Molecular Genetics of Small Mature B-cell Lymphomas … Small B cell... · Molecular Genetics of...
Transcript of Molecular Genetics of Small Mature B-cell Lymphomas … Small B cell... · Molecular Genetics of...
Molecular Genetics of Small Mature B-cell Lymphomas
Elias CampoHospital Clinic, University of Barcelona
Barcelona, Spain
Genetic Drivers in Lymphomagenesis
Overt Lymphoid Neoplasia
Progression/TransformationClonal Population
Primary Genetic Alterations
Secondary Genetic Alterations
Microenvironment Genetic alterations
Impact of NGS in Small B-cell Neoplasms
• Orient management estrategies
• Understanding evolution of the disease
• Prognostic groups and risk stratification
• Disease specific profiles
ICGC: Puente et al. Nature. 2015, DFCI: Landau et al. Nature 2015,
Driver mutated Genes and CNA in CLL ICGC & Danna-Farber (DFCI)
2336 21
ICGC DFCI
Driver genes (n=80)
ICGC (n=428): (Puente et al. Nature 2015). DFCI/Broad (n=123) Only pretreatment cases consideredCLL8 (n=278): Clinical trial (Landau et al. Nature 2015)
Driver mutated Genes in CLL genomes (ICGC vs Danna Farber (DFCI)
Population based vs Clinical trialMedian age DFCI cohort10 yr younger than ICGC
Puente et al Nature 2015
452 CLL & 54 MBL (150 whole genomes)13.631 mut in coding genes: 27 per tumor951 CNA (2 per tumor)59 driver genes
Genomic Heterogeneity and Driverless in CLL
Puente et al, Nature 2011, 2015
NOTCH1 Mutations in CLL
0
5
10
15
20
25
Richter Transformation
Mutated wt
Stilgenbauer S et al. Blood 2014;123:3247-3254
FCR- NOTCH1 wt
FCR- NOTCH1 mut
E52DEL E52DEL wt L265Pwt wt wt L265P L265P
CCL2CCL4CCL3IL6
IL1RA
TLR/MYD88 mutated pathway in CLLA specific subgroup of patients?
Martínez-Trillos A, et al. Blood 2014; 123:3790–3796.
Age at diagnosis (years)
30
20
10
0
20 30 60 70 80 9040 50
Patie
nts
(%)
1.0
0.8
0.4
0.2
0.0
0.6
0 5 10 15 20
MYD88 mut
MYD88 wt
p=0.024
Rel
ativ
e su
rviv
alYears from diagnosis
NFĸB pathwayMAPK pathways
Inflammatory cytokinesand chemokines
IRAKs
MYD88
TLR
Clinical relevance of individual mutated genes in CLL
Mansouri et al. 2015, JEM
NFKBIE
Young et al. 2017, Leukemia
EGR2
Ljungström et al. 2015, Blood
RPS15
SF3B1
Quesada et al. 2011, Nat Genetics
NOTCH1
Puente et al. 2011, NaturePOT1
Herling et al. 2016, Blood
?
Prognostic impact of number of mutated drivers and subclonal alterations in CLL
Puente X et al Nature 2015 Landau DA et al Cell 2013
Number of drivers Clonal/subclonal heterogeneity
TP53 variant CLL cell
Diagnosis Chemotherapy Progression Chemotherapy Refractoriness
Small TP53 variant subclone mixed with
TP53 wt clones
Removal of TP53 wt clones and selection of the
TP53 variant subclone
Expansion of the TP53 variant clone
Rosi D et al Blood 2013
• 406 untreated patients• 28 CLL driver genes• Mean coverage 1500x NGS • Sensitive pipeline 0.1% allelic frequency
Small mutated subclones are identified in virtually all genes
Nadeu F et al Leukemia 2017 (In press)
Clinical Impact of clonal and sublconal mutations in CLL
Nadeu F et al Blood 2016; Leukemia 2017 (In press)
Ibrutinib-resistant mutations may be detected before clinical relapse
Ahn IE et al. Blood 2017;129:1469-1479; Woyach JA et al JCO 2017, 35, 1437-1443; Arnason & Brown Curr Oncol Report 2017
Detection of mutated subclones may precede treatment Median 8-9 months (3-15 months)
BTK
Somatic Mutations in LymphoplasmacyticLymphoma
Tiacci et al NEJM 2011; Ngo Nature 2011; Puente Nature 2011; Xi L et al Blood 2012; Schmidt et al Br J Haematol 2015; Cao et al Leukemia, 2015 29, 169–176.; Xu L et al Blood 2017
MYD88 L265P
• 95% WM/LPL• 29% DLBCL-ABC• 6% MZL• 3% CLL
• Useful information in the differential diagnosis of LPL• Need to be interpreted in the global context of the disease
CXCR4
• 25-35% WM/LPL• Associated with MYD88• More active disease• Less lymphadenopathy• More resistant disease
to new drugs
Patients treated with Ibrutininb
Mutations before clinical progression
When should MYD88 L265P mutations be studied in small B-cell neoplasms?
Swerdlow SH et al Virchows Arch. 2016;468(3):259-75
• Differential diagnosis of LPL strongly considered but findings not conclusive
• Differential diagnosis between LPL and IgM PCM
• If LPL is not in the differential diagnosis MYD88 mutations may not be useful
Somatic Mutations in HCL and HCLv
Hairy Cell Leukemia
HCL-v
Tiacci E, et al. N Engl J Med. 2011; Waterfall J et al Nat Genet. 2014; Durham BH et al Blood 2017
MAP2K1
• 50 % HCLv50% HCL IgH V4-3450% Pediatric Type FL
CCND3, U2AF1
• NF1/NF2 downregulation• KRAS mutation
Vemurafenib Resistance
7q deletion
Okosun J et al Nat Genet. 2014 , 46:176-81.
Follicular lymphoma Mutational Landscape
Krysiak K et al Blood 2017; 129: 473-483
Early and Late Mutations in Follicular Lymphoma
Okosun J et al Nat Genet. 2014 , 46:176-81.
Early driver mutations in chromatin regulator genes (CREBBP, EZH2 and KMT2D (MLL2)),
Gained at transformation : EBF1 and regulators of NF-κB signaling(MYD88 and TNFAIP3)
Clinical Impact of FL Mutations
EZH2 inhibitor (Tazemetostat) in Refractory/Relapsed FL• OR: 63% in patients with EZH2 mutations (N = 8)
28% in with wild type EZH2 (N = 46)
Morschhauser F et al Hematol Oncol 2017; 35, S2: 24–25
Pastore et al Lancet Oncol. 2015 Sep;16(9):1011-1012
Risk Stratification
Target Therapy
NOTCH1/2 Mutations in FL
7/112 FL cases (6.3%)5 NOTCH12 NOTCH2
NOTCH3/4 (4%)
PEST/TADD domainTruncanting mutations
FemaleSplenic/extranodal involvementNegative for t(14;18)DLBCL component
CD21
BCL2 LMO2Karube K et al J Pathol 2014; 234:423-30Krysiak K et al Blood 2017; 129: 473-483
Pediatric Type Follicular Lymphoma
BCL2
GenesPTFL
(n=71)(%)
t(14;18)-neg FL (%)
t(14;18)-pos FL(%) P-value
TNFRSF14 33-51 36 18-46 Ns
KMT2D 14-16 36 67-82 Ns
CREBBP 3* 45 33-64 0.001
FOXO1 5 27 - Ns
GNA13 11 0 - Ns
EZH2 3* 18 7-20 0.0049* >18yr
Genes PTFL
MAP2K1 38-49%
MAPK1 10%
IRF8 15-50%
Louissaint A et al Blood 2016; Ozawa et al Mod Pathol 2016; Schmidt J et al Blood 2016 & 2017
Mutational Profile of Nodal and Splenic Marginal ZoneLymphoma
Targeted Pathways
• KLF2, NOTCH2, activation (40-25%)• NFkB activation (36-51%)• BCR signaling (SMZL 8%)• PTPRD (NMZL 20%)
Arcaini L, Rossi D. Haematologica. 2012; Rossi et al J Exp Med 2012; Clipson et al Leukemia 2015; Parry et al Clin Cancer Res 2015; Spina V et al; 2016; Piris MA Blood 2016;
Clinical Impact of Somatic Mutations in SMZL
Parry et al Clin Cancer Res 2015 Campos-Martin Y et al Haematologica 2017
Steven H. Swerdlow et al. Blood 2016;127:2375-2390
Molecular Pathogenesis and Clinical Subtypes of MCL
CCND1 neg MCL
Classic MCL
Cyclin D1
Cyclin D1 Sox11
Mozos et al Haematologica 2009; Salaverria et al Blood 2013
Mantle cell lymphomaCCND1-negative variant
Rearrangements No. (%)
CCND2 22 (55%)
CCND3 0
No Cyclin D gene translocation
18 (45%)
Cryptic IG-CCND1/2/3 rearrangements in MCL
IGK/CCND3
Bea S et al unpublished
IHC CCND1+Break apart CCND1 neg
CrypticIGK-CCND1
…CACTAGGGCTCCCACA CCTTGGGCGC…
Chr2 IgK
110kb
Chr11 CCND1chr11
CCND1/2 neg MCL
Recurrent gene mutations in MCL
0
10
20
30
40
50
60
70
ATM CCND1 TP53 MLL2 MLL3 TRAF2 NOTCH1 WHSC1 BIRC3 NOTCH2 UBR5 S1PR1 MEF2B CARD11
Bea et al. & unpubl (n=170) Greiner et al (n=92) Zhang et al (n=56) Rahal et al (n=165)Kridel et al (n=108) Meissner et al (n=102) Rossi et al (n=151) Wu et al (n=13)
Zhang 2014 Bea 2013
ATMCCND1MLL2
TP53BIRC3WHSC1
MCL primary tumors (n=29)
SOX11 -
SOX11 +
IGHV-unmut
IGHV-mut
-80
-60
-40
-20
0
No. of somatic mutations (WES) No. of CNA (SNP-array)
20
40
60
80
0
0 SOX11IGHV
ATMCCND1TP53
WHSC1MLL2BIRC3
MEF2BCHMP4C
LUZP4RGS4
PDLIM3KCNC2
SLC17A6DCP1BPCSK2SP140TLR2
TRMP6DLGAP2DNAJC6TNRC6BCRYBG3ABCA3
KIAA1671ABCC9
No.
of
alte
ratio
ns
Somatic Mutations in MCL
M00
1M
007
M01
9M
023
M02
9M
028
M00
2M
011
M02
2M
025
M00
8M
020
M03
1M
030
M01
3M
014
M01
0M
006
M01
2M
024
M02
6M
018
M00
4M
003
M02
7M
009
M01
5M
021
M01
6
Bea S et al PNAS 2013
SOX11 +ATM (55%)
Chromatin (10%)
NOTCH1/2 (5%)TP53 23%)BIRC3 (7%)
High CNA
SOX11 -TLR2 (29%)
NOTCH1/2 (5%)TP53 23%)
BIRC3 (14%)
Low CNA
Prognostic impact of 17pTP53 aberrations in MCL
Royo C et al, Leukemia 2012TP53 wt
P =0 .045
17p/TP53 wt
17p/TP53 altered
SOX11-negative MCL
17p/TP53 wt
17p/TP53 altered
SOX11 +
Eskelund CW et al Blood 2017
• Diagnostic criteria to refine entities• Identification of subsets of patients• Prognostic and predictive significance• Monitoring disease evolution: Dynamic evolution of
mutational landscape• Targets for therapy decisions:
– Selection of patients
– Actionable mutations
Clinical Relevance of Mutational Profiles in Lymphoid Neoplasms