applications of single-cell mass cytometry in hematologic...

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

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

T cells

• 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-Kim Le, ICCS, Ask an expert -Lyberger et al., unpublished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

Blasts

Normal MarrowClinical Cytometry

Blasts

Mass Cytometry

Normal Marrow

Blasts

Mass Cytometry

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)

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

High parameter data allows visualization of hematopoiesis

Normal

Variable patterns of immature cell expansion in AML

Normal Normal

Cell Numbers

Cell NumbersCell Numbers

Normal AML10 inv(16)

Normal AML35 t(8;21)

Normal APL3 t(15;17)

Normal AML14 NK

Distinct patterns of cell frequency across AML subtypes

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)

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

SampleSummed

Aberrancies

AML23 1AML13 0AML42 1AML30 1Repeat for all 25 surface markers

and sum aberrancies

SampleSummed

Aberrancies

Repeat for all 25 surface markers

and sum aberrancies

SampleSummed

Aberrancies

Normal AML27 AML30

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

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

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

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

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

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

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

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

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

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

NK-FLT3wt

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

Normal

• Aberrancy persists into mature cells

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

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

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

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

NK-FLT3wt

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

Normal

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

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

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

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

NK-FLT3wt

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

Normal

• 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

FLT3wtNK-AML

AdverseKaryotype

AMLn=6

MLL-rearranged

AMLn=1

CD33 Expression on HSPCs (CD34+CD38low)

Karyotype- and genotype-specific immunophenotypic aberrancies in AML

Normaln=14 (5)

CBFAMLn=5

APLn=4

(FLT3+)

FLT3-ITDNK-AML

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

AML10 - inv(16) AML32 - inv(16) AML26 – t(8;21)AML5 – t(8;21) AML35 – t(8;21)

tSNE-1

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

Normal #5

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

-Behbehani et al., unpublished

Normal #5

Mature Granulocytes

Mature Monocytes

Normal #5Low IPSS(MDS19)

Int-1 IPSS(MDS23)

sAML(MDS17)

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

Intercalator Ir193

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)

Normal AML 35 t(8;21)

Normal APL 3 t(15;17)

Normal AML14 NK

Cell cycle distribution is developmentally regulated

RBCB-CELL

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

RBCB-CELL

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

Is proliferative fraction sub-type specific?CBF AML?

RBCB-CELL

LSCs from CBF AML have a higher S phase fraction

RBCB-CELL

LSCs from CBF AML have a higher S phase fraction

p=0.0019

p=0.00071

p=0.0069

p=0.0019

p=0.10

• Does proliferative fraction correlate with clinical risk?

• What is the effect of FLT3-ITD mutation?

• Does proliferative fraction correlate with clinical risk?

• What is the effect of FLT3-ITD mutation?

LSCs from FLT3+ AML have a lower proliferative fraction

RBCB-CELL

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

Treatment stratification by cell cycle?

4.5% 2.4%

2.2%3.4%

-Persistent LSCs have reduced S-phase after treatment

AML Day 14

4.5% 2.4%

2.2%3.4%

-Persistent LSCs have reduced S-phase after treatment

AML Day 14

4.5% 2.4%

2.2%3.4%

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?

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

B-CELL

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

B-CELL

pERK is up-regulated in almost all AML cell populationsIncreasedDecreased

B-CELL

tSNE-1

Normal 4Normal 3 Normal 6Normal 5Normal 1

z = p-STAT5

Aberrant signaling correlates with aberrant immunophenotype

tSNE-1

z = p-STAT5

AML26 – t(8;21) AML35 – t(8;21)

tSNE-1

AML22 – t(10;11)

z = p-STAT5

AML26 – t(8;21) AML35 – t(8;21)

tSNE-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)

Monitoring therapy response in rare cell subsets

Healthy Donor samples

Day 0TKI Responding Patient

Day 0 Day 14TKI Responding Patient

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

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)

Schematic of mass cytometry

Nebulizer – Single cell droplets

High-pass Filter

Ion Detector

-DVS Sciences / Fluidigm

Occurs at a rate of ~1000 cells per second

Ion Detector

High-pass Filter

Ion Detector

High-pass Filter

Ion Detector

High-pass Filter

Ion Detector

High-pass Filter

Progenitor cells

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

High-dimensional analysis of an AML cohortPatient Characteristics

2x 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

2x 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)

RBCB-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

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

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

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

NK-FLT3wt

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

Normal

last Pr

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

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

p-histone H3

Additional markers allow for complete cell cycle state assignment

p-histone H3

Uridine

p-histone H3

Uridine

p-histone H3

Uridine

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

Normal (Nl-6)

MDS-23 RCMD, IPSS=0.5/Int-1

Normal (Nl-6)

MDS-3 RAEB-1, IPSS=1.5/Int-2

Normal (Nl-6)

MDS-17 RAEB-T/AML, IPSS=High

Normal (Nl-6)

MDS-3 RAEB-1, IPSS=1.5/Int-2

Normal (Nl-6)

MDS-17 RAEB-T/AML, IPSS=High

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

HU treatment does not cause cell cycle arrest in vivo

RBCB-CELL

• Granulocyte differentiation can be visualized by gating out other cell lineages

Normal (Nl-6)

• Granulocyte differentiation can be visualized by gating out other cell lineages

Normal (Nl-6)

• Monocyte differentiation can be visualized by gating out other cell lineages

Normal (Nl-6)

• Monocyte differentiation can be visualized by gating out other cell lineages

NormalLow riskInt-1Int-2High

CD34 included in clustering CD34 excluded from clustering

Secondary clustering of SPADE clusters groups patients by clinical risk

Normal

Leukemia

Normal #4Normal #3 Normal #6

APL #3 APL #5 AML #22(MLL-rearrangement)CD33

Markers of LSCs may also be subtype-specific

Normal

Leukemia

Normal #4Normal #3 Normal #6

APL #3 APL #5 AML #22(MLL-rearrangement)CD33

Markers of LSCs may also be subtype-specific

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

RBCB-CELL

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

RBCB-CELL

p4EBP1 is down-regulated in almost all AML cell populationsIncreasedDecreased

B-CELL

applications of single-cell mass cytometry in hematologic...

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