Multivariate Analysis of the immunologic system s reconstitution after an Allogeneic Stem Cell Transplantation

Transcription

1 Pediatrics Hematology and Oncology Lab of SCT and Immunotherapy Johann Wolfgang Goethe-University Frankfurt / Main Umesoft GmbH Steinbacher Str Eschborn Multivariate Analysis of the immunologic system s reconstitution after an Allogeneic Stem Cell Transplantation Emilia Salzmann & Melanie Bremm

2 Allogeneic Stem Cell Transplantation (SCT) Search: HLA-matched related or unrelated donor Graft: - Bone Marrow - PBSC - Cord Blood Patient Conditioning Transplantation Antonio Pezzutto, Taschenatlas der Immunologie, Thieme Verlag,

3 Immune Reconstitution post-sct Cells / µl 5000 Chemotherapy SCT Cell-Regeneration Immuno-therapy New immune Immuno-therapy cells are built from donor stem cells Relapse Days 3

4 Immune Reconstitution post-sct A rapid immune reconstitution following SCT is of central importance to protect patients from relapse and infectious complications An adequate immune reconstitution can efficiently eliminate recipient s residual malignant cells Patients with delayed immune reconstitution show an enhanced probability of relapse and severe infections 4

5 Therapeutic Intervention Today s standard Intervention is mainly based on minimal residual disease (MRD) * diagnostic and donor/recipient chimerism Unfortunately part of the patients already exhibit a manifest relapse at this time point Future aim Identify patients following SCT with an increased risk of relapse and/or lifethreatening infections on the basis of immune reconstitution Introducing prophylactic celltherapies (for ex. DLI, NK cell donation) may be beneficial for highrisk patients to overcome their susceptibility for relapse or infectious complications * Evidence for the presence of residual malignant cells 5

6 Study Design Aim: Generate a multivariate model to divide leukemia patients following SCT into risk-groups Strategy: 1) Choose adequate leukocyte subpopulations to use for model generation 2) Design a multivariate model on the basis of age-matched leukocyte subpopulation values of 100 healthy children and adolescents 3) Classify the immune reconstitution of the patients post allo-sct on the basis of the new model 4) Create risk-groups on the basis of the classification result 6

7 Dimensionality Reduction Input Result p[2] Leukocytes Lymphocytes CD3 + (T cells) CD3 + CD4 + (helper T cells) CD3 + CD8 + (cytotoxic T cells) CD3 - CD56 + (NK cells) CD19 + (B cells) CD14 + (Monocytes) Dimensionality Reduction (PCA) p[1] p[3] 7

8 Reference Model 3-component multivariate model: t[3] t[1] t[2] Age-matched lymphocyte subpopulation reference values from 100 healthy children are used to generate a multivariate model. This model defines the range of normal lymphocyte values. 8

9 Prediction Classification: Do immune-status values of transplanted children reach the reference ellipsoid? Evaluation of leukocyte subsets of 32 pediatric patients undergoing allogeneic SCT 9

10 T 2 Range Values Prediction - Example a tps[3] b tps[3] tps[1] tps[1] tps[2] tps[2] c Monitoring Time (Days Following SCT) M. Koenig et al., BMT,

11 Overall Survival a b Classification at day 200 Classification at day 300 Significantly higher number of long-time survivors among the low-risk group compared to the high-risk group 11

12 Hierarchical model Leukocytes Lymphocytes CD3 + (T cells) CD3 + CD4 + (helper T cells) CD3 + CD8 + (cytotoxic T cells) CD3 - CD56 + (NK cells) CD19 + (B cells) CD14 + (Monocytes) PCA to 3 component Model (t 1, t 2, t 3 ) Distance from the centre of the ellipsoid T2-Range OPLS Risk-Index 12

13 Risk- Index the risk Index to classify a given patient's life expectancy "an event" or "a non-event" is: - non-event patients : - risk-index = 0 : For long-time survivors the event-risk converges to zero - event-patients : - risk-index > 1 : patient who dies within the first year - risk-index = 1 : patient who dies exactly in 1 year - risk-index < 1 : patient who dies after 1 year 13

14 Comp[1]P Early prediction First scenario Data from first 3 months to predict 1 year Training data-set: 16 patients with acute leukemia Use time dependence variables T 2 - dd for modeling risk-index. Where dd is the day post SCT Result 2 component OPLS-model (orthogonal PLS) 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 Model (OPLS/O2PLS) R2Y(cum) Q2(cum) 0,0 Comp No. SIMCA-P :14:42 (UTC+1) 14

15 Prediction 1 year from the first 3 months 15

16 Prediction 1 year from the first 3 months 16

17 Prediction Training-Set (n= 16) predict-event predict-no-event Event in 1st. year 3 3 No Event 1 9 Prediction-Set (n= 46) predict-event predict-no-event Event in 1st. year 7 7 No Event 9 19 Data from first 3 months to predict 1 year 17

18 po[1] Loading PLot Model (OPLS/O2PLS) pq[comp. 1]/po[Last comp.] 0, ,60 0,50 0,40 0, ,20 0,10 0,00-0,02 0,00 0,02 0,04 0,06 0,08 0,10 0,12 0,14 0,16 0,18 0,20 0,22 0,24 0,26 0,28 0,30 0,32 0,34 0,36 0,38 0,40 0,42 pq[1] R2X[1] = 0, R2X[XSide Comp. 1] = 0, SIMCA-P :55:37 (UTC+1) 18

19 Lab of SCT and Immunotherapy Johann Wolfgang Goethe-University Umesoft GmbH University of Applied Sciences Frankfurt Thank You for Your Attention PD Dr. U. Köhl Prof. A. Orth Prof. E. Falkenberg Dr. R. Esser E. Salzmann Prof. M. Behl Dr. S. Klöß Prof. A. Orth Dr. S. Hünecke E. Salzmann C. Brehm E. Auth A. Quaiser S. Wehner R. Müller S. Erben R. El Kalaäoui T. Gardlowski S. Betz O. Zimmermann This project was supported by DFG (GRK-1172), BMBF (FKZ 01FP09120B), Frankfurter Stiftung für krebskranke Kinder and Hilfe für krebskranke Kinder Frankfurt e.v. 19

20 20

21 Flowcytometric Monitoring 21

22 FS CD8 ECD CD19 ECD SS CD14 PC7 CD4 PE CD56 PE Flowcytometric Measurement Gate: ungated CD45+ Lymph + Check FSC Lymph Lymph + Check FSC Lymph CD45 FITC SS CD3 PC5 CD3 PC5 Gate: Lymph Lymph + Check FSC Lymph Lymph + Check FSC Lymph Lymph + Check FSC Lymph SS CD3 PC5 CD3 PC5 CD3 PC5 22

23 log(t-helper/µl+1) log(t-helper/µl+1) T-Helper/µl Age Correction 11 years Age (months) 11 years Age (months) Sqrt(Age (months)) 23

24 Infections/ GvHD post-sct GvHD Grade IV 24

25 Example Pat-ID. PS.ObsID.Obs. ID..Dauer.. M1.T2Range PS Risk_Index. L ,8594 1,26736 L ,7786 1,26736 L ,8722 1,26736 L ,7368 1,26736 L ,2886 1,26736 L ,0817 1,26736 L ,5839 1, M1.YPred[1] (Risk_Index _M1_14) M1.YVar(Risk _index_m1_1 4) Obs ID (Primary) Obs ID (Number) L-2 1 0, ,07353 L-3 2 0, ,26736 L-5 3 0, L-6 4 0, L-7 5 1, , L , ,34795 L , L , ,81592 L ,007 0, L , L ,14 0, L , L ,683 0, L , ,34686 L ,954 0, L , L ,8538 0, L , ,77184 M1.T2Rang M1.T2Rang M1.T2Rang M1.T2Rang M1.T2Rang M1.T2Rang M1.T2Rang M1.T2Rang M1.T2Rang M1.T2Rang L ,43 0, eps _ eps _ eps _ eps _ eps _ eps _ L-23 eps _ eps _ 14 eps _ 1,35444 eps _ Risk_Index_ 2,26708 L ,21 Primary 0, ID Number M1_14 M1_27 M1_33 M1_41 M1_50 M1_61 L-108 M1_69 M1_77 15 M1_86 0, M1_96 0, M1_14 L ,068 Phase 0, M1 M1 M1 M1 M1 M1 L-110 M1 M1 16-0, M1 M1 M1 0 L ,946 Maturity 0, M1.T2Rang M1.T2Rang M1.T2Rang M1.T2Rang M1.T2Rang M1.T2Rang M1.T2Rang M1.T2Rang M1.T2Rang M1.T2Rang Source eps eps eps eps eps eps eps eps eps eps L-2 1-1, , , , , , , , , , L-3 2-1, , , , , , , , , ,50687 L-5 3-1, , , , , , , , ,34483 L-6 4 0, , , , , , , , , , L-7 5 0, , , , , , , , , , L , , , , , , , , , , L , , , , ,1509 1, , , , , L ,3823 0, , , ,9835 2, , , , ,71117 L , , , , , , , , , , L , , , , , , , , , , L , , , , , , , , , , L , , , , , , , , , , L , , , , ,5312 1, , , , ,3785 L , , , , , , , , , ,46404 L , , , , , , , , , , L , , , , , , , , , ,14421 PAI 0, , , , , , , , , , , , , , , ,