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Webinar Series

Advancing precision medicine through multi-omicsAn integrated approach to tumor profilingSeptember 16, 2015

Sponsored by:

Participating ExpertsBrought to you by the Science/AAAS Custom Publishing Office Giovanni Martinelli, M.D.

University of BolognaItaly

Nicholas Foreman, M.B.Ch.B.Children’s Hospital Colorado Aurora, CO

Webinar Series


Sponsored by:

Giovanni Martinelli, MDInstitute of Hematology and Medical Oncology “L.e A. Seragnoli”

University of Bologna, Italy

Technology Webinars

Advancing precision medicine through multi-omics:An integrated approach to tumor profiling

Wednesday, September 16, 201512:00 pm ET

AIM of webinar• While technological breakthroughs of the last decade have greatly

advanced our understanding of the cancer genome, they have alsorevealed the immense complexity and heterogeneity of tumors.

• The key to further advancing our understanding and treatment ofcancer is to distinguish between genes and pathways that drivetumorigenesis (driver events) and neutral, passenger events.

• A growing body of evidence has shown that not all DNA‐levelalterations have biological implications, making it necessary toexamine multiple ꞌomics levels including the transcriptome andproteome, to identify critical driver events.

• We will discuss how using a multi‐omics–based approach toleukemia research can advance our understanding of leukemiabiology as well as lead to the discovery of novel biomarkers.

Background

• For instance, the genetic hallmark of APL is the t(15;17) (detected by karyotype) resulting in the fusion of the promyelocytic leukemia (PML) gene and retinoic acid receptor α (RARα) gene (PML‐RARα) (detected by RT‐PCR).

• PML‐RARα is necessary but not sufficient for the development of APL Are additional cooperating genetic events also required for its pathogenesis?

• The development of single nucleotide polymorphism (SNP) Cyto ScanHD®‐arrays now allows to perform genome‐wide screens for submicroscopic genomic alterations with unprecedented informativity and to map all the genes involved in these alterations.

Aim and methodsTo explore, in the clinical content, the potential of SNP array for a high‐resolution screening of additional submicroscopic genomic alterations which characterize APL and may be used to better classify genomic subsets.

Nexus Copy NumberTM 7.0PARTEK Genomics SuiteGenotyping Console 3.1 softwaredCHIP

Genome‐Wide Human SNP Assay CytoScan®HD Array, Affymetrix

(1.85 million SNPs; median physical distance

between SNPs: 700 bp)

Genomic DNA from bone marrow

mononuclear cells

Leukemia cases in remission(paired and unpaired analysis)

COPY NUMBER ALTERATIONS

SNP ARRAY: by the new potent CytoScan® HD* Array Affymetrix

CytoScan®HD ARRAY 2.67 milions di markers 750,000 SNP probes 1.9 milioni non‐polymorphic probes •100% Sanger cancer gene coverage•100% ICCG constitutional gene coverage •12,000 OMIM® genes•36,000 RefSeq genes

FDA version approved for diagnostic use

*for research use only

Patients Variable n Patients (number) 105Median age, yrs (range) 50 (18‐84)Male/Female 64/41De novo AML

M0M1M2M3M4M5Bi‐lin

86 (82%)6 (7%)11 (13%)11 (13%)28 (33%)14 (16%)15 (17%)1 (1%)

Secondary AML 19 (28%)Cytogenetics

NormalComplex*t(15;17)t(8;21)inv(3)inv(16)other**+8NA***

35 (33%)8 (8%)28 (27%)3 (3%)3 (3%)1 (1%)22 (21%)2 (2%)3 (3%)

* Presence of at least 3 chr abnormalities in the absence of t(8;21), inv(16)/t(16;16), t(15;17), and t(11q23); ** rare traslocations; *** NA: not available

Principal Component Analysis (PCA)

Karyotype

• NA• Normal• Complex• inv(3)• inv(16)• t(15;17)• t(8;21)• other

APL

PCA showed an evident separation between APL and other AML subtypes→ a peculiar genome profile characterize APL patients

Results I

From Partek Genomics Suite

Identification of multiple copy numberalterations

A wide spectrum of different genetic lesions (gains/losses) involving completechromosome arms or submicroscopic genomic intervals were identified in allcases.

No significant difference in the average number of alterations was detectedamong different karyotpye subgroups, except for complex karyotype group.

Karyotype Average CNAs* (n) Range

Normal 14 3‐44

Complex 55 35‐92

t(15;17) 8 1‐24

t(8;21) 12 6‐16

Trisomy 8 5.5 5‐6

Others 9 6‐18

p < 0.01

* CNAs: Copy Number Alterations; they including both deletions and amplifications

Alteration= region which has a copy number state lower or higher than 2. At least 5 probesets have been considered.

Results IIMacroscopic alterations in APL (>1.5 Mbp)

+8q‐6 loss

gain

For each type of aberration, each line represents a different case (from Affymetrix Genotyping Console v3.1).

Trisomy 8

‐20q

Microscopic alterations (<1.5 Mb)

Chr Type ofCNA

Median size(Kbps) # samples Candidate genes* Function (GO**)

1q23.3 gain 258 1 LMX1A Transcription factor

6q25.1 loss 202 2 AKAP12 Signal transduction

7q11.23 loss 224 3 MLXIPL, BCL7 Negative regulation oftranscription

8q24.21 gain 1,907 3 PVT1, MYC Cell cycle progression

11q23.1 gain 238 1 NCAM1 Cell adhesion

12q24.12 loss 226 1 ALDH2, BRAP, MAPKAPK5 Negative regulation of signalingtansduction/MAP Kinase activity

**GO: Gene Ontology* After comparison with the Database of Genomic Variants (http://projects.tcag.ca/variation/ )

Overexpression of the PVT1 oncogene

Pts with gain of 8q24 Pts with normal 8q24

p<0.0001

Results V

CytoScan® HD Array Affymetrix SNP arrays allow classification of Acute Promyelocitic Leukemia genomic

subgroups 1. No additional chromosomalabnormalities and low burden ofCNAs

3. Additional chromosomalabnormalities and high numberof CNAs

vs

2. Additional chromosomalabnormalities and low burden ofCNAs

Results VCopy number alterations (CNAs) worsen outcome

ACA: Additional chromosomal abnormalities

ACA+ CNAs > 10 (Group III)

(Group I + Group II)

Event Free Survival Time (months)

Stratification according to additional chromosomal abnormalities and a high number ofCNAs is associated with a highly significant shorter event‐free survival

Deletoma and Mutations in AML patientsWe may construct the “deletoma” of most recurrent deleted region in AML genome and combine with point mutations of gene included into the deleted region, pointing the suspect on potential oncogene

0

10

20

30

40

50

CDKN2A CDKN2B ANRIL

Diagnosis

Relapse

29%

47%

24%29%

40%

43%

%

P = 0.06

P = ns

An almost significant increase in the detection rate of CDKN2A loss(47%) was found at the relapse compared to diagnosis (p = 0.06).

Iacobucci I et al, Clin Cancer Res 2011 Iacobucci I et Al. PLos1 2013

Other Hematological Malignancies: CDKN2A/B deletions in 112 adult Ph+ ALL patients

SNP CytoScan HDSNPS array analysis and GEP profiling

CDKN2A/ARF loss and outcome

Months since CR

p=0.0033

Disease Free Survival according to CDKN2A deletion

CDKN2A wt : 55% (C.I. 95%: 47.3‐64.1)

CDKN2A del: 22.2% (C.I. 95%: 18.8‐26.3)

Months since CR

Cumulative Incidence of Relapse according to CDKN2A deletion

p=0.0043

CDKN2A del: 73.3% (C.I. 95%: 71.6‐75.1)

CDKN2A wt : 40.4% (C.I. 95%: 39.3‐41.6)

Deletions of CDKN2A/ARF are significantly associated with poor outcome both in terms of disease free‐survival and cumulative incidence of relapse.

Istituto “Seragnoli” Clin Cancer Research 2011

SNP ARRAY: AMPLIFICATION OF LONG ARM OF CHROMOSOME 1

ID 343 CHROMOSOME 1

Istituto di Ematologia “L. e A. Seràgnoli”

Shaughnessy J, Hematology 2005: Amplification and overexpression of CKS1B at chromosome band 1q21 is associated with reduced levels of p27 Kip1 and an aggressive clinical course in multiple myeloma

… See what’s been missing

Gene MDM4 1q32.1: 3NGene CKS1B 1q21.3: 2N

ID 293CHROMOSOME 1


Leone PE, Clin Canc Res 2008

Gene CDKN2C 1p32.3: 2N Gene FAM46C 1p12: 1N

SNP ARRAY: DELETIONS OF SHORT ARM OF CHROMOSOME 1… See what’s been missing

SNP ARRAY: DELETION OF SHORT ARM OF CHROMOSOME 17

Chr 17: deletion of gene including TP53

Not ALL the patients have the same quantity of p53!

LOH

DEL

AMP

TP53

MUTATION and + ALLELIC Burden

Mutational ANALYSIS OF p53 status

… See what’s been missing

CHROMOTHRIPSIS is frequently associated to p53 structural alteration


Tubio J, Nature 2011

Single CATASTROFIC EVENT with breakageof multiple sites of the region of the chromosome

• Rearrangements• Deletions

CHROMOTHRIPSIS by SNP ARRAY in MM: CROMOSOMA 16

Marina Martello et Al. Unpublished Istituto di Ematologia “L. e A. Seràgnoli”

FRAGILE SITE FRA16D

Analysis of genes involved in chromosomal translocations

Comparison between AML and ALL subtypes: aneuploidy vs.

euploidy

2. How does this approach enable the better identification and validation of actionable biomarkers, translating these into clinicalutility and personalized therapy

Use of Human Transcriptome ®Array

>285,000 full-length transcripts covered:n >245,000 coding transcriptsn >40,000 non-coding transcriptsn >339,000 probe sets covering exon-exon junctions

Probes designed to maximize exon coverage enable you tomeasure all transcript isoforms

Confidence in your results:n Reproducible: Intra-lot correlation coefficient ≥0.99n <6.5% coefficient of variation observed for all tissues tested

Minimum total RNA required: 50 ng

GeneChip® Human Transcriptome Array 2.0

Use of Affymetrix Arrays (Human Transcriptome Array -HTA and Cytoscan HD Array)

in hematological malignancy studies

Gene expression analysis

Benefits of GeneChip Human Transcriptome Array 2.0:

The extent of IKZF1 deletions correlated with the expression of dominant‐negative or untranslated Ikaros isoforms

Deletion

ex1 ex2 ex3 ex4 ex5 ex6 ex7 ex8

5’ 3’

Δ4‐7 deletion (65%)

Δ2‐7 deletion (30%)

Deletion

ex1 ex8

5’ 3’

ex2 ex3 ex4 ex5 ex6 ex7

Dominant-negative Ik6 isoform

Untranslated isoform

Iacobucci I et al, Blood 2009

IKZF1 Deletions Are Associated With High Rate Of Cumulative Incidence of Relapse and with Short Disease Free Survival

Martinelli G, Iacobucci I, et al JCO 2009

Ph+ ALL

Acute Myeloid Leukemia: Samples characteristics

Cytogenetics abnormalities - no.

Normal karyotype 27/50One-two abnormalities 6/50Monosomal karyotype 5/50Complex karyotype 4/50Other abnormalities 8/50

Rare translocations

Sample FISH RNAseq

# 20 t(6;17)(p21;q11) STK38 (chr 6p21) – PSMD11 (chr 17q11)RPL7L1 (chr 6p21) – BC062794 (chr 17q12)

# 59810 t(2;14) t(11;12)

ZEB2 (chr 2q22) – BCL11B (chr 14q)FAM128A/B (chr 2q21) – CDC42BPB (chr 14q32)

ANO3 (chr 11p14) – CORO1C (chr 12q24)AL049692.1 (chr 11p13) – CNOT2 (chr 12q15)

HINFP (chr 11q23) – RSRC2 (chr 12q24)NUMA1 (chr 11q13) – SLC35E3 (chr 12q15)

FEZ1 (chr 11q24) – TAOK3 (chr 12q24)WT1 (chr 11p13) – CNOT2 (chr 12q15)

# 21t(3;12)(p22;q24)

monosomal karyotype

LIPH (chr 3q27) – PCBP2 (chr 12q13)NICN1 (chr 3p21) – SPATS2 (chr 12q13)

Simonetti G.

Reference group

Increased expression of ZEB2, BCL11B, NUMA1 and HINFP upon gene fusions

GENES

0.87 1.00 1.05 1.06 1.18 1.30 1.36 1.48 1.52 1.63 1.75 1.791.99

2.36

3.91

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Fold

diff

eren

ce(R

NA

seq

sam

ple/

ref s

ampl

es)

Simonetti G. et al. Unpublished personal communication

14q32

2q22

Improving the diagnostics of leukemia by Affymetrix arrays

PHARMACOGENOMICSDMET

GENE EXPRESSION

COPY NUMBERCytoscan HD

Gene Chip/Transcriptome

Methods

DNA processing and genotype identification for each patient sample were performed using the Affymetrix Drug‐Metabolizing Enzyme and Transport (DMET Plus) platform.

Recommended by Key Opinion Leaders in Industry and Academia – 147 markers

DMET Core – 169 markers

Features markers for all FDA validated genes

DMET Extended

Supplemental ADME – 1,621 markers

1,936 markers in 225 genes

Results II Association among SNPs and response to the Induction Chemotherapy Cycle 1 (FLAI + GO)

1,936 markers

DMET PLUS 2

AML responderPatients (88%)

AML non‐responderpatients (12%)

1 2 3 4 5 6 7 8 9

ADH1A

‐224,712 G > A(rs1826909)

‐201,946 T > C(rs6811453)

4q23

Conclusions

• Despite the overall number of lesions across the patient cohort, novel regions of micro and macro genetic alteration were identified in de novo AML‐ALL‐MM patients by Gene CHIP Human Transcriptome® 2.0 array and by Cyto ScanHD®‐arrays : • Molecular karyotyping is easy obtained• Copy neutral loss of heterozygousity (CN‐LOH) are friendly

analysed• Chromothripsis could be identified• New expressed biomarkers (Bcr‐ABL like) for target leukemia

therapyThe identification of additional cytogenetic abnormalities and a high

number of microscopic genetic alterations allows to define subgroups with worse prognosis, and rapidly stratify these patients to individualized, personalized leukemia therapy.

Institute of Hematology “L. and A. Seràgnoli”, Bologna

Acknowledgments

Supported by: FP7, European LeukemiaNet, AIL, AIRC, FIRB 2006, Fondazione del Monte di Bologna e Ravenna

Emanuela Ottaviani, Antonella Padella, Viviana Guadagnuolo, Giorgia Simonetti, Stefania Paolini, Anna Ferrari, ValentinaRobustelli, Carolina Terragna, Marina Martello, Ilaria Iacobucci, Andrea Luserna di Rora Margherita Perricone, Maria Chiara Fontana, Marco Manfrini, Michele Cavo, Daniel Remondini, Italo Do Valle, Elisa Zuffa, Nicoletta Testoni, Carmen Baldazzi, Sarah Parisi, Maria Chiara Abbenante, Chiara Sartor, Giovanni Marconi, Simona Soverini, Caterina De Benedittis, Emanuela Mancini, Gabriele Galli, Eugenia Franchini, Maria Teresa Bocchicchio, Enrica Imbrogno, Cristina Papayannidis.

Participating ExpertsBrought to you by the Science/AAAS Custom Publishing Office Giovanni Martinelli, M.D.

University of BolognaItaly


Webinar Series


Sponsored by:

Development of Immunotherapy for Pediatric

Ependymoma

Correlation of outcome with gene expression microarray data = Unbiased identification of

prognostic factors • Surgical tumor samples used from initial

presentation of EPN in patients that either recurred or did not recur– All patients were treated uniformly, receiving complete

resection and radiation.

• mRNA extracted from surgical tumor specimens and applied to Affymetrix HG-U133-plus2 microarray chips– mRNA Expression profiles for ~18,000 genes created

for each sample

Identification and functional grouping of outcome-associated genes

1) Identification of outcome associated genes:A) Differentially expressed (>2-fold; p<0.05) between non-

recurrent (n=9) and recurrent (n=10) phenotypesB) Correlated (p<0.05) with time to progression as a continuous

variable in those EPN that recurred (n=10). The median time to progression was 24 months (range 1 to 51 months)

2) Ontological analysis of outcome associated genes to unravel functional and biological content

A) GSEA (Gene Set Enrichment Analysis). Downloadable from The Broad Institute, MIT. http://www.broadinstitute.org/gsea

B) DAVID (Database for Annotation, Visualization and Integrated Discovery). Online resource http://david.abcc.ncifcrf.gov

Genes associated with both the non-recurrent phenotype and positively correlated with time to

progression are almost entirely immune-function related

*

*no documented immune function

Immune functions of these genes include:

• Innate immune response– complement activity – macrophage activity

• Adaptive immune response– phagocytosis of antibody

coated cells– antigen presentation– lymphocyte activation– lymphocyte tethering and

rolling

• Success of Phase III trials of GD2-therapeutic antibody in childhood neuroblastoma• Response largely dependent on antibody dependent cell-dependent cytotoxicity (ADCC)• Combination of therapeutic antibody (TxAb) with GM-CSF and IL-2 critical for success

• EPN are infiltrated with large numbers of microglia/macrophages that express CD64• Receptor for Fc-region of antibody• Facilitates clearance of antibody bound pathogens (or tumor) by phagocytic or cytotoxic cells• EPN infiltrating microglia therefore primed for ADCC

AIF1

Rationale for therapeutic antibody as immunotherapeutic strategy

% tumor infiltrating microglia % CD64 positive microgliaEPN infiltrating microglia

3 molecular subgroups of EPN

0 25 50 75 100 125 150 175 2000

25

50

75

100 Group B (n=17)

Group A (n=14)

p=0.02HR=7.7

OS (months)

Perc

ent s

urvi

val

0 50 1000

25

50

75

100

PFS (months)

Perc

ent

0 25 50 75 100 1250

25

50

75

100

PFS (months)

Perc

ent s

urvi

val

Overall survival

Diagnosis to 1st recurrence

1st to 2nd recurrence

Hoffman et.al. Acta Neuropath 2014

Copy Number Variation Differs in Primary Groups A & B

Group A Group B


A B

CFigure 4: T-cell inhibitorypathways are activated inGroup A relative to Group BEPN . (A) flow cytometricmeasurement of PD1 proteinexpression on tumorinfiltrating CD4 and CD8 T-cells in GpA and B EPN. (B)whole tumor mRNAmicroarray levels of PD1-related T-cell exhaustionmarkers (p-values: *<0.05,**<0.01, ***<0.005) (C) T-cellinhibitory mRNA markersmicroarray levels in isolatedcellular subpopulations (tum=tumor, myld = myeloid)

CD4 CD80

50

100

150

200GpA (n=7)GpB (n=7)

*re

lativ

e PD

1 ex

pres

sion

PD-L

1

TIM

3

IL10

RA

IL10

RB

Blim

p-1

BA

TF20

2

4

6

8

10GpA (n=21) GpB (n=20)

*

*

**

*

***

***

rela

tive

mRN

A ex

pres

sion

GpA GpBtum myld T‐cell tum myld T‐cell

relativ

e RN

A expression

2.4 5.8 10.2 2.4 4.4 2.4 CTLA43.4 2.6 9.1 3.3 3.4 3.5 PD‐L12.3 2.3 5.1 2.3 3.1 2.3 PD‐L22.3 2.3 7.7 2.3 3.6 2.3 CD2444.1 12.3 10.7 2.3 12.1 2.7 TIM32.4 2.4 7.8 2.4 2.4 2.5 FASLG2.5 7.9 9.1 2.4 6.3 6.2 TRAIL2.9 5.8 6.7 2.9 9.4 2.9 TRAIL‐R12.4 2.4 4.3 2.4 2.7 2.4 IL102.6 7.6 8.5 2.4 8.9 4.5 IL10RA6.1 5.2 7.8 2.9 8.5 4.3 IL10RB2.7 7.9 10.5 2.7 8.0 2.7 Blimp‐18.9 10.9 8.1 4.0 10.0 2.6 EGR2

Group A-infiltrating CD4 T-cells at diagnosis demonstrate impaired cytokine release

Tumor-infiltrating CD4 and CD8 T-cells are more prevalent in Group B at recurrence


STAT3 gene set is significantly enriched in Group A

IL-6IL-8CHI3L1 (YKL-40)CCL2SOCS3PTGS2 (COX2)

Notable STAT3 up-regulated genes, with FC>10 in Group A

STAT3 signaling has pro-survival role

Metabolic Activity(MTS)

DNA Synthesis(H3 Incorporation)

Apoptosis(cleaved Caspase 3/7)

Group A EPN tumor cells secrete significantly more IL-6 than Group B tumor cells which correlated with STAT3

activation.

IL-6 secretion from flow sorted patient tumors after 48hr incubation

Ratio of pSTAT3:Total STAT3 whole tumor lysate

EPN-secreted IL-6 induces CD14+ monocytes to secrete key pro-inflammatory

cytokine IL-8.

Griesinger et.al Cancer Immunology Research, under review

IL-8-mediated signaling between monocytes perpetuates inflammatory

signaling in the tumor microenvironment

Griesinger et.al Cancer Immunology Research, under review

• Screened transcriptomic database of pediatric brain tumors and normal brain for targets of FDA approved therapeutic antibodies

• ERBB2 was the top hit – targeted by trastuzumab (Herceptin®)

• ERBB2 previously shown to be overexpressed in EPN (Gilbertson et al. CCR, 2002)

Selection of EPN-targeted therapeutic antibody

target therapeutic AbERBB2 trastuzumab

VEGFC bevacizumab

MUC1 cantuzumab

VEGFA bevacizumab

CA9 girentuximab

VIM Pritumumab

EGFR Cetuximab

TRAIL-R Conatumumab

FAP Sibrotuzumab

EPN NORM. HGG LGG MED

high lowgene expression

ERBB2 mRNAFDA-approved therapeutic antibody screen

0 4 8 12

0

50

100

no PBMCtumor and PBMC

GM-CSF onlytras only

tras+GMtras+GM+IL-2

Time (hrs)

clea

ved

casp

ase-

3/7

(cou

nts

per m

m2 )

Preclinical testing of trastuzumab: in-vitro

Pilot clinical study: trastuzumab and immunostimulant combinatorial therapy in recurrent EPN

• Repeat surgery is standard of care for recurrent EPN - provides opportunity to examine treatment effects directly in tumor samples

• Proposed strategy:

• Study 1. Impact of GM-CSF on microglia/macrophage and T-cell activation status

• Study 2. Add trastuzumab through intrathecal delivery into the CNS

• Study 3. Add immunomodulatory treament

• Reversal of IL6-STAT3-IL8 inflammatory phenotype in Group A

• Reversal of T-cell immunosuppression: PD-1 or CTLA4 inhibitor

Pilot clinical study: GM-CSF in recurrent EPN • GM-CSF delivered intravenously to patients daily for 5 days prior to surgery

4 patients currently treated

• Tumor sample analyzed by gene expression microarray to measure GM-CSF treatment effects

• This geneset includes a number of genes shown to correlate with improved survival in prior studies (Donson et al. J Immunol, 2009)

Genes upregulated at recurrence after GM-CSF pre-treatment (n=4)1 Antigen processing and presentation2 Antigen processing and presentation … via MHC class II3 Nucleoside metabolic process4 Induction of apoptosis5 Induction of programmed cell death6 Regulation of protein transport7 Ribonucleoside metabolic process8 Antigen processing and presentation of peptide antigen9 Regulation of establishment of protein localization

10 Negative regulation of anti-apoptosis

Genes upregulated at recurrence in untreated patients (n=13)1 Regulation of neurotransmitter levels2 Cell adhesion3 Biological adhesion4 Transmission of nerve impulse5 Neurotransmitter secretion6 Synaptic transmission

Benjamini false discovery rate <0.05H

LA-D

PA1

HLA

-DQ

A1

PSM

B9

HLA

-DM

A

HLA

-DR

B1

TAPB

PL

IFI3

0

MR

1

ERA

P1

PSM

B8

HFE

FCG

RT

HLA

-F

-1

0

1

2

3

4

GM-CSF treated (n=4)untreated (n=13)

**

**

* *

*

** *

* * **

*

error bar = SEM** p<0.01* p<0.05

mRN

A fo

ld-in

crea

se (l

og 2

)

AcknowledgmentsNick Foreman LabAndrew DonsonDiane BirksVladimir AmaniLindsey HoffmanAndrea Griesinger

Rajeev Vibhakar LabSujatha VenkataramanEric PrinceIrina AlimovaIlango BalakrishnanAngela Pierce

Jean Mulcahy Levy LabShadi Zahedi

FundingNIH R01 CA140614Morgan Adams FoundationTanner Seebaum Foundation

Phil Reigan LabSteffanie FurtekChris Matheson

CollaboratorsKarim El KasmiHideho Okada, UCSF

Cancer Center Microarray Core and Flow Cytometry Core

Participating ExpertsBrought to you by the Science/AAAS Custom Publishing Office To submit your

questions, click theAsk a Question

button

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Sponsored by:

Giovanni Martinelli, M.D.University of BolognaItaly


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