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The BIOLOGY of HIGH RISK CLL
Division of Hematology
Department of Translational Medicine
University of Eastern Piedmont
Novara-Italy
Gianluca Gaidano, M.D., Ph.D.
Disclosures
Roche (Advisory Board)
Janssen (Advisory Board)
Gilead (Advisory Board)
Amgen (Advisory board; research support)
Morphosys (Advisory Board)
Abbvie (Advisory Board)
Outline
• The genetic landscape of CLL
• Molecular prognosticators of CLL
• Molecular predictors of CLL
• Richter syndrome biomarkers
• Novel biomarkers
Pathogenesis of CLL
InitiationProgression
ChemorefractorinessTransformation
MicroenvironmentInteractions
TrasformingLesion
SecondaryLesion
Predisposition Promotion/Accumulation
PolygenicIRF4IRF8MYCOther
del13q+12MYD88
TP53NOTCH1SF3B1BIRC3ATMMYCCDKN2A
Signaling pathwaysBCRNF-kBTLRCD38VLA-4 integrinsNOTCHCXCR4
CLL is genetically heterogeneous and lacksdisease defining genetic lesions
The wordcloud shows the genes that are reported as mutated in CLL by the v77 of the Catalogue of Somatic Mutations in Cancer (COSMIC). The size of the font is proportional to the mutation frequency
• One of the tumor with the lowest background mutation load (0.6 per Mb)• No unifying gene mutations• TP53, NOTCH1, SF3B1, ATM mutated in >5% CLL
NF-kB
BIRC3TRAF3NFKBIE
NOTCHTLR
MYD88
Apoptosis
BCL2
del13q (miR15/16)
DNA damage response
P
TP53P
TP53P
Cellcycle
BCR
NOTCH1FBXW7SPEN
TP53ATMSF3BRPS15POT1
CDKN2A
MAPK
RASBRAFMAP2K1
Moia et al, 2018
Outline
• The genetic landscape of CLL
• Molecular prognosticators of CLL
• Molecular predictors of CLL
• Richter syndrome biomarkers
• Novel biomarkers
Genetic-based models of CLL prognosis
Döhner H, et al. N Engl J Med. 2000Rossi et al, Blood 2013
(N=325)
Surv
ival
Months
13q-Deletion
Trisomy 12
0 2 4 6 8 10 12 140
.00
.20
.40
.60
.81
.0
Years from diagnosis
Cu
mu
lative
pro
ba
bility o
f O
S (
%)
Matched general population
Cytogenetic model Mutational- cytogenetic model
N
del13q 26%
Normal/+12 40%
NOTCH1 M/SF3B1 M/del11q 17%
TP53 DIS/BIRC3 DIS 17%
CLL-IPI
Variable Adverse factor Coeff. HR
TP53 (17p) deleted and/or mutated 1.442 4.2
Grading
4
Prognostic Score 0 – 10
IGHV status Unmutated 0.941 2.6
B2M, mg/L > 3.5 0.665 2.0
Clinical stage Binet B/C or Rai I-IV 0.499 1.6
Age > 65 years 0.555 1.7
2
1
2
1
Risk group Score PatientsN (%)
5-year OS, %
HR (95% CI) p value
Very High 7 – 10 62 (5) 23.3 3.6 (2.6 - 4.8) < 0.001
High 4 – 6 326 (27) 63.6 1.9 (1.5 - 2.3) < 0.001
Intermediate 2 – 3 464 (39) 79.4 3.5 (2.5 - 4.8) < 0.001
Low 0 – 1 340 (29) 93.2
International CLL-IPI working group. Lancet Oncol 2016
Time (months)
Overa
ll surv
ival
Low
Intermediate
High
Very high
Overall survival (all patients)
Outline
• The genetic landscape of CLL
• Molecular prognosticators of CLL
• Molecular predictors of CLL
• Richter syndrome biomarkers
• Novel biomarkers
Predictive biomarkers Prognostic biomarkers
Treatment tailoring Patient counseling
Frequency of follow-up
Identify those apropriate for early intervention trials
Clinical applications of predictive and prognostic biomarkers in CLL
Antigen-experienced,
memory B-cells
Naive
B-cells
Germinal Center
B-cells
Ag
T-cell dependent affinity maturation
M-CLL
MBL
• Genetic lesions
• BCR stimulation
• Microenvironmental interactions
Ag
T-cell independent immune response
(no somatic hypermutation)
Antigen-experienced
B-cells
U-CLLMBL
IGHV mutated patients gain the greatest benefit from FCR
a p-value vs. matched general populationFCR: fludarabine, cyclophosphamide, rituximab; MDACC: MD Anderson Cancer Center; OS: overall survival; PFS: progression-free survival
1. Thompson PA, et al. Blood 2016; 127:303–309. 2. Fischer K, et al. Blood 2016; 127:208–215. 3. Rossi D et al. Blood 2015; 126 1921–1924.
2 4 6 8 10 12 140 16
p<0.00010
25
50
75
100
PFS
(%
)
Time (years)
PFS
(%
)
Time (years)
PFS
(%
)
Time (years)
0
25
50
75
100
0 102 4 6 8
OS
(%)
Time (years)
0
25
50
75
100
0 102 4 6 8
IGHV mutated
IGHV unmutated
Matched general population
IGHV mutated, FCR
IGHV mutated, FC
p<0.001
p<0.0001a
p<0.0001a
High-risk (del[17p])
Intermediate-risk (IGHV unmutated and/or del[11q])
Low-risk (IGHVmutated)
p=0.227a
MDACC Phase II study (N=300)1 CLL8 study vs. FC (N=817)2
Italian retrospective analysis (N=404)3
100
0
25
50
75
0 2 4 6 8
IGHV unmutated, FC
IGHV unmutated, FCR
Hallek et al. Blood. 2008;Oscier et al. Br J Haematol. 2013Zelenetz et al J Natl Cancer Inst 2015Eichhorts et al, Ann Oncol 2015.
Guideline recommendations for IGHV analysis in clinical practice
Recommendation When
iwCLLNot generally
indicated-
BCSH Not recommended -
NCCNNot generally
indicated-
ESMO Desirable Before treatment
PFS
(%
)
Time (months)
0 2 4 6 8 10 12 14 16 18 20 22 24 26
100
80
60
40
20
0P
FS (
%)
Time (months)
0
20
40
60
80
100
0 6 12 18 24 30 36 42 48 54 60 66 72
IGHV mutated (n=16)
IGHV unmutated (n=79)
Analysis of ibrutinib-treated patients with R/R CLL/SLL from Phase Ib/II Study PCYC-1102/1103
Sharman JP, et al. ASH 2014, #330; O’Brien S, et al. ASH 2016, #233
Analysis of idelalisib-treated patients with R/R CLL from Phase III Study 116/117
IGHV unmutated (n=91)
IGHV mutated (n=19)
BCRi are efficacious regardless of IGHV status
12%
majorsubsets
high propensity for Richter’s transformation
#2
#1
#4
#8
the paradigmatic indolent subset
very aggressive
Major stereotyped subsets represent distinct CLL variants
Tobin et al. Blood 2003; Ghiotto et al. J Clin Invest 2004; Stamatopoulos et al. Blood 2007; Chu et al. Blood 2008; Catera et al. Mol Med 2008; Rossi et al. Clin Cancer Res 2009; Sutton et al. Blood 2009; Chu et al. Blood 2010; Marincevic et al. Haematologica 2010; Maura et al. PLosOne 2011; Ntoufa et al. Mol Med 2012; Agathangelidis et al. Blood 2012; Strefford et al. Leukemia 2013; Rossi et al. Blood 2013; Papakonstantinou et al Mol Med 2013; Vardi et al. Clin Cancer Res 2013; Sutton et al. Mol Med 2014; Mansouri et al. J Exp Med 2015
very aggressive
Courtesy of K Stamatopoulos
BcR stereotypy refines prognostication in CLL
Distinct clinical outcomes for subsets #1, #2 and #4, independently of genomic aberrations or SHM status
Baliakas et al. Lancet Haematol. 2014
All express IGHV4-34, all concern IG mutated CLL, yet the outcome is different
Xochelli et al. Clin Cancer Res 2017
Subset #2 is as bad as CLL with TP53 aberrations though essentially devoid of such lesions
Baliakas et al. Blood 2015
Subset #8: the highest risk for Richter’s transformation amongst all CLL
Rossi et al. Clin Cancer Res. 2009
#4
#1
#2
0 5 10 15 20 25 30
Time years
0%
25%
50%
75%
100%
% u
ntr
ea
ted
#2, n=212 del(17p), n=284
p=0.5
Courtesy of K Stamatopoulos
TP53 abnormalities in CLL
5’ 3’
1 DNA BINDING
EX4 EX9
393
Missense Nonsense Frameshift
TP53
Freq
uen
cy
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
MBL Early stage CLL
CLL requiring treatment
F-refactory CLL
Richter syndrome
TP53 M
del17p13
TP53 M/del17p13
N=1/63(1.5%)
N=30/318(9.4%)
N=44/99(44.4%)
N=25/38(65.7%)
N=13/268(4.8%)
Dohner et al, New Engl J Med 2000 ; Rasi et al, Haematologica 2012; Zainuddin et al, Leuk Res 2011; Zenz et al J Clin Oncol 2010;Rossi et al Blood 2011; Rossi et al Blood 2014
Chr17
DNA damage
P
p53P
p21
cyclin B
p53P
cyclin B
cdc2p21
BAXCaspase 9
Apoptosis
Cell cycle arrest
M P
del(17p)
p53wt
(rare)
M P
del(17p)p53mut
(>80%)
P P
LOH
p53mut
M P
p53wt/mut
(rare)
M P
normal
p arm
p53
q arm
17p-censored
11q-censored
+12q-censored
13q-single-censored
No aberration-censored
Months
Hallek et al, Lancet 2010
0 6 12 18 24 30 36 42 48
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0 6 12 18 24 30 36 42 48 54PFS Months
FCR
17p- on FCR
TP53 abnormalities in CLL
Stilgenbaueret al, Blood 2014
FC and TP53WT
FC and TP53mut
FCR and TP53WT
FCR and TP53mut
Badoux Blood 2011; Fisher J Clin Oncol 2011; O’Brien, ASH 2014; Sharman ASH 2014; Byrd ASH 2015; Stilgenbauer, ASH 2015
Chemoimmunotherapy (CIT) vs novel agents in TP53 disrupted CLL
0%
20%
40%
60%
80%
100%
Re
spo
nse
rate
0%
20%
40%
60%
80%
100%
CR PR PR-L
35%
7%
83% 78% 79%
12
-mo
nth
s P
FS
18% 22%
80% 79% 72%
Response rate PFS
CIT Novel agents CIT Novel agents
Relapsed/Refractory CLL
0
0.2
0.4
0.6
0.8
1.0
Pro
po
rtio
n W
ith
PFS
0 6 12 18 24 30 36 42
Months
del(17p)del(11q)No del(17p) or del(11q)
+ Censored
TP53 disruption is a prognostic biomarker in CLLtreated with novel agents
Ibrutinib in trials
VenetoclaxIdelalisib+R
Ibrutinib in real-world practice
Byrd JC, Blood 2015; Thompson PA, Cancer 2015; Winqvist M, Haematologica 2016; Barrientos, ASCO,2015, 7011; Roberts, et al New Engl J Med 2016
Hallek et al. Blood. 2008;Oscier et al. Br J Haematol. 2013Pospisilova et al. Leukemia. 2012Zelenetz et al J Natl Cancer Inst 2015Eichhorts et al, Ann Oncol 2015.
Guideline recommendations for TP53 analysis in clinical practice
When What
iwCLL Before treatment 17p deletion
ERIC Before treatment TP53 mutation
BCSH Before treatment 17p deletion and TP53 mutation
NCCN Before treatment 17p deletion and TP53 mutation
ESMO Before treatment 17p deletion and TP53 mutation
Thessaloniki
Brno
Uppsala
Ulm
London
NovaraMadrid
Paris
Amsterdam
Copenhagen
Bellinzona
Harmonization of TP53 mutation analysis
TP53 GuidelinesTP53 NetworkTP53 CertificationTP53 Manual
http://www.ericll.org/pages/networks/tp53network/ericmanualfortp53mutationalanalysis/!
a In patients who are not eligible for any other therapiesChl: chlorambucil; CIRS: Cumulative Illness Rating Scale; Cr: creatinine
FCR
Chl + anti-CD20
TP53
Chemo + anti-CD20
Age;CIRS;
Cr clearance
BR
IbrutinibIdelalisib + Ra
IGHV Ibrutinib
Mutated and/or deleted
Wild type
Unmutated
Mutated
Biomarkers may inform first line treatment
Personal view
Outline
• The genetic landscape of CLL
• Molecular prognosticators of CLL
• Molecular predictors of CLL
• Richter syndrome biomarkers
• Novel biomarkers
BM PB
Lymph node biopsy
BIOPSY IS MANDATORY
(PET-guided)
Clinical suspicion of RS
• Bulky disease
• Extranodal involvement
• B symptoms
• High LDH
Clinical clues of Richter transformation
NOTCH1 wt
NOTCH1 M
p<.001
No. at Risk
NOTCH1 wt 531 279 92 31 11 3 1 0 0
NOTCH1 M 74 28 8 1 0 0 0 0 0
Events Total 5-year risk 95% CI
NOTCH1 wt 18 531 3.9% 2.0-5.8
NOTCH1 M 12 74 18.6% 7.3-29.9
No. at Risk
NOTCH1 wt & no IGHV4-39 519 273 90 30 11 3 1 0 0
NOTCH1 wt & IGHV4-39 12 12 12 12 0 0 0 0 0
NOTCH1 M & no IGHV4-39 67 27 8 1 0 0 0 0 0
NOTCH1 M & IGHV4-39 7 1 0 0 0 0 0 0 0
Events Total 5-year risk 95% CI
NOTCH1 wt & no IGHV4-39 18 519 4.0% 2.1-5.9
NOTCH1 wt & IGHV4-39 0 12 0
NOTCH1 M & no IGHV4-39 8 67 12.5% 2.9-22.1
NOTCH1 M & IGHV4-39 4 7 75.0% 32.5-100
NOTCH1 wt & no IGHV4-39
NOTCH1 M & no IGHV4-39
NOTCH1 M & IGHV4-39
NOTCH1 wt & IGHV4-39
p<.001
p<.001
Risk of Richter transformation according to NOTCH1 mutation status and IGHV usage at CLL diagnosis
MYC
activation
TP53
disruption
CLL DLBCL
(Richter)
transformation
MYC
translocation
amplification
NOTCH1
mutations
TP53 disruption, MYC activation and CDKN2A loss contribute to CLL transformation to Richter syndrome
MGA
mutations
Fabbri, et al. J Exp Med 2011
Rossi, et al. Blood 2011
Rossi, et al. Blood 2012
Rossi, et al. Leuk Lymphoma 2012
Chigrinova et al, Blood, in press
CDKN2A
loss
Clonal relationship of Richter syndrome
CLL
80%
20%
Clonally relatedRichter
Clonally unrelatedRichter
V4-39 D6 J4
V4-39 D6 J4
V4-34 D2-2 J3
The genetic profile of clonally unrelated RS
differs from that of clonally related RS
p=.018
p=.009
Clonally unrelated
Clonally related
p=.017
Rossi et al, Blood 2011; Rossi and Gaidano, 2018
Outline
• The genetic landscape of CLL
• Molecular prognosticators of CLL
• Molecular predictors of CLL
• Richter syndrome biomarkers
• Novel biomarkers
Thompson PA, ASH 2014, Oral Presentation #22
Complex Karyotype in the novel agent era
Ibrutinib MDACC Ibrutinib PCYC-1102/1103
O’Brien S, et al. ASH 2016; 233
Venetoclax
Anderson MA, Blood 2017
0 2 4 6 8 1 0 1 2 1 4 1 6 1 8 2 0 2 2 2 4 2 6 2 8 3 0 3 2 3 4 3 6 3 8 4 0
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
1 0 0
T im e (M o n th s )
Pro
ba
bil
ity
of
PF
S (
%)
C K T & E ith e r d e l(1 7 p )/T P 5 3 -M (n = 1 6 )
C K T & N e ith e r d e l(1 7 p )/T P 5 3 -M (n = 1 0 )
N o C K T & E ith e r d e l(1 7 p )/T P 5 3 -M (n = 1 7 )
N o C K T & N e ith e r d e l(1 7 p )/T P 5 3 -M (n = 2 2 )
Idelalisib-R GS 0116/0117
Kreuzer, et al. Abstract #192, ASH, 2016.
BCR
CD
79
A
CARD11
CD
79
B BTK
MALT1 BCL10
IKBKB
PLCG2
Non-canonicalNF-κB pathway
TRAF3 TRAF2
MAP3K14
IKK
BIRC3
NF-kBactivation
Ibrutinib
Molecular mechanisms of resistance to ibrutinib
BCR pathway
CLL
DLBCL
MCL
In CLL:BTK C481S mutationPLCG2 mutationAbsent in ibrutinib naïve patients
Davis, Nature 2010Woyach, NEJM 2014Furman, NEJM 2014Famà, Blood 2014Rahal, Nat Med 2014
Clonal architecture of TP53 mutated CLL
Scenario 2Scenario 3 Scenario 1
Nadeu F et al Blood 2016
subclonal M clonal M+subclonal M
clonal M wt
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Alle
le f
req
ue
ncy
corr
ect
edfo
rtu
mo
rre
pre
sen
tati
on
TP53 mutations
Sanger sequencingnegative n=50
Sanger sequencingpositive n=35
TP53 mutations
Small TP53 mutated subclones in untreated CLL
85%(N=263)
N=309
6%(N=18)
3%(N=10)
6%(N=18)
Ultra deep-NGS85 TP53 mutations in 46/309 (15%) CLL
* TP53 mutations and 17p13 deletion
Median allele frequency: 2.1% (range: 0.3-11%)
TP53 unmutatedSolely subclonal TP53 MClonal TP53 M
- .0042 .<.0001.0042 - .6926
<.0001 .6926 -
p from pairwise comparisons
Rossi, Blood 2014
INCREASING CCF
TP53 subclone evolution in CLL treated within the CLL8 trial
Landau, Nature 2015
Selection of the
TP53 mutated subclone
Expansion of the
TP53 mutated clone
Diagnosis Chemotherapy Progression Chemotherapy Refractoriness
TP53 mutated CLL cell
NGS Sanger
Rossi, Blood 2014; Malcikova, Leukemia 2015; Landau, Nature 2015; Nadeu, Blood 2016
Summing up
• CLL is characterized by a marked degree of molecular heterogeneity,since few mutations recur patients at a frequency >5%
• TP53 disruption identifies a genetic category of high risk CLL,predicts chemoimmunotherapy failure and mandates treatment withinnovative drugs, including ibrutinib, idelalisib or venetoclax
• Mutated IGHV gene represent a predictive biomarker for identifyingpatients that may benefit the most from chemoimmunotherapy withFCR
• Novel molecular prognosticators and predictors are under scrutiny,but their application in the clinical practice is premature
Lodovico Terzi di BergamoValeria SpinaDavide Rossi
Fary DiopRiccardo MoiaLorenzo De PaoliGloria Margiotta CasaluciClara DeambrogiChiara FaviniAhad Ahmed KodipadSruthi SagirajuNawwar MaherSimone Favini
Ilaria Del GiudiceSabina ChiarettiMonica MessinaAnna Guarini
Robin Foà
CROAVIANO
Michele Da BoValter Gattei
Roberto Marasca
Carol Moreno
Giovanni Del Poeta
Silvia Deaglio
Francesco Forconi
Grant support:
Luca Laurenti
CROAVIANO
Paolo Ghia
Laura PasqualucciGiulia Fabbri
Riccardo Dalla Favera
DSL
NOTCH1ICN
S3 γ-secretase
S2 Metalloprotease
Pro-NOTCH ICN
ER-Golgi
RPBJ
MAML
Other Co-Activators
gene expression
Degradation
SPEN
RPBJ
DTX1
Ubiquitination
Degradation
5’ 3’
EGF repeats (1-36) LNR RAMHDTM
Ankyrin
1 2556
2155 2556
CLL
Missense Nonsense Frameshift
TAD PEST
TAD PEST
NOTCH1 mutations in CLL
0%
10%
20%
30%
40%
50%
Fre
quency (
%)
N=60/539
(11%)
N=18/58
(31%)N=10/48
(20%)
N=2/134
(1%)
***
***
P<0.001
***
P<0.05 **
**
N=2/63
(3%)
de novo
DLBCL
MBL CLL
diagnosis
Richter
syndrome
**
F-ref CLL
Arruga F et al. Leukemia 2013
Arruga F et al. Leukemia 2016
Fabbri G et al. PNAS 2017
Pozzo F et al. Leukemia 2017
Fabbri, et al. J Exp Med 2011
Puente, et al. Nature 2011
Wang, et al. New Engl J Med 2011
Rossi, et al. Blood 2012
Rasi, et al. Haematologica 2012
MYC (proliferation)DUSP22 (migration)CD20 (anti CD20)
3’ UTR
NOTCH1 mutations as predictive marker for no benefitfrom addition of rituximab to chemotherapy
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96
PFS Months
Stilgenbauer S et al. Blood 2013
GCLLSG CLL8
PFS Months
GCLLSG CLL11
Estenfelder S et al. Blood 2016 128:3227