Should the therapy of AML be driven by MRD quantification? Sergio - - PowerPoint PPT Presentation
Should the therapy of AML be driven by MRD quantification? Sergio Amadori Dept. Hematology Tor Vergata University Hospital Rome COHEM 09/2010 Should the therapy of AML be driven by MRD quantification? YES It makes sense Independent
COHEM 09/2010
Biologically heterogenous group of disorders Disease recurrence major obstacle to cure Risk-stratification schemes (based on pre-therapy variables)
inadequate for predicting relapse in individual patients
Can we do better in terms of predicting risk and guiding treatment
decisions? SWOG
(Medeiros et al, Blood 2010)
Presence of occult disease (MRD) at morphologic CR is
predictive of relapse
Quantification of MRD defines the risk
1012 1010 108 106 104 102 100 Time
Relapse Cure CR MRD
Morphology has no value More sensitive techniques needed
Detection of LAIP
Detection of LSGT
Minimum 4-colour technology Aberrant LAIP identified at diagnosis in >90% of AMLs Average sensitivity 0.01% (10-4)
Multiple staining at diagnosis Identification of LAIP (average 3 LAIPs per patient) Definition of patient-specific “immunologic fingerprint” Immunologic fingerprint used during follow-up
Many studies published in the last decade Main conclusions
MRD monitoring feasible Most relevant checkpoints
Independent prognostic factor for
Tor Vergata University Hospital (TVUH) 142 adults with AML in CR (median age 52y, range 18-75; 50 ≥ 60y) EORTC-GIMEMA AML-10, AML-12, AML-13 MRD+: ≥ 3.5 x 10-4 (0.035%)
Excel: Ind – Cons – Good: Ind + Cons – Poor: Ind + Cons + Ugly: Ind – Cons +
Cytogenetic and molecular diagnostic characterization combined to postconsolidation minimal residual disease assessment by flow- cytometry improves risk stratification in adult acute myeloid leukemia
TVUH 143 adults with AML in CR (median age 50y, range 18-75; 40 ≥ 60y) EORTC-GIMEMA AML-10, AML-12, AML-13, AML-17 MRD+: ≥ 3.5 x 10-4 (0.035%)
Low-Risk High-Risk
Good K / MRD- Int K / MRD- Adverse K FLT3+ Good K / MRD+ Int K / MRD+
MRD quantification Predicts outcome Refines risk-stratification
GIMEMA
(Lo Coco et al, Semin Hematol 2002)
PETHEMA
(Esteve et al, Leukemia 2007)
Prospective Minimal Residual Disease Monitoring to Predict Relapse of Acute Promyelocytic Leukemia and to Direct Pre-Emptive Arsenic Trioxide Therapy
Improve risk-stratification and guide post-remission therapy Inform timing and type of transplantation in CR1 Detect impending relapse and guide preemptive therapy Improve outcome
MRD monitoring MRD+ MRD- Treatment intensification Novel therapies Treatment reduction
TVUH Pediatric AML
N=42 MRD+ post-cons N=37 MRD- post-cons
Maurillo et al, JCO 2008
B
auSCT alloSCT Total L-risk 26 6 32 H-risk 30 17 47 Total 56 23 79
Low-Risk High-Risk
Good K / MRD- Int K / MRD- Adverse K FLT3+ Good K / MRD+ Int K / MRD+ Buccisano et al, Blood 2010
1 2 3 4 5
Time (yrs)
0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0
Disease Free Survival
P=0.003
AlloSCT (ITT) N=21 AlloSCT N=15 AutoSCT N=53
85% 44% 20% Buccisano et al, 2010 unpublished
MRD monitored by FCM in 95% of pts MRD+: ≥ 0.1% cells with LAIP among BM mononuclear cells Used to intensify timing or components of subsequent therapy
Day 22 MRD ≥ 0.1% → intensified timing (ADE) Day 22 MRD ≥ 1% → ADE + GO Persistent MRD ≥ 0.1% → eligible for HSCT
SR
(with donor)
HR
Enrollment, Randomization, Initial Risk Assignment H-ADE ADE ± GO Final Risk Assignment
SCT CI CII CIII
ADE MRD MRD
LR SR
(w/o donor)
SR
(with donor)
HR
(Rubnitz et al, Lancet Oncology 2010)
Risk- and MRD-adapted therapy resulted in 71% OS Day 22 MRD >1% significantly associated with worse
OS, EFS, CIR
71% ± 4% OS 63% ± 4% EFS 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1 2 3 4 5 6 7 19% ± 3% Years on Study 9% ± 2% Relapse Death
N=230 CR rate 94% MDR+ 37% (Ind1) MDR+ 20% (Ind2)
Low-risk: CBF/Kitwt; NPM1+/FLT3- Int-risk: all others High-risk: Adverse K; FLT3+
Diagnosis Low-risk Int-risk High-risk MRD- MRD+
MRD marker LAIP Risk stratif CG, molecular MRD assess LAIP FLA-I salvage No CR CR
CR
Induction
(1 or 2 courses)
Consolidation 1
autoSCT alloSCT alloSCT: MRD, MUD, UCB, HRD
Improve outcome by
Refining risk stratification Using MRD to guide type of transplant in Int-risk AML
Primary endpoint
Overall survival
Secondary endpoints
CIR DFS EFS
20-30% relapse rate in MRD negative Why are we unable to predict it? Technical reasons
Increase sensitivity/specificity Define more significant thresholds Define more relevant checkpoints
Biological reasons
Quantification of LSC (CLL-1)
Independent predictor of outcome Can be used as an early endpoint to assess efficacy Refines pre-therapy risk-stratification, providing a
framework for development of more tailored treatment approaches
Routinely used to guide management of patients with
APL
MRD-directed treatment strategies likely to improve the
management of other subtypes of AML
Tor Vergata Univ. Hospital
Adriano Venditti Francesco Buccisano Luca Maurillo Maria I. Del Principe Francesco Lo Coco William Arcese
GIMEMA Group
Marco Vignetti Paola Fazi Giulio D’Alfonso
Emperor Caesar Augustus (63 B.C. – 14 A.D.)
“Sic Est”