applications of single-cell mass cytometry in hematologic...
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Greg Behbehani, MD, PhDDivision of Hematology,The Ohio State University
Disclosures: Paid consultant and travel award recipient of DVS Sciences / Fluidigm
Applications of single-cell mass cytometry in hematologic malignancies
I currently have, or I have had in the past two years, an affiliation or financial interest with business corporation(s):(1) Consulting fees, patent royalties, licensing fees: None(2) Research funding: Fluidigm(3) Others (Travel Awards): Fluidigm
대한혈액학회 Korean Society of Hematology
COI disclosureGregory K. Behbehani, MD, PhD
Embracing Complexity in Cancer
Genotype Phenotype
- TCGA, 2013, N. Engl. J. Med. -Hanahan & Weinberg, 2011, Cell
Intro to mass cytometry and analysis workflow AML as a model Distinct differentiation and surface marker patterns
• Leukemic cells are not uniform• Specific patterns in certain karyotypes• Aberrant differentiation in MDS
Cell cycle differences across subtypes• Method of cell cycle analysis• Genotype and karyotype-specific S-phase fractions
Chemotherapy response in vivo Combined analysis of surface markers and
intracellular signaling
Quality from quantity:A systems biology approach to myeloid malignancies
Intro to mass cytometry and analysis workflow AML as a model Distinct differentiation and surface marker patterns
• Leukemic cells are not uniform• Specific patterns in certain karyotypes• Aberrant differentiation in MDS
Cell cycle differences across subtypes• Method of cell cycle analysis• Genotype and karyotype-specific S-phase fractions
Chemotherapy response in vivo Combined analysis of surface markers and
intracellular signaling
Quality from quantity:A systems biology approach to myeloid malignancies
Intro to mass cytometry and analysis workflow AML as a model Distinct differentiation and surface marker patterns
• Leukemic cells are not uniform• Specific patterns in certain karyotypes• Aberrant differentiation in MDS
Cell cycle differences across subtypes• Method of cell cycle analysis• Genotype and karyotype-specific S-phase fractions
Chemotherapy response in vivo Combined analysis of surface markers and
intracellular signaling
Quality from quantity:A systems biology approach to myeloid malignancies
Intro to mass cytometry and analysis workflow AML as a model Distinct differentiation and surface marker patterns
• Leukemic cells are not uniform• Specific patterns in certain karyotypes• Aberrant differentiation in MDS
Cell cycle differences across subtypes• Method of cell cycle analysis• Genotype and karyotype-specific S-phase fractions
Chemotherapy response in vivo Combined analysis of surface markers and
intracellular signaling
Quality from quantity:A systems biology approach to myeloid malignancies
Intro to mass cytometry and analysis workflow AML as a model Distinct differentiation and surface marker patterns
• Leukemic cells are not uniform• Specific patterns in certain karyotypes• Aberrant differentiation in MDS
Cell cycle differences across subtypes• Method of cell cycle analysis• Genotype and karyotype-specific S-phase fractions
Chemotherapy response in vivo Combined analysis of surface markers and
intracellular signaling
Quality from quantity:A systems biology approach to myeloid malignancies
Intro to mass cytometry and analysis workflow AML as a model Distinct differentiation and surface marker patterns
• Leukemic cells are not uniform• Specific patterns in certain karyotypes• Aberrant differentiation in MDS
Cell cycle differences across subtypes• Method of cell cycle analysis• Genotype and karyotype-specific S-phase fractions
Chemotherapy response in vivo Combined analysis of surface markers and
intracellular signaling
Quality from quantity:A systems biology approach to myeloid malignancies
76,800 Mass Spectrum Scans Per Second
Metal Isotope Masses
Pulse Scans
Fresh PrimarySamples
Cross-link Proteins PermeabilizeCell Membrane
Metal-chelatedAntibody Stain
Nebulize To Single Cell DropletsIonize In Plasma (7500K)ToF Mass Spec
Integrate Pulse Scans Into40+Dimensional Cell Events
Stored In FCS Format
High-dimensional Single Cell Mass Cytometry
Isotopically enriched
lanthanide ions (+3)
30-site chelating polymer
x 6 polymers= 180 atoms per antibody
Staining nearly identical to fluorescent cytometry
• Running the mass cytometer is relatively easy, getting consistent high-quality data is not
• Careful attention to antibody titration, blocking, cell numbers, machine tuning, and fluidics during data acquisition
• Internal bead standards are a must• Barcoding is very helpful
Progenitor cells
Staining nearly identical to fluorescent cytometry
• Running the mass cytometer is relatively easy, getting consistent high-quality data is not
• Careful attention to antibody titration, blocking, cell numbers, machine tuning, and fluidics during data acquisition
• Internal bead standards are a must• Barcoding is very helpful
T cells
Staining nearly identical to fluorescent cytometry
• Standard clinical blast gates are defined using CD45 and light side scatter• Use of antibodies against cell granule proteins can enable an approximation
of side scatter by mass cytometry and allow identification of blasts• In a preliminary series this shows high correlation with clinical flow cytometry
-Kim Le, ICCS, Ask an expert -Lyberger et al., unpublished
Blasts
Clinical Cytometry
Correlation with clinical blasts
Staining nearly identical to fluorescent cytometry
• Standard clinical blast gates are defined using CD45 and light side scatter• Use of antibodies against cell granule proteins can enable an approximation
of side scatter by mass cytometry and allow identification of blasts• In a preliminary series this shows high correlation with clinical flow cytometry
-Kim Le, ICCS, Ask an expert -Lyberger et al., unpublished
Blasts
Normal MarrowClinical Cytometry
Blasts
AML
Blasts
Mass Cytometry
Correlation with clinical blasts
Staining nearly identical to fluorescent cytometry
• Standard clinical blast gates are defined using CD45 and light side scatter• Use of antibodies against cell granule proteins can enable an approximation
of side scatter by mass cytometry and allow identification of blasts• In a preliminary series this shows high correlation with clinical flow cytometry
Correlation with clinical blasts
-Kim Le, ICCS, Ask an expert -Lyberger et al., unpublished
Normal Marrow
Blasts
AML
Blasts
Mass Cytometry
Flow
Cyt
omet
ry B
last
%
Mass Cytometry Blast%
r = 93%
• AML is a malignancy of blood forming cells Expansion of immature myeloid cell types and loss loss of
mature blood cells U.S. incidence is ~20,000/ yr and rising AML survival is highly variable by subtype; good risk 79-97%,
poor risk <5% The 5-yr overall survival for patients with AML is ~20%
• Lack of standardized therapy for consolidation and relapsed disease
• AML is an ideal model for understanding tumor heterogeneity Developmental heterogeneity Genetic heterogeneity Numerous new therapies
Acute Myeloid Leukemia (AML)
• AML is a malignancy of blood forming cells Expansion of immature myeloid cell types and loss loss of
mature blood cells U.S. incidence is ~20,000/ yr and rising AML survival is highly variable by subtype; good risk 79-97%,
poor risk <5% The 5-yr overall survival for patients with AML is ~20%
• Lack of standardized therapy for consolidation and relapsed disease
• AML is an ideal model for understanding tumor heterogeneity Developmental heterogeneity Genetic heterogeneity Numerous new therapies
Acute Myeloid Leukemia (AML)
• AML is a malignancy of blood forming cells Expansion of immature myeloid cell types and loss loss of
mature blood cells U.S. incidence is ~20,000/ yr and rising AML survival is highly variable by subtype; good risk 79-97%,
poor risk <5% The 5-yr overall survival for patients with AML is ~20%
• Lack of standardized therapy for consolidation and relapsed disease
• AML is an ideal model for understanding tumor heterogeneity Developmental heterogeneity Genetic heterogeneity Numerous new therapies
Acute Myeloid Leukemia (AML)
Workflow
Bone marrow biopsy5 normal control samples
40 patient samples
Fix and freeze in aliquots
Thaw in groups of 20Red cell lysis
Mass tag barcoding~2 million cells / well
Combine all cells into a single tube ~40 million cells
Blocking and surface stain
Methanol permeabilization
Intracellular stain
DNA stain
Decode mass tags Run on mass cytometer
Patient 1=
Patient 2=
Patient 3=
Patient 4=
Patient 5=
Biaxial plots are not a scalable solution
Parameters: 481432Plots: 62891496
Sean Bendall, Erin Simonds. Science, May 2011
SPADE: Spanning-tree Progression Analysis of Density-normalized Events – Peng Qiu
1. Determine Tree Structure
1. Overlay regions with surface marker expression levels
• Each node is a cluster of similar cells
• Color = expression of indicated marker
• Size = approximate cell number (minimum size enforced)
• Clustering performed using 19 surface markers
CD45
High parameter data allows visualization of hematopoiesis
Normal
Variable patterns of immature cell expansion in AML
CD45
Normal Normal
Cell Numbers
Variable patterns of immature cell expansion in AML
Cell NumbersCell Numbers
Normal AML10 inv(16)
Variable patterns of immature cell expansion in AML
Cell NumbersCell Numbers
Normal AML35 t(8;21)
Variable patterns of immature cell expansion in AML
Cell NumbersCell Numbers
Normal APL3 t(15;17)
Variable patterns of immature cell expansion in AML
Cell NumbersCell Numbers
Normal AML14 NK
Distinct patterns of cell frequency across AML subtypes
HSC
MPP
CM
PG
MP
CD
34- C
D38
+un
diff.
Mye
lo/m
onob
last
Prom
onoc
yte
CD
14-m
onoc
yte
CD
14+
mon
ocyt
ePr
omye
locy
teM
yelo
cyte
Met
amye
locy
teM
atur
e gr
anul
ocyt
ePr
oery
thro
blas
tEr
ythr
obla
stLa
te e
ryth
robl
ast
Pre-
B c
ell
Mat
ure
B c
ell
Plas
ma
T ce
llsN
K c
ells
APLNK-AML, FLT3-ITDCBF-AMLARK-AMLNK-AML, FLT3wtNormal
Distinct patterns of cell frequency across AML subtypes
HSC
MPP
CM
PG
MP
CD
34- C
D38
+un
diff.
Mye
lo/m
onob
last
Prom
onoc
yte
CD
14-m
onoc
yte
CD
14+
mon
ocyt
ePr
omye
locy
teM
yelo
cyte
Met
amye
locy
teM
atur
e gr
anul
ocyt
ePr
oery
thro
blas
tEr
ythr
obla
stLa
te e
ryth
robl
ast
Pre-
B c
ell
Mat
ure
B c
ell
Plas
ma
T ce
llsN
K c
ells
APLNK-AML, FLT3-ITDCBF-AMLARK-AMLNK-AML, FLT3wtNormal
Normal Marrow
Fold CD33
AML27 (FLT3-ITD)
Fold CD33
Aberrant marker expression in AML
-Behbehani et al., Cancer Discovery, 2015
Normal Marrow
Fold CD123
AML27 (FLT3-ITD)
Fold CD123
Aberrant marker expression in AML
Gate Populations(MPP)
CD34
CD
38
CD33
CD
90
CD
45
Nor
mal
AML2
6AM
L35
AML5
AML1
0AM
L32
AML2
7AM
L7AM
L9AM
L23
AML1
3AM
L42
AML3
0
SampleSummed
AberranciesExtract population median
Sum aberrancies for each
population
Define normal range
Identify aberrant samples
Systematic detection of multiple small aberrancies defines large immunophenotypic changes across hematopoiesis
Normal 0AML26 0AML35 0AML5 0
AML10 0AML32 0AML27 1AML7 0AML9 0
AML23 1AML13 0AML42 1AML30 1
Gate Populations(MPP)
CD34
CD
38
CD33
CD
90
CD
45
Nor
mal
AML2
6AM
L35
AML5
AML1
0AM
L32
AML2
7AM
L7AM
L9AM
L23
AML1
3AM
L42
AML3
0
SampleSummed
Aberrancies
Systematic detection of multiple small aberrancies defines large immunophenotypic changes across hematopoiesis
Normal 0AML26 0AML35 0AML5 0
AML10 0AML32 0AML27 1AML7 0AML9 0
AML23 1AML13 0AML42 1AML30 1Repeat for all 25 surface markers
and sum aberrancies
Gate Populations(MPP)
CD34
CD
38
CD33
CD
90
CD
99
Nor
mal
AML2
6AM
L35
AML5
AML1
0AM
L32
AML2
7AM
L7AM
L9AM
L23
AML1
3AM
L42
AML3
0
SampleSummed
Aberrancies
Systematic detection of multiple small aberrancies defines large immunophenotypic changes across hematopoiesis
Repeat for all 25 surface markers
and sum aberrancies
Normal 0AML26 0AML35 0AML5 1
AML10 1AML32 1AML27 2AML7 0AML9 1
AML23 2AML13 1AML42 1AML30 2
Normal 0AML26 0AML35 0AML5 0
AML10 0AML32 0AML27 1AML7 0AML9 0
AML23 1AML13 0AML42 1AML30 1
Gate Populations(MPP)
CD34
CD
38
CD33
CD
90
CD
99
SampleSummed
Aberrancies
Systematic detection of multiple small aberrancies defines large immunophenotypic changes across hematopoiesis
Normal 0AML26 0AML35 0AML5 1
AML10 1AML32 1AML27 2AML7 0AML9 1
AML23 2AML13 1AML42 1AML30 2
CD45
CD
99
Normal AML27 AML30
Systematic detection of multiple small aberrancies defines large immunophenotypic changes across hematopoiesis
HSC
MPP
MPP
/CM
P
CMP/
GM
P
Mye
lo/
Mon
obla
st
Prom
onoc
yte
CD14
-Mon
ocyt
e
CD14
+ M
onoc
yte
Prom
yelo
cyte
Mye
locy
te
Met
amye
locy
te
Mat
ure
Gra
nulo
cyte
ProE
ryth
robl
ast
Eryt
hrob
last
Pre-
B Ce
ll
Mat
ure
B ce
ll
T Ce
ll
NK
Cell
AML26t(8;21)
3 3 3 5 4 5 4 4 1 3 1 1 1 0 9 1 0 3AML35 5 3 4 5 4 3 8 5 6 6 3 3 4 0 10 0 0 3AML5 2 2 3 5 5 6 6 6 8 3 3 3 9 2 10 1 2 1
AML10 Inv(16) 3 3 4 3 6 7 4 5 10 8 7 6 7 3 11 0 0 2AML32 5 5 3 7 6 7 8 5 8 9 1 1 3 2 10 1 2 3
AML34
t(15;17)
1 3 4 3 6 5 7 1 8 12 3 1 5 2 13 0 2 2APL3 0 0 3 2 6 2 1 2 6 1 0 0 0 0 12 0 0 1
AML24 2 3 6 5 7 5 6 5 5 8 1 2 1 3 11 0 2 0APL1 1 2 4 3 6 8 6 1 8 1 2 0 8 2 12 1 5 1
AML27
NK-FLT3-ITD
6 5 7 4 5 8 9 4 8 10 2 2 3 3 9 0 1 1AML7 4 1 8 4 5 6 6 4 4 4 6 4 1 4 6 2 1 3AML9 3 3 4 5 3 4 4 2 1 1 4 3 3 3 8 1 2 2
AML20 4 2 3 3 6 3 4 5 5 3 3 3 3 2 8 1 2 2AML21 4 4 5 5 6 6 2 0 7 4 1 0 2 3 2 3 4 1AML23 3 4 5 3 6 5 4 4 10 5 2 2 1 3 10 1 2 2AML30 4 5 9 4 4 7 7 5 2 2 2 2 1 3 11 0 2 5APL2 2 2 5 5 5 6 8 3 3 4 3 1 5 2 11 0 0 1
AML37 7 6 8 6 7 9 9 4 8 6 9 7 6 4 9 1 2 6AML38 2 3 7 4 4 8 6 5 6 5 6 5 1 3 12 1 1 9AML40 4 4 4 5 7 11 3 3 8 6 5 6 5 3 9 5 2 2
AML13
NK-FLT3wt
3 3 5 4 5 3 3 3 1 0 2 0 0 1 9 2 3 2AML36 4 4 3 3 4 3 4 3 2 2 2 1 0 0 6 1 2 2AML33 3 2 3 4 6 9 3 3 2 1 2 2 4 1 1 4 1 1MDS1 1 1 7 3 2 3 6 4 1 1 2 3 1 0 4 1 2 3AML14 3 2 4 4 3 5 3 3 1 2 1 1 0 1 0 0 4 2AML15 2 4 4 4 5 7 5 2 8 11 3 4 1 1 5 1 2 1
AML18
Poor-risk
3 2 2 5 5 10 4 0 8 4 0 0 4 3 1 1 3 1AML4 5 4 4 4 5 4 4 2 3 2 2 0 2 3 6 1 1 2
AML19 3 3 3 4 6 9 7 5 7 12 10 7 8 2 6 3 4 2AML29 6 5 4 4 6 4 5 4 11 7 5 2 2 2 6 2 4 4AML41 2 1 3 1 1 2 4 3 1 3 4 4 0 2 8 3 4 3AML25 2 2 6 2 4 2 2 3 3 3 8 7 0 0 7 2 1 1
Nl-1
Normal
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0NL-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0NL-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0NL-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
• High levels of aberrancy in the blast population
Systematic detection of multiple small aberrancies defines large immunophenotypic changes across hematopoiesis
HSC
MPP
MPP
/CM
P
CMP/
GMP
Mye
lo/
Mon
obla
st
Prom
onoc
yte
CD14
-M
onoc
yte
CD14
+ M
onoc
yte
Prom
yelo
cyte
Mye
locy
te
Met
amye
locy
te
Mat
ure
Gra
nulo
cyte
ProE
ryth
robl
ast
Eryt
hrob
last
Pre-
B Ce
ll
Mat
ure
B ce
ll
T Ce
ll
NK
Cell
AML26t(8;21)
3 3 3 5 4 5 4 4 1 3 1 1 1 0 9 1 0 3AML35 5 3 4 5 4 3 8 5 6 6 3 3 4 0 10 0 0 3AML5 2 2 3 5 5 6 6 6 8 3 3 3 9 2 10 1 2 1
AML10 Inv(16) 3 3 4 3 6 7 4 5 10 8 7 6 7 3 11 0 0 2AML32 5 5 3 7 6 7 8 5 8 9 1 1 3 2 10 1 2 3
AML34
t(15;17)
1 3 4 3 6 5 7 1 8 12 3 1 5 2 13 0 2 2APL3 0 0 3 2 6 2 1 2 6 1 0 0 0 0 12 0 0 1
AML24 2 3 6 5 7 5 6 5 5 8 1 2 1 3 11 0 2 0APL1 1 2 4 3 6 8 6 1 8 1 2 0 8 2 12 1 5 1
AML27
NK-FLT3-ITD
6 5 7 4 5 8 9 4 8 10 2 2 3 3 9 0 1 1AML7 4 1 8 4 5 6 6 4 4 4 6 4 1 4 6 2 1 3AML9 3 3 4 5 3 4 4 2 1 1 4 3 3 3 8 1 2 2
AML20 4 2 3 3 6 3 4 5 5 3 3 3 3 2 8 1 2 2AML21 4 4 5 5 6 6 2 0 7 4 1 0 2 3 2 3 4 1AML23 3 4 5 3 6 5 4 4 10 5 2 2 1 3 10 1 2 2AML30 4 5 9 4 4 7 7 5 2 2 2 2 1 3 11 0 2 5APL2 2 2 5 5 5 6 8 3 3 4 3 1 5 2 11 0 0 1
AML37 7 6 8 6 7 9 9 4 8 6 9 7 6 4 9 1 2 6AML38 2 3 7 4 4 8 6 5 6 5 6 5 1 3 12 1 1 9AML40 4 4 4 5 7 11 3 3 8 6 5 6 5 3 9 5 2 2
AML13
NK-FLT3wt
3 3 5 4 5 3 3 3 1 0 2 0 0 1 9 2 3 2AML36 4 4 3 3 4 3 4 3 2 2 2 1 0 0 6 1 2 2AML33 3 2 3 4 6 9 3 3 2 1 2 2 4 1 1 4 1 1MDS1 1 1 7 3 2 3 6 4 1 1 2 3 1 0 4 1 2 3AML14 3 2 4 4 3 5 3 3 1 2 1 1 0 1 0 0 4 2AML15 2 4 4 4 5 7 5 2 8 11 3 4 1 1 5 1 2 1
AML18
Poor-risk
3 2 2 5 5 10 4 0 8 4 0 0 4 3 1 1 3 1AML4 5 4 4 4 5 4 4 2 3 2 2 0 2 3 6 1 1 2
AML19 3 3 3 4 6 9 7 5 7 12 10 7 8 2 6 3 4 2AML29 6 5 4 4 6 4 5 4 11 7 5 2 2 2 6 2 4 4AML41 2 1 3 1 1 2 4 3 1 3 4 4 0 2 8 3 4 3AML25 2 2 6 2 4 2 2 3 3 3 8 7 0 0 7 2 1 1
Nl-1
Normal
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0NL-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0NL-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0NL-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
• High levels of aberrancy in the blast population
• Aberrancy persists into mature cells
Systematic detection of multiple small aberrancies defines large immunophenotypic changes across hematopoiesis
HSC
MPP
MPP
/CM
P
CMP/
GM
P
Mye
lo/
Mon
obla
st
Prom
onoc
yte
CD14
-M
onoc
yte
CD14
+ M
onoc
yte
Prom
yelo
cyte
Mye
locy
te
Met
a-m
yelo
cyte
Mat
ure
Gra
nulo
cyte
ProE
ryth
robl
ast
Eryt
hrob
last
Pre-
B Ce
ll
Mat
ure
B ce
ll
T Ce
ll
NK
Cell
AML26t(8;21)
3 3 3 5 4 5 4 4 1 3 1 1 1 0 9 1 0 3AML35 5 3 4 5 4 3 8 5 6 6 3 3 4 0 10 0 0 3AML5 2 2 3 5 5 6 6 6 8 3 3 3 9 2 10 1 2 1
AML10 Inv(16) 3 3 4 3 6 7 4 5 10 8 7 6 7 3 11 0 0 2AML32 5 5 3 7 6 7 8 5 8 9 1 1 3 2 10 1 2 3
AML34
t(15;17)
1 3 4 3 6 5 7 1 8 12 3 1 5 2 13 0 2 2APL3 0 0 3 2 6 2 1 2 6 1 0 0 0 0 12 0 0 1
AML24 2 3 6 5 7 5 6 5 5 8 1 2 1 3 11 0 2 0APL1 1 2 4 3 6 8 6 1 8 1 2 0 8 2 12 1 5 1
AML27
NK-FLT3-ITD
6 5 7 4 5 8 9 4 8 10 2 2 3 3 9 0 1 1AML7 4 1 8 4 5 6 6 4 4 4 6 4 1 4 6 2 1 3AML9 3 3 4 5 3 4 4 2 1 1 4 3 3 3 8 1 2 2
AML20 4 2 3 3 6 3 4 5 5 3 3 3 3 2 8 1 2 2AML21 4 4 5 5 6 6 2 0 7 4 1 0 2 3 2 3 4 1AML23 3 4 5 3 6 5 4 4 10 5 2 2 1 3 10 1 2 2AML30 4 5 9 4 4 7 7 5 2 2 2 2 1 3 11 0 2 5APL2 2 2 5 5 5 6 8 3 3 4 3 1 5 2 11 0 0 1
AML37 7 6 8 6 7 9 9 4 8 6 9 7 6 4 9 1 2 6AML38 2 3 7 4 4 8 6 5 6 5 6 5 1 3 12 1 1 9AML40 4 4 4 5 7 11 3 3 8 6 5 6 5 3 9 5 2 2
AML13
NK-FLT3wt
3 3 5 4 5 3 3 3 1 0 2 0 0 1 9 2 3 2AML36 4 4 3 3 4 3 4 3 2 2 2 1 0 0 6 1 2 2AML33 3 2 3 4 6 9 3 3 2 1 2 2 4 1 1 4 1 1MDS1 1 1 7 3 2 3 6 4 1 1 2 3 1 0 4 1 2 3AML14 3 2 4 4 3 5 3 3 1 2 1 1 0 1 0 0 4 2AML15 2 4 4 4 5 7 5 2 8 11 3 4 1 1 5 1 2 1
AML18
Poor-risk
3 2 2 5 5 10 4 0 8 4 0 0 4 3 1 1 3 1AML4 5 4 4 4 5 4 4 2 3 2 2 0 2 3 6 1 1 2
AML19 3 3 3 4 6 9 7 5 7 12 10 7 8 2 6 3 4 2AML29 6 5 4 4 6 4 5 4 11 7 5 2 2 2 6 2 4 4AML41 2 1 3 1 1 2 4 3 1 3 4 4 0 2 8 3 4 3AML25 2 2 6 2 4 2 2 3 3 3 8 7 0 0 7 2 1 1
Nl-1
Normal
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0NL-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0NL-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0NL-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
• High levels of aberrancy in the blast population
• Aberrancy persists into mature cells
Systematic detection of multiple small aberrancies defines large immunophenotypic changes across hematopoiesis
HSC
MPP
MPP
/CM
P
CMP/
GM
P
Mye
lo/
Mon
obla
st
Prom
onoc
yte
CD14
-M
onoc
yte
CD14
+ M
onoc
yte
Prom
yelo
cyte
Mye
locy
te
Met
a-m
yelo
cyte
Mat
ure
Gra
nulo
cyte
ProE
ryth
robl
ast
Eryt
hrob
last
Pre-
B Ce
ll
Mat
ure
B ce
ll
T Ce
ll
NK
Cell
AML26t(8;21)
3 3 3 5 4 5 4 4 1 3 1 1 1 0 9 1 0 3AML35 5 3 4 5 4 3 8 5 6 6 3 3 4 0 10 0 0 3AML5 2 2 3 5 5 6 6 6 8 3 3 3 9 2 10 1 2 1
AML10 Inv(16) 3 3 4 3 6 7 4 5 10 8 7 6 7 3 11 0 0 2AML32 5 5 3 7 6 7 8 5 8 9 1 1 3 2 10 1 2 3
AML34
t(15;17)
1 3 4 3 6 5 7 1 8 12 3 1 5 2 13 0 2 2APL3 0 0 3 2 6 2 1 2 6 1 0 0 0 0 12 0 0 1
AML24 2 3 6 5 7 5 6 5 5 8 1 2 1 3 11 0 2 0APL1 1 2 4 3 6 8 6 1 8 1 2 0 8 2 12 1 5 1
AML27
NK-FLT3-ITD
6 5 7 4 5 8 9 4 8 10 2 2 3 3 9 0 1 1AML7 4 1 8 4 5 6 6 4 4 4 6 4 1 4 6 2 1 3AML9 3 3 4 5 3 4 4 2 1 1 4 3 3 3 8 1 2 2
AML20 4 2 3 3 6 3 4 5 5 3 3 3 3 2 8 1 2 2AML21 4 4 5 5 6 6 2 0 7 4 1 0 2 3 2 3 4 1AML23 3 4 5 3 6 5 4 4 10 5 2 2 1 3 10 1 2 2AML30 4 5 9 4 4 7 7 5 2 2 2 2 1 3 11 0 2 5APL2 2 2 5 5 5 6 8 3 3 4 3 1 5 2 11 0 0 1
AML37 7 6 8 6 7 9 9 4 8 6 9 7 6 4 9 1 2 6AML38 2 3 7 4 4 8 6 5 6 5 6 5 1 3 12 1 1 9AML40 4 4 4 5 7 11 3 3 8 6 5 6 5 3 9 5 2 2
AML13
NK-FLT3wt
3 3 5 4 5 3 3 3 1 0 2 0 0 1 9 2 3 2AML36 4 4 3 3 4 3 4 3 2 2 2 1 0 0 6 1 2 2AML33 3 2 3 4 6 9 3 3 2 1 2 2 4 1 1 4 1 1MDS1 1 1 7 3 2 3 6 4 1 1 2 3 1 0 4 1 2 3AML14 3 2 4 4 3 5 3 3 1 2 1 1 0 1 0 0 4 2AML15 2 4 4 4 5 7 5 2 8 11 3 4 1 1 5 1 2 1
AML18
Poor-risk
3 2 2 5 5 10 4 0 8 4 0 0 4 3 1 1 3 1AML4 5 4 4 4 5 4 4 2 3 2 2 0 2 3 6 1 1 2
AML19 3 3 3 4 6 9 7 5 7 12 10 7 8 2 6 3 4 2AML29 6 5 4 4 6 4 5 4 11 7 5 2 2 2 6 2 4 4AML41 2 1 3 1 1 2 4 3 1 3 4 4 0 2 8 3 4 3AML25 2 2 6 2 4 2 2 3 3 3 8 7 0 0 7 2 1 1
Nl-1
Normal
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0NL-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0NL-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0NL-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
• High levels of aberrancy in the blast population
• Aberrancy persists into mature cells
• Multiple marker aberrancies can be found in HSC and MPP populations
Karyotype- and genotype-specific immunophenotypic aberrancies in AML
Normaln=14 (5)
CBFAMLn=5
APLn=4
(FLT3+)
FLT3-ITDNK-AML
n=11
FLT3wtNK-AML
n=6
AdverseKaryotype
AMLn=6
MLL-rearranged
AMLn=1
CD33 Expression on HSPCs (CD34+CD38low)
CD34
CD
38
Karyotype- and genotype-specific immunophenotypic aberrancies in AML
CD34
CD
38
Normaln=14 (5)
CBFAMLn=5
APLn=4
(FLT3+)
FLT3-ITDNK-AML
n=11
FLT3wtNK-AMLn=6 (4+2)
AdverseKaryotype
AMLn=6
MLL-rearranged
AMLn=1
CD123 Expression on HSPCs (CD34+CD38low)
Subtype-specific high dimensional phenotypesNormal 1 Normal 3 Normal 4 Normal 5 Normal 6
tSNE-1
tSN
E-2
CD34
CD
38
Subtype-specific high dimensional phenotypesNormal 1 Normal 3 Normal 4 Normal 5 Normal 6
AML10 - inv(16) AML32 - inv(16) AML26 – t(8;21)AML5 – t(8;21) AML35 – t(8;21)
tSNE-1
tSN
E-2
Subtype-specific high dimensional phenotypesNormal 1 Normal 3 Normal 4 Normal 5 Normal 6
AML10 - inv(16) AML32 - inv(16) AML26 – t(8;21)AML5 – t(8;21) AML35 – t(8;21)
AML27-FLT3-ITD+ AML30-FLT3-ITD+ AML37-FLT3-ITD+ AML38-FLT3-ITD+ AML39-FLT3-ITD+
tSNE-1
tSN
E-2
Grans
Monos
Normal #5
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
-Behbehani et al., unpublished
Grans
Monos
Normal #5
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
-Behbehani et al., unpublished
HSC
HSC
Mature Granulocytes
Mature Monocytes
Grans
Monos
Normal #5Low IPSS(MDS19)
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
-Behbehani et al., unpublished
Grans
Monos
Normal #5Low IPSS(MDS19)
Int-1 IPSS(MDS23)
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
-Behbehani et al., unpublished
Grans
Monos
Normal #5Low IPSS(MDS19)
Int-1 IPSS(MDS23)
sAML(MDS17)
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
-Behbehani et al., unpublished
Summary 1: The big picture of abnormal myeloid differentiation
• High-Resolution Immunophenotyping Mass-tag barcoding and internal reference samples enable detection of
subtle differences Comparison of malignant cells to developmentally similar normal cells
• Surface marker aberrancies detected as early as HSCs • Abnormal patterns of differentiation may have diagnostic utility in
AML and MDS• Stem and early progenitor immunophenotyping:
Subtype classification Identification of therapeutic targets LSC markers are complex and different in different subtypes
Summary 1: The big picture of abnormal myeloid differentiation
• High-Resolution Immunophenotyping Mass-tag barcoding and internal reference samples enable detection of
subtle differences Comparison of malignant cells to developmentally similar normal cells
• Surface marker aberrancies detected as early as HSCs • Abnormal patterns of differentiation may have diagnostic utility in
AML and MDS• Stem and early progenitor immunophenotyping:
Subtype classification Identification of therapeutic targets LSC markers are complex and different in different subtypes
Summary 1: The big picture of abnormal myeloid differentiation
• High-Resolution Immunophenotyping Mass-tag barcoding and internal reference samples enable detection of
subtle differences Comparison of malignant cells to developmentally similar normal cells
• Surface marker aberrancies detected as early as HSCs • Abnormal patterns of differentiation may have diagnostic utility in
AML and MDS• Stem and early progenitor immunophenotyping:
Subtype classification Identification of therapeutic targets LSC markers are complex and different in different subtypes
Summary 1: The big picture of abnormal myeloid differentiation
• High-Resolution Immunophenotyping Mass-tag barcoding and internal reference samples enable detection of
subtle differences Comparison of malignant cells to developmentally similar normal cells
• Surface marker aberrancies detected as early as HSCs • Abnormal patterns of differentiation may have diagnostic utility in
AML and MDS• Stem and early progenitor immunophenotyping:
Subtype classification Identification of therapeutic targets LSC markers are complex and different in different subtypes
Summary 1: The big picture of abnormal myeloid differentiation
• High-Resolution Immunophenotyping Mass-tag barcoding and internal reference samples enable detection of
subtle differences Comparison of malignant cells to developmentally similar normal cells
• Surface marker aberrancies detected as early as HSCs • Abnormal patterns of differentiation may have diagnostic utility in
AML and MDS• Stem and early progenitor immunophenotyping:
Subtype classification Identification of therapeutic targets LSC markers are complex and different in different subtypes
What about function?
AML and the stem cell hypothesis
• Leukemia stem cell (LSC) hypothesis of tumor resistance – LSCs mediate leukemia relapse – LSCs are protected from chemotherapy
• quiescent state• bone marrow niche
• Why can certain leukemia types be cured by chemotherapy (e.g. t(8;21), inv(16), APL)?– Not all leukemias have LSCs?– Not all LSCs are resistant to chemotherapy?
• Analysis of chemotherapy response in vivo
-With Bruno Medeiros
AML and the stem cell hypothesis
• Leukemia stem cell (LSC) hypothesis of tumor resistance – LSCs mediate leukemia relapse – LSCs are protected from chemotherapy
• quiescent state• bone marrow niche
• Why can certain leukemia types be cured by chemotherapy (e.g. t(8;21), inv(16), APL)?– Not all leukemias have LSCs?– Not all LSCs are resistant to chemotherapy?
• Analysis of chemotherapy response in vivo
-With Bruno Medeiros
AML and the stem cell hypothesis
• Leukemia stem cell (LSC) hypothesis of tumor resistance – LSCs mediate leukemia relapse – LSCs are protected from chemotherapy
• quiescent state• bone marrow niche
• Why can certain leukemia types be cured by chemotherapy (e.g. t(8;21), inv(16), APL)?– Not all leukemias have LSCs?– Not all LSCs are resistant to chemotherapy?
• Analysis of chemotherapy response in vivo
-With Bruno Medeiros
Intercalator Ir193
IdU
I127
No IdU 5 min 10 min 15 min
30 min 60 min 120 min
0.3%S phase
21.9%S phase
20.6%S phase
24.2%S phase
24.3%S phase
26.6%S phase
28.9%S phase
IdU incorporates rapidly into S-phase cells
-Behbehani, et al., 2012, Cytometry A
Distinct patterns of proliferation in AML
S phase fractionS phase fraction
Normal AML10 inv(16)
S phase fractionS phase fraction
Normal AML 35 t(8;21)
Distinct patterns of proliferation in AML
S phase fractionS phase fraction
Normal APL 3 t(15;17)
Distinct patterns of proliferation in AML
S phase fractionS phase fraction
Normal AML14 NK
Distinct patterns of proliferation in AML
Cell cycle distribution is developmentally regulated
HSPC
MONO
GRAN
RBCB-CELL
B
AML cells have a lower proliferative fraction but still follow developmental patterns
HSPC
MONO
GRAN
RBCB-CELL
B
AML cells have a lower proliferative fraction but still follow developmental patterns
MONO
AML cells have a lower proliferative fraction but still follow developmental patterns
GRAN
AML cells have a lower proliferative fraction but still follow developmental patterns
Is proliferative fraction sub-type specific?CBF AML?
HSPC
MONO
GRAN
RBCB-CELL
B
LSCs from CBF AML have a higher S phase fraction
HSPC
MONO
GRAN
RBCB-CELL
B
LSCs from CBF AML have a higher S phase fraction
HSPC
B
LSCs from CBF AML have a higher S phase fraction
HSPC
B
p=0.0019
p=0.00071
p=0.0069
p=0.0019
p=0.10
AML and the stem cell hypothesis
• Does proliferative fraction correlate with clinical risk?
• What is the effect of FLT3-ITD mutation?
AML and the stem cell hypothesis
• Does proliferative fraction correlate with clinical risk?
• What is the effect of FLT3-ITD mutation?
LSCs from FLT3+ AML have a lower proliferative fraction
HSPC
MONO
GRAN
RBCB-CELL
B
LSCs from FLT3+ AML have a lower proliferative fraction
HSPC
MONO
GRAN
RBCB-CELL
B
LSCs from FLT3+ AML have a lower proliferative fraction
HSPC
MONO
GRAN
RBCB-CELL
B
LSCs from FLT3+ AML have a lower proliferative fraction
HSPC
B
LSCs from FLT3+ AML have a lower proliferative fraction
HSPC
B
p=0.00026
p=0.00010
p=0.0021
p=0.0087
p=0.55
In vivo chemotherapy response
• LSC proliferative fraction correlates known clinical risk
• How might the differences in cell cycle impact chemotherapy response?
cytarabine
AML Pre-TreatmentNormal Marrow
Ki-67
IdU
(S-P
hase
)
Treatment stratification by cell cycle?
CD34
CD
38
4.5% 2.4%
2.2%3.4%
-Persistent LSCs have reduced S-phase after treatment
AML Pre-TreatmentNormal Marrow
Ki-67
IdU
(S-P
hase
)
Treatment stratification by cell cycle?
CD34
CD
38
AML Day 14
4.5% 2.4%
2.2%3.4%
0.0%
0.6%
-Persistent LSCs have reduced S-phase after treatment
AML Pre-TreatmentNormal Marrow
Ki-67
IdU
(S-P
hase
)
Treatment stratification by cell cycle?
CD34
CD
38
AML Day 14
4.5% 2.4%
2.2%3.4%
0.0%
0.6%
7+3CPX-351
Intracellular signaling correlates
• Can we detect abnormal intracellular signaling?• How does it vary with development and proliferation?• Are their AML sub-type specific differences?• Do these correlate with immunophenotypic changes?
• Can we use this information therapeutically?
Intracellular signaling correlates
• Can we detect abnormal intracellular signaling?• How does it vary with development and proliferation?• Are their AML sub-type specific differences?• Do these correlate with immunophenotypic changes?
• Can we use this information therapeutically?
Intracellular signaling correlates
• Can we detect abnormal intracellular signaling?• How does it vary with development and proliferation?• Are their AML sub-type specific differences?• Do these correlate with immunophenotypic changes?
• Can we use this information therapeutically?
Basal pSTAT5 is up-regulated in FLT3-ITD+ AMLIncreasedDecreased
HSPC
MONO
GRAN
RBC
B-CELL
B
Basal pSTAT5 is up-regulated in FLT3-ITD+ AMLIncreasedDecreased
HSPC
MONO
GRAN
RBC
B-CELL
B
pERK is up-regulated in almost all AML cell populationsIncreasedDecreased
HSPC
MONO
GRAN
RBC
B-CELL
B
tSNE-1
tSN
E-2
Normal 4Normal 3 Normal 6Normal 5Normal 1
z = p-STAT5
Aberrant signaling correlates with aberrant immunophenotype
CD34
CD
38
tSNE-1
tSN
E-2
Normal 4Normal 3 Normal 6Normal 5Normal 1
z = p-STAT5
AML26 – t(8;21) AML35 – t(8;21)
Aberrant signaling correlates with aberrant immunophenotype
tSNE-1
tSN
E-2
Normal 4Normal 3 Normal 6Normal 5Normal 1
AML22 – t(10;11)
z = p-STAT5
AML26 – t(8;21) AML35 – t(8;21)
Aberrant signaling correlates with aberrant immunophenotype
tSNE-1
tSN
E-2
Normal 4Normal 3 Normal 6Normal 5Normal 1
APL4 – FLT3-ITD+ APL5 – FLT3-ITD+AML22 – t(10;11)
AML21 – FLT3-ITD+AML9 – FLT3-ITD+ AML23 – FLT3-ITD+ AML27 – FLT3-ITD+
z = p-STAT5
AML26 – t(8;21) AML35 – t(8;21)
Aberrant signaling correlates with aberrant immunophenotype
Monitoring therapy response in rare cell subsets
Healthy Donor samples
Monitoring therapy response in rare cell subsets
Healthy Donor samples
Day 0TKI Responding Patient
Monitoring therapy response in rare cell subsets
Healthy Donor samples
Day 0 Day 14TKI Responding Patient
Monitoring therapy response in rare cell subsets
Healthy Donor samples
Day 0 Day 14 Day 0 Day 14TKI Responding Patient Non-Responding Patient
Conclusions• Mass cytometry enables detection of subtle immunophenotypic and
functional abnormalities– Mass-tag barcoding and normalization– Reference samples– Distinct patterns of abnormal differentiation in AML
• Malignant cell populations recapitulate normal development:– Immunophenotypic – Functional
• Karyotype and genotype-specific patterns:– Cell population frequency– Aberrant immunophenotypes– Cell cycle distribution– Intracellular signaling
• Properties of LSCs are not universal – Karyotype and genotype-specific variations– May account for differences in AML relapse
• Mass cytometry can measure these complex phenotypes and assess their interaction with therapy
Conclusions• Mass cytometry enables detection of subtle immunophenotypic and
functional abnormalities– Mass-tag barcoding and normalization– Reference samples– Distinct patterns of abnormal differentiation in AML
• Malignant cell populations recapitulate normal development:– Immunophenotypic – Functional
• Karyotype and genotype-specific patterns:– Cell population frequency– Aberrant immunophenotypes– Cell cycle distribution– Intracellular signaling
• Properties of LSCs are not universal – Karyotype and genotype-specific variations– May account for differences in AML relapse
• Mass cytometry can measure these complex phenotypes and assess their interaction with therapy
Conclusions• Mass cytometry enables detection of subtle immunophenotypic and
functional abnormalities– Mass-tag barcoding and normalization– Reference samples– Distinct patterns of abnormal differentiation in AML
• Malignant cell populations recapitulate normal development:– Immunophenotypic – Functional
• Karyotype and genotype-specific patterns:– Cell population frequency– Aberrant immunophenotypes– Cell cycle distribution– Intracellular signaling
• Properties of LSCs are not universal – Karyotype and genotype-specific variations– May account for differences in AML relapse
• Mass cytometry can measure these complex phenotypes and assess their interaction with therapy
Conclusions• Mass cytometry enables detection of subtle immunophenotypic and
functional abnormalities– Mass-tag barcoding and normalization– Reference samples– Distinct patterns of abnormal differentiation in AML
• Malignant cell populations recapitulate normal development:– Immunophenotypic – Functional
• Karyotype and genotype-specific patterns:– Cell population frequency– Aberrant immunophenotypes– Cell cycle distribution– Intracellular signaling
• Properties of LSCs are not universal – Karyotype and genotype-specific variations– May account for differences in AML relapse
• Mass cytometry can measure these complex phenotypes and assess their interaction with therapy
Conclusions• Mass cytometry enables detection of subtle immunophenotypic and
functional abnormalities– Mass-tag barcoding and normalization– Reference samples– Distinct patterns of abnormal differentiation in AML
• Malignant cell populations recapitulate normal development:– Immunophenotypic – Functional
• Karyotype and genotype-specific patterns:– Cell population frequency– Aberrant immunophenotypes– Cell cycle distribution– Intracellular signaling
• Properties of LSCs are not universal – Karyotype and genotype-specific variations– May account for differences in AML relapse
• Mass cytometry can measure these complex phenotypes and assess their interaction with therapy
AcknowledgementsNolan Lab – Stanford
-Garry Nolan-Wendy Fantl-Burno Medeiros-Peter Greenberg
-Rachel Finck-Matt Hale-Nikolay Samusik-Nikesh Kotecha-Sean Bendall-Zach Bjornson-Astraea Jager-Angelica Trejo
Behbehani Lab – Ohio State-Palak Sekhri-Chi Chang-Ray Devine-Justin Lyberger-HussamAlkhalaileh
Hematology-John Byrd-Bill Blum-Steve Devine-Mike Caligiuri-Ronnie Freud
The patients of Stanford and Ohio State University
AcknowledgementsNolan Lab – Stanford
-Garry Nolan-Wendy Fantl-Burno Medeiros-Peter Greenberg
-Rachel Finck-Matt Hale-Nikolay Samusik-Nikesh Kotecha-Sean Bendall-Zach Bjornson-Astraea Jager-Angelica Trejo
Behbehani Lab – Ohio State-Palak Sekhri-Chi Chang-Ray Devine-Justin Lyberger-HussamAlkhalaileh
Hematology-John Byrd-Bill Blum-Steve Devine-Mike Caligiuri-Ronnie Freud
Extra Slides
TOF (Time of Flight)
ICP
Schematic of mass cytometry
Nebulizer – Single cell droplets
High-pass Filter
Ion Detector
-DVS Sciences / Fluidigm
TOF (Time of Flight)
ICP
Schematic of mass cytometry
Occurs at a rate of ~1000 cells per second
Nebulizer – Single cell droplets
Ion Detector
-DVS Sciences / Fluidigm
High-pass Filter
TOF (Time of Flight)
ICP
Schematic of mass cytometry
Occurs at a rate of ~1000 cells per second
Nebulizer – Single cell droplets
Ion Detector
-DVS Sciences / Fluidigm
High-pass Filter
TOF (Time of Flight)
ICP
Schematic of mass cytometry
Occurs at a rate of ~1000 cells per second
Nebulizer – Single cell droplets
Ion Detector
-DVS Sciences / Fluidigm
High-pass Filter
TOF (Time of Flight)
ICP
Schematic of mass cytometry
Occurs at a rate of ~1000 cells per second
Nebulizer – Single cell droplets
Ion Detector
-DVS Sciences / Fluidigm
High-pass Filter
Staining nearly identical to fluorescent cytometry
• Running the mass cytometer is relatively easy, getting consistent high-quality data is not
• Careful attention to antibody titration, blocking, cell numbers, machine tuning, and fluidics during data acquisition
• Internal bead standards are a must• Barcoding is very helpful
Progenitor cells
Staining nearly identical to fluorescent cytometry
• Running the mass cytometer is relatively easy, getting consistent high-quality data is not
• Careful attention to antibody titration, blocking, cell numbers, machine tuning, and fluidics during data acquisition
• Internal bead standards are a must• Barcoding is very helpful
T cells
High-dimensional analysis of an AML cohort
5 Normal bone marrow samples
33 AML samples (8 good-risk)
5 Recovering marrows (CR after chemo)
2x samples
2 High-Risk MDS samples
All samples fresh with IdU added
-Behbehani et al., Cancer Discovery, 2015
High-dimensional analysis of an AML cohortPatient Characteristics
5 Normal bone marrow samples
33 AML samples (8 good-risk)
5 Recovering marrows (CR after chemo)
2x samples
2 High-Risk MDS samples
All samples fresh with IdU addedSample Sex Age Cytogenetics Mutations Status at Bx CR? Treatment Survival (days) Blast % HU?AML5 F 44 t(8;21) None New Dx. Yes 4+3 619 75% No
AML26 F 45 t(8;21) NRAS+ New Dx. Yes Vori+3+4 Alive 15% NoAML35 M 58 t(8;21) Kit N822K+ New Dx. Yes 7+3 (90) Alive 57% NoAML10 M 37 Inv(16) None New Dx. Yes 7+3 (90) Alive 76% YesAML32 F 60 t(16;16),+22 FLT3-TKD+ 4th Relapse ? Aza + Mylotarg unknown 82% NoAPL4 M 55 t(15;17) PML-RAR+; FLT3-ITD+; New Dx. Yes Arsenic + ATRA Alive 93% NoAPL5 M 48 t(15;17) PML-RAR+; FLT3-ITD+ New Dx. N/A ATRA + Dauno 2 94% YesAPL3 M 18 t(15;17) FLT3-ITD+ New Dx. Yes Arsenic + ATRA Alive 21% NoAML6 M 66 Failed (Normal) FLT3 TKD+ New Dx. Refractory 7+3 x2 142 76% YesAML8* M 66 Normal FLT3 TKD+ Refractory Refractory Decitabine 107 34% NoAML31 F 37 Normal NPM1+, FLT3-TKD+, IDH1+, Relapse No MEC Alive (BMT) 92% YesAML7 F 72 Normal FLT3-ITD+ New Dx. N/A No Tx. 32 78% NoAML9 F 26 Normal FLT3-ITD+, NPM1+ New Dx. Yes 7+3 x2 533 21% No
AML18 F 30 Inv(1), t(11;15) FLT3-ITD+, sCEBPa Relapsed PR MEC x 2 unknown 81% NoAML20 M 29 Normal FLT3-ITD+, BiCEBPa New Dx. Yes G-CLAC 229 90% YesAML21* F 27 Normal FLT3-ITD+, NPM1+ Relapsed No G-CLAC x 2 173 76% NoAML 39 M 59 Normal DNMT3a+, FLT3-ITD+ New Dx. PR 7+3, 5+2 (90) Alive (BMT) 69% YesAML23 F 62 Normal DNMT3a+, FLT3-ITD+, sCEBPa New Dx. N/A No Tx. 11 92% NoAML27 M 53 Normal NPM1+; FLT3-ITD+ New Dx. Yes 7+3 (90) Alive 96% Yes
AML30 F 67 Normal NPM1+, FLT3-ITD+, DNMT3a+ New Dx. YesDecitabine + midostaurin Alive 89% Yes
AML37 M 71 Normal NPM1+, FLT3-ITD+, DNMT3a+ New Dx. N/A No Tx. 2 84% YesAML38 M 33 Normal FLT3-ITD+, NRAS+ New Dx. PR 4+3 Alive 39% NoAML 40 M 58 Normal FLT3-ITD+, NPM1+, sCEBPa Residual PR GCLAC 267 49% / 68% NoAML4 M 66 Monosomal None New Dx. No Aza-Rev 6 43% No
AML13* M 50 Normal None Relapsed Refractory Aza Mylotarg 74 27% NoAML14 F 55 Normal None Relapsed Refractory Aza Mylotarg 65 43% NoAML15 M 72 Normal BiCEBPa Refractory Refractory NEDD-8 181 24% NoAML19 F 76 Monosomy 7 None Relapse No Temozolomide 22 82% YesAML22 F 42 t(10;11) [MLL] Kit(D816V)+, KRAS(G12V)+ New Dx. No 7+3 x 2 257 95% NoAML25 F 67 Monosomy 7 JAK2(V617F)+ New Dx. N/A No Tx. 98 28% No
AML29 M 57Hyperdiploid; Complex; 5q- DNMT3a+ New Dx. Yes Vori+3+4 Alive 90% No
AML41 F 62 t(1;3) NRAS+, SF3b1+ Residual PR 7+3 + Panobino 13% / 8% NoMDS1 M 80 Normal None Progression PR decitabine 390 16%/11% No
AML36 M 68 Normal none Residual N/A No Tx. Alive 9%/12.5% NoAML33 M 32 normal BiCEBPa Relapse Yes MEC Alive 28% NoAPL1 M 64 t(15;17) FLT3-ITD+ New Dx. Yes Ida + ATRA 96% YesRec2 F 48 Normal IDH2+ CR s/p 7+3 N/A N/A Alive 1% NoRec3 F 53 Normal PM1+,FLT3+, IDH1+ CR s/p 4+3 N/A N/A 269 2% / 1% NoRec5 M 49 Normal None CR s/p 7+3 N/A N/A 185 2% NoRec6 F 81 Normal None CR N/A N/A 327 4% NoRec7 F 80 del 13q None CR N/A N/A 112 4% No
54% Male, Median Age = 57, 56% New Diagnosis, 49% CR (of treated), 40% Alive (of treated, F/U = 603d), 11/41 on HU
-Behbehani et al., Cancer Discovery, 2015
5 Normal bone marrow samples
33 AML samples (8 good-risk)
5 Recovering marrows (CR after chemo)
2x samples
2 High-Risk MDS samples
All samples fresh with IdU added
High-dimensional analysis of an AML cohort
25 Surface Markers
Staining panel
Element Mass Antibody Element Mass AntibodyIn 113 CD3 Sm 152 CD33 (p67)In 115 CD45 Tb 159 CD38La 139 CD45RA (A) Gd 160 CD14Pr 141 CD133 (A) Dy 161 CD16Nd 142 CD7 Dy 162 CD11bNd 143 CD71 Dy 164 CD15Nd 144 CD235 Er 166 CD321Nd 145 CD47 (A) Er 167 CD99Sm 147 CD56 Er 168 CD13 (A)Nd 148 CD34 Yb 172 CD10Sm 149 CD90 (A) Yb 173 CD19Nd 150 CD117 Yb 174 CD20Eu 151 CD123
25 Intracellular MarkersElement Mass Antibody Element Mass Antibody
La 139 pRPS6 (B) Gd 158 Ki-67Pr 141 pATM (B) Ho 165 pRb (A)Nd 145 pAKT (B) Ho 165 pNFkB (B)Nd 146 p21 (A) Er 168 pERK (B)Nd 146 H3K9ac (B) Tm 169 pCDK1 (A)Sm 149 cMyc (B) Tm 169 B-Catenin (B)Sm 154 Cyclin A (A) Er 170 pH2AX (A)Sm 154 pMAPKAPK2 (B) Er 170 p4EBP1 (B)Gd 156 Cyclin B1 (A) Yb 171 cPARPGd 156 pSTAT3 (B) Lu 175 pRPS6 (A)Gd 157 PCNA (A) Lu 175 pSMAD1/5 (B)Gd 157 pSTAT5(B) Yb 176 pHH3 (A)
Yb 176 pCreb (B)
-Behbehani et al., Cancer Discovery, 2015
Distinct patterns of cell frequency across AML subtypes
HSPC
MONO
GRAN
RBCB-CELL
B
Distinct patterns of cell frequency across AML subtypes
HSPC
MONO
GRAN
RBCB-CELL
B
Systematic detection of multiple small aberrancies defines large immunophenotypic changes across hematopoiesis
HSC
MPP
MPP
/CM
P
CMP/
GM
P
Mye
lo/
Mon
obla
st
Prom
onoc
yte
CD14
-M
onoc
yte
CD14
+ M
onoc
yte
Prom
yelo
cyte
Mye
locy
te
Met
a-m
yelo
cyte
Mat
ure
Gra
nulo
cyte
Pro-
Eryt
hrob
last
Eryt
hrob
last
Pre-
B Ce
ll
Mat
ure
B ce
ll
T Ce
ll
NK
Cell
AML26t(8;21)
3 3 3 5 4 5 4 4 1 3 1 1 1 0 9 1 0 3AML35 5 3 4 5 4 3 8 5 6 6 3 3 4 0 10 0 0 3AML5 2 2 3 5 5 6 6 6 8 3 3 3 9 2 10 1 2 1
AML10 Inv(16) 3 3 4 3 6 7 4 5 10 8 7 6 7 3 11 0 0 2AML32 5 5 3 7 6 7 8 5 8 9 1 1 3 2 10 1 2 3
AML34
t(15;17)
1 3 4 3 6 5 7 1 8 12 3 1 5 2 13 0 2 2APL3 0 0 3 2 6 2 1 2 6 1 0 0 0 0 12 0 0 1
AML24 2 3 6 5 7 5 6 5 5 8 1 2 1 3 11 0 2 0APL1 1 2 4 3 6 8 6 1 8 1 2 0 8 2 12 1 5 1
AML27
NK-FLT3-ITD
6 5 7 4 5 8 9 4 8 10 2 2 3 3 9 0 1 1AML7 4 1 8 4 5 6 6 4 4 4 6 4 1 4 6 2 1 3AML9 3 3 4 5 3 4 4 2 1 1 4 3 3 3 8 1 2 2
AML20 4 2 3 3 6 3 4 5 5 3 3 3 3 2 8 1 2 2AML21 4 4 5 5 6 6 2 0 7 4 1 0 2 3 2 3 4 1AML23 3 4 5 3 6 5 4 4 10 5 2 2 1 3 10 1 2 2AML30 4 5 9 4 4 7 7 5 2 2 2 2 1 3 11 0 2 5APL2 2 2 5 5 5 6 8 3 3 4 3 1 5 2 11 0 0 1
AML37 7 6 8 6 7 9 9 4 8 6 9 7 6 4 9 1 2 6AML38 2 3 7 4 4 8 6 5 6 5 6 5 1 3 12 1 1 9AML40 4 4 4 5 7 11 3 3 8 6 5 6 5 3 9 5 2 2
AML13
NK-FLT3wt
3 3 5 4 5 3 3 3 1 0 2 0 0 1 9 2 3 2AML36 4 4 3 3 4 3 4 3 2 2 2 1 0 0 6 1 2 2AML33 3 2 3 4 6 9 3 3 2 1 2 2 4 1 1 4 1 1MDS1 1 1 7 3 2 3 6 4 1 1 2 3 1 0 4 1 2 3AML14 3 2 4 4 3 5 3 3 1 2 1 1 0 1 0 0 4 2AML15 2 4 4 4 5 7 5 2 8 11 3 4 1 1 5 1 2 1
AML18
Poor-risk
3 2 2 5 5 10 4 0 8 4 0 0 4 3 1 1 3 1AML4 5 4 4 4 5 4 4 2 3 2 2 0 2 3 6 1 1 2
AML19 3 3 3 4 6 9 7 5 7 12 10 7 8 2 6 3 4 2AML29 6 5 4 4 6 4 5 4 11 7 5 2 2 2 6 2 4 4AML41 2 1 3 1 1 2 4 3 1 3 4 4 0 2 8 3 4 3AML25 2 2 6 2 4 2 2 3 3 3 8 7 0 0 7 2 1 1
Nl-1
Normal
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0NL-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0NL-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0NL-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
RAE
B-T
Hig
her R
isk
Low
er R
isk
ICU
SN
orm
al
HSC
MPP
MPP
/CM
P
CMP/
GM
P
Mye
lo/M
onob
last Pr
omon
ocyt
e
CD14
-Mat
ure
Mno
ocyt
e
Mat
ure
Mon
ocyt
e
Prom
yelo
cyte
Mye
locy
te
Met
amye
locy
te
Mat
ure
Gra
nulo
cyte
Proe
ryth
robl
ast
Eryt
hrob
last
Pre-
B Ce
ll
Mat
ure
B-Ce
ll
T Ce
ll
NK
Cell
MDS17 4 4 3 3 5 2 2 2 1 2 3 4 0 0 11 0 1 1MDS15 4 5 6 3 2 1 3 1 7 6 6 1 8 5 11 3 1 3MDS4 2 3 6 3 1 3 2 2 2 1 0 2 0 1 2 4 2 2
MDS21 3 4 1 3 5 3 1 1 0 1 2 0 0 0 6 0 0 2MDS3 2 2 1 1 5 3 2 2 1 1 0 1 0 0 6 0 0 2
MDS13 3 4 3 1 5 1 2 2 1 3 4 1 0 0 6 0 0 1MDS16 0 0 0 2 1 1 0 0 1 0 0 0 4 3 3 0 1 1MDS1 0 4 5 4 2 0 5 3 3 0 1 0 0 0 6 1 1 3
MDS21 4 5 2 2 5 0 2 2 2 2 2 2 0 0 6 0 1 3MDS26 1 2 1 1 3 2 2 1 1 0 2 0 0 0 4 0 0 1MDS27 1 1 1 0 4 1 1 0 1 0 0 0 2 3 7 2 2 1MDS2 5 5 3 4 6 3 4 3 3 1 3 2 5 4 7 4 1 2MDS3 2 3 1 2 5 1 2 2 2 3 2 1 0 0 6 1 0 1
MDS23 2 1 3 2 2 1 1 1 0 0 0 2 0 1 3 0 0 1MDS25 3 7 7 1 2 0 0 0 0 0 2 0 0 1 6 1 1 2MDS5 2 5 5 3 1 1 4 2 5 4 4 3 5 2 11 0 2 6MDS6 0 2 3 2 2 2 3 1 3 1 0 0 2 3 3 1 0 1MDS8 1 0 11 0 1 2 2 1 1 0 0 0 1 2 3 0 0 1MDS9 0 2 0 0 4 6 5 2 1 0 0 0 1 2 6 0 0 2
MDS12 2 4 4 4 1 1 2 1 0 0 1 1 0 0 2 1 1 3MDS14 3 5 7 1 2 4 0 0 2 1 0 1 0 1 0 1 0 2MDS19 0 0 0 1 0 2 2 1 1 1 1 1 0 0 3 2 0 1MDS20 0 1 4 0 2 4 2 2 1 0 0 1 1 0 3 3 0 2MDS28 1 2 1 1 3 1 2 4 1 0 2 2 3 0 2 0 0 1MDS5 3 1 6 1 0 2 4 1 4 4 4 2 5 1 9 1 2 6
ICUS11 3 3 9 2 2 0 0 1 1 0 0 0 2 3 4 0 0 0ICUS18 0 0 0 1 3 1 2 0 1 0 0 0 0 1 4 0 0 1ICUS22 0 0 0 0 2 3 4 3 1 1 1 2 0 2 0 0 0 1ICUS24 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Nl-1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Nl-6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Systematic detection of multiple small aberrancies defines large immunophenotypic changes across hematopoiesis
-Andrew Hughes, Gene Ther Mol Biol., 2006; 10:41
p-histone H3
Additional markers allow for complete cell cycle state assignment
p-histone H3
Additional markers allow for complete cell cycle state assignment
p-R
b (S
807/
S811
)
Uridine
G0
p-histone H3
Additional markers allow for complete cell cycle state assignment
p-histone H3
Uridine
Cyc
lin B
1
G0/G1
S
G2
p-histone H3
Additional markers allow for complete cell cycle state assignment
p-histone H3
Uridine
p-H
isto
ne H
3 (S
28)
M
HU treatment of HL-60 cells in culture
HU22hr
• In cell culture, HU treatment leads to cell cycle arrest
• Almost no cells enter S phase
• Treatment is associated with increased apoptotic markers
Distinct patterns of cell frequency across AML subtypes
Normal (Nl-6)
MDS-23 RCMD, IPSS=0.5/Int-1
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
-Behbehani et al., unpublished
Normal (Nl-6)
MDS-3 RAEB-1, IPSS=1.5/Int-2
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
-Behbehani et al., unpublished
Normal (Nl-6)
MDS-17 RAEB-T/AML, IPSS=High
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
-Behbehani et al., unpublished
Normal (Nl-6)
MDS-23 RCMD, IPSS=0.5/Int-1
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
-Behbehani et al., unpublished
Normal (Nl-6)
MDS-14 RCMD, IPSS=1.0/Int-1
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
-Behbehani et al., unpublished
Normal (Nl-6)
MDS-3 RAEB-1, IPSS=1.5/Int-2
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
-Behbehani et al., unpublished
Normal (Nl-6)
MDS-17 RAEB-T/AML, IPSS=High
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
-Behbehani et al., unpublished
Nl#5 – ANl#5 – BNl#3 – A Nl#3 – B Nl#4 – A Nl#4 – B Nl#6 – ANl#6 – B Nl#1 – ANl#1 – B CR#2CR#6CR#3AML#25APL#3APL#4AML#36AML#42CR#5AML13AML8AML#41AML#14AML#10AML#12CR#7AML#19AML#20AML#18AML#33AML#4AML#5AML#15AML#26AML#35AML#32AML#22AML#7AML#39AML#23AML#6AML#40AML#27AML#38AML#9AML#21AML#30AML#31APL#5APL#1AML#37AML#29
NormalNormalNormalNormalNormalNormalNormalNormalNormalNormalNK – FLT3wt NK – FLT3wtFLT3-ITD+PRK – Mono 7t(15;17) – FLT3-ITDt(15;17) – FLT3-ITDNK – FLT3wtNK – FLT3wtNK – FLT3wtNK – FLT3wtFLT3 - TKDPRK – t(1;3)NK – FLT3wtCBF – inv(16)Myeloid Sarcomadel(q13)PRK – Mono 7FLT3-ITD *PRK – ComplexNK – FLT3wtPRK - ComplexCBF – t(8;21)NK – FLT3wtCBF – t(8;21)CBF – t(8;21)CBF – t(16;16)MLL – t(10;11)NK – FLT3-ITDNK – FLT3-ITDNK – FLT3-ITDNK – FLT3-TKDNK – FLT3-ITDNK – FLT3-ITDNK – FLT3-ITDNK – FLT3-ITDNK – FLT3-ITDNK – FLT3-ITDNK – FLT3-TKDt(15;17) – FLT3-ITDt(15;17) – FLT3-ITDNK – FLT3-ITDPRK - Complex
Subtype-specific high dimensional phenotypes
Nl#5 – ANl#5 – BNl#3 – A Nl#3 – B Nl#4 – A Nl#4 – B Nl#6 – ANl#6 – B Nl#1 – ANl#1 – B CR#2CR#6CR#3AML#25APL#3APL#4AML#36AML#42CR#5AML13AML8AML#41AML#14AML#10AML#12CR#7AML#19AML#20AML#18AML#33AML#4AML#5AML#15AML#26AML#35AML#32AML#22AML#7AML#39AML#23AML#6AML#40AML#27AML#38AML#9AML#21AML#30AML#31APL#5APL#1AML#37AML#29
NormalNormalNormalNormalNormalNormalNormalNormalNormalNormalNK – FLT3wt NK – FLT3wtFLT3-ITD+PRK – Mono 7t(15;17) – FLT3-ITDt(15;17) – FLT3-ITDNK – FLT3wtNK – FLT3wtNK – FLT3wtNK – FLT3wtFLT3 - TKDPRK – t(1;3)NK – FLT3wtCBF – inv(16)Myeloid Sarcomadel(q13)PRK – Mono 7FLT3-ITD *PRK – ComplexNK – FLT3wtPRK - ComplexCBF – t(8;21)NK – FLT3wtCBF – t(8;21)CBF – t(8;21)CBF – t(16;16)MLL – t(10;11)NK – FLT3-ITDNK – FLT3-ITDNK – FLT3-ITDNK – FLT3-TKDNK – FLT3-ITDNK – FLT3-ITDNK – FLT3-ITDNK – FLT3-ITDNK – FLT3-ITDNK – FLT3-ITDNK – FLT3-TKDt(15;17) – FLT3-ITDt(15;17) – FLT3-ITDNK – FLT3-ITDPRK - Complex
Subtype-specific high dimensional phenotypes
Myelodysplastic syndrome
• Incidence of 5-10 per 100,000 per year• Generally poor prognosis
– Median survival • high risk disease is 5 – 12 mo.• 3 – 6 years in low risk
• Highly variable progression
• Management still based on clinical factors
• What is different about stem and progenitor cells in MDS? • Can unique markers be used to target them?• How can disordered hematopoiesis be quantitated?
-With Peter Greenberg
Myelodysplastic syndrome
• Incidence of 5-10 per 100,000 per year• Generally poor prognosis
– Median survival • high risk disease is 5 – 12 mo.• 3 – 6 years in low risk
• Highly variable progression
• Management still based on clinical factors
• What is different about stem and progenitor cells in MDS? • Can unique markers be used to target them?• How can disordered hematopoiesis be quantitated?
-With Peter Greenberg
Myelodysplastic syndrome
• Incidence of 5-10 per 100,000 per year• Generally poor prognosis
– Median survival • high risk disease is 5 – 12 mo.• 3 – 6 years in low risk
• Highly variable progression
• Management still based on clinical factors
• What is different about stem and progenitor cells in MDS? • Can unique markers be used to target them?• How can disordered hematopoiesis be quantitated?
-With Peter Greenberg
HU treatment does not cause cell cycle arrest in vivo
HSPC
MONO
GRAN
RBCB-CELL
B
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
• Granulocyte differentiation can be visualized by gating out other cell lineages
Normal (Nl-6)
-Behbehani et al., unpublished
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
• Granulocyte differentiation can be visualized by gating out other cell lineages
Normal (Nl-6)
-Behbehani et al., unpublished
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
Normal (Nl-6)
-Behbehani et al., unpublished
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
Normal (Nl-6)
-Behbehani et al., unpublished
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
Normal (Nl-6)
• Monocyte differentiation can be visualized by gating out other cell lineages
-Behbehani et al., unpublished
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
Normal (Nl-6)
• Monocyte differentiation can be visualized by gating out other cell lineages
-Behbehani et al., unpublished
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
-Behbehani et al., unpublished
Mass Cytometry allows for the study of disrupted differentiation in MDS patients
-Behbehani et al., unpublished
NormalLow riskInt-1Int-2High
CD34 included in clustering CD34 excluded from clustering
Secondary clustering of SPADE clusters groups patients by clinical risk
MD
S17
MD
S21
MD
S3M
DS1
3M
DS3
MD
S21
MD
S26
MD
S23
MD
S9IC
US1
8M
DS2
5M
DS1
1M
DS8
MD
S6IC
US2
2IC
US2
4N
l-1N
l-6N
l-6N
l-3N
l-3N
l-4N
l-4N
l-5M
DS1
9M
DS1
4M
DS2
8M
DS2
0M
DS1
2M
DS1
6M
DS2
7M
DS1
MD
S2M
DS4
MD
S5M
DS5
MD
S15
MD
S17
MD
S21
MD
S3M
DS1
3M
DS3
MD
S21
MD
S23
MD
S9IC
US1
8M
DS2
5M
DS1
1M
DS8
MD
S6IC
US2
2IC
US2
4N
l-1N
l-6N
l-6N
l-3N
l-3N
l-4N
l-4N
l-5M
DS1
9M
DS1
2M
DS1
4M
DS2
8M
DS2
0M
DS1
6M
DS2
7M
DS5
MD
S5M
DS4
MD
S1M
DS2
MD
S26
MD
S15
Normal
Leukemia
Normal #4Normal #3 Normal #6
APL #3 APL #5 AML #22(MLL-rearrangement)CD33
HLA
-DR
Markers of LSCs may also be subtype-specific
CD34
CD
38
Normal
Leukemia
Normal #4Normal #3 Normal #6
APL #3 APL #5 AML #22(MLL-rearrangement)CD33
HLA
-DR
Markers of LSCs may also be subtype-specific
CD34
CD
38
0.048%
In vivo chemotherapy response
• LSC proliferative fraction correlates known clinical risk
• How might the differences in cell cycle impact chemotherapy response?
Can we measure it?-10 patients treated with hydroxyurea (HU)
cytarabine
HU treated
Untreated
Treatment of AML bone marrow cells in vivo• Primary cells display reduced IdU incorporation• Most cells remain in cycle• Minimal or no change in apoptosis occurs in vivo
HU treatment has little effect on S phase fraction in vivo
HSPC
MONO
GRAN
RBCB-CELL
B
HU treatment has little effect on S phase fraction in vivo
HSPC
MONO
GRAN
RBCB-CELL
B
HU treatment has little effect on S phase fraction in vivo
HSPC
MONO
GRAN
RBCB-CELL
B
HU treatment causes a large decrease in IdU incorporation into S phase cells
HSPC
MONO
GRAN
RBCB-CELL
B
p4EBP1 is down-regulated in almost all AML cell populationsIncreasedDecreased
HSPC
MONO
GRAN
RBC
B-CELL
B