Molecular Testing of Lymphomas John Goodlad Department of Pathology Western General Hospital & University of Edinburgh Edinburgh RCPath Symposium Molecular Diagnosis on Tissues and Cells Friday 25th November 2011

Molecular Techniques in Haematological Malignancy Spectrum of disease Lymphoma Leukaemia  Lymphoid  Myeloid Myelodysplasia Myeloproliferative disorders Areas of application Diagnosis / classification Therapy  Identification of specific targets  Newly tailored drugs Assessing response to treatment Techniques available Polymerase Chain Reaction  Standard PCR  Reverse transcriptase PCR  Real time / Quantitative PCR (Q-PCR) Fluorescence in situ hybridisation  Interphase  Spectral Karyotype Imaging (SKI)  Fluorescence immunophenotyping & interphase cytogenetics (FICTION) Comparative Genomic Hybridisation  Conventional  Array based Gene expression profiling Massively parallel (next generation) sequencing Proteomics

Molecular Testing in Lymphoma 1. Establishing a diagnosis of lymphoma What is the significance of clonality? 2. Classification of lymphoma 3. Discovery and future developments Refining prognostic and diagnostic categories Developing new therapeutic regimens

1. Clonality testing in lymphoma Dominant clonality often used as a marker of lymphoid malignancy (Neoplastic versus benign lymphoproliferations) Based on the premise that: Neoplastic lymphocytes are clonal Reactive (‘benign’) populations of lymphocytes are polyclonal PCR is method of choice for clonality assays: Strategies directed towards lymphocyte antigen receptor IG genes TCR genes Important to be aware of the limitations and pitfalls of this approach

IGH gene rearrangement No encounter with antigen DEATH Encounter with appropriate antigen SURVIVAL Naïve B-cell CD34+ Progenitor B cell Pre-B cell Mature B cell: IgM+/IgD+ Immature B cell: IgM+/IgD- IGK+/-L gene rearrangement Immunoglobulin gene rearrangements

5’5’ VnD1D2D3J1DnJ2J3Jn CC CC CC C… 3’ V2V1 V3 Immunoglobulin heavy chain gene rearrangement: generation of diversity 5’5’ Vn D1 D2J2 J3Jn CC CC CC C… 3’ V2 V1 V3 1. D-J joining (incomplete DNA rearrangement) 5’5’ D2J2 J3Jn CC CC CC C… 3’ V2 V1 2. V-DJ joining (complete DNA rearrangement) 3. Transcription D2J2J3Jn CC CC CC C…V2 precursor IGH mRNA 4. RNA splicing D2J2 CC V2 mature IGH mRNA 5. Translation

Gene segmentsIGHIGKIGL V segments Functional (family)44 (7)7656 Rearrangeable (family)66 (7) D segments Rearrangeable (family)27 (7)-- J segments Functional654 Rearrangeable655 Van Dongen et al Leukemia 2003 Immunoglobulin heavy chain gene rearrangement: generation of diversity Potential functional rearrangements of IGH = 44 x 27 x 6 = 1188 Potentail functional rearrangements of IGK = 76 x 5 = 380 Potential functional rearrangements of IGL = 56 x 4 = 224 Number of possible different IG molecules = 1188 x 380 x 224 = 101,122,560

B T B B B B In the presence of antigen T- and B-lymphocytes combine to produce: Plasma cells/specific antibody An expanded clone of memory B-cells

A reactive lymphocyte proliferation is polyclonal; Each expanded clone has different gene re-arrangement

A neoplastic lymphocyte proliferation is clonal Same gene rearrangement Same chromosomal abnormality

Polymerase Chain Reaction for IGH chain gene (and TCR gene) re-arrangement can be used to determine pattern of clonality within a lymphoid infiltrate Implication is that clonality = maligancy primers Products: Same size in monoclonal population Different sizes in polyclonal population

Limitations and Pitfalls of Molecular Clonality Studies 1.Limited sensitivity 2.Clonality does not equate with malignancy 3.Ig & TCR re-arrangements are not markers of lineage 4.Pseudoclonality 5.Oligoclonality 6.False positive results 7.False negative results

How and when do we test for clonality

BIOMED 2: antigen receptor PCR targets for clonality studies * V-J re-arrangements Number of possible IG molecules = 101,122,560 Number of possible TCRA/B heterodimers = 2,979,236 Number of possible TCRG/D heterodimers = 2880 IGH A (FR1*) IGH B (FR2*) IGH C (FR3*) IGH D (D-J) IGH E (D-J IGK A (V-J) IGK B (Kde) IGL (V-J) TCRB A (V-J) TCRB B (V-J) TCRB C (D-J) TCRG A (V-J) TCRG B (V-J) TCR D (V-J) Primer design / Multiplex PCR primers designed to cover maximum number of possible combinations for each re-arrangement Product size means effective with FFPE tissues (<300bp) use in multiplex reactions without cross annealing to each other Majority of re-arrangements covered by: 83 upstream primers 39 downstream primers 14 tubes (reaction mixtures) WHAT WE USE

BIOMED 2 : Immunoglobulin gene re-arrangement: Different assays have different sensitivities Sensitivity of assay varies with lymphoma subtype (especially pre- or post GC) In 31 cases (20%) clonality demonstrated by only one assay Any one assay not suitable for all types of lymphoma Combination of assays should be performed to increase the sensitivity Modified from Liu H et al. Br J Haematol 2007; 138:31-43 Pre-GC (%)GC & postGC (%) MCLSLL/CLLFLMALTDLBCL (n=4)(n=9)(n=30)(n=29)(n=24) IGH A (FR1) IGH B (FR2) IGH C (FR3) IGH D (D-J) IGH E (D-J) IGK A (V-J) IGK B (Kde) IGL (V-J) ALL

BIOMED 2 in action: routine strategy e.g. Liu et al Leukaemia 2007 DNA sample DNA size ladder PCR IGH B + IGK A +IGK B IGH A + IGH C + IGH D IGL + IGH E TCRG A + TCRG B TCRB A + TCRB B TCRB C + TCRD %+ %+ with >1 reaction 58% 79% 91% 100% 99% 80% %+ 94% 100% 98% %+ with >1 reaction 30% 73% 82% DNA >300 bp

WHEN DO WE TEST? 1. Demonstration of clonality used as supportive evidence for neoplasia in morphologically or immunophenotypically abnormal lymphoproliferations that do not fully fulfill criteria for malignancy. N.B. Clonality does not equate with malignancy Dominant clones can be found in many conditions that are not overtly malignant

Some clearly benign/reactive processes, e.g: Reactive and progressively transformed germinal centres Peripheral blood from patients infected with EBV or CMV Any lymphoid proliferation in context of immunosuppression Lichen planus Lichen sclerosus et atrophicus Drug hypersensitivity reactions B-cutaneous lymphoid hyperplasia Lymphoid proliferations that may be associated with progression to overt lymphoma in some, but by no means all cases, e.g: MGUS Monoclonal B-lymphocytosis “Cutaneous lymphoid dyscrasias” Pigmented purpuric dermatoses Atypical lobular panniculitis Pityriasis lichenoides “In situ lymphomas”: Follicular Mantle cell

Example 1: 55 year old male with peripheral blood lymphocytosis Found to have infectious mononucleosis Example 2: 65 year old female with breast carcinoma, axillary lymph node sample “In situ follicular lymphoma”

2. Absence of clonality (polyclonal result) may help confirm a diagnosis. Other haematolymphoid malignancies that should not have re-arranged IG or TCR genes, e.g: NK cell lymphomas Myeloid sarcoma Plasmacytoid dendritic cell neoplasms Montypic but polyclonal lymphoid proliferations, e.g: HHV8-asscociated Castlemans disease HHV8(KSHV8)- and EBV- associated germinotropic lymphoproliferative disorder Atypical marginal zone hyperplasia of MALT Du et al Blood 2001, Du et al Blood 2002, Attygale et al Blood 2004

Example: 14 year old male with enlarged tonsils: Massively expanded marginal zones Lambda restricted population of cells on flow cytometry Polyclonal IG gene rearrangement (Fr1,2,3, IGK, IGL) Atypical marginal zone hyperplasia

LYMPHOMA CLASSIFICATION: HISTORICAL PERSPECTIVE 1832: Thomas Hodgkin Often accredited with first description of Hodgkin’s disease: "On Some Morbid Appearances of the Absorbent Glands and Spleen". Medico- Chirurgical Transactions, 17, 1832, 68– : Marcello Malpighi Publishes the first recorded description of any lymphoma (Hodgkin's disease) “De viscerum structuru exercitatio anatomica” 2. MOLECULAR TESTING AND LYMPHOMA CLASSIFICATION

An early indication of the limitations of early some lymphoma diagnoses/classifications Original specimens of Thomas Hodgkin still preserved in Guy’s museum Histological examination in /7 cases diagnosed correctly Tuberculosis Other forms of lymphoma Thomas Hodgkin’s diagnoses were based entirely on gross appearances

Jackson & Parker1944 Lukes1963 Rye1965 Rappaport1966 Lukes & Collins1974 Kiel1978 Working Formulation1985 Updated Kiel1988 Hodgkin’s disease NHL Primarily LN Subsequent classification systems based purely on light microscopic appearances First real lymphoma classification in 1944, followed rapidly by many others* *all based entirely on light microscopic appearances Giemsa Haematoxylin & eosin

Limitations of morphology based classifications: lymphomas with nodular/follicular growth pattern Small cell lymphomas with nodular/follicular growth pattern circa 1980 centroblastic-centrocytic (small) & centrocytic: Kiel follicular, predominantly small cleaved cell: W-F

Overall survival for this group of patients Median = 6.93 years 5-year = approx 62% 10-year = 35.3%

This group of lymphomas contains subsets of cases with different chromosomal translocations t(14;18)(q32;q21) t(11;14)(q13;q32) BCL2 Cyclin D1 Follicular lymphomaMantle cell lymphoma

Follicular lymphoma has much better outcome than mantle cell lymphoma FLMCL Median Survival:8-10 years3-4 years 5-year OS:>70%<40% Treatment dictated by classification:  CHOP-like followed by myeloablative regimens and allogeneic stem cell transplant in younger patients  Wait and watch or symptomatic only

Fundamentals of modern lymphoma classification The International Lymphoma Study Group Pathologists/haematopathologists Clinicians US, Europe and rest of world Nancy Harris - Boston Elaine Jaffe - Bethesda Harald Stein - Berlin Peter Banks - San Antonio John Chan - Hong Kong Michael Cleary - Stanford George Delsol - Toulouse Chris De Wolf-Peters - Leuven Brunangelo Falini - Perugia Kevin Gatter - Oxford Thomas Grogan - Tucson Peter Isaacson - London Daniel Knowles - Cornell David Mason - Oxford Konrad Muller-Hermelink - Wurzburg Stefano Pileri - Bologna Miguel Piris - Toledo Elizabeth Ralfkiaer - Copenhagen Roger Warnke - Stanford

1994: A consensus list of lymphoid neoplasms that appear to be distinct clinical entities All available information used to define entities Morphology Immunophenotype Genetic features Clinical features Reproducibility proven in consistency studies (Blood Jun 1;89(11): ) Clinical utility verified (Blood Jun 1;89(11): ) Understanding that modifications would be required as knowledge increase Internationally acceptable!

WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues Treatment is dictated largely by the diagnostic category into which a tumour is placed

BreakapartDual fusion IGH- IGK- IGL- BCL2IGH/BCL2 BCL6- MALT1 IGH/MALT AP12/MALT MYC MYC/IGH CCND1 CCND1/IGH ALK1 In Edinburgh: 1.Probes currently in routine use: 2.3  m tissue sections 3.Break-apart probes in first instance 4.Negative controls run on each test to determine cut-off value 5.Scoring on basis of number of abnormal versus normal signals

USE OF FISH +/- KARYOTYPING 1.Occasional as adjunct to clonality testing, often in atypical follicular proliferations; IGH, IGL, IGK BCL2 BCL6 2.Facilitate subclassification when pathological features inconclusive 3.All large B-cell lymphomas Mandatory to make diagnosis of Burkitt lymphoma  MYC Identify “double-hit” lymphoma  MYC, BCL2, BCL6, IGH, IGK, IGL

Example: 14 year old female with lesion on scalp ALK1 Small cell infiltrate in skin Relatively few ALK+ cells by IHC ALK translocation confirmed with breakapart probe Anaplastic large cell lymphoma, small cell variant

Techniques now available that permit analysis of thousands of genetic, epigentic and proteomic changes in tumours in relatively short space of time Array based technologies Massively parallel sequencing Vast quantities of information can be obtained from a large number of samples in a relatively short period of time. Traditional methods allowed researchers to survey only a relatively small numbers of genes/abnormalities at any one time: ‘Chipping away at the coal face.’ ‘Industrial strength processing’ IMPACT OF NEW HIGH THROUGHPUT TECHNOLOGIES 3. DISCOVERY AND FUTURE DIRECTIONS

Example 1: Diffuse large B-cell lymphoma Advances in classification and treatment

Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Alizadeh AA, et al. Nature 2000; 403: Two main prognostic groups Germinal centre B-like: good prognosis Activated B-like; bad prognosis Gene expression profiling studies on DLBCL show that ‘cell of origin’ is an important determinant of outcome

Gene expression profiling has identified a number of potential therapeutic targets e.g./ GEP and other investigations show evidence of constitutive activation of NFkB pathway in ABL-DLBCL but not GCB- DLBCL Antiapoptotic effects of NFkB counteract action of conventional doxorubicin-based cytotoxic chemotherapy in DLBCL Inhibition of NFkB in ABL-DLBCL cell lines in vitro is toxic Inhibition of NFkB in vivo may sensitize tumour cells to chemotherapy and improve outcome Trial of bortezomib in conjunction with doxorubicin based chemotherapy in patients with relapsed/refractory DLBCL

Inactive NFkB exists as protein complex in cytoplasm During NFkB activation, IκB kinase (IKK) phosphorylates IκBα IκBα dissociates from NF-κB Freed NF-κB translocates to the nucleus and alters gene expression Bortezomib blocks IκBα degradation Prevents translocation of NF-κB to the nucleus Bortezomib and NFkB Activation

Copyright ©2009 American Society of Hematology. Copyright restrictions may apply. Dunleavy, K. et al. Blood 2009;113: Figure 2 Overall survival in patients with DLBCL Bortezomib significantly improves survival in relapsed/refractory ABL-DLBCL but not GCB-DLBCL

Clinical trials already open to assess efficacy of Bortezomib (Velcade) as front line treatment in ABC-DLBCL, eg UK: ISRCTN A Randomized Evaluation of Molecular Guided Therapy for Diffuse Large B-Cell Lymphoma With Bortezomib (REMoDL-B); ISRCTN GEP will be undertaken on samples of trial patients to stratify into GC and ABC type DLBCL

Example 2: Classic Hodgkin lymphoma; tumour cell genetics impact on microenvironment to the benefit the tumour Steidl et al Nature Common lymphoma associated translocations are rare in cHL Whole transcriptome paired end sequencing (next-generation) Genome wide mapping of base pair sequences  Translocation breakpoints  Mutations  Gains and losses Applied to two Hodgkin cell lines KM-H2 (89.2 million base pair readings) L428 (61.5 millon base pair readings)

C Steidl et al. Nature 000, 1-5 (2011) doi: /nature09754 CIITA–BX gene fusion observed using paired-end massively parallel whole transcriptome sequencing. Found three translocations: 9q34.13 (BAT2LI) / 10q26.3 (MGMT) 7p (ELMO1) / 15q26.1 (SLCO3A1) 15q21.3 (BX648577) / 16p13.13 (CIITA)

Studied incidence of CIITA translocations further by FISH; breakapart probe: 15% classic Hodgkin lymphoma (8/55 cases) 38% primary mediastinal large B-cell lymphoma (29/77) 27% mediastinal grey zone lymphoma 3% diffuse large B-cell lymphoma (4/131) 11% testicular DLBCL 0% primary DLBCL of CNS In cases of PMBCL presence of CTIIA correlates with: Poorer disease specific survival (63.0% vs 85.0% at 10 years) Steidl et al, Nature 2011 CIITA: a major MHC class II transactivator

Fusion partners sought for CIITA using 3’ rapid amplification of cDNA ends (RACE) Several different partners 9p24 a frequent partner Several genes at 9p24, including JAK2 Programmed cell death ligand 1 (PD-L1) (CD273) Programmed cell death ligand 2 (PD-L2) (CD274) Breakpoints typically in region of CD273 and CD274 genes HRS cells also shown to have copy number gains of 9p24 Green et al, Blood 2010; 116: 3268 TRANSLOCATIONS INVOLVING CIITA

CONSEQUENCES OF t(9;16)(q34.13; p13.13) CIITA / CD273 or CIITA / CD274 Translocation interferes with MHCII expression but upregulates PD-L expression Downregulation of MHCII Upregulation of CD273 or CD274 Decreased MHCII expression correlates with poor survival in variety of lymphomas including cHL and DLBCL, eg Rimsza LM et al, Blood 2008 Diepstra A et al, JCO 2007 Roberts RA et al, Blood 2006 Upregulation of PD1 ligands correlates with inferior survival in several cancers Blank C et al, Cancer Immunol Immuntherapy Effects mediated via modulation of anti-tumour immune response

Anti-tumour host immune response Cytotoxic T-cells are critical in recognition and elimination of altered self antigens Virus infected cells Tumour cells MHC class I restricted - recognize antigen-MHC I complexes on specific target cells Activated by Th1 cells Recognize specific antigen-MHC II complexes Th1 Tc MHC IIMHC I

Downregulation of MHC II helps tumour cell evade recognition by tumour specific T-cells Th1 Tc MHC II MHC I

Programmed cell death 1 and its ligands PD.1 expressed on a variety of cell types, including T-lymphocytes Binding of PD.1 with one of its ligands (PD-L1 and PD-L2); Inhibits activated T-cells(Induction of a resting state) In some circumstances may facilitate apoptosis Normally functions to induce self tolerance prevent development of autoimmune disease

t(9;16)(q34.13; p13.13): a Novel Translocation Recurrent genetic event with fusion that impacts through both sides of the translocation First recurrent abnormality shown to favour tumour growth through effects on microenvironment, rather than tumour cell division, differentiation and death Provides opportunity for therapeutic manipulation Blocking PD.1:PD-L interactions may restore anti-tumour T-cell immunity Specific PD.1 receptor blocking antibodies exist Already in clinical trials; lymphoma, carcinoma and melanoma Gordon L et al, Ann Oncol 2011;22 (suppl4): iv102 (prelim report in DLBCL)

CONCLUSIONS (i) Molecular testing is already well established in lymphoma diagnosis Differentiating reactive and neoplastic populations Classification Modern lymphoma classification systems define entities basis of shared biological and clinical characteristics, allowing them to be arranged into clinically relevant groupings Diagnostic category dictates treatment and likely prognosis Molecular studies have changed our perception of cancer from that of a genetic disease to complex signaling network Highlight biological and clinical heterogeneity within disease categories Identification of new prognostic markers Identification of pathogenetic pathways of potential relevance Identification of potential therapeutic targets

These advances will allow diagnostic categories to be refined and incorporated into updated lymphoma classifications Ultimately may permit Molecular diagnosis Integration of diagnosis and therapeutics Individually tailored treatment CONCLUSIONS (ii)

John Goodlad, MD, FRCPath Western General Hospital Edinburgh Scotland Ahmet Dogan, MD, PhD Mayo Clinic Rochester Minnesota, USA Andrew Wotherspoon, MBChB, FRCPath Royal Marsden NHS Foundation Trust London UK Daphne de Jong, MD, PhD The Netherlands Cancer Institute Amsterdam The Netherlands 1st EDINBURGH HAEMATOPATHOLOGY TUTORIAL: “INTEGRATING TECHNOLOGICAL ADVANCES INTO DIAGNOSTIC PRACTICE” JUNE 7-8,