Molecular diagnostics in haematopathology

Mini-symposium: diagnostic molecular pathology

Molecular diagnostics in haematopathology

and sought to relate each to a normal lymphoid ­counterpart cell or compartment.1 It was a major advance on previous classifications, but it contains many groups (e.g. follicular lymphoma, diffuse large B-cell lymphoma) which have a wide range of outcomes.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 conventional views as to the pathobiology of lymphoma in general.

Richard Byers Eleni Tholouli

Abstract

Integrated diagnostics in haematological malignancy

Haematological malignancies comprise a complex range of disorder with widely differing outcomes and response to treatments. Precise diagnosis and prognostication is vital in directing optimal treatment and this is ­ relies increasingly on identification of molecular features, specifically 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 rearrangements 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 malignancy, the molecular ­features of haematological malignancies and recent ­advances from microarray studies.

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 quantitative reverse transcriptase PCR (qRT-PCR) or PCR for translocations 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 individual 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, followup 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 nodules confirms diagnosis in almost all cases (though some BCL2­negative cases require demonstration of immunoglobulin (IG) gene rearrangement for diagnosis). Morphology is usually sufficient for diagnosis of reactive lymphadenopathy, but a restricted immunohistochemistry 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 measurement 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 treating the patient rather than by laboratory staff; this has potential 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 lymphoma in Wales had a benign lymphoma, a situation likely to be the same in England. Consequently, 400 people each year may

Keywords haematological malignancy; microarray; molecular diagnosis; translocations

Haematological malignancies comprise a complex range of disorders with widely differing outcomes and response to treatments, features underscored by the 2001 WHO classification of haematological malignancies.1 Precise diagnosis is vital in directing optimal treatment, and this is becoming increasingly important as new treatments are developed. Prognostic markers are becoming more important as tailored treatment becomes possible. Haematological 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.

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Mini-symposium: diagnostic molecular pathology

Levels of diagnosis of haematological malignancy Morphology Clinical features PCR & real-time PCR

DIAGNOSIS

Cytogenetics FISH Flow cytometry Microarrays (future platforms) 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 discordance 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 alteration 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

DIAGNOSTIC HISTOPATHOLOGY 14:5

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 ‘Improving Outcomes Guidance (IOG) for Haemato-oncology cancer’ in 2003, which stated the information shown below. “In order to reduce errors, every diagnosis of possible haematological 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 specialist haemato-pathology diagnostic services in a single laboratory.” The guidance went on to state that: “Improving the consistency and accuracy of diagnosis is probably the single most important aspect of improving outcomes in haematological cancer.” 224

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It is therefore imperative that such clinical services are established and that one is available for each cancer network nationally. The service provided by the Haematological Malignancy Diagnostic Service in Leeds, UK, acts as a model for such an integrated service, but few other such services exist.3,9 The implementation 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 workflows 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 implications 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 scientists to the diagnostic process must also be required.

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 rearrangements are present in reactive lymphoid proliferations. In a lymphoma, 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 separated 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 multiplex 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 populations yield clear bands or peaks. To maximize the results from the resultant multiple PCRs, the BIOMED-2 programme developed 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 associated with more false-negatives than fresh or frozen tissue, usually 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 disease; patient-specific primers can detect down to 1 tumour cell in 105 normal cells (though this is not routinely done in diagnostic practice).

Molecular characteristics of lymphoma Diagnosis of lymphoma is usually made using morphological and immunohistochemical characteristics, in the appropriate clinical 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 subtypes, which benefit from specific treatment; analysis of clonal rearrangement and chromosomal translocation are used in these cases. Clonality studies Lymphomas arise due to clonal expansion of neoplastic cells and, in contrast to reactive lymphoid infiltrate, show clonal rearrangement 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 peptide 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. Bcells further extend the variability by maturation in the germinal centres after antigenic stimulation, resulting in somatic hypermutation, whereby single nucleotide mutations or deletions/­ insertions occur in the exonic regions of IGH and IG κ and λ DIAGNOSTIC HISTOPATHOLOGY 14:5

Molecular abnormalities in specific B-cell lymphomas In addition to IG and TCR clonality analysis using PCR, lymphomas also carry a range of chromosomal translocations that can 225

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Mini-symposium: diagnostic molecular pathology

Suspected lymphoid proliferation of unknown origin

Suspected B-cell proliferation

Suspected T-cell proliferation

TCRB V-J TCRB D-J

IGH VH-JH IGK V-J IGK Kde

Clonality TCRG

No clonality

No clonality

IGH DH-JH with IGL

Clonality

No clonality

TCRG TCRD No clonality

Probably polyclonal

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.

gene on Chr18, resulting in upregulation of BCL2, inhibition of apoptosis, and increased proliferation of follicle centre cell Bcells.16 Five common breakpoints are present, and different PCR primers are required to detect each one17; the BIOMED-2 programme 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.

Follicular lymphoma The 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) t(1;14) t(14;18)

0–40% 5% 5%

API2-MALT1 Ig/BCL10 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) t(8;14) t(3;14)

20% 10% 5–10%

Ig/BCL2 Ig/c-myc Ig/BCL6

ALCL

t(2;5) t(1;2)

70–80% 10–20%

NPM-ALK TPM3-ALK

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) translocations should be confirmed using PCR or FISH. Marginal zone lymphoma (MZL) About 25% of marginal zone lymphomas (MZL) harbour translocations involving the MALT1 gene on Chr11, the commonest of which is t(11;14), present in 13.5% of lung and gastrointestinal 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 translocations present in MALT are 4(3;14) involving IGH and FOXp1, seen particularly in ocular, skin and thyroid MALTs23; rare cases

Table 1

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

Molecular characteristics of leukaemia CML CML 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 neutrophilic 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 relapse initial 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 resistance have a mutation in the kinase domain.

Small lymphocytic lymphoma (SLL)/chronic lymphocytic ­leukaemia (CLL) SLL and CLL are usually easily diagnosed on clinical, morphological and immunophenotypic data, and all cases show rearrangements 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 deletions confer a good prognosis and 17p the worst.25,26 CLL is generally 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; surrogate 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 chromosome 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. DLBCL The t(14;18) typically present in follicular lymphoma is present 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 abnormalities 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 disease. 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).

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 mutation 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

Burkitt’s lymphoma Burkitt’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 different 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 partnered 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 immunohistochemistry and confirmed using FISH. Its presence correlates positively with survival, though it is negative in primary cutaneous ALCL, which has a better prognosis than nodal ALCL.1

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Mixed myelodysplastic syndromes/myeloproliferative diseases This category includes: • chronic myelomonocytic leukaemia (CMML) • juvenile myelomonocytic leukaemia • atypical chronic myeloid leukaemia • myelodysplastic syndrome/myeloproliferative disorder unclassifiable (diagnosis is made on morphological and clinical criteria). 227

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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 regulates 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 inhibitors are under way.

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 syndromes Molecular studies are, with the exception of FISH to follow monosomies or trisomies, of limited use in the diagnosis of myelodysplastic 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 syndromes that may lead to AML.

Acute lymphoblastic leukaemia (ALL) and lymphoma ALL 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 associated with various specific molecular abnormalities of prognostic ­significance.

AML Acute promyelocytic leukaemia (APML) results from translocation of the RARα gene on chromosome 17, in most cases with the PML gene on chromosome 15.44 RAR is a nuclear hormone 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.

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 primitive 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.

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 leukaemia. 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) TELAML1; qRT-PCR is useful for monitoring treatment response and detection of minimal residual disease.

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 precursor T lymphoblastic neoplasms are less common, they constitute 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 abnormalities include t(1;14), involving TAL1, t(5;14) involving HOX11L2.58

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, detection is via FISH, though this will not detect internal tandem repeat duplications, which occur in a subset of cases.

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Recent advances in the molecular pathology of haematological malignancy In the past, identification of genetic abnormalities involved painstaking cytogenetic or molecular biological methods that resulted 228

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in a slow accretion of molecular characteristics, usually one at a time. The introduction of gene expression microarrays (spotted 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 subgroups of tumours (specifically in DLBCL), new prognostic and predictive markers (in AML) and new mechanisms of disease progression (in follicular lymphoma). Microarray RNA profiling has identified microRNA markers in several haematological malignancies, including CLL.61

expression of BCL-2. The secondary alterations leading to full development of the lymphoma are poorly defined and several 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 transformed to diffuse large B-cell lymphoma. Glas identified a set of 81 genes discriminatory between low- and high-grade follicular 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, respectively. The immune response-1 signature was characterized by T-cell-associated genes; the immune response-2 signature contained 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 panT-cell markers and survival, indicating that a subset of T-cells is responsible for the effect; FOX-P3-positive T-cells and CD7­positive cells have been implicated in some studies.76,77

Microarray profiling studies in DLBCL DLBCL 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 Rosenwald 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 compared to the ABC group. This was independent of the international prognostic index, but did not show correlation with the morphological subgroups of DLBCL. Shipp et al64 used a supervised clustering approach and identified a 13-gene prognostic model predictive of survival at 5 years. Some of the genes, notably 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 initially 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 studies 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, independent 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 AML The new classification of myeloid neoplasms according to the WHO incorporates immunological and cytogenetic findings of prognostic significance.78 Since the late 1990s, cytogenetic abnormalities 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 further 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 therapy.80,81 The WHO classification allows subsequent addition of such markers, but it does not fully reflect the molecular heterogeneity of the disease, particularly for intermediate-risk AML. New molecular markers, alone or in combination, that can separate

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

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+

+

GCB +

CD10 –

MUM-1 –

Bcl-6 –

–

GCB

MUM-1 +/–

Non-GCB

Group 1 +

Bcl-2 +

Non-GCB

Group 1 –

CD10 –

MUM-1

Group 1 +

Group 2

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 samples 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 hybridized to gene chips containing oligonucleotides from 6817 human genes (Affymetrix). Fifty genes identified to be uniformly overexpressed 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 (‘discovery’), 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 leukaemogenic 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 showing 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

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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 development of many tumours, including haematological malignancies.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 signatures, 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 generically 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. 230

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Gene microarrays rely on relatively large amounts of fresh starting tissue, obviating measurement of indicator genes in routine 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 centres. 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 simultaneously 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 (indicator 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 polyadenylated 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 homegenates 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

cDNA archive – stored @ -70˚C

Retrospective analysis Archived lymph nodes

Lymph nodes, aspirates & Peripheral blood

qRT-PCR measurement of Indicator genes

PolyA RT-PCR

Lysis & RNA extraction

Globally amplified cDNA “the molecular block”

Gene expression analysis by specific real-time PCR for 36 indicator genes

Prospective analysis Analysis for subgroup discrimination and correlation with outcomes (esp. for retrospective cases) and other prognostic features

Pre-analytical – Preparation of globally amplified cDNA Analytical –expression profiling for specific indicator genes within 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).

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Analysis

Signatures probes

In-situ hybridisation

Paraffin embedded tissue Q-dot labelled oligonucleotide probe

Spectral Imaging

Biopsy

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 aetiology. 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, paraffin-embedded tissue biopsies at diagnosis (Figure 5).99,100 ◆

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45 Reiter A, Lengfelder E, Grmwade D. Pathogenesis, diagnosis and monitoring of residual disease in acute promyelocytic leukaemia. Acta Haematol 2004; 112: 55–67. 46 Licht JD. AML1 and the AML1-ETO fusion protein in the pathogenesis of t(8;21) AML. Oncogene 2001; 20: 5660–79. 47 Dimartino JF, Cleary ML. Mll rearrangements in haematological malignancies: lessons from clinical and biological studies. Br J Haematol 1999; 106: 614–26. 48 Thiede C, Steudel C, Mohr B, et al. Analysis of FLT3-activating mutations in 979 patients with acute myelogenous leukemia: association with FAB subtypes and identification of subgroups with poor prognosis. Blood 2002; 99: 4326–35. 49 Murphy KM, Levis M, Hafez MJ, et al. Detection of FLT3 internal tandem duplication and D835 mutations by a multiplex polymerase chain reaction and capillary electrophoresis assay. J Mol Diagn 2003; 5: 96–102. 50 Golub TR, Barker GF, Bohlander SK, et al. Fusion of the TEL gene on 12p13 to the AML1 gene on 21q22 in acute lymphoblastic leukemia. Proc Natl Acad Sci U S A 1995; 92: 4917–21. 51 Shurtleff SA, Buijs A, Behm FG, et al. TEL/AML1 fusion resulting from a cryptic t(12;21) is the most common genetic lesion in pediatric ALL and defines a subgroup of patients with an excellent prognosis. Leukemia 1995; 9: 1985–9. 52 Raynaud S, Mauvieux L, Cayuela JM, et al. TEL/AML1 fusion gene is a rare event in adult acute lymphoblastic leukemia. Leukemia 1996; 10: 1529–30. 53 Heerema NA, Sather HN, Sensel MG, et al. Frequency and clinical significance of cytogenetic abnormalities in pediatric T-lineage acute lymphoblastic leukemia: a report from the Children’s Cancer Group. J Clin Oncol 1998; 16: 1270–8. 54 Crist W, Carroll A, Shuster J, et al. Philadelphia chromosome positive childhood acute lymphoblastic leukemia: clinical and cytogenetic characteristics and treatment outcome. A Pediatric Oncology Group study. Blood 1990; 76: 489–94. 55 Khalidi HS, O’Donnell MR, Slovak ML, Arber DA. Adult precursor-B acute lymphoblastic leukemia with translocations involving chromosome band 19p13 is associated with poor prognosis. Cancer Genet Cytogenet 1999; 109: 58–65. 56 Uckun FM, Nachman JB, Sather HN, et al. Poor treatment outcome of Philadelphia chromosome-positive pediatric acute lymphoblastic leukemia despite intensive chemotherapy. Leuk Lymphoma 1999; 33: 101–6. 57 Ferrando AA, Neuberg DS, Staunton J, et al. Gene expression signatures define novel oncogenic pathways in T cell acute lymphoblastic leukemia. Cancer Cell 2002; 1: 75–87. 58 Cavé H, Suciu S, Preudhomme C, et al. EORTC-CLG. Clinical significance of HOX11L2 expression linked to t(5;14)(q35;q32), of HOX11 expression, and of SIL-TAL fusion in childhood T-cell malignancies: results of EORTC studies 58881 and 58951. Blood 2004; 103: 442–50. 59 Wadlow R, Ramaswamy S. DNA microarrays in clinical cancer research. Curr Mol Med 2005; 5: 111–20. 60 Golub TR, Slonim DK, Tamayo P, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 286: 531–7. 61 Calin GA, Pekarsky Y, Croce CM. The role of microRNA and other non-coding RNA in the pathogenesis of chronic lymphocytic leukemia. Best Pract Res Clin Haematol 2007; 20: 425–37.

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62 Alizadeh AA, Eisen MB, Davis RE, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000; 403: 503–11. 63 Rosenwald A, Wright G, Chan WC, et al. The use of molecular profiling to predict survival after chemotherapy for diffuse largeB- cell lymphoma. N Engl J Med 2002; 346: 1937–47. 64 Shipp MA, Ross KN, Tamayo P, et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 2002; 8: 68–74. 65 Robertson MJ, Kahl BS, Vose JM, et al. Phase II study of enzastaurin, a protein kinase C beta inhibitor, in patients with relapsed or refractory diffuse large B-cell lymphoma. J Clin Oncol 2007; 25: 1741–6. 66 Hans CP, Weisenburger DD. Greiner, et al. Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray. Blood 2004; 103: 275–82. 67 De Paepe P, de Wolf-Peeters C. Diffuse large B-cell lymphoma: a heterogeneous group of non-Hodgkin lymphomas comprising several distinct clinicopathological entities. Leukemia 2007; 21: 37–43. 68 Nyman H, Adde M, Karjalainen-Lindsberg ML, et al. Prognostic impact of immunohistochemically defined germinal center phenotype in diffuse large B-cell lymphoma patients treated with immunochemotherapy. Blood 2007; 109: 4930–5. 69 Polo JM, Juszczynski P, Monti S, et al. Transcriptional signature with differential expression of BCL6 target genes accurately identifies BCL6-dependent diffuse large B cell lymphomas. Proc Natl Acad Sci U S A 2007; 104: 3207–12. 70 Bende RJ, Smit LA, van Noesel CJ. Molecular pathways in follicular lymphoma. Leukemia 2007; 21: 18–29. 71 Husson H, Carideo EG, Neuberg D, et al. Gene expression profiling of follicular lymphoma and normal germinal center B cells using cDNA arrays. Blood 2002; 99: 282–9. 72 Glas AM, Kersten MJ, Delahaye LJ, et al. Gene expression profiling in follicular lymphoma to assess clinical aggressiveness and to guide the choice of treatment. Blood 2005; 105: 301–307. 73 Dave SS, Wright G, Tan B, et al. Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells. N Engl J Med 2004; 351: 2159–69. 74 Farinha P, Masoudi H, Skinnider BF, et al. Analysis of multiple biomarkers shows that lymphoma-associated macrophage (LAM) content is an independent predictor of survival in follicular lymphoma (FL). Blood 2005; 106: 2169–74. 75 Lee AM, Clear AJ, Calaminici M, et al. Number of CD4+ cells and location of forkhead box protein P3-positive cells in diagnostic follicular lymphoma tissue microarrays correlates with outcome. J Clin Oncol 2006; 24: 5052–9. 76 Carreras J, Lopez-Guillermo A, Fox BC, et al. High numbers of tumorinfiltrating FOXP3-positive regulatory T cells are associated with improved overall survival in follicular lymphoma. Blood 2006; 108: 2957–64. 77 Yang ZZ, Novak AJ, Stenson MJ, Witzig TE, Ansell SM. Intratumoral CD4+CD25+ regulatory T-cell-mediated suppression of infiltrating CD4+ T cells in B-cell non-Hodgkin lymphoma. Blood 2006; 107: 3639–46. 78 Vardiman JW, Harris NL, Brunning RD. The World Health Organization (WHO) classification of the myeloid neoplasms. Blood 2002; 100: 2292–302. 79 Grimwade D, Walker H, Oliver F, et al. The importance of diagnostic cytogenetics on outcome in AML: analysis of 1,612 patients entered into the MRC AML 10 trial. Blood 1998; 92: 2322–33.

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exponentially from sub-picogram quantities of mRNA. Nat Biotechnol 2002; 20: 940–3. 96 Byers R, Roebuck J, Sakhinia E, Hoyland J. PolyA PCR amplification of cDNA from RNA extracted from formalin-fixed paraffin-embedded tissue. Diagn Mol Pathol 2004; 3: 144–150. 97 Sakhinia E, Farahangpour M, Tholouli E, et al. Routine expression profiling of microarray gene signatures in acute leukaemia by realtime PCR of human bone marrow. Br J Haematol 2005; 130: 233–48. 98 Sakhinia E, Glennie C, Hoyland JA, et al. Clinical quantitation of diagnostic and predictive gene expression levels in follicular and diffuse large B-cell lymphoma by RT-PCR gene expression profiling. Blood 2007; 109: 3922–8. 99 Byers RJ, Di Vizio D, O’Connell F, et al. Semiautomated multiplexed quantum dot-based in situ hybridization and spectral deconvolution. J Mol Diagn 2007; 9: 20–9. 100 Tholouli E, Hoyland JA, Di Vizio D, et al. Imaging of multiple mRNA targets using quantum dot based in situ hybridization and spectral deconvolution in clinical biopsies. Biochem Biophys Res Commun 2006; 348: 628–36.

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DIAGNOSTIC HISTOPATHOLOGY 14:5

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.

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