myelodysplastic syndromes · myelodysplastic syndromes flow cytometry in diagnosis and prognosis...

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


Antigen expression during monocytic differentiation: the concept


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

http://www.leukemia-net.org/content/e35

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.


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.

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

http://www.leukemia-net.org/content

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


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)


• 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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