Multivariate Analysis of the immunologic s reconstitution ... Files/drupal/goetheuniv... · Antonio...

Pediatrics Hematology and OncologyLab of SCT and Immunotherapy Johann Wolfgang Goethe-University60596 Frankfurt / Main

Umesoft GmbHSteinbacher Str. 14-24

65760 Eschborn

Multivariate Analysis of the immunologic

system´s reconstitution after an Allogeneic

Stem Cell Transplantation

Emilia Salzmann & Melanie Bremm

Umetrics User Meeting - 2011

Allogeneic Stem Cell Transplantation (SCT)

219.08.2011

Search: HLA-matched related or unrelated donor

Graft:- Bone Marrow- PBSC- Cord Blood

Patient Conditioning

Antonio Pezzutto, Taschenatlas der Immunologie, Thieme Verlag, 2006

Transplantation


Immune Reconstitution post-SCT

319.08.2011

Chemotherapy

SCT

-10 0 +10 +20 +30 +40

Cells / µl

5000

0

Cell-Regeneration

New immune cells are

built from donor stem

cells

Immuno-therapy

Days

Immuno-therapy

Relapse


Immune Reconstitution post-SCT

419.08.2011

• 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


Therapeutic Intervention

519.08.2011

• 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

Today’s standard

*Evidence for the presence of residual malignant cells

• Identify patients following SCT with

an increased risk of relapse and/or life-

threatening infections on the basis of

immune reconstitution

Introducing prophylactic cell-

therapies (for ex. DLI, NK cell

donation) may be beneficial for high-

risk patients to overcome their

susceptibility for relapse or infectious

complications

Future aim


Study Design

619.08.2011

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


Dimensionality Reduction

719.08.2011

p[1]

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)

Input Result

Dimensionality

Reduction (PCA)

p[3]


Reference Model

819.08.2011

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.

t[1]t[2]

t[3]

3-component multivariate model:


Prediction

919.08.2011

Classification: Do immune-status values of transplanted

children reach the reference ellipsoid?

Evaluation of leukocyte subsets of 32 pediatric

patients undergoing allogeneic SCT


Prediction - Example

1019.08.2011

Monitoring Time (Days Following SCT)

tPS[1]

tPS[2]

tPS[3]a

c

T2

Ra

ng

e V

alu

esb

tPS[1]

tPS[3]

tPS[2]

M. Koenig et al., BMT, 2009


Overall Survival

1119.08.2011

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


Hierarchical model

1219.08.2011

PCA

Leukocytes

Lymphocytes

CD3+ (T cells)

CD3+CD4+ (helper T cells)

CD3+CD8+ (cytotoxic T cells)

CD3-CD56+ (NK cells)

CD19+ (B cells)

CD14+ (Monocytes)

to 3 – component

Model(t1, t2, t3)

OPLST2-Range Risk-Index

Distance from the centre of the ellipsoid


Risk- Index

1319.08.2011

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


Early prediction

1419.08.2011

• 2 component OPLS-model

(orthogonal PLS)

Result• Data from first 3 months to predict 1

year

Training data-Set: 16 patients with

acute leukemia

Use time dependence variables T2 -

dd for modeling risk-index . Where dd

is the day post SCT

First scenario

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

Com

p[1

]P

Comp No.

Model (OPLS/O2PLS) R2Y(cum)

Q2(cum)

SIMCA-P+ 12.0.1 - 2011-06-01 11:14:42 (UTC+1)


Prediction 1 year from the first 3 months

1519.08.2011


Prediction 1 year from the first 3 months

1619.08.2011


Prediction

1719.08.2011

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


Loading PLot

1819.08.2011

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

-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

po[1

]

pq[1]

Model (OPLS/O2PLS)pq[Comp. 1]/po[Last comp.]

R2X[1] = 0,499719 R2X[XSide Comp. 1] = 0,395335

14

2733

4150

61

6977

86

96

SIMCA-P+ 12.0.1 - 2011-06-07 17:55:37 (UTC+1)


Thank You for Your Attention

1919.08.2011

PD Dr. U. Köhl

Dr. R. Esser

Dr. S. Klöß

Dr. S. Hünecke

C. Brehm

E. Auth

A. Quaiser

S. Wehner

R. Müller

S. Erben

R. El Kalaäoui

T. Gardlowski

S. Betz

O. Zimmermann

Lab

of

SC

T a

nd

Im

mu

no

thera

py

Jo

han

n W

olf

gan

g G

oeth

e-U

niv

ers

ity Prof. E. Falkenberg

Prof. M. Behl

Prof. A. Orth

E. Salzmann

Un

ivers

ity o

f A

pp

lied

Scie

nces F

ran

kfu

rt

Prof. A. Orth

E. Salzmann

Um

eso

ft G

mb

H

This project was supported by ‘DFG (GRK-1172)’, ‘BMBF (FKZ 01FP09120B)’,

‘Frankfurter Stiftung für krebskranke Kinder’ and ‘Hilfe für krebskranke Kinder Frankfurt e.V.’


19.08.2011


Flowcytometric Monitoring

2119.08.2011


CD3 PC5

CD

8 E

CD

Lymph + Check FSC Lymph

Flowcytometric Measurement

2219.08.2011

CD45 FITC

SS

Gate: ungated

CD3 PC5


SS

FS

Gate: Lymph

CD

14

PC

7

CD45+

SS CD3 PC5

CD

56

PE


CD3 PC5

CD

19

EC

D


CD

4 P

E

CD3 PC5



Age Correction

2319.08.2011

11 years

11 years

T-H

elp

er/µ

l

Age (months)

Age (months) Sqrt(Age (months))

log(

T-H

elp

er/µ

l+1

)

log(

T-H

elp

er/µ

l+1

)


Infections/ GvHD post-SCT

2419.08.2011

GvHD Grade IV


Example

2519.08.2011

Pat-ID

PS.ObsID.Obs.

ID..Dauer..

M1.T2Range

PS.1...3. Risk_Index...

L-3 10 52,8594 1,26736

L-3 13 76,7786 1,26736

L-3 21 23,8722 1,26736

L-3 28 40,7368 1,26736

L-3 35 27,2886 1,26736

L-3 41 74,0817 1,26736

L-3 49 31,5839 1,26736...

L-7 15 463,007 0,404208

L-7 19 312,14 0,404208

L-7 27 178,683 0,404208

L-7 36 110,954 0,404208

L-7 48 93,8538 0,404208

L-7 54 149,43 0,404208

L-7 61 102,21 0,404208

L-7 75 104,068 0,404208

L-7 83 105,946 0,404208

Obs ID

(Primary)

Obs ID

(Number)

M1.YPred[1]

(Risk_Index

_M1_14)

M1.YVar(Risk

_index_M1_1

4)

L-2 1 0,903135 1,07353

L-3 2 0,521172 1,26736

L-5 3 0,230717 0

L-6 4 0,50773 0

L-7 5 1,02757 0,404208

L-11 6 0,566901 0,34795

L-13 7 0,780602 0

L-14 8 1,61624 1,81592

L-15 9 0,599637 0

L-16 10 0,639127 0

L-18 11 0,320025 1,34686

L-19 12 0,331459 0

L-21 13 1,54062 1,77184

L-23 14 1,35444 2,26708

L-108 15 0,391192 0,732932

L-110 16 -0,482898 0

Primary ID Number

M1.T2Rang

ePS.1...3._

M1_14

M1.T2Rang

ePS.1...3._

M1_27

M1.T2Rang

ePS.1...3._

M1_33

M1.T2Rang

ePS.1...3._

M1_41

M1.T2Rang

ePS.1...3._

M1_50

M1.T2Rang

ePS.1...3._

M1_61

M1.T2Rang

ePS.1...3._

M1_69

M1.T2Rang

ePS.1...3._

M1_77

M1.T2Rang

ePS.1...3._

M1_86

M1.T2Rang

ePS.1...3._

M1_96

Phase M1 M1 M1 M1 M1 M1 M1 M1 M1 M1

Maturity 14 27 33 41 50 61 69 77 86 96

Source

M1.T2Rang

ePS.1...3.

M1.T2Rang

ePS.1...3.

M1.T2Rang

ePS.1...3.

M1.T2Rang

ePS.1...3.

M1.T2Rang

ePS.1...3.

M1.T2Rang

ePS.1...3.

M1.T2Rang

ePS.1...3.

M1.T2Rang

ePS.1...3.

M1.T2Rang

ePS.1...3.

M1.T2Rang

ePS.1...3.

L-2 1 -1,67197 -1,91224 -1,23188 -0,929507 -1,00966 -0,541067 -0,187641 -0,558269 -0,143506 -0,836282

L-3 2 -1,66196 -1,42348 -1,47412 -0,309718 -0,766982 -0,537983 -0,830685 -0,863842 -1,08623 -1,50687

L-5 3 -1,15128 -1,52646 -1,47608 -1,38188 -1,52171 -1,14752 -1,41737 -1,29394 -1,34483

L-6 4 0,0626525 -0,392714 -0,266382 -0,808636 -0,298793 -0,323792 -0,433484 -0,451272 -0,387277 -0,357159

L-7 5 0,992867 -0,0612148 -0,0845685 0,0785673 0,373838 0,357161 0,537244 0,51752 0,716233 0,740481

L-11 6 -0,13474 0,426421 -0,687178 -0,912154 -0,727428 -0,594267 -0,215358 -0,244603 -0,276336 -0,0408475

L-13 7 0,956142 1,29953 1,25385 1,44639 1,1509 1,05323 0,579315 0,791941 0,762827 0,452327

L-14 8 0,3823 0,979317 1,68164 2,11873 1,9835 2,03344 2,09829 1,71106 1,80616 1,71117

L-15 9 -1,11901 -1,04536 -0,910187 -0,16009 -0,384889 -0,556257 -0,48036 -0,579415 -0,47443 -0,972214

L-16 10 0,296386 0,551828 0,79582 0,750663 0,439351 0,248163 0,198917 0,145894 0,297474 0,312267

L-18 11 0,311971 0,531029 0,0926541 0,364508 0,339701 -0,440982 -0,522234 -0,349162 -0,672595 0,0737407

L-19 12 -0,976239 -0,364278 -0,0345314 0,0443366 -0,323212 -1,07351 -0,798123 -0,562394 -0,328668 -0,582896

L-21 13 0,812092 0,596723 1,46484 1,39453 1,5312 1,84158 1,79598 1,79942 1,60244 1,3785

L-23 14 1,17534 0,805715 0,0761837 -0,185552 0,364449 0,760569 0,932098 1,52143 1,37599 1,46404

L-108 15 1,11653 0,810736 0,0555947 -1,08214 0,249403 0,168306 0,0574737 -0,254304 -0,495755 -0,692051

L-110 16 0,608923 0,724441 0,74435 -0,428049 -1,39967 -1,24707 -1,31406 -1,33007 -1,35151 -1,14421

Risk_Index_

M1_14

M1

14

PAI

0,486223

0,731464

-0,87203

-0,87203

-0,360617

-0,431795

-0,87203

1,42551

-0,87203

-0,87203

0,832052

-0,87203

1,36975

1,99633

0,0552916

-0,87203

Multivariate Analysis of the immunologic s reconstitution ... Files/drupal/goetheuniv... · Antonio...

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