04 Octobre 2012
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Transcript of 04 Octobre 2012
Towards optimization of Acute Myeloid Leukemia treatment: a data-driven model of cell population dynamics
Annabelle Ballesta, Faten Mehri, Xavier Dupuis, Pierre Hirsh, Ruoping Tang, Jean-Pierre Marie, Jean Clairambault
04 Octobre 2012
OUTLINE Introduction on Acute Myeloid Leukemia
1. Cell Dynamics in the absence of drugs
1. Experimental results for Patient #7
2. Time- and age-structured mathematical model
3. Fit to experimental results for Patient #7
2. Cell Dynamics in presence of Anticancer drugs
(Aracytine and AC220)
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Normal Hematopoiesis
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Hematopoietic Stem Cell
Acute Myeloid Leukemia
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Hematopoietic Stem Cell
Acute Myeloid Leukemia
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• Blockage in the differenciation of progenitors of the myeloid linage.
• Cancer cells proliferate in the bone marrow and eventually invade the blood and other organs.
• AMLs are the most frequent leukemias in adults. They are associated to a high mortality (40 000/year in Europe).
-> optimizing therapies against AML still a clinical challenge
Cell Dynamics in the absence of drugs
1
Annabelle Ballesta
Experimental protocol
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• Experiments on AML patient blood sample.
• Assumption: only cancer progenitors in blood, healthy progenitors being in the bone marrow.
• Three membrane markers to characterize cell populations: CD 34, CD 38 and CD 33
Surface marker
• Three cell population: from immature to mature cells
Able to self-renew, to differenciate
Able to differenciate
Unable to differenciate
Three Cell compartments
Cancer Hematopoietic stem cells CD34+/CD38-
Cancer progenitors CD38+/CD33-
Mature cancer cells ”blast”
CD38+/CD33+
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Annabelle Ballesta
Experimental protocol
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• J0 8am: Blood sample collection
• J0: Sort white blood cells by Ficoll
• J0: Sort CD34/38/33+ cell population by immunomagnetic technique
• J0 2pm to J5: cell culture in standard medium supplemented with
growth factors (SCF, G-CSF, Il-3, Flt-3)
Patient #7: Markers at J0
• Markers:
• ->cell sorting of CD 38+ population
- +CD34 97.34 2.65
CD33- CD33+CD38+ 10.84 69.62CD38- 16.33 3.2
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• Methods: count using the Malassez cell
• Average of 2 to 3 independant measurements
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Patient #7: Cell count
0 1000 2000 3000 4000 5000 6000 7000 80000
200000
400000
600000
800000
1000000
1200000
Cell number (control conditions)
Time (min)
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0 1000 2000 3000 4000 5000 6000 7000 800070
75
80
85
90
95
Viable cells (Annexin -/PI-)
• Methods: Annexin/ Propidium iodure (PI)
• Average of 2 to 3 independant measurements
Time (min)
Patient #7: Cell Death
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J1 J2 J3 J4
G0 (%) G1 (%) S (%) G2/M (%)
J0 79.9 20.1 0 0J1 51.4 16.73 31.41 0.46J2 21.9 34.52 42.82 0.76J3 22 39.18 38.7 0.05J4 45.6 21.22 33.02 0.07J5 64.46 0.64 34.48 0
Patient #7: Cell cycle (PI+Ki67)
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0 1000 2000 3000 4000 5000 6000 7000 80000
10
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CD38+/33+controlCD38+/33- control
Patient #7: CD 38 and CD 33 markers
Time (min)
Mathematical model
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• One population : CD 38+• Model is structured in time and age• 4 phases: G0 (r), G1 (g), S (s), G2/M (m)
• NB: model incorporates cell cycle phases in view of modeling of phase-specific anticancer drugs.
g r
γ
Tg
Tr
sTs
mTm
γ γ γ
Mathematical model
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• Equation for g(t,a):
• Initial condition in age:
• Initial condition in time:
Mathematical model
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• Tr, Tg, Ts, Tm: Transitions functions in the form:
• -> 3 parameters to be estimated for each phase= 12 parameters
• Initial instant: cells in G0 and G1, same age assumed for all cells, age to be estimated
Parameter estimation
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• 16 parameters to be estimated in total: 12 for transition functions, age in G0 and G1 at t=0, 2 death rates gamma and delta.
• Least square approach, minimization task performed with the CMAES algorithm
Results for Patient #7: cell number
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Results for Patient #7: cell death
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Results for Patient #7: cell cycle
G0 G1, S , G2/M
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Results for Patient #7: parameter values
a_min_r= 30.2 hµ_r= 53σ_r= 7.1
a_min_g= 18.9 hµ_g= 27.7σ_g=7
a_min_s= 28.3 hµ_s=39.9σ_s=-=6.59
a_min_m= 0.9 hµ_m=0.16σ_m=0.05
a0_G0= 53 ha0_G1= 235 h
γ=0.002 h-1δ=0 h-1
Conclusions and Perspectives
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• Experimental characterization of cell dynamics in control conditions for Patient #7, mathematical model calibrated to data achieve a satisfying fit.
• Perspective: model two populations (CD 38+/CD 33- and CD38+/33+) to improve fit.
• Same modeling approach for other patient data (10 patients)
Cell Dynamics in presence of Aracytin (ARA-C) and FLt 3 inhibitor AC220
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Annabelle Ballesta
AML therapeutics
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• Aracytine (ARA-C) widely used in clinics against AML.
• Aracytine targets cells in S-phase.
Annabelle Ballesta
Cell dynamics : cell count
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Temps (min)
Patient #7: CD38+ population
0 1000 2000 3000 4000 5000 6000 7000 80000
200000
400000
600000
800000
1000000
1200000
nombre de cellules controlARA0.5ARA1ARA2
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Cell dynamics: Annexin/PI
Temps (min)
% d
e ce
llule
s A
nnex
in-/I
P-
0 1000 2000 3000 4000 5000 6000 7000 80000
10
20
30
40
50
60
70
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90
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nombre de cellules controlARA0.5ARA1ARA2
Forte mort cellulaire due à l’ARA-C, dépendante du temps d’exposition (pas de la dose pour ce patient)
Patient #7: CD38+ population
Annabelle Ballesta
Cell dynamics: cell cycle analysis
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J1 J2 J3
Patient #7: CD38+ population, ARA-C 0.5ng/mL
G0 G1 S G2/MJ0 80 20 0 0
J1 90,25 1,52 8,23 0
J2 91,95 0,26 7,79 0
J3 95,9 4,1 0 0
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Cell dynamics: differenciation
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0 1000 2000 3000 4000 5000 6000 7000 80000
200000
400000
600000
800000
1000000
1200000
CD38+/33+ Nbre cellulesCD38+/33- Nombre celluleARA 0.5 CD38+/33+ARA à.5 CD38+/33-
Temps (min)
Patient #7: CD38+ population, ARA-C 0.5ng/mL
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Cell dynamics in presence of Flt3 inhibitor AC220
• Flt 3 is atyrosine kinase which is often mutated in AML cells, giving a proliferative advantage
• AC220 inhibits Flt3 activty.
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Cell dynamics : cell count
Patient #8 (53LAM2012), polpulation CD38+/33-
Temps (min)
0 1000 2000 3000 4000 5000 6000 7000 80000
200000
400000
600000
800000
1000000
1200000
nombre de cellules controlAC50 microMAC200 microMAC1000 microM
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Cell dynamics : Annexin/PI
Patient #8 (53LAM2012), population CD38+/33-
Temps (min)0 1000 2000 3000 4000 5000 6000 7000 8000
0
20
40
60
80
100
120
33- ControlAC50AC200AC1000
Annabelle Ballesta
Cell dynamics: cell cycle analysis
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Patient #8 (53LAM2012), Flt3-, polpulation CD38+/33-G0 G1 S G2/M
J0 87,7 6,43 5,87 0
J1 70,2 7,21 22,59 0
J2 28,8 43,22 24,79 3,19
J3 23,8 51,54 22,1 2,56
J4 77,14 21,18 1,68
J5 79,56 19,04 1,4
G0 G1 S G2/MJ0 87,7 6,43 5,87 0
J1 90.9 0 9,7 0
J2 75.7 0 33,48 0,77
J3 50,4 23,22 23,33 3,05
J4 81,85 16,31 1,84
J5 83,51 13,81 2,68
Normal conditions
AC-220, 1000µM
Annabelle Ballesta 04/10/2012
Conclusions and Perspectives
Cytotoxic activity of ARA-C on AML cells
Cytostatic activity of AC220 on AML cells
Next:
Mathematically Model ARA-C and AC220 activities.
Perform optimization procedure to optimize co-
administration.
Thank you
www.inria.fr
AML: FAB Classification
FAB Maturation
M0 Indifférenciée
M1 Myéloblastique sans différenciation
M2 Myéloblastique avec différenciation
M 3 Promyélocytaire
M4 Myélomonocytaire
M5 Monoblastique et monocytaire
M6 érythroleucémique
M7 mégacaryocytoblastique
M0
M1
M2
M3
M4 M5a
M5b
M6 M7
Expression des antigènes: CD33, CD13, CD117, CD65, CD14, MPO
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Annabelle Ballesta
Experimental protocol
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Ficoll on blood samples to isolate white blood cells:
Annabelle Ballesta
Cell sorting by immunomagnetic technique:
Antibody against CD 34, CD38 and CD33
Experimental protocol
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Specific antibody
Magnetic particule
Surface antigen
Introduction rates: functions
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Adimy et al., J of Biological systems, 2008 :