Ron Lee, MD Hematopathologist, Esoterix Pathology Practice Group, PC Brentwood, TN Office Cellular

Transcription

1 Ron Lee, MD Hematopathologist, Esoterix Pathology Practice Group, PC Brentwood, TN Office Cellular

2 Disclosure In the past 12 months, I have not had a significant financial interest or other relationship with the manufacturer(s) of the product(s) or provider(s) of the service(s) that will be discussed in my presentation.

3 Goals/Objectives A basic review of the predominant normal populations seen by routine clinical flow cytometry in peripheral blood, bone marrow, and tissue samples - includes: granulocytic/monocytic cells erythroid cells lymphoid cells

4 This presentation will not cover Technical aspects of flow cytometry International Cytometry Certification Exam (ICCE)

5 General Approach to Clinical Flow Cytometry Interpretation Determine sample composition and account for all events/populations using multiple antibody combinations, crosschecks, FSC, and gating strategies (including ungated data with specific markers v SSC and CD45-negative events/debris) Characterize each population as thoroughly and accurately as possible Determine if all populations are immunophenotypically normal and/or have normal maturation patterns Characterize lineage/immunophenotype of any abnormal populations and level of involvement Summary: account for all events!!

6 Specific populations Blasts B-cell precursors/hematogones Mature lymphocytes Monocytic cells Neutrophilic cells Eosinophils Basophils Plasma cells Erythroid cells Others- mast cells, histiocytes, Langerhans cells, dendritic cells, CD45-negative events Other considerations- treatment (chemotherapy, anti-cd20, methotrexate, etc.), growth factors, autoimmunity, B12/folate deficiency, viral infection, Hodgkin, viability

7 Review of Typical Population Regions (marrow sample, CD45 v SSC log)

8 General Impressions CD45 v SSC (blood, marrow, tissue)

9 General Impressions FSC v SSC Large lymphoid cells, plasma cells, monocytic cells, basophils, blasts FS Lin Neutrophilic cells Aggregates, non-hematolymphoid cells, other large cells Pronormoblasts Eosinophilic cells Small lymphoid cells SS Log

10 Granulocytic/Monocytic Cells Neutrophilic maturation Myeloid blasts/blast maturation Monocytic maturation Eosinophils, basophils, mast cells

11 Neutrophilic Cells Patterns to remember: Nike swoosh (CD13 v CD16) CD11b v HLA DR other combinations CD10, CD64 CD56 SSC

12 Neutrophilic Maturation

13 Neutrophilic Maturation

14 Neutrophilic maturation pattern abnormalities

15 Illustrative case with multiple abnormalities

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17

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19 Myeloid Blasts Patterns to remember CD34 v CD38 CD11b v HLA DR (in conjunction with CD34, CD117) CD11c, CD15, CD13, CD33 CD2, CD5, CD7, CD25

20 Myeloid Blast Maturation

21 MPD (JAK2+) CD117+ Gating

22 Hemodilute marrow, ~1% atypical CD34+ myeloid blasts

23 Monocytic Cells Patterns to remember CD14 v CD64 CD11b v HLA DR CD13, CD33 CD2, CD4 CD56 Others - CD15, CD16, CD1a

24 Monocytic Maturation

25 Abnormal Monocytic Cells (monoblastic leukemia)

26

27 Eosinophils Increased SSC Patterns to remember CD45 v CD16, CD45 v SSC CD13 v CD16 CD16 v SSC, CD64 v SSC, several other combos and markers negative v increased SSC CD11b v HLA DR

28 Eosinophils

29 Eosinophils

30 Basophils Patterns to remember CD45 v SSC CD11b v HLA DR CD13, CD16, CD33 CD22

31 Basophils

32 Basophils

33 Basophils

34 Mast Cells Patterns to remember CD117 (bright) v SSC CD2, CD25

35 Mast Cells CD45 PC7 102 CD117 PC R SS Log SS Log

36 Abnormal Mast Cells

37 Abnormal Mast Cells

38 Erythroid maturation Patterns to remember CD71, GlyA, CD117

39 Erythroid maturation 71 FITC-A R SSC-A

40 Erythroid maturation 71 FITC-A R SSC-A CD45 B APC-H7-A SSC-A CD45 B APC-H7-A R SSC-A glya PE-A _ _4.10 R _ _ FITC-A 25 yo BM core, newly diagnosed HIV, erythroid hyperplasia, CD45-negative and expanded blast region gating: erythroid cells mature from the blast region with increasing SSC and decreasing CD45, acquire CD71, acquire GlyA, then lose CD71 and CD45 with decreasing SSC. The majority of precursors detected by flow are typically intermediate stage (bright GlyA/bright CD71).

41 Lymphoid Cells Precursor B-cells/Hematogones Precursor T-cells/Thymus Mature B-cells Mature T-cells NK cells Plasma Cells

42 Precursor B-cells/Hematogones Patterns to remember CD45 v SSC CD10 v CD20 CD34, TdT CD38+ (bright)

43 Precursor B-cells/Hematogones

44 Precursor B-cells/Hematogones

45 Precursor T-cells/Thymus Patterns to remember CD4 v CD8 CD7 v CD3 CD1a v CD3 CD10, CD34

46 Precursor T-cells/Thymus

47 Mature B-cells Patterns to remember CD19, CD20 v kappa, lambda cross-checks CD10, CD5 Other markers CD23, FMC7, CD22, CD103, CD11c, CD25

48 Mature B-cells CD19 PC R SS Log CD20 ECD R SS Log KAPPA FITC _2.32 R _ _ _ LAMBDA PE CD20 ECD _ _0.00 R _ _ KAPPA FITC CD20 ECD _2.36 R _ _ _ LAMBDA PE

49 Mature B-cells CD20 ECD SS Log R2 KAPPA FITC _0.13 R _ _ _ LAMBDA PE

50 Mesocolon / Lymph Node CD19+ gating

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52 Neck, FNA CD19+ gating

53 Mature B-cells (FNA, back lesion)

54 Remember: Mature T-cells Use multiple markers and gating strategies for crosschecks with respect to T-cells and NK cells cross-check, cross-check, cross-check Markers/patterns: CD4 v CD8 CD2, CD3, CD5, CD7 (with CD4, CD8) CD16, CD56, CD57 (LGL s in PB, marrow; NLPHL in tissues) CD10 Other markers TCRGD, CD103, CD25, CD52, CD26, CD30, CD103

55 Mature T-cells CD45 APC-H7-A R SSC-A CD8 PE-Cy7-A _ _4.51 R _ _ CD4 PerCP-Cy5-5-A

56 Patterns to remember CD3 v CD56 CD16+, CD56+, CD57+/- CD2+, CD4-, CD5-, CD7+, CD8-/+ CD158

57 NK cells CD45 PC R1 R SS Log

58

59 Plasma Cells Patterns to remember CD45, CD38, CD19, CD20, CD138 CD56, CD117, HLA DR surface and cytoplasmic light chains cytoplasmic heavy chains

60 Plasma Cells

61 Residual myeloma post treatment

62 Hodgkin Lymphoma

63 Non-hematolymphoid neoplasia (small cell carcinoma)

64 Non-hematolymphoid neoplasia (carcinoma, neck lymph node, FNA)

65 Reference Intervals (31 May 2011) Mean (SD) Mean +/- 2SD Negative peripheral blood (N=91) CD45-negative events/debris 1.5 (1.0) Lymphocytes 21 (8.2) 5-37 Monocytes 5.4 (1.8) Neutrophils 68 (8.9) Eosinophils 2.8 (1.6) Basophils 0.7 (0.3) CD4+ T-cells 12 (5.2) CD4+/CD57+ T-cells 0.6 (0.6) CD8+ T-cells 4.2 (2.3) CD8+/CD57+ T-cells 1.5 (1.2) CD4:CD8 ratio 3.3 (1.7) CD19+ B-cells 2.5 (1.9) NK cells 2.4 (1.7) Viability (N=77) 95 (4) S-phase (N=78) 0.2 (0.2) Negative bone marrow (N=72) CD45-negative events/debris 6.5 (3.1) Lymphocytes 9.5 (4.3) Hematogones 1.3 (1.4) Plasma cells 0.3 (0.3) Monocytic cells 3.7 (1.2) Neutrophilic cells 74 (5.9) Eosinophils 2.4 (1.0) Basophils 0.4 (0.2) Myeloid blasts 0.9 (0.3) CD4+ T-cells 4.1 (2.3) CD4+/CD57+ T-cells 0.4 (0.3) CD8+ T-cells 2.8 (1.4) CD8+/CD57+ T-cells 1.0 (0.9) CD4:CD8 ratio 1.6 (0.8) CD19+ B-cells 1.4 (1.0) NK cells 1.1 (0.9) Mast cells 0.02 (0.04) Viability (N=55) 90 (4) S-phase (N=60) 10 (3.0) 4-16

66 Lymph node (N=118) CD4:8 5.1 (2.6) 0-10 CD19+ (% of sample) 34 (12) NK cells 1.1 (0.7) /debris 2.5 (2.1) Granulocytes 0.2 (0.6) Monocytes/histiocytes 0.6 (0.9) Viability (N=113) 94 (5) S-phase (N=114) 2.1 (1.4) Lymphocytes 97 (2.4) Pleural Fluid (N=46) CD4:8 5.1 (3.6) 0-12 CD19+ (% of sample) 9.4 (9.7) 0-29 NK cells 4.5 (4.1) 0-13 Lymphocytes 79 (23) /debris 5.0 (6.0) 0-17 Neutrophils 5.9 (11) 0-28 Eosinophils 0.4 (1.2) Monocytes/histiocytes 8.8 (15) 0-39 Viability 87 (14) S-phase 0.8 (0.7) CSF (N=16) CD4:8 3.1 (2.4) CD19+ (% of sample) 0.5 (0.9) NK cells 2.0 (3.2) Neutrophils 1.4 (3.4) Monocytes/histiocytes 2.1 (3.7) S-phase (N=9) 1.0 (0.7) Tonsil (N=28) CD4:8 7.9 (3.9) CD19+ (% of sample) 50 (14) NK cells 0.7 (0.4) Neutrophils 0.5 (0.7) Monocytes/histiocytes 0.5 (0.6) S-phase 3.7 (2.0) Viability 95 (5)

67 BM PB Tissue k:l ratios AVG SD AVG+/-2SD AVG+/-3SD

68 Selective References US-Canadian Consensus recommendations on the immunophenotypic analysis of hematologic neoplasia by flow cytometry Bethesda, Maryland, November 16-17, Cytometry Oct 15;30(5): PubMed PMID: Baumgarth N, Roederer M. A practical approach to multicolor flow cytometry for immunophenotyping. J Immunol Methods Sep 21;243(1-2): PubMed PMID: Chen K, Liu J, Heck S, Chasis JA, An X, Mohandas N. Resolving the distinct stages in erythroid differentiation based on dynamic changes in membrane protein expression during erythropoiesis. Proc Natl Acad Sci U S A Oct 13;106(41): PubMed PMID: ; PubMed Central PMCID: PMC Craig FE, Foon KA. Flow cytometric immunophenotyping for hematologic neoplasms. Blood Apr 15;111(8): PubMed PMID: Davis BH, Holden JT, Bene MC, Borowitz MJ, Braylan RC, Cornfield D, Gorczyca W, Lee R, Maiese R, Orfao A, Wells D, Wood BL, Stetler-Stevenson M Bethesda International Consensus recommendations on the flow cytometric immunophenotypic analysis of hematolymphoid neoplasia: medical indications. Cytometry B Clin Cytom. 2007;72 Suppl 1:S5-13. PubMed PMID: Harrington A, Olteanu H, Kroft S. The specificity of immunophenotypic alterations in blasts in nonacute myeloid disorders. Am J Clin Pathol Nov;134(5): PubMed PMID: Kussick SJ, Fromm JR, Rossini A, Li Y, Chang A, Norwood TH, Wood BL. Four-color flow cytometry shows strong concordance with bone marrow morphology and cytogenetics in the evaluation for myelodysplasia. Am J Clin Pathol Aug;124(2): PubMed PMID: Kussick SJ, Wood BL. Four-color flow cytometry identifies virtually all cytogenetically abnormal bone marrow samples in the workup of non-cml myeloproliferative disorders. Am J Clin Pathol Dec;120(6): PubMed PMID: Kussick SJ, Wood BL. Using 4-color flow cytometry to identify abnormal myeloid populations. Arch Pathol Lab Med Sep;127(9): PubMed PMID: Stetler-Stevenson M, Arthur DC, Jabbour N, Xie XY, Molldrem J, Barrett AJ, Venzon D, Rick ME. Diagnostic utility of flow cytometric immunophenotyping in myelodysplastic syndrome. Blood Aug 15;98(4): PubMed PMID: van de Loosdrecht AA, Westers TM, Westra AH, Dräger AM, van der Velden VH, Ossenkoppele GJ. Identification of distinct prognostic subgroups in low- and intermediate-1-risk myelodysplastic syndromes by flow cytometry. Blood Feb 1;111(3): PubMed PMID: Wells DA, Benesch M, Loken MR, Vallejo C, Myerson D, Leisenring WM, Deeg HJ. Myeloid and monocytic dyspoiesis as determined by flow cytometric scoring in myelodysplastic syndrome correlates with the IPSS and with outcome after hematopoietic stem cell transplantation. Blood Jul 1;102(1): PubMed PMID: Westers TM, Ireland R, Kern W, Alhan C, Balleisen JS, Bettelheim P, Burbury K, Cullen M, Cutler JA, Della Porta MG, Dräger AM, Feuillard J, Font P, Germing U, Haase D, Johansson U, Kordasti S, Loken MR, Malcovati L, te Marvelde JG, Matarraz S, Milne T, Moshaver B, Mufti GJ, Ogata K, Orfao A, Porwit A, Psarra K, Richards SJ, Subirá D, Tindell V, Vallespi T, Valent P, van der Velden VH, de Witte TM, Wells DA, Zettl F, Béné MC, van de Loosdrecht AA. Standardization of flow cytometry in myelodysplastic syndromes: a report from an international consortium and the European LeukemiaNet Working Group. Leukemia Jul;26(7): PubMed PMID: Wood BL, Arroz M, Barnett D, DiGiuseppe J, Greig B, Kussick SJ, Oldaker T, Shenkin M, Stone E, Wallace P Bethesda International Consensus recommendations on the immunophenotypic analysis of hematolymphoid neoplasia by flow cytometry: optimal reagents and reporting for the flow cytometric diagnosis of hematopoietic neoplasia. Cytometry B Clin Cytom. 2007;72 Suppl 1:S PubMed PMID:

69 Additional References Appendix A: Recommended Reading materials