HIGH  DIMENSIONAL  FLOW  CYTOMETRY  FOR  COMPREHENSIVE  IMMUNE  MONITORING  IN  CLINICAL  TRIALS    

Dominic  Gagnon,  Yoav  Peretz,  Marylène  For8n,  Claire  Landry,  and  David  Favre    ImmuneCarta  Services,  2901  Rachel  Est,  Suite  22,    Montréal,  QC,  Canada,  H1W  4A4    

Introduc8on  

Immune  Monitoring  

Data  Analysis  

Precision,  Stability  and  Reproducibility  

Conclusions  

Study  Cases  of  Immune  Monitoring  

Freq

uency)(%

CD8))

Freq

uency)(%

CD4))

Figure  14:  Study  protocol  and  immune  monitoring  of  the  AGS-­‐004  pilot  study  to  invesNgate  the  safety  and  immunologic  acNvity  of  an  autologous  HIV  immunotherapeuNc  agent.  (NCT00381212)  ICS   and   CFSE   prolifera8on   assays   were   performed   as   indicated   in   the   study   protocol   (leW).   The   frequencies   of   the   HIV(GNRV)-­‐specific   CD8   and   CD4   T   cell   responses   and   their  func8onal   profiles   (CD107a,   IFNγ   and   IL-­‐2)   (right)   are   compared   between   baseline   and   visit   8   aWer   vaccine   administra8on.   Pie   charts   represent   the   rela8ve   distribu8on   of   the  func8onal   subsets  within   the   total   CD8   and   CD4   T   cell   pools.   Sta8s8cal   significant   differences   (P   <   0.05)   between   pre   (V2)-­‐   and   post   (V8)-­‐vaccine   8me   points   are   indicated   by  Wilcoxon-­‐Rank  and  a  Student’s  t-­‐test  (#  and  +,  respec8vely).  The  90th  and  75th  percen8le  threshold  applied  to  background  subtracted  CD8  and  CD4  T  cell  responses  were  0.01%  and  0.005%,  respec8vely.  

Pre-­‐treatment  Phase  

Vaccine  ART  

Vaccine  ARTI  

Booster  Phase  

Follow-­‐up  &  Safety  

ART:  an8-­‐retroviral  treatment  ARTI:  ART  interrup8on  

V6  V2   V9  

90  mL   90  mL   90  mL   90  mL   90  mL   90  mL  

Client    PRODUCT  PRODUCTION  

ImmuneCarta  DC  TARGET  PRODUCTION  

w0   w4   w8   w12   w16   w20  

V8  V3   V13  

Vaccine  Treatment  

Visit/Week    

Samples    

wk0   wk8   wk16   wk20   wk24  

BASELINE    IM  SAMPLES  

POST-­‐THERAPY  IM  SAMPLES  

Immune  Monitoring    

ICS   ICS  and  CFSE  

CFSE   CFSE   CFSE   CFSE  

Exported  as  “Experiment”  from  Diva  database  to  Raw  

Data  Server  

.fcs  files  read  from  Raw  Data  Server  on  FlowJo  template  

.jo,  .csv,  and  .PDF  from  FlowJo  saved  in  

Derived  Data  Server    

Batch  layout  PDF,  and  data  table  saved  in  Derived  Data  Server    

QA  if  needed  

Assay   Acquisi8on  Sta8on  

Raw  Data  Server  

Analysis  Sta8on  

Derived  Data  Server  

QC  Sta8on   Client  Report  

• Another  analyst  on  another  iMac  analysis  sta8on  

• Verify  ga8ng  and  documenta8on  

• Confirm  data  report  across  all  files  

• Phenotyping  • Func8onal  profiling  • Enumera8on  •  ICS  • Prolifera8on  • PhosFlow  • Cyotkine  bead  arrays  

• BD  Diva/  LSR  II  • Daily  and  monthly  QC  • Acquisi8on  template  applied  

• Applica8on  sepngs  used  • Compensa8on  and  staining  references  

• Delete/modify  restricted  

•   Regular  backup  

•   Presenta8ons  •   Data  Table  •   PDF  

•   iMac  analysis  sta8on  •  Import  FlowJo  assay  template  

• Exploratory  analysis  can  also  be  done  with  SPICE/PRISM  

• Users  can  only                                          create  or  read  files  

•   Regular  backup  

Figure  9:   Flow  of  work   for  data  analysis.  Sample  processing,   assays   and  data  acquisi8on  are  based  on  pre-­‐approved  worksheets   related   to   study   requirements.   Samples   are  acquired  on  BD   LSR   II  cytometers  based  on  pre-­‐defined  sepngs  and  templates.  The  raw  FACS  data  are  exported  directly  to  the  Raw  Data  server  and  used  on  FlowJo  analysis  template  on  the  Derived  Data  server.  Once  analysis  has  been  completed  and  documented,  a  full  QC  of  data  analysis  is  performed.  Final  client  data  and  report  are  generated,  while  the  whole  process  is  audited  by  the  Quality  Assurance  (GLP  studies).  

Figure  2:  Overview  of  immune  monitoring  services.  ImmuneCarta  Services  include  all  steps  from  strategic  planning  to  final  reports  to  clients.  It  oWen  involves  study  set-­‐up,  lab  manual  wri8ng  and  sample  management  which  are  cri8cal  steps  for  cell-­‐based  analysis  as  flow  cytometry  assays.  Customized  assays  as  per  client  needs  require  assay  development  and  valida8on  prior  to  immune  monitoring  of  clinical  samples.  All  steps  include  an  ac8ve  follow-­‐up  with  the  sponsor,  are  performed  using  proprietary  SOPs  and  worksheets  and  are  documented  as  per  applicable  GLP  regula8ons.  

Strategic  Planning  and  Communica8on  

Pro-­‐ac8ve  Rela8onship  with  Sponsors,  CRO,  and  Clinical  Sites  

Standard  and  Customized  Immune  Monitoring  Assays  

Study  Progress  and  Final  Report  to  Sponsor  

• Innova8on  and  development  program    • Design  of  immune  monitoring  strategy  and  workplan    • Ph.D.-­‐level  Principal  Scien8sts  assigned  to  each  study    

• Involved  in  study  set-­‐up,  lab  manual  wri8ng,  and  sample  management      • On-­‐site  training  of  clinical  sites  for  maximum  sample  viability  and  recovery  

• Assay  development,  qualifica8on,  and  valida8on;  SOP  &  worksheets  • Data  integrity,  high  quality  and  high  throughput  processing  and  analysis  • Documenta8on,  sample  tracking  and  control,  GLP/GCLP  training  

• Conference  calls,  interim  and  final  scien8fic  reports  • Quality  Assurance  Statement  • Ac8ve  par8cipa8on  in  scien8fic  posters  and  publica8ons  

Figure  8:  Reproducibility  and  %CV   from  external  QC  samples:   results  obtained  with  BD  MulN-­‐check  CD4  low  controls  in  14  experiments  with  lot  #  40L  in  April  2010  and  17  experiments  with  lot  #  50L  in  May  2010.  The  first  graph  represents  the  enumera8on  of  lymphocytes  (CD45),  T  cell  popula8ons  (CD3,  CD4,  and  CD8),  B  cells,  and  NK  cells.  The  second  graph  represents  the  enumera8on  of  the  same  popula8ons.  Coefficient  of  varia8on  are  indicated  in  blue.  

CD4 Low control, lot # 40L

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Figure  4:  Stability  of  the  signal  using  applicaNon  seangs  coupled  with  CS&T.  Compbeads  were   stained   45   8mes  with   either   FITC,   PE   or   PerCP   (3  month   survey)   and  were   used   to  determine  compensa8on  for  81  experiments  using  cytometer  #  1    and  31  experiments  using  cytometer   #   2.   Voltages   were   determined   by   the   Applica8on   Sepngs   linked   to   the   daily  CS&T   Performance   check.   First   row:   11-­‐color   cytometer,   second   row,   18-­‐color   cytometer  with  yellow-­‐green  laser  (see  increased  posi8ve  PE  signal  without  increasing  the  background).    

FITC   PE   PerCP  FSC/SSC  

LSR  II  #  1  

LSR  II  #  2  

Serum  aliquoNng/storage  

ELISA  assays:  -­‐   Vaccine  Nters  -­‐   Soluble  markers  

Other  soluble  markers  

ImmuKnow    Assay    

Performed  at  ImmuneCarta  Services  

Performed  at  collaborator  sites  

Flow  cytometry  T  cell  panel    (12  colors)    Innate  panel  (12  colors)  

Cell  pellet    cryopreservaNon  

DNA  analysis  (SNP/telomeres)  

Paxgene  tube    storage  

RNA/mRNA  analysis    

Ficoll  

PhosFlow  

Flow  cytometry  B  cell  panel  (11  colors)  

PBMC    cryopreservaNon  

Figure  3:  Example  of  sample  management  for  assays  performed  at  ImmuneCarta  Services  and  at  collaborator  sites.  In  this  vaccine  clinical  trial  on  200  subjects,  ImmuneCarta  provided  mul8ple  services  including  study  design,  central  lab  ac8vi8es,  assays  and  integrated  analy8cal  report.  In  this  example,  fresh  blood  samples  from  each  subject  and  each  8me  point  were  collected  at  a  CRO  site  nearby  and  immediately  processed  for  analysis  at  ImmuneCarta  or  stored    at  ImmuneCarta  for  analysis  at  collaborator  sites,  e.g.  genomics,  gene8c,  other  soluble  markers  or  PhosFlow.  

Figure  5:  Reproducibility  of  CD34+  absolute  counts  using  reference  QCs  over  Nme.    Three  levels  of  CD-­‐Chex  CD34  reference  controls  were  used  (3,  35,  and  124  CD34  cells/µL)  and  two  BD  Stem  Cell  Control  Kit  (12.1   and   35.9   CD34   cells/µL).     Coefficient   of   varia8on   for   CD-­‐Chex   level   1,   2,   and   3   are   respec8vely  8.98%,   3.84%,   and   1.98%.     For   BD   Stem   cell   low   and   high   reference   controls,   the   CV   are   respec8vely  6.94%  and  3.48%.  

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Discovery   Pre-­‐clinical   Phase  I   Phase  II   Phase  III   Phase  IV  /  

Market  

Figure  13:  SchemaNc  of  the  drug  discovery  process.  

Table  I:  Overview  of  ImmuneCarta  assays  in  different  fields  of  applicaNon  and  therapeuNc  areas.  

ImmuneCarta  Services  is  a  leading  provider  of  services  for  preclinical  and  clinical  studies  related  to  immunology.  Over  the  past  7  years,  we  have  developed  a  broad  bayery  of   innova8ve  assays  to  characterize  cell  popula8ons  and  immune  responses  in  the  sepng  of  infec8ous  diseases,  cancer,  vaccine  trials  and  immune-­‐based  therapies.  Based   in   Montréal,   ImmuneCarta   Services   is   specialized   in   advanced   mul8parametric   flow-­‐based   assays  performed   according   to   GLP   regula8ons,   GCLP   guidelines   governed   by   Quality   Management   Systems   and  standard  opera8ng  procedures.     ImmuneCarta  exper8se   includes   the  assessment  of  phenotypic  and   func8onal  markers,   the   characteriza8on  of   cell   subset   lineages,   ac8va8on   states,   and   signaling  molecules,   as  well   as   the  quan8ta8ve  analysis  of  vaccine-­‐,  pathogen-­‐  or  drug-­‐specific   responses  based  on  an8body  signatures,   cytokine/chemokine  profiles,  and  signaling  pathways.  We  describe  here  our  experience  as  a  contract  research  organiza8on  providing   services   to   the   biopharmaceu8cal   industry,   in   the   execu8on   of   high   dimensional   flow   cytometry  analysis  of  subjects  enrolled  in  Phase  I/II  clinical  trials.    

Flow  cytometry  is  a  unique  way  to  address  complex  cellular  immunological  profiling  for  drug  development  and  Phase  I-­‐III  clinical  trials  in  infec8ous  diseases,  cancer,  vaccine,  transplanta8on,  autoimmune  disorders  and  related  immunomodula8on-­‐based  therapies.  High  dimensional  mul8parametric  single-­‐cell  analysis  is  not   only   aimed   to   define  mul8ple  markers   of   different   cell   popula8ons   simultaneously   -­‐though  helpful  when  clinical  sample  availability  is  limited-­‐,  it  is  also  one  of  very  few  analy8cal  plazorms  that  can  address  complex  protein-­‐based  signatures  (biomarkers,  disease  stage,  etc.)  and  func8onal  networks  (mechanisms)  from  relevant  and  well-­‐characterized  primary  human  or  animal  cells  at  the  single  cell  level.    The   immune   monitoring   of   Phase   I   to   Phase   III   clinical   trials   aims   to   design,   perform   and   interpret  immunological   data   that   enable   industry   to   move   vaccines,   immunotherapeu8cs   and   drug   candidates  through   the   regulatory   process   (FDA,   EMEA,   others).   High   dimensionality   flow   cytometric   analysis   also  allows  for  the  defini8on  of  immunological  profiles  that  are  disease  and  stage  specific,  enabling  elimina8on  of  many   unsuitable   drug,   vaccine   or   therapy   candidates   prior   or   at   the   8me   of   “in-­‐man”   studies.   This  requires   both   a   scien8fic   exper8se   in   immunology,   physiology   and   pathology   as   well   as   a   clear  understanding  of  technicali8es  related  to   instruments,  reagents  and  high  dimensional  data  mining.  As  a  service   company   for   the   pharmaceu8cal   industry,   ImmuneCarta   regulatory   process   and   standardized  procedures  are  cri8cal  to  ensure  data  integrity  and  quality,  especially  when  interpre8ng  complex  data  sets  to   define   disease   stage,   drug   efficacy   or   toxicity.   Overall,   immune   assays   for   diagnos8c,   research   or  biomarker   discovery   may   impact   on   all   aspects   and   stages   of   immune   system   tes8ng,   vaccine   and  immunotherapeu8c  design  and  development  as  well  as  drug  screening.  They  are  enablers,  permipng  GO/NO-­‐GO   decision-­‐making,   thus   saving   both   8me   and  money,   enhancing   safety   and   providing   surrogate  markers  of  clinical  efficacy  and/or  mechanis8c  insights.    

Flow   cytometry   is   based   on   fluorescence,   fluidic   and   op8cal   tools   with   the   help   of   signal   and   image   computer  treatment.  ImmuneCarta  Services  uses  3-­‐  and  4-­‐laser  LSR  II  Becton  Dickinson  instruments.  These  instruments  are  high  performance   systems   allowing   simultaneous   analysis   of   up   to   18   colors   using   automated   sampler   in   96-­‐well   plate  format.   High   dimensional   flow   cytometry   requires   sensi8ve   and   precise   methods   with   op8mal   stability   and  reproducibility  of  the  signal.  For  customized  an8body  panels,  qualifica8on  or  valida8on  steps  are  necessary  to  address  specificity,   precision,   accuracy,   lower   and   upper   limits   and   range   of   detec8on,   stability   and   reproducibility   of   the  analysis.  The  precision  and  accuracy  of  flow  cytometry  experiments  also  depends  on  stable  applica8on  sepngs  (CS&T  beads)  as  well  as  internal  and/or  external  quality  control  (QC)  samples.    

Since   2004,   ImmuneCarta   has   applied   a   broad   array   of   innova8ve   assays   for   the   biopharmaceu8cal  industry   and   government   ins8tu8ons   to   characterize   the   immune   profiling   of   adap8ve   and   innate  immunity  and  the  potency  of   immune-­‐related  drugs  or  vaccines   in  exploratory  and  Phase   I  and  Phase   II  clinical  trials.  ImmuneCarta  Services  recently  formed  a  strategic  alliance  with  Caprion  Proteome  Inc.,  the  leading  company  in  proteomics  and  biomarker  discovery  (www.caprion.com)  in  order  to  integrate  single-­‐cell  mul8parametric  flow  cytometry  analysis  with   soluble  markers,   serological  measurements  and  other  large  datasets  including  genomics  and  proteomics.  

High   throughput   analysis   of   high   dimensional   flow   cytometry   data   requires   advanced   soWware   and  methods.   Data  analysis  performed  at  ImmuneCarta  Services  relies  on  flow  data  acquisi8on  using  DiVa  soWware  (BD  Biosciences),  and  data  analysis  with  FlowJo  (Treestar  Inc.),  Excel  (Windows),  PESTLE/SPICE  (NIH),  Cluster  (Open  source,  Stanford),  Java  TreeView   (Open   source),   Prism   (GraphPad   SoWware)   and/or   other   specialized   soWware   for   sta8s8cal   analysis   and  systems  biology.  All  data  are  acquired  and  analyzed  in  compliance  with  21  CFR  Part11  to  ensure  quality  and  integrity  of   the   raw  data  and   its  analysis.  Pre-­‐defined  FlowJo   templates  are  qualified  or  validated   (GLP   study)  prior   to  being  used  throughout  studies  and  require  minimal  ga8ng  adjustments  that  are  documented  accordingly.  

Over  the  past  7  years,  we  have  implemented  assays  using  high  throughput  analy8cal  methods  with  10  to  18-­‐flow  cytometry   parameters   on   fresh   and   cryopreserved   human   peripheral   blood   samples.   Overall,   immune  monitoring   of   clinical   trials   involve   study   planning,   assay   valida8on,   specimen   handling,   assay   execu8on,  monitoring,   repor8ng,   and   quality   review   performed   as   per   applicable   GLP   regula8ons   and   GCLP   guidelines  governed  by  quality  systems  and  standard  opera8ng  procedures.    

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Switched IgGSwitched IgAUnswitched IgMMZ-likeImmature B cellNaive IgM-IgD+Naive IgM+IgD+Naive IgM+IgD-Memory IgA+Memory IgG+Memory IgM+IgD+Memory IgM+IgD-Memory IgM-IgD+Plasma B cellsPlasma IgA+Plasma IgD+Plasma IgG+Plasma IgM+

% Parent

Figure  6:  Reproducibility  of  high  dimensional  flow  cytometry.    Phenotyping  of  T,  B,  and  innate  cells  using    11-­‐12  color  an8body  panels  was  assessed  independently  on  two  blood  samples  from  20  subjects  at  Visit  2  (V2  and  V2-­‐redo).    Distribu8on  of  popula8ons  and  cell  counts  are  highly  correlated  with  R2  >  0.99.  

Innate  cell  Counts  

0 20 40 60 80 1000

20

40

60

80

100CD28- (CD8)CD4 (CD3)CD8 (CD3)PD-1 (CD4)PD-1 (CD8)CD57 (CD4)CD57 (CD8)Naive (CD8)CM (CD8)EM (CD8)TEM1 (CD8)TEM2 (CD8)

ILD (CD8)LD (CD8)Naive (CD4)CM (CD4)EM (CD4)TEM1 (CD4)TEM2 (CD4)ILD (CD4)LD (CD4)Mem CD45RA- (CD8)Mem CD45RA- (CD4)

V2

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o

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V2-R

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CD3 CD4CD8DPDNB cells HLA-DR+ B cells HLA-DR- MonocytespDCmDC1mDC2All NKNK 56-16+ NK 56bright16-NK 56low16-NK 56low16+NK 56bright16low

Cell counts (cells/µL)

R2  =  0.9956   R2  =  0.9955   R2  =  0.9954  

Figure  7:  Reproducibility  and  %CV  from  internal  QC  samples  (frozen  PBMC,  L747)  on  11-­‐color  T,  B,  and  Innate  cell  phenotype  panels.  Results  from  32  experiments  are  shown  over  one  year  (July  2010  to  September  2011).    The  first  graph   represents   the  enumera8on  of   T   cell   popula8ons   (CD3,  CD4,   and  CD8).     The   second  graph   represents   the  enumera8on   of   B   cells,   the  monocytes,   and   sub-­‐popula8ons   of   dendri8c   cells.     The   third   graph   represents   the  enumera8on  of  natural  killer  (NK)  sub-­‐popula8ons.  Coefficient  of  varia8on  are  indicated  in  blue.  

B, mono, and DC L747

B ce

lls HLA

-DR+

B ce

lls H

LA-D

R- Mon

oPDC

mDC1mDC2

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1500

B, Mono, and DC Populations

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T Cells Populations

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NK Populations

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18                      18                    19                      39                  38      36              66              44                29              24              31    18              36            23              38                19              23    

Subj-­‐1  

Subj-­‐1  

Subj-­‐2  

Flow  Cytometry   Single-­‐cell  analysis  (%  funcNonal  response)   StaNsNcal  analysis  and  idenNficaNon  of  group  differences  in  funcNonal  responses  

Sample  

Figure  1:  Overview  of  ImmuneCarta  flow  cytometry  and  analyNcal  processes  (example  of  immune  profiling  of  mucosal  CD4+  T  cells  by  intracellular  cytokine  detecNon)  

Plamorm   Field  of  applicaNon   Main  therapeuNc  area  

Assays   Targeted  Sample    or  Cell  PopulaNon   Markers  

Custom

izab

le    

Flow

 Cytom

etry  

Cytokine

 bead  array  

ELISA/ELISPO

T  

Chem

iluminescence  

Molecular  biology  

Drug  develop

men

t  

Biom

arkers  

Biologics/Biosim

ilars  

Immun

e  Mon

itorin

g  

Vaccine  

Epito

pe  M

apping  

Immun

otoxicology  

Infec8ou

s  dise

ases  

Allergy  

Oncology  

Cardiology  

Autoim

mun

ity  

Transplanta8

on  

Immun

e  Sene

scen

ce  

Chronic  Inflamma8

on  

Cell  EnumeraNon  

HematopoieNc  stem  cells    Circula8ng  CD34+    CD34,  CD45,  others   ✓   •                       •   •   •                       •           •          Immune  Cells    T,  B  and  Innate  cells    CD3,  CD4,  CD8,  CD16/56,  CD19,  CD14,  CD11c,  CD45,  CD123,  others   ✓   •                   •   •   •   •           •   •   •   •   •   •   •   •   •  Tumor  Cells    Circula8ng  Tumor  Cells    DNA,  Cytokera8n,  CD45       •                           •   •                       •                      

Immune  Phenotyping  

Cell  Lineage    T,  B,  NK  and  NKT  cells,  Dendri8c  cells,  Basophils,  Monocytes    CD3,  CD4,  CD8/CD19,  HLA-­‐DR/CD16,  CD56,  Lin-­‐/Vα24,  αGalCerCD1d    tetramer,  CD3/CD11c,  CD123,  Lin-­‐/CD123,  Lin-­‐/CD14   ✓   •                   •   •   •   •   •       •   •   •   •   •   •   •   •   •  

Cell  Subsets  DifferenNaNon    T,  B,  NK  and  NKT  cells,  Dendri8c  cells,  Basophils,  Monocytes    CD45RA/RO,  CD27,  CCR7,  CD28,  CD62L/CD27,  IgD/M,  CD20,                          

CD38,CD10,IgA/G/E/CD94/NKG2A,  CD7,  KIR2DL/DS/CD11c,/CD123,    BDCA-­‐2/3/4,  /FcγRs,  IgE,  CCR3/CD16,  CD64,  FcγRs  

✓   •                   •   •   •   •   •       •   •   •   •   •   •   •   •   •  

AnNgenic  specificity    Tetramer-­‐posi8ve  an8gen-­‐specific  CD8+/CD4+  T  cells    An8gen-­‐specific,  HLA-­‐restricted  TCR  (using  Tetramer/Pentamer/Dexamer)   ✓   •                   •       •   •   •   •       •       •       •   •          AcNvaNon/InhibiNon/ExhausNon/  Immune  

Senescence    T,  B  and  Innate  cells    HLA-­‐DR,  CD38,  ICOS,  OX40,  4-­‐1BB,  Ki67,  CD40,  CD95,  PD-­‐1,  CD57,  CD83,    CD80,  CD86,  CD160,  Lag-­‐3,  2B4,  CTLA-­‐4,  Tim-­‐3   ✓   •                   •   •   •   •   •       •   •   •   •   •   •   •   •   •  

Homing  Receptors/Co-­‐receptors    T  cells,  B  and  Innate  cells    CCR4,  CCR5,  CCR6,  CCR7,  CCR9,  CXCR3,  α4β7  integrins,  others   ✓   •                   •   •   •   •   •       •   •   •   •       •   •       •  TranscripNonal  Factors    Treg,  Th1,  Th2,  Th17,  T~    FoxP3,  T-­‐bet,  GATA-­‐3,    RORγt,  BCL-­‐6   ✓   •               •   •   •   •   •   •       •   •   •           •           •  

FuncNonal  Profiling  

Intracellular  Cytokine/Chemokine  Staining    T,  B,  NK,  NKT,  Dendri8c  cells,  Monocytes    IL-­‐2,  TNFα,  IFNγ,  IL-­‐4,  IL-­‐17,  IL-­‐22,    IL-­‐10,  TGFβ,  IL-­‐9,  IL-­‐21,  Mip1β,  others   ✓   •               •   •   •   •   •   •       •   •   •   •   •   •   •   •   •  Apoptosis/Necrosis    T,  B  and  Innate  cells    Annexin,  caspase  3,  CD95,  PARP,  TUNEL,  Live/Dead,  7-­‐AAD   ✓   •                   •   •   •   •   •       •   •   •   •   •   •   •   •   •  

PhosphorylaNon  (PhosFlow)    T,  B  and  Innate  Cells,  Tumor  cells    Akt,  Btk,  Elk,  EGF-­‐R,  Lck,  LAT,    Zap70,  Syk,    MEK1,    NFκB,  PKC,  PLC-­‐γ1,    PLC-­‐γ2,  p38MAPK,  ERKk1/2  Src,  STAT1  to  STAT-­‐6   ✓   •       •   •   •   •   •   •   •           •   •   •   •       •   •   •   •  

Lymphocyte  acNvity  (ImmuKnow®)    Total  CD4  cells    ATP               •       •   •   •   •           •                   •   •   •   •  ProliferaNve  response/cell  cycling    T  cells  and  subsets    CFSE,  Ki67,  BrDU   ✓   •                   •   •   •   •   •   •   •   •   •   •   •   •   •   •   •  

ELISPOT    CD8+/CD4+  T  cells,  B  cells    IFNγ  and/or    IL-­‐2,  TNFα, IgG,  IgM   ✓           •           •           •   •   •       •       •       •   •          Cytotoxicity/DegranulaNon    T,  NK,  NKT  cells  and  subsets,  Basophils    CD107a,  Granzyme,  Perforin,  CD63,  others   ✓   •                   •   •   •   •   •       •   •   •   •   •   •   •   •   •  

Serology  &  Soluble  Markers  

AnNbody  Titers    Serum,  plasma    Tetanus,  Diphtheria,  Hepa88s  B,  Cholera  toxin  B,  CMV,  others               •   •       •   •       •   •       •   •   •           •       •   •  Cytokines/Chemokines/Adhesion  

Molecules/Growth  Factors    Serum,  plasma,  cell  culture  supernatant    Interleukins,  sICAM-­‐1,  sICAM-­‐3,  sVCAM-­‐1,  sPECAM-­‐1,  sE-­‐Selec8n,  sP-­‐  Selec8n,  G-­‐CSF,  IL-­‐8,  MCP-­‐1,  MIG,  MIP-­‐1α,  MIP-­‐1β,  others   ✓       •   •   •       •   •   •   •   •       •   •   •   •   •   •   •   •   •  

Gene  Expression    &  DNA  Analysis  

mRNA  expression  (real-­‐Nme  PCR)    Cells,  8ssue    Specific  mRNA  quan8fica8on   ✓                   •   •   •   •   •           •   •   •   •   •   •   •       •  T  cell  receptor  excision  circles  (TREC)    Cells,  8ssue    sjTREC                       •           •   •           •   •       •           •       •  

Final  data  and  report  exported    in  Study  Server    

Figure   12:   Boolean   gaNng   analysis   of   5  markers   in  mucosal   CD4+   T   cells   using   SPICE   analysis.  CorrelaNon   of   specific   funcNonal   signatures   with  immune  acNvaNon  (Ki67+).  Combina8on  of  3  among  5   markers   (IFNγ,   IL-­‐2,   IL-­‐17,   MIP1β,   and   TNFα)   are  displayed   as   dots   and   their   means,   as   grey  histograms.   Sta8s8cally   significant   differences  between  HIV  viral  controllers  and  non-­‐controllers  are  shown  by  #  (Wilcoxon-­‐Rank)  and  +  (Student’s  t-­‐test).  

Figure  10:  Example  of  a  simple  hierarchical  gaNng  for  T,  B  and  NK  cell  enumeraNon.     Lymphocytes  and  BD  TruCount  Beads  are  gated  based   on   SSC/CD45   expression.   CD3-­‐posi8ve   and   CD3-­‐nega8ve  popula8ons   are   defined.   From   CD3-­‐   gate,   natural   killer   (NK)   and   B  cells   are   discriminated   based   on   CD16/CD56+     (NK)   and   CD19+   (B  cells).    From  CD3+  gate,  CD4+  and  CD8+  T  cells  are  defined  as  well  as  double  nega8ve  and  double  posi8ve  T  cells.      

<FITC-A>: CD3

70.829.2

<APC-Cy7-A>: CD8

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-A>:

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4

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SSC

-A

<APC-A>: CD19

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C-A

Beads

Lymph CD3+

NK

B Cells

CD4 DP

CD8DNCD3-

Figure  11:  Example  of  Boolean  gaNng  of  6  funcNonal  markers  expressed  in  CD4+,  CD8+  or  memory  subsets  and  effector  T  cells  by   ICS  aoer  anNgen-­‐sNmulaNon   of   cryopreserved   PBMCs.   Posi8ve   responses   for   each   of   the  markers  are  defined  from  the  template  analysis  (IFNγ,  CD107a,  TNFα,  IL-­‐2,  IL-­‐4,   and   IL-­‐17).   Boolean   ga8ng   is   generated   by   FlowJo   for   all   possible  combina8ons  (IFNγ+/-­‐  and  CD107a  +/-­‐  and  TNFα+/-­‐,  and  IL-­‐2+/-­‐  and  IL-­‐4+/-­‐  and   IL-­‐17+/-­‐   ),   e.g.   leading   to   2n  different   gates.     In   this   example,   n   =   6,  genera8ng  64  gates  for  8  popula8ons  of  interest  per  sample.  

Boolean Gating (total CD4+ and CD8+ T cells):

CD107a TNFa IL-2IFNg IL-17IL-4

CD

8+ T

cel

lsC

D4+

T c

ells

0.130.354

0.157 0.05540.0294

1.68e-3

0.6720.734

0.623 0.06140.0154

3.07e-3

Titer  DistribuNons  of  Vaccinated  Subjects   Cytometric  Vaccine  Response  Examples  

Plasmacyte Counts before (V2) and after (V3) immunization (left) All Plasmacytes (right) IgG+ Plasmacytes

High Dimensionality Analysis of Hyporesponsiveness to Protein Subunit Vaccines in the Elderly -- Introduction to Study MK0000-131

Carayannopoulos LN1,, Railkar RA1, Favre D2, Landry C2, Schaeffer AK1, Wiener MC1, Chastain M1, Loboda A1, Lukac S1, Duguay D3, Audet D3, St-Maurice F3, Kaslow DC1, Beals CR1, Sekaly RP2,4

1Merck Research Laboratories, USA | 2National Immune Monitoring Laboratory – Genome Quebec, Canada | 3Anapharm-Pharmanet Quebec, Canada | 4VGTI-Florida

6

wk24   wk28  

Figure  15:  High  dimensionality  analysis  of  vaccine  hyporesponse  in  healthy  elderly  subjects  (NCT01119703)  Titer  distribu8on  of  vaccine  responses   to  Tetanus,  Diphtheria  and  Hepa88s  B  vaccines   in  elderly  subjects   (leW)  and  example  of  increase  frequency  of  highly  characterized  B  cell  popula8ons  one  week  (V3)  aWer  vaccine  administra8on  (V2)  (middle).  “Plasma  B  cells”  are  characterized  as  singlet/lymphocyte/CD19+/HLA-­‐DR+/CD3-­‐/CD27+/CD10-­‐/CD20-­‐  cells.  “Plasma  IgG+    B  cells”  are  plasma  B  cells  expressing  IgG  on  the  cell  surface.  An  example  of  heatmap  represen8ng  unsupervised  clustering  of  high  dimensional  flow  cytometric  Boolean  datasets  of  T,  B  and  innate  immune  phenotyping  (y-­‐axis)  is  shown  on  the  same  cohort  (N=120  subjects,  x-­‐axis)  (right).  Such  unsupervised  clustering  of  large  flow  cytometric  datasets    allows  further  analysis  of  genomic  datasets  or  other  large  datasets  by  quan8ta8ve  regression  analysis  related  to  individual  immune  profiling,  as  described  in  Loke,  Favre  et  al.,  Blood  2010.