Myelodysplastic Syndromes

Diagnostics and Flow Cytometry

Machine Learning and Artificial Intelligence

Arjan A. van de Loosdrecht

Department of Hematology

Amsterdam UMC

VU University Medical Center

Cancer Center Amsterdam (CCA)

Amsterdam, The Netherlands

Annual Meeting

Israel Society of Hematology and Transfusion Medicine

September 5-7, 2019

Pastoral, Kfar Blum

Diagnostic tool Diagnostic value Priority

Peripheral blood

smear

• Evaluation of dysplasia in one or more cell lines

• Enumeration of blastsMandatory

Bone marrow

aspirate

• Evaluation of dysplasia in one or more

myeloid cell lines

• Enumeration of blasts

• Enumeration of ring sideroblasts

Mandatory

Bone marrow biopsy • Assessment of cellularity, CD34+ cells, and fibrosis Mandatory

Cytogenetic analysis

• Detection of acquired clonal chromosomal

abnormalities that can allow a conclusive diagnosis

and also prognostic assessment

Mandatory

FISH

• Detection of targeted chromosomal abnormalities

in interphase nuclei following failure of standard G-

banding

Recommended

Flow cytometry

immunophenotype

• Detection of abnormalities in erythroid,

immature myeloid, maturing granulocytes,

monocytes, immature lymphoid compartments

Recommended*

If according to

ELN guidelines

SNP-array

• Detection of chromosomal defects at a high

resolution in combination with metaphase

cytogenetics

Suggested (likely to

become a

diagnostic tool in

the near future)

Mutation analysis of

candidate genes

• Detection of somatic mutations that can allow

a conclusive diagnosis and also reliable

prognostic evaluation

Suggested (likely

to become a

diagnostic tool in

the near future)

Diagnostic approach to suspected myeloid

neoplasms/MDS 2019 (EU guidelines 2013)

Malcovati L, et al., ELN guidelines. Blood 2013;122:2943-64; Greenberg P, et al., J Nat Compr Netw

Canc 2013;11:838-74; *Westers TM, et al., Leukemia 2012;26:1730-41

Fig. 1

Diagnostic algorithm for lower-risk

myelodysplastic syndromes

Mufti GJ, Van de Loosdrecht AA, et al. Leukemia 2018;32;1679-1696

WHO2016:

classifying MDS: role of flow cytometry

Morphology:

no changes

– dysplasia cut-off levels remains 10% in all lineages

– blast cell counts by cytology: not by FCM

– due to IPSS-R push towards counts of <2% vs 2-5% (500

cells)

Cytogenetics:

no changes

Flow cytometry:

in suspected MDS if performed according to recommended

panels

as part of an integrated report

Arber DA and Hasserjian RP. Hematology 2015;294-298

Porwit A, et al., Leukemia 2014:28:1793-98

Arber DA, et al., Blood 2016;127:2391-2405

Antigen expression during neutrophil

differentiation: the concept

103

102

101

Adapted from: A Orfao, ELNet Flow MDS 2008-2018, Amsterdam

BAND/

NEUTROPHILMETAMYELOCYTEMYELOCYTEPROMYELOCYTEMYELOBLAST

CD13

CD11b

CD13

CD16

Antigen expression during neutrophil

differentiation: the concept

103

102

101

BAND/

NEUTROPHILMETAMYELOCYTEMYELOCYTEPROMYELOCYTEMYELOBLAST

CD34

HLA-DRCD117

CD13

CD33

CD11b

CD64

CD65

CD54

CD10

CD35

CD13

MPO

CD15

CD16

Adapted from: A Orfao, ELNet Flow MDS 2008-2018, Amsterdam

Antigen expression during monocytic

differentiation: the concept

Adapted from: A Orfao, ELNet Flow MDS 2008-2018, Amsterdam

5203

Antigen expression during erythroid

differentiation: the concept

Wangen JR, et al, Int J Lab Hem 2014;36:184-96; Eidenschink-

Broderson L, et al., Cytometry B Clin Cytom 2015;88:125-135

Van de Loosdrecht AA, et al., Haematologica 2009; 94:1124-34

Westers TM, et al., Leukemia 2012;36:422-30; Porwit A, et al., Leukemia 2014:28:1793-98

Standardization of flow cytometry in MDS:ELNet 2014 recommendations

CD45

SS

C

granulocyte

mono

lympho

progenitors

control MDS

My My

My

CD34+ cells

B

4-parameter diagnostic score consists of: (≥2 possible MDS)

1. SSC of granulocytes (ratio to lymphocytes)(>6)

2. % CD34+ myeloid progenitor cells among all nucleated cells (<2%)

3. % CD34+ B cell precursors among all CD34+ cells (>5%)

4. CD45 expression of myeloid progenitor cells (ratio to lymphocytes)(4-7.5)

B

FCM in diagnostics: Cardinal Parameters Ogata Score

Ogata K, et al., Blood 2006;108;1037-1044; Ogata K, et al., Haematologica

2009;94:1066-74; Della Porta MG, et al., [ELNet] Haematologica 2012;97:1209-17

Examples of the diagnostic score (normal)

Westers TM and Van de Loosdrecht AA. 2018, in:

(Multiparameter Flow Cytometry in the Diagnosis of Hematologic Malignancies; ed. Porwit and Bené)

D E F

A B C

SSC 9.5 CD45 4.5

CD34+ 1.0% CD34+B 4.8%

Examples of the diagnostic score (MDS)

G H I

J K L

SSC 3.1 CD45 5.5

CD34+ 1.1% CD34+B 0%

Westers TM and Van de Loosdrecht AA. 2018, in:

(Multiparameter Flow Cytometry in the Diagnosis of Hematologic Malignancies; ed. Porwit and Bené)

FCM of dyserythropoiesis: a sensitive and powerful diagnostic

tool for myelodysplastic syndromes: The RED score

Mathis S, et al., Leukemia 2013;27:1981-198; Westers TM, et al., Haematologica 2017:102:308-19

Red score treshold points

CD71: CV <80; ≥80 0 vs 3

CD36: CV <65; ≥65 0 vs 2

Hb level >10.5f or

>11.5m;

≤10.5f or

≤11.5m

0 vs 2

Red Score ≥ 3: 80% correctly scored MDS/non-MDS;

Ogata score + Red Score:

sensitivity of 49% 88%

CD71 CD71

MDS

controls

parameter exp(B) 95% CI p-value CD36 CV 3.65 1.57 – 8.48 0.003

CD71 CV 3.20 1.61 – 6.37 0.001

CD71 MFI 2.18 1.07 – 4.45 0.033

%CD117 1.74 0.92 – 3.23 0.084

Methods:

Collection of flow cytometry data (2012-2014):

Learning cohort (18 centers); 142 NBM, 290 pathological controls and 245

MDS cases + 8 RAEB

Validation cohort (9 centers); 49 NBM, 153 pathological controls and 129

MDS cases + 21 RAEB

Normalization of expression levels (different fluorochromes and instruments)

and percentages of subsets

Results of multivariate analysis; multicenter

approach within iMDSflow ELN WG

Westers TM, et al., Haematologica 2017:102:308-19

Integrated flowcytometric (iFC) diagnostic algorithm

Diagnostic score <2 <2 <2 <2 <2 <2 <2 <2 ≥2 ≥2 ≥2 ≥2 ≥2 ≥2 ≥2 ≥2

Dysplasia by FC

myeloid prog.

- - - - + + + + - - - - + + + +

Dysplasia by FC

- Neutrophils

- Monocytes

- - + + - - + + - - + + - - + +

Dysplasia by FC

- Erythrocytes

- + - + - + - + - + - + - + - +

Conclusion* A B B C B C C C B C C C C C C C

Cremers EMP, et al., Haematologica 2017;102:320-26

Van de Loosdrecht AA, et al., J Nat Compr Cancer Netw 2013;11:892-902

Westers TM, et al., Leukemia 2012;26:1730-41

Porwit A, et al., Leukemia 2014:28:1793-98

A = ‘results show no MDS-related features’ ‘as good as normal’

B = ‘results show limited number of changes associated with MDS’ ‘borderline benign’

C = ‘results are consistent with MDS’ ‘consider MDS’

Duetz C, et al. Pathobiology 2018 ;18:85;274-283.

Diagnostic

Algorithm:

role of FCM

• Flow cytometry is 1 diagnostic approach; the report should include:

– PB cytopenias including differential count

– BM Cytomorphology

– BM trephine/Immunohistochemistry

– Cytogenetics/FISH

– Molecular data

• Note: dysmegakaryopoiesis is not included in the flow cytometric analysis

• Note: repeat analysis after 6 month in inconclusive cases and/or if disconcordance between diagnostic tools is evident

• Note: no prognostic and prediction of response information yet!

Van de Loosdrecht AA, et al., J Nat Compr Cancer Netw 2013;11:892-902

Westers TM, et al., Leukemia 2012;26:1730-41

Porwit A, et al., Leukemia 2014;28:1793-98

Additional comments in final integrated report

Current approach (iFS)*

Flow cytometry panel:

7 tubes, 25 unique markers

Scoring:

For every cell subset

(progenitors, erythroid,

monocyte, neutrophils) aberrant

expression outside 2SD range of

normal bone-marrow, is scored.

Conclusion:

- MDS

- Inconclusive

- No-MDS

*Duetz et al., Clinical implications of multi parameter flow cytometry in myelodysplastic syndromes

Duetz C, et al. Pathobiology 2018 ;18:85;274-283.

Can we improve diagnostic MDS-iFS?

-Accuracy and robustness

-Sensitivity and specificity

-Objectivity

-User friendliness

-Time investment

-Required level of expertise

-Costs

Duetz C, et al., 2019 (submitted)

Clustering Classification

Concept of the automated pipeline

Automated interpretation

-A self-learning algorithm

determines based on the

parameters of the clustering

whether a sample is more

similar to MDS or a control-

sample.

Automated analysis of

FCS files

- Perform grouping of

cells of all FCS files based

on similarity of marker

expression and scatter

parameters

Pre-processing

Preparing FCS files for

automated clustering

- Quality control

- Compensation

- Pre-gating

- Transformation

- Enrichment

Feature generation

Duetz C, et al., 2019 (submitted)

Feature selection by AI

Features derived from

metaclusters:

- Relative abundance

- MFI

- CV

Training cohort (n=148)

2013-2017

MDS-patients (n=67)

Blast count < 5%

Pathological controls/healthy

controls (n=69/n=12)

Patient cohorts

Validation cohort (n=57)

2017-2018

MDS-patients (n=30)

Blast count < 5%

Pathological controls/healthy

controls (n=19/n=8)

Duetz C, et al., 2019 (submitted)

Flow cytometry panel: according to MDS ELNet recommendations

(2014)

Tube FITC PEPerCP-CY5.5

PC7 APC APC-H7 V450 KO

1 CD34 CD117 HLA-DR CD45

2 CD16 CD13 CD34 CD117 CD11b CD10 HLA-DR CD45

3 CD2 CD64 CD34 CD117 IREM2 CD14 HLA-DR CD45

4 CD36 CD105 CD34 CD117 CD33 CD71 HLA-DR CD45

5 CD5 CD56 CD34 CD117 CD7 CD19 HLA-DR CD45

6 CD15 CD25 CD34 CD117 CD123 CD38 HLA-DR CD45

7 CD7 CD235a CD34 CD117 CD13 CD71 HLA-DR CD45

Cremers EMP, et al., Haematologica 2017;102:320-26

Duetz C, et al 2019 (submitted)

Sensitivity Specificity

AI analysis 90% 93%

iFS 80% 86%

6-tubes workflow

Sensitivity Specificity

AI analysis 97% 95%

iFS 80% 86%

Single-tube workflow

Duetz C, et al., 2019 (submitted)

Performance in validation cohort by usingartificial intelligence

Most discriminative features

Duetz C, et al., 2019 (submitted)

Most discriminative features

Duetz C, et al., 2019 (submitted)

Practical evaluation: computational analysis

Time needed for analysis:

60 to 90 minutes 30 seconds

Amount of bone-marrow and materials needed:

Seven fold decrease (from 7 to 1 tube)

Validation in independent cohorts of Dresden, Paris and

Munich (MLL) groups within ELN-WP8/MDS-Right (2019):

Duetz C, et al., 2019 (submitted)

FCM in CMML:

Normal Monocyte subsets: definitions

Zawada A et al., Immunobiol 2017:831-840; Ziegler-Heitbrock-L. Front Immunol 2015:6;423; Wong KL et al.,

Blood 2011;118:e16-31; Wong KL et al., Immunol Res 2012;53:41-57; Selimoglu-Buet, L, et al., Blood

2015;125:3618-26

Abnormal repartition of monocyte subsets in

CMML

Selimoglu-Buet, L, et al.,

Blood 2015;125:3618-26

ROC:

Cut-off: 94% MO1

Sensitivity 90%;

specificity 95%

Monocyte subset profile as a biomarker of

disease evolution

Selimoglu-Buet, D, et al., Blood 2015;125:3618-26

Delimoglu-Buet, D. et al.,

Blood 2017

CMML-like MDS

(not fulfiling CMML

criteria) with >94

MO1 evolve to overt

CMML (A-F/M)

(K-L): Associated

inflammatory

conditions gave

rise to fals-neg FCM

CMML diagnosis

Repartition of monocytes subsets in

different groups of cases with monocytosis

Picot T, et al., Front Oncol 2018;8:109

monocytosis

Morphology/cytogenetics/NGS

+/- 10% monocytes

+/- 1x109/l monocytes

+/- <10% dysplasia/non-specific dysplasia

Not met WHO2016 criteria

WHO2016

criteria

CMML No CMML

Monocytosis of Unknown

significance (pre-CMML conditions)

”specific FCM

abberancies” (ELN/EuroFlow/MO1-3)/BM/PB

Other causes

(AML/CML/etc)NGS: no mutations beyond DAT

NGS: not available

YES

NO

Provisional Diagnostic Algorithm of FCM in MDS

monocytosis: (Van de Loosdrecht, Orfao, Kern; Vienna 24-26 Aug 2018)

Valent P, et al., 2019

(submitted)

Conclusions MDS/CMML

Suspected MDS or CMML:

• Follow ELN recommendation for FCM analysis

• (all lineages)

• Computational analysis of FCM may largely improve sensitivity

and specificity with a major reduction of costs in time and

materials

• In CMML: focus on specific monocyte subsets (MO1:

CD14+/CD16-)

• Note: PB vs BM (under investigation within ELN WG FCM)

• Note: no specific phenotypes discriminate CMML vs MDS/MPN

• Note: Most frequent but not specific: CD56; HLA-Dr

Acknowledgements

Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam

Department of Hematology, Amsterdam, The Netherlands

MDS Group AmsterdamMarisa Westers, Claudia Cali, Canan Alhan, Eline Cremers,

Margot van Spronsen, Nathalie Kerkhoff, Carolien Duetz,

Luca Janssen, Yvonne van der Vreeken, Adrie Zevenbergen,

Geja Heeremans, Guus Westra, Costa Bachas,

Arjan van de Loosdrecht

National and International/ELN MDS WGAustria, Australia, France, Greece,

Germany, Italy, Japan, Netherlands,

Spain, Sweden, Taiwan, United Kingdom, USA

Artificial Intelligence Group

University of Ghent/Saeys lab

Yvan Saeys

Sophie van Gassen

Grants/support

- MDS Foundation Inc. USA

- Amsterdam UMC/VU University Medical Center

- Cancer Center Amsterdam

- Dutch Society for Cytometry

- ELN WP8/WP10 on MDS

- HOVON The Netherlands

- Dutch Cancer Foundation (KWF)

- European Science Foundation (ESF)

www.mds-europe.eu