Analysis and interpretation of Ig and TCR gene rearrangements in the conventional, low-throughput way have their limitations in terms of resolution, coverage, and biases. With the advent of high-throughput, next-generation sequencing (NGS) technologies, a deeper analysis of Ig and/or TCR (IG/TR) gene rearrangements is now within reach, which impacts on all main applications of IG/TR immunogenetic analysis. To bridge the generation gap from low- to high-throughput analysis, the EuroClonality-NGS Consortium has been formed, with the main objectives to develop, standardize, and validate the entire workflow of IG/TR NGS assays for 1) clonality assessment, 2) minimal residual disease detection, and 3) repertoire analysis. This concerns the preanalytical (sample preparation, target choice), analytical (amplification, NGS), and postanalytical (immunoinformatics) phases. Here we critically discuss pitfalls and challenges of IG/TR NGS methodology and its applications in hemato-oncology and immunology.

Specific Ag recognition of the adaptive immune system is mediated by a remarkably diverse repertoire of Ag receptors—Igs on B lymphocytes (plus Abs secreted by plasma cells) and TCRs on T lymphocytes—showing high affinity for a particular Ag. Fundamental to Ig and/or TCR (IG/TR) diversity is the combined effect of molecular [mainly V(D)J recombination] and cellular diversification processes during maturation of B and T lymphocytes (15).

The result of Ag-independent B and T lymphocyte differentiation and diversification that occurs in bone marrow and thymus is a broadly diverse, polyclonal repertoire of Ag-specific receptors (naive or primary repertoire), whereas Ag-dependent maturation in the periphery further shapes the IG/TR repertoire through selection processes (immunocompetent or antigen-experienced repertoire) (6, 7). IG/TR polyclonality is one end of a continuum of immune profiles (Fig. 1A). Upon Ag-specific triggering and during inflammation certain IG/TR specificities can predominate and lead to one or more small clones (i.e., the offspring of particular B or T lymphocytes) on top of the polyclonal repertoire, thus reflecting a more oligoclonal immune profile. At the other end of the continuum, significant outgrowth of a single lymphocyte clone with particular Ag specificity would lead to a monoclonal immune profile, the hallmark of lymphoid malignancies (Fig. 1A).

FIGURE 1.

IG/TR repertoire diversity translating into clinical applications in low-throughput methodology and high-throughput immunogenetics. (A) Continuum of IG/TR repertoire diversity ranging from true polyclonality (far left end) through oligoclonality and low level clonality (middle) to clear monoclonality (far right end). (B) Schematic representation of how IG/TR repertoire diversity translates into low-throughput methodology-based detection of repertoire, MRD, and clonality testing (left to right). (C) Schematic representation of how high-throughput methodology discloses the full IG/TR sequence information of the entire cell population, thus allowing much more information for repertoire analysis, MRD monitoring, and clonality assessment to be drawn.

FIGURE 1.

IG/TR repertoire diversity translating into clinical applications in low-throughput methodology and high-throughput immunogenetics. (A) Continuum of IG/TR repertoire diversity ranging from true polyclonality (far left end) through oligoclonality and low level clonality (middle) to clear monoclonality (far right end). (B) Schematic representation of how IG/TR repertoire diversity translates into low-throughput methodology-based detection of repertoire, MRD, and clonality testing (left to right). (C) Schematic representation of how high-throughput methodology discloses the full IG/TR sequence information of the entire cell population, thus allowing much more information for repertoire analysis, MRD monitoring, and clonality assessment to be drawn.

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In view of the extensive diversification mechanisms, the probability that two independent B or T cell clones carry exactly the same IG/TR gene rearrangement by chance alone is virtually negligible. IG/TR gene rearrangements thus form unique genetic markers that can be justifiably viewed as molecular signatures (DNA fingerprints), which have been instrumental for understanding both normal and pathologic immune responses (822). In addition, the spectrum of IG/TR repertoire diversity also translates into immunogenetic profiles that are especially useful for clinical/diagnostic purposes for pathophysiological conditions, that is, clonotypic repertoire analysis, minimal residual disease (MRD) detection and monitoring, and clonality assessment (Fig. 1B) (2326).

Multiplex PCR assays for the detection of clonally rearranged IG/TR genes have been designed, standardized, and validated by the BIOMED-2/EuroClonality Consortium (euroclonality.org) (27), which also established guidelines for interpretation of clonality results (28). These BIOMED-2 multiplex assays are now widely used to obtain corroborative evidence for the differential diagnosis of reactive lesions versus malignant lymphomas and for assessing clonal identity (25, 2931), as well as for identification of appropriate PCR targets for MRD detection. Highly sensitive (10−4 to 10−5) MRD analysis of follow-up samples upon treatment is achieved by using IG/TR rearrangements for design of patient-specific oligonucleotides in real-time quantitative (RQ-)PCR. Efforts by the EuroMRD Consortium (euromrd.org) to standardize IG/TR-based RQ-PCR MRD diagnostics have resulted in improved technical guidelines and definitions of MRD positivity and MRD quantitative range to express MRD levels in a reproducible way across multiple centers and therapeutic protocols (32). Such accurate MRD quantitation is relevant for use as surrogate markers for clinical outcome and for actual monitoring in view of risk-based clinical decisions or group stratification (3340). Finally, IG/TR repertoire at the genomic or transcript level has been studied in many immunological conditions, although the true diversity of the IG/TR repertoire is mostly only partly disclosed using low-throughput Sanger-based sequencing. Nevertheless, clonal repertoire analysis has proven clinically relevant, as exemplified by chronic lymphocytic leukemia (CLL) where identifying IGHV gene mutational status is established as one of the most robust prognostic markers in CLL (standardized by the European Research Initiative on CLL [termed ERIC]; ericll.org) (41, 42).

Even though many of the low-throughput IG/TR assays have thus been optimized and standardized to a high level, inherently they may occasionally provide suboptimal and even misleading results. Depending on the diagnostic application, the causes of concern as well as the issues at stake are different. However, fundamental to all is the enormous potential diversity of IG/TR gene rearrangements necessitating the use of multiplex PCR assays with multiple primers that—even in the most optimal situation—are always a compromise. Indeed, PCR biases due to differential performance or misannealing of primers can lead to artificial asymmetries with regard to gene frequencies, resulting in a false impression of repertoire skewing or even clonality status. The use of consensus primers in the amplification protocols, though practical, implies a less than complete coverage and thus a less comprehensive view of repertoire diversity. Moreover, consensus primers have a tendency to miss clonal IG rearrangements in the presence of somatic hypermutation (SHM). Both have merged as relevant problems in studies of, especially, nonmalignant (i.e., oligo/polyclonal) repertoires, for example, in normal individuals as well as in settings of vaccination, autoimmunity, or immune reconstitution after allogenic hematopoietic stem cell transplantation or drug-induced lymphocyte depletion. SHM can also be the cause for the inability to detect the clonal B cell population in B cell malignancies with high SHM load, for example, follicular lymphoma (FL), multiple myeloma, and others.

Furthermore, the IG/TR low-throughput approaches have their specific limitations in all diagnostic applications. GeneScan analysis/spectratyping and heteroduplex analysis, widely used for evaluation of the clonality status (4345), usually ensure diagnostic accuracy with an analytical sensitivity of maximally 5%, dependent upon the type of lymphoma and the context (e.g., specimen type and size, DNA quality and integrity). This limited dynamic range of spectratyping renders this approach suboptimal for determining low-level dissemination or for monitoring purposes. RQ-PCR–based quantification of clone-specific IG/TR gene rearrangements can be highly informative for the detection of MRD; however, sensitivity varies, depending on the PCR protocol and the relative size of the background of normal (polyclonal) B and T lymphocytes. Also, oligoclonality at initial diagnosis and clonal evolution of IG/TR gene rearrangements between diagnosis and relapse, both relevant for acute lymphoblastic leukemia (ALL) (4648), or the occurrence of (ongoing) SHM (for some mature B cell malignancies) (49), may give false-negative results. Traditional repertoire analysis, conducted by Sanger sequencing of (subcloned) rearranged IG/TR PCR amplicons or by single cell strategies, is limited in depth by the strain on laboratory manpower and resources required, hampering study of the dynamics of immune responses (e.g., in vaccination) and clonal evolution (e.g., intraclonal diversification of Ig genes in B cell malignancies). Moreover, using multiplex assays that do not encompass the entire V domain encoding region may lead to 1) ambiguities in IG/TR gene and allele identification, and 2) incomplete appreciation of the true impact of SHM (41, 42).

With the introduction of next-generation sequencing (NGS) in immunogenetics, also collectively termed Repertoire Sequencing (RepSeq) analysis (5059), a deeper analysis of IG/TR gene rearrangements is now within reach, which could have a profound impact on the three main applications of immunogenetic analysis (clonality assessment, MRD detection and monitoring, and repertoire analysis; Table I). Based on the wealth of IG/TR sequences generated, much more information on the entire IG/TR repertoire diversity of the cells can thus be disclosed (Fig. 1C).

Table I.
Potential impact of IG/TR RepSeq analysis on different clinical applications
ApplicationImpact
Clonality testing Accurate evaluation of IG/TR clonal relationship in multiple lesions (true relapse or dissemination versus new or secondary malignancy) 
 Assessment of low-level dissemination of malignant clone 
 Assessment of intraclonal diversity of malignant clone 
MRD monitoring More easy and unbiased identification of IG/TR targets 
 Sensitive monitoring of malignant clone 
 Evaluation of oligoclonal IG/TR heterogeneity at diagnosis and clonal evolution of resistant clone 
Repertoire analysis Assessment of higher depth/coverage of IG/TR rearrangements 
 Assessment of higher number of IG/TR clonotypes 
ApplicationImpact
Clonality testing Accurate evaluation of IG/TR clonal relationship in multiple lesions (true relapse or dissemination versus new or secondary malignancy) 
 Assessment of low-level dissemination of malignant clone 
 Assessment of intraclonal diversity of malignant clone 
MRD monitoring More easy and unbiased identification of IG/TR targets 
 Sensitive monitoring of malignant clone 
 Evaluation of oligoclonal IG/TR heterogeneity at diagnosis and clonal evolution of resistant clone 
Repertoire analysis Assessment of higher depth/coverage of IG/TR rearrangements 
 Assessment of higher number of IG/TR clonotypes 

However, there are still many challenges toward routine clinical application that need to be overcome (Table II; see also next paragraph for more details concerning the main applications). These are all subjects of study in the EuroClonality-NGS Consortium (euroclonalityngs.org; coordinated by a steering group chaired by A. W. Langerak), which consists of several EuroClonality laboratories experienced in the design of assays for detecting IG/TR rearrangements, supplemented by laboratories with expertise in IG/TR gene-based MRD studies (from the EuroMRD network) or IG/TR repertoire studies and immunoinformatics (from the ERIC network).

Table II.
Challenges of IG/TR RepSeq analysis for different clinical applications
ApplicationChallenge
Clonality testing Multiplexing and complementarity of IG/TR targets 
 Defining amount of starting material in the context of specimen type, DNA integrity, and estimated neoplastic cell load 
 Defining value of clonal size, numerical cut-off values, and limits of detection 
 Reappraisal/redefinition of the meaning of clonality 
 Data analysis pipeline including visualization 
MRD monitoring Multiplexing and complementarity of IG/TR targets 
 Amount and type of starting material 
 Use of internal controls (e.g., spike-in) 
 Definition of limits of detection (quantifiable, sensitivity) 
 Correct for disproportional PCR amplification of rearrangements 
 Data analysis pipeline including visualization 
Repertoire analysis Multiplexing and complete coverage of genes 
 Equal amplification and thus representation of genes 
 Sequence information of entire V gene (IG loci) 
 Error correction prior to accurately defining mutations, polymorphisms 
 Data analysis pipeline including visualization 
ApplicationChallenge
Clonality testing Multiplexing and complementarity of IG/TR targets 
 Defining amount of starting material in the context of specimen type, DNA integrity, and estimated neoplastic cell load 
 Defining value of clonal size, numerical cut-off values, and limits of detection 
 Reappraisal/redefinition of the meaning of clonality 
 Data analysis pipeline including visualization 
MRD monitoring Multiplexing and complementarity of IG/TR targets 
 Amount and type of starting material 
 Use of internal controls (e.g., spike-in) 
 Definition of limits of detection (quantifiable, sensitivity) 
 Correct for disproportional PCR amplification of rearrangements 
 Data analysis pipeline including visualization 
Repertoire analysis Multiplexing and complete coverage of genes 
 Equal amplification and thus representation of genes 
 Sequence information of entire V gene (IG loci) 
 Error correction prior to accurately defining mutations, polymorphisms 
 Data analysis pipeline including visualization 

Clonality assessment.

For NGS-based clonality assessment the following issues are at stake.

Assessing the clonal relationship between lesions.

Although GeneScan analysis/spectratyping takes (identical) size of the generated IG/TR amplicons as the basis for clonality assessment, NGS also provides the individual sequences to create so-called clonotypes, a sequence-based compilation of identical rearrangements. This allows a quantitative consideration of the individual IG/TR rearrangements, and also helps to confirm the clonal relationship of the amplified rearrangements in multiple lesions from different locations or in multiple lesions over time (Fig. 2). NGS will also enable rapid identification and evaluation of bone marrow involvement in lymphoma patients. In addition, intraclonal diversity in mature B cell malignancies that undergo continuous SHM processes can be evaluated via NGS.

FIGURE 2.

NGS-based clonality assessment. (A) IGK NGS analysis in a tonsil sample showing high reproducibility of the diversity in three different laboratories. Data presented as IGKV gene (x-axis) versus frequency (y-axis), highlighting different IGKJ genes in different colors. (B) Clonal identity in two lymph node biopsies (1 y time difference) as evidenced from identical clonotypes in the two consecutive samples. Data presented as CDR3 aa length (x-axis) versus frequency (y-axis), highlighting clonotype sequences in different colors. Visualization using ARResT/Interrogate (92).

FIGURE 2.

NGS-based clonality assessment. (A) IGK NGS analysis in a tonsil sample showing high reproducibility of the diversity in three different laboratories. Data presented as IGKV gene (x-axis) versus frequency (y-axis), highlighting different IGKJ genes in different colors. (B) Clonal identity in two lymph node biopsies (1 y time difference) as evidenced from identical clonotypes in the two consecutive samples. Data presented as CDR3 aa length (x-axis) versus frequency (y-axis), highlighting clonotype sequences in different colors. Visualization using ARResT/Interrogate (92).

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Complementarity of IG/TR targets to reduce the risk of missing clones.

NGS-based clonality assessment still relies on an initial multiplex PCR step, which will be hampered by the same limitations as all other PCR-based IG/TR studies, that is, polymorphisms and especially SHM that prevent primer annealing. Additionally, the diverse amplification efficiency leading to unequal amplification of different rearrangements in multiplex PCR potentially prevents precise quantification. NGS-based clonality assessment thus also requires multiple complementary IG/TR targets to ensure a high detection rate; for B cell malignancies a combination of IGH V-D-J and IGK V-J (and preferably also IGH D-J), and for T cell malignancies a combination of TRB and TRG assays. Costs for NGS-based clonality assays are affordable—also when compared with the traditional approaches by GeneScan analysis. This can be accomplished by multiplexing of targets and samples for the sequencing step following initial separate PCRs. In considering costs, it is worth noting that the required depth of NGS clonality approaches is not as deep as for MRD purposes, thus allowing running more patient samples in parallel for the various clonality targets. Thus, NGS-based clonality assessment has the potential to replace the classical GeneScan approach.

Capture-based detection of clonal IG/TR rearrangements and translocations.

An alternative approach is to use a hybridization capture or pull-down methodology to enrich for IG/TR loci DNA using small probes or baits, potentially allowing for less biased sequencing of all target regions. In addition, this approach is suitable for parallel identification of chromosomal translocations involving IG/TR gene loci, present in lymphoma and leukemia samples, in the same workflow (60, 61). Finally, hybridization capture approaches allow the analysis of all the IG/TR targets—including unproductive rearrangements—in a single assay, saving precious material, and also facilitates the use of a given target as a reference or baseline for normalization against the others, that is, comparing the level of total rearranged B lymphocytes versus T lymphocytes, or clonal B lymphocytes out of total B lymphocytes.

Reappraisal of the meaning of clonality.

The depth of resolution provided by NGS-based clonality assessment will almost certainly require a reappraisal of the term clonality. In particular, defining clonality simply based on the fact that a certain percentage of identical IG/TR rearrangement sequences can be found does not accurately address the clinical meaning and implications of the term. On the other hand, the future amassment of a large body of NGS-based IG/TR sequence data obtained in the context of clonality assessment will certainly reveal the true extent of repertoire skewing in healthy individuals, in patients with reactive lesions or other immunological conditions such as postinfection or vaccination, or in oncology patients receiving immunotherapeutics. Therefore, a major challenge in the quantitative NGS era will be to evaluate and possibly revise the current definitions as to what constitutes clonality and what is the border, if any, to accurately distinguish reactive from malignant lymphoproliferations in a clinicopathological context. One of the challenges is to define how and where borders should be drawn in the spectrum of mono-, oligo-, and polyclonality in patients with lymphoproliferations associated with viruses (e.g., HIV, EBV, or hepatitis C virus) and with lymphoproliferations suspicious for malignant lymphoma. Now for the first time, to our knowledge, the question can be addressed whether clonal sizes and numerical cut-offs are of (any) biological or clinical value.

MRD detection.

Despite recent developments offering proof of principle that NGS-based MRD assessment using IG/TR genes is workable in lymphoid leukemias and lymphomas and potentially even more sensitive than alternative options (RQ-PCR, multicolor flow cytometry) (54, 55, 6265), several issues remain to be addressed, which will be the topic of the sections below.

Identification of the correct index clone.

The standard way to identify a leukemia/lymphoma-associated index sequence is to perform IG/TR multiplex PCR followed by amplicon sequencing and clonotype selection based on a frequency threshold ≥5% of all analyzed sequences (Fig. 3A). This procedure is error prone, because—depending on the clinical setting—IG/TR gene rearrangements of unrelated B and T lymphocyte clones can account for a considerable fraction of amplified sequences and might be misinterpreted as leukemia/lymphoma-specific rearrangements (53), in particular if the true malignant IG/TR rearrangement is missed by the applied primer set, for example, due to harboring extensive SHM rates (66).

FIGURE 3.

Clonotype frequencies and accurate MRD quantification. (A) IGH clonotype frequency in the peripheral blood of 12 cases of follicular lymphoma at diagnosis (left) and in normal buffy coat (right). A threshold of 5% is generally used to identify lymphoma/leukemia-related clonotypes at diagnosis. A significant number of clonotypes >2 and <5% is seen in buffy coat. Dashed lines indicate the 5% threshold for index clone selection and 2 and 1% thresholds. (B) Correct MRD quantification is dependent on the background level of polyclonal B lymphocytes. MRD levels may greatly differ following chemotherapy versus B cell depletion therapy, yet might give rise to the same relative frequency of index sequences, thus necessitating the use of internal references for accurately calculating MRD levels.

FIGURE 3.

Clonotype frequencies and accurate MRD quantification. (A) IGH clonotype frequency in the peripheral blood of 12 cases of follicular lymphoma at diagnosis (left) and in normal buffy coat (right). A threshold of 5% is generally used to identify lymphoma/leukemia-related clonotypes at diagnosis. A significant number of clonotypes >2 and <5% is seen in buffy coat. Dashed lines indicate the 5% threshold for index clone selection and 2 and 1% thresholds. (B) Correct MRD quantification is dependent on the background level of polyclonal B lymphocytes. MRD levels may greatly differ following chemotherapy versus B cell depletion therapy, yet might give rise to the same relative frequency of index sequences, thus necessitating the use of internal references for accurately calculating MRD levels.

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Correct MRD quantification.

Published studies on IG/TR amplicon sequencing frequently quantify the MRD level by counting the number of index sequences and dividing them by the total number of sequenced amplicons. Given that the applied multiplex PCR assays only amplify rearranged IG/TR genes, although cells with the respective IG/TR gene in germline configuration are not targeted, this might lead to considerable errors in MRD quantification, particularly in situations with a low number of polyclonal background B lymphocytes. For example, when IGH NGS is performed in a B cell malignancy after anti–B cell treatment resulting in a subtotal depletion of polyclonal B lymphocytes, preferential sequencing of IGH-rearranged B lymphocytes might lead to a considerable overestimation of MRD (Fig. 3B). Therefore, adequate internal controls have to be included, for which different approaches have been proposed: 1) different plasmids containing known IGH gene rearrangements (62); 2) synthetic control templates spiked at limiting dilution into each sample to compute the average number of reads for each sequenced spiked synthetic template (67); 3) spike-in of defined amounts of buffy coat (68). Again, in mature B cell malignancies like FL, where clonal heterogeneity is caused by ongoing SHM, one has to follow the presence of evolved clonotypes to assess the correct MRD level during treatment or at relapse.

Detection of clonal heterogeneity and clonal evolution.

In lymphoid malignancies different degrees and ways of IG/TR heterogeneity have been documented. In ALL ongoing gene rearrangements lead to IG/TR oligoclonality (e.g., IGH D-J to IGH V-D-J changes, and VH substitutions of complete VH-DH-JH rearrangements). Using an IGH NGS approach, IGH oligoclonality was computationally identified in the vast majority of childhood B cell precursor–ALL cases by comparing the IGH D-J stem of different complete IGH gene rearrangements with the IGH D-J stem of the index sequence; ∼10% of cases showed >1000 related sequences (53). Whether such potentially related sequences should also be tracked in follow-up samples has not yet been prospectively analyzed. In contrast, in mature B cell malignancies, IGH clonal heterogeneity is mainly the result of ongoing SHM, leading to intraclonal diversity (69). This might lead to a decrease of amplification efficacy of the respective rearrangement in NGS and thereby to a low or even false-negative MRD result. This can at least partly be compensated by relying on additional index sequences from other IG targets, if present.

Validation, quality control, and standardized interpretation of NGS-based MRD results.

Technical guidelines, as developed for RQ-PCR based MRD analysis, are currently lacking for NGS-based MRD; moreover, data interpretation regarding the definition of MRD positivity/negativity is very heterogeneous in the published literature. Considering the potential higher sensitivity of NGS, minimal technical requirements have therefore to be defined including the theoretical sensitivity for a single sample analyzed for MRD. This is particularly relevant with respect to the prognostic impact of certain MRD thresholds; the sensitivity of NGS could indeed be higher than other methods, but is in fact a function of the DNA amount or the number of cells analyzed in a single sample. Furthermore, a clear definition of MRD positivity/negativity is needed based on technical assay performance, the number of good quality reads, and the total number of cells analyzed. Given the complexity of NGS-based MRD assessment, successful participation in external quality control rounds should be a prerequisite for laboratories generating MRD data for clinical decision making. Notably, quality control rounds for RQ-PCR and NGS-based MRD are being organized for experienced laboratories by the EuroMRD Consortium twice yearly.

Repertoire analysis.

NGS-based repertoire analysis requires optimization at the level of sequence methodology as well as at the level of data analysis.

Methodological challenges and possible solutions.

Considering that Ag receptor gene repertoires may vary considerably between different lymphocyte subpopulations (7072), sorted cells should be used whenever possible, especially when analyzing complex repertoires of nonclonal lymphoid populations. In order to cover representative diversity and draw meaningful conclusions from experimental data, it is important to ensure adequate sampling, including biological as well as sequencing replicates. One microgram of DNA corresponds to only 150,000 cells or up to 300,000 copies of a given IG/TR locus, which represents a minor proportion of the total repertoire. Given that IG/TR transcript levels may vary considerably between cell populations (for instance more than 100-fold difference in IG transcript levels between naive B cells and plasma cells), mRNA use complicates the quantification of clonal expansions and may lead to erroneous conclusions about clonal architecture. Nevertheless, starting with mRNA allows the use of 5′ RACE/template switching protocols that reduce amplification biases, but often at a cost of shorter sequence length (7074). Within the EuroClonality-NGS Consortium, gDNA was chosen as the template in multiplex PCR with 5′ IGH primers annealing to the peptide leader region to amplify the complete VH sequence. This is important for 1) accurate identification of the rearranged germline V gene and allele, and 2) robust determination of the SHM load in the IG repertoire. Optimization of primer sets was performed using a collection of plasmids containing most functional V genes to ensure proper recognition of the targets and minimize amplification biases. Errors introduced during the amplification and sequencing phases complicate data interpretation, particularly in the case of IG gene repertoire analysis, where discriminating between SHM versus error may prove challenging. Among the error correction algorithms that have been proposed (7577), the most promising approach relies on molecular barcoding of each individual template molecule to improve data quality and sequencing accuracy (7880). Barcoding strategies have also been developed on an mRNA template but suffer from limited sequencing depth (79). Reliable repertoire analysis by amplification-based NGS strategies is dependent on comparable amplification efficiency, which can be difficult to control in highly multiplexed strategies. Preferential amplification can be detected by comparing repertoires during normal development, as demonstrated for TRD rearrangements during thymic development (Fig. 4A). IG/TR loci that undergo deletion, such as IGK and TRD, also present particular challenges for repertoire analysis.

FIGURE 4.

NGS-based repertoire analysis. (A) TRD repertoire during human thymocyte development. Different types of TRD rearrangements are present at different frequencies during human thymic development. (B) Additional clonotypic sequences as detected by NGS in different CLL samples. Each of the two dominant clonotypic sequences (a productive IGHV3-11 rearrangement and a nonproductive IGHV3-47 rearrangement) is surrounded by minor satellite sequences that differ from the main one by unique point mutations. Whether they are true SHM or PCR/sequence artifacts is currently unknown. Visualization using Vidjil software (91).

FIGURE 4.

NGS-based repertoire analysis. (A) TRD repertoire during human thymocyte development. Different types of TRD rearrangements are present at different frequencies during human thymic development. (B) Additional clonotypic sequences as detected by NGS in different CLL samples. Each of the two dominant clonotypic sequences (a productive IGHV3-11 rearrangement and a nonproductive IGHV3-47 rearrangement) is surrounded by minor satellite sequences that differ from the main one by unique point mutations. Whether they are true SHM or PCR/sequence artifacts is currently unknown. Visualization using Vidjil software (91).

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Finally, Ag receptors are heterodimers and sequence information of both chains is necessary for thorough repertoire analysis. Although, in lymphoid tumors, the chain pair of the clonotypic Ag receptor can be determined fairly easily from the dominant clonotypes, it is virtually impossible to do so in polyclonal populations with a vastly heterogeneous sequence composition. NGS technologies are evolving to investigate paired chain repertoires; methods to circumvent this problem include 1) two-dimensional barcoding system tagging H chain and L chain V genes of the BcR Ig within individual cells (81), and 2) partitioning single cells in emulsion-based droplets followed by a linkage RT-PCR producing a composite BcR Ig H chain and L chain V region product (51, 8284).

NGS-based repertoire data analysis in malignant and nonmalignant lymphocytes.

To test whether NGS could replace the conventional Sanger sequencing for accurate assessment of the SHM status in CLL, both approaches need to be compared. Following a pilot study showing identical results in the vast majority of CLL cases (93 out of 95, 98%), validation of the technique is currently ongoing in several EuroClonality-NGS laboratories. Of note, satellite clones differing from the predominant clone by nucleotide substitutions and more rarely by indels are often observed, which as mentioned earlier, could represent true SHM or artifacts (Fig. 4B). Furthermore, additional unrelated clonal VDJ sequences are detected at a lower frequency in a number of cases; whether these represent minor independent CLL clones or derive from other concomitant lymphoproliferation(s) still remains elusive.

NGS profiling of complex repertoires may also prove instrumental in dissecting normal and pathological immune responses (e.g., infection, immune reconstitution, vaccination, allergy, or autoimmunity). Importantly, the prospective large-scale accumulation of immune receptor sequence data in various pathological contexts may help unveil immunogenetic signatures that are distinctive in certain entities, and provide valuable insight regarding pathogenic mechanisms of diseases and effect of immunomodulating therapies, immune surveillance defects, or even normal immune system constitution.

Our recent NGS study of the T cell repertoire in CLL documented oligoclonal expansions, with T cell clones being persistent and further expanding over time and other T cell clones being shared by different patients, hence appearing to be disease specific. Altogether, these findings revealed an as yet unappreciated extent of TCR skewing, with implications for future interventions into CLL microenvironment interactions and interdependencies (85).

In all cases, molecular analysis of IG/TR genes eventually concerns the in silico analysis of the nucleotide sequences of their rearrangements. Although NGS enables us to explore immune repertoires and responses in their immense variability and complexity, it also naturally produces vast amounts of complex data that render these steps highly nontrivial.

Raw NGS data processing is a critical but often neglected first step. It can involve statistical demultiplexing to ensure minimal digital contamination (i.e., misassignment of reads to samples due to noisy barcodes) while maximizing read recovery and thus depth (86), prefiltering of capture data to remove non-IG/TR sequences and thus aid computational efficiency, paired-end and multilane joining to maximize quality and information content, and primer annotation and trimming. This last component has many important functions: assay development, assessment of run quality and amplicon (and thus junctional) completeness, removal of the artificial primer sequences before immunogenetic annotation, amplification bias correction when combined with a control sample (e.g., highlighting misbehaving primers), and computational efficiency.

Processed reads are then immunogenetically annotated (e.g., involved V and J genes and alleles, and exact CDR3 sequence). Standardization is important here, and although the use of different underlying algorithms is arguably inevitable, it is important to at least safeguard the consistent use of germline sequences, rules for CDR3 identification and translation (including C104 and W/F 118 anchor positions), and nomenclature of V, D, and J genes and alleles. At this stage, and especially for clinical applications, it is also desirable to identify incomplete and special/uncommon rearrangements. The eventual classification of reads into all these rearrangement types is in fact necessary when the experimental design, (i.e., when the amplification of specific rearrangements takes place in separate tubes) requires the normalization of abundances. It also allows classifying reads with no signs of any rearrangement as unknown and excluding them from the total sample read count used as the basis for relative abundances. This supervised abundance calculation is critical in clinical applications when thresholds are used for diagnosis or prognosis or therapy decisions.

Basic immunogenetic annotation can be used to construct clonotypes (73). The exact definition of a clonotype has been diverse in the literature and has depended on the underlying question, experiment, and data. In general, a clonotype can be considered as a distinct rearrangement event, in which case it can and has been used to report repertoires in an expression-independent manner.

Eventually, mining these inherently complex data for information requires additional functionalities, such as filtering and visualization, within an interactive graphical environment that provides flexibility and enables expert user input. However, this interactivity needs to be bidirectional and include feedback and guidance to the user toward unambiguous and consistent conclusions.

Taken together and properly standardized and validated, these key elements (i.e., comprehensive and consistent annotation of sequences; true representation of the repertoire; meaningful clonotype definition and quantitation; end-user-friendly but unambiguous visualization) can form a consistent computational platform usable by both researchers and clinicians. Within EuroClonality-NGS complementary immunoinformatics expertise is available that covers these key elements with a collection of dedicated computational resources and tools in the form of IMGT (imgt.org) (8790), Vidjil (vidjil.org) (91), and most recently ARResT/Interrogate (bat.infspire.org/arrest/) (92).

Specific challenges for MRD detection.

A true computational challenge for clonotype assessment in the MRD setting is the pronounced ongoing SHM of the IG locus in FL leading to clonal evolution and heterogeneity during treatment course and at relapse. Here, the acceptance criteria for the numbers of mismatches in relation to the diagnostic clonotype have to respect a continuous mutation process of FL cells, and the clonal relationship of heterogeneous sequences has therefore to be carefully defined to assess a correct MRD value over time. Overall, high reproducibility and a standardized NGS-based quantification are particularly important for the comparability of NGS-based MRD in the setting of prospective multicenter trials, as is the goal of EuroClonality-NGS.

Specific challenges for repertoire analysis.

In addition to raw data processing, V(D)J gene assignment, and clonotype identification, Ag receptor gene repertoire studies require additional sequence analysis extending from diversity profiles to clonal architecture, CDR3 length and characteristics, clonal dynamics (if temporal samples are analyzed), and clonotype comparisons between different lymphoid populations/individuals/disease entities. The complexity of the data generated would argue for concomitant analysis via independent existing pipelines (8792) or newly published ones (93, 94).

RepSeq analysis has quickly found its way into hematology and immunology research. Implementation of this high-throughput technology into routine clinical applications requires standardization, validation, and application-specific challenges that should be covered in a network of laboratories with specialists that bring immunobiological knowledge, technical experience on NGS methodology, and immunoinformatics expertise. Only then will RepSeq analysis be fully exploited for its high potential in diagnostic and translational research, with the full benefit for patients. The EuroClonality-NGS Consortium was formed to bridge the immunogenetics generation gap from low-throughput IG/TR analysis to high-throughput RepSeq analysis. Main objectives of the EuroClonality-NGS Consortium are to develop, standardize, and validate IG/TR NGS assays for the different clinical applications in a platform-independent way. Even though several such assays have already been published, there still is a need for optimization in assay development with the aim to ensure better coverage of all the genes and also to evaluate other types of rearrangements (partial IGH D–J rearrangements, IGK locus rearrangements including those involving the κ-deleting element, partial TRB D–J rearrangements, IG/TR translocations, etc.).

One of the most important aspects in the implementation of RepSeq analysis in both research and routine diagnostic practice concerns standardization of the entire NGS workflow, which pertains not only to the analytical phase, but also to the preanalytical (e.g., sample preparation and target choice) and the postanalytical phases (e.g., immunoinformatics pipeline, data visualization, and interpretation). Another very important aspect of implementing RepSeq analysis for different applications relates to validation of the technology against standard methodologies (GeneScan analysis, Sanger sequencing, RQ-PCR, and multiparameter flow cytometry) via large-scale, multilaboratory testing of clinical samples in the context of clinical trials. Eventually, this should provide robust tools and methodology, which will allow exploiting the full potential of this powerful new technology in diagnostic patient care. An additional confounding factor, at least for diagnostic applications of RepSeq analysis, is the lack of expertise and guidelines for implementation, evaluation, and clinical translation of the results. Both validation studies and guideline development are among the key activities of the EuroClonality-NGS Consortium.

We thank all members of the EuroClonality-NGS Consortium for their input in the discussions, Jos Rijntjes and Florian Thonier for preparing figures, Marieke Bitter for artwork and general support in management of the EuroClonality-NGS Consortium, and Bibi van Bodegom for secretarial assistance.

See related articles in this issue: IJspeert et al. (J. Immunol. 198, 4156; DOI: https://doi.org/10.4049/jimmunol.1601921) and Boyer et al. (J. Immunol. 198, 4148; DOI: https://doi.org/10.4049/jimmunol.1601924).

This work was supported by EuroClonality.

Abbreviations used in this article:

     
  • ALL

    acute lymphoblastic leukemia

  •  
  • CLL

    chronic lymphocytic leukemia

  •  
  • FL

    follicular lymphoma

  •  
  • IG/TR

    Ig and/or TCR

  •  
  • MRD

    minimal residual disease

  •  
  • NGS

    next-generation sequencing

  •  
  • RepSeq

    Repertoire Sequencing

  •  
  • RQ-PCR

    real-time quantitative PCR

  •  
  • SHM

    somatic hypermutation.

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