The Role of Cytogenetics in Elderly patients with Myeloma Dr Faith Davies Cancer Research UK Senior Cancer Fellow Centre for Myeloma Research Divisions of Molecular Pathology, Cancer Therapeutics and Clinical Studies Royal Marsden Hospital and The Institute of Cancer Research London

Stages of Disease clinically and biologically Morgan, Walker & Davies Nat Rev Cancer 2012 12:335

Advances in technology have led to an increasing knowledge of myeloma genetics Translocations of C14 G band FISH 1995

Conventional Cytogenetics G-banding Wikipedia et al !!

Chromosome 14 FISH - translocation Centromere Telomere Dual, Break Apart probe c. 250 kb c. 900 kb Constant seg Variable segments J segs D segs IGH 3’ Flanking Probe IGHV Probe 14q32 region Immunoglobulin heavy chain locus Kindly provided by Dr Fiona Ross, Wessex Regional Cytogenetics Laboratory

Molecular classification of myeloma Translocations Early events Translocations t(4;14) t(11;14) t(6;14) t(14;16) t(16;20) Hyperdiploidy Chromosome gain 3, 5, 7, 9, 11, 15, 19, 21 Kuehl & Bergsagel 2005

Normal Isotype Switching on Chromosome 14q32 telomere switch region = 1-3kb long, tandem pentameric repeats) centromere        VDJ   S C VDJ S2 C2 VDJ C2 - Intervening DNA deleted - Hybrid switch formed S S2

Illegitimate switch recombination in Myeloma VDJ          VDJ C2 Gene X Gene Y VDJ Gene X Gene Y C2

Translocations into 14q32 Various partner chromosomes are linked to 14q32, in cell line studies. Some have also been identified in patients. Up to 70% of patients have a translocation - thought to be a primary event. t(11;14)(q13;q32) 30% cyclin D1 t(4;14)(p16:q32) 15% FGFR3 and MMSET t(6;14)(p25;q32) 4% cyclin D3 and IRF4 t(14;16)(q32;q23) 5% cMAF (and WWOX) many other regions may be involved often the partner is not identified.

Advances in technology have led to an increasing knowledge of myeloma genetics Translocations of C14 Gene expression arrays Global mapping G band methylation FISH TC classification miRNA NGS Translocations Hyperdiploid t(4;14) t(6;14) t(11;14) t(14;16) t(14;20) Chromosome gain 3, 5, 7, 9, 11, 15, 19, 21 Normal MGUS MM 1995 2000 2005 2010 2015

Hyperdiploidy Gain of chromosomes (between 48-74) Mostly odd numbered chromosomes 3, 5, 7, 9, 11, 15, 19, 21 gain of chromosomes 15, 9 and 19 are most frequent mechanism of gain not understood 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Walker et al. Blood 2006

Myeloma specific copy number variation Deletion Gain Deletion 1p (30%) CDKN2C, FAF1, FAM46C Deletion 6q (33%) Deletion 8p (25%) Deletion 13 (45%) RB1, DIS3 Deletion 11q (7%) BIRC2/BIRC3 Deletion 14q (38%) TRAF3 Deletion 16q (35%) WWOX, CYLD Deletion 17p (8%) TP53 Deletion 20 (12%) Deletion 22 (18%) Deletion X (28%) Gain 1q (40%) CKS1B, ANP32E Gain 12p LTBR Gain 17p TACI Gain 17q NIK 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Boyd KD, et al. Leukemia. 2012;26:349-355. Walker BA, et al. Blood. 2010;116:e56-e65.

Myeloma Abnormalities Number of common abnormalities Deletions 13q (45%) and 17p (8%) Other regions – 1p, 1q (40%), 16q Translocations Hyperdiploidy odd number chromosomes (3,7,9,11,17)

The Incidence of Abnormality Changes With Disease Progression MGUS (%) SMM (%) MM (%) t(11;14) 10 16 14 t(14;16) 3 t(14;20) 5 <1 1.5 del(13q) 24 37 45 del(17p) 1 8 1q+ 22 39 41 del(CDKN2C) 4 15 Ross et al. Haematologica 2010 95:1221 Leone et al. Clinical Cancer Research 2008 14:6033 Lopez-Corral et al. Clinical Cancer Research 2011 17:1692

Myeloma Disease Progression and Genetic Events Morgan, Walker & Davies Nat Rev Cancer 2012 12:335

Inter relationship of abnormalities 6 16 20 ? No Data HRD HRD+t(#;14) None All t(4;14) have del(13) 17p evenly distributed Boyd KD, et al. Leukemia. 2012;26:349-355. Walker BA, et al. Blood. 2010;116:e56-e65.

Inter relationship of abnormalities 6 16 20 ? No Data HRD HRD+t(#;14) None All t(4;14) have del(13) 17p evenly distributed Boyd KD, et al. Leukemia. 2012;26:349-355. Walker BA, et al. Blood. 2010;116:e56-e65.

Myeloma IX trial: del(13) by FISH not associated with poor survival outcome* Survival according to del(13) with “bad” IgH and del(17)(p53) removed Survival according to del(13) by FISH 20 40 60 80 100 20 40 60 80 100 No del(13) del(13) No del(13) del(13) only Bad IgH or del(17p) n = 568 ms 48.3 months n = 283; ms not reached Patients (%) Patients (%) n = 568 ms 48.3 months n = 478 ms 40.9 months n = 191 ms 27.7 months p = 0.024 p < 0.001 10 20 30 40 50 60 70 10 20 30 40 50 60 70 Survival (months) Survival (months) * In the absence of other adverse prognostic features.

Inter-relationship of Adverse Lesions Genetic abnormalities are not solitary events and can occur together Strong positive association with adverse IGH and 1q+ -72% of IGH translocations with 1q+ Implications In order to understand the prognosis of any lesion need to know if other lesions are present. Lesions may collaborate to mediate prognosis. Boyd et al. Leukemia 2011

Frequency in the Elderly

Frequency of abnormalities with age Ross et al Leukemia 2006

Frequency of abnormalities with age N = 1890, median age 72, range 66-94 Avet Loiseau et al 2013 JCO

Clinical and prognostic significance in the Elderly

Myeloma IX trial: effect of “bad” IgH translocations on survival Combined “bad” IgH translocations 20 40 60 80 100 “Bad” IgH Rest No “bad” IgH translocations Any “bad” IgH translocation n = 858 ms 49.6 months Patients (%) n = 170 ms 25.8 months p < 0.001 10 20 30 40 50 60 70 Survival (months) Intensive arm Non-intensive arm 20 40 60 80 100 20 40 60 80 100 Note to Dr. Davies: Please note the suggested change in the title. Do you agree? n = 495 ms not reached Patients (%) Patients (%) n = 363 ms 33.4 months n = 170 ms 36 months n = 63 ms 13.1 months p < 0.001 p < 0.001 10 20 30 40 50 60 70 10 20 30 40 50 60 Survival (months) Survival (months) ms = median survival. 24

Myeloma IX trial: effect of deletion 17p53 on survival Survival of patients with del(17)(p53) 20 40 60 80 100 No del(17)(p53) del(17)(p53) n = 929 ms 45.8 months Patients (%) del(17p) Rest n = 87 ms 22.2 months p < 0.001 10 20 30 40 50 60 70 Survival (months) del(17)(p53): intensive arm del(17)(p53): non-intensive arm 20 40 60 80 100 20 40 60 80 100 Note to Dr. Davies: Please note the suggested change in the title. Do you agree? n = 545 ms not reached Patients (%) Patients (%) n = 384 ms 32.6 months n = 48 ms 40.9 months n = 39 ms 19.2 months p = 0.004 p = 0.017 10 20 30 40 50 60 70 10 20 30 40 50 60 Survival (months) Survival (months) 25

Prognostic Impact of Lesions N = 1890, median age 72, range 66-94 Avet Loiseau et al JCO 2013

Any bad IgH translocation + del(17)(p53) Myeloma IX trial: effect of combined deletion 17p53 and “bad” IgH on survival n = 754 Patients (%) Survival (days) 20 40 60 80 100 500 1,000 1,500 2,000 Any bad IgH translocation + del(17)(p53) p < 0.001 n = 214 n = 18 Rest Bad IgH translocation Bad IgH translocation + del(17p) Note to JD: dp53 should be del (p53); can we Note to Dr. Davies: Please note the suggested change in the title. Do you agree? 27

Impact of Combined Lesions The number of adverse markers has an additive effect on overall survival 60 months 40 months 23.4 months 9.1 months Boyd et al. Leukemia 2011

Defining high risk according to the ISS: “bad” IgH and del(17p) Myeloma IX trial: effect of adverse prognostic features on survival n = 125 Patients (%) Survival (days) 20 40 60 80 100 500 1,000 1,500 2,000 p < 0.001 n = 244 n = 269 n = 76 1 2 3 4 ISS + any bad IgH translocation + del(17)(p53) 1 = 1 excluding bad IgH or del(17)(p53) 2 = ditto + 1 including, etc. Group 1 ISS1 Group 2 ISS2 Group 3 ISS3 Group 4 bad IgH or del(17p) Note to Dr. Davies: Please note the suggested modification of the titles. Do you agree? ie having something bad doesn’t always mean it is! Boyd et al. Leukemia 2011 29

Non-intensive pathway – chemotherapy regimens Baseline assessment Response Every 28 Days to maximal response. 6 - 9 cycles CHEMOTHERAPY RANDOMISATION C yclophosphamide 500 mg po Days 1, 8, 15, 22 T halidomide 50 - 200 mg po Daily D a examethasone ttenuated 20 mg po Days 1- 4, 15- 18 elphalan 7 mg/m2 od po Days 1 - 4 P rednisolone 40 mg od po Maximal response THALIDOMIDE RANDOMISATION M Primary endpoints: PFS and OS Secondary endpoints: Response, QoL and toxicity Morgan et al Blood 2011

Summary of patient characteristics at trial entry MP (N=423) CTDa (N=426) Age (years) Median Range 73 57–89 73 58–87 Gender (N (%)) Male Female 231 (54.6) 192 (45.4) 242 (56.8) 184 (43.2) ISS (N (%)) I II III Missing Data 64 (15.1) 156 (36.9) 165 (39.0) 38 (9.0) 46 (10.8) 156 (36.6) 168 (39.4) 56 (13.1) β2M (mg/l) 4.9 0.3-40.4 5.0 0.4–64.0

Summary of cytogenetics at trial entry Trans- location MP % CTDa Total Favour-able 125 58.1 129 57.3 254 57.7 Adverse 90 41.9 96 42.7 186 42.3 Adverse group includes t(4;14), t(14;20) t(14,16), gain 1q and del 17p Morgan et al Blood 2011

PFS and OS according to cytogenetics Favourable 14 months 95% CI 12-17 range 0-65 37 months 95% CI 22-44 range 0-69 Adverse 12 months 95% CI 10-13 range 0-67 24 months 95% CI 20-28 range 0-68 Morgan et al Blood 2011

OS according to treatment group in patients with favorable cytogenetics CTDa MP Morgan et al Blood 2011

OS in favorable cytogenetics according to treatment; landmark at 1 OS in favorable cytogenetics according to treatment; landmark at 1.5 years CTDa median not reached MP 42 months CTDa not reached vs 42 months Morgan et al Blood 2011

Influence of cytogenetics on survival among patients achieving a CR Favourable Adverse Morgan et al Blood 2011

NGS results inform myeloma biology No single mutation responsible for myeloma – hundreds of mutations identified. Deregulation of pathways is an important molecular mechanism. Including NF-κB pathway, histone modifying enzymes and RNA processing. Morgan GJ, Walker BA and Davies FE. Nature Reviews Cancer. Vol 12 May 335-348, 2012,

Mutational landscape of myeloma Acute leukaemia 8 non-synonymous variants per sample Myeloma 35 non-synonymous variants per sample Solid tumours 540 non-synonymous variants per sample Hallmarks Of Myeloma Morgan G, et al. Nat Rev Cancer. 2012;12:335-48.

Comparative analysis of cancer evolutionary trees Comparison across disease states and curability Paediatric ALL Myeloma Solid cancer

Linear and branching models for myeloma evolution Morgan, Walker and Davies Nature Reviews Cancer 2012

Linear and branching models for myeloma evolution Morgan, Walker and Davies Nature Reviews Cancer 2012

“Nothing in biology makes sense except in the light of evolution” Theodosius Dobzhansky, 1973

“Nothing in biology makes sense except in the light of evolution” Theodosius Dobzhansky, 1973 Adaption and survival of the fittest

Charles Darwin “Applying the ideas developed initially by Darwin, to explain the origin of the species, can inform us of how cancer develops and how best to treat it”

Clonal evolution of myeloma Ecosystem 1 Single founder cell (stem or progenitor) Ecosystem 3 Ecosystem 5 Selective pressures Treatment Ecosystem 4 PCL MM MGUS Diffuse Focal Ecosystem 2 EMM Adaption and survival of the fittest Subclones with unique genotype/”driver” mutations Adapted from Greaves MF, Malley CC. Nature. 2012;481:306-13.

MIGRATION AND FOUNDER EFFECT A Model of MM Disease Progression A model based on the random acquisition of genetic hits and Darwinian selection Primary genetic events IgH translocations Hyperdiploidy Copy number abnormalities DNA hypomethylation Acquired mutations MGUS Smouldering myeloma Myeloma Plasma cell leukaemia Initiation Progression Bone marrow Peripheral blood Germinal centre Post-GC B cell Inherited variants COMPETITION AND SELECTIVE PRESSURE MIGRATION AND FOUNDER EFFECT Clonal advantage Myeloma progenitor cell Tumour cell diversity Genetic lesions Secondary genetic events Morgan G, et al. Nat Rev Cancer. 2012;12:335-48: page 338; figure 2. Morgan G, et al. Nat Rev Cancer. 2012;12:335-48.

A Darwinian View of Induction, maintenance and relapse Clones can be eradicated - cured Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012

A Darwinian view of induction, maintenance and relapse Clones can be eradicated - cured Post treatment Evolutionary / Treatment Bottleneck Myeloma progenitor cell Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012

Intraclonal heterogeneity and targeted treatment Clones with a distinct pattern of mutations Target

Intraclonal heterogeneity and targeted treatment Clones with a distinct pattern of mutations Suboptimal response at 30%

A Darwinian View of Induction, maintenance and relapse Clones can be eradicated - cured Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012

A Darwinian view of induction, maintenance and relapse Clones can be eradicated - cured Post treatment Evolutionary / Treatment Bottleneck Myeloma progenitor cell Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012

Myeloma progenitor cell Clonal Tides During Myeloma Treatment Relapse can come from any one of a number of clones Relapse Original clone – treatment resistant Myeloma progenitor cell Differential sensitivity to treatment treatment sensitive Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012

Clonal dynamics over multiple relapses Clinical evidence supports this - a t(4;14) case Keats JJ, et al. Blood. 2012;120:1067-76.

Conclusions Myeloma is biologically and genetically diverse. Genetic complexity develops early before clinical symptoms develop. Linking biological data to clinical data is beginning to identify clinically distinct subgroups with different disease characteristics and outcomes. The frequency of the different subgroups differs with age, but the prognostic significance remains Darwinian style processes can describe the multistep pathogenesis of myeloma. The impact of clonal heterogeneity needs to be considered when making treatment choices

Conclusion Knowledge of the patients genetic sub group is important regardless of the patients age This has been incorporated into the UKMF/BCSH guidelines C14 translocation, 17p, HRD, C1

Centre for Myeloma Research, ICR Davies Lab Mike Bright Lei Zhang Lauren Aronson Jade Strover Jackie Fok Daniel Izthak Morgan Lab Brian Walker Chris Wardell David Johnson Li Ni David Gonzalez Ping Wu Fabio Mirabella Lorenzo Melchor AnnaMaria Brioli Charlotte Pawlyn Elileen Boyle Matthew Jenner Kevin Boyd Martin Kaiser Chief Investigators JA Child GJ Morgan GH Jackson NH Russell CTRU, Leeds K Cocks W Gregory A Szubert S Bell N Navarro Coy F Heatley P Best J Carder M Matouk D Emsell A Davies D Phillips Leeds RG Owen AC Rawstron R de Tute M Dewar S Denman G Cook S Feyler D Bowen Birmingham MT Drayson K Walker A Adkins N Newnham Salisbury F Ross L Chieccio MRC Leukaemia Trial Steering Committee MRC Leukaemia Data Monitoring and Ethics Committee NCRI Haematological Oncology Clinical Studies Group UK Myeloma Forum Clinical Trials Committee Myeloma UK Funding Medical Research Council Pharmion Novartis Chugai Pharma Bayer Schering Pharma OrthoBiotech Celgene Kay Kendall Leukaemia Fund