Myeloma Genetics what do we know and where are we going?

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1 in partnership with Myeloma Genetics what do we know and where are we going? Dr Brian Walker Thames Valley Cancer Network 14 th September 2015 Making the discoveries that defeat cancer

2 Myeloma Genome: Summary 2 Primary events: IGH translocations t(4;14) t(6;14) t(11;14) t(14;16) t(14;20) Hyperdiploidy Secondary events: t(8;14) Copy Number Abnormalities: Deletions: 1p, 6q, 8p, 12p, 13q, 14q, 16q, 17p Gains: 1q+, trisomic chromosomes Morgan, Walker & Davies Nat Rev Cancer :335

3 Myeloma Disease Progression 3 Morgan, Walker & Davies Nat Rev Cancer :335

4 The Incidence of Abnormality Changes With Disease Progression 4 Abnormality MGUS (%) SMM (%) MM (%) t(4;14) t(11;14) t(14;16) t(14;20) 5 <1 1.5 del(13q) del(17p) q del(cdkn2c) Ross et al. Haematologica :1221 Leone et al. Clinical Cancer Research :6033 Lopez-Corral et al. Clinical Cancer Research :1692

5 Mutation Number Increases as Disease Progresses Walker et al. Leukemia 2013

6 Genetic Prognostic Markers in Myeloma Translocations t(4;14) t(14;16) t(14;20) vs months 32.9 vs months 16.9 vs months t(11;14) t(6;14) Adverse vs Neutral 51.6 vs months Not reached vs months 25.8 vs months

7 Genetic Prognostic Markers in Myeloma Copy Number Changes 7 del(1p32.3) Gain 1q 34.5 months >70 months 52.1 months >70 months del(13q) del(17p) >70 months >70 months 37 months 40.9 months 67.8 months Walker et al Blood 116:e56

8 Inter-relationship of Adverse Lesions 8 Genetic abnormalities are not solitary events and can occur together Strong positive association with adverse IGH and 1q+ -72% of IGH translocations with 1q+ 16/418 (4%) samples had all three poor markers Boyd et al. Leukemia 2011

9 Impact of Combined Lesions 9 The number of adverse markers has an additive effect on overall survival 60 months 40 months 23.4 months 9.1 months A significant proportion of patients with no adverse lesions still die early Boyd et al. Leukemia 2011

10 Genome Sequencing in Myeloma 10 There is no unifying mutation in myeloma Most frequently mutated genes found at 20% in untreated patients Requires biological pathway analysis Identified NF-κB pathway, histone modifying enzymes and RNA processing as enriched pathways

11 The Prevalence of Somatic Mutations 11 Alexandrov et al. Nature 2013

12 UK Myeloma XI Trial Genomic Strategy newly diagnosed myeloma patients Copy Number Analysis MLPA Translocation Determination qpcr Targeted Sequencing Mutational Analysis Exome Sequencing Biochemical Data β 2 m, Serum Albumin Clinical Data Patient characteristics, PFS, OS

13 UK Myeloma XI Trial Overview 13 Intensive (n=261) Non-intensive (n=202) Randomise Randomise CTD (n=132) CRD (n=129) CTDa (n=99) CRDa (n=103) Assess response Assess response NC + PD CR + VGPR PR + MR NC + PD CR + VGPR PR + MR Randomise (n=60) Randomise (n=25) CVD (n=14) Nothing (n=27) CVD (n=33) CVD (n=10) Nothing (n=12) CVD (n=13) Assess response Assess response Assess response Assess response High-dose melphalan + ASCT Assess response Randomise No maintenance (n=105) Lenalidomide (n=106) Lenalidomide + Vorinostat (n=42)

14 The Mutational Landscape of Myeloma 14

15 RAS Pathways 15

16 RAS Pathway Mutations Summary 16 KRAS 21%, NRAS 19%, BRAF 7% at presentation Myeloma is a disease characterised deregulation of the RAS/MAPK pathway so may constitute good clinical targets No prognostic value with current treatments Walker et al. JCO 2015

17 Results: interaction between genetic abnormalities Positive correlations: t(11;14) and CCND1 t(4;14) and FGFR3 t(4;14) and PRKD2 del(17p) and TP53 del(13q) and DIS3 Negative correlations NRAS and KRAS Hyperdiploïdy and translocations Co-segregation of the adverse prognostic features Walker et al. JCO 2015

18 Results: IRF4 and positive outcome Transcription factor Mutated in 3.2% of patients Most common is K123R Not the same as CLL (L116R) Walker et al. JCO 2015

19 Results: IRF4 and positive outcome Shaffer AL, Nature, 2008

20 Results: EGR1 and positive outcome Mutated in 3.5% of patients Transcriptional regulator The biological role of mutations at the 5 end of EGR1 remains to be ascertained (?activating) EGR1 has been involved in Myeloma at the expression level: in the recruitment of MYC to promote the p53-independent apoptosis and in response Bortezomib treatment In the response to IMiDs as it is one of the candidate genes for del(5q) myelodysplastic syndromes Walker et al. JCO 2015

21 Results: Adverse prognostic features 21 We identified in our cohort a series of genes associated with an adverse prognosis. - Some of them were expected and/or had previously been described - TP53 mutations (3%) - ATM (3%) and ATR (2%) mutations - Some were novel - CCND1 mutations (2.2%) - ZFHX4 mutations (4.0%) - NCKAP5 mutations (2.2%) Gene PFS present PFS absence p-value 2y OS Present 2y OS Absent p-value Frequency ZFHX < % 80% NS 4% TP % 81% % ATM/ATR mutations % 81% % CCND NS 38% 80% % NCKAP NS 53% 80% % Walker et al. JCO 2015

22 Poor Prognostic Genetic Markers 22 CKS1B PFS CKS1B OS TP53 PFS TP53 OS ATM/ATR PFS ATM/ATR OS Walker et al. JCO 2015

23 Poor Prognostic Genetic Markers 23 ZFHX4 PFS CCND1 OS NCKAP5 OS Walker et al. JCO 2015

24 Results: Multivariate Analysis 24 C=0.69, optimism 0.01 Walker et al. JCO 2015

25 Results: Cumulative impact on survival (ISS-FISH) 25 The more adverse features are present, the worse the outcome is Nevertheless, 35% of patients that progress before 18 months do not have an adverse lesion 25% of patients that die before 24 months do not have an adverse feature Walker et al. JCO 2015

26 26 Results: Improved prognostic discrimination using ISS-MUT Group I: Group II: Or ISSI/II no CNSA/mut ISS III no CNSA/mut ISS I/II/III +1 CNSA/mut Group III: 2+ CNSA/mut Walker et al. JCO 2015

27 Results: Improved prognostic discrimination using ISS-MUT Walker et al. JCO 2015

28 The relevance of intra-clonal heterogeneity to myeloma 28 Multiple Myeloma Many sites at which tumour cells reside in the body Each population is derived from a common ancestor But, each population may evolve independently mutations may be present at one site but not another Over time clonal dominance will change clonal tiding differential responses to treatment over time

29 Darwinian evolution through natural selection 29 Genetic analyses (FISH, NGS ) have unravelled intratumour heterogeneity. Branching or Darwinian evolution in many different human cancers (ALL, breast cancer, gastric cancers ) Anderson et al Nature 2011, Nik-Zainal et al Cell 2012, Gerlinger et al NEJM 2012 Understanding intraclonal heterogeneity and tumour evolution will be key to improve targeted therapies, and to describe the mechanisms of treatment-resistance Greaves M & Maley C, Nature, 2012

30 A Model of Myeloma Disease Progression COMPETITION AND SELECTIVE PRESSURE MIGRATION AND FOUNDER EFFECT Clonal advantage Myeloma progenitor cell TUMOUR CELL DIVERSITY GENETIC LESIONS Morgan G, et al. Nat Rev Cancer. 2012;12:

31 Clonal Dynamics in High Risk Myeloma 31 8 FISH assays to interrogate clonal dynamics of one patient Keats et al., Blood (2012)

32 Patients and Methods 32 Whole Exome Sequencing Alignment, calibration, and de-duplication 463 samples from NCRI Myeloma XI trial (NCT ) CD138+ cells as tumour DNA WCP as normal DNA HiSeq 2000 Illumina FastQC BWA Stampy GATK Picard Mutation Calling MuTect Variants filtering SnpEff Oncotator Copy number assessment Control-FREEC MLPA data Measurement of Sub-clonal diversity GAUCHO Number of mutations Mutated genes Mutation signatures Somatic aberrations Hyperdiploidy Cancer clonal fraction Number of sub-clones Berger-Parker index Inverse Simpson index

33 Determining heterogeneity at the single cell level 33 BM aspiration CD138+ cell purification DNA isolation Fixed/Frozen Cells MM Patient Whole Exome Sequencing Single Cell Analysis List of Mutations List of Genomic Aberrations Clonal phylogeny

34 A linear pattern of MM evolution 34 - Consecutive accumulation of somatic mutations and genomic aberrations Melchor L et al Leukemia 2014

35 A linear pattern of MM evolution 35 - Consecutive accumulation of somatic mutations and genomic aberrations Melchor L et al Leukemia 2014

36 A branching pattern of MM evolution 36 Divergent clonal lineages arise from common clonal ancestors Melchor L et al Leukemia 2014

37 A branching pattern of MM evolution 37 Divergent clonal lineages arise from common clonal ancestors Melchor L et al Leukemia 2014

38 Parallel evolution in MM 38 Under the same selective pressure and environmental conditions, independent but not far-related clones may acquire similar mutations conferring growth and selective advantages For example, a patient with both an NRAS and KRAS mutation Melchor L et al Leukemia 2014

39 Parallel evolution in MM 39 Melchor L et al Leukemia 2014

40 RAS mutations subclonal heterogeneity 40

41 Cancer Evolutionary Trees 41 Hairy Cell Leukaemia Myeloma Solid Cancer Common BRAF V600 mutation 5 NS SNVs No common mutations Primary translocations/hyperdiploidy 25 NS SNVs No common mutations No common translocations 540 NS SNVs

42 Chop down the trunk, don t aim for the branches? 42 BRAF inhibitors RAS inhibitors BRD inhibitors Copy Number Abnormalities Primary Translocations/ Hyperdiploidy DNA methylation inhibitors MMSET/CCNDx inhibitors

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