PROGNOSTICATION IN CLL: PRESENT AND FUTURE

PROGNOSTICATION IN CLL: PRESENT AND FUTURE Gianluca Gaidano, M.D., Ph.D. Division of Hematology Department of Translational Medicine Amedeo Avogadro University of Eastern Piedmont Novara-Italy

CLL: Homogeneous phenotype but heterogeneous clinical course Highly stable Lymphocytes years Slowly progressive Lymphocytes Tx years Rapidly progressive Lymphocytes Tx Tx Tx Tx months

Pathogenesis of CLL Trasforming Lesion Microenvironment Interactions Secondary Lesion Predisposition Initiation Promotion/Accumulation Progression Chemorefractoriness Transformation Polygenic IRF4 IRF8 MYC Other del13q +12 Signaling pathways BCR NF-kB TLR CD38 VLA-4 integrins NOTCH CXCR4 TP53 NOTCH1 SF3B1 BIRC3 ATM MYC CDKN2A

CLL mutations Font size according to gene mutation prevalence 3601 genes mutated in CLL from the COSMIC v71 database 100 genes mutated in 2 or more CLL 4 genes recurrently mutated in >5% of CLL Fabbri, J Exp Med 2011; Puente, Nature 2011; Rossi, Blood 2011; Quesada, Nat Genet 2011; Wang, N Engl J Med 2011; Rossi, Blood 2012

Applications of genetic biomarkers in CLL Survival Treatment tailoring Prognosticator Factors that provide information on the likely outcome of CLL Predictor Factors that provide information on the likely benefit from a specific CLL treatment Italiano A, J Clin Oncol 2011

Prognosticators and predictors in CLL Prognosticators Predictors Richter transformation TP53 disruption NOTCH1 mutations Gene mutations IGHV mutation FISH karyotype CLL BCR pathway mutations

FISH karyotype: Pivotal evidence that genetics stratifies CLL survival 13q-Deletion Survival Trisomy 12 (N=325) Months Döhner H, et al. N Engl J Med. 2000

Prognosticators and predictors in CLL Prognosticators Predictors Richter transformation TP53 disruption NOTCH1 mutations Gene mutations IGHV mutation FISH karyotype CLL BCR pathway mutations

UK LRF CLL4: validation of the impact of NOTCH1 and SF3B1 mutations on OS 100 NOTCH1 100 SF3B1 Survival Probability 80 60 40 20 Wild-type Survival Probability 80 60 40 20 Wild-type Mutant Mutant 0 0 0 50 100 150 200 Time from Randomization (mths) NOTCH1 Sur. (mths) 95% CI Events P-value Wild-type 75 68-81 308 Mutant 55 31-79 40 0.02 0 50 100 150 200 Time from Randomization (mths) SF3B1 Sur. (mths) 95% CI Events P-value Wild-type 79 72-86 250 Mutant 54 47-61 66 <0.001 Courtesy of D. Oscier and J. Strefford Oscier DG, et al. Blood 2013;121:468 475.

Integrating mutation and cytogenetics for CLL survival prognostication Survival Cumulative probability of OS (%) 0.0 0.2 0.4 0.6 0.8 1.0 Matched general population 0 2 4 6 8 10 12 14 Years from diagnosis N 10-year OS 10-year relative OS del13q 26% 69% 84% Normal/+12 40% 57% 70% NOTCH1 M/SF3B1 M/del11q 17% 37% 48% TP53 DIS/BIRC3 DIS 17% 29% 37% Rossi et al, Blood 2013

Impact of gene mutations on treatment free survival from diagnosis Binet A cases 100% % untreated 75% 50% 25% all negative, n=211 TP53abn, n=56 SF3B1, n=43 NOTCH1, n=55 del(11q), n=76 trisomy 12, n=83 del(13q), n=294 p<0.0001 Median TTFT (years) del13q 11.8 +12 4.1 del11q 2.7 NOTCH1 M 3.3 SF3B1 M 3.1 TP53 DIS 2.9 0% 0 5 10 15 20 Time (years) Baliakis et al, Leukemia 2014

Prognosticators and predictors in CLL Prognosticators Predictors Richter transformation TP53 disruption NOTCH1 mutations Gene mutations IGHV mutation FISH karyotype CLL BCR pathway mutations

NOTCH1 mutations predispose to the development of Richter syndrome MYC activation TP53 disruption Trasnformation Chemoresistance Rapid disease kinetics Driving forces Rossi, et al. Blood 2012 Villamor, et al. Leukemia 2013 NOTCH1 mutation CLL CDKN2A disruption DLBCL (Richter) NOTCH1 wt NOTCH1 NOTCH1 wt wt NOTCH1 wt NOTCH1 M NOTCH1 M p<.001 p<.001 NOTCH1 M NOTCH1 M Events Total 5-year Events risk Total 5-year risk 10-year risk 10-year risk NOTCH1 wt NOTCH1 18 wt 531 5% 18 531 NOTCH1 5% wt NOTCH1 6% wt 6% NOTCH1 M NOTCH1 12 M 74 45% 12 74 NOTCH1 45% M NOTCH1 41% M 41%

Risk of Richter transformation according to NOTCH1 status and stereotyped BCR at CLL diagnosis NOTCH1 wt & no IGHV4-39 NOTCH1 wt & IGHV4-39 NOTCH1 M & no IGHV4-39 NOTCH1 M & IGHV4-39 p<.001 p<.001 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 Rossi, et al. Br J Haematol 2012

Prognosticators and predictors in CLL Prognosticators Predictors Richter transformation TP53 disruption NOTCH1 mutations Gene mutations IGHV mutation FISH karyotype CLL BCR pathway mutations

TP53 abnormalities in CLL Missense Nonsense Frameshift NOTCH1 SF3B1 SF3B1 10 1010 10 1010 TP53 5 3 EX4 EX9 1 DNA BINDING 393 TP53 TP53 17p 17p 11q 11q 12 12 10101010 10101010101010 01010101010101010101010 01010101010101010 010101010101010 13q 13q 010101010101010101010101010101010101010101010 IGHV-U 1 0 10101010101010101010101010101010101010101010 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 1 Chr17 All IGHV U IGHV M normal 13q- +12 11q- 17p- Frequency 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% N=1/63 (1.5%) MBL TP53 M del17p13 TP53 M/del17p13 N=13/268 (4.8%) Early stage CLL N=30/318 (9.4%) CLL requiring treatment N=44/99 (44.4%) F-refactory CLL N=25/38 (65.7%) Richter syndrome Caspase 9 cdc2 p21 cyclin B Cell cycle arrest Apoptosis BAX p21 cyclin B p53 P DNA damage p53 P P 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, Stilgenbauer, Blood 2014

TP53 abnormalities in CLL 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 FCR 17p- on FCR 17p-censored 11q-censored +12q-censored 13q-singlecensored No aberrationcensored FC and TP53 WT FC and TP53 mut FCR and TP53 WT FCR and TP53 mut 0 6 12 18 24 30 36 42 48 54 PFS Months Months Hallek et al, ASH 2009 Stilgenbaueret al, ASH 2012 EHA-20: Ljungstrom S121; Tausch LB2070 (novel FCR prognosticators)

Allo-SCT in High-Risk CLL CLL3X: multicenter GCLLSG Genomic 100 unknown (18) aberrations 17p- (13) 11q- (26) other (21) 13q- (12) 50 Event-free Survival 0 0 24 48 72 96 Months from SCT Dreger P, et al., Blood 2010; Dreger P, et al. Blood 2013

Clonal architecture of TP53 mutated CLL Scenario 1 Scenario 2 Scenario 3 TP53 mutation representation 80% Detectable by Sanger sequencing TP53 mutation representation 20% Barely detectable by Sanger sequencing TP53 mutation representation 1% Not detectable by Sanger sequencing

Small TP53 mutated subclones account for ~30% of all cases harboring TP53 defects Allele frequency corrected for tumor representation 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Sanger sequencing positive n=35 TP53 mutations Ultra deep-ngs 85 TP53 mutations in 46/309 (15%) CLL Sanger sequencing negative n=50 Median allele frequency: 2.1% (range: 0.3-11%) 6% TP53 mutations N=309 N=309 TP53 lesions* (N=18) 85% 7% 84% (N=263) (N=22) (N=259) 3% (N=10) 6% (N=18) 4% (N=13) 5% (N=15) subclonal M clonal M * TP53 mutations and 17p13 deletion clonal M+subclonal M wt subclonal M clonal M+subclonal M clonal M+del17p del17p subclonal M+del17p clonal M+subclonal M+del17p clonal M wt Rossi, Blood 2014

Small TP53 mutated subclones have the same unfavorable prognostic impact as clonal TP53 defects p from pairwise comparisons -.0042.<.0001.0042 -.6926 <.0001.6926 - TP53 unmutated Solely subclonal TP53 M Clonal TP53 M ID9245 del17p p.g244d TP53 M (p.g244d) 100% 80% 66.0% 60% 40% 58.0% 20% 0.9% 0% -20% -10-5 0 5 10 15 20 25 30 35 40 45 Allele frequency months No. at risk 263 122 15 0 18 4 0 0 28 6 0 0 Diagnosis FCR CR Relapse Refractoriness Events Total 5-year OS 95% CI 77 263 75.1% 69.5-80.7% 9 18 46.3% 22.0-70.6% 19 28 34.6% 15.8-53.4% Rossi, Blood 2014

Small TP53 mutated subclones are selected by treatment because of their chemoresistance Diagnosis Chemotherapy Progression Chemotherapy Refractoriness TP53 unmutated Solely subclonal TP53 M Clonal TP53 M Small TP53 mutated subclone admixed with TP53 wild type clones Removal of TP53 wild type clones and selection of the TP53 mutated subclone Expansion of the TP53 mutated clone Poor outcome TP53 mutated CLL cell Rossi, Blood 2014

Guidelines recommendations on TP53 lesions in CLL NCI-IWCLL 2008 guidelines In the clinical practice, cytogenetics (FISH) del(17p) in the peripheral blood lymphocytes is desirable before treatment Repetition of FISH analyses seems justified before subsequent, second- and third-line treatment ERIC recommendations on TP53 mutation analysis TP53 mutations (exons 4-9) should be investigated immediately before treatment decision Outside clinical trials in patients requiring therapy who would be eligible to an allogeneic stem cell transplantation or other intensive therapies (e.g., FCR and BR) Previously treated patients with wild-type TP53 at the time of treatment, should be retested when further therapy is needed and results can be expected to influence choice of therapy British Committee for Standards in Haematology (BCSH) Patients should be screened for the presence of a TP53 abnormality prior to initial and subsequent treatment. Currently, TP53 loss should be assessed by FISH. Patients should also be screened for TP53 mutations when this assay becomes routinely avaliable (Grade B2). Hallek et al, Blood 2008 Oscier et al, Br J Haematol 2012 Pospisilova et al, Leukemia 2012

Prognosticators and predictors in CLL Prognosticators Predictors Richter transformation TP53 disruption NOTCH1 mutations Gene mutations IGHV mutation FISH karyotype CLL BCR pathway mutations

NOTCH1 mutations as predictive marker for no benefit from addition of anti-cd20 MoAb to chemotherapy GCLLSG CLL8 COMPLEMENT 1 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 et al, Blood 2014 Tausch et al, ASH 2013

Prognosticators and predictors in CLL Prognosticators Predictors Richter transformation TP53 disruption NOTCH1 mutations Gene mutations IGHV mutation FISH karyotype CLL BCR pathway mutations

IGHV mutated patients devoid of del17p and del11q gain the greatest benefit from FCR 1 del17p del_17p p<0.001 p < 0.001 IGHV M IGHV UM/del11q del17p 2 del11q del_11q p=0.024 p = 0.024 No 0 Yes 0 3 IGHV IGHV UM p p=0.011 = 0.011 0 0 No Yes No 0 Yes 0 Progression free survival (%) 1 0.8 0.6 0.4 0.2 0 Node 4 (n = 86) 1 0.8 0.6 0.4 0.2 0 Node 5 (n = 233) 1 0.8 0.6 0.4 0.2 0 Node 6 (n = 54) 1 0.8 0.6 0.4 0.2 0 Node 7 (n = 30) p from pairwise comparisons - 0.004 <.001 0.004-0.002 <.001 0.002-0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Months Months Months Months Hazard of progression Hazard Rate 0.000 0.005 0.010 0.015 0.020 0.025 IGHV M IGHV UM/del11q del17p IGHV UM and/or del11q Overall survival (%) 0 20 40 60 80 100 Matched general population IGHV M IGHV UM/del11q del17p N Observed Expected 5-year relative 5-year OS (%) events events* OS* p* 194 4 2.6 91.3 95.9% 0.364 212 30 7.4 81.8 85.9% <.001 82 14 1.6 57.5 60.4% <0.001 0 10 20 30 40 50 60 Months Follow-up time (months) 0 12 24 36 48 60 72 84 96 108 120 Months

Clonally related vs unrelated Richter syndrome 50/63 (80%) Clonally related RS V4-39 D6 J4 13/63 (20%) p=.018 p=.009 CLL V4-39 D6 J4 Clonally unrelated RS V4-34 D2-2 J3 Clonally unrelated Clonally related p=.017 Rossi et al, Blood 2011

Prognosticators and predictors in CLL Prognosticators Predictors Richter transformation TP53 disruption NOTCH1 mutations Gene mutations IGHV mutation FISH karyotype CLL BCR pathway mutations

Molecular mechanisms of resistance to ibrutinib BCR pathway Non-canonical NF-κB pathway BCR Ibrutinib CD79A CD79B BTK PLCG2 CLL TRAF3 TRAF2 BIRC3 CARD11 DLBCL MAP3K14 MALT1 BCL10 MCL IKBKB IKK BTK C481S mutation PLCG2 mutation Absent in ibrutinib naïve patients NF-kB activation Davis, Nature 2010 Woyach, NEJM 2014 Furman, NEJM 2014 Famà, Blood 2014 Rahal, Nat Med 2014

Mutations that are inert under chemotherapy may become dangerous under new agents and vice versa FCR kinase inhibitors KI resistant subclone FCR resitant subclone

IS THERE STILL A ROLE FOR IMMUNOPHENOTYPE IN CLL PROGNOSTICATION?

CCR1 CCR5 CCL3 CCL4 Mo CLL CD49d TNF VCAM VCAM VCAM VCAM VCAM VCAM Pro-survival signals CRO AVIANO

CD49d is the strongest flow cytometry-based CLL prognosticator Bulian P et al. JCO 2014;32:897-904

MRD as a POST-TREATMENT PROGNOSTICATOR for CLL

MRD-negativity may indicate deeper remission MRD-negative patients have fewer CLL cells after treatment Detectable Tumor Burden Baseline tumor burden 50% reduction Detection limit of standard staging techniques (~ 1 CLL cell in 10 100) Clinical disease status SD PR 50% increase = PD Decrease 0 <50% = SD Decrease 50 <100% = PR Below this level = CR* Definition of MRD-negativity (< 1 CLL cell in 10,000) Deeper remission CR Cure? MRD+ CR MRD CR * May be PR for cytopenia or organ enlargement Hallek M, et al. Blood 2008; 111:5446 5456.

MRD can indicate depth of remission and predict relapse Remission Relative frequency of CLL cells 1 10 1 10 2 10 3 10 4 10 5 10 6 10 7 0 MRDnegative Time < 10 4 = iwcll definition of MRD-negativity 2 Detection limit of cytology/ct scan 1 : 10 1 10 2 Detection limits of flow cytometry and PCR techniques 3 : 10 4 10 6 Still in remission and MRD-negative 1 Böttcher S, et al. Hematol Clin N Am 2013; 27:267 288; 2. Hallek M, et al. Blood 2008; 111:5446 5456; 3. Moreno C, et al. Best Pract Res Clin Haematol 2010; 23:97 107.

Clinical significance of MRD in CLL8 Patients in CLL8 were grouped by MRD level (blood) at initial response assessment Patients achieving MRD-negative status had the best outcome, regardless of treatment The extent of MRD reduction was important for outcome 1.0 PFS probability 0.8 0.6 0.4 0.2 0.0 p < 0.0001 for all comparisons 10 2 (n = 45) 0 6 12 18 24 30 36 42 48 54 60 66 72 78 Time (months) < 10 4 (negative) (n = 141) 10 4 to < 10 2 (n = 104) MRD by 4-color flow cytometry, validated against ASO-RQ-PCR Böttcher S, et al. J Clin Oncol 2012; 30:980 988.

PFS by MRD status in patients treated with G-Clb Goede V, et al. N Engl J Med 2014; 370:1101 1110. supl material

BUT: With some new drugs (eg: BCR inhibitors), MRD is of no/little use

Summary: clinical implications of mutations for the management of CLL TP53 assessment for treatment tailoring (chemoimmuno vs new inhibitors) Comprehensive genetic assessment to sort out patients who benefit most of FCR NOTCH1 mutations to identify patients at high risk of RS and resistance to anti-cd20 MoAb NGS as a new tool for highly sensitive detection of TP53 mutations Clonal relationship between CLL and DLBCL to identify prognostically favourable unrelated RS (de novo DLBCL) BTK and PLCG2 mutation testing to identify acquired resistance to KI

NOTCH1, SF3B1, BIRC3, MYD88, & other.. Not ready for prime time because: Most evidence is based on retrospective cohorts Lack of robustness Dig into the across depth clinical of TP53 trials and avoid (eg: SF3B1 mutations predict OS in UK CLL4 but not in GCLLSG CLL8) random strolling across other biomarkers! Lack of uniform methodologies of detection across studies EHA-19 Lunch Debate: Stamatopoulos vs Gaidano Milan 2014

Suggestions for every day life Outside of clinical trials/research purposes, limit analysis of prognosticators/predictors to what is recommended by guidelines Do not modify (decrease/increase) the sensitivity threshold of the methodology recommended by guidelines Consider that the value of each prognosticator/predictor may be highly treatment-specific

University of Eastern Piedmont, Novara Davide Rossi Michaela Cerri Lorenzo De Paoli Silvia Rasi Alessio Bruscaggin Valeria Spina Lorenzo De Paoli Clara Deambrogi Sapienza University, Rome Anna Guarini Ilaria Del Giudice Francesca R Mauro Monica Messina Sabina Chiaretti Robin Foà Columbia University, New York Laura Pasqualucci Raul Rabadan Riccardo Dalla Favera