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Myelodysplastic Syndromes
flow cytometry in diagnosis and prognosis
Arjan A. van de Loosdrecht
Department of Hematology
VU University Medical Center VU-Institute of Cancer and Immunology (V-ICI)
Cancer Center Amsterdam (CCA) Amsterdam, The Netherlands
Utrecht
European Pathology Bone Marrow Seminars Monday, May 29, 2017
The Netherlands
DISCLOSURES A.A. van de Loosdrecht 2017
Name of Company
Research support
Employee Consultant Major Stockholder
Speaker’s Bureau
Advisory Board
Other
Celgene x x
Novartis x x
Alexion x
Amgen x
Myelodysplastic Syndromes: Heterogeneity
• Heterogeneous: MD-Syndrome Part of: bone marrow failure syndromes stem cell disease Anemia is presenting symptom in >80% of cases
• Heterogeneous: Morphology/FlowCytometry/Cytogenetics/Molecular
• Heterogeneous: clinical course
Diagnostic tool Diagnostic value Priority
Peripheral blood smear
• Evaluation of dysplasia in one or more cell lines • Enumeration of blasts Mandatory
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 MDS 2017 Dutch/EU guidelines
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
WHO2016: classifying Myelodysplastic Syndromes
MDS with single lineage dysplasia (MDS-SLD) MDS with multilineage dysplasia (MDS-MLD)
MDS with single lineage dyplasia and RS (MDS-RS-SLD) MDS with multilineage dysplasia and RS (MDS-RS-MLD)
MDS with excess blasts-1 (MDS-EB1) MDS with excess blasts-2 (MDS-EB2) MDS-U
MDS del(5q) Familial myeloid neoplasms with germ line mutations
Arber DA and Hasserjian RP. Hematology 2015;294-298 Arber DA, et al., Blood 2016;127:2391-2405
WHO2016 vs WHO2008 classifying MDS: comments
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 and 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
WHO2016 vs WHO2008 classifying MDS: comments
Additinal new concepts: Add: SF3B1 mutation and RS <15%
= MDS-RS-SLD / MDS-RS-MLD
Add: MDSdel(5q): isolated or with one add chrom abnormality, excl monosomy 7 Delete: non-eythroid blast cell count to distinguish pure erythroleukemia vs MDS-EB2 if >50% erythroid precursors Add: Familial myeloid neoplasms with germ line mutations (TERT, RUNX1, GATA2)
Arber DA and Hasserjian RP. Hematology 2015;294-298; educational session
Antigen expression during neutrophil differentiation: the concept
103
102
101
Adapted from: A Orfao, ELNet Flow MDS 2008-2017, Amsterdam
BAND/ NEUTROPHIL METAMYELOCYTE MYELOCYTE PROMYELOCYTE MYELOBLAST
CD13
CD11b
CD13
CD16
Antigen expression during neutrophil differentiation: the concept
103
102
101
BAND/ NEUTROPHIL METAMYELOCYTE MYELOCYTE PROMYELOCYTE MYELOBLAST
CD34
HLA-DR CD117
CD13
CD33
CD11b
CD64
CD65
CD54
CD10
CD35
CD13 MPO
CD15
CD16
Adapted from: A Orfao, ELNet Flow MDS 2008-2017, Amsterdam
Antigen expression during monocytic differentiation: the concept
Adapted from: A Orfao, ELNet Flow MDS 2008-2017, 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
Standardization of flow cytometry in MDS: ELNet 2014 recommendations
CD45
SSC
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. 2017, 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%
Het afbeeldingonderdeel met relatie-id rId1 is niet aangetroffen in het bestand. Het afbeeldingonderdeel met relatie-id rId1 is niet aangetroffen in het bestand.
Het afbeeldingonderdeel met relatie-id rId1 is niet aangetroffen in het bestand. Het afbeeldingonderdeel met relatie-id rId1 is niet aangetroffen in het bestand.
Examples of the diagnostic score (MDS)
G H I
J K L
SSC 3.1 CD45 5.5
CD34+ 1.1% CD34+B 0%
Het afbeeldingonderdeel met relatie-id rId1 is niet aangetroffen in het bestand. Het afbeeldingonderdeel met relatie-id rId1 is niet aangetroffen in het bestand.
Het afbeeldingonderdeel met relatie-id rId1 is niet aangetroffen in het bestand.Het afbeeldingonderdeel met relatie-id rId1 is niet aangetroffen in het bestand.
Westers TM and Van de Loosdrecht AA. 2017, in: (Multiparameter Flow Cytometry in the Diagnosis of Hematologic Malignancies; ed. Porwit and Bené)
Diagnostic power of FCM in MDS: the learning (validation) cohort (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
Flow cytometry in MDS: the erythroid lineage
CD45 CD34 HLA-DR CD117 CD105 CD36 CD71 CD235a
CD34+ MyProg CD34+ EryProg proerythroblast basophilic erythrobl. polychr. erythrobl. orthochr. erythrobl.
5561
51971
Flow cytometry in MDS: the erythroid lineage
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’
Specificity: 95%
Sensitivity: 80% By FCM dysplasia in myeloid / erytroid lineage
Validation of FCM acc. to integrated Flow Score within a prospective clinial trial: HOVON 89 (n=174)
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
Integrated Flow Score: inconclusive by cytomorphology
Cremers EMP, et al., Eur J Cancer 2016; 54:49-56
iFS VUmc sensitivity MDS-CM: 61% specificity (PaCo): 95%
MDS in follow-up by CM
2.6% (3/114) (based on DysM by CM)
25% (2/8) (reactive/fe-def/drug-induced)
0%10%20%30%40%50%60%70%80%90%
100%
??
CM? CG-FC+
CM ? CG-FC-/+
CM? CG-FC-
n= 379 total: cytopenia n = 218 diagnosis MDS/non-MDS) n = 161 CM/CG inconclusive
7%
t=0 FC-analysis Ogata score + myelomonocytoid
FU 122/161
C
B
A
• 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
Revised International Prognostic Scoring System (IPSS-Revised) for MDS: clinical heterogeneity
Greenberg P, et al., Blood 2012;120:2454-65; Van Spronsen M.F., et al., Eur J Cancer 2014;50:3198-3205; Van Spronsen M.F. et al., Eur J Cancer 2016;56:10-20;
Van Spronsen M.F. et al., Eur J Cancer 2017;72:269-271
45 173 2 1 29 18 86 51 4 16 4 44 46 55
severe
moderate
normal to mild
Westers TM, et al., unpublished data 10-15 Wells D, et al., Blood 2003;102(1):394-403
Van de Loosdrecht AA, et al., Blood 2008;111:1067-1077
Flow Cytometric Scorings System and prognosis
n=574
p=0.019
Alhan C, et al., Br J Haematol 2014:167:100-109 Chu S, et al., Leuk Res 2011:35:868-73; Wells D, et al., Blood 2003;102:394-403
Dysplasia by FC and prognosis
Even within prognostic risk groups (WHO), e.g. RCMD
FCSS has been developed as prognostic score
A B
Time after diagnosis (months) Time to disease evolution (months)
The FCSS distinguishes prognostic subgroups within the IPSS-R cytogenetic good risk group
Alhan C, Westers TM, et al., Br J Haematol 2014;167:100-109
MFS risk categories low risk; 0 points intermediate risk; 1 point high risk; 2-3 points
Learning cohort
Prognosis according to FC: validation: A 3-parameter approach
Alhan C, et al. Leukemia, 2016; 39:658-65
Eluding those FCSS parameters with highest prognostic relevance: Multivariate analysis •Decreased SSC of myeloid progenitors •Increased CD117 MFI of MyProg •Normal to increased monocyte CD13 MFI
Haferlach T, et al., Leukemia 2014;28:241-7
The landscape of genetic aberrations in MDS: genomic architecture (targeted deep seq)
n = 944, 104 genes, 47 genes with mutations
median age: 72.8 y (range 23.3-90.8)
patients with cytogenetic aberrations: 31.4%
patients with molecular [at least 1] aberrations: 89.5%
Prognostic models beyond IPSS-R: genetics incorporated or as an isolated strong prognostic marker? [training cohorts]
Model 1: 14 genes + age + WBC, Hb, Plt, % blasts, Cytogenetics according IPSS-R [integrated model]
Model 2: 14 genes only
(13/14 from Model 1)
Haferlach T, et al., Leukemia 2014;28:241-7
Bejar R et al., NeJM 2011;364:2496 Kennedy JA, Ebert BL. J Clin Oncol 2017;35:968-74
Somatic mutations in any of TP53, EZH2, RUNX1, ASXL1, or ETV6 and prognosis in the IPSS-R lower risk categories.
Bejar R et al., J Clin Oncol 2012;30:3376-82; Alhan C, et al. Leukemia, 2016; 39:658-65
Emerging prognostic models in lower risk MDS; FCM and molecular abnormalities
IPSS-R low
Aberrant immunophenotype as biomarker for prediction of response to Erythropoietin
Westers TM, van de Loosdrecht AA, et al. Blood 2010:115:1779
Cremers EMP, et al. Best Pract Res Clin Haematol 2015:28;14-21
Figure 2. Monitoring of lenalidomide response exemplarily shown for three MDS patients with singulary del(5q). (A, B) These two cases show the typical del(5q) immunophenotypic profile before treatment and in pCR. The disappearance of the del(5q) immunophenotype later on predicts cCR reliably at all appropriate investigation points; (C) this patient achieved an intermittent cCR only; already 10 months after the start of the treatment FCM again showed the typical del(5q) profile further increasing over time reaching the maximum score seven month ahead of the full cytogenetic and hematological relapse (month 30).
FCM score at diagnosis or relapse
cCR cCR cCR
cCR
threshold (≥15.0) of del(5q) FCM score
cCR FCM score at complete cytogenetic response pCR FCM score at partial cytogenetic response
5-parameter del(5q) immunophenotypic profile
Oelschlaegel U, et al., the Dresden/Amsterdam group
Haematologica 2015:100;e93-96
Lenalidomide with or without erythropoietin and granulocyte-colony stimulating factor shows efficacy in patients with low and intermediate-1
risk myelodysplastic syndrome with or without del 5q refractory or unlikely to respond to erythropoietin. Results of a HOVON 89 Phase II
randomized multicenter study. (EudraCT 2008-002195-10)
A.A. van de Loosdrecht, D.A. Chitu, E.M.P. Cremers, T.M. Westers, C. Alhan, P. Da Silva, A. de Graaf, B. van der Reijden, H. Visser-Wisselaar, A.
Verbrugge, P. Muus, G.E. de Greef, P.W. Wijermans, S.K. Klein, E. Vellenga, M.C. Legdeur, W. Deenik, M. Jongen-Lavrencic, R. van Marwijk-Kooy, B.C.
Tanis, J. Wegman, T.M. van Maanen, J. Jansen, G.J. Ossenkoppele For the HOVON89 study group
Department of Hematology
VU University Medical Center VU-Institute of Cancer and Immunology (V-ICI)
Cancer Center Amsterdam (CCA) Amsterdam, The Netherlands
• Hematological Improvement-Erythroid (HI-E): • 41% (according to IWG criteria). • 39% and 42% of the patients for arm A and B, respectively (p = 0.45).
• Hematological Improvement-Erythroid (HI-E):
• non-del5q versus del5q: 34% vs 79%.
• Time-to-Hematological Improvement-Erythroid (HI-E): • 3.1 months (median; range 1.6-12.3) for both arms.
• Duration of Hematological Improvement-Erythroid (HI-E):
• 10.6 months (median; range 1.4 – 76.1).
Results HOVON89: primary and secondary endpoints
Van de Loosdrecht AA, et al., 2017 (in preparation)
Mutations by Next Generation Sequencing in HOVON89 (TruSight-Illumina)
Cremers EMP, et al. 2017 (in preparation)
• Presence of 2 or more mutations were inversely related to HI-E (p=0.004)
• Presence of 1 or more splicing factor mutations were inversely related to HI-E (p<0.0001)
• Of the 7 most frequently mutated genes i.e. TET2, ASXL1, DNMT3, ATRX, RUNX1, only SRSF2 (p=0.021) and SF3B1 (p=0.004) were significantly associated with lack of response to lenalidomide (HI-E)
Results: NGS and response to lenalidomide
Cremers EMP, et al. 2017 (in preparation)
Results: Flow Cytometry and response to lenalidomide
• Presence of Aberrant Myeloid Progenitor cells were inversely related to HI-E (p=0.028)
• Presence of Aberrant Erythroid Progenitor cells were inversely related to HI-E (p=0.032)
• A significant correlation between FCM aberrancies and number of mutations (NGS) irrespective of WHO classification is observed (r=0.28; p=0.02)
• A significant correlation between transcription factor mutations and: • - aberrant granulocytes (p = 0.02) • - aberrant monocytes (p = 0.02) • - aberrant myeloid progenitors (p = 0.009)
• A significant correlation between mutations in an epigenetic regulator
and presence of aberrant myeloid progenitors (p = 0.005)
Correlations between FCM and NGS (preliminary data set)
Cremers EMP, et al. 2017 (in preparation)
• Use FCM application for MDS diagnostics – Follow International MDS Flow recommendations (ELN/NCCN)
• For screening purposes
– Follow a mini-panel based on the so-called Ogata score
• For extended analysis: perform FCM in all cell compartments – Myeloid and lymphoid progenitor cells – Maturing myelomonocytic cells – Immature en mature erythroid cells Extended panel: Integrated Flow Score
• FCM abnormalities
– associated with IPSS-R/cytogenetic abnormalities – transfusion dependency – Prognosis and risk of transformation – prediction of treatment response (Epo/Aza) – Parallels cytogenetic responses in MDS(del5q)
ELN Recommendations/conclusions and implications of Flow Cytometry in MDS 2017
Malcovati L, et al., ELN guidelines 2013: 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; Van de Loosdrecht AA,
Westers TM. J Natl Comp Canc Netw 2013;11:892-902; Porwit A, et al., Leukemia 2014;28:1793-98; Cremers EMP, et al., Haematologica 2017;102:320-26
Projects and Data collection: • Multicenter data collection on MDS FC (>2000 cases) • Implementation of the diagnostic algorithm in EU diagnostic guidelines • define FCM/molecular correlates • role of FCM in Monitoring MDS within prospective clinical trials • role of FCM in ICUS/CCUS
Perspectives of Flow Cyotometry in MDS within the ELN based iMDSflow WG (2017-2018)
Acknowledgements
VU University Medical Center, Cancer Center Amsterdam Department of Hematology, Amsterdam, The Netherlands MDS FCM team Marisa Westers, Claudia Cali, Canan Alhan, Eline Cremers, Margot van Spronsen, Nathalie Kerkhoff, Carolien Duetz, Yvonne van der Vreeken, Adrie Zevenbergen, Gert Ossenkoppele National and International/ELNet MDS WGs Austria, Australia, France, Greece, Germany, Italy, Japan, Natherlands, Spain, Sweden, Taiwan, United Kingdom, USA
Grants/support
- MDS Foundation Inc. USA
- 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)
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