acute leukemia in older patients - monaco age...
TRANSCRIPT
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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
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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
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AML incidence and prevalence
Available at: http://www.cancerresearchuk.org/
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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
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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
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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
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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
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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
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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)
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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
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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
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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
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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
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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).
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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
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Genomic classifier of older AML
Derived from the ALFA-1200 study (#471)
R. Itzykson et al. Annual ASH Meeting 2018 (abstract #993)
NPM1mut
FLT3ITD
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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
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• 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
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HMA vs ICT decision-making
In favor of ICT In favor of HMAs
Age
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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
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CPX-351 – Mechanism of action
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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
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Venetoclax – Mechanism of action
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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
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And older ALL…?
Ph+ ALL
TKI + Blinatumomab
Ph-negative BCP ALL
Inotuzumab ozogamicin + low-intensity CTx
T-ALL
No major advance to date
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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