Molecular diagnostics in haematopathology

Mini-syMposiuM: diagnostic Molecular pathology

Molecular diagnostics in haematopathologyrichard Byers

eleni tholouli

Abstracthaematological malignancies comprise a complex range of disorder with

widely differing outcomes and response to treatments. precise diag-

nosis and prognostication is vital in directing optimal treatment and

this is relies increasingly on identification of molecular features, spe-

cifically in the myeloproliferative disorders, acute myeloid leukaemia,

chronic myeloid leukaemia and in the lymphomas, whilst determination

of clonality by analysis of immunoglobulin or t-cell receptor gene rear-

rangements are also becoming more important in diagnosis. the Who

classification formulated each disease subtype on the basis of a quartet

of clinical, morphological, immunohistochemical and genetic features but

still contains numerically large groups, such as follicular lymphoma and

diffuse large B-cell lymphoma, which contain a wide range of outcomes.

Microarray studies have sought to further dissect these groups and many

prognostic and predictive markers have been identified for these groups,

as well as for aMl and cll. in addition other insights from these studies

have challenged conventional views as to the pathobiology of lymphoma

in general. the above themes will be expanded in this review which

will cover, the role of integrated diagnostics in haematological malig-

nancy, the molecular features of haematological malignancies and recent

advances from microarray studies.

Keywords haematological malignancy; microarray; molecular diagnosis;

translocations

Haematological malignancies comprise a complex range of disor-ders with widely differing outcomes and response to treatments, features underscored by the 2001 WHO classification of haemato-logical malignancies.1 Precise diagnosis is vital in directing opti-mal treatment, and this is becoming increasingly important as new treatments are developed. Prognostic markers are becoming more important as tailored treatment becomes possible. Haema-tological malignancy has acted as a forerunner for other tumour types, and the scientific, clinical and technological advances that have been made in its diagnosis are expected to be transferable. A consideration of these issues is likely to be of benefit beyond haematopathology.

The WHO classification formulated each disease subtype on clinical, morphological, immunohistochemical and genetic features,

Richard Byers PhD FRCPath is a Senior Lecturer in Pathology, University of

Manchester/Consultant Histopathologist, Manchester Royal Infirmary, UK.

Eleni Tholouli PhD MRCPath is a Locum Consultant Haematologist at

Manchester Royal Infirmary, Manchester, UK.

diagnostic histopathology 14:5 2

and sought to relate each to a normal lymphoid counterpart cell or compartment.1 It was a major advance on previous classifi-cations, but it contains many groups (e.g. follicular lymphoma, diffuse large B-cell lymphoma) which have a wide range of out-comes.1 Recent microarray studies have sought to subclassify these groups, and many prognostic and predictive markers have been identified for these groups, as well as for acute myeloid leukaemia (AML) and chronic myeloid leukaemia (CLL), though measurement of these markers have been translated into clinical practice.2 Other insights from these studies have challenged con-ventional views as to the pathobiology of lymphoma in general.

Integrated diagnostics in haematological malignancy

Haematological malignancies are diverse and complex, and accurate diagnosis requires multiple analyses.3 These include morphology, immunohistochemistry, flow cytometry, molecular analyses (e.g. polymerase chain reaction (PCR), such as quan-titative reverse transcriptase PCR (qRT-PCR) or PCR for trans-locations and/or gene rearrangements) and cytogenetics (e.g. fluorescence in situ hybridization (FISH)).

These methods can be seen as increasingly detailed layered analytical platforms, but are more appropriately viewed as indi-vidual elements in a constellation of data required for diagnosis (Figure 1). Each of the diagnostic methods assumes a different level of importance in different diseases. For instance, follow-up of CML requires qRT-PCR for detection and quantification of BCR–ABL transcript principally; other tests are less important, except for morphological measurement of the number of blasts because of the possibility of transformation to AML. For follicular lymphoma, characteristic morphology with BCL2-positive nod-ules confirms diagnosis in almost all cases (though some BCL2- negative cases require demonstration of immunoglobulin (IG) gene rearrangement for diagnosis). Morphology is usually sufficient for diagnosis of reactive lymphadenopathy, but a restricted immuno-histochemistry panel is advisable in atypical cases, particularly if the clinical context suggests lymphoma, if only to exclude the possibility of a neoplastic process. Cutaneous T-cell infiltrates can be difficult to segregate into benign or neoplastic lesions; T- cell receptor gene rearrangement studies can be invaluable.

It is unsurprising that discrepancies arise between diagnoses made in specialist departments and those made in non-specialist departments, given the: • complexity of diagnosis of haematological malignancy • increasing need for ever-more precise subtyping and measure-

ment of associated prognostic markers • need for increasingly specialized tests in problematic cases.Even if all required analyses are done, integration of the data from the different tests is usually done by the clinician treat-ing the patient rather than by laboratory staff; this has poten-tial for error because the results of each test are best interpreted with reference to the others. Diagnosis of lymphoma is difficult because it is relatively uncommon. Due to these confounding factors, several regional, national and international studies have shown a misdiagnosis rate of 10–30% between non-expert and expert diagnosis of lymphoma.4

Specifically, it was found that ≤5% of people treated for lym-phoma in Wales had a benign lymphoma, a situation likely to be the same in England. Consequently, 400 people each year may

23 © 2008 elsevier ltd. all rights reserved.


Morphology

Flow cytometry

Cytogenetics FISH

PCR &real-timePCR

Microarrays(future platforms)

DIAGNOSIS

Clinical features

Levels of diagnosis of haematological malignancy

Figure 1 optimal diagnosis of haematological malignancy requires interpretation and integration of several analytical methods which are central to

the process.

suffer the distress and upheaval of cancer diagnosis and undergo unnecessary treatment, with resultant morbidity. Up to 10% may receive suboptimal treatment due to incorrect classification of the disease.4 A study in Wales revealed major diagnostic dis-cordance in 20% of 275 lymph nodes reviewed centrally over a two-year period.5 A follow-up study using case notes from a random sample of 33 patients in whom the diagnosis had been changed found that the treatment should have been changed in one-half of patients, and that first-line treatment was changed in about one-third of cases.4 A study in north-west England found diagnostic disagreement between a Lancashire hospital and a regional centre in nearly one-quarter of cases, 26% of which were major.6,7 An audit in north-east England found a diagnostic discrepancy of 26%.8 Review of suspected Hodgkin’s lymphoma by the Scottish and Newcastle Lymphoma Group led to altera-tion of the histological subtyping in 28% of cases, resulting in a change in management for 10% of patients.4

These errors have significant potential medico-legal cost, while it is estimated that an integrated service could provide precise diagnosis for a unit cost of £150–200, a fraction of the


cost of treatment, particularly in the era of tailored treatment and immunotherapy.4

This has been addressed by the UK National Institute for Health and Clinical Excellence (NICE), who published ‘Improv-ing Outcomes Guidance (IOG) for Haemato-oncology cancer’ in 2003, which stated the information shown below.

“In order to reduce errors, every diagnosis of possible haematologi-cal malignancy should be reviewed by specialists in diagnosis of haematological malignancy. Results of tests should be integrated and interpreted by experts who work with local haemato-oncology multi-disciplinary teams (MDTs) and provide a specialised service at network level. This is most easily achieved by locating all special-ist haemato-pathology diagnostic services in a single laboratory.”

The guidance went on to state that:

“Improving the consistency and accuracy of diagnosis is prob-ably the single most important aspect of improving outcomes in haematological cancer.”



It is therefore imperative that such clinical services are estab-lished and that one is available for each cancer network nation-ally. The service provided by the Haematological Malignancy Diagnostic Service in Leeds, UK, acts as a model for such an inte-grated service, but few other such services exist.3,9 The imple-mentation of the IOG is expected to increase the number of such integrated services, but must also take account of existing local arrangements when doing so. Such pre-existing arrangements have resulted in organizational barriers to wide adoption of the guidance, but these are gradually being overcome as pressure to institute the guidance grows.

Robust information technology support is required for such a service, configured upon rapid integration of a complex array of tests. Such a system should also underlie the laboratory work-flows required to deliver results rapidly. Other considerations for such a service are the constantly expanding range of diagnostic tests and platforms; services must have the capacity to grow if new developments arise, in terms of expanding and updating repertoire, and introducing new test platforms. This has implica-tions for the skillmix in the service, which must be technically well-trained and adaptive; flexibility to adopt new tests, new methods of diagnosis and an increasing input from clinical scien-tists to the diagnostic process must also be required.

Molecular characteristics of lymphoma

Diagnosis of lymphoma is usually made using morphological and immunohistochemical characteristics, in the appropriate clini-cal context, to assign lineage and subtype. The advent of more sophisticated types of flow cytometry has heralded the possibility of diagnosing some types of lymphoma this way, but this is not yet routine. Difficulty in interpretation remains in many cases, whereas in others specific translocations indicate particular sub-types, which benefit from specific treatment; analysis of clonal rearrangement and chromosomal translocation are used in these cases.

Clonality studiesLymphomas arise due to clonal expansion of neoplastic cells and, in contrast to reactive lymphoid infiltrate, show clonal rearrange-ment of IG or T-cell receptor (TCR) genes that are detectable by PCR.10 The IG heavy genes and TCR genes contain many different variable and joining segments, together with diversity segments. These are rearranged such that one gene segment from each of the loci is represented in the gene product, which is then transcribed into the peptide components of the IG and TCR complexes. There are 38–200 variable regions, 23 diversity regions and 6 joining regions. Rearrangement of these regions to give a functional pep-tide enables a combinational repertoire estimated at: • ×106 for IG molecules • ×106 for TCRα and β molecules • ×103 for TCRγδ molecules.The IG κ light-chain region also undergoes rearrangement, though does not contain diversity regions. If this is non-productive, then the κ region is deleted and the λ region is rearranged instead. B-cells further extend the variability by maturation in the germinal centres after antigenic stimulation, resulting in somatic hyper-mutation, whereby single nucleotide mutations or deletions/insertions occur in the exonic regions of IGH and IG κ and λ


genes. The TCR genes undergo VDJ or VJ rearrangements in the sequential order TCRδ, TCRγ, TCRα and TCRβ. Most T-cells are of α/β type, but a few are γ/δ type. Many different rearrange-ments are present in reactive lymphoid proliferations. In a lym-phoma, a single, or dominant, clonal rearrangement, whereby the same VDJ loci are represented in each cell, is present. This is most easily detected by PCR.10,11

PCR is carried out using multiple primer sets to target different gene segments that may potentially be rearranged. Primers for the antigen receptor genes are designed to the variable and joining regions and are termed ‘concensus primers’ because they are not a perfect match for one gene region. Different laboratories have developed their own primer sets, but the BIOMED-2 programme undertook a pan-European multicentre evaluation of primers that resulted in publication of a validated set in 2003.10 This set was tested and its utility confirmed in a set of publications in 2007.11–15 PCR often results in a ‘smear’ or ‘ladder’ with gel-based detection systems because it amplifies clonal and background polyclonal cells. Fluorescent primers result in a product that can be sepa-rated by size using capillary electrophoresis, enabling clearer separation of PCR products and more confident discrimination of clonal populations from polyclonal background cells.

The BIOMED-2 programme resulted in validation of a series of multiplex PCR tests for detection of rearrangements in IGH, IGK, IGL, TCRβ, TCRγ and TCRδ. Each of these is represented by mul-tiplex PCR tests split across various different multiplex reactions (termed ‘tubes’),11–15 specifically, for: • complete IGH (3 tubes) • incomplete IGH (2 tubes) • IGK (2 tubes) • IGL (1 tube) • complete TCRβ (2 tubes) • incomplete TCRβ (1 tube) • TCRγ (2 tubes) • TCRδ (1 tube).Between 2 and 32 primers are present in each tube. Because of the many primers, greater specificity is obtained by subjecting initial PCR products to heteroduplex analysis or to GeneScan fragment analysis; polyclonal populations resulting in polyclonal smears or Gaussian curves, respectively, whereas clonal popula-tions yield clear bands or peaks. To maximize the results from the resultant multiple PCRs, the BIOMED-2 programme devel-oped a testing algorithm for the different tubes (Figure 2).

These PCR techniques target genomic DNA and can therefore be done on paraffin-embedded material (though this is associ-ated with more false-negatives than fresh or frozen tissue, usu-ally quoted at ∼20%). False-negative results may be due to DNA degredation, lineage infidelity, and a low level of the clonal population in an overwhelming reactive background. PCR can detect disease at a level of 1% of the total cells present. More sensitive tests are required for detection of minimal residual dis-ease; patient-specific primers can detect down to 1 tumour cell in 105 normal cells (though this is not routinely done in diagnostic practice).

Molecular abnormalities in specific B-cell lymphomas

In addition to IG and TCR clonality analysis using PCR, lympho-mas also carry a range of chromosomal translocations that can



Suspected lymphoid proliferation

of unknown origin

Clonality

No clonalityNo clonality

No clonalityNo clonality

Suspected T-cell proliferationSuspected B-cell proliferation

TCRB V-J

TCRB D-J

TCRG

IGH VH-JH

IGK V-J

IGK Kde

Clonality

Probably polyclonal

TCRG

TCRD

IGH DH-JH

with IGL

Figure 2 algorithm for BioMed-2 primers in the investigation of suspected B- or t-lymphomatous lesions, starting with most informative primer sets

and moving through additional sets if clonality remains uncertain.

be detected by conventional cytogenetics or by FISH (Table 1). The former can detect all translocations and deletions; FISH can detect a narrower range of translocations, but is applicable to FFPET.

Follicular lymphomaThe t(14;18) translocation is present in 70–80% of follicular lymphoma, and in 17–38% of diffuse large B-cell lymphoma (DLBCL). It juxtaposes the IGH gene on Chr14 with the BCL2

Common translocations in lymphoma (percentage incidence in each type of lymphoma)

Lymphoma type Chr translocation Frequency Gene involved

Malt lymphoma t(11;18) 0–40% api2-Malt1

t(1;14) 5% ig/Bcl10

t(14;18) 5% ig/Malt1

Follicular

lymphoma

t(14;18) 90% ig/Bcl2

Mantle cell

lymphoma

t(11;14) 95% ig/cyclind1

Burkitt’s

lymphoma

t(18;14) 100% ig/c-myc

dlBcl t(14;18) 20% ig/Bcl2

t(8;14) 10% ig/c-myc

t(3;14) 5–10% ig/Bcl6

alcl t(2;5) 70–80% npM-alK

t(1;2) 10–20% tpM3-alK

Table 1


gene on Chr18, resulting in upregulation of BCL2, inhibition of apoptosis, and increased proliferation of follicle centre cell B-cells.16 Five common breakpoints are present, and different PCR primers are required to detect each one17; the BIOMED-2 pro-gramme validated 9 primer sets in 3 multiplex tubes.11–15 The t(14;18) translocation has also been detected by PCR in normal peripheral blood and reactive lymph nodes,18–20 suggesting that it can occur in few cells without development of lymphoma. This highlights the need for integration of molecular data with other analyses (particularly morphology and the clinical context) before assigning a diagnosis of lymphoma.

Mantle cell lymphoma (MCL)MCL is characterized by the t(11;14) translocation involving the heavy chain gene on Chr14 and the BCL1/PRAD1 gene on Chr11.21 BCL1 encodes for cyclinD1, a cell cycle protein that results in proliferation. MCL is a relatively aggressive tumour, the immunophenotype and morphology of which is otherwise similar to chronic lymphocytic leukaemia (CLL), an indolent lymphoma; distinction between them is critical.1 Usually this is made by immunohistochemistry for cyclin D1, but t(11;14) trans-locations should be confirmed using PCR or FISH.

Marginal zone lymphoma (MZL)About 25% of marginal zone lymphomas (MZL) harbour trans-locations involving the MALT1 gene on Chr11, the commonest of which is t(11;14), present in 13.5% of lung and gastrointes-tinal tumours,22 and which involves AP12 (apoptosis inhibitor) on Chr11; it is associated with a lack of response to Helicobacter pylori therapy in gastric MALT. The t(14;18) translocation involves the IGH and MALT1 genes, and occurs in 11% of cases, particularly ocular, salivary gland and cutaneous tumours.

MALT1 translocations are present in only extra-nodal MZL (not in splenic MZL or primary nodal MZL), and evolution to a higher grade is associated with loss of the translocation. Other translo-cations present in MALT are 4(3;14) involving IGH and FOXp1, seen particularly in ocular, skin and thyroid MALTs23; rare cases



have t(1;14) involving BCL10 and IGH.24 FISH is the most efficient method for detecting these translocations.

Small lymphocytic lymphoma (SLL)/chronic lymphocytic leukaemia (CLL)SLL and CLL are usually easily diagnosed on clinical, morpholog-ical and immunophenotypic data, and all cases show rearrange-ments of immunoglobulin heavy genes. Characteristic cytogenetic abnormalities have been identified in CLL, and include deletions of 13q14, 11q, 17p and 6q, together with trisomy 12; 13q dele-tions confer a good prognosis and 17p the worst.25,26 CLL is gen-erally indolent, but lack of hypermutation in the IGH gene is associated with risk of progression and more aggressive disease. It can be detected by PCR but the technique is laborious; surro-gate markers (e.g. ZAP70) are usually preferentially used.27,28

Lymphoplasmacytic lymphoma (LPL)Up to 50% of LPL have the t(9;14) translocation29 also present in some MZL and DLBCL. It involves the PAX5 gene on chromo-some 9, and the IGH gene on Chr14. PAX5 regulates proliferation and differentiation of B-cells. It is not routinely tested for, even though FISH probes have been developed for it.

DLBCLThe t(14;18) typically present in follicular lymphoma is pres-ent in 17–38% of DLBCL, and has been suggested to be a poor prognostic indicator in DLBCL.30 Up to one-third of DLBCL have abnormalities involving the BCL6 gene on Chr3.31 BCL6 abnor-malities are also found in high-grade follicular lymphoma with associated DLBCL, indicating a link with progression.32 BCL6 is usually associated with translocation of the IHγ gene, the IGκ or IGλ genes, and is also more frequent in extranodal dis-ease. The significance of BCL6 rearrangements is controversial, though many consider it to confer a poor prognosis.33 This field has recently been overshadowed by development of prognostic models on the basis of genome-wide gene expression profiling in DLBCL (see below).

Burkitt’s lymphomaBurkitt’s lymphoma is associated with translocation of the c-myc gene on Chr8, usually with the IGH gene, though the IGκ or IGλ genes are also partners.34 The breakpoints show variability, and detection is best made by FISH using break-apart probes. It is important to consider the possibility and test for the translocation because Burkitt’s lymphoma requires aggressive treatment differ-ent from that of DLBCL, particularly in cases of high-grade B-cell lymphoma in which there is a high proliferation index.

T-cell lymphomas

The commonest translocation seen in T-cell lymphomas is ALK on chr2 in anaplastic large cell lymphoma.35 It is usually part-nered with NPM on Chr5, but other partners include TPM3 on Chr1, TFG on Chr3, ATIC on Chr2 and CTLC on chr17, all of which result in overexpression of the ALK protein, a tyrosine kinase. ALK overexpression can be detected by immunohisto-chemistry and confirmed using FISH. Its presence correlates posi-tively with survival, though it is negative in primary cutaneous ALCL, which has a better prognosis than nodal ALCL.1


Molecular characteristics of leukaemia

CMLCML is characterized by the Philadelphia chromosome, t(9;22)/BCR–ABL, which is essential for diagnosis.1 The translocation t(9;22)/BCR–ABL results in fusion of BCR on Chr 22 and ABL on Chr9, resulting in unregulated activity of tyrosine kinase, and downstream activation of other pathways, including RAS, JUN and PI3, leading to increased cell proliferation and reduced apoptosis. Three different-sized variants of the BCR–ABL fusion product have been identified: p190, p210 and p230. The p210 variant is most often associated with CML, p190 with ALL, and p230 with neutro-philic CML. Imatinib mesylate blocks tyrosine kinase activity and has enabled haematological remission to be achieved in CML.36

Cytogenetic analysis can confirm t(9;22) and identify other prognostic factors, after which molecular methods are used to define the specific breakpoints present, enabling response to treatment to be monitored by qRT-PCR.37 Response is usually measured in terms of log-reduction, though this can result in problems of data correlation between different institutions if patients move. A reduction in BCR–ABL mRNA transcripts of ≤3 logs in 12 months predicts 100% progression-free survival though, overall, patients fall into one of three groups:initial response with eventual relapseinitial response followed by a decline (though not to the extent of relapse)initial response followed by a plateau.38,39

Resistance to imatinib mesylate can develop and is due to amplification and overexpression of BCR–ABL, development of BCR–ABL point mutations, or clonal evolution of a leukaemic population. Between 50% and 90% of patients developing resis-tance have a mutation in the kinase domain.

Other chronic myeloproliferative disorders (CMPDs)Other CMPDs include: • chronic neutrophilic leukaemia • chronic eosinophilic leukaemia • polycythemia rubra vera • chronic idiopathic myelofibrosis • essential thrombocythemia.Abnormalities in expression of tyrosine kinase have been linked with each of these, of which the most important is the JAK2 mutation in polycythemia rubra vera (though the same muta-tion is also present in a minority of cases of myelofibrosis and thrombocythemia).40 Testing for this has resulted in a lot of work for molecular laboratories; this test is an example of the extra work a new test can generate, particularly if the result is poorly understood. Of the other CMPDs, t(5;12) (q33;p13) involving the PDGFβ receptor and TEL is present in some cases of chronic eosinophilic leukaemia.41

Mixed myelodysplastic syndromes/myeloproliferative diseasesThis category includes: • chronic myelomonocytic leukaemia (CMML) • juvenile myelomonocytic leukaemia • atypical chronic myeloid leukaemia • myelodysplastic syndrome/myeloproliferative disorder un-

classifiable (diagnosis is made on morphological and clinical criteria).



Karyotypic abnormalities have been identified in these disorders, though are not used for diagnosis. In common with other CMPDs, BCR–ABL testing is done to exclude unusual cases of CML. The translocation t(15;12), involving PDGFβ and TEL is present in CMML with eosinophilia.42

Myelodysplastic syndromesMolecular studies are, with the exception of FISH to follow mono-somies or trisomies, of limited use in the diagnosis of myelo-dysplastic syndrome. A notable exception to this is 5q-minus syndrome, characterized by loss of part of the long arm (Q arm) of human chromosome 5.43 This syndrome affects myeloid cells, causing treatment-resistant anaemia, and myelodysplastic syn-dromes that may lead to AML.

AMLAcute promyelocytic leukaemia (APML) results from trans-location of the RARα gene on chromosome 17, in most cases with the PML gene on chromosome 15.44 RAR is a nuclear hor-mone receptor that represses transcription when complexed with retinoic-X receptors. Upon PML-RAR fusion, physiological levels of retinoic acid are insufficient and the effects of RAR on gene transcription are blocked. Treatment with all-trans retinoic acid (ATRA) restores control with consequent good prognosis for this type of acute leukaemia. The t(15;17) translocation is detected by karyotype analysis, and molecular detection is via RT-PCR or FISH (RT-PCR is the most sensitive); three fusion isoforms exist, and RT-PCR assays must be designed to identify them.45 Other translocations include fusion with PLZF at 11q23, NUMA at 11q13, NPM at 5q35 and STAT5b at 17q11, but these account for <1% of patients with APML overall. The RAR translocation must be identified because patients lacking it, or translocations involving PLZF or STAT5b, do not respond to ATRA and require different treatment.

Core binding factor leukaemias: core binding factor is a protein complex that includes elements encoded by AML1 and CBFb, translocations of which are present in many cases of acute leu-kaemia. The t(8;21) translocation results in an AML-ETO fusion, causing loss of transcriptional activation.46 This is present in 12% of acute leukaemias, and represents a distinct subtype in the WHO classification. Inv(16) is associated with eosinophilia and results in fusion of CBFb and MYH11 on chromosome 11, with suppression of AML1 activity. Most core binding factor translocations can be detected by karyotyping, but RT-PCR and FISH are more useful for detection of inv(16) and t(12;21) TEL-AML1; qRT-PCR is useful for monitoring treatment response and detection of minimal residual disease.

AML with 1q23/MLL mutations: AML with 11q23 abnormalities is a specific subtype in the WHO classification.1 MLL is altered in 5% of AML overall and is associated with a poor prognosis, but this category is heterogeneous with ≤40 fusion partners, while further non-balanced abnormalities can also occur (though the commonest partners are chromosomes 6q27 (AF6), 9p22 (AF9), 19p13.3 (ENL), 19p13.1 (ELL), 19p13.3 (EEN), 16p13 (CBP) and 22q13)47 Because of the high number of fusion partners, detec-tion is via FISH, though this will not detect internal tandem repeat duplications, which occur in a subset of cases.


Other genetic changes in AML: FLT3 is important for stem cell survival and myeloid differentiation and is mutated in 20–30% of adult AML.48 It can occur in any type of AML, but is more frequent in APML. FLT3 encodes for a tyrosine kinase that regu-lates proliferation and differentiation, and is mutated by ITD or point mutation, both of which can be detected by PCR49; ITD can also be detected by gel electrophoresis and the D835 mutation by restriction enzyme digestion. FLT3 is an important prognostic marker, and clinical trials of FLT3-specific tyrosine kinase inhibi-tors are under way.

Acute lymphoblastic leukaemia (ALL) and lymphomaALL is of B- or T-precursor lineage and is termed a leukaemia if the bone marrow shows >25% involvement at diagnosis and a lymphoma if presenting primarily nodally. They are associ-ated with various specific molecular abnormalities of prognostic significance.

ALL with t(12;21)/TEL-RUNX1: the t(12;21) translocation is common in paediatric ALL and uncommon in adults.50–52 Such cases show aberrant expression of CD13, lack CD9 and CD20, and are associated with a good prognosis. The translocation results in fusion of TEL with RUNX1, causing redirection of the repressor activity of TEL to AML1.

ALL with t(4:11): MLL abnormalities, noted above for AML, also occur in ALL, typically in infants but also in ≤6% of adults.53 They carry a poor prognosis and are associated with a primi-tive pro-B-cell immunophenotype without CD10, and aberrant myeloid expression of CD15.

ALL with E2A abnormalities: the E2A gene on chromosome 19 is most frequently involved in t(1;19), resulting in fusion with the PBX1 gene, a homeodomain-containing HOX cofactor, with generation of a chimeric transcription factor. E2A occasionally fuses with HLF on chromosome 17, with resultant anti-apoptotic activity. It is common in paediatric ALL, but can occur at any age.54,55 Detection is by FISH or RT-PCR, and this subtype has a poor prognosis.

ALL with t(9;22) BCR–ABL: the Philadelphia chromosome occurs in 20–25% of adult ALL, which are of precursor B-cell lineage, often with aberrant expression of the myeloid antigens CD13 and CD33. The p190 variant fusion protein is commonest in ALL, though some have the p210 variant, both of which confer a poor prognosis.56

Precursor T lymphoblastic leukaemia/lymphoma: though pre-cursor T lymphoblastic neoplasms are less common, they consti-tute 85–90% of those presenting as lymphomas. Improved survival is associated with the t(10;14) translocation involving HOX11, expression of which confers a good prognosis.57 Other abnormali-ties include t(1;14), involving TAL1, t(5;14) involving HOX11L2.58

Recent advances in the molecular pathology of haematological malignancy

In the past, identification of genetic abnormalities involved pains-taking cytogenetic or molecular biological methods that resulted



in a slow accretion of molecular characteristics, usually one at a time. The introduction of gene expression microarrays (spot-ted or printed) in the 1990s revolutionized molecular pathology because large numbers of genes to be assayed simultaneously. It is now possible to assay all genes on a single chip.59

The first application of this new method to diagnosis was by Golub et al, who distinguished between AML and ALL as proof of concept.60 Others have used this approach to identify new sub-groups of tumours (specifically in DLBCL), new prognostic and predictive markers (in AML) and new mechanisms of disease progression (in follicular lymphoma). Microarray RNA profil-ing has identified microRNA markers in several haematological malignancies, including CLL.61

Microarray profiling studies in DLBCLDLBCL has a wide range in outcome, and current prognostic markers are insufficient to inform long-term survival. Many research groups have carried out gene expression profiling to identify prognostic markers.

Two studies from the same group, Alizadeh et al62 and Rosen-wald et al63 using unsupervised hierachical clustering identified two main prognostic groups in DLBCL, characteristic of normal germinal centre B-cells (GBC) or of activated B-cells (ABC). The GBC subgroup correlated with significantly better prognosis com-pared to the ABC group. This was independent of the interna-tional prognostic index, but did not show correlation with the morphological subgroups of DLBCL. Shipp et al64 used a super-vised clustering approach and identified a 13-gene prognostic model predictive of survival at 5 years. Some of the genes, nota-bly PKCβ, were also correlated with survival in the lymphochip study of Alizadeh et al62 and have subsequently informed clinical trials of novel treatments based on the expression of this gene.65

The segregation of DLBCL into GBC or ABC subtypes was ini-tially undertaken using a lymphochip cDNA microarray platform, but has been validated using an Affymetrix oligonucleotide array platform, indicating the importance of validation across multiple test platforms. Distinction between the two subtypes can be made by immunohistochemistry using a limited immunohistochemistry panel of Bcl-6, CD10 and MUM-1 (Figure 3).66 This is attractive as a diagnostic algorithm, but the usefulness of this approach to identify GBC and ABC subgroups is controversial; many studies confirmed the utility of bcl-6, MUM-1 and CD10 for the distinction, but others did not, some proposing an alternative algorithm using bcl-2 instead of bcl-6.67 This follows the finding of restriction of bcl-2 rearrangement and t(14;18) to the GBC subgroup, the ABC subgroup showing activation of the NFKB pathway. Other stud-ies have shown loss of prognostic difference between GBC and ABC subgroups in patients treated with rituximab.68 This led Polo et al to undertake gene expression profiling in untreated cases of DLBCL followed by unsupervised clustering to identify subgroups characterized by activation of different biological pathways, inde-pendent of outcome, to identify novel therapeutic targets. This approach identified a cluster characterized by BCR activation and proliferation in which there was upregulation of BCL-6, which may be amenable to treatment with BPI, a BCL-6 inhibitory peptide.69

Microarray profiling studies in follicular lymphomaThe t(14;18) translocation is found in ∼90% of cases of follicular lymphoma, and is the initiating genetic event causing constitutive


expression of BCL-2. The secondary alterations leading to full development of the lymphoma are poorly defined and sev-eral gene expression studies have sought to identify the genes responsible for the wide variation in natural history and outcome observed in follicular lymphoma.70

Husson et al compared purified follicular lymphoma cells from six patients with recurrent disease with normal GC B-cells using a cDNA array. They found increased expression of SMAS1, the MAP kinase MAP3k11, the cell cycle regulator CDKN1A, and heat shock proteins HSPB1 and HSPF1.71 Other groups compared expression profiles of low-grade follicular lymphoma (grades 1–2) with those of high grade (grade 3) and those that have trans-formed to diffuse large B-cell lymphoma. Glas identified a set of 81 genes discriminatory between low- and high-grade follicu-lar lymphoma, expression of which was used to assess clinical behaviour in a further set of follicular lymphoma.72 Aggressive disease was associated with upregulation of cell cycle genes and genes associated with metabolism genes and DNA synthesis; indolent disease showed upregulation of the T-cell gene CD3D and the stromal cell gene CXCL12. This agrees with the findings of Dave et al who showed the importance of the host immune response in determining outcome in follicular lymphoma.73 Global gene expression profiling of whole follicular lymphoma samples from 191 patients identified prognostic signatures i.e. immune response-1 and immune response-2 signatures, which associate with favourable and unfavourable prognosis, respec-tively. The immune response-1 signature was characterized by T-cell-associated genes; the immune response-2 signature con-tained genes expressed by macrophages and dendritic cells. This suggested that the prognostic signatures were reflective of non-tumoural T-cells and macrophages, a supposition confirmed by cell sorting that showed they resided in the CD19-negative fraction. This study was the first to relate prognostic signature to host immune response to the tumour rather than to tumour cells as the major determinant of outcome. A follow-up study using immunohistochemistry to measure T-cell and macrophage infiltration in follicular lymphoma showed a poor prognosis in cases with high numbers of macrophages, but failed to show an association of T-cell infiltration with good prognosis,74 though this has been shown independently of a macrophage response in other studies.75 These studies showed no correlation with pan-T-cell markers and survival, indicating that a subset of T-cells is responsible for the effect; FOX-P3-positive T-cells and CD7- positive cells have been implicated in some studies.76,77

Microarray profiling studies in AMLThe new classification of myeloid neoplasms according to the WHO incorporates immunological and cytogenetic findings of prognostic significance.78 Since the late 1990s, cytogenetic abnor-malities have been primarily used to stratify treatment intensity by defining three major groups: good, intermediate and poor risk of disease.79 Only a few recently identified markers allow fur-ther definition of relapse risk. Such markers include the fms-like tyrosine kinase 3 (FLT3) gene mutation and the mixed-lineage leukaemia (MLL) gene, which provide potential targets for ther-apy.80,81 The WHO classification allows subsequent addition of such markers, but it does not fully reflect the molecular heteroge-neity of the disease, particularly for intermediate-risk AML. New molecular markers, alone or in combination, that can separate



CD10 MUM-1

Bcl-6 GCB

MUM-1 +/– Non-GCB

GCB Non-GCB

Bcl-2 Group 1

CD10 Group 1

MUM-1 Group 2+

–

–

+

–

+

–

+

–

+

+

–Group 1

Figure 3 a immunohistochemical algorithms for subdividing dlBcl into germinal centre (gc) or non-gc subtypes. b alternative algorithm for

subdivision into favourable (group 1) and unfavourable (group 2) subtypes (adapted from 67).

AML types further, allowing optimization of treatment, must be identified.

Golub et al was the first to apply microarrays to patient sam-ples for class prediction and discovery of cancer, proving a very important principle for this technique.60 RNA extracted from bone marrow samples from patients with AML and ALL was hybrid-ized to gene chips containing oligonucleotides from 6817 human genes (Affymetrix). Fifty genes identified to be uniformly over-expressed in AML but not in ALL and vice versa were selected and retested in an independent set of samples. This ‘weighted voting’ model correctly discriminated between AML and ALL samples (‘prediction’). To identify new classes of cancer (‘dis-covery’), patterns of gene expression were clustered according to similarity by ‘self-organizing maps’ (SOMs) and successfully distinguished between AML and ALL, as well as T-cell and B-cell ALL. In the same study, HOXA9 was identified to be the single gene most highly correlating to poor-prognosis AML. Its leukae-mogenic role in murine and human AML when overexpressed has subsequently been shown by other groups.82–85

Many studies investigating the applications of microarrays in cancer have been done since then. Most have focused on the potential for medical applications by identifying novel genes, linking them to therapy responses and adverse events.86 In AML, three large-scale studies have been published, all of which show-ing the correct recognition of known, pre-existing groups of AML with gene expression profiling.87–89

Bullinger et al used cDNA microarrays to determine gene expression in 116 adults with AML, subdividing them into a supervised group (in whom gene-expression and outcomes were linked) and an unsupervised group (were the system was tested). They identified clustering of samples with recurrent cytogenetic abnormalities such as t(15;17), t(8;21) and inv(16). New molecular subtypes of AML, including two prognostically


relevant subgroups in patients with normal karyotype, were identified.87 One of these subgroups showed a high expression of the methyltransferases DNMT3A and DNMT3B, suggesting their potential role in leukaemic pathogenesis. This is consistent with previous studies that showed aberrant hypermethylation of tumour suppressor genes play an important part in the devel-opment of many tumours, including haematological malignan-cies.90,91 Gene expression levels for DNA methyltransferases DNMT1 as well as the de novo methyltransferases DNMT3A and DNMT3B were shown to be increased. It is also thought that DNA methylation plays an integral part within the p53 network. Esteve et al showed the interaction between DNMT1 and p53, and showed overall methylation of the surviving promotor region contributed to gene repression.92

In a similar study, Valk et al examined 285 cases of AML using an Affymetrix GeneChip. Unsupervized cluster analysis correctly aggregated samples containing the cytogenetic abnormalities mentioned above. Abnormalities such as 11q23 and CEBPA, as well as novel gene clusters, were identified.88 Similar results were obtained from paediatric AML samples by Ross et al. In addition to the translocations t(15;17) and t(8;21), the MLL gene rearrangement and AML M7 displayed clustering.89

Translation of new methods to clinical practise

Microarray gene expression profiling has identified gene signa-tures, or ‘indicator’ genes, predictive of outcome in many cancer types, including DLBCL and follicular lymphoma.63,64,71 These have identified novel powerful diagnostic, prognostic and generi-cally applicable markers, promising more specific diagnosis and treatment, together with improved understanding of patho-biology. There is an urgent need to translate these signatures to clinical use.



Gene microarrays rely on relatively large amounts of fresh starting tissue, obviating measurement of indicator genes in rou-tine practice. Development of another, simple, robust, relatively inexpensive and sensitive method for their translation to clinical use is needed. Nearly all clinical material is routinely fixed in formalin and processed in paraffin, and this practice is likely to continue for the foreseeable future, particularly outside large cen-tres. Effective screening has resulted in a diametric shift in stage at diagnosis, with smaller tumours diagnosed on formalin-fixed, paraffin-embedded core biopsies. Initial treatment decisions are usually based on such material. There is a need to develop a method to profile gene signatures in routinely processed tissue and to localize gene expression to particular cell populations. It would be ideal if multiple gene targets could be seen simultane-ously in situ, preferably in paraffin-embedded archival material.

We have piloted two methods that may enable this: • real-time PCR measurement of specific prognostic genes (indi-

cator genes) in globally amplified polyA cDNA • multiplexed in situ hybridization for prognostic genes, using

quantum dot-based in situ hybridization.


PolyA PCR co-ordinately amplifies cDNA copies of all polyad-enylated mRNAs, thereby generating a PCR product (polyA cDNA) whose composition reflects the relative abundance of all expressed genes in the starting sample.93–95 PolyA PCR enables global mRNA amplification from picogram amounts of RNA and has been routinely used to analyse expression in small samples, including single cells. The polyA cDNA pool generated is also indefinitely renewable, and represents a ‘molecular block’.96,97 Real-time PCR measurement using gene-specific primers and probes of the expression levels of specific indicator genes allows gene signatures to be detected within the polyA cDNA, thereby enabling expression profiling of very small amounts of starting material (Figure 4).97,98 Microarray studies use tissue home-genates with corresponding loss of spatial, architectural and cellular information. Recent studies, particularly those detailed for follicular lymphoma shown above, have demonstrated that prognostic and biologically significant gene signatures may relate to the tumour cells but also to adjacent cells (e.g. immune cells, stromal cells) and definition of the spatial relationship of gene expression is likely to be important in understanding their role

PolyA global cDNA amplification qRT-PCR measurement of Indicator genes

Retrospective analysis

Archivedlymph nodes

Lymph nodes,aspirates &Peripheralblood

Lysis & RNAextraction

Prospective analysis

PolyART-PCR

cDNA archive –stored @ -70˚C

Globally amplified cDNA “the molecular block”

Gene expression analysis by specificreal-time PCR for 36 indicator genes

Analysis for subgroup discrimination and correlation withoutcomes (esp. for retrospective cases) and otherprognostic features

Analytical –expression profiling for specific indicatorgenes within globally amplified cDNA

Pre-analytical – Preparation of globally amplified cDNA

Figure 4 polya pcr creates a ‘molecular block’ (pre-analytical phase) that can be subsequently probed by real-time pcr for expression levels of

signature genes of interest (analytical phase).

© 2008 elsevier ltd. all rights reserved.


Signaturesprobes

Q-dot labelledoligonucleotide probe

Biopsy

Paraffin embeddedtissue

In-situhybridisation

Spectral Imaging

Analysis

Figure 5 overview of multiplex in situ gene expression. gene signatures are used to choose diagnostic or prognostic genes from which in situ

probes or antibodies are sourced. these are labelled with quantum dots of different fluorescence and used to probe tissue sections. imaging

resolves the different fluorescent signals from which the distributions, colocalizations and expression levels of the different genes are analysed.

in cancer subtype classification, treatment response and aetiol-ogy. The goal is the development of a widely applicable enabling method for in situ measurement of microarray-identified gene signatures in paraffin sections. This is required for widespread development and use of molecular surgical pathology, outside large research centres and, hopefully, eventually outside the ‘developed’ world. It will also allow pathology to move from the expert to an objective generically useable level. The authors have combined quantum dot labelling and spectral imaging to produce a novel technique for multiplex in situ hybridization that can be used to apply gene expression signatures to formalin-fixed, paraf-fin-embedded tissue biopsies at diagnosis (Figure 5).99,100 ◆

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Practice points

• diagnosis of haematological malignancy relies on integration

of several diagnostic modalities.

• Molecular diagnostic and prognostic features are becoming

increasingly important in acute and chronic myeloid leukaemia.

• JaK2 is becoming increasingly important for the diagnosis of

myeloproliferative disorders.

• Microarray studies have identified several prognostic gene

signatures for lymphomas and acute myeloid leukaemia,

though few of these have been introduced into clinical practice.

• immunoglobulin and t-cell receptor gene rearrangement

analysis is useful for determination of clonality in lymphoma

and is best determined using the BioMed-2 protocols.

• Microarray studies have identified several new biological

paradigms in lymphoma, including favourable germinal centre

and unfavourable non-germinal centre phenotypes in diffuse

large B-cell lymphoma and favourable t-cell and unfavourable

macrophage infiltration in follicular lymphoma.

• this field is expanding rapidly and will require greater

involvement of histopathologists with molecular diagnostic

tests, in an integrated manner, validation of new prognostic

and diagnostic biomarkers and development of new

technologies for their clinical measurement.

© 2008 elsevier ltd. all rights reserved.

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Molecular diagnostics in haematopathology