Acute Leukemia in Older Patients

Hervé Dombret

Saint-Louis Institute for Research, University of Paris

Saint-Louis Hospital (AP-HP)

Paris, France

Monaco, MAO, March 2019

Disclosures2017-present

Honoraria

- Consulting

- Advisory role

- or Symposia

Amgen

Celgene

Pfizer

Incyte

Novartis

Jazz Pharma

Cellectis

Immunogen

Daiichi Sankyo

Sunesis

Astellas

Janssen

Servier

Shire-Baxalta

Abbvie

Otsuka

Menarini

Research Funding Amgen

Novartis

Pfizer

Jazz Pharma

Incyte

Servier

AML incidence and prevalence

Available at: http://www.cancerresearchuk.org/

Less frequent good-risk AML features

• Very low incidence of CBF-AML (translocation 8;21, inversion 16)

More frequent adverse-risk AML features

• Higher incidence of secondary (post-MDS, post-MPN) and therapy-related AML

• Higher incidence of unfavorable cytogenetics, including complex/monosomal karyotypes

• Higher incidence of unfavorable somatic gene mutations, including MDS-like and TP53 mutations

• More frequent pre-leukemic clonal hematopoiesis

Less favorable health status

• Associated comorbidities

• Chronic medications

Older AML characteristics

CBF: core binding factor; CK: complex karyotype ; MK: monosomal karyotype; MDS: myelodysplastic syndromes; TP: tumor protein

Intensive chemotherapy (ICT)

• 7+3 with or without a 3rd agent (midostaurin, GO…)

Low-intensity chemotherapy

• Low-dose cytarabine (LDAC)

• Azacitidine (AZA)

• Decitabine (DAC)

Hematopoietic stem cell transplantation (HSCT)

Clinical trials with new agents

Supportive Care

Standard options for older AML

GO: gemtuzumab ozogamicin

Results in patients 60/65y+

selected for clinical trials

ICT

ALFA-12001LDAC

AZA-AML-0012AZA

AZA-AML-0013DAC

DACO-0164

Patients, N 509 158 241 242

Median age 68 years 75 years 75 years 73 years

Adverse-risk AML 17% 34.2% 35.3% 36.1%

Response

CR 71.5% 24% 20% 15.7%

CR/CRp/CRi 72.5% 26% 28% 27.7%

60-day mortality 9.4% NA 16.2% 19.7%

OS

Median 20.7 months 6.4 months 10.4 months 7.7 months

1y-OS 63.6% 34% 46.5% NA

1. Gardin C, et al., Blood. 2017;130:466;

2. Seymour JF, et al. 20th EHA Congress 2015; Poster Presentation: Abstract E954

3. Dombret H, et al., Blood. 2015;126:291-99;

4. Kantarjian H, et al., J Clin Oncol. 2012;30:2670-77

Prognostic factors

• ECOG-PS

• Geriatric assessment

• Comorbidities

• Concomitant AEs

“Age alone should not be the decisive determinant to guide therapy”

ELN-2017

How these factors could be used to guide AML treatment choice?

• WBC

• Secondary AML

• Therapy-related AML

• Cytogenetics

• WHO AML-MRC

• ELN-risk

• Gene mutation profile

• Pre-existing CHIP

Results of ICT in selected patients

ALFA-1200 study (2012-2016), 60y+ (#509)

C. Gardin et al. Annual ASH Meeting 2017 abstract #466, updated

Patients, N 509

Median age, years (range) 68 years (60-85)

Patients aged >70 years, N (%) 162 (32%)

ECOG-PS 0/1/2/3/NA, N 219/219/57/9/5

HCT-CI 0/1/2/3/4+/NA, N 226/92/66/61/54/10

Secondary AML, N (%) 88 (17%)

ELN-2010 risk, N (%) -

Favorable 76 (15%)

Intermediate 347 (68%)

Adverse 86 (17%)

CR rate

82%

75%

56%

Overall results:

CR rate, 72.5%

Induction death rate, 8.5%

60-day mortality, 9.4%

Median OS, 21 monthsClinicalTrial.gov ID, NCT01966497

IDA 12 mg/m2 D1 to D3

AraC 200 mg/m2 D1 to D7

AraC 1.5g/m2/12h* D1/3/5

AraC 1.5g/m2/12h* D1/3/5

CR or CRp

* reduced to 1g/m2/12h if age≥70y

Median OS

NR

2 years

9 months

Median follow-up, 3.8 years

RIC-SCT in older AML patients

• Reported studies using a time-dependent analysis to compare

RIC-SCT vs CTx outcomes in older patients with AML in CR1

Ref. Group Study PeriodPts

eligible, NAge

Pts

transplanted, NCIR NRM

Russell, 2015 NCRI AML16 2006-2009 964 60y+ 145 (15%) NA NA

Versluis, 2015HOVON

SAKKAML42/43/81/92 2001-2010 640 60-70y 97 (15%) 50% at 5y 18% at 5y

Gardin, 2017 ALFA ALFA-1200 2012-2016 214 60-70y 90 (42%) 23% at 2y 20% at 2y

Devillier, 2018 FILO Retrospective 2007-2017 521 60-70y 199 (38%) 26% at 5y 20% at 5y

Courtesy of N. Russell et al. EHA Meeting 2015 (abstract)

J. Versluis et al. Lancet Haematol. 2015;2:427-436

C. Gardin et al. ASH Meeting 2017 (abstract)

R. Devillier et al. ASH Meeting 2018 (abstract)

RIC-SCT in older AML patients

ALFA-1200 study

• Adverse-risk

HR, 0.16 (0.05-0.48); p=0.001

• Intermediate-risk

HR, 0.86 (0.55-1.36): p=0.53

• Interaction

p=.002

C. Gardin et al. ASH Meeting 2017 (updated)

Manuscript in preparation

Clinical predictors of ICT outcome

The ALFA-1200 study

Multivariate analysis for OS

C. Gardin et al. Annual ASH Meeting 2017 abstract #466

ALFA, data on file

Age ≥70y

ECOG-PS

HCT-CI

sAML

WBC ≥15

ELN-2010

Clinical predictors of HMA outcome

The international E-ALMA series

• 702 patients with AML aged 60y+

• Treated in EU with front-line AZA therapy between 2011 and 2014

J. Falantes et al. Leuk & Lymphoma 2017;24:1-8

Multivariate analysis

Interim conclusion

In the absence of randomized HMA vs ICT comparisons,

does it mean that only fitness should be taken into account?

The same factors (age, ECOG-PS, WBC, cytogenetics)

predict the outcome of older patients receiving either ICT or HMAs

Scores for fitness

Description

General status assessments

Karnofsky performance status Numbered scale (0 – 100) to classify patients according to functional impairment.

ECOG performance status Numbered scale (0 – 5) to define functioning of clinical trial population.

Comprehensive geriatric assessment 1 A comprehensive evaluation of cognitive and physical functions that may be used to improve risk stratification.

Comorbidity indexes

Charlson (CCI) 2 Method of classifying comorbidity to estimate risk of death from comorbid disease.

Sorror (HCT-CI) 3 Simple, validated, reliable index of pre-SCT comorbidities that predicts non-relapse mortality and survival.

SIE/SIES/GITMO consensus 4 A uniform and feasible characterisation of unfitness for intensive and non-intensive chemotherapy in AML.

Composite prognostic scores

MRC-NCRI score 5 A risk index based on regression coefficients of cytogenetics, age, WBC, PS and type of AML.

SWOG/MDACC 6 Does not include the cytogenetic/molecular risk. “Age is primarily a surrogate for other covariates”.

German SAL score 7 A web-based application for prediction of older AML outcomes.

Sorror AML model 8 HCT-CI augmented by hypoalbuminemia, thrombocytopenia and LDH level + age + cytogenetic/molecular risk.

NCCN guidelines 9 Treatment decision-making algorithm, which predicts the probability of achieving CR and the risk for an early

death

1. Klepin HD, et al. Blood. 2013;121:4287-4294.

2. Charlson ME, et al. J Chronic Dis. 1987;40:373-383.

3. Sorror ML, et al. Blood. 2005;106:2912-2919.

4. Ferrara F, et al. Leukemia. 2013;27:997-999.

5. Wheatley K, et al. Br J Haematol. 2009;145:598-605.

6. Walter RB, et al. J Clin Oncol. 2011;29:4417-4423.

7. Krug U, et al. Lancet. 2010;376:2000-2008.

8. Sorror ML, et al. JAMA. 2017;[Epub ahead of print].

9. NCCN. Acute Myeloid Leukemia (Version 3.2017).

Physician’s judgment

US. Schuler et al. Blood 2007;110:2863 (ASH Meeting abstract)

• Clinical judgment (without formal comorbidity scoring or functional assessment)

is of prognostic value in older patients treated intensively

• Independently of cytogenetics (available at judgment time only in a minority of

cases) in a multivariate Cox model

Genomic classifier of older AML

Derived from the ALFA-1200 study (#471)

R. Itzykson et al. Annual ASH Meeting 2018 (abstract #993)

NPM1mut

FLT3ITD

Decitabine in TP53-mutated AML

116 patients with AML/MDS treated with 10-d courses of decitabine

• Higher response rates in patients

• with an unfavorable-risk cytogenetic profile (67% vs. 34%, P

• Patients with TP53 or

NRAS mutation have a

better outcome when

treated with AZA than

with CCR

• FLT3 mutations have a

negative impact in the

AZA arm, not in the

CCR arm

Genomic predictors of AZA response

H. Döhner et al. Leukemia 2018;32(12):2546-2557

A subgroup analysis of the AZA AML-001 study

HMA vs ICT decision-making

In favor of ICT In favor of HMAs

Age

HMAs ICTVyxeos®Venclexta®

Quizartinib

Rydapt®

Xospata®Daurismo®

Mylotarg®

Crenolanib

Idhifa®

Tibsovo®

Durvalumab

Nivolumab

Pembrolizumab

Mocetinostat

Entinostat

SL-401

Pevonedistat

Avelumab

FT-2102

Sorafenib

Vorinostat

PF-8600

Utomilumab

APR-246

IbrutinibPanobinostat

HMPL-523

Brentuximab vedotin

Pracinostat

Hu5F9-G4

Tosedostat

Milademetan

Nintedanib

Lenalidomide

ARGX-110

CPX-351 – Mechanism of action

CPX-351 Phase III study

ITT analysis population

CPX-3517+3

104/153132/156

9.56 months (6.60–11.86)5.95 months (4.99–7.75)

Events/N Median survival (95% CI)

122110

9277

7956

6243

4631

3420

2112

167

113

52

153156

10

CPX-3517+3

6 9 12 15 18 21 24 27 30 33 360 3Months from randomisation

100

80

60

40

20

0

Surv

ival

(%

)

HR=0.69, P=0.005

Clinical Care Options Oncology

Available at: https://www.clinicaloptions.com/Oncology/Treatment%20Updates/AML%20Induction/Module/Slideset.aspx

• Higher CR rate

47.7 vs 33.3%

https://www.clinicaloptions.com/Oncology/Treatment Updates/AML Induction/Module/Slideset.aspx

Venetoclax – Mechanism of action

Venclexta® + HMAs study

Phase 1b study (NCT02203773)

• Patients 65y+ ineligible for ICT

• Treated with 400 mg VEN + AZA / DCA

• Median age, AZA 75 / DCA 72 years

• Median FU, AZA 8.2 / DCA 16.2 months

• CR+CRi, AZA 70% / DCA 74%

• MRD negativity, AZA 47% / DCA 39%

• Median time to response, AZA 1.2 / DCA 1.9 months

• Median OS, AZA 14.9 / DCA 16.2 months

A phase 3 trial is ongoing (NCT02993523)

Pollyea DA, et al., ASH Congress, Blood 2018;132:285

And older ALL…?

Ph+ ALL

TKI + Blinatumomab

Ph-negative BCP ALL

Inotuzumab ozogamicin + low-intensity CTx

T-ALL

No major advance to date

The Paris Saint-Louis Leukemia Team

• Hervé Dombret, MD

• Nicolas Boissel, MD PhD

• Raphael Itzykson, MD PhD

• Emmanuel Raffoux, MD

• Etienne Lengliné, MD

• Nathalie Dhédin, MD

• Pierre Fenaux, MD

• Lionel Ades, MD PhD

• Marie Sebert, MD

• Delphine Réa, MD PhD

• Jean-Jacques Kiladjian, MD PhD

• Marie Robin, MD PhD

• Régis Peffault de Latour, MD PhD

• Gérard Socié, MD

• Karine Celli-Lebras, RN

• Blandine Beve, RN

• Martine Meunier, RN

• Marie-Thérèse Trémorin, RN

• Catherine Fauvaux, RN

• Jean Soulier, MD PhD

• Hugues de Thé, MD PhD

• Alexandre Puissant, PhD

• Emmanuelle Clappier, MD PhD

• Christine Chomienne, MD PhD

• Stéphane Giraudier, MD PhD

• Stéphanie Mathis, PhD

• Maria-Elena Noguera, PhD

• Jean-Michel Cayuela, PhD

• Wendy Cuccuini, MD

• Odile Maarek, MD

• Bruno Cassinat, PhD

• Véronique Meignin, PhD

• Véronique Lhéritier (Lyon), RN

• Claude Preudhomme (Lille), MD PhD