The Role of Cytogenetics in Elderly patients with Myeloma

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The Role of Cytogenetics in Elderly patients with MyelomaDr 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

Stages of Diseaseclinically and biologically

Morgan, Walker & Davies Nat Rev Cancer 2012 12:335

Advances in technology have led to an increasing knowledge of myeloma genetics

1995

G band

FISH

Translocations of C14

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 segmentsJ se

gsD

seg

s

IGH 3’ Flanking Probe IGHV Probe

14q32 regionImmunoglobulin heavy chain locus

Kindly provided by Dr Fiona Ross, Wessex Regional Cytogenetics Laboratory

Translocations

Hyperdiploidy

• Translocations– t(4;14)– t(11;14)– t(6;14)– t(14;16)– t(16;20)

Early events

• Chromosome gain– 3, 5, 7, 9, 11, 15, 19, 21

Molecular classification of myeloma

Kuehl & Bergsagel 2005

Normal Isotype Switching on Chromosome 14q32

VDJ S C S2 C2

VDJ

S S2

C2- Intervening DNA deleted- Hybrid switch formed

VDJ 2 2

switch region = 1-3kb long, tandem pentameric repeats)telomere

centromere

Illegitimate switch recombination in Myeloma

VDJ 2 2

VDJ C2

VDJ Gene X Gene Y C2

Gene X Gene Y

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

1995 2010

Global mappingGene expressionarrays

miRNAmethylation

G band

FISH

Translocations of C14

TC classification NGS

Translocations

Hyperdiploid

Translocationst(4;14)

t(11;14)

t(6;14)

t(14;16)

t(14;20)

Chromosome gain3, 5, 7, 9, 11, 15, 19, 21

Normal MGUS MM

2000 2005 2015

11

1 2 3 4 5

6 7 8 9 10 11 12

13 14 15

19 20

16 17 18

21 22 X

Hyperdiploidy

Walker et al. Blood 2006

• 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

Myeloma specific copy number variationDeletion-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%)

GainGain 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 202122 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)

14The Incidence of Abnormality Changes With Disease Progression

Ross et al. Haematologica 2010 95:1221Leone et al. Clinical Cancer Research 2008 14:6033Lopez-Corral et al. Clinical Cancer Research 2011 17:1692

Abnormality MGUS (%) SMM (%) MM (%)

t(11;14) 10 16 14

t(14;16) 3 3 3

t(14;20) 5 <1 1.5

del(13q) 24 37 45

del(17p) 3 1 8

1q+ 22 39 41

del(CDKN2C) 4 10 15

15Myeloma Disease Progression and Genetic Events

Morgan, Walker & Davies Nat Rev Cancer 2012 12:335

16

All t(4;14) have del(13)17p evenly distributed

t(4;14) t(11;14) 6 16 20? No Data HRD HRD+t(#;14) None

Inter relationship of abnormalities


17

All t(4;14) have del(13)17p evenly distributed

t(4;14) t(11;14) 6 16 20? No Data HRD HRD+t(#;14) None

Inter relationship of abnormalities


18

Myeloma IX trial: del(13) by FISH not associated with poor survival outcome*

n = 568ms 48.3 months

Patie

nts

(%)

Survival (months)

0

20

40

60

80

100

0 10 20 30 40 50 60 70

Survival according to del(13) by FISH

p = 0.024


n = 283; ms not reached

Patie

nts

(%)

Survival (months)

0

20

40

60

80

100

0 10 20 30 40 50 60 70

Survival according to del(13) with “bad” IgH and del(17)(p53) removed

p < 0.001



* In the absence of other adverse prognostic features.

No del(13)del(13) onlyBad IgH or del(17p)

No del(13)del(13)

19Inter-relationship of Adverse Lesions

Genetic abnormalities are not solitary eventsand can occur together

Strong positive association with adverse IGH and 1q+

-72% of IGH translocations with 1q+

Boyd et al. Leukemia 2011

Implicationsi. In order to understand the

prognosis of any lesion need to know if other lesions are present.

ii. Lesions may collaborate to mediate prognosis.

Frequency in the Elderly

Frequency of abnormalities with age

Ross et al Leukemia 2006

N = 228

Frequency of abnormalities with age

Avet Loiseau et al 2013 JCO

N = 1890, median age 72, range 66-94

Clinical and prognostic significance in the Elderly

Myeloma IX trial: effect of “bad” IgH translocations on survival



Patie

nts

(%)

Survival (months)

0

20

40

60

80

100

0 10 20 30 40 50 60 70

Combined “bad” IgH translocations

p < 0.001

n = 495ms not reached

n = 170ms 36 months

Patie

nts

(%)

Survival (months)

0

20

40

60

80

100

0 10 20 30 40 50 60 70

Intensive arm

p < 0.001


Patie

nts

(%)

Survival (months)

0

20

40

60

80

100

0 10 20 30 40 50 60

Non-intensive arm

p < 0.001

“Bad” IgHRest


No “bad” IgH translocationsAny “bad” IgH translocation

ms = median survival.

Myeloma IX trial: effect of deletion 17p53 on survival



Patie

nts

(%)

Survival (months)

0

20

40

60

80

100

0 10 20 30 40 50 60 70

Survival of patients with del(17)(p53)

p < 0.001

n = 545ms not reached


Patie

nts

(%)

Survival (months)

0

20

40

60

80

100

0 10 20 30 40 50 60 70

del(17)(p53): intensive arm

p = 0.004


Patie

nts

(%)

Survival (months)

0

20

40

60

80

100

0 10 20 30 40 50 60

del(17)(p53): non-intensive arm

p = 0.017n = 39

ms 19.2 months

del(17p)Rest

No del(17)(p53)del(17)(p53)

26Prognostic Impact of Lesions

Avet Loiseau et al JCO 2013

N = 1890, median age 72, range 66-94

Myeloma IX trial: effect of combined deletion 17p53 and “bad” IgH on survival

n = 754

Patie

nts

(%)

Survival (days)

0

20

40

60

80

100

0 500 1,000 1,500 2,000

Any bad IgH translocation + del(17)(p53)

p < 0.001

n = 214

n = 18

Rest

Bad IgH translocationBad IgH translocation + del(17p)

28Impact of Combined Lesions

The number of adverse markers has an additive effect on overall survival

60 months40 months23.4 months9.1 months

Boyd et al. Leukemia 2011

Defining high risk according to the ISS: “bad” IgH and del(17p)

Group 1 ISS1Group 2 ISS2Group 3 ISS3Group 4

bad IgH or del(17p)

bad IgH or del(17p)

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.

n = 125

Patie

nts

(%)

Survival (days)

0

20

40

60

80

100

0 500 1,000 1,500 2,000

p < 0.001

n = 244

n = 269

n = 76

1234

ie having something bad doesn’t always mean it is! Boyd et al. Leukemia 2011

Non-intensive pathway – chemotherapy regimens

Baseline assessment

Response assessment

Every 28 Days to maximal response. 6 - 9 cycles

Every 28 Days to maximal response. 6 - 9 cycles

CH

EM

OTH

ER

AP

Y R

AN

DO

MIS

ATION

Cyclophosphamide 500 mg po Days 1, 8, 15, 22

Thalidomide 50 - 200 mg po Daily

Da

examethasone

ttenuated

20 mg po Days 1- 4, 15- 18

elphalan 7 mg/m2 od po Days 1 - 4

Prednisolone 40 mg od po Days 1 - 4

Maximalresponse

THA

LIDO

MID

E R

AN

DO

MIS

ATION

M

Primary endpoints: PFS and OSSecondary 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) MedianRange

7357–89

7358–87

Gender(N (%))

MaleFemale

231 (54.6)192 (45.4)

242 (56.8)184 (43.2)

ISS (N (%)) IIIIIIMissing 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) MedianRange

4.90.3-40.4

5.00.4–64.0

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


PFS and OS according to cytogenetics

PFS OSFavourable 14 months

95% CI 12-17 range 0-6537 months

95% CI 22-44 range 0-69

Adverse 12 months95% CI 10-13 range 0-67

24 months95% CI 20-28 range 0-68


OS according to treatment group in patients with favorable cytogenetics

MP

CTDaP=0.1041


OS in favorable cytogenetics according to treatment; landmark at 1.5 years

CTDamedian not reached

MP42 months

CTDa not reached vs 42 months Morgan et al Blood 2011

Influence of cytogenetics on survival among patients achieving a CR

Favourable

Adverse


NGS results inform myeloma biology

Morgan GJ, Walker BA and Davies FE. Nature Reviews Cancer. Vol 12 May 335-348, 2012,

• 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.

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

HallmarksOf

Myeloma

Morgan G, et al. Nat Rev Cancer. 2012;12:335-48.

Comparative analysis of cancer evolutionary treesComparison across disease states and curability

Paediatric ALL Myeloma Solid cancer

40Linear and branching models for myeloma evolution

Morgan, Walker and Davies Nature Reviews Cancer 2012

41Linear 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

Adapted from Greaves MF, Malley CC. Nature. 2012;481:306-13.

Subclones with unique genotype/”driver” mutations

Ecosystem 1

Single founder cell (stem or progenitor)

Ecosystem 3 Ecosystem 5

Selective pressures Treatment

Ecosystem 4

PCLMMMGUS

Diffuse

Focal

Ecosystem 2

EMM

Adaption and survival of the fittest

A Model of MM Disease ProgressionA model based on the random acquisition of genetic hits and Darwinian selection

Morgan G, et al. Nat Rev Cancer. 2012;12:335-48.

Primary genetic eventsIgH translocationsHyperdiploidy Copy number abnormalities

DNA hypomethylationAcquired mutations

MGUS Smouldering myeloma Myeloma Plasma cell

leukaemia

Initiation Progression

Bone marrow Peripheral bloodGerminal centre

Post-GC B cell

Inherited variants

COMPETITION AND SELECTIVE PRESSURE MIGRATION AND FOUNDER EFFECT

Clonal advantage

Myeloma progenitor

cell

TUMOUR CELL DIVERSITYGENETIC LESIONS

Secondary genetic events

A Darwinian View of Induction, maintenance and relapseClones can be eradicated - cured

Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012

Myeloma progenitor

cell

A Darwinian view of induction, maintenance and relapseClones can be eradicated - cured

Evolutionary / TreatmentBottleneck

Post treatment


Clones with a distinct pattern of mutations

Target

Intraclonal heterogeneity and targeted treatment

Clones with a distinct pattern of mutations

Intraclonal heterogeneity and targeted treatment

Suboptimal response at 30%

A Darwinian View of Induction, maintenance and relapseClones can be eradicated - cured


Myeloma progenitor

cell

A Darwinian view of induction, maintenance and relapseClones can be eradicated - cured

Evolutionary / TreatmentBottleneck

Post treatment


Myeloma progenitor

cell

Clonal Tides During Myeloma TreatmentRelapse can come from any one of a number of clones

Relapse


Original clone – treatment resistant

treatment sensitiveDifferential sensitivity to treatment

Clonal dynamics over multiple relapsesClinical 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

in partnership with

Centre for Myeloma Research, ICRDavies LabMike BrightLei ZhangLauren AronsonJade StroverJackie FokDaniel Izthak

Morgan LabBrian WalkerChris WardellDavid JohnsonLi NiDavid GonzalezPing WuFabio MirabellaLorenzo MelchorAnnaMaria BrioliCharlotte PawlynElileen BoyleMatthew JennerKevin BoydMartin Kaiser

LeedsRG OwenAC RawstronR de TuteM DewarS Denman

G Cook

S Feyler

D Bowen

BirminghamMT Drayson

K Walker

A Adkins

N Newnham

SalisburyF Ross

L Chieccio

MRC Leukaemia Trial Steering CommitteeMRC Leukaemia Data Monitoring and Ethics CommitteeNCRI Haematological Oncology Clinical Studies GroupUK Myeloma Forum Clinical Trials CommitteeMyeloma UK

FundingMedical Research CouncilPharmion Novartis Chugai Pharma Bayer Schering PharmaOrthoBiotech CelgeneKay Kendall Leukaemia Fund

Chief InvestigatorsJA ChildGJ MorganGH JacksonNH Russell

CTRU, LeedsK CocksW GregoryA SzubertS BellN Navarro CoyF HeatleyP BestJ CarderM MatoukD EmsellA DaviesD Phillips

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The Role of Cytogenetics in Elderly patients with Myeloma