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Greg Behbehani, MD, PhD Division 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

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Page 1: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 2: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 3: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

Embracing Complexity in Cancer

Genotype Phenotype

- TCGA, 2013, N. Engl. J. Med. -Hanahan & Weinberg, 2011, Cell

Page 4: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 5: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 6: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 7: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 8: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 9: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 10: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 11: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 12: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 13: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 14: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 15: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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%

Page 16: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

• 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)

Page 17: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

• 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)

Page 18: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

• 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)

Page 19: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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=

Page 20: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

Biaxial plots are not a scalable solution

Parameters: 481432Plots: 62891496

Sean Bendall, Erin Simonds. Science, May 2011

Page 21: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

SPADE: Spanning-tree Progression Analysis of Density-normalized Events – Peng Qiu

1. Determine Tree Structure

1. Overlay regions with surface marker expression levels

Page 22: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

• 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

Page 23: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

Variable patterns of immature cell expansion in AML

CD45

Normal Normal

Cell Numbers

Page 24: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

Variable patterns of immature cell expansion in AML

Cell NumbersCell Numbers

Normal AML10 inv(16)

Page 25: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

Variable patterns of immature cell expansion in AML

Cell NumbersCell Numbers

Normal AML35 t(8;21)

Page 26: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

Variable patterns of immature cell expansion in AML

Cell NumbersCell Numbers

Normal APL3 t(15;17)

Page 27: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

Variable patterns of immature cell expansion in AML

Cell NumbersCell Numbers

Normal AML14 NK

Page 28: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 29: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 30: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

Normal Marrow

Fold CD33

AML27 (FLT3-ITD)

Fold CD33

Aberrant marker expression in AML

-Behbehani et al., Cancer Discovery, 2015

Page 31: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

Normal Marrow

Fold CD123

AML27 (FLT3-ITD)

Fold CD123

Aberrant marker expression in AML

Page 32: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 33: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 34: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 35: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 36: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 37: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

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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

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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

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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

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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)

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Subtype-specific high dimensional phenotypesNormal 1 Normal 3 Normal 4 Normal 5 Normal 6

tSNE-1

tSN

E-2

CD34

CD

38

Page 43: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 44: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 45: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

Grans

Monos

Normal #5

Mass Cytometry allows for the study of disrupted differentiation in MDS patients

-Behbehani et al., unpublished

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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

Page 47: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

Grans

Monos

Normal #5Low IPSS(MDS19)

Mass Cytometry allows for the study of disrupted differentiation in MDS patients

-Behbehani et al., unpublished

Page 48: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 49: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 50: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 51: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 52: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 53: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 54: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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?

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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

Page 56: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 57: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

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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

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Distinct patterns of proliferation in AML

S phase fractionS phase fraction

Normal AML10 inv(16)

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S phase fractionS phase fraction

Normal AML 35 t(8;21)

Distinct patterns of proliferation in AML

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S phase fractionS phase fraction

Normal APL 3 t(15;17)

Distinct patterns of proliferation in AML

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S phase fractionS phase fraction

Normal AML14 NK

Distinct patterns of proliferation in AML

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Cell cycle distribution is developmentally regulated

HSPC

MONO

GRAN

RBCB-CELL

B

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AML cells have a lower proliferative fraction but still follow developmental patterns

HSPC

MONO

GRAN

RBCB-CELL

B

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AML cells have a lower proliferative fraction but still follow developmental patterns

MONO

Page 66: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

AML cells have a lower proliferative fraction but still follow developmental patterns

GRAN

Page 67: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

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

Page 68: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

LSCs from CBF AML have a higher S phase fraction

HSPC

MONO

GRAN

RBCB-CELL

B

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LSCs from CBF AML have a higher S phase fraction

HSPC

B

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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

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AML and the stem cell hypothesis

• Does proliferative fraction correlate with clinical risk?

• What is the effect of FLT3-ITD mutation?

Page 72: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

AML and the stem cell hypothesis

• Does proliferative fraction correlate with clinical risk?

• What is the effect of FLT3-ITD mutation?

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LSCs from FLT3+ AML have a lower proliferative fraction

HSPC

MONO

GRAN

RBCB-CELL

B

Page 74: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

LSCs from FLT3+ AML have a lower proliferative fraction

HSPC

MONO

GRAN

RBCB-CELL

B

Page 75: Applications of single-cell mass cytometry in hematologic …plan.medone.co.kr/70_icksh2019/data/SS14-2_Gregory... · 2019-06-27 · Normal AML26 AML35 AML5 AML10 AML32 AML27 AML7

LSCs from FLT3+ AML have a lower proliferative fraction

HSPC

MONO

GRAN

RBCB-CELL

B

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LSCs from FLT3+ AML have a lower proliferative fraction

HSPC

B

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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

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In vivo chemotherapy response

• LSC proliferative fraction correlates known clinical risk

• How might the differences in cell cycle impact chemotherapy response?

cytarabine

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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

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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

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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

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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?

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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?

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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?

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Basal pSTAT5 is up-regulated in FLT3-ITD+ AMLIncreasedDecreased

HSPC

MONO

GRAN

RBC

B-CELL

B

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Basal pSTAT5 is up-regulated in FLT3-ITD+ AMLIncreasedDecreased

HSPC

MONO

GRAN

RBC

B-CELL

B

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pERK is up-regulated in almost all AML cell populationsIncreasedDecreased

HSPC

MONO

GRAN

RBC

B-CELL

B

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tSNE-1

tSN

E-2

Normal 4Normal 3 Normal 6Normal 5Normal 1

z = p-STAT5

Aberrant signaling correlates with aberrant immunophenotype

CD34

CD

38

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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

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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

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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

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Monitoring therapy response in rare cell subsets

Healthy Donor samples

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Monitoring therapy response in rare cell subsets

Healthy Donor samples

Day 0TKI Responding Patient

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Monitoring therapy response in rare cell subsets

Healthy Donor samples

Day 0 Day 14TKI Responding Patient

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Monitoring therapy response in rare cell subsets

Healthy Donor samples

Day 0 Day 14 Day 0 Day 14TKI Responding Patient Non-Responding Patient

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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

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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

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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

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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

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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

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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

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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

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Extra Slides

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TOF (Time of Flight)

ICP

Schematic of mass cytometry

Nebulizer – Single cell droplets

High-pass Filter

Ion Detector

-DVS Sciences / Fluidigm

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Distinct patterns of cell frequency across AML subtypes

HSPC

MONO

GRAN

RBCB-CELL

B

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Distinct patterns of cell frequency across AML subtypes

HSPC

MONO

GRAN

RBCB-CELL

B

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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

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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

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-Andrew Hughes, Gene Ther Mol Biol., 2006; 10:41

p-histone H3

Additional markers allow for complete cell cycle state assignment

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p-histone H3

Additional markers allow for complete cell cycle state assignment

p-R

b (S

807/

S811

)

Uridine

G0

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p-histone H3

Additional markers allow for complete cell cycle state assignment

p-histone H3

Uridine

Cyc

lin B

1

G0/G1

S

G2

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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

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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

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Distinct patterns of cell frequency across AML subtypes

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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HU treatment does not cause cell cycle arrest in vivo

HSPC

MONO

GRAN

RBCB-CELL

B

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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

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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

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Mass Cytometry allows for the study of disrupted differentiation in MDS patients

Normal (Nl-6)

-Behbehani et al., unpublished

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Mass Cytometry allows for the study of disrupted differentiation in MDS patients

Normal (Nl-6)

-Behbehani et al., unpublished

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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

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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

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Mass Cytometry allows for the study of disrupted differentiation in MDS patients

-Behbehani et al., unpublished

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Mass Cytometry allows for the study of disrupted differentiation in MDS patients

-Behbehani et al., unpublished

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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

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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

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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%

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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

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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

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HU treatment has little effect on S phase fraction in vivo

HSPC

MONO

GRAN

RBCB-CELL

B

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HU treatment has little effect on S phase fraction in vivo

HSPC

MONO

GRAN

RBCB-CELL

B

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HU treatment has little effect on S phase fraction in vivo

HSPC

MONO

GRAN

RBCB-CELL

B

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HU treatment causes a large decrease in IdU incorporation into S phase cells

HSPC

MONO

GRAN

RBCB-CELL

B

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p4EBP1 is down-regulated in almost all AML cell populationsIncreasedDecreased

HSPC

MONO

GRAN

RBC

B-CELL

B