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The Role of Cytogenetics in Elderly patients with Myeloma

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.

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The Role of Cytogenetics in Elderly patients with Myeloma

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  1. The Role of Cytogenetics in Elderly patients with Myeloma • Dr Faith DaviesCancer Research UK Senior Cancer FellowCentre for Myeloma ResearchDivisions of Molecular Pathology, Cancer Therapeutics and Clinical StudiesRoyal Marsden Hospital and The Institute of Cancer ResearchLondon

  2. Stages of Diseaseclinically and biologically Morgan, Walker & Davies Nat Rev Cancer 2012 12:335

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

  4. Conventional CytogeneticsG-banding Wikipedia et al !!

  5. 14q32 region Dual, Break Apartprobe Centromere Telomere Constant seg Variable segments J segs D segs c. 250 kb c. 900 kb IGH 3’ Flanking Probe IGHV Probe Chromosome 14 FISH - translocation Immunoglobulin heavy chain locus Kindly provided by Dr Fiona Ross, Wessex Regional Cytogenetics Laboratory

  6. 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

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

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

  9. 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.

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

  11. 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 4 5 2 3 7 8 11 12 6 9 10 13 14 15 16 17 18 22 19 20 21 X Walker et al. Blood 2006

  12. 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.

  13. 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)

  14. The Incidence of Abnormality Changes With Disease Progression Ross et al. Haematologica2010 95:1221 Leone et al. Clinical Cancer Research 2008 14:6033 Lopez-Corral et al. Clinical Cancer Research 2011 17:1692

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

  16. t(4;14) t(11;14) 6 16 20 ? No Data HRD HRD+t(#;14) None Inter relationship of abnormalities 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.

  17. t(4;14) t(11;14) 6 16 20 ? No Data HRD HRD+t(#;14) None Inter relationship of abnormalities 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.

  18. 100 100 80 80 60 60 40 40 20 20 0 0 0 0 10 10 20 20 30 30 40 40 50 50 60 60 70 70 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 No del(13) No del(13) del(13) del(13) only Bad IgH or del(17p) n = 283; ms not reached n = 568ms 48.3 months Patients (%) Patients (%) n = 568ms 48.3 months n = 478ms 40.9 months n = 191ms 27.7 months p = 0.024 p < 0.001 Survival (months) Survival (months) * In the absence of other adverse prognostic features.

  19. 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

  20. Frequency in the Elderly

  21. Frequency of abnormalities with age N = 228 Ross et al Leukemia 2006

  22. Frequency of abnormalities with age N = 1890, median age 72, range 66-94 AvetLoiseau et al 2013 JCO

  23. Clinical and prognostic significance in the Elderly

  24. Myeloma IX trial: effect of “bad” IgH translocations on survival 100 100 100 80 80 80 60 60 60 “Bad” IgH 40 40 40 Rest 20 20 20 0 0 0 0 0 10 10 20 20 30 30 40 40 50 50 60 60 70 70 Combined “bad” IgH translocations No “bad” IgH translocations Any “bad” IgH translocation n = 858ms 49.6 months Patients (%) n = 170ms 25.8 months p < 0.001 Survival (months) Intensive arm Non-intensive arm n = 495 ms not reached Patients (%) Patients (%) n = 363ms 33.4 months n = 170ms 36 months n = 63ms 13.1 months p < 0.001 p < 0.001 0 10 20 30 40 50 60 Survival (months) Survival (months) ms = median survival.

  25. Myeloma IX trial: effect of deletion 17p53 on survival 100 100 100 80 80 80 60 60 60 40 40 40 20 20 20 0 0 0 0 0 10 10 20 20 30 30 40 40 50 50 60 60 70 70 Survival of patients with del(17)(p53) No del(17)(p53) del(17)(p53) n = 929ms 45.8 months del(17p) Patients (%) Rest n = 87ms 22.2 months p < 0.001 Survival (months) del(17)(p53): intensive arm del(17)(p53): non-intensive arm n = 545ms not reached n = 384ms 32.6 months Patients (%) Patients (%) n = 48ms 40.9 months n = 39ms 19.2 months p = 0.004 p = 0.017 0 10 20 30 40 50 60 Survival (months) Survival (months)

  26. Prognostic Impact of Lesions N = 1890, median age 72, range 66-94 AvetLoiseau et al JCO 2013

  27. Myeloma IX trial: effect of combined deletion 17p53 and “bad” IgH on survival Any bad IgH translocation + del(17)(p53) 100 80 60 p < 0.001 40 20 0 n = 754 Patients (%) n = 214 n = 18 Bad IgH translocation + del(17p) 0 500 1,000 1,500 2,000 Rest Survival (days) Bad IgH translocation

  28. 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

  29. Defining high risk according to the ISS: “bad” IgH and del(17p) 100 80 60 40 20 0 Myeloma IX trial: effect of adverse prognostic features on survival ISS + any bad IgH translocation + del(17)(p53)1 = 1 excluding bad IgH or del(17)(p53)2 = ditto + 1 including, etc. 1 2 3 4 p < 0.001 Group 1 ISS1 Group 2 ISS2 Group 3 ISS3 Group 4 bad IgH or del(17p) n = 125 Patients (%) n = 244 bad IgH or del(17p) n = 269 n = 76 0 500 1,000 1,500 2,000 Survival (days) ie having something bad doesn’t always mean it is! Boyd et al.Leukemia 2011

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

  31. Summary of patient characteristics at trial entry

  32. Summary of cytogenetics at trial entry Adverse group includes t(4;14), t(14;20) t(14,16), gain 1q and del 17p Morgan et al Blood 2011

  33. PFS and OS according to cytogenetics Morgan et al Blood 2011

  34. OS according to treatment group in patients with favorable cytogenetics P=0.1041 CTDa MP Morgan et al Blood 2011

  35. 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

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

  37. 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,

  38. 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.

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

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

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

  42. “Nothing in biology makes senseexcept in the light of evolution” Theodosius Dobzhansky, 1973

  43. “Nothing in biology makes senseexcept in the light of evolution” Theodosius Dobzhansky, 1973 Adaption and survival of the fittest

  44. 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”

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

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

  47. A DarwinianView of Induction, maintenance and relapseClones can be eradicated - cured Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012

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

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

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

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