Sage Journals: Discover world-class research

Abstract

Currently, distinguishing between benign and malignant lymphoid proliferations is based on a combination of clinical characteristics, cyto/histomorphology, immunophenotype and the identification of well-defined chromosomal aberrations. However, such diagnoses remain challenging in 10–15% of cases of lymphoproliferative disorders, and clonality assessments are often required to confirm diagnostic suspicions. In recent years, the development of new techniques for clonality detection has allowed researchers to better characterize, classify and monitor hematological neoplasms. In the past, clonality was primarily studied by performing Southern blotting analyses to characterize rearrangements in segments of the IG and TCR genes. Currently, the most commonly used method in the clinical molecular diagnostic laboratory is polymerase chain reaction (PCR), which is an extremely sensitive technique for detecting nucleic acids. This technique is rapid, accurate, specific, and sensitive, and it can be used to analyze small biopsies as well as formalin-fixed paraffin-embedded samples. These advantages make PCR-based approaches the current gold standard for IG/TCR clonality testing. Since the completion of the first human genome sequence, there has been a rapid development of technologies to facilitate high-throughput sequencing of DNA. These techniques have been applied to the deep characterization and classification of various diseases, patient stratification, and the monitoring of minimal residual disease. Furthermore, these novel approaches have the potential to significantly improve the sensitivity and cost of clonality assays and post-treatment monitoring of B- and T-cell malignancies. However, more studies will be required to demonstrate the utility, sensitivity, and benefits of these methods in order to warrant their adoption into clinical practice. In this review, recent developments in clonality testing are examined with an emphasis on highly sensitive systems for improving diagnostic workups and minimal residual disease assessments.

Keywords

clonality hematological neoplasms high-throughput sequencing PCR

Introduction

In recent years, the development of new techniques to detect clonality has allowed researchers to better characterize, classify, and monitor hematological neoplasms. It is now appreciated that neoplasms are populations of cells that share many of the same characteristics and can be, in theory, derived from the proliferation of a single common precursor; or in other words, neoplasms are usually ‘clonal’. Therefore, all cells within such cancers would be expected to contain identical DNA sequences, which could be used as specific tumor markers. The vast majority of lymphoid malignancies (>98%) contain identical (i.e. clonal) rearrangements of the immunoglobulin (IG) and/or T-cell receptor (TCR) genes, and other well-defined chromosomal aberrations are found in 25–30% of cases, any of which can serve as clonality markers [van Dongen and Wolvers-Tettero, 1991a]. Thus, such clonality assessments represent a qualitative leap in our ability to characterize and monitor lymphoproliferative diseases.

These types of analyses allow for more objective diagnostic definitions of lymphoid disorders, and they can help clarify the lineage of tumors [Langerak et al. 1997; Mannu et al. 2011; van Dongen et al. 2003]. Presently, distinctions between lymphoproliferative malignant disorders and non-neoplastic processes are usually based on clinical characteristics, cyto/histomorphology, immunophenotype and the identification of well-defined chromosomal aberrations. However, in 10–15% of cases such diagnoses remain unclear, and additional analyses, such as clonality assessments, are required to confirm diagnostic suspicios [Langerak et al. 1997; Mannu et al. 2011; van Dongen et al. 2003].

In addition, the presence of a clonality marker can be used to monitor minimal residual disease (MRD) in the context of therapeutic programs that aim to eradicate neoplastic clones. Therefore, clonality assessments can be used for both prognostic and therapeutic purposes [Langerak et al. 1997; Mannu et al. 2011; van Dongen et al. 2003]. In this review, we describe various tests to detect clonality, with an emphasis on the importance of developing sensitive systems for improving diagnostic workups and MRD assessments.

Clonality investigation

It was first suspected that hematological malignancies were clonal from the observation of monomorphic cell populations as well as the identification of specific cell populations using immunopathological methods to detect specific monoclonal antigens [Rezuke et al. 1997]. Today, there are much more precise and accurate molecular techniques [Rezuke et al. 1997] to identify the presence of chromosomal translocations and rearrangements in antigen-receptor genes in lymphoid tumors.

The use of chromosomal aberrations, fused transcripts and breakpoint-fusion regions as markers for clonality can provide useful clinical information; however, given the frequency of these types of translocations, they permit the study of only a subset of patients[Bruggemann et al. 2010]. They are widely applicable for use in MRD monitoring, as they are stable over the course of a disease and are directly related to oncogenic processes, irrespective of biological changes due to clonal selection [Gabert et al. 2003].

The tracking of antigen-receptor gene rearrangements for clonality analyses and MRD monitoring can be applied to virtually all patients. During early B- and T-cell development, the germline variable (V), diverse (D), and joining (J) fragments of the IG and TCR genes become rearranged through the random deletion or insertion of nucleotides within the junctional region, generating specific and unique sequences within each lymphocyte. Therefore, cancer cells that arise from alterations in single lymphoid precursors possess clonal IG or TCR junctional regions, which can be thought of as ‘DNA fingerprints’ for the malignant cells and can be used as tumor-specific markers [Beldjord et al. 1993; Tonegawa, 1983; van Dongen and Wolvers-Tettero, 1991a].

IG proteins are composed of two heavy chains (IGH) and two light chains (IGK or IGL). The variable domain of IGH is encoded by a single exon that originates from the rearranged V, D and J segments, whereas a combination of the V and J gene segments encode the variable domains of IGK and IGL. The constant domain is encoded by gene segments within the C (constant) region.

In B lymphoproliferative disorders, it is often possible to identify clonal rearrangements of the IGH, IGK and/or IGL genes, indicating that these pathologies are characterized by the expansion of B cells that have been blocked at different levels of differentiation. In contrast, studies involving TCR gene rearrangements are more complex. In the case of T cells, two types of receptors are known: TCRαβ and TCRγδ; in particular, the γδ chain rearranges earlier than the αβ chain during ontogenesis. In fact during T-cell differentiation, first the TCR D chains (TRD) genes rearrange, then TCR G chains (TRG), potentially resulting in TCRγδ expression or followed by further TCR B chains (TRB) rearrangement and TCRD deletion with subsequent TCR A chains (TRA) rearrangement, potentially followed by TCRαβ expression. Furthermore, rearrangements of the TCR B and/or G chains have been observed in almost all types of T lymphoproliferative disorders [van Dongen et al. 2003]. Nevertheless is important to underline that there are rare T-cell neoplasms that have lacked TRB or TRG gene rearrangements, in this cases the evaluation of TRD could be helpful [O’Connor et al. 1985; Weiss et al. 1988].

Assessing the state of such markers makes it possible to identify hematological neoplasms through the detection of clonality; however, these analyses should always be interpreted in the context of other clinical, morphological, and immunophenotypic data [van Dongen et al. 2003]. It should be stressed that the context in which clonality is observed is extremely important, and although clonality is strongly predictive of neoplasia in certain situations, it is not sufficient to demonstrate a neoplastic hypothesis or malignancy [van Dongen et al. 2003]. For example, there are types of inflammatory diseases and viral infections that can induce lymphoproliferative disorders that display clonality (i.e. AIDS-related lymphoproliferative disorders) [van Dongen et al. 2003]. Currently, a number of different approaches can be used to detect clonality, including Southern blotting analyses, polymerase chain reaction (PCR) assays, and most recently, high-throughput sequencing [or next-generation sequencing (NGS)]; see Table 1.

Table 1.

A comparison of different techniques for clonality detection.

	Time	Material load	Sensitivity	Specificity
Southern blotting	Days	10,000–20,000 ng	Low/ intermediate	Very high
Polymerase chain reaction	Hours	100–500 ng	Very high	high
Next-generation sequencing	Hours	20 ng	High?^*	High?^*

To be validated.

Clonality evaluation by Southern blotting analysis

Southern blotting analysis is the gold-standard technique (currently, at least when fresh tissue is available) for molecular studies of clonality. Such studies generally focused on rearrangements of the IG and TCR gene segments and took advantage of the combinatorial repertoires observed in these genes. In particular, Southern blotting can be used to detect genetic rearrangements based on changes in the distance between restriction-enzyme cleavage sites in genomic DNA.

First, DNA samples are digested with restriction enzymes [Sambrook et al. 1989]. Next, the resulting DNA fragments are separated by size using agarose-gel electrophoresis, blotted onto a nitrocellulose membrane and immobilized. The membrane is then incubated with a radiolabeled DNA probe complementary to either the IG or TCR genes [van Dongen and Wolvers-Tettero, 1991b]. Finally, the unbound probe is washed off, and the targeted fragments are detected using autoradiography or phosphorimaging.

Given that appropriate combinations of restriction enzymes and probes are used, the detected fragments from the rearranged IG or TCR genes will differ between neoplastic and germline samples [Beishuizen et al. 1993; Breit et al. 1991b; van Dongen and Wolvers-Tettero, 1993]. Well-chosen restriction enzymes (i.e. those that result in fragments 2–15 kb in length) and well-positioned DNA probes (e.g. those downstream of the J segment) allow for the detection of virtually all types of IG and TCR gene rearrangements, as well as other types of chromosomal aberrations [Beishuizen et al. 1993; Breit et al.; Langerak et al.; Moreau et al. 1999; Tumkaya et al. 1995, 1996; van Dongen and Wolvers-Tettero, 1991a].

Ideally, tumor cells will give rise to a clearly visible rearranged band (monoclonal pattern), whereas reactive lymphoid cells, which contain many different IG and TCR gene rearrangements, will yield a characteristic background pattern or a smear of multiple faint bands (polyclonal pattern) [van Dongen and Wolvers-Tettero]. Furthermore, the presence of two bands of comparable intensity is associated with biallelic rearrangements, and the presence of multiple bands of differing intensity is associated with olicoglonal populations [van Dongen and Wolvers-Tettero, 1991a and b]. Tumor cell population can be detected with a sensitivity of approximately 5%, whereas the detection limit is 10–15% if a clonal cell population has to be identified within a background of polyclonal, reactive cells [van Dongen and Wolvers-Tettero, 1991a and b].

Southern blotting tests to detect clonality are usually carried out against the IGH, IGK and TCR B chain (TRB) loci, as these genes have extensive combinatorial repertoires and possess relatively simple gene structures that can be evaluated using only one or two DNA probes [Beishuizen et al. 1993; Breit et al. 1993; Langerak et al. 1999]. The IGL and TCR A chain (TRA) genes are more complex and require multiple probe sets to fully characterize [Hara et al. 1987; Tumkaya et al. 1995, 1996]. Finally, the TCR G chain (TRG) and TCR D chain (TRD) genes have limited combinatorial repertoires, which are less suitable for discriminating between monoclonal and polyclonal cell populations through Southern blotting analysis [Breit et al. 1991; Cossman et al. 1988; van Dongen and Wolvers-Tettero, 1991b].

Despite the high reliability of Southern blotting analyses, they are increasingly being replaced by PCR-based techniques, not only for the purposes of clonality assessment but also for MRD evaluation, due to several disadvantages inherent to Southern blotting procedures. These disadvantages include significant demands in terms of time and technical skill, the need for large quantities of high-quality DNA, and limited sensitivity (in 5–10% of cases, Southern blotting is not suitable for tracking clones, such as during specific disease phases or during incipient recurrence) [van Dongen and Wolvers-Tettero, 1991b].

Clonality evaluation by PCR analysis

In recent years, PCR-based analyses have gradually replaced other techniques for routine clonality workups, and they have become the basis for the diagnosis and prognosis of lymphoproliferative malignancies. PCR is fast, accurate, and requires only small amounts of nucleic acid as a template; in addition, PCR can also amplify partially degraded DNA. Therefore, PCR-based techniques can be used to analyze small biopsies (e.g. skin biopsies) or formalin-fixed paraffin-embedded samples (FFPE), which generally yield low-quality DNA, making this reliable, specific, and sensitive technique widely applicable for clonality assessment [Bruggemann et al. 2006; van der Velden et al. 2003; van der Velden et al. 2007; van Dongen et al. 2003; White et al. 1989].

Currently, two different types of PCR analysis are used for clonality assessments: qualitative and quantitative. Although qualitative assays can provide significant information, they do not allow for precise MDR analysis, making them suitable only for clonality assessment. However, quantitative approaches are crucial for correctly assessing treatment responses and monitoring MDR.

Regardless of the approach, the detection of IG/TCR gene rearrangements using PCR-based techniques requires prior, precise knowledge of the rearranged gene segments in order to design appropriate primers at opposite sides of the junctional regions. Indeed, clonality studies involving PCR are almost always based on the selective amplification of junctional regions of rearranged IG and TCR gene segments. Namely, amplification is only possible when the IG and TCR segments are juxtaposed through rearrangement, as the distance between these genes in the germline configuration is far too large to allow for PCR amplification.

Clonality analysis by qualitative PCR

The detection of IG and TCR gene rearrangements using PCR is fast, simple, precise, and very accurate, and such tests can identify a single T or B monoclonal cell among 100 B or T polyclonal cells with a sensitivity of 0.01% [van Dongen et al. 2003]. However, a significant disadvantage of PCR-based IG or TCR gene analyses is that the types of rearrangements that can be detected are limited by the primers that are used, raising the possibility of false-negative results. In particular, for optimal results, the distance between primers should be less than 500 bp, and in the case of FFPE tissues, less than 200 bp. These considerations are particularly relevant for discriminating between monoclonal and polyclonal IG and TCR gene rearrangements based on changes in amplicon size and composition [Bertness et al. 1985; Weiss et al. 1985].

In 2003, the BIOMED-2 group established standardized protocols and primers for multiplex PCR clonality analysis, which have both increased the efficiency and reproducibility of clonality detection across different laboratories [van Dongen et al. 2003]. This effort has resulted in standardized multiplex PCR assays for nearly all IG/TCR targets, which collectively show extraordinarily high rates of detection of the most common B- and T-cell malignancies [Bruggemann et al. 2007; Evans et al. 2007; Langerak et al. 2007; van Krieken et al. 2007]. This high detection rate was achieved not only through optimized primer design, but also through the inclusion of additional IG/TCR targets (e.g. IGK and TRB as well as incomplete IGH D-J and TRB D-J rearrangements) [van Dongen et al. 2003]. In addition, the BIOMED-2/EuroClonality PCR protocols have been extensively validated by many groups outside of the consortium, making them the world standard for PCR-based IG/TCR clonality testing [Bruggemann et al. 2007; Halldorsdottir et al. 2007; Liu et al. 2007; McClure et al. 2006; Patel et al. 2010; Sandberg et al. 2007].

To date, IGH and TRG gene rearrangements have been the most commonly tested loci in PCR-based clonality studies, due to the small number of primers needed to detect V-J rearrangements. In the case of suspected B non-Hodgkin’s lymphoma (NHL), IGH targets (FR1, FR2, and FR3) are generally chosen, either in parallel with or followed by IGK targets [Evans et al. 2007]. Although assessing a combination of IGH V-J and IGK targets should be sufficient to detect clonality in the vast majority of cases (>95%), evaluating IGH D–J and IGL targets may sometimes be useful as a second-line approach [Evans et al. 2007].

With respect to T lymphoproliferative disorders, TRB and TRG can be analyzed in parallel or consecutively, and they show a detection rate of nearly 100% [Bruggemann et al. 2007; van Dongen et al. 2003]. Importantly, TRD should only be analyzed in the case of well-defined clinical requests, as most TRD rearrangements are removed in TCRαβ-lineage T cells following rearrangement of the TRA locus, which can give rise to preferential amplification and pseudoclonality [Beldjord et al. 1993; Bruggemann et al. 2007; van Krieken et al. 2007].

Assessing patterns in clonality tests is not always straightforward, as it can be difficult to discern whether rearrangements are clonal, oligoclonal, polyclonal, or pseudoclonal [Groenen et al. 2012] (see Figures 1 and 2). In an attempt to make interpretation less subjective, reading algorithms have been introduced [Greiner and Rubocki, 2002; Kuo et al. 2007; Miller et al. 1999]. These algorithms consider peak heights and peak ratios to define ‘truly clonal’ rearrangements. Although obviously clonal samples can be readily identified by these algorithms, such cutoff values can create a false sense of security and may even lead to false-positive or false-negative interpretations. Indeed, the primary problem remains that multiplex clonality PCRs are quantitative and not qualitative assays, and strictly quantitative approaches using criteria based on ratio determinations can easily miss clonal processes or erroneously diagnose clonality.

Figure 1.

IGH BIOMED-2 polymerase chain reaction analysis of polyclonal (A) and monoclonal (B) cell populations.

Figure 2.

IGH BIOMED-2 polymerase chain reaction analysis of irregular polyclonal (A) and biallelic or biclonal (B) cell populations.

To correctly interpret results, it is crucial (and in some cases mandatory) to carry out analyses in duplicate, integrate the results with histopathological information, and finally, analyze multiple or alternative targets in all cases in which the gene-rearrangement patterns are difficult to interpret [Groenen et al. 2012; Langerak et al. 2012]. Applying these recommendations will help to avoid the main problems encountered when evaluating clonality: (a) limited sensitivity associated with normal polyclonal background; (b) false-negative results due to somatic hypermutation; (c) false-positive results due to a lack of genetic material; (d) oligoclonality; and (e) pseudoclonality derived from a limited number of B or T cells in the tissue sample analyzed. This phenomenon is an artifactual finding of an apparently clonal lymphoid population secondary to small biopsies. In particular, due to the sensitivity of PCR, a small number of IG or TCR gene rearrangements may be selectively amplified from a small number of B or T cells present in the tissue. This is particularly seen when TRG is used as PCR targets in skin biopsies. In these cases duplicates of PCR are strongly recommended to avoid overinterpretation of an apparent clone [Elenitoba-Johnson et al. 2000; van Dongen et al. 2003]. pseudoclonality is rare by Southern blot analysis [Elenitoba-Johnson et al. 2000; van Dongen et al. 2003].

Another complicating feature of this type of molecular analysis involves the extraction of DNA from FFPE blocks. The major disadvantage of using such blocks is the effect of formalin fixation on DNA architecture and nucleoside fragmentation, which can lead to lower amplification yields and diagnostic utility [Bagg et al. 2002; Liu et al. 2007]. Despite these issues, molecular testing of FFPE specimens is becoming increasingly common, and in such cases, appropriate target selection can help optimize analysis and mitigate amplification problems [Langerak et al. 2012].

Analysis of minimal residual disease by real-time quantitative polymerase chain reaction

To accurately assess MRD, three types of real-time quantitative polymerase chain reaction (RQ-PCR) analyses can be used, involving one of the following types of probes: SYBR Green I (TaqMan and LightCycler), hydrolysis (TaqMan) or hybridization (LightCycler) probes. The first is the simplest RQ-PCR technique and is based on the detection and quantification of PCR products using the DNA-intercalating dye SYBR Green I. However, this method is not sequence specific and can therefore also detect nonspecific PCR products. In contrast, systems involving hydrolysis or hybridization probes are more sensitive and can be used to analyze RQ-PCR results in a sequence-specific manner. In these assays, signal generation requires two types of hybridization (two primers and one/two probes), leading to higher accuracy levels [Campana, 2004]. Independent of the type of RQ-PCR analysis used, different types of oligonucleotides can also be used to increase specificity, including allele-specific oligonucleotide (ASO) probe, ASO forward and reverse primers, as well as germline probes and primers [Campana, 2010; van der Velden et al. 2003].

In lymphoid tumors, the primary targets used in PCR-based analyses of MRD are clonal antigen-receptor rearrangements [Campana, 2010; van der Velden et al. 2003]. Although these MRD targets can be assessed in virtually all patients, the tests can be relatively complex to perform, due to the fact that the junctional rearrangements must be identified prior to performing the patient-specific RQ-PCR assays. In particular, it is first necessary to screen the DNA using PCR to determine the precise clonal rearrangement at diagnosis [van der Velden et al. 2007]. Then, the PCR product is sequenced to define the junctional regions and to allow for the design of patient-specific and ASO primers. These primers are then used to assess MRD in peripheral blood (PB)/bone marrow (BM) mononuclear cells from sequential specimens collected at different time points post-therapy.

Despite the difficulty of RQ-PCR approaches, they remain widely used because they are reliable, accurate, specific, and extremely sensitive [Bruggemann et al. 2006; Campana, 2010; van der Velden et al. 2007], being able to detect 1 cancer cell among 10,000–100,000 normal cells (sensitivity = 10^–4–10^–5). In practice, the sensitivity of this assay will vary based on the type of rearrangement and number of normal lymphoid cells with identical or similar junctional regions within the analyzed sample [Szczepanski et al. 1999; Szczepanski et al. 2002a; van der Velden et al. 2002]. In addition, IGH and TCR genes are present at only one copy per cell, allowing for extremely precise quantification of MRD [Szczepanski et al. 1999].

In contrast, the main pitfalls and drawbacks of RQ-PCR are the significant technical costs necessary to perform patient-specific assays, as well as the possibility of false-negative results due to phenomena such as oligoclonality [Bird et al. 1988; Davis and Bjorkman, 1988; Deane et al. 1991; Szczepanski et al. 2002b; van der Velden et al. 2004] (minor pathologic clones that were undetected at diagnosis become more predominant during the course of the disease) or clonal evolution (clonal markers being replaced by a secondary rearrangement) [Bird et al. 1988; Marshall et al.]. To reduce the risk of false-negative results, it is suggested that two or more different rearrangements be monitored [van der Velden et al. 2003; van der Velden et al. 2007].

In cases where only one suitable genetic rearrangement has been identified, the use of another MRD assay in parallel may help avoid false-negative results. However, such practices may prove too stringent and reduce the number of patients being correctly monitored. Importantly, there is a need for standardized criteria for the uniform interpretation of RQ-PCR MRD data, especially among disparate nations. Towards this end, several European groups have already joined together with the aim of establishing common guidelines for data analysis. These networks are working to standardize technologies to introduce RQ-PCR-based MRD detection methods to clinics in order to abolish problems stemming from the current diversity of evaluation systems [van der Velden et al. 2003; van der Velden et al. 2007].

Clonality analysis by NGS

Since the complete sequencing of the first human genome, there has been a rapid development of technologies to facilitate DNA analysis using NGS. The potential applications for these novel technologies are wide-ranging, including: (1) the deep characterization and classification of diseases (in fact diagnosis by traditional morphologic analysis of pathologic material will be complemented by NGS assays to analyze the tumor on a molecular level); and (2) patient stratification and highly specific MRD monitoring. Actually, prognosis and response to therapy will be more precisely defined by these novel approaches having the potential to significantly improve the sensitivity and costs of clonal detection assays in post-treatment monitoring of B- and T-cell malignancies [Kohlmann et al. 2012].

The deep sequencing of immune receptor gene populations offers the possibility of specific and detailed molecular characterization, and these techniques will likely transform our understanding of the human immune system [Boyd et al. 2009, 2010]. For example, these technologies may identify both stable and dynamic aspects of the immune repertoire that differ under normal and diseased conditions. Such data would provide a high-resolution picture of the spectrum of immunity found in normal individuals and patients with lymphoid malignancies. In the latter case, these findings could illuminate both the initial behaviors of clonal tumor populations as well as the later suppression or re-emergence of these populations following treatment [Boyd et al. 2009].

These technologies are highly parallelized processes that enable the sequencing of thousands to millions of molecules at once. Popular NGS methods include: (1) pyrosequencing which makes use of luciferase to read out signals as individual nucleotides are added to DNA templates; (2) reversible dye-terminator techniques that adds a single nucleotide to the DNA template in each cycle; and (3) sequences by preferential ligation of fixed-length oligonucleotides.

NGS can be applied to either the whole genome or to selected DNA regions. NGS studies are based on the fragmentation of target DNA probes, with subsequent amplification and sequencing of the obtained DNA fragments. The massive production of parallel sequences generates several reads for each genome position: the number of reads per stretch of DNA is referred to as ‘coverage’. A high coverage improves the detection of point mutations and small insertions/deletions by filtering out the noise due to possible DNA contaminants. A number of reads above or below the mean coverage respectively indicates gains or losses of DNA material, whereas the mapping of variable reads in two distant regions stands for the presence of chromosomal translocations [Meyerson et al. 2010].

Despite the promise of these approaches, the translation of these innovative technologies into routine diagnostic practice will be complex and difficult.

A recent study by He and colleagues presented an interesting combination of targeted capture and a sequencing approach called IgCap [He et al. 2011]. Fragmented DNA sequences with adapters at each end are hybridized to a set of probes that bind to the germline-encoded fragments of the IGH locus, which are then sequenced using Illumina high-throughput technology. This methodology allowed the researchers to detect the majority of sequences within the IGH locus, although the hybridization step does not distinguish between rearranged and nonrearranged sequences [He et al. 2011].

Other direct methods for sequencing rearranged IG and TCR genes have been developed over the past few years [Boyd et al. 2010; Freeman et al. 2009; Robins et al. 2011]. These methods use primers that are specific to each pair of the V and J segments, which are used to attempt to directly amplify and sequence all possible rearrangements. Although, such studies are important steps towards demonstrating that high-throughput sequencing of the adaptive immune-receptor repertoire can have direct clinical applications, more experiments will be required to prove that the utility, sensitivity, and benefits of these systems warrant their use in clinical practice. Indeed, despite the fact that several IG/TR assays have already been described in the literature, there remains a need for further optimization in assay design and development in order to ensure better coverage of the relevant genes and genetic rearrangements (e.g. partial IGHD-IGHJ rearrangements and IGK-locus rearrangements involving the kappa-deleting element); see Figures 3 and 4.

Figure 3.

IGH high-throughput sequencing analysis of polyclonal (A) and monoclonal (B) cell populations.

Figure 4.

IGH high-throughput sequencing analysis of irregular polyclonal (A) and biallelic or biclonal (B) cell populations.

The ability of NGS to detect and quantify small numbers of circulating tumor cells has important implications with respect to direct therapies and predicting patient prognoses [Kohlmann et al. 2012; Robins, 2011]. In particular, NGS can improve MRD detection methods by identifying specific genomic alterations or detecting small amounts of mutant or clonal DNA in the absence of prior knowledge concerning the mutant DNA sequences. Such technologies would extend MRD evaluation to all patients, eliminate the problems inherent to patient-specific assays, reduce errors due to operator- and laboratory-specific data interpretation, and increase the sensitivity and informativeness of post-therapy monitoring, all of which could significantly affect clinical treatment decisions [Kohlmann et al. 2012; Robins, 2011].

Currently, there are many groups interested in novel NGS applications. In a study that elegantly demonstrated several of benefits of these approaches, Wu and colleagues [Wu et al. 2012] used NGS of the T-cell antigen-receptor genes to track MRD in T-lineage acute lymphoblastic leukemia/lymphoma. They found that TCR NGS could not only be used to identify clonality at the time of diagnosis but also to detect subsequent MRD, which was missed by flow cytometry in a subset of cases. This study highlights the potential of this technology to lower detection thresholds for MRD, and indeed, this may become one of the first applications of high-throughput sequencing to be adopted by clinical molecular laboratories. Similarly, NGS of IGH gene rearrangements were also performed; an example of this approach was carried by Boyd and colleagues [Boyd et al. 2009] who used NGS to characterize B-cell repertoires in normal patients and detect small numbers of clonal B cells in patients with B-cell lymphomas. To date, NGS of B-cell receptors (BCRs) have mainly focused on classifying the IGHV, D, and J recombination frequencies to understand the diversity of the BCR repertoire [Boyd et al. 2009; Campbell et al. 2008; Maletzki et al. 2012; Sanchez et al. 2003; Stewart et al. 1997; Wu et al. 2012]. However, computational assignment of V-D-J sequences to reference databases results in many incompletely assigned IGHV, D, and J genes, even when the germ-line alleles are known. This is most likely due to the presence of mutations masking the identity of the germ-line genes present in the NGS, or the existence of allelic variation relative to the reference IGH genes.

Thus, modern DNA-sequencing technologies have opened new windows of investigation into the complex topic of genetic rearrangement, and they promise unprecedented sensitivity and specificity for the tracking of monoclonal B-cell expansions. However, it remains to be seen whether such novel modes of tracking will be useful for elucidating the clinical and molecular variables that lead certain clonal expansions to progress.

To address the challenges presented by NGS technologies with respect to IG/TR gene analysis, a consortium has been formed to set standards for IG/TR NGS methodologies and their applications in hemato-oncology, which is known as the EuroClonality-NGS consortium. The main objectives of the EuroClonality-NGS consortium are to develop, standardize, and validate IG/TR NGS assays for: (i) clonality assessment; (ii) MRD analysis; and (iii) repertoire analysis. The primary aims of the consortium involve the standardization of these practices, not only at the pre-analytical stages (e.g. sample preparation and target choice), but also the post-analytical stages (e.g. bioinformatics pipelines), as well as the validation of these technologies for large-scale use in clinical trials.

Taking a broader perspective, deep-sequencing approaches to the analysis of lymphocyte populations may provide insights into the nature of a variety of autoimmune and infectious diseases, and they may elucidate how medical manipulations (e.g. vaccinations) affect the immune system and how harmful outcomes result from current therapies (e.g. graft-versus-host disease following stem-cell transplantations). We expect that immune-receptor sequencing in medical scenarios involving lymphoid malignancies or other immune-mediated diseases will be broadly used for gathering diagnostic, prognostic, and disease-monitoring information in the future.

All of these studies highlight that the emergence of NGS has opened the door to a new era in diagnostic medicine, bringing the vision of ‘personalized medicine’ closer to reality. In fact, NGS opens the possibility to combine several molecular assays into one from antigen receptor sequencing to identification of translocation fusion transcripts. Hence, this technology becomes available for healthcare applications, physicians and patients will increasingly demand refined diagnosis and treatment strategies tailored to the clinical needs of an individual patient. However, prior to the widespread application of NGS for molecular diagnostic testing, several critical processes need to be addressed in a way that results in practical, actionable solutions and effective patient care.

Footnotes

Funding

This work was supported by AIRC (grant number IG10519; 5xMille10007), Centro Interdipartimentale per la Ricerca sul Cancro ‘G. Prodi’, BolognAIL, RFO (to Professor Pileri, Dr. Piccaluga), FIRB Futura 2011 (grant number RBFR12D1CB to Professor Piccaluga), Fondazione Cassa di Risparmio in Bologna, Fondazione della Banca del Monte e Ravenna, and Progetto Strategico di Ateneo 2006 (to Professor Pileri and Dr Piccaluga).

Conflict of interest statement

The authors have no conflicting financial interests to declare.

References

Bagg

Braziel

Arber

Bijwaard

Chu

(2002) Immunoglobulin heavy chain gene analysis in lymphomas: a multi-center study demonstrating the heterogeneity of performance of polymerase chain reaction assays. J Mol Diagn 4: 81–89.

Beishuizen

Verhoeven

Mol

Breit

Wolvers-Tettero

van Dongen

(1993) Detection of immunoglobulin heavy-chain gene rearrangements by Southern blot analysis: recommendations for optimal results. Leukemia 7: 2045–2053.

Beldjord

Macintyre

Even

Sigaux

(1993) Peripheral selection of V delta 1+ cells with restricted T cell receptor delta gene junctional repertoire in the peripheral blood of healthy donors. J Exp Med 178: 121–127.

Bertness

Kirsch

Hollis

Johnson

Bunn

Jr (1985) T-cell receptor gene rearrangements as clinical markers of human T-cell lymphomas. N Engl J Med 313: 534–538.

Bird

Galili

Link

Stites

Sklar

(1988) Continuing rearrangement but absence of somatic hypermutation in immunoglobulin genes of human B cell precursor leukemia. J Exp Med 168: 229–245.

Boyd

Gaeta

Jackson

Fire

Marshall

Merker

. (2010) Individual variation in the germline Ig gene repertoire inferred from variable region gene rearrangements. J Immunol 184: 6986–6992.

Boyd

Marshall

Merker

Maniar

Zhang

Sahaf

. (2009) Measurement and clinical monitoring of human lymphocyte clonality by massively parallel VDJ pyrosequencing. Sci Transl Med 1(12): 12–23.

Breit

Wolvers-Tettero

Beishuizen

Verhoeven

van Wering

van Dongen

(1993) Southern blot patterns, frequencies, and junctional diversity of T-cell receptor-delta gene rearrangements in acute lymphoblastic leukemia. Blood 82: 3063–3074.

Breit

Wolvers-Tettero

Hahlen

van Wering

van Dongen

(1991) Limited combinatorial repertoire of gamma delta T-cell receptors expressed by T-cell acute lymphoblastic leukemias. Leukemia 5: 116–124.

10.

Bruggemann

Raff

Flohr

Gokbuget

Nakao

Droese

. (2006) Clinical significance of minimal residual disease quantification in adult patients with standard-risk acute lymphoblastic leukemia. Blood 107: 1116–1123.

11.

Bruggemann

Schrauder

Raff

Pfeifer

Dworzak

Ottmann

. (2010) Standardized MRD quantification in European ALL trials: proceedings of the Second International Symposium on MRD assessment in Kiel, Germany, 18–20 September 2008. Leukemia 24: 521–535.

12.

Bruggemann

White

Gaulard

Garcia-Sanz

Gameiro

Oeschger

. (2007) Powerful strategy for polymerase chain reaction-based clonality assessment in T-cell malignancies Report of the BIOMED-2 Concerted Action BHM4 CT98-3936. Leukemia 21: 215–221.

13.

Campana

(2004) Minimal residual disease studies in acute leukemia. Am J Clin Pathol 122(Suppl.): S47–S57.

14.

Campana

(2010) Minimal residual disease in acute lymphoblastic leukemia. Hematology Am Soc Hematol Educ Program 2010: 7–12.

15.

Campbell

Pleasance

Stephens

Dicks

Rance

Goodhead

. (2008) Subclonal phylogenetic structures in cancer revealed by ultra-deep sequencing. Proc Natl Acad Sci U S A 105: 13081–13086.

16.

Cossman

Uppenkamp

Sundeen

Coupland

Raffeld

(1988) Molecular genetics and the diagnosis of lymphoma. Arch Pathol Lab Med 112: 117–127.

17.

Davis

Bjorkman

(1988) T-cell antigen receptor genes and T-cell recognition. Nature 334: 395–402.

18.

Deane

Pappas

Norton

(1991) Immunoglobulin heavy chain gene fingerprinting reveals widespread oligoclonality in B-lineage acute lymphoblastic leukaemia. Leukemia 5: 832–838.

19.

Elenitoba-Johnson

Bohling

Mitchell

Brown

Robetorye

(2000) PCR analysis of the immunoglobulin heavy chain gene in polyclonal processes can yield pseudoclonal bands as an artifact of low B cell number. J Mol Diagn 2: 92–96.

20.

Evans

Pott

Groenen

Salles

Davi

Berger

. (2007) Significantly improved PCR-based clonality testing in B-cell malignancies by use of multiple immunoglobulin gene targets. Report of the BIOMED-2 Concerted Action BHM4-CT98-3936. Leukemia 21: 207–214.

21.

Freeman

Warren

Webb

Nelson

Holt

(2009) Profiling the T-cell receptor beta-chain repertoire by massively parallel sequencing. Genome Res 19: 1817–1824.

22.

Gabert

Beillard

van der Velden

Grimwade

Pallisgaard

. (2003) Standardization and quality control studies of ‘real-time’ quantitative reverse transcriptase polymerase chain reaction of fusion gene transcripts for residual disease detection in leukemia - a Europe Against Cancer program. Leukemia 17: 2318–2357.

23.

Greiner

Rubocki

(2002) Effectiveness of capillary electrophoresis using fluorescent-labeled primers in detecting T-cell receptor gamma gene rearrangements. J Mol Diagn 4: 137–143.

24.

Groenen

van Raaij

van Altena

Rombout

van Krieken

(2012) A practical approach to diagnostic Ig/TCR clonality evaluation in clinical pathology. J Hematopathol 5: 17–25.

25.

Halldorsdottir

Zehnbauer

Burack

(2007) Application of BIOMED-2 clonality assays to formalin-fixed paraffin embedded follicular lymphoma specimens: superior performance of the IGK assays compared to IGH for suboptimal specimens. Leuk Lymphoma 48: 1338–1343.

26.

Hara

Benedict

Mak

Gelfand

(1987) T cell receptor alpha-chain gene rearrangements in B-precursor leukemia are in contrast to the findings in T cell acute lymphoblastic leukemia. Comparative study of T cell receptor gene rearrangement in childhood leukemia. J Clin Invest 80: 1770–1777.

27.

Jiao

Wagner-Johnston

Ambinder

Diaz

Jr . (2011) IgH gene rearrangements as plasma biomarkers in Non- Hodgkin’s lymphoma patients. Oncotarget 2: 178–185.

28.

Kohlmann

Grossmann

Haferlach

(2012) Integration of next-generation sequencing into clinical practice: are we there yet? Semin Oncol 39: 26–36.

29.

Kuo

Hall

Longtine

(2007) A novel method for interpretation of T-cell receptor gamma gene rearrangement assay by capillary gel electrophoresis based on normal distribution. J Mol Diagn 9: 12–19.

30.

Langerak

Groenen

Bruggemann

Beldjord

Bellan

Bonello

. (2012) EuroClonality/BIOMED-2 guidelines for interpretation and reporting of Ig/TCR clonality testing in suspected lymphoproliferations. Leukemia 26: 2159–2171.

31.

Langerak

Molina

Lavender

Pearson

Flohr

Sambade

. (2007) Polymerase chain reaction-based clonality testing in tissue samples with reactive lymphoproliferations: usefulness and pitfalls. A report of the BIOMED-2 Concerted Action BMH4-CT98-3936. Leukemia 21: 222–229.

32.

Langerak

Szczepanski

van der Burg

Wolvers-Tettero

van Dongen

(1997) Heteroduplex PCR analysis of rearranged T cell receptor genes for clonality assessment in suspect T cell proliferations. Leukemia 11: 2192–2199.

33.

Langerak

Wolvers-Tettero

van Dongen

(1999) Detection of T cell receptor beta (TCRB) gene rearrangement patterns in T cell malignancies by Southern blot analysis. Leukemia 13: 965–974.

34.

Liu

Bench

Bacon

Payne

Huang

Scott

. (2007) A practical strategy for the routine use of BIOMED-2 PCR assays for detection of B- and T-cell clonality in diagnostic haematopathology. Br J Haematol 138: 31–43.

35.

Maletzki

Jahnke

Ostwald

Klar

Prall

Linnebacher

(2012) Ex-vivo clonally expanded B lymphocytes infiltrating colorectal carcinoma are of mature immunophenotype and produce functional IgG. PLoS One 7(2): e32639.

36.

Mannu

Gazzola

Bacci

Sabattini

Sagramoso

Roncolato

. (2011) Use of IGK gene rearrangement analysis for clonality assessment of lymphoid malignancies: a single center experience. Am J Blood Res 1: 167–174.

37.

Marshall

Kwan

Haber

Brisco

Sykes

Morley

. (1995) Characterization of clonal immunoglobulin heavy chain and I cell receptor gamma gene rearrangements during progression of childhood acute lymphoblastic leukemia. Leukemia 9: 1847–1850.

38.

McClure

Kaur

Pagel

Ouillette

Holtegaard

Treptow

. (2006) Validation of immunoglobulin gene rearrangement detection by PCR using commercially available BIOMED-2 primers. Leukemia 20: 176–179.

39.

Meyerson

Gabriel

Getz

(2010) Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet 11: 685–696.

40.

Miller

Wilson

Jaye

Kronenberg

(1999) An automated semiquantitative B and T cell clonality assay. Mol Diagn 4: 101–117.

41.

Moreau

Langerak

van Gastel-Mol

Wolvers-Tettero

Zhan

Zhou

. (1999) Easy detection of all T cell receptor gamma (TCRG) gene rearrangements by Southern blot analysis: recommendations for optimal results. Leukemia 13: 1620–1626.

42.

O’Connor

Wainscoat

Weatherall

Gatter

Feller

Isaacson

. (1985) Rearrangement of the T-cell-receptor beta-chain gene in the diagnosis of lymphoproliferative disorders. Lancet 1: 1295–1297.

43.

Patel

Pan

Wang

Maitta

Xue

. (2010) Comparison of BIOMED-2 versus laboratory-developed polymerase chain reaction assays for detecting T-cell receptor-gamma gene rearrangements. J Mol Diagn 12: 226–237.

44.

Rezuke

Abernathy

Tsongalis

(1997) Molecular diagnosis of B- and T-cell lymphomas: fundamental principles and clinical applications. Clin Chem 43: 1814–1823.

45.

Robins

(2011) Detecting and monitoring lymphoma with high-throughput sequencing. Oncotarget 2: 287–288.

46.

Robins

Campregher

Srivastava

Wacher

Turtle

Kahsai

. (2009) Comprehensive assessment of T-cell receptor beta-chain diversity in alphabeta T cells. Blood 114: 4099–4107.

47.

Sambrook

Fritsch

Maniatis

(1989) Molecular Cloning, A Laboratory Manual, 2nd edn. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press.

48.

Sanchez

Almeida

Gonzalez

Garcia-Marcos

Balanzategui

. (2003) Incidence and clinicobiologic characteristics of leukemic B-cell chronic lymphoproliferative disorders with more than one B-cell clone. Blood 102: 2994–3002.

49.

Sandberg

Verhaaf

van Gastel-Mol

Wolvers-Tettero

de Vos

Macleod

. (2007) Human T-cell lines with well-defined T-cell receptor gene rearrangements as controls for the BIOMED-2 multiplex polymerase chain reaction tubes. Leukemia 21: 230–237.

50.

Stewart

Lee

Ibrahim

Watts

Shlomchik

Weigert

. (1997) A Shannon entropy analysis of immunoglobulin and T cell receptor. Mol Immunol 34: 1067–1082.

51.

Szczepanski

Flohr

van der Velden

Bartram

van Dongen

(2002a) Molecular monitoring of residual disease using antigen receptor genes in childhood acute lymphoblastic leukaemia. Best Pract Res Clin Haematol 15: 37–57.

52.

Szczepanski

Pongers-Willemse

Langerak

van Dongen

(1999) Unusual immunoglobulin and T-cell receptor gene rearrangement patterns in acute lymphoblastic leukemias. Curr Top Microbiol Immunol 246: 205–213; discussion 214–205.

53.

Szczepanski

Willemse

Brinkhof

van Wering

van der Burg

van Dongen

(2002b) Comparative analysis of Ig and TCR gene rearrangements at diagnosis and at relapse of childhood precursor-B-ALL provides improved strategies for selection of stable PCR targets for monitoring of minimal residual disease. Blood 99: 2315–2323.

54.

Tonegawa

(1983) Somatic generation of antibody diversity. Nature 302: 575–581.

55.

Tumkaya

Beishuizen

Wolvers-Tettero

van Dongen

(1996) Identification of immunoglobulin lambda isotype gene rearrangements by Southern blot analysis. Leukemia 10: 1834–1839.

56.

Tumkaya

Comans-Bitter

Verhoeven

van Dongen

(1995) Southern blot detection of immunoglobulin lambda light chain gene rearrangements for clonality studies. Leukemia 9: 2127–2132.

57.

van der Velden

Bruggemann

Hoogeveen

de Bie

Hart

Raff

. (2004) TCRB gene rearrangements in childhood and adult precursor-B-ALL: frequency, applicability as MRD-PCR target, and stability between diagnosis and relapse. Leukemia 18: 1971–1980.

58.

van der Velden

Cazzaniga

Schrauder

Hancock

Bader

Panzer-Grumayer

. (2007) Analysis of minimal residual disease by Ig/TCR gene rearrangements: guidelines for interpretation of real-time quantitative PCR data. Leukemia 21: 604–611.

59.

van der Velden

Hochhaus

Cazzaniga

Szczepanski

Gabert

van Dongen

(2003) Detection of minimal residual disease in hematologic malignancies by real-time quantitative PCR: principles, approaches, and laboratory aspects. Leukemia 17: 1013–1034.

60.

van der Velden

Wijkhuijs

Jacobs

van Wering

van Dongen

(2002) T cell receptor gamma gene rearrangements as targets for detection of minimal residual disease in acute lymphoblastic leukemia by real-time quantitative PCR analysis. Leukemia 16: 1372–1380.

61.

van Dongen

Langerak

Bruggemann

Evans

Hummel

Lavender

. (2003) Design and standardization of PCR primers and protocols for detection of clonal immunoglobulin and T-cell receptor gene recombinations in suspect lymphoproliferations: report of the BIOMED-2 Concerted Action BMH4-CT98-3936. Leukemia 17: 2257–2317.

62.

van Dongen

Wolvers-Tettero

(1991a) Analysis of immunoglobulin and T cell receptor genes. Part I: Basic and technical aspects. Clin Chim Acta 198: 1–91.

63.

van Dongen

Wolvers-Tettero

(1991b) Analysis of immunoglobulin and T cell receptor genes. Part II: Possibilities and limitations in the diagnosis and management of lymphoproliferative diseases and related disorders. Clin Chim Acta 198: 93–174.

64.

van Krieken

Langerak

Macintyre

Kneba

Hodges

Sanz

. (2007) Improved reliability of lymphoma diagnostics via PCR-based clonality testing: report of the BIOMED-2 Concerted Action BHM4-CT98-3936. Leukemia 21: 201–206.

65.

Weiss

Wood

Moulds

Cleary

Warnke

. (1985) Clonal rearrangements of T-cell receptor genes in mycosis fungoides and dermatopathic lymphadenopathy. N Engl J Med 313: 539–544.

66.

Weiss

Picker

Grogan

Warnke

Sklar

(1988) Absence of clonal beta and gamma T-cell receptor gene rearrangements in a subset of peripheral T-cell lymphomas. Am J Pathol 130: 436–442.

67.

White

Arnheim

Erlich

(1989) The polymerase chain reaction. Trends Genet 5: 185–189.

68.

Sherwood

Fromm

Winter

Dunsmore

Loh

. (2012) High-throughput sequencing detects minimal residual disease in acute T lymphoblastic leukemia. Sci Transl Med 4: 134–163.

The evolution of clonality testing in the diagnosis and monitoring of hematological malignancies

Abstract

Keywords

Introduction

Clonality investigation

Clonality evaluation by Southern blotting analysis

Clonality evaluation by PCR analysis

Clonality analysis by qualitative PCR

Analysis of minimal residual disease by real-time quantitative polymerase chain reaction

Clonality analysis by NGS

Footnotes

Funding

Conflict of interest statement

References