diagnostics and flow cytometry machine learning and artificial … · 2019-09-12 ·...
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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