a step size of 2500 bp.The OEC sample was used as a control sample in CNVseq analyses of primary, CCR, CMR and relapse data. CNVs were retained whose log ratio of copy number relative to the normal sample was more than 2 or less than −0.8. SVs were generated using Lumpy [19] by analyzing the BAM file with default parameters. Lumpy used multiple SV signals to detect variations and the program used from Lumpy was ‘lumpyexpress’. Only SVs with at least eight supporting reads in LUMPY were retained. Results Case history A 31-year-old male presented Ph+ MPAL (Blymphoid + myeloid) according to the criteria of both EGI and WHO in 2013. During the first year of treatment, the patient gradually underwent the stages of CCR and CMR, and remained CMR thereafter for two years. However, the patient relapsed at the third year and died of active leukemia nine months after the relapse. We collected the bone marrow samples at four time points (Fig. 1) and performed whole genome sequencing (WGS) analysis of matched primary, CCR, CMR, relapse DNA samples of the patient. Clonal evolution of MPAL We obtained 156 somatic SNVs and indels that were present in exonic regions or annotated in COSMIC database (Supplementary S2). Nine nonsynonymous mutations were only present in the primary sample and then disappeared (Fig. 2A). In contrast, seven mutations that were present at primary stage became disappeared at CCR and CMR stages, and then emerged at relapse (Fig. 2B). CMR was the stage when the bone marrow of the patient was hematological, immunological, and molecular normal as assessed by smear staining, FACS, and BCR-ABL fusion gene qPCR. However, we detected nascent mutations in ten genes, including SMO and ERBB2, at this stage. These mutations were also detectable at relapse stage with equivalent or increased VAF (Fig. 2C). These together indicated the distinct evolving paths of mutations along the treatment course (Fig. 2A–C). The mutated genes existed at primary and relapse stage were also mutated in AML and ALL samples [20,21] but did not share the same location with the MPAL sample (Supplementary Fig. 1 A, B). Cluster analyses were conducted using PyClone [17] to classify the mutations according to their evolutionary behaviors. Four clusters were identified from the 156 mutations (Fig. 2D). The cellular
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Fig. 2. Clone evolution along disease progression. A-C, Emergence and disappearing of mutations along treatment. Variant allele frequencies were shown for mutations that were present in A) only primary; B) both primary and relapse; C) both CMR and relapse. D, Cellular prevalence of four mutational clusters. Clusters were determined by PyClone according to the allele frequencies of 156 mutations. E, Cellular prevalence alteration of 156 mutations from primary to relapse by means of supraHex map. Mutations with similar cellular prevalence across samples were mapped onto the same or nearby regions. F, The profile of variant allele frequencies of 156 mutations in the primary and relapse samples.
prevalence (CP) of cluster I was dramatically dropped during the transition from the primary (24%) to the CCR (0%) stage, and kept at 0% in the following two stages. In contrast, cluster II and III exhibited the opposite trend. Both started with 0% at primary stage, remained quiescent at CCR stage. Cluster II became evident (24%) at CMR, and continued to increase to 46% at relapse, while clus-
ter III became evident until the relapse stage (29%) (Fig. 2D). The fourth cluster underwent a course of initial decrease, from 55% at primary to 0% at CCR, and then increasing, from 0% at CMR to 88% at relapse. CP alterations were graphically shown by pentagrams (Fig. 2E). The cluster patterns indicated the genetic heterogeneities of MPAL sample and the evolutionary course of each cell type. The
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Fig. 3. Comparison of CNVs and SVs across four samples. A, Comparison of copy number loss and SVs among the four samples of primary, CCR, CMR and relapse. The y-axis showed the copy number log ratio relative to the normal (OEC) sample, and the x-axis presented the chromosome position (Mb). B, The copy number log ratio of twelve genes. The copy number loss region, after expanding 500 kb upstream and downstream, was divided into 2500 bp-windows. C-D, Karyotyping analysis of the leukemia cells of the B) primary and C) relapse samples. Red arrows indicated the aberrant chromosomes. E, The links in the circle showed the SVs shared by the primary and relapse samples (red), only in the primary sample (black), or only in the relapse sample (green).
first type of cells containing cluster IV mutations acquired cluster I mutations to form the second type of cells. The first (blue, harboring Cluster IV mutations) and second type (orange, harboring Cluster I mutations) both existed in the primary sample (Supplementary Fig. 2). The first type of cells obtained cluster II and cluster III mutations to become the third and fourth type, respectively. So the relapse samples had the first, third and fourth type of cells, while the second type of cells almost disappeared (Supplementary Fig. 2). The four clusters included 17, 10, 122, and 7 mutations respectively. Comparison between the primary and relapse stages revealed the heterogeneity of VAFs even within one cluster (Fig. 2F). Mutations in cluster I had VAFs spanning from 10% to 39% (median 13%), and mutations in cluster III had VAFs spanning from 10% to 59% (median 14%), indicating these mutations, even within one cluster, probably emerged at different time along leukemia progression (Fig. 2F). However, the genetic heterogeneity did not affect the uniform behavior during the treatment course. Mutations in cluster I were only present in primary and sensitive to treatment. Cluster II appeared even with the continuing treatment of DCTER, suggesting that mutations in cluster II conferred tumor cells drug resistance. One mutation in cluster II, ERBB2 (P1170A), was closely related to drug resistance in cancer [22,23]. Mutations in cluster III only existed in relapse, indicating their roles in pathogenesis and drug resistance. Genes HP1BP3 (p.E9D) could lead to radio-resistance, chemo-resistance in
cancer cells [24]; HIPK1 (p.G93A) formed a complex with AML1 [25] and FLT3 (p.T227M), thereby contributing to the myeloid and lymphoid cell proliferation and survival [26]. Because of the high CP of cluster IV, we inferred that mutations in cluster IV appeared to be the early events in the tumor development. Of the cluster IV mutations, mutations in gene BRINP2 (p.R371C) and c10orf71 (p.P439T) also existed in TCGA database. Besides that, DDX6 (p.G143R), KLHL18 (p.Y219D) and PTPRZ1 (p.V2249M) were located in the protein domain and may affect the structure of proteins. Copy number loss reveals disease progression We assessed somatic CNVs of the MPAL patient using CNVSeq [18] (Supplementary S3). Copy number loss closely correlated with MPAL progression. While no losses were detected in remission samples, 7 and 383 losses were detected in primary and relapse tumors respectively (Fig. 3A). Interestingly, all the seven losses of primary tumor were shared by the relapsed tumor (Fig. 3A), suggesting the crucial role of these regions for leukemia growth. Losses were unevenly distributed along the genome of relapse sample. Chrom1 and chrom5 had 7.6 Mb (41%, 18 Mb in total) and 8.2 Mb (44%) loss regions, while eight chromosomes did not contain any loss regions (Fig. 3A). The length of loss regions could be as long as 500 kb, and as short as 10 kb (Supplementary S3). We identified the loss of IKZF1, FBXW7 and ETV6 (Fig. 3B), which was concordant with the target sequencing results of 31 MPAL patients
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Fig. 4. Genomic overview of the Ph+ MPAL. A, Distribution of various genomic aberrations that resided in oncogenes or tumor suppressor genes. Oncogene and tumor suppressor genes were obtained from ONGene and TSGene. The column and row represented genes and samples respectively. The right panel showed the number of each type of variations in each sample. Blocks of red, light red or light blue in the TCGA/GTEX line indicated the genes whose expression was dramatically dysregulated (2), dysregulated (1), or not dysregulated (0) in AML patients of TCGA/GTEX database. Blocks of dark blue, red, green, or light blue indicate the genes of tumor suppressor genes (tsgene), oncogenes, both tsgenes and oncogenes, or others. B, Functional analysis of the genes was shown in Supplementary S1-S4 by using GSEA. The size of the circle indicated the gene number mapped to the GO process, and the different colors indicated the FDR value.
[10]. The loss of the TARP (7p14.1) gene in the TCR region were detected in both primary and relapse samples, which reinforced the findings using the aCGH in Ph+AML [27] and CML [28]. As previously reported [29–31], we found that the loss of ABL1, PAK2 and RUNX1 were associated with the progression of lymphomas or leukemia. By comparing with the CNV data of TCGA [20], we found some of the genes exhibit the loss alteration in the TCGA AML data, such as APC, TARP, IKZF1, TARP, ETV6 and PAK2 (Supplementary Fig. 3). Furthermore, karyotyping analysis detected two chromosome aberrations in primary tumor (Fig. 3C) and four aberrations in relapse tumor (Fig. 3D), which were concordant with the more prevalent copy number loss aberrations in the relapse sample. Except CNVs, twenty-four SVs were identified in the four samples (Supplementary S4). Five gene fusions, BCR/ABL, DPP10/SET, NAV3/NAV3, BCR/HMCN2 and GXYLT1/SGCZ were both presented in the primary and relapse, and the breakpoints of these fusions occurred at exactly the same position in the primary and relapse samples (Fig. 3E). DPP10(2q14.1)/SET(9q34.11) gene fusion was previously identified using an algorithm called ZoomX [32]. Especially, BCR-ABL1 gene fusion was also detected using RNA-seq data (manuscript in submission) in the primary and relapse tumors, which indicated that BCR/ABL chromosomal rearrangement was likely to be crucial oncogenic event in the development of MPAL.
Whole genomic variation of MPAL By examining the relevance of these variations to cancer, we selected 53 genes according to the ONGene [33] and TSG2.0 databases [34]. We compiled these mutations, CNVs, and SVs together to get the overall picture of genomic aberrations across the four stages of the MPAL patient (Fig. 4A). Surprisingly, CMR sample (n = 4) exhibited more aberrations than CCR (n = 2) although CMR was considered to be clinically better than CCR (Fig. 4A). Non-synonymous SNVs (n = 18) and copy number loss (n = 33) were the two most prevalent aberrations. Noticeably, four gene fusions were present in this list, and three of them were shared by two samples. An online database, Gene Expression Profiling Interactive Analysis (GEPIA) [35], which was based on TCGA and GTEx dataset for transcriptomic analysis, was used to determine the mRNA expression level of these genes in AML patients. Analysis results demonstrated that 70% (40/57) of these genes were differently expressed in AML patients. We performed a gene set enrichment analysis (GSEA) [36] to discover the most relevant biological process in hallmark and KEGG gene sets. Consistent with the hematological source and malignancy nature of MPAL, immunological pathways, such as NEGATIVE_REGULATION_OF_RESPONSE_TO_STIMULUS, and proliferative pathways, such as CELL_CYCLE_PROCESS, were highly enriched (Fig. 4B).
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Discussion In this study, we tracked the clonal dynamics of one MPAL patient for over three years and investigated the dynamic changes of somatic variations across the whole genome. By classifying the variants across multiple time points, it was recognized that the appearance and disappearance of the variants including mutations, CNVs and SVs during the disease progression. Clonal evolution had been shown to be a powerful approach to dissect the clonal architecture and temporal alterations in AML patients [37]. In this study, we applied this approach to the MPAL patient. Detailed clonal structures were dissected (Fig. 2D–F). And their longitudinal changes across the stages of primary, CCR, CMR, and relapse were demonstrated (Fig. 2D–F). Six SNVs and one indel existed in both primary and relapse, including BRINP2 (p.R371C), c10orf71 (p.P439T), SLC4A8, DDX6 (p.G143R), KLHL18 (p.Y219D), PTPRZ1 (p.V2249M) and FAM212A (Fig. 2C). For this MPAL patient, the primary and relapse sample presented similar morphology, immunology, and molecular biology phenotypes, but they exhibited different genetic heterogeneities. The next-generation sequencing technology is able to identify the rare mutations at base resolution across the whole genome [38], making it feasible to reveal such genetic heterogeneities. CNV loss correlated well with the disease development (Fig. 3A and D). No copy number loss was detected in remission samples while a cluster of losses was present in malignant samples (Fig. 3A and D), indicating copy number loss could be a potential indicator for MPAL progression. This study was valuable in demonstrating the genomic features of MPAL patient, and to provide clues for target therapy. Declaration of Competing interest We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. Acknowledgments The authors would like to thank Dekang Lv and Xuehong Zhang that offered invaluable advice. This work was supported by National Natural Science Foundation of China (No. 81472637, 81672784, and 81602200), the Pandeng Scholar Program from the Department of Education of Liaoning Province (to Dr. Zhiguang Li), and startup funds from Dalian Medical University (to Dr. Zhiguang Li). Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.cancergen.2019.11.007. References [1] Khan M, Siddiqi R, Naqvi K. An update on classification, genetics, and clinical approach to mixed phenotype acute leukemia (MPAL). Ann Hematol 2018;97:945–53. doi:10.10 07/s0 0277- 018- 3297- 6. [2] Weinberg OK, Seetharam M, Ren L, Alizadeh A, Arber DA. Mixed phenotype acute leukemia: a study of 61 cases using World Health Organization and European Group for the immunological classification of leukaemias criteria. Am J Clin Pathol 2014;142:803–8. doi:10.1309/AJCPPVUPOTUVOIB5. [3] Weinberg OK, Arber DA. Mixed-phenotype acute leukemia: historical overview and a new definition. Leukemia 2010;24:1844–51. doi:10.1038/leu.2010.202. [4] Roberts KG, Mullighan CG. Genomics in acute lymphoblastic leukaemia: insights and treatment implications. Nat Rev Clin Oncol 2015;12:344–57. doi:10. 1038/nrclinonc.2015.38. [5] Matutes E, Pickl WF, Van’t Veer M, Morilla R, Swansbury J, Strobl H, et al. Mixed-phenotype acute leukemia: clinical and laboratory features and outcome in 100 patients defined according to the WHO 2008 classification. Blood 2011;117:3163–71. doi:10.1182/blood- 2010- 10- 314682.
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Longitudinal whole-genome sequencing reveals the evolution of MPAL Yu Zhang a,1, Zhijie Kang b,1, Dekang Lv a, Xuehong Zhang a, Yuwei Liao a, Yulong Li a, Ruimei Liu a, Peiying Li a, Mengying Tong c, Jichao Tian a, Yanyan Shao a, Chao Huang a, Dongcen Ge a, Jingkai Zhang a, Wanting Bai a, Yichen Wang a, Quentin Liu a,∗, Zhiguang Li a,∗, Jinsong Yan b,∗∗ a b c
Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, Liaoning, China The Second Hospital of Dalian Medical University, Dalian, Liaoning, China The First Hospital of Dalian Medical University, Dalian, Liaoning, China
a r t i c l e
i n f o
Article history: Received 6 June 2019 Revised 21 October 2019 Accepted 21 November 2019
Keywords: Longitudinal WGS MPAL Evolution Genomic variation
a b s t r a c t Purpose: Mixed phenotype acute leukemia (MPAL) is a rare subtype of acute leukemia and its progressive genomic basis over time remains unclear. We aimed to investigate the longitudinal genomic evolution of MPAL from diagnosis to relapse. Methods: We performed whole genome sequencing (WGS) on bone marrow (BM) samples obtained at the four stages of this disease in a male patient with Philadelphia chromosome positive (Ph+) MPAL, including primary, complete cytogenetic remission (CCR), complete molecular remission (CMR), and relapse stage during the 3 year follow-up period. Results: 156 single-nucleotide variants (SNVs) and indels were detected, which exhibited distinctive evolutionary behaviors. Seventeen mutations disappeared quickly upon DCTER treatment and never came back. Seven mutations, although disappeared initially, reoccurred with the withdrawal of TKI treatment. Notably, ten mutations emerged in spite of the active DCTER chemotherapy. Moreover, copy number loss played critical roles in monitoring MPAL progression, displaying 7, 0, 0, and 383 losses at the stages of primary, CCR, CMR, and relapse respectively. Conclusion: This longitudinal genomic investigation of the Ph+ MPAL patient established one MPAL evolution model in which the primary tumor acquired additional variations leading to tumor relapse. Moreover, the event of copy number loss remained a valuable hallmark in the progression of MPAL. © 2019 Elsevier Inc. All rights reserved.
Introduction Mixed phenotype acute leukemia (MPAL) is a recently defined disease entity, characterized by the blast cells of multilineage origin or blasts expressing markers specific to several lineages [1]. MPAL is rare and comprised 2–5% of all acute leukemia [2]. The WHO definition of MPAL is based on the expression of strictly specific immunological markers, which principally detected by either flow cytometry (FCM) or cytochemistry and/or clear evidence of monocytic differentiation [3]. Generally, patients with MPAL are
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Corresponding authors. Corresponding author at: Department of Hematology, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China. E-mail addresses: [email protected] (Q. Liu), [email protected] (Z. Li), [email protected] (J. Yan). 1 Yu Zhang and Zhijie Kang contributed equally for this work. ∗∗
https://doi.org/10.1016/j.cancergen.2019.11.007 2210-7762/© 2019 Elsevier Inc. All rights reserved.
considered to have poor outcomes. The analysis of SEER registry data demonstrated that the risk of death of a patient with MPAL was increased by 59% and 26% when compared with the patients with ALL or AML, respectively [4]. Patients with MPAL often present with abnormal karyotypes or complex chromosomal abnormalities, which may account for the mixed phenotype and distinct characteristics of MPAL. Two major retrospective analyses of MPAL patients revealed that 64% (59/92) and 87% (66/76) patients had an abnormal karyotype [5,6]. In the 2016 revision of WHO, two types of MPAL were classified based on their genetic variation: MPALs with BCR-ABL1 or MLL rearrangements [7]. Genome-wide studies of clinical samples using nextgeneration sequencing (NGS) have improved our understanding of the molecular mechanisms of MPAL [8–10]. Although there were no hallmarks applying in clinical for MPAL patients, mutations of DNMT3A were identified in 33% of MPAL patients [11]. Besides mutations, copy number variations (CNVs) have been reported to be of
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Fig. 1. The case history of a patient with MPAL. Four bone marrow samples of the Ph+ MPAL patient were collected at different time points of primary, CCR, CMR and relapse, respectively, and were analyzed by WGS. OEC was collected at CCR as control. CCR: complete cytogenetic remission, CMR: complete molecular remission. VDCLP and DCTER were used for the chemotherapy treatment and tyrosine kinase inhibitor (TKI) Dasatinib was used for induction therapy (Supplementary S1).
independent prognostic relevance of AML [12], and structural variations (SVs), such as rearrangement of ZNF384 and biallelic WT1, are common in B/M MPAL and T/M MPAL respectively [8]. Although these recent genomic findings have provided the features of MPAL genome at a single time point, the genetic aberrations that drive MPAL progression remain unclear. To investigate the genetic changes associated with MPAL relapse, and to determine whether clonal evolution contributes to relapse, we performed whole-genome sequencing of a B/Myeloid (Ph+) MPAL patient at four time points throughout the disease course, and made in-depth analysis about the dynamic variations of single-nucleotide variants (SNVs), indels, CNVs and SVs. Materials and methods Patient samples Patient samples were acquired with patient consent in accordance with the Ethics Committee of Dalian Medical University (Dalian, Liaoning, China). Bone marrow (BM) samples of the Ph+ MPAL patient were collected at primary diagnosis (Primary), cytogenetic remission (CCR), complete molecular remission (CMR) and relapse respectively. Oral epithelial cell (OEC) was obtained at the time of CCR for use as control. Whole-genome sequencing BM and OEC total DNA was isolated using the TIANGEN DNA/RNA isolation kit (cat#DP422). DNA concentration and purity were measured with Qubit 2.0 Fluorometer (Life Technologies) and Bioanalyzer 2100 (Agilent Technologies). Libraries were prepared according to the protocol of the NEBNext® UltraTM Directional DNA Library Prep Kit for Illumina. Massively parallel DNA sequencing (DNA-seq) was performed on the HiSeq 2500 platform with paired-end 150-bp read-length in Novogene Company (Beijing, China). Sequencing reads were aligned to NCBI human reference genome (hg19) using BWA (v0.5.9) [13] with default parameters. Picard (v1.54) (http://picard.sourceforge.net/) was used to mark duplicates and followed by Genome Analysis Toolkit (v1.0.6076, GATK IndelRealigner) [14] to improve alignment accuracy. The average depth of each sample was 30x. Single-nucleotide variants Somatic SNVs and indels were detected by MuTect [15] and by strelka [16] with default parameters, respectively. Both MuTect and Strelka apply a Bayesian approach to detect somatic variations from matched normal and tumor samples. All high confidence SNVs and indels were filtered by the following rules: 1) depth of the SNV/indel site is more than 10; 2) variant allele frequency (VAF) of the SNVs/indels is more than 10%; 3) SNV/indel is falling in exonic region or recorded in cosmic database. 156 SNVs/indels were obtained and these variants were clustered by PyClone[17]. PyClone uses a Bayesian clustering method to estimate the cellular
prevalence and accounts for allelic imbalances. The program used from PyClone was ‘run_analysis_pipeline’ with default parameters with 10,0 0 0 iterations. Copy number and structural variations CNVs were detected in a read-depth based approach using CNVSeq [18] with a window size of 50 0 0 bp and a step size of 2500 bp.The OEC sample was used as a control sample in CNVseq analyses of primary, CCR, CMR and relapse data. CNVs were retained whose log ratio of copy number relative to the normal sample was more than 2 or less than −0.8. SVs were generated using Lumpy [19] by analyzing the BAM file with default parameters. Lumpy used multiple SV signals to detect variations and the program used from Lumpy was ‘lumpyexpress’. Only SVs with at least eight supporting reads in LUMPY were retained. Results Case history A 31-year-old male presented Ph+ MPAL (Blymphoid + myeloid) according to the criteria of both EGI and WHO in 2013. During the first year of treatment, the patient gradually underwent the stages of CCR and CMR, and remained CMR thereafter for two years. However, the patient relapsed at the third year and died of active leukemia nine months after the relapse. We collected the bone marrow samples at four time points (Fig. 1) and performed whole genome sequencing (WGS) analysis of matched primary, CCR, CMR, relapse DNA samples of the patient. Clonal evolution of MPAL We obtained 156 somatic SNVs and indels that were present in exonic regions or annotated in COSMIC database (Supplementary S2). Nine nonsynonymous mutations were only present in the primary sample and then disappeared (Fig. 2A). In contrast, seven mutations that were present at primary stage became disappeared at CCR and CMR stages, and then emerged at relapse (Fig. 2B). CMR was the stage when the bone marrow of the patient was hematological, immunological, and molecular normal as assessed by smear staining, FACS, and BCR-ABL fusion gene qPCR. However, we detected nascent mutations in ten genes, including SMO and ERBB2, at this stage. These mutations were also detectable at relapse stage with equivalent or increased VAF (Fig. 2C). These together indicated the distinct evolving paths of mutations along the treatment course (Fig. 2A–C). The mutated genes existed at primary and relapse stage were also mutated in AML and ALL samples [20,21] but did not share the same location with the MPAL sample (Supplementary Fig. 1 A, B). Cluster analyses were conducted using PyClone [17] to classify the mutations according to their evolutionary behaviors. Four clusters were identified from the 156 mutations (Fig. 2D). The cellular
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Fig. 2. Clone evolution along disease progression. A-C, Emergence and disappearing of mutations along treatment. Variant allele frequencies were shown for mutations that were present in A) only primary; B) both primary and relapse; C) both CMR and relapse. D, Cellular prevalence of four mutational clusters. Clusters were determined by PyClone according to the allele frequencies of 156 mutations. E, Cellular prevalence alteration of 156 mutations from primary to relapse by means of supraHex map. Mutations with similar cellular prevalence across samples were mapped onto the same or nearby regions. F, The profile of variant allele frequencies of 156 mutations in the primary and relapse samples.
prevalence (CP) of cluster I was dramatically dropped during the transition from the primary (24%) to the CCR (0%) stage, and kept at 0% in the following two stages. In contrast, cluster II and III exhibited the opposite trend. Both started with 0% at primary stage, remained quiescent at CCR stage. Cluster II became evident (24%) at CMR, and continued to increase to 46% at relapse, while clus-
ter III became evident until the relapse stage (29%) (Fig. 2D). The fourth cluster underwent a course of initial decrease, from 55% at primary to 0% at CCR, and then increasing, from 0% at CMR to 88% at relapse. CP alterations were graphically shown by pentagrams (Fig. 2E). The cluster patterns indicated the genetic heterogeneities of MPAL sample and the evolutionary course of each cell type. The
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Fig. 3. Comparison of CNVs and SVs across four samples. A, Comparison of copy number loss and SVs among the four samples of primary, CCR, CMR and relapse. The y-axis showed the copy number log ratio relative to the normal (OEC) sample, and the x-axis presented the chromosome position (Mb). B, The copy number log ratio of twelve genes. The copy number loss region, after expanding 500 kb upstream and downstream, was divided into 2500 bp-windows. C-D, Karyotyping analysis of the leukemia cells of the B) primary and C) relapse samples. Red arrows indicated the aberrant chromosomes. E, The links in the circle showed the SVs shared by the primary and relapse samples (red), only in the primary sample (black), or only in the relapse sample (green).
first type of cells containing cluster IV mutations acquired cluster I mutations to form the second type of cells. The first (blue, harboring Cluster IV mutations) and second type (orange, harboring Cluster I mutations) both existed in the primary sample (Supplementary Fig. 2). The first type of cells obtained cluster II and cluster III mutations to become the third and fourth type, respectively. So the relapse samples had the first, third and fourth type of cells, while the second type of cells almost disappeared (Supplementary Fig. 2). The four clusters included 17, 10, 122, and 7 mutations respectively. Comparison between the primary and relapse stages revealed the heterogeneity of VAFs even within one cluster (Fig. 2F). Mutations in cluster I had VAFs spanning from 10% to 39% (median 13%), and mutations in cluster III had VAFs spanning from 10% to 59% (median 14%), indicating these mutations, even within one cluster, probably emerged at different time along leukemia progression (Fig. 2F). However, the genetic heterogeneity did not affect the uniform behavior during the treatment course. Mutations in cluster I were only present in primary and sensitive to treatment. Cluster II appeared even with the continuing treatment of DCTER, suggesting that mutations in cluster II conferred tumor cells drug resistance. One mutation in cluster II, ERBB2 (P1170A), was closely related to drug resistance in cancer [22,23]. Mutations in cluster III only existed in relapse, indicating their roles in pathogenesis and drug resistance. Genes HP1BP3 (p.E9D) could lead to radio-resistance, chemo-resistance in
cancer cells [24]; HIPK1 (p.G93A) formed a complex with AML1 [25] and FLT3 (p.T227M), thereby contributing to the myeloid and lymphoid cell proliferation and survival [26]. Because of the high CP of cluster IV, we inferred that mutations in cluster IV appeared to be the early events in the tumor development. Of the cluster IV mutations, mutations in gene BRINP2 (p.R371C) and c10orf71 (p.P439T) also existed in TCGA database. Besides that, DDX6 (p.G143R), KLHL18 (p.Y219D) and PTPRZ1 (p.V2249M) were located in the protein domain and may affect the structure of proteins. Copy number loss reveals disease progression We assessed somatic CNVs of the MPAL patient using CNVSeq [18] (Supplementary S3). Copy number loss closely correlated with MPAL progression. While no losses were detected in remission samples, 7 and 383 losses were detected in primary and relapse tumors respectively (Fig. 3A). Interestingly, all the seven losses of primary tumor were shared by the relapsed tumor (Fig. 3A), suggesting the crucial role of these regions for leukemia growth. Losses were unevenly distributed along the genome of relapse sample. Chrom1 and chrom5 had 7.6 Mb (41%, 18 Mb in total) and 8.2 Mb (44%) loss regions, while eight chromosomes did not contain any loss regions (Fig. 3A). The length of loss regions could be as long as 500 kb, and as short as 10 kb (Supplementary S3). We identified the loss of IKZF1, FBXW7 and ETV6 (Fig. 3B), which was concordant with the target sequencing results of 31 MPAL patients
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Fig. 4. Genomic overview of the Ph+ MPAL. A, Distribution of various genomic aberrations that resided in oncogenes or tumor suppressor genes. Oncogene and tumor suppressor genes were obtained from ONGene and TSGene. The column and row represented genes and samples respectively. The right panel showed the number of each type of variations in each sample. Blocks of red, light red or light blue in the TCGA/GTEX line indicated the genes whose expression was dramatically dysregulated (2), dysregulated (1), or not dysregulated (0) in AML patients of TCGA/GTEX database. Blocks of dark blue, red, green, or light blue indicate the genes of tumor suppressor genes (tsgene), oncogenes, both tsgenes and oncogenes, or others. B, Functional analysis of the genes was shown in Supplementary S1-S4 by using GSEA. The size of the circle indicated the gene number mapped to the GO process, and the different colors indicated the FDR value.
[10]. The loss of the TARP (7p14.1) gene in the TCR region were detected in both primary and relapse samples, which reinforced the findings using the aCGH in Ph+AML [27] and CML [28]. As previously reported [29–31], we found that the loss of ABL1, PAK2 and RUNX1 were associated with the progression of lymphomas or leukemia. By comparing with the CNV data of TCGA [20], we found some of the genes exhibit the loss alteration in the TCGA AML data, such as APC, TARP, IKZF1, TARP, ETV6 and PAK2 (Supplementary Fig. 3). Furthermore, karyotyping analysis detected two chromosome aberrations in primary tumor (Fig. 3C) and four aberrations in relapse tumor (Fig. 3D), which were concordant with the more prevalent copy number loss aberrations in the relapse sample. Except CNVs, twenty-four SVs were identified in the four samples (Supplementary S4). Five gene fusions, BCR/ABL, DPP10/SET, NAV3/NAV3, BCR/HMCN2 and GXYLT1/SGCZ were both presented in the primary and relapse, and the breakpoints of these fusions occurred at exactly the same position in the primary and relapse samples (Fig. 3E). DPP10(2q14.1)/SET(9q34.11) gene fusion was previously identified using an algorithm called ZoomX [32]. Especially, BCR-ABL1 gene fusion was also detected using RNA-seq data (manuscript in submission) in the primary and relapse tumors, which indicated that BCR/ABL chromosomal rearrangement was likely to be crucial oncogenic event in the development of MPAL.
Whole genomic variation of MPAL By examining the relevance of these variations to cancer, we selected 53 genes according to the ONGene [33] and TSG2.0 databases [34]. We compiled these mutations, CNVs, and SVs together to get the overall picture of genomic aberrations across the four stages of the MPAL patient (Fig. 4A). Surprisingly, CMR sample (n = 4) exhibited more aberrations than CCR (n = 2) although CMR was considered to be clinically better than CCR (Fig. 4A). Non-synonymous SNVs (n = 18) and copy number loss (n = 33) were the two most prevalent aberrations. Noticeably, four gene fusions were present in this list, and three of them were shared by two samples. An online database, Gene Expression Profiling Interactive Analysis (GEPIA) [35], which was based on TCGA and GTEx dataset for transcriptomic analysis, was used to determine the mRNA expression level of these genes in AML patients. Analysis results demonstrated that 70% (40/57) of these genes were differently expressed in AML patients. We performed a gene set enrichment analysis (GSEA) [36] to discover the most relevant biological process in hallmark and KEGG gene sets. Consistent with the hematological source and malignancy nature of MPAL, immunological pathways, such as NEGATIVE_REGULATION_OF_RESPONSE_TO_STIMULUS, and proliferative pathways, such as CELL_CYCLE_PROCESS, were highly enriched (Fig. 4B).
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Discussion In this study, we tracked the clonal dynamics of one MPAL patient for over three years and investigated the dynamic changes of somatic variations across the whole genome. By classifying the variants across multiple time points, it was recognized that the appearance and disappearance of the variants including mutations, CNVs and SVs during the disease progression. Clonal evolution had been shown to be a powerful approach to dissect the clonal architecture and temporal alterations in AML patients [37]. In this study, we applied this approach to the MPAL patient. Detailed clonal structures were dissected (Fig. 2D–F). And their longitudinal changes across the stages of primary, CCR, CMR, and relapse were demonstrated (Fig. 2D–F). Six SNVs and one indel existed in both primary and relapse, including BRINP2 (p.R371C), c10orf71 (p.P439T), SLC4A8, DDX6 (p.G143R), KLHL18 (p.Y219D), PTPRZ1 (p.V2249M) and FAM212A (Fig. 2C). For this MPAL patient, the primary and relapse sample presented similar morphology, immunology, and molecular biology phenotypes, but they exhibited different genetic heterogeneities. The next-generation sequencing technology is able to identify the rare mutations at base resolution across the whole genome [38], making it feasible to reveal such genetic heterogeneities. CNV loss correlated well with the disease development (Fig. 3A and D). No copy number loss was detected in remission samples while a cluster of losses was present in malignant samples (Fig. 3A and D), indicating copy number loss could be a potential indicator for MPAL progression. This study was valuable in demonstrating the genomic features of MPAL patient, and to provide clues for target therapy. Declaration of Competing interest We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. Acknowledgments The authors would like to thank Dekang Lv and Xuehong Zhang that offered invaluable advice. This work was supported by National Natural Science Foundation of China (No. 81472637, 81672784, and 81602200), the Pandeng Scholar Program from the Department of Education of Liaoning Province (to Dr. Zhiguang Li), and startup funds from Dalian Medical University (to Dr. Zhiguang Li). Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.cancergen.2019.11.007. References [1] Khan M, Siddiqi R, Naqvi K. An update on classification, genetics, and clinical approach to mixed phenotype acute leukemia (MPAL). Ann Hematol 2018;97:945–53. doi:10.10 07/s0 0277- 018- 3297- 6. [2] Weinberg OK, Seetharam M, Ren L, Alizadeh A, Arber DA. Mixed phenotype acute leukemia: a study of 61 cases using World Health Organization and European Group for the immunological classification of leukaemias criteria. Am J Clin Pathol 2014;142:803–8. doi:10.1309/AJCPPVUPOTUVOIB5. [3] Weinberg OK, Arber DA. Mixed-phenotype acute leukemia: historical overview and a new definition. Leukemia 2010;24:1844–51. doi:10.1038/leu.2010.202. [4] Roberts KG, Mullighan CG. Genomics in acute lymphoblastic leukaemia: insights and treatment implications. Nat Rev Clin Oncol 2015;12:344–57. doi:10. 1038/nrclinonc.2015.38. [5] Matutes E, Pickl WF, Van’t Veer M, Morilla R, Swansbury J, Strobl H, et al. Mixed-phenotype acute leukemia: clinical and laboratory features and outcome in 100 patients defined according to the WHO 2008 classification. Blood 2011;117:3163–71. doi:10.1182/blood- 2010- 10- 314682.
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