Histologic transformation of CLL and other indolent lymphomas

Transcription

1 Histologic transformation of CLL and other indolent lymphomas Davide Rossi, M.D., Ph.D. Hematology IOSI - Oncology Institute of Southern Switzerland IOR - Institute of Oncology Reserach Bellinzona - Switzerland

2 The genetics of transformed DLBCL CLL FL LPL MZL?? DLBCL variant of Richter syndrome DLBCL variant of transformed FL Transformed LPL Transformed MZL Font size according to gene mutation prevalence DLBCL genes are from COSMIC V71 Richter syndrome genes are from Fabbri G et al, J Exp med 2013; 210: Transformed FL genes are from Pasqualucci L et al, Cell Rep 2014; 6:130-40

3 1. Definition of Richter syndrome

4 Richter syndrome 95-99% DLBCL LN BM PB CLL 1-5% LN HL LN CD15 CD30 Swerdlow SH. WHO classification of tumours of haematopoietic and lymphoid tissues 2008

5 Differential diagnosis: prolymphocytoid evolution CLL Richter syndrome Prolymphocytoid evolution RS Ginè et al, Haematologica 2010

6 Morphology and phenotype of Richter syndrome Rossi et al, Clin Cancer Res 2009 (n=61) CD20 expression: 100% CD5 expression: ~60% CD23 expression: ~50% GCB FOXP1 - NonGC FOXP1 + MUM1 - MUM1 + CD10 + CD10 - Mao et al, Am J Surg Pathol 2007 Rossi et al Clin Cnacer Res 2009 Rossi et al, Blood 2011 Choi et al, Clin cancer Res 2009 BCL6 + BCL6 - GCET1 + GCET1-76/82 (92.7%) 6/82 (7.3%)

7 Transformation sites Mao et al, Am J Surg Pathol 2007 (n=34) Rossi et al, Clin Cancer Res 2009 (n=99) Mao et al, Am J Surg Oathol 2007 Rossi et al Clin Cnacer Res 2009 Rossi et al, Blood 2011

8 2. Epidemiology of Richter syndrome

9 The prevalence of RS Reference Study design CLL patients included in the study Study population Treatment Patients that developed RS RS prevalence Maddocks-Christianson, 2007 Retrospective 962 Unselected na 14 1% Any Robak, 2004 Retrospective 1487 Unselected na 15 1% Any Byrd, 2014 Clinical trial 391 Relapsed Ibrutinib, ofatumumab 4 1% Any Catovsky, 2007 Clinical trial 777 Progressive untreated Clb, F, FC 13 2% Any Mauro, 1999 Retrospective 1011 Unselected na 22 2% Any Parikh, 2013 Observational 1641 Unselected na 37 2% DLBCL O'Brien, 2014 Clinical trial 29 Progressive untreated Ibrutinib 1 3% Any Tsimberidou, 2006 Retrospective 3986 Unselected na 148 4% Any Fisher, 2012 Clinical trial 817 Progressive untreated FC, FCR 33 4% Any Jain, 2015 Clinical trial 127 Relapsed/Refractory Ibrutinib 7 5% Any Alipour, 2008 Retrospective 465 Unselected na 24 5% Any Tabuteau, 1999 Retrospective 620 Unselected na 37 6% Any Farooqui, 2015 Clinical trial 51 17p deleted Ibrutinib 3 6% Any Keating, 1998 Clinical trial 174 Progressive untreated F 13 7% Any Solh, 2013 Clinical trial 521 Progressive untreated Clb, F, Clb+F 34 7% Any Byrd, 2013 Clinical trial 85 Relapsed/Refractory Ibrutinib 7 8% Any Benjamini, 2014 Clinical trial 234 Progressive untreated FCR 21 9% Any Rossi, 2009 Retrospective 783 Unselected na 69 9% DLBCL Rossi, 2008 Retrospective 185 Unselected na 17 9% DLBCL Thornton, 2005 Retrospective 101 Unselected na 12 12% Any Strati, 2014 Clinical trial 63 17p deleted Heterogeneous 15 23% Any Parikh, 2015 Observational 3887 Unselected na 26 0,7% HL RS type

10 Cumulative incidence of RS 0.5% per year of observation Parikh et al Br J Haematol 2013

11 3. Risk factors for Richter syndrome

12 Role of previous treatment in priming RS Treatment Richter syndrome CALGB 9011 F 7% CLB 5% F-CLB 8% LRF CLL4 CLB 2% F 1% FC 1% CLL2007FMP FCR 0% FCA 6% RESONATE Ofa 1% Rai et al, N Engl J Med 2000 Solh et al, Leuk Lymphoma 2012 Catovsky et al, Lancet 2007 Lepretere et al, Blood 2012 Byrd et al, N Engl J med 2014 Ibrutinib 1%

13 4. Molecular pathogenesis of Richter syndrome

14 The genetic profile of Richter sydrome (RS) differs from that of de novo DLBCL Frequency 100% RS de novo DLBCL 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% TP53 CDKN2A/B MYC NOTCH1 Lesions of TP53, NOTCH1, MYC and CDKN2A recapitulates 90% RS Rossi D, et al. Blood 2011; 117: Fabbri G, et al. J Exp Med 2011; 208: Fabbri G, et al. J Exp Med 2013; 210: Chirginova et al. Blood 2013; 122: Monti, et al. Hematol Oncol 2014; 32: 155-7

15 TP53 abnormalities in Richter syndrome Frequency Missense Nonsense Frameshift cdc2 p21 cyclin B TP EX4 EX9 1 DNA BINDING 393 Caspase 9 Cell cycle arrest BAX p21 cyclin B p53 P p53 P P Chr17 Apoptosis DNA damage 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 Döhner H, et al. New Engl J Med 2000;343:1910 6;Rasi S, et al. Haematologica 2012;97:153 4; Zainuddin N, et al. Leuk Res 2011;35:272 4; Zenz T, et al J Clin Oncol 2010;28:4473 9; Rossi D, et al. Blood 2011;117: ; Stilgenbauer S, et al. Blood 2014;123: ; Fabbri G, et al. J Exp Med 2013;210:

16 TP53 disruption predicts survival in Richter syndrome No TP53 disruption TP53 disruption Variables associated with RS survival by multivariate analysis Final model Bootstrap parameters (mean) HR LCI UCI p HR LCI UCI Bootstrap selection (%) ECOG PS > < CR after RS treatment < TP53 disruption p<.001 Low risk vs intermediate risk Intermediate risk vs high risk Low risk vs high risk p=.022 p<.001 p<.001 Low risk: ECOG PS <1, no TP53 disruption, CR after RS treatment Intermediate risk: ECOG PS <1, TP53 disruption, or no CR after RS treatment High risk: ECOG PS >1 Rossi D, et al, Blood 2011; 117:

17 CDKN2A/B abnormalities in Richter syndrome Frequency (%) Rinaldi A, et al, Br J Haematol 2011; 154: Edelmann J et al, Blood 2012; 120: Fabbri G et al, J Exp Med 2013; 210: Chirginova et al, Blood 2013; 122: % 40% 30% N=14/59 (24%) 20% 10% 0% N=0/305 (0%) CLL diagnosis N=0/144 (0%) CLL requiring treatment Richter syndrome

18 MYC abnormalities in Richter syndrome Frequency (%) Mutation Missense MYC Translocation IgH MYC Aberrant cell proliferation Metabolic reprogramming Genomic instability 50% 40% Amplification CASC11 MYC MYC MYC 30% 20% 10% N=6/161 (3%) N=7/27 (26%) 0% CLL diagnosis Richter syndrome Rossi D, et al. Blood 2011;117: ; Brown J, et al. Clin Cancer Res 2012;18: ; Fabbri G, et al. J Exp Med 2013;210: ; Monti S, et al. Hematol Oncol 2014;32:155-7

19 NOTCH1 mutations in Richter syndrome Frequency (%) 5 3 S3 γ-secretase TM EGF repeats (1-36) LNR HD RAMAnkyrin 1 TAD PEST 2556 Missense Nonsense Frameshift CLL training series CLL validation series DSL NOTCH1 S2 Metalloprotease ICN Pro-NOTCH Ubiquitination Degradation ER-Golgi ICN DTX1 SPEN RPBJ 2155 TAD PEST 2556 Degradation Arruga, et al. Leukemia 2013 Other Co-Activators MAML RPBJ gene expression 50% *** 40% 30% 20% 10% 0% P<0.001 *** P<0.05 ** N=2/134 (1%) de novo DLBCL N=2/63 (3%) MBL ** N=60/539 (11%) CLL diagnosis ** *** N=10/48 (20%) F-ref CLL N=18/58 (31%) Richter syndrome Fabbri G, et al, J Exp Med 2011; 208: Puente X, et al. Nature 2011; 475: Rossi D, et al. Blood 2012; 119: Rasi S, et al. Haematologica 2012; 97: Fabbri G et al, J Exp Med 2013; 210:

20 NOTCH1 mutations predispose to the development of Richter syndrome p<.001 NOTCH1 wt NOTCH1 M NOTCH1 wt NOTCH1 M Events Total 10-year risk NOTCH1 wt % NOTCH1 M % 10-year risk NOTCH1 wt 6% NOTCH1 M 41% Rossi D, et al. Blood 2012; 119: Villamor, et al. Leukemia 2013; 27:1100-6

21 Richter syndrome show biased usage of the BCR in the subset 8 (IGHV4-39) configuration Subset 8 usage 100% 80% 60% 40% 20% 0% p<.001 3/18 1/9 2/13 6/69 14/753 CLL RS RS RS RS Rossi 2009 Mao 2007 Smit 2006 Timar 2004 BCR from subset 8 CLL display extreme antigen polyreactivity del BIRC3 +12 del17p13 Subset 2 Subset 8 Subset 8 CLL clones respond avidly to stimulation by multiple antigens del13q14 del11q22 Subset 1 Rossi D, et al, Clin Cancer Res 2009; 15: Chu, et al, Blood 2011; 117: Rossi D, et al, Blood 2013; 121: Gounari M, et al, Blood 2015; 125: Normal BIRC3 M SF3B1M NOTCH1 M TP53 M Subset 3 Subset 4 Subset 5 Subset 6 Subset 7B

22 Subset 8 usage predisposes to the development of Richter syndrome Sample Diagnosis Subset IGHV IGHD IGHJ % homology HCDR3 aa sequence 3542 RS 8 IGHV4-39*01 IGHD6-13*01 IGHJ5* CAASRGYSSGWYEGVNWFDPW 5675 RS 8 IGHV4-39*01 IGHD6-13*01 IGHJ5* CARIYGYSSSWYGGSNWFDPW 7342 RS 8 IGHV4-39*01 IGHD6-13*01 IGHJ5* CARSMGYSSSWYGGGNWFDPW 7599 RS 8 IGHV4-39*01 IGHD6-13*01 IGHJ5* CARRSGYSSSWYALKNWFDPW 7842 RS 8 IGHV4-39*01 IGHD6-13*01 IGHJ5* CARMTGYSSSWYKRD-WFDPW 5889 Non-transformed CLL 8 IGHV4-39*01 IGHD6-13*01 IGHJ5* CARRVGYSSSWYGQKNWFDPW VG_770 Non-transformed CLL 8 IGHV4-39*01 IGHD6-13*01 IGHJ5* CARITGYSSSWFAS-NWFDPW HR and 95% CI Subset 8 IGHV4-39/no stereotyped HCDR3 No IGHV4-39/stereotyped HCDR3 No IGHV4-39/no stereotyped HCDR3 Rossi D, et al, Clin Cancer Res 2009; 15:

23 Proliferation and apoptosis are the master cellular programs deregulated in Richter syndrome CLL DLBCL BCR (subset 8) MYC TP53 Trasnformation Chemoresistance Rapid disease kinetics Driving forces NOTCH1 CDKN2A/B

24 Is EBV infection involved in Richter syndrome transformation? DLBCL variant of Richter syndrome EBV+ Hodgkin lymphoma variant of Richter syndrome EBV+ EBV- EBV- 5% 70% Abruzzo et al, Am J Surg Pathol 2002; 26: O Brien et al, Cancer 2003; 98: Thornton et al, Leuk Res 2005; 29: Ammatuna et al, Leuk Lymphoma 2009; 50:; Kanzler et al, Blood 2000; 95: Tsimberidou et al, Cancer 2006; 107: Rossi D, et al, Clin Cancer Res 2009; 115:

25 5. How to make diagnsosis of Richter syndrome

26 Clinical clues of transformation Clinical suspicion of transformation Declining performance status B symptoms Bulky disease Discordant growth of localized lymphnodes Unusual extranodal involvement Sudden and excessive rise in levels of LDH Rossi et al, Br J Haematol 2008 (n=50) Bruzzi et al, J Nucl Med 2006 (n=17)

27 Detection of transformation by 18FDG-CT/PET Richter syndrome diagnosis RS Sensitivity 91% Specificity 80% Positive predictive value 53% Negative predictive value 97% Max SUV cut off=5 Bruzzi et al, J Nucl Med 2006

28 Open biopsy is recommended to reach RS diagnosis Prolymphocytoid evolution Richter syndrome

29 6. Clinical course of Richchter syndrome

30 Post-transformation survival is generally poor RS Tsimberidou et al, J Clin Oncol 2006 Rossi et al, Blood 2011 Events/N= 128/148 Median: 8 months Events/N= 52/86 Median: 16 months

31 Richter syndrome clinical score (RS score) Tsimberidou et al., J Clin Oncol 2006 Parikh et al, Br J Haematol 2013 Tadmor et al, Am J Hematol 2014 Features associates with poor post trasnformation OS HR ECOG PS > LDH >1.5 ULN Platelets <100x10 9 /l Prior tx > Tumor size >5 cm p

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

33 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

34 7. Treatment options for Richter syndrome

35 Clinical trials in RS: R-CHOP and OFAR are the best available induction treatments Response 1-year PFS 1-year OS G3-4 Hematotoxicity G3-4 Infection TRM Dabaja, Leuk Lymphoma 2001; Tsimberidou, Leuk Lymphoma 2002; Tsimberidou, Cancer 2003;Tsimberidou, Cancer 2004; Tsimberidou J Clin Oncol 2008; Jenke, ASH 2011

36 Post remission auto vs allo SCT in RS (EBMT) Allo-SCT=25 3y relapse: 47% Auto-SCT=34 3y relapse: 43% Factors affecting RFS HR p 3y NRM: 26% 3y NRM: 12% Age >60y PD at SCT RIC Cwynarski et al, J Clin Oncol y OS: 36% 3y OS: 59%

37 Clinical management of RS Clinical suspicion of RS Adopt a biopsy policy Bulky Close monitoring of CLL with Extranodal IGHV4-39 with stereotyped HCDR3 (subset 8) B symptoms NOTCH1 mutations High LDH Manage as a CLL - PET + PET tailored open biopsy Suggested management of RS (Current guidelines for CLL lack specific recommendations for RS) CLL or accelerated CLL DLBCL Second cancer Clonal + - relationship Clonally related RS Clonally unrelated RS Clinical trial or R-CHOP or OFAR Manage as a de novo DLBCL (i.e. R-CHOP) RIC allo SCT + + Fit - Auto SCT - Donor Clinical trial or follow-up

38 Perspectives Biology and genetics of Richter syndrome after targeted agents? Signalling kinase inhibitors for the treatment of Richter syndrome after chemimmunotherapy? Treatment of Richter syndrome after signalling kinase inhibitors?

39 1. Definition of tfl

40 Transformed follicular lymphoma Sheets of centroblasts and/or immunoblasts FL 80-90% ~10% 1-5% DLBCL BCLU Blastoid GCB profile (CD10+, BCL6+, BCL2+) Features intermediate between DLBCL and Burkitt lymphoma GCB profile (CD10+, BCL6+, BCL2+) Ki67 <95% Sheets of immature B cells with fine chromatin, absent nucleoli, frequent mitoses GCB profile (CD10+, BCL6+, BCL2+) ; TdT- Swerdlow SH. WHO classification of tumours of haematopoietic and lymphoid tissues 2008

41 2. Epidemiology of tfl

42 The cude prevalence of tansformation is heterogeneous and depends on biopsy policy Follicular lymphoma transformation Author Patients (N) tfl prevalence FL Hubbard, % Horning, % Conconi, % Acker, % Oviatt, % Al-Tourah, % Ersboll, % 27% Bastion, % Montoto, 2007* % *70% biopsy rate Montoto et al. J Clin Oncol 2007

43 3. Risk factors of tfl

44 Tumor load at presentation as prognosticator of follicular lymphoma transformation Cumulative risk of tfl by FLIPI Multivariate analysis for tfl risk HR p Stage III-IV FLIPI int 1.4 FLIPI hi IPI hi W&W Montoto et al, J Clin Oncol 2007 Gine et al, Ann Oncol 2006

45 Role of previous treatment in priming transormation Follicular lymphoma transformation Treatment Study p Montoto W&W vs Tx Conconi 2012 Brice 1997 NS NS Al-Tourah Dox vs no dox Gine 2006 Conconi 2012 NS NS Brice et al, J Clin Oncol 1997 Gine et al, Ann Oncol 2006 Montoto et al, J Clin Oncol 2007 Al-Tourah et al, J Clin Oncol 2008 Conconi et al, Br J Haematol 2012

46 4. Molecular pathogenesis of tfl

47 Clonal evolution models The linear model Phase 1 The branching model Earlier precursor +Lesions Phase 1 +Lesions +Lesions Phase 2 Phase 2 All Phase 1 lesions present in Phase 2 Unique lesions in Phase 2 Set of shared lesions in Phases 1&2 Unique lesions in Phase 1 Unique lesions in Phase 2 Pasqualucci L, et al. Cell rep 2014;6:130-40

48 Richter syndrome displays mostly unique genomic aberrations Fabbri G, et al, J Exp med 2013; 210:

49 Transformed FL: divergent evolution Richter syndrome: linear evolution n=10/12 (83.4%) n=7/9 (77.8%) Carlotti E, et al. Blood 2009;113: Pasqualucci L, et al. Cell rep 2014;6: Okosun J, et al. Nat Genet 2014;46: Fabbri G, et al, J Exp med 2013; 210:

50 FL and transformed FL display both shared and unique genomic aberrations * Shared SNVs in cases w/o paired normal DNA only include known genes Pasqualucci L, et al. Cell rep 2014;6:130-40

51 Transformed FL: divergent evolution Richter syndrome: linear evolution n=10/12 (83.4%) n=7/9 (77.8%) Carlotti E, et al. Blood 2009;113: Pasqualucci L, et al. Cell rep 2014;6: Okosun J, et al. Nat Genet 2014;46: Fabbri G, et al, J Exp med 2013; 210:

52 Recurrent targets of mutations in transformed FL Pasqualucci L, et al. Cell rep 2014;6:130-40

53 Transformed FL display a unique genomic profile compared to de novo DLBCL Pasqualucci L, et al. Cell rep 2014;6:130-40

54 Aberrant somatic hypermutation (ASHM) is a mechanism of genetic instability in transformed FL Prevalence of ASHM across patients 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% CLL, n=9 RS, n=9 FL, n=15 tfl, n=39 de novo DLBCL; n=102 0% CLL 10% DLBCL variant of RS 20% FL 87% DLBCL variant of tfl 80% de novo DLBCL Rossi D, et al, Haematologica 2006;91: Pasqualucci L, et al, Cell Rep 2014;6:130-40

55 Physiological and aberrant SHM in FL SHM FL FL ASHM tfl

56 BCL2 mutations associate with increased risk of transformation and shortened survival in FL BCL2 mutations ~3 fold increased risk of tfl 10-year incidence of tfl ~80% Marker of the genetic instability of the FL clone promoted by ASHM? Correia C, et al. Blood 2015;125:

57 Molecular pathogenesis of Richter syndrome and transformed FL Richter syndrome Transformed FL 13q loss or +12 B-cell B-cell BCL2 tx MLL2 M BCR stimulation (subset 8) CPC CPC CDKN2A/B loss TP53 loss MYC deregulation CDKN2A/B loss TP53 loss MYC deregulation NOTCH1 M CLL FL DLBCL DLBCL

58 5. How to make diagnsosis of tfl

59 Clinical clues of transformation Clinical suspicion of transformation Declining performance status B symptoms Bulky disease Discordant growth of localized lymphnodes Unusual extranodal involvement Sudden and excessive rise in levels of LDH Bodet-Milin et al, Haematologica 2008 (n=23)

60 Detection of transformation by 18FDG-CT/PET Follicular lymphoma transformation diagnosis tfl Sensitivity 94% Specificity 95% Positive predictive value 94% Negative predictive value 95% Max SUV cut off=14 Bodet-Milin et al, Haematologica 2008

61 6. Clinical course of tfl

62 Post-transformation survival is generally poor tfl Montoto et al, J Clin Oncol 2007 Al-Tourah et al, J Clin Oncol 2008 Events/N= 76/88 Median: 14 months Events/N= 114/170 Median: 20 months

63 Response to induction treatment: one of the most important factors affecting post transformation OS Post trasnformation OS according to induction treatment response in tfl N of patients Median OS after tfl (mo) Median OS if CR (mo) Median OS if no CR (mo) Hubbard Acker Not reached 6 Yuen Montoto

64 7. Treatment options for tfl

65 Clinical trials in tfl: R-CHOP is the best available induction treatment Study design Treatment Patients ORR CR Yuen, 1995 Retrospective Doxorubicin-based 43-40% Montoto, 2007 Retrospective Doxorubicin-based 86 54% - Bastion, 1997 Retrospective Doxorubicin-based 30 60% - Kaminsky, 2001 Clinical trial RIT (I 131 ) 23 39% 13% Vose, 2000 Clinical trial RIT (I 131 ) 10 60% 50% Davies, 2004 Clinical trial RIT (I 131 ) 7 71% 28% Witzing, 2002 Clinical trial RIT (Y 90 ) 9 55% -

66 Survival (%) Post remission auto SCT in tfl (EBMT) tfl outcome after auto-sct tfl vs FL treated with auto-sct 5y OS: 51% 5y PFS: 30% Factors affecting PFS p NR at SCT.047 LDH >normal.003 Williamns et al, J Clin Oncol

67 Approach to treatment of transformed FL. Casulo C et al. Blood 2015;125:40-47