the biology of high risk cll - er congressi

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






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






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






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. 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






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


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

the biology of high risk cll - er congressi

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