Personalization of Therapy for MDS: The Role of Biology, Risk … · 2014. 9. 10. · EZH2 TK...

Personalization of Therapy for MDS: The Role of Biology, Risk Stratification, and Evolving TherapiesGuillermo Garcia-Manero, MDThe University of Texas MD Anderson Cancer Center

Age‐adjusted incidence rates of MDS (2001‐2003)

Characteristic (Rate) Count %Total 3.27 24 798Year of diagnosis200120022003

3.13.293.45

7 8878 5088 403

31.834.333.9

SexMaleFemale

4.432.53

13 51811 280

54.545.5

Age (years)<4040-4950-5960-6970-79≥80

0.140.621.957.14

20.0535.49

592728

1 7273 9318 5639 257

2.537

1634.537.3

Rollison D et al. Blood 2008;112:45‐52 (Rate per 105; age‐adjusted for 2000 US population)

Diagnosis of MDS is based on morphology

Courtesy of Dr. Carlos Bueso‐Ramos

Discordance between referral diagnoses and final diagnoses at MDACC. (A) By French-American-British.

Naqvi K et al. Blood 2011;118:4690-4693

©2011 by American Society of Hematology

Cause of death in lower risk MDS

0

10

20

30

40

50

60

Transf. Infectious Cardiac Bleed Others

Dayyani. Cancer 2010;116:2174‐9

%

What are the major needs in MDS?(problems that limit significant cure rate)

• Identification of poor prognosis “lower risk” patients– By default sparing patients with no need of therapy– Concept of early intervention

• Development of new targeted therapies for patients with lower risk MDS

• Development of new therapies for patients with higher risk MDS

• Understanding mechanisms of resistance to epigenetic modulators in MDS (critical)

• Understanding mechanisms of transformation to AML• Incorporation of alloSCT in MDS• Minimizing risk of relapse post alloSCT in MDS

Why do we need prognostic models?• Predict survival without therapy

• Therefore decide on who needs therapy• Shorter the survival associated with increased need of therapy

• Predict survival with therapy• Example: SCT

• Predict survival in the context of the patient• Comorbidities

• Molecularly adapted therapy– Early intervention– Targeted approaches

Prognostic models• IPSS• WPSS• Global MDACC model• MDACC lower risk model• Impact of comorbidities• New “revised IPSS”

Survival by MDS model within IPSS Risk IPSS-low IPSS-int-1

IPSS-int-2 IPSS-high

Kantarjian Cancer 2008;113:1351-61

Comorbidities in MDS. ResultsComorbidities by ACE-27None (0)Mild (1)Moderate (2)Severe (3)

N/(%)137 (23)254 (42)127 (21)82 (14)

Comorbidities by System

050

100150200250300350 328

9740

1

168

351

48 1453

17 32

Naqvi et al. JCO 2011

Comorbidities in MDS. Multivariate Survival Model with Risk Score

Prognostic factor

Coefficient Score

Age >65 0.582 2

Comorbidityscore

(ACE -27)

Mild or moderate

0.301 1

Severe 0.782 3

IPSS Int 2 0.512 2High 0.769 3

*Score points were obtained by dividing estimated coefficients by 0.3

Comorbidities in MDS. Survival by Risk ScoreSurvival Proposed

risk groupScore No pts (%) Median(mos)

5-yr %

0 45 (8) 60 53 Low (score: 0-1)1 71 (12) 39 38

2 52 (9) 26 19 Intermediate(score : 2-4)3 180 (30) 23 22

4 56 (9) 15 245 109 (18) 9 8 High

(score:5-8)6 62 (10) 7 27-8 20 (3) 7 0

1999-2003: Survival by % Blasts

Weighting of Cytogenetics in Relation to BM blast counts in IPSS

0 10 20 30 40 50 60

Months

Cum

ulat

ive

inci

denc

e fu

nctio

n fo

r AM

L

00.

10.

20.

3

IPSS cytogenetic subgroupsgoodintermediatepoor

p(Gray-test) = 0.132

B

0 10 20 30 40 50 60

Months

Cum

ulat

ive

inci

denc

e fu

nctio

n fo

r AM

L

00.

10.

20.

3

Bone marrow blast count<5%5-10%11-20%21-30%

p(Gray-test) < .001

D

Figure 1 A-DOverall survival and cumulative risk of AML-transformation in IPSS cytogenetic and FAB bone marrow blast count subgroups (univariate analysis; pts. treated with supportive care exclusively)

0 50 100 150 200 2500.0

0.2

0.4

0.6

0.8

1.0

goodintermediatepoor

A

IPSS cytogenetic subgroup

p (log-rank) <0.0001

Months

Cum

ulat

ive

surv

ival

0 50 100 150 200 2500.0

0.2

0.4

0.6

0.8

1.0

<5%5-10%11-20%21-30%

CBone marrow blast count

p (log-rank) <0.0001

Months

Cum

ulat

ive

surv

ival

Category OS(months)

OS (HR)

Poor (IPSS)

7.5 3.2

Complex(non 5/7)

7.4 3.0

Complex(5/7)

5.6 5.4

Blasts(21-30%)

7.4 3.2

Schanz J et al. J Clin Oncol. 2012;30:820-829.

New Cytogenetic Scoring System in MDS

Schanz J et al. J Clin Oncol. 2012;30:820-829.

chr 7 abn alone7%

chr 7 abn + other abn13%

Tri X alone2%

Tri Y alone2%XXYY

2%

Tri 8 alone13%

Tri 8 + Tri 52%

Tri 9 alone2%

Tri 11 alone2%

Tri 13 alone2%Tri 14 or Add 14q32

alone%

Add 18q232%

Add 19p13.3

2%

Tri 214%

5p del alone4%

Y del alone2%

Del 1p34.1p36.22%

del 3q21q274%

del9q12q224%

del12p11p134%

del 13q12q222%

del14q22q312%

del17q22q242%

t(2;9)(p14p21)2%

t(3;18)(p21q21.3)2% t(6;9)

4%

Complex abn exclud 7 abn

7%

Other abn5%

Cytogenetic acquisition in MDS

Jabbour. ASH 2011 abstract 3802

0 20 40 60 80 100 120

Time (Months)

0.0

0.2

0.4

0.6

0.8

1.0

Prob

abilit

y of

Ove

rall

Surv

ival

Non-CACA

Jabbour. ASH 2011 abstract 3802

Effect of cytogenetic acquisition in MDS

Diploid

Diploid

Abnormal

LR MDS

LR MDS

AML

AML

ACA

ACA

Diploid

Abnormal

Abnormal

Diploid

Abnormal

7 months

8 months11 months

6 months

A)

B)

Early identificationpatients at risk

Early identificationpatients at risk

Intervention

Intervention

Hypothesis #5: identification of drivers of transformation

NRASFL

T3 MLL NF1CSF

R1KIT

CDKN2BTP

53CDKN2A RB1

EV11

IRF1

RUNX1CEB

PAWT1NPM

1

0

10

20

30

40

50Pe

rcen

t

Bernasconi. BJH 2008

Molecular lesions in MDS

UPD chromosome 7 in MDS

Heinrichs. Leukemia 2010

Point Mutations in MDSAssociations With Clinical Features

Independent Predictors of Overall Survival

Bejar et al. N Engl J Med. 2011;364:2496-2506.

DNA Sequencing454 sequencingRUNX1TP53TET2NPM1

Laura Macconaill & Charlie Hatton Omar Abdel‐Wahab & Ross Levine

Sanger sequencingASXL1EZH2KDM6A (UTX)

Björn Nilsson

CDKN2APTENCBL

IDH1IDH2ETV6


Somatic mutations in 18 genes were identified.

51% of samples had at least one mutation ...

... including 52% of cases with a normal karyotype.

Mutation Frequency


TP53

TET2

ASXL1

RUNX1

NRAS/KRAS/BRAF

CBL

JAK2

EZH2

IDH1/IDH2

ETV6

NPM1

5q‐ alone

Complex

‐7/7q‐ alone or +1 abnormality

Normal and –Y alone

+8 alone

Other

‐20q alone

Unknown

Karyotype

20.5%

14.4%

8.7%

5.0%

2.3%

3.0%

6.4%

3.4%

2.7%

7.5%

1.8%

Mutation Frequency and Distribution

Bernd Boidol & Bennett Caughey Bejar et al. N Engl J Med. 2011;364:2496-2506.

Kristen Stevenson & Donna Neuberg

Survival Curves II


Multivariate Model


HR (95% CI) p‐value

Age

≥55 yrs vs. <55 yrs 1.81 (1.20‐2.73) 0.004

IPSS Risk Group

Int1 vs. Low 2.29 (1.69‐3.11) <0.001

Int2 vs. Low 3.45 (2.42‐4.91) <0.001

High vs. Low 5.85 (3.63‐9.40) <0.001

Mutational Status ‐ Present vs. Absent

TP53Mutation 2.48 (1.60‐3.84) <0.001

EZH2Mutation 2.13 (1.36‐3.33) <0.001

ETV6Mutation 2.04 (1.08‐3.86) 0.029

RUNX1Mutation 1.47 (1.01‐2.15) 0.047

ASXL1Mutation 1.38 (1.00‐1.89) 0.049

137/439 (31.2%) samples carry one or more prognostic mutations

LowInt1

Int2High

IPSS Risk Groups


Molecular genetic landscape (2010)RUNX1 20%

poor prognosisMDS-EV1 n/a ~5% cases over-expressedTP53 10% poor prognosis, therapy related MDSRAS 15% potential treatment targets in downstream

pathway membersJAK2 5% most often seen in RARS-T

potential treatment with JAK2 inhibitorsTET2 12-26% seen in various myeloid malignancies

potential link to DNA methylation and treatment with hypomethylating agents

ASXL-1 11-18% potential treatment targets in histone modifying enzymes and ATRA

EZH2 6% seen in various myeloid and solid tumor malignanciespotential treatment targets in histone modifying enzyme

Cortesy Ross Levine. MSKCC

Identification of poor prognosis “lower risk” patients with MDS

Bejar et al. J Clin Oncol 2012 (e‐pub).

Mutation Distribution in the LR‐PSS

SF3B1

SRSF2

TET2

TP53

RUNX1

ASXL1

EZH2

TK Pathway

ETV6

IDH1/IDH2

NPM1

U2AF1

DNMT3A

SF3B1

SRSF2

TET2

TP53

RUNX1

ASXL1

EZH2

TK Pathway

ETV6

IDH1/IDH2

NPM1

U2AF1

DNMT3A

Category 1 Category 3

TK Pathway Genes = NRAS, KRAS, BRAF, CBL, JAK2

46% 90%


U2AF1 Normal (n=242)Mutated (n=46)

p = 0.026

SRSF2 Normal (n=246)Mutated (n=42)

p = 0.016

SF3B1 Normal (n=226)Mutated (n=62)

p = 0.11

Overall Survival in Lower Risk MDSLower Risk MDS Set (n=288)


DNMT3A Normal (n=252)Mutated (n=36)

p = 0.89

Mutated Genes with Shorter OS NRAS p < 0.001

EZH2 p < 0.001

RUNX1 p < 0.001

ASXL1 p < 0.001

SRSF2 p = 0.016

U2AF1 p = 0.026

TP53 p = 0.054


Other molecular alterations in MDS• Aberrant DNA methylation CpG islands

• NUP98-HOXD13: transplantable animal model

• EVI1: role in erythropoiesis (GATA-1, PU.1)

• SPARC, c-CBL, RUNX-1, Tet-2

• miRNA (5q-)

• Disrupted mesenchymal stem cell differentiation

Boumber Cancer Res 2007;67:1997-2005. Chung PNAS 2008;105:140088-93. Pellagatti, PNAS 2007;104:11406-11. Dunbar, Cancer Res 2008;68:10349-57. Delhommeau NEJM 2009;360:2289-301.Laricchia-Robbio Cancer Res 2009, in press. Lopez-Villar Leukemia 2009, in press

MDS

Low-risk(IPSS low, INT-1)

(BM blasts < 10%)

High-risk(IPSS INT-2, high)(BM blasts > 10%)

• Iron chelation• Growth factors

(Epo + G-CSF)• MTI (5-AZA/decitabine)• Lenalidomide (5q-)• Immunemodulation• Clinical trial

Age < 60• Intensive

chemotherapy• MTI (5-AZA/decitabine)• Clinical trial

Age ≥ 60• MTI (5-AZA/decitabine)• Clinical trial• Intensive

chemotherapy1

1Consider in younger patients with diploid cytogenetics

2Consider earlier in younger patients

Failure/ Progression

Allo SCT

Proposed treatment algorithm for patients with MDS

Any age

Atallah. Cancer Inv. 2008;26:208-16

Low dose decitabine in LR MDS: Study Design

Study Arms• Arm A: DAC 20 mg/m2 SC Q DAY x 3 every 28 days• Arm B: DAC 20 mg/m2 SC Q WEEK x 3 every 28 days

Statistical Methods• First 40 patients 1:1 randomization• Then adaptive randomization procedure for a maximum of

80 patients• Modified intent-to-treat-analysis• Expected overall response rate of 20% for each arm• Continuous data monitoring• Overall improvement included: CR, marrow CR, PR or HI

by IWG 2006• An arm will considered superior if the posterior probability

was 0.95 and inferior if it was 0.1 • A maximum of N=80 patients to be treated

Garcia-Manero. Blood 2009;114:54:54 (abstract 119)

Low dose DAC in LR MDS: Transfusion Independence Rate

Arm A(Daily)N(%)

Arm B(Weekly)

N (%)RBC TransfusionIndependence 20 (62.5) 15 (68.2)

Platelet Transfusion Independence 22 (68.8) 18 (81.8)

RBC/PLT Transfusion Independence

20 (62.5) 13 (59.1)

Phase 1 Oral Aza Study

• MDS or AML• ECOG PS 0-2• Prior Aza/Dec

allowed

Treat to Progression

• Cycle 1: SC Aza 75 mg/m2 x 7 days• Cycle 2 and beyond: PO Aza x 7 days• 3 + 3 design• 28-day cycles

• PK Cycles 1, 2 and 7• PD every Cycle• End of Cycle 7:

– Assessment of response– Crossover to SC permitted for non-

responders• Intrasubject dose escalation permitted

Garcia-Manero. JCO 2011

Phase 1 Oral Aza Study Response to Therapy (N=41)

DispositionMDS

(N=29)N (%)

CMML (N=4) N (%)

AML (N=8)N (%)

Ongoing 8 (28) 2 (50) 2 (25)Terminated 21 (72) 2 (50) 6 (75)

Median duration of oral therapy,

# of cycles, (range) 6.0 (1–23+) 7.0 (3–17+) 4.5 (1– 14+)

Cycle 7 Response Assessment* 13 (45) 2 (50) 2 (25)CR / PR / HI 4 (31) 1 (50) 0 (0)SD 8 (61) 1 (50) 2 (100)†

Progression 1 (8) 0 (0) 0 (0)

* IWG 2003 or 2006†Subjects did not meet criteria for progression or response by IWG 2003

Duration on Study of MDS Subjects (N=29)

Extrapolated Cumulative AZA Exposure per Cycle*

*Percentage of exposure as compared to SC azacitidine.

0

2000

4000

6000

8000

10000

SC-75 mg/m2

qd×7d (n = 44)Oral-480 mg

qd×7d (n = 15)Oral-300 mg

qd×14d (n = 13)Oral-300 mg

qd×21d (n = 13)

Mea

n cu

mul

ativ

e ex

posu

re

(AU

C) p

er c

ycle

(ng*

hr/m

L)

100%

58%

36% 38%

LK08-02-11_1.ppt

A Decision Analysis of RIC Allogeneic HSCT for Older Patients

with De-Novo MDS: Early Transplantation Offers Survival

Benefit in Higher-Risk MDS

CIBMTR Study #LK08-02

COI Disclosures per ASH

Datasets Natural History Cohorts (Low/Int-1 IPSS): BSC: IMRAW (Greenberg), Pavia (Cazzola, Malcovati) ESA: GFM (Park), Nordic (Hellstrom-Lindberg,

Jadersten)

Hypomethylating Agent Cohorts (Int-2/High IPSS): AZA-001 (Fenaux, Beach, Gale) GFM Compassionate use Azacytidine (Itzykson, Ades) MDACC Decitabine (Garcia-Manero)

RIC HSCT Cohorts (All IPSS risk): CIBMTR-MDS DFCI-MDS FHCRC-MDS (Deeg)

LK08-02-11_7.ppt

Monte Carlo – Low/int-1 IPSSSurvival Estimates

LK08-02-11_15.ppt

Test of Equality over Strata

Test p

Log‐Rank <.0001

Wilcoxon <.0001

‐2Log(LR) <.0001

RIC HSCT

Non-transplant

Pleiotropic effectson Progenitors and AML blasts

Dual Inhibition of p38 and Tie2 by ARRY-614 Blocks Myelosuppression in Bone Marrow

Ang-1 Ang-2

• Major regulator of the cellular pathways which sense stress (Cuadrado et al. Biochem. J. 2010. 429: 403.)

• Over-activated, leading to inappropriate production of myelosuppressive cytokines (Navas et al. Blood. 2006. 108: 4170.)

• Dysregulated, may be a survival factor for the AML blast (Keith et al. BJH. 2007. 137: 206.) (Wakabayashi et al. Hematol. J. 2004. 5: 353.)

• Increased signaling associated with poor prognosis (Cheng et al. BJC. 2011. 105: 975.)

p38 MAPK in MDS Tie2 in MDS – Emerging Target

p38

Stress/Inflammatory Stimuli (Cytokines, Hypoxia, FasL)

TNF-α, IL-6, ChemokinesDecreased RBC, WBC,

platelets

Apoptosis

Komrokji R. ASH 2011.

Prior Therapies

AZA=azacitidine, ESA=erythropoiesis stimulating agents, DEC=decitabine, LEN=lenalidomide, GCSF=granulocyte colony stimulating factor, HDACi=histone deacetylase inhibitor

80%

58%

42%

22%

49%

40%

24%

16%13%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%Pe

rcen

t of P

atie

nts

(N=4

5)

HMAs Other

• Median number of prior therapies (range): 3 (0 – 9)• 80% received treatment with ≥ 1 HMA


0%

10%

20%

30%

40%

50%

400 (n=15) 600 (n=10) 900 (n=3) 1200 (n=16)

HI R

ate

Total Daily Dose (mg)

Trend for Increased HI by DoseIWG 2006 HI by Total Daily Dose

• 38% HI at highest dose (1200 mg daily)• 67% bi-lineage responses

Bi-lineage

Uni-lineage

38 %33 %

30 %

20 %


HI Responses• Overall, durable HI observed in 13 of 44 evaluable patients (30%)

• 5 bi-lineage responses

1Includes one each: Pseudodiploid Clone, T(1:8)(Q21;Q22), Del (7), Pseudo Hyperdiploid Clone del (20q), del 5q, and Trisomy 8

• Responder (n = 13) baseline characteristics• All ≥ 1 prior HMA• 12 IPSS Int-1• 11 2-3 cytopenias• 6 abnormal cytogenetics1

N patients HI-E HI-P HI-N Total HIn = 41 n = 25 n = 16 N = 44

Total (%) 8 (20) 5 (20) 5 (31) 13 (30)

Median duration, weeks (range) 32 (9-80) 16 (8-67) 21 (14-26)5 of 7 platelet transfusion-dependent patients became transfusion independent (TI) for a median duration of 20 weeks (range 15 – 31)3 of 28 RBC transfusion-dependent patients became TI for a median duration of 21 weeks (range 11 – 72)


Azacitidine survival studyAZA 75 mg/m2/d x 7 d q28 d

CCRRandomization

BSC was included with each armTx continued until unacceptable toxicity or AML transformation or disease progression

• Best Supportive Care (BSC) only• Low Dose Ara-C (LDAC,

20 mg/m2/d x 14 d q28-42 d)• Std Chemo (7 + 3)

Screening/CentralPathology Review

Investigator CCRTx Selection

Fenaux et al. Lancet Oncology 2010

Overall Survival: Azacitidine vs CCR ITT Population

Log-Rank p=0.0001HR = 0.58 [95% CI: 0.43, 0.77]Deaths: AZA = 82, CCR = 113

0 5 10 15 20 25 30 35 40Time (months) from Randomization

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Prop

ortio

n Su

rviv

ing

CCRAZA

Difference: 9.4 months

24.4 months

15 months

50.8%

26.2%

Fenaux et al. Lancet Oncology 2010

Hazard Ratio and 95% CI for Overall SurvivalITT Subgroups Total - Event / N

Cytopenias: 0/1 20 / 53

WHO: RAEB-1 15 / 31

0.125 0.250 0.500 1 2 4

Favors Azacitidine Favors CCR

≥ 65 150 / 258

Female 61 / 107FAB: RAEB 95 / 207

RAEB-T 80 / 123

RAEB-2 102 / 193IPSS: INT-2 71 / 146

High 98 / 167Cytogenetics: Good 80 / 167

Intermediate 38 / 76Poor 67 / 100

2/3 167 / 290

Karyotype: -7/del (7q) 42 / 57

≥ 21% to < 31% 58 / 99

AGE: < 65 45 / 100

LDH: ≤ 240 U/I 97 / 208

RAEB & RAEB-T: AGE ≥ 65 138 / 240

> 240 U/I 94 / 145

ITT 195 / 358

≥ 75 50 / 87

≥ 11% to < 21% 98 / 192

Male 134 / 251

BM Blasts: ≥ 5% to < 11% 34 / 61

Update on AZA-001 study• Subset analysis of pts ≥ 75 years in AZA-001• 87 of 358 (24%) in that category in AZA-001• OS not reached for 5-aza treated vs 10.8 months

for BSC (HR 0.48, 0.26-0.89, p=0.01)• OS at 2 years 55% 5-aza vs 15% BSC• Subset analysis WHO AML in AZA-001• 113 of 358 WHO AML (blasts 20-30%)• Median age 70; 24% abnormal CG• Median N of cycles 8 (1-39)• OS at 2 years 50% vs 16% p=0.0007• No differences in CR

Seymour. Blood 2008;112:1241 (abstract 3629)

Fenaux P. JCO 2010; 28:562

Overall Survival: Azacitidine (n=55) or Conventional Care Regimens (CCR,n=58)

Outcome in MDS post hypomethylating failure

Synergistic apoptotic effect of the combination of decitabine with VPA

Yang H, et al. Leuk Res 2005;29:739-748

05

101520253035404550

Control VPA DAC DAC + VPA% Apoptosis

VPA(mg/kg) N CR CRp BM OR

N (%)Courses to response

50 40 10 3 6 19 (47) 1(1-3)

62.5 7 1 0 0 1 (14) 2

75 6 1 0 1 2 (33) 1

Total 53 12 3 7 22 (42) 1 (1-3)

Untreated 33 11 3 3 17 (52) 1 (1-3)

Previously Treated 20 1 0 4 5 (25) 1(1-2)

5-AZA + VPA + ATRA in Leukemia: Responses

Soriano. Blood 2007; 110:2302-2308

5-AZA + VPA + ATRA in Leukemia: Overall survival

Soriano. Blood 2007;110:2302-2308

TET2 and response to 5-azacitidine• TET genes transform 5mC to 5(OH)mC

• Mutations may result in passive hypomethylation

• N=103 patients: median age 72 (43 to 91)

• Standard 5-aza in France

• Median N of cycles 7 (1 to 39)

• TET2 MUT in 21 pts (17%)

• RR 11/17 (65%) in MUT vs 26/86 (30%) in WT (p=0.01)

• OS 15.3 months in MUT vs 16.2 in WT (NS)Itzykson. ASH 2010 abst 439

Len + Aza Phase 2: Response Results

• ORR (n=36): 26/36 (72%)– 15 CR (42%)– 10 HI (28%)– 1 BM CR (3%)

• Median CR duration: 16+ months (range, 3-36+)

• Median OS among CR pts: 27+ months (range, 7-55+)

Sekeres et al. Blood 2011;118:607a

North American Intergroup Randomized Phase 2 MDS Proposal (S1117)

AZAN=80

AZA + LENN=80

AZA + VorinN=80

Higher-risk MDS

(IPSS >1.5 or Blasts

>5%)

Groups: SWOG, ECOG,CALGB, NCIC

Total Sample Size: 240

Primary Objective: 20%improvement of RR based on 2006 IWG Criteria

Secondary Objectives: OS,RFS, LFS

Power 81%, alpha 0.05 for each combo arm vs. AZA

Anticipated time: 2.5 years

Sekeres et al. Blood 2011;118:607a

Monte Carlo – Int-2/High IPSSSurvival Estimates

LK08-02-11_16.ppt

Test of Equality over Strata

Test p

Log‐Rank <.0001

Wilcoxon <.0001

‐2Log(LR) <.0001

RIC HSCT

Non-transplant

Effect of monosomy 7 in MDS/AML post alloSCT

• EBMT analysis from 1981 to 2006. N=278 patients• N=192 (70%) related• N=63 (22%) monosomy (one monosomy and at least one

other abnormality)• 5-year OS was 28% and RFS 26%• Relapse rate at 3, 12 and 60 months was 9, 29 and 39%• Non-relapse mortality was 21, 36 and 40% respectively• For patients with monosomy:

– OS 0% (HR 2.4, 95% CI 1.6 to 3.6); p=0.0001– RFS 0% (HR 2.4 95% CI 1.7 to 3.9); p=0.0001

Van Gelder. Blood 2009:114: 123 (abstract 293)

AZA POST ALLOSCT – overall survivalMedian follow-up is 16 months

5 10 15 20 25 30 35 40 45

Months

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Cum

ulat

ive

Prop

ortio

n Su

rviv

ing

Median follow-up is 16 months

Survival - patients that received AZA

DeLima Cancer 2010

ON 01910.Na: Results in +8 MDS• ON 01910.Na new styryl sulfone; decreases

cyclin D1 levels in primary MDS• Phase I/II trial. N=15 (int-1 to AML CG +8)• Dose 800 to 1500 mg/m2 2 to 5 days every 2 to 4

weeks • No toxicity• Decrease in aneuploidy and cyclin D1• HI in 46% (7 of 15)

–4 patients with prior 5-aza: 2 responses• Potential new agent in MDS

Sloand et al. Blood 2009; 114: abst 120

Phase 3 ON 01910 vs BSC Pivotal Trial• Randomized study (2:1) of ON 01910 vs

BSC; post AZA/DAC failures• Primary endpoint survival• ON 01910 1800 mg /m2 CI over 24 hrs

daily x 3; Qow x 8 then Qmos• Total 270: ON 01910 180 pts; BSC 90 pts

Treatment No./Eval Marrow CR (%)ON 01910 17/13 5(38)LD ara C 5/4 0

Garcia Manero. 2012

Investigational Strategies in MDS• Clofarabine• Romiplostin (AMG531)• Vaccines (PR1)• TLK199• Farnesyltransferase inhibitors• CD33 MoAb (GO, lintuzumab)• P38 inhibitors• Sapacitabine• Homoharringtonine• HDAC inhibitors (VPA, vorinostat, MGCD0103) ±

hypomethylating agents• Intensive chemoRx

Clofarabine in High-Risk MDS• 58 pts with MDS with ≥5% blasts or

IPSS ≥1–Median age 68y (25-89)–26% secondary MDS– IPSS karyotype poor 38%

• No prior chemohterapy(biologic/targeted Rx allowed)–60% prior hypomethylating agents

• Randomized to clofarabine (5 days/mo): 15 mg/m2 (N=37) or 30 mg/m2 (N=21)

• Ambulatory therapy 22%Jabbour et al. ASCO 2010; abst #6504

Response to Clofarabine in HR-MDS

Dose (mg/m2)

All ptsFailed

hypomethylating agents

N% Response % 8-wk

mortality

% Response

ORR CR CRp N CR CRp15 37 41 27 17 14 22 14 530 21 29 24 5 29 13 15 0

Total 58 36 26 10 19 35 14 3• Median survival 7.4 mo• Median response duration 8.8 mo• Deaths on Study: 11 patients (19%)

Jabbour et al. ASCO 2010; abst #6504

66

Sapacitabine in MDS • 39 pts; all failed AZA/DAC; all int-2 or

high risk IPSS• Randomized Rx to 3 doses of sapa: 200

mg bid x 7, 300 mg bid x 7, 400 mg bid x 6 (D1-3, D8-10)

• Median age 73 yrs; age > 70 yrs 79%• Marrow blasts > 10% 18 pts (56%)• Response: 1 CR+8 major HI = 9/39 (23%)

RR 23% at 200, 38% at 300, 8% at 400Garcia-Manero et al. Blood 114:1758, 2009

Thank you

• Guillermo Garcia-Manero• [email protected]

• Cell 281 380 7813

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