ESTRATEGIAS ACTUALES DE ANALISIS DE DATOS POR CMF: EL PROGRAMA INFINICYT DIAGNOSTICS IN HEMATO-ONCOLOGY APPLICATIONS OF FLOW CYTOMETRY

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1 ESTATEGIAS ATUALES DE ANALISIS DE DATOS PO MF: EL POGAMA INFINIYT ANE ESEAH ENTE, UNIVESITY & UNIVESITY HOSPITAL of SALAMANA (SPAIN) urso Avanzado de Actualización en Oncohematología por citometria de flujo. Buenos Aires, 30 de mayo de 2011 DIAGNOSTIS IN HEMATO-ONOLOGY 1. Making the diagnosis Normal reactive/regenerating malignant Annually > 300,000 new patients with a hematological malignancy in developed countries 2. lassification of hematopoietic malignancies - relation with prognosis - relevance of risk-group definition in treatment protocols Based on differentiation characteristics and particularly on chromosome aberrations, resulting in fusion gene transcripts or aberrantly (over) expressed genes 3. Evaluation of treatment effectiveness Detection of minimal residual disease (MD): MD-based risk-group stratification (treatment reduction or treatment intensification) Annually > 400,000 follow-up samples in leukemia patients (ALL, AML, ML) Prepared by JJM van Dongen APPLIATIONS OF OW YTOMETY Flow cytometry: where can it be applied? 70s-90s Hybridoma technology Monoclonal antibodies Fluorochrome-conjugates GENEATION OF OW YTOMETY DATA LASES BLUE LASE (Aligned and focused) LASE LIGHT HANGES: SS - Different direction (Scatter) - Different colour (Fluorescence) Photon in SS FS PMT Voltage In Power Supply From research laboratories to clinical diagnostics GENEATION OF OW YTOMETY DATA OW YTOMETY IMMUNOPHENOTYPING APPOAH LASES BLUE LASE (Aligned and focused) LASE LIGHT HANGES: SS - Different direction (Scatter) - Different colour (Fluorescence) Photon in SS FS PMT Voltage In Power Supply

2 OW YTOMETY IMMUNOPHENOTYPING APPOAH STOAGE & DISPLAY OF MULTIPAAMETE OW YTOMETY DATA Single parameter storage THE GATING ONEPT SS D8 FS D4 GATE & EVALUATE Dual parameter storage STOAGE & DISPLAY OF MULTIPAAMETE OW YTOMETY DATA IMMUNOPHENOTYPI HAATEIZATION OF ELL POPULATIONS Single parameter storage List mode data storage SET GATES PLAE QUADANT MAKES Dual parameter storage From FS 1.0 (1984) to FS 3.0 data format respo et al, N Engl J Med, 2003 BOOLEAN GATING STATEGY OW YTOMETY: TYPE OF INFOMATION - Identification of cell populations - Enumeration of cell numbers - haracterization of cell populations Prepared by A.Salvador 2

3 D 45 PEPY5.5 -> D4 FIT -> L.el.Dendrt.D4/D8 cd3 FIT -> HLAD FIT -> D34 AP -> ntdt FIT -> D2 FIT -> D36 FIT -> D11B FIT -> BDA4 PE -> L.el.DendrtBDA4/D138 D65 FIT -> D61 FIT -> OW YTOMETY IMMUNOPHENOTYPING APPOAH IMMUNOPHENOTYPI FEATUES OF NEOPLASTI ELLS Transformed SS D45 PerPy5.5 D123 PE HLA-D FIT D56 PE D2 FIT HLA-D FIT BDA4 (D304) PE D8 PE D4 FIT D117 PE D34 FIT D64 PE D36 FIT 7.1 PE D65 FIT D7 PE y-d3 FIT yt MPO PE ntdt FIT D13 PE D11b FIT D33 PE D61 FIT MDS/AML: 3-OLO 3 STAINING PANEL - FIT PE PerP/y5.5 - cont. cont. D45 - ntdt cmpo D45 - cd3 D7 D45 - D19 cd79a D45 - sont sont D45 - HLAD D117 D45 - HLAD D123 D45 - D11b D13 D45 - D15 D16 D45 - D36 D64 D45 - D33 D61 D45 - D71 GphA D45 - D D45 - D2 D56 D45 TANSFOMED SS -> T-SS D138-PerP/y5.5 D138 PerP/y5 -> MONOLONAL GAMMOPATHIES: IDENTIFIATION OF LONAL PLASMA ELLS D38 FIT -> D38-FIT D38 FIT -> TANSFOMED SS -> T-SS D138-PerP/y5.5 D138 PerP/y5 -> MONOLONAL GAMMOPATHIES: IDENTIFIATION OF LONAL PLASMA ELLS D38 FIT -> D38-FIT D38 FIT -> Normal P D19-Pcpy5 D 1 9 D56-PE D 5 6 P E D38-FIT gated P D 4 5 A P D45-AP lonal P Perez-Andres, J Biol eg, 2004 Do not forget.fm language is OMPLEX WIIYGIWYG What Is In Your Gate Is What You Get Prepared by A.Salvador 3

4 Do not forget.fm language is OMPLEX OW YTOMETY IMMUNOPHENOTYPING APPOAH WINIYGIWY IYGIWYF What Is Not In Your Gate Is What You Forget Prepared by A.Salvador MDS: OEXISTENE OF NOMAL & HYPOGANULA MATUING NEUTOPHILS Immunophenotypic Myeloid Abnormalities in MDS D13-PE Normal MDS D11b-FIT SS D45-PerPy5.5 SS D13-PE D11b-FIT Normal MDS Stetler-Stevenson M et al, Blood 2001;98: Examples of various myeloid and monocytic aberrant antigenic patterns in MDS MIOSOPE VS FM OLOFUL IMAGES ompatible with MDS Wells, D. A. et al. Blood 2003;102:

5 MIOSOPE VS FM OLOFUL IMAGES WHIH POBLEMS AE WE FAING? - Many reagents: costly and complex panels - Need expertise in normal (reference) cell populations - Time consuming - Technical limitations - Many (my) suboptimal strategies to reach a similar result Monocytes X=D4 vs Y=SS - Not standardized: reproducibly harmonized? - Partial and more limited clinical utility than expected D4+-T-cells IMMUNOPHENOTYPI HAATEISTIS OF NOMAL vs LEUKEMI B-ELLS D19+ B-ELLS 8- OLO flow cytometry: Bcp-ALL EuroFlow panel (450 bivariate plots) EQUIED DEVELOPMENTS IN OW YTOMETY (status in 2005) Immunobeads introduce combined cellular/immunobead assays special immunobead for leukemias Novel antibodies test new (academic) antibodies for application in intracellular stainings development of new antibodies against oncoproteins and aberrant signalling pathways SS SS D45-PO D19-PEy7 Multicolor flow cytometry: 8 color comprehensive panels inclusion of solid state violet laser selection of appropriate fluorochromes compare conjugated antibodies (multiple companies) Development of novel software for complex pattern recognition combining multiple tubes: calculate data & multivariate analyses mapping of diagnosis and follow-up leukemia samples against templates of reference normal/control samples Lucio et al, Leukemia, 1999 OW YTOMETY IMMUNOPHENOTYPING APPOAH INEASING DISIMINATION POWE N-multiplication of individual parameters Improved Data visualization Data analysis Data interpretation LAMBDA B-ELLS KAPPA B-ELLS NK ELLS D4+D8+ T ELLS D8+ T ELLS D4+ T ELLS LAMBDA B-ELLS D4+D8+ T ELLS Duplication of D3 allows a better discrimination between D8+ T-cells and NK-cells KAPPA B-ELLS NK ELLS D8+ T ELLS D4+ T ELLS D4-D8- T ELLS D4-D8- T ELLS 5

6 AUTOMATED SEPAATION AMONG DIFFEENT ELL POPULATIONS (APS view) AUTOMATED SEPAATION AMONG DIFFEENT ELL POPULATIONS (APS view) D8+ T- LYMPHOYTES MONOYTES B-ELLS NEUTOPHILS D4+ T- LYMPHOYTES NK-ELLS FM DATA OVELAYED ON EFEENE IMAGES HOW TO SIMPLIFY DATA ANALYSIS - onstruct reference data files for normal and pathologic cell populations (e.g.: per disease category) - Multi-n-dimensional comparison of normal vs pathologic cell populations (e.g.: at diagnosis and follow-up): - Automated PA-guided for homogeneous cell populations (e.g. Blood lymphocyte subsets) - Maturation tools for heterogeneous cell populations (e.g. Maturing BM B-cell precursors) OW YTOMETY IMMUNOPHENOTYPING APPOAH MEGED DATA FILES FO SINGLE STEP GATING Full phenotypic profile onventional New Merge data files alculate data not actually measured Learn normal patterns Automated guided data analysis A single gating step for 5 different data files (tubes) 6

7 OW YTOMETY IMMUNOPHENOTYPING APPOAH DATA IN A MEGED DATA FILE PAAMETE TUBE No FS-HEIGHT onventional New Merge data files alculate data not actually measured Learn normal patterns SS-HIGHT D11b-FIT D13-PE D45-PerP D34-AP D2-FIT D56-PE HLAD-FIT D117-PE D123-PE D15-FIT D16-PE D22-FIT D25-PE D65-FIT 7.1-PE Automated guided data analysis D61-FIT D33-PE D71-FIT Glyphorin-PE DATA ALULATION IN THE INFINIYT TM SOFTWAE MEGED & ALULATED LISTMODE DATA FILE Tube 1 SS Exp Low-> Tube 2 Files Event X in tube 1. D19 PEP Y5.5-> FS-> SS Exp Low->. D19 PEP Y5.5-> FS-> Event Y: Nearest neighbour of event X in tube 2 6 Files x alculated FM7 value for event X in tube 1 (based on data from event Y in tube 2) Parameters 10 parameters x events 1 File x 25 parameters x Parameter coding = ommon = Measured E = alculated events MEGED AND ALULATED DATA FILE ALULATED VS MEASUED DATA Full phenotypic profile Measured D11c-AP 2: LL 4 colors D5-PE D11c-AP D20-PE D43-AP FM7-FIT Imposible phenotypic profile alculated D11c-AP D11c-AP D5-AP D20-AP D43-FIT D43-AP Measured D23-PE D79b-AP sigk-ap D24-PE sigk-ap D24-PE alculated D23-PE sigk-pe D79b-PE D24-PE sigk-pe D24-PE Pedreira et al, ytometry A,

8 OW YTOMETY IMMUNOPHENOTYPING APPOAH EFEENE DATAFILES FO LL B-ELLS LL case 1 onventional New Merge data files alculate data not actually measured Learn normal patterns Automated guided data analysis LL case 2 LL case 3 LL case 4 Merge alculated Data files D19-PEy7 ase number D19+ LL B-cells esponsible scientist: Sebastian Bottcher haracterization markers: LAI1(D305) haracterization markers: D10 D10 PE BL LL DLBL HL LPL ML MZL BL LL DLBL HL LPL ML MZL esponsible scientist: Sebastian Bottcher esponsible scientist: Sebastian Bottcher haracterization markers: D10 B-LPD panel: clinical utility D10 PE LL ML BL LL DLBL HL LPL ML MZL esponsible scientist: Sebastian Bottcher esponsible scientist: Sebastian Bottcher 8

9 B-LPD panel: clinical utility B-LPD: omparative analysis of our case vs multiple reference groups sigm D200 D79b D23 D LL ML esponsible scientists: Sebastian Bottcher esponsible scientist: Sebastian Bottcher osta et al, Leukemia, 2010 HOW TO SIMPLIFY MD STATEGIES - Improve the design of MD panels for a greater efficiency and higher reproducibility. - onstruct reference data files for normal and neoplastic cells (e.g.: per disease category) - Multi-n-dimensional comparison of normal vs neoplastic cell populations (e.g.: at diagnosis and follow-up): - Automated PA-guided for homogeneous cell populations (e.g. lymphoid) - Maturation tools for heterogeneous cell populations (e.g. myeloid) ONSTUTION OF EuroFlow MD PANELS: MM MM #2 MM #1 Normal BM #2 Normal BM #1 SS SS SS SS Identify P D38-FIT D38-FIT D38-FIT D38-FIT SS SS SS SS Select P D38-FIT D38-FIT D38-FIT D38-FIT Merge P (n-cases) Principal component analysis (n=12 markers) MOST INFOMATIVE MAKES D D D D D D D Aim of Euroflow MD for B-LPD EFEENE DATAFILES: NOMAL vs. TUMO B-ELLS - To find a marker combination to discriminate all neoplastic B-cells in every B-LPD case vs all normal/reactive PB and BM mature B-cells/hematogones Normal PB D19+ B-cells D19-PEy7 D19-PEy7 D19+ tumor B-cells NOT an individualized per patient ase number ase number the should work without knowledge of the exact initial immunophenotype D19-PEy7 ase number Normal PB (n=8) Patients (n=6) Tumor PB B-cells Normal PB B cells 9

10 OMPAE A ASE VS NOMAL & NEOPLASTI B-ELLS OMPAE A ASE VS NOMAL & NEOPLASTI B-ELLS EFEENE DATA FILES EFEENE DATA FILES DIAGNOSTI SAMPLES vs. EFEENE DATA FILES Tumor Normal OMPAE A ASE VS NOMAL & NEOPLASTI B-ELLS UTILITY OF THE NEW SOFTWAE TOOLS Standardization of FM immunophenotyping EFEENE DATA FILES DIAGNOSTI SAMPLES vs. EFEENE DATA FILES MD SAMPLES vs. EFEENE DATA FILES How to get optimal and comparable measurements? Which are the most appropriate fluorochromes? What is the optimal sample preparation protocol? EPODUIBLE & OBJETIVE ESULTS esults of standardized experiments EuroFlow Panel for Plasma ell Disorders: an we identify the center? Normal BM P from 24 samples from different centres, acquired over a one year period. EuroFlow participants University Institutes / Medical Schools Erasmus M, otterdam, NL J.J.M. van Dongen, V.H.J. van der Velden USAL, Salamanca, ES A. Orfao, J. Flores, J. Almeida, Q. Lecrevisse IMM, Lisbon, PT P. Lucio, A. Mendonça, A. Parreira a.o UNIKIEL, Kiel, DE M. Kneba, S. Böttcher, M. itgen, M. Brüggemann AP-HP, Paris, F E. Macintyre, L. Lhermitte, V. Asnafi UNIVLEEDS, Leeds, GB S. ichards, A.. awstron. P. Evans DPH/O, Prague, Z O. Hrusak, T. Kalina, E. Mesjstrikova SAM, Zabrze, PL T. Szczepanski, L. Sedek DOG, The Hague, NL E. Sonneveld, A. van der Sluijs-Gelling... KUL, Leuven, BE N. Boeckx HGSA, Porto, PT M. Lima, AH Santos UFJ, io de Janeiro, B. Pedreira, E.S. osta ompanies (SME s) DYNOMIS, otterdam, NL E. Dekking, F. Weerkamp YTOGNOS, Salamanca, ES M. Martin, J. Bensadon, J. Hernandez, M. Muñoz 10

11 MUHAS GAIAS 11