Current next-generation sequencing (NGS) technologies allow unprecedented insights into the mutational profiles of tumors. Recent studies in myeloproliferative neoplasms have further demonstrated that, not only the mutational profile, but also the order in which these mutations are acquired is relevant for our understanding of the disease. Our ability to assign mutation order from NGS data alone is, however, limited. Here, we present a strategy of highly multiplexed genotyping of burst forming unit-erythroid colonies based on NGS results to assess subclonal tumor structure. This allowed for the generation of complex clonal hierarchies and determination of order of mutation acquisition far more accurately than was possible from NGS data alone.Work in ARG lab has been supported by the Leukemia and Lymphoma Society (grant 7001-12), the National Institute of Health Research (grant NF-SI-0512-10079) and core support grants by the MRC and Wellcome Trust to the Cambridge Institute for Medical Research (100140/Z/12/Z) and Wellcome Trust-MRC Cambridge Stem Cell Institute (097922/Z/11/Z). Work in ARG's laboratory has in addition been supported by Cancer Research UK (grants C1163/A12765 and C1163/A21762), Bloodwise (grant 13003) and the Wellcome Trust (grant 104710/Z/14/Z

Green, Anthony R

Klampfl, Thorsten

Massie, Charlie E

Nice, Francesca L

Apollo (Cambridge)

BRIEF COMMUNICATIONSDetermination of complex subclonal structures of hematological malignanciesby multiplexed genotyping of blood progenitor coloniesFrancesca L. Nicea,b, Charlie E. Massiea,b, Thorsten Klampfla,b, and Anthony R. Greena,b,caDepartment of Haematology, Cambridge Institute for Medical Research University of Cambridge, Cambridge CB2 0XY, United Kingdom; bWellcome Trust/Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge CB2 1QR, United Kingdom; cDepartment of Haematology,Addenbrooke’s Hospital, Cambridge CB2 0QQ, United Kingdom(Received 26 September 2017; accepted 27 September 2017)Current next-generation sequencing (NGS) technologies allow unprecedented insights into themutational profiles of tumors. Recent studies in myeloproliferative neoplasms have further dem-onstrated that, not only the mutational profile, but also the order in which these mutations areacquired is relevant for our understanding of the disease. Our ability to assign mutation orderfrom NGS data alone is, however, limited. Here, we present a strategy of highly multiplexedgenotyping of burst forming unit-erythroid colonies based on NGS results to assess subclonaltumor structure. This allowed for the generation of complex clonal hierarchies and determi-nation of order of mutation acquisition far more accurately than was possible from NGS dataalone. © 2018 ISEH – Society for Hematology and Stem Cells. Published by Elsevier Inc. Thisis an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Next-generation sequencing (NGS) methods have providedunprecedented insights into the somatic mutationsassociated with hematological malignancies, including my-eloproliferative neoplasms (MPNs) [1–3]. However, althoughwe are now able to acquire detailed lists of mutations presentin tumors at a given state of development, we are only be-ginning to understand how these mutations are associated intumor subclones and the history of mutation acquisition duringtumor development. The relevance of analyzing the makeupof tumors in detail has been demonstrated recently in studiesof MPN patients and have shown, for the first time in anycancer, that the order in which somatic mutations are ac-quired influences tumor biology and clinical presentation [4,5].However, determining the subclonal architecture of tumorsaccurately remains challenging. In particular, mutant alleleburdens determined by NGS have a limited ability to predictthe clonal landscape and clonal history within a patient. Clonalanalysis of hematopoietic colonies provides a powerful ap-proach to circumvent the problems associated with sequencingpooled cell populations [1,2,4,5]. Here, we present our strat-egy to determine complex subclonal tumor structures usinga combination of NGS and highly multiplexed genotypingof burst forming unit-erythroid colonies (BFU-Es).MethodsClustering analysisClustering was carried out using MClust Version 3 for R [6] on thebasis of NGS-derived mutational allele burden data for patientPD4772. MClust utilizes normal mixture modeling for univariatedata to classify the allele burdens into clusters as a prediction forthe subclonal structure of the tumor.Blood acquisition and processingPatient PD4772 was recruited from Addenbrookes Hospital afterwritten informed consent and ethical approval consistent with theDeclaration of Helsinki. As described previously [4], mono-nuclear cells (MNCs) were isolated from 40 mL of peripheral bloodCEM’s present address: Cancer Research UK Cambridge Institute, Uni-versity of Cambridge, Robinson Way, Cambridge CB2 0RE, United Kingdom.FLN’s present address: Cancer Molecular Diagnostics Laboratory (CMDL),University of Cambridge, Cambridge Biomedical Campus, CB2 0AH, UnitedKingdom.Offprint requests to: Anthony R. Green, University of Cambridge,Wellcome Trust/MRC Cambridge Stem Cell Institute, Department ofHaematology, Cambridge Institute of Medical Research, Hills Road,Cambridge CB2 0XY, UK; E-mail: Arg1000@cam.ac.ukSupplementary data related to this article can be found online at https://doi.org/10.1016/j.exphem.2017.09.011.Experimental Hematology 2018;57:60–640301-472X/© 2018 ISEH – Society for Hematology and Stem Cells. Published by Elsevier Inc. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/).https://doi.org/10.1016/j.exphem.2017.09.011using a sodium diatrizoate/polysaccharide density gradient(Lymphoprep; Axis Shield PLC, Oslo, Norway) according to themanufacturer’s instructions. MNCs were plated at a density of 1 × 106cells/mL in MethoCult H4034 (StemCell Technologies, Vancou-ver, Canada). BFU-Es were identified and picked into 100 µL ofPBS before vigorous pipetting to break the colony apart. Of the100 µL cell suspension, 10 µL was used for capillary sequencingand 10 µL for Fluidigm SNP genotyping.Fluidigm SNP genotypingFluidigm SNP genotyping was performed according to the manu-facturer’s instructions (SNP Genotyping User Guide, PN68000098 M2, Appendix C: SNP Type Assays for SNP Genotypingon the 192.24 Dynamic Array Integrated Fluidics Circuit, IFC).Briefly, SNPType genotyping assays were designed for all muta-tions identified previously in patient PD4772 by NGS according tothe manufacturer’s recommendations and ordered from Fluidigm(Supplementary Table E1, online only, available at www.exphem.org).Predefined regions of DNA were amplified using polymerase chainreaction (PCR) with specific target amplification primers for 22 cyclesbefore a 1:100 dilution of the amplified products was prepared. Thediluted amplified product was loaded onto the 192.24 Dynamic ArrayIFC for SNP Genotyping (BMK-M-192.24GT, Fluidigm) along-side a sample premixture including ROX reference dye and real-time master mixture. Assays were composed of allele-specific primerstagged with either FAM or HEX and an untagged common locus-specific PCR primer. The array was processed using the BioMarksystem (Fluidigm), which performs the thermal cycling and imageacquisition.Data were analyzed using the Biomark SNP Genotyping Anal-ysis software version 3.1.2 to obtain genotype calls. Briefly, thesoftware calculates the relative fluorescence intensities of FAM andHEX compared with the background ROX signal, classifying eachof the data points into one of three genotypes (wild-type, hetero-zygous mutant, or homozygous mutant) using k-means basedclustering methods.Capillary sequencingCapillary sequencing was carried out as described in Ortmann et al[4]. Sequences of the primers used in this study are providedin Supplementary Table E2 (online only, available fromwww.exphem.org).Results/discussionWhole-exome sequencing of bulk granulocyte DNA from poly-cythemia vera patient PD4772 revealed 16 somatic mutationswith a range of mutant allele burdens (SupplementaryTable E1, online only, available from www.exphem.org [1]).We set out to determine the subclonal tumor composition inthis patient from a comparison of mutant allele burdens aloneby means of a clustering analysis (Fig. 1). The result of thisanalysis revealed two separate clusters of mutations indicat-ing the presence of two tumor subclones. The JAK2V617Fmutation had the highest allele burden (46.9%), defining cluster1 (Fig. 1). The second cluster contained the remainder of allother mutations, where allele burdens ranged from 5.4% to23.6% (cluster 2, Fig. 1). Although the algorithm was not ableto determine further clusters given the wide range of mutantallele burdens within cluster 2, we hypothesized a morecomplex subclonal makeup of this tumor. Given the relativelylow allele burdens, a potential serial acquisition of two mu-tations cannot be distinguished from a biclonal acquisitionusing analysis that is based on allele burden alone. More-over, the analysis did not provide insights in the historicaldevelopment of the tumor.In order to gain a more comprehensive understanding ofthis patient’s tumor, we analyzed 176 BFU-E colonies thatwere cultured from peripheral-blood-derived mononuclearcells. Each of these colonies originated from a single bloodprogenitor cell. Genotyping each colony therefore providesinformation of the genetic makeup of the tumor at single-cell resolution. The combined interpretation of genotypes froma large number of individual colonies allows conclusions tobe drawn about the subclonal composition of the tumor.To genotype simultaneously and efficiently a large numberof BFU-E colonies, for all 16 mutations identified, we es-tablished a multiplexed assay based on custom SNP genotypingtechnology provided by Fluidigm. Firstly, we compared theperformance of Fluidigm multiplexed SNP genotyping withclassical capillary sequencing by genotyping a subset of 96colonies for five mutations with both technologies. FluidigmSNP genotyping returned a genotype in 476 of the 480genotyping reactions (96 colonies × 5 mutations, an averagecall rate of 99.1%). In contrast, capillary sequencing re-sulted in a total call rate of 409/480 or 85.2% (Fig. 2). Thegenotyping results were then compared using only those colo-nies for which both methods yielded a genotype. On average,87.7% of reactions returned concordant calls between the twomethods (range 81.5–95.6%) (Fig. 2). Given the increasedtime efficiency with which multiplexed genotyping can beperformed and the higher genotyping call rates, in additionFigure 1. Prediction of clonal structure from mutant allele burden alone forpatient PD4772. Predicted mutant allele burdens from whole-exome se-quencing were visualized using the MClust classification plot. Each linerepresents a mutation shown both as part of the two identified clusters (redand blue) and combined (black). Cluster 1 is defined only by the JAK2V617Fmutation and all other mutations are found in cluster 2.61F.L. Nice et al. / Experimental Hematology 2018;57:60–64to the considerable overlap in results compared with capil-lary sequencing, we decided to use multiplexed genotypingassays to genotype BFU-E colonies.All 176 colonies from patient PD4772 were then genotypedfor all 16 mutations using Fluidigm SNP genotyping. To bestassess the order of mutation acquisition, the results were com-piled in a tabular format to show the particular genotype (wild-type, heterozygous, or homozygous) for a specific mutationfor a specific colony (Fig. 3A). The columns (genes) wereordered based on the frequency of the mutation in the 176colonies so that the gene showing highest overall mutant alleleburden is on the left. Colonies in rows are ordered to gen-erate clusters of colonies with the same genotype (Fig. 3, nodesi–viii). The clusters represent genetically defined subclonesof the tumor and the number of colonies within each clusterreflects the relative size of the respective subclone. Two colo-nies of a similar genotype were required as a minimum tocall a separate cluster. When only one colony showed a certainmutational profile (which occurred in 34 of the 176genotyped), the colony was removed from the analysis. Finally,all of the clusters were reordered so that the order of acqui-sition of subclones was readily visible.These data were then converted into a clonal hierarchy(Fig. 3B). Within the hierarchy, each node represents a ge-netically defined subclone. Lines between nodes reflect theevolutionary relationship of subclones so that a more re-cently established subclone is connected to the clone fromwhich it arose. Genetically, such a subclone carries all of themutations of the parental clone and any newly acquiredmutations.Our results demonstrate that there is an unrecognized com-plexity to the subclonal architecture of the patient’s tumorclone. After the initial mutation acquisition, JAK2V617F (Fig. 3B,node ii), two independent subclones (Fig. 3B, nodes iii andv), which were identified by two distinct subsets of muta-tions, arose from the same parental tumor clone with theJAK2V617F mutation. Within node iii, additional mutationsweare acquired sequentially (Fig. 3B, node iv). After the ac-quisition of KSR2c.2582+7G>T, an additional bifurcation of thehierarchy occurred, with two sets of mutations acquired se-quentially within the JAK2V617F/KSR2c.2582+7G>T clone (vi andvii/viii).Previous studies in MPNs have shown that mutational datafrom NGS alone can only be used to call mutation order inunder half of cases [4,5]. In the case of low mutant alleleburden, it is also not possible to tell whether mutations areacquired in a linear or biclonal fashion from NGS data alone.Here, we showed that combining NGS with highly multi-plexed genotyping of BFU-E colonies is one method that canaccurately determine the subclonal structure of a tumor. Wecould determine a highly complex subclonal structure, showingboth the linear and biclonal acquisition of mutations, whichwas far more complex than the structure predicted from mutantallele burdens alone.AcknowledgmentsWork in ARG’s laboratory was supported by grants from theLeukemia & Lymphoma Society (7001-12), the National In-stitutes of Health (NF-SI-0512-10079), joint grants fromMedical Research Council (MRC) and Wellcome Trust to theCambridge Institute for Medical Research (100140/Z/12/Z)and the Wellcome Trust–MRC Cambridge Stem Cell Insti-tute (097922/Z/11/Z), Cancer Research UK (C1163/A12765and C1163/A21762), Bloodwise (13003), and the WellcomeTrust (104710/Z/14/Z) FLN and CM designed the genotypingpanel. FLN performed all experiments. FLN, TK, and ARGFigure 2. Multiplexed SNP genotyping is an efficient method for genotyping BFU-E colonies. Number of colonies for which a genotype could be called outof 96 colonies for each mutation is shown. Five genes were genotyped by capillary sequencing and multiplexed SNP genotyping. Shaded regions highlightthe number of colonies for which the same genotype was called by both technologies.62 F.L. Nice et al. / Experimental Hematology 2018;57:60–64ABFigure 3. Schematic representation of the subclonal structure and evolution of the tumor for patient PD4772. Wild-type colonies with no mutations are shownin peach and heterozygous mutations are shown in blue. No colonies with homozygous mutations were identified in this patient. Each individual cluster ofcolonies representing a single subclone is highlighted by a Roman numeral and the number of colonies within the subclone. (A) Row-wise representation ofthe mutational status of each colony for each mutation. Each row is one colony and each column is one gene. Red lines delineate clusters of colonies withthe same mutational characteristics. (B) Clonal hierarchy derived from the data in (A). A node further down the hierarchy includes all mutations that precedeit on the branch.63F.L. Nice et al. / Experimental Hematology 2018;57:60–64directed the research and wrote the paper. All authors re-viewed the manuscript.References1. Nangalia J, Massie CE, Baxter EJ, et al. Somatic CALR mutations inmyeloproliferative neoplasms with nonmutated JAK2. N Engl J Med.2013;369:2391–2405.2. Lundberg P, Karow A, Nienhold R, et al. Clonal evolution and clinicalcorrelates of somatic mutations in myeloproliferative neoplasms. Blood.2014;123:2220–2228.3. Klampfl T, Gisslinger H, Harutyunyan AS, et al. Somatic mutations ofcalreticulin in myeloproliferative neoplasms. N Engl J Med. 2013;369:2379–2390.4. Ortmann CA, Kent DG, Nangalia J, et al. Effect of mutation order onmyeloproliferative neoplasms. N Engl J Med. 2015;372:601–612.5. Nangalia J, Nice FL, Wedge DC, et al. DNMT3A mutations occurearly or late in patients with myeloproliferative neoplasms andmutation order influences phenotype. Haematologica. 2015;100:e438–e442.6. Fraley C, Raftery AE. Model-based clustering, discriminant analysis anddensity estimation. J Am Stat Assoc. 2002;97:611–631.64 F.L. Nice et al. / Experimental Hematology 2018;57:60–64Supplementary dataTable E1.Gene Protein Change DNA Change Chr Position NGS allele burden (%)JAK2 p.V617F c.1849G>T 9 5073770 46.9CPN2 p.V292F c.874G>T 3 194062558 21.3HADHA p.R291Q c.872G>A 2 26437358 21.5CHEK2 p.E231D c.693A>T 22 29120993 16.3SETD1A p.Y382C c.1145A>G 16 30976208 14.8POLR2F p.R154R c.462G>T 22 38437084 23.6KSR2 p.? c.2582+7G>T 12 117914262 20.5ZFP161 p.N409N c.1227C>T 18 5290980 18.8BAI3 p.E1391V c.4172A>T 6 70071337 16.1SLC24A1 p.E492E c.1476G>A 15 65917894 14.7RNF19B p.I573R c.1718T>G 1 33404025 13.3UPF2 p.T531A c.1591A>G 10 12043738 13KCNMA1 p.A220G c.659C>G 10 78944618 10.7LRRC67 p.G203R c.607G>A 8 67900698 10.5TTC3L p.V1299V c.3897T>C 21 38538413 7.1UNC45B p.? c.1547+6G>T 17 33496956 5.4Mutations, mutation locations and allele burdens for all mutations found by exome sequencing for patient PD4772 [1]. p., protein; p.?, splice sitemutation; c., cDNA; Chr, chromosome; NGS, next generation sequencing. Genomic coordinates are from the hg19 reference genome.Table E2.Gene Protein Change DNA Change Chr Position Forward Primer Reverse PrimerJAK2 p.V617F c.1849G>T 9 5073770 CAAGCAGCAAGTATGATGAGCAAGCCTGACACCTAGCTGTGATCCTGAACPN2 p.V292F c.874G>T 3 194062558 TGGGAGGTGGGTAATGGCATTGTATCCATCTTTGCCTCCCTGGGTAATHADHA p.R291Q c.872G>A 2 26437358 TGGTCCAGAATGGCAATAAGGAGGAACAGAATTGACAGCGTATGCCATGACHEK2 p.E231D c.693A>T 22 29120993 CACGCCCAGCAACTTACTCATCTTGAAGATCACAGTGGCAATGGAACCSETD1A p.Y382C c.1145A>G 16 30976208 CTCCTCATTGTCCTCGTCCTCCTCAGGAGGTGTAAGAAGGTGGGAAGCMutation locations and primer details used for capillary sequencing. p., Protein; c., cDNA; Chr, chromosome. Genomic coordinates are from the hg19reference genome.64.e1F.L. Nice et al. / Experimental Hematology 2018;57:60–64

Determination of complex subclonal structures of hematological malignancies by multiplexed genotyping of blood progenitor colonies.

Francesca L. Nice

Charlie E. Massie

Thorsten Klampfl

Anthony R. Green

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Experimental Hematology

Determination of complex subclonal structures of hematological malignancies by multiplexed genotyping of blood progenitor colonies

https://www.repository.cam.ac.uk/bitstream/1810/276067/3/1-s2.0-S0301472X17308159-main.pdf

Determination of complex subclonal structures of hematological malignancies by multiplexed genotyping of blood progenitor colonies.

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