Integrative Genomic Approaches to Identify Biomarkers and Therapeutic Targets in NF1

Walter Jessen Integrative genomic approaches to identify biomarkers and therapeutic targets in [email protected]

Integrative genomic approaches to identify biomarkers and therapeutic targets in NF1

Walter J. Jessen, Ph.D.Cincinnati Children’s Hospital


“Any living cell carries with it the experiences of a billion years of experimentation by its ancestors.”

Max Delbrück, theoretical physicist


Principlethe whole is greater than the sum of its parts

Research goal

‣ develop and apply integrative genomic approaches to better organize and evaluate high-throughput genomic data

‣ effectively interpret the results and achieve a greater understanding of the signals and mechanisms regulating disease development and progression


Neurofibromatosis (NF)

‣ Set of autosomal dominant genetic disorders of the nervous system that cause tumors to form on peripheral nerves

‣ Approximately 50% of those affected have a prior family history of NF

‣ The other 50% are a result of spontaneous genetic mutation

‣ Although most tumors are benign, can cause serious morbidity

Two major forms of NF:

- Type 1 (NF1) von Recklinghausen NF or Peripheral NF‣ Most common hereditary tumor predisposition syndrome‣ Occurs in 1:3500 births‣ Tumors (neurofibromas) form on peripheral nerves

- Type 2 (NF2) Bilateral Acoustic NF‣ Occurs in 1:40,000 births‣ Tumors (schwannomas) form on cranial and spinal nerves

Malignant peripheral nerve sheath tumors (MPNST)‣ Highly aggressive soft tissue sarcomas‣ Localized recurrence, chemo-resistance, frequent metastasis‣ Median age of onset: 26, five year survival: 34%


NF1 tumor subtypes

25%

10 – 13%

No effective treatments exist for either neurofibroma or MPNST

Dermal neurofibromas (dNF)‣ Tumors that appear as multiple, firm rubbery

bumps of varying size on the skin‣ Benign, but a significant source of morbidity

Plexiform neurofibromas (pNF)‣ Associated with major nerve trunks‣ Expand within the perineurium to displace

surrounding tissue‣ Capable of becoming malignant

% patients affected

95%


NF1 tumor composition is complex

Neurofibroma

Nerve

Tumors contain all the cell types of normal peripheral nerves, including:

Schwann cells are the pathogenic cell type in peripheral nerve tumors

Lines of evidence

1. LOH is observed in NF1-derived Schwann cells but not fibroblasts2. NF1-derived Schwann cells are invasive3. Mice with Schwann cell lineage-specific ablation of Nf1 develop tumors

‣ Schwann cells (ensheath axons)‣ Fibroblasts (give rise to connective tissue)‣ Mast cells (wound healing)‣ Axons (nerve fiber)

Two types of Schwann cells


Corfas et al., Mechanisms and roles of axon-Schwann cell interactions. J Neurosci. 2004 Oct 20;24(42):9250-60.

A. Myelinated Axon B. Unmyelinated Axons

S Schwann cell nucleus

Ax Axon

Ax

S

Myelin: electrically insulating material (glycolipd and protein) produced by Schwann cells that ensheathes axons; increases speed of electrical impulses

Ax

S

myelin sheath


NF1 encodes neurofibromin, a GTPase activating protein

Reduced NF1 expression results in increased Ras activation

Downward J, Cancer: A tumour gene's fatal flaws. Nature. 2009 Nov 5;462(7269):44-5.

RAS activation stimulates downstream signaling pathways


Very little is known about the pathways that drive NF1 tumor progression

Normal Schwann cell

Dermal neurofibroma

Plexiform neurofibromaNF1-associated

MPNST

NF1 mutation

H-, K-, N-Ras

PDGFRA

EGFR

KIT

cAMP

S6kinase

p53

Rb

CDKN2A (p16)

CDKN2D(p19)

Transformation

Growth factorreceptors

Tumor suppressorgenesSignaling

Benign Malignant

INTEGRATE


Research objectives: 1.Gain insight into the biological pathways and processes that drive

NF1 tumor formation and transformation2. Identify molecular differences between tumor subtypes

- dermal vs. plexiform - benign vs. malignant

3.Provide candidate genes for diagnostics and treatment strategies

Hypothesis: purified Schwann cells from NF1 tumors will continue to express tumor gene programs in culture

Human cell culture, human tumor, mouse Schwann cell development

Study: Biological pathways that drive NF1 tumor progression


Profiling gene expression using Affymetrix DNA microarrays

DNA microarray technology:

Enables researchersto simultaneously survey the expressionof a large number of genes.

Microarray or GeneChip:

A tool used to analyzegene expression,consisting of a small glass slide containingsamples of many genes arranged in a regular pattern.

Samples:

Sets of probes which represent gene transcripts


What is clustering?

‣ Technique used to group similar genes and samples together

‣ Allows for the identification of potentially meaningful relationships

‣ Genes that have similar patterns of expression are grouped together in clusters

‣ Cluster genes are likely to be co-regulated or part of the same biological process or pathway

‣ Statistics are used to identify over-representation or enrichment of biological processes or pathways in gene clusters

Analyze gene expression data from DNA microarrays by clustering


Two common types of clustering methods

‣ Hierarchical clustering: subdivides each cluster into smaller clusters, forming a dendrogram (tree-shaped data structure)

Algorithm summary1. Place all points into their own clusters2. While there is more than one cluster, merge the closest pair of clusters

Weakness: doesn’t really produce clusters, user must decide where to split the tree into groups

‣ K-means clustering: subdivides data into a predetermined number of clusters without any implied hierarchical relationship between clusters

Algorithm summary 1. Assign all points to a cluster at random2. Repeat until stable:

a. Compute the centroid for each clusterb. Reassign each point to the nearest centroid

Weakness: must choose k parameter in advance; sensitive to outliers, which can distort centroid positions

➡ Comparative studies have shown that K-means outperforms hierarchical clustering on expression data(Gibbons et al., 2002; Datta and Datta, 2003; Costa et al., 2004)


Robust Multi-array Average (RMA)

Robust Multi-array Average (RMA): an algorithm for normalizing and summarizing probe-level intensity measurements from DNA microarrays

Boxplot and histogram of signal intensities before RMA pre-processing

The normalization procedure is intended to make the intensity distributions identical across arrays

Boxplot and histogram of signal intensities after RMA pre-processing

Dai et al. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 2005 Nov 10;33(20):e175.

R: language and environment for statistical computing and graphicsBioconductor: open source software project for genomic data analysis


Affymetrix probe specificity and annotation issues

Reorganize probes and use updated transcript definitions to increase gene detection confidence and identification

Chip definition file (CDF) and annotation library updates only affect the qualitative attributes of probe sets without any degree of control on the effective matching of probes and genome sequences

Novel system for associating probes with genomic information; custom defined probes meet the following criteria: 1. Probes must have only one perfect match on the genomic sequence2. Because EST sequences are subject to a relatively high error rate, probes must

perfectly match a genomic region that can be aligned with mRNA/EST sequences in the UniGene database

3. Probes must target the same transcript strand 4. Updated probe sets must contain a minimum of 3 probes5. Transcript annotation is based on updated reference sequences (RefSeq)

Dai et al. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 2005 Nov 10;33(20):e175.

R: language and environment for statistical computing and graphicsBioconductor: open source software project for genomic data analysis


Analysis of Variance (ANOVA)

Analysis of Variance (ANOVA): a statistical technique for helping to infer whether there are real differences between the means of three or more groups in a population based on sample data

In general, an ANOVA:

‣ measures the overall variation within a group‣ finds the variation between group means‣ combines these to calculate a single test statistic‣ uses this to carry out a hypothesis test

Assumptions with an ANOVA:1. observations are independent2. dependent variable is normally distributed3. homogeneity of variances

➡ The advantage of using ANOVA rather than making multiple comparisons using individual t-tests is that it reduces the probability of a false positive (type-I error)


Hypothesis testing and error‣ P-value was invented for testing individual hypotheses

‣ Problem with data collected by DNA microarrays, usually involves testing thousands of hypotheses simultaneously

‣ The False Discovery Rate (FDR) is a statistical method used for testing multiple hypotheses that corrects for multiple comparisons

‣ False Discovery Rate (FDR): the expected proportion of false positives (type I errors) among the results declared significant

example: 1,000 genes at an FDR = 0.05- expect a maximum of 50 genes to be false positives (1000 x 0.05)- no such interpretation exists for P-value

‣ At least four factors determine FDR characteristics for a microarray study (Pawitan et al., 2005)

1. proportion of truly differentially expressed genes2. distribution of the true differences3. measurement variability4. sample size➡ Benjamini and Hochberg FDR

Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Ser. 1995, B 57 289-300.


Integrate genomic data from NF1 tumor-derived cell culture samples and tumor samples

Analysis strategy: 1. Identify genes differentially expressed in cultured Schwann cells2. Identify genes similarly deregulated in NF1 cell cultures & human tumors

NHSC

dNFSC

pNFSC

MPNST cell

dNF

pNF

MPNSTPlexiform NF Schwann cells

Normal human Schwann cells

Dermal NF Schwann cells

# samples

10

11

11 MPNST

Plexiform NF

Dermal NF

MPNST cell lines 13

# samples

13

13

6

Human tumorsNormal and NF-derived Schwann cells


Genes differentially expressed in cultured Schwann cellsPrinciple patterns

Genes upregluated in MPNST cell lines

Genes upregluated in NFSC

Genes upregluated in all

Genes downregulatedin MPNST cell lines andclass 2 NFSC

Genes downregulated in MPNST cell lines

Miller*, Jessen* et al., Integrative genomic analyses of neurofibromatosis tumors identify SOX9 as biomarker and survival gene. EMBO Mol Med 2009 July, 1(4);236-248.

Two classes of NFSC

Statistical test:ANOVA, FDR ≤ 0.001

Dermal and plexiform neurofibromas mix together


Genes similarly deregulated in NF1 cell cultures & human tumors

Cell culture Tumors

Functional enrichment

‣Cytoskeletal organization and biogenesis

‣Glycoprotein metabolism

‣Nervous system development

‣Neurogenesis ‣Sphingolipid metabolism

‣Cell adhesion‣Nervous system development

‣Chromosome organization and biogenesis

‣Extracellular matrix organization and biogenesis‣Nervous system development

‣Cell adhesion‣JAK-STAT cascade

‣Skeletal development

‣Cell adhesion‣Morphogenesis‣WNT receptor signaling pathway

dNF and pNFNormal and benign Schwann cells MPNST



Stage 1 Stage 2 Stage 3 Stage 4

Buchstaller et al. Efficient isolation and gene expression profiling of small numbers of neural crest stem cells and developing Schwann cells. J Neurosci. 2004 Mar 10;24(10):2357-65.

Four stages of Schwann celldevelopment


Neural crest cell gene signature

Schwann cell precursorgene signature

Immature Schwann cellgene signature

E9 E12 E14 E16 E18 P0E9 E12 E14 E16 E18 P0E9 E12 E14 E16 E18 P0E9 E12 E14 E16 E18 P0

Stage 1 Stage 2 Stage 3

Three activated gene signatures of Schwann cell development

4,75

0 pr

obe

sets


Compare genes to clusters C6 – C11



Gen

es e

xpre

ssed

in N

F1 c

ell c

ultu

res

and

tum

ors


Boxes in red (up-regulated) or blue (down-regulated) are statistically significant

Neurofibromas and MPNSTs have gene signatures characteristic of different stages of Schwann cell development


Results are consistent with recently published data

E8.5

E12.5

E18.5

Stage 1 Stage 2 Stage 3 Stage 4

Developed a mouse model: DhhCre; Nf1 flox/flox

Jianqiang Wu

DhhCre; Nf1 flox/flox mice die by 13 months


0 2 4 6 8 10 12 140

25

50

75

100

Nf1 flox/flox; DhhCre (n=28)Nf1 flox/+; DhhCre (n=22)Nf1 flox/flox (n=10)Nf1 flox/+ (n=8)

Months

Perc

ent s

urvi

val

Jianqiang Wu


Mice have dermal- and plexiform-like neurofibromas N

orm

al m

ouse

Der

mal

neu

rofib

rom

asP

lexi

form

neu

rofib

rom

as

DhhCre; Nf1 flox/floxmice show symptomsof tumor developmentas early as 5½ months of age

Human tumors DhhCre; Nf1 fl/fl mouse model

Wu et al., Plexiform and dermal neurofibromas and pigmentation are caused by Nf1 loss in desert hedgehog-expressing cells. Cancer Cell. 2008 Feb;13(2):105-16.

Jianqiang WuBioinformatics and biology suggest NF1 loss later in

Schwann cell development gives rise to neurofibromas


SOX9

Fold change NHSC to dNFSC

Fold change NHSC to pNFSC

Fold change NHSC to MPNST cell lines

Fold changeNHSC to dNF

Fold changeNHSC to pNF

Fold change NHSC to MPNST

9.72 7.76 46.46 27.97 28.08 63.06

‣ Encodes a high-mobility group box-containing transcription factor

‣ Modulates glial specification and differentiation in the peripheral nervous system and spinal cord (Kordes et al., 2005)

‣ Regulates neural crest stem cell survival (Cheung et al., 2005)

Schwann cell cultureSchwann cell culture Human tumor

Perform immunohistochemistry on tumor sections to evaluate protein expression


Immunohistochemical analysis of SOX9 protein expression

42 NF1 tumor sections (10 independent)

Brown = SOX9+SOX9 is a biomarker for NF1

Anat Stemmer-RachamimovMiller*, Jessen* et al., Integrative genomic analyses of neurofibromatosis tumors identify SOX9 as biomarker and survival gene. EMBO Mol Med 2009 July, 1(4);236-248.

Neurofibroma Schwann cells MPNST cellsCorresponding phase contrast images

Corresponding phase contrast images


Test a role for SOX9 in tumor survival

*

‣ Use shRNA to reduce SOX9 expression‣ Infected cells with a lentivirus-expressing shSOX9 or shGFP control‣ Plated 7 days post-selection in puromycin, measured survival (MTS)‣ Plated 3 days post-selection in puromycin, counted cells

p ≤ 0.05

Shyra MillerMiller*, Jessen* et al., Integrative genomic analyses of neurofibromatosis tumors identify SOX9 as biomarker and survival gene. EMBO Mol Med 2009 July, 1(4);236-248.

‣ Use shRNA to reduce SOX9 expression‣ Infected cells with a lentivirus-expressing shSOX9 or shGFP control‣ Plated 1–4 days post-selection in puromycin, measured survival (MTS)‣ Plated 3 days post-selection in puromycin, assayed for apoptosis


SOX9 is a survival gene for NF1 and a potential therapeutic target

Test MPNST cells for survival

MPNST cells

p ≤ 0.002


Summary

‣ Gene expression distinguishes benign and malignant NF1 Schwann cell cultures and solid tumors

‣ Gene expression fails to distinguish dermal and plexiform neurofibroma subtypes

‣ NF1 Schwann cell culture and tumor transcription patterns are enriched for genes activated during Schwann cell development

‣ SOX9 is biomarker and survival gene for NF1

‣ Reduction in SOX9 expression kills MPNST cells

Human cell culture, human tumor, mouse cell developmentIdentify enrichment of developmental programs in NF1 tumors



Model of NF1 tumor formation

Neural Crest Cell

Schwann cellprecursors

Mature Schwann cellsImmature

Schwann cells


Stage 4


Walter Jessen Integrative genomic approaches to peripheral nerve [email protected]

Onging research objectives: 1. Identify core biological processes and pathways for

tumorigenesis and malignancy that are conserved between mouse and human

2.Translate findings from mouse NF1 models to human therapeutics

Hypothesis: there are biological processes and pathways similarly changed in human NF1 tumors and tumors from mouse models of NF1

Mouse tumor, human tumor

Study: Leverage mouse NF1 models for translation to human therapeutics

INTEGRATE

Evaluate three classes of transgenic mouse and human samples


MPNST

Control nerve

Neurofibroma

HumanTransgenic mice# samples of

each genotype

5–5–5

4–7–4

3–3–5–3–4 MPNST

Neurofibroma

Normal nerve

# samples

3

26

6

‣ Each data set is referenced to control/normal nerve

‣ Evaluate gene signatures that are shared across tumor subtypes for each species


Method to identify similarly expressed gene orthologs conserved between mouse and human

Filter for genes similar in neurofibroma

Human Mouse

UP

Human Mouse

DOWNin 80% of samples >1.2 in 80% of samples <0.8

Filter for genessimilar in MPNST UP DOWN

in 80% of samples >1.2 in 80% of samples <0.8

UP DOWNin 80% of samples >1.2 in 80% of samples <0.8

UP DOWNin 80% of samples >1.2 in 80% of samples <0.8

2,212 orthologs similarly expressed

1,016

398 414

758

HumanANOVA (FDR ≤ 0.05)

Nerve vs. NF vs. MPNST

MouseANOVA (FDR ≤ 0.05)

Control nerve vs. NF vs. MPNST

Human Mouse Human Mouse

Identify genesstatistically different

integrate, identify orthologous genes


Gene orthologs similarly expressed between mouse and human tumors

‣ Axonogenesis

‣ Induction of apoptosis ‣Negative regulation of

MAP kinase activity‣ Regulation of

neurotransmitter levels

‣ Actomyosin structure and organization‣ Negative regulation of

cell cycle progression‣Peripheral nervous

system development

‣ Apoptosis

‣ Negative regulation of MAP kinase activity ‣ Phosphoinositide-

mediated signaling

‣ Regulation of mitosis

‣ Axon ensheathment

‣Axonogenesis‣ Catecholamine

metabolism‣ Peripheral nervous

system development



HumanTransgenic mice HumanTransgenic mice Neurofibroma

MPNST

C1 C2 C3 C4

2,21

2 Tr

ansc

ripts


Gene signatures shared or unique between NF1 tumors and GEM NF1 models

Similar expression patterns Contrasting expression patterns


Species-specific gene signatures

Human-specific expression patterns Mouse-specific expression patterns


Perform comparative enrichment analysis on expression signatures(ToppCluster facilitates co-functional enrichment analysis of multiple gene signatures)

http://toppcluster.cchmc.org/17 clusters C11 – C27ToppGene: FDR ≤ 0.05

Generates relationships in high-dimensional space, visualize interaction network using the open source bioinformatics software platform Cytoscape.

ToppGene: uses cluster assignment as a classification parameter and the Gene Set Enrichment Algorithm to identify significant gene set over-representation of several features: gene ontologies, pathways, co-expression, gene-disease, gene-drug, mouse and human phenotypes, microRNAs, cytobands and transcription factor binding site (TFBS).

http://toppcluster.cchmc.org




Structure based on the force-directed layout paradigmyFiles (Java Graph Layout and Visualization Library) Organic Algorithm

Clusters

Gene

Pathway

Cytoband

Ontology

Gene sets

TFBS

(disease associations)

Protein domain

Drug

Nodes: 2,653Edges: 7,938


Shared activationApoptosis Cell cycle controlCell proliferationRegulation of MAP kinase activity

Shared repressionCell-cell signalingLipid metabolism MyelinationNervous system development

HS unchanged, MM activatedHS activated, MM repressedCell-cell signalingGlutathione metabolismGABA-B receptor signalingPotassium/calcium transportSmall GTPase mediated signal transduction Synaptic vesicle trafficking

HS unchanged/repressed, MM activatedAngiogenesis, Apoptosis, Immune response,Ras protein signal transductionNote: only ontologies and pathways are listed

Four “Feature Domains”


Gene orthologs and biological themes shared between mouse and human

‣ Axonogenesis

‣ Induction of apoptosis ‣Negative regulation of

MAP kinase activity‣ Regulation of

neurotransmitter levels

‣ Actomyosin structure and organization‣ Negative regulation of

cell cycle progression‣Peripheral nervous

system development

‣ Apoptosis

‣ Negative regulation of MAP kinase activity ‣ Phosphoinositide-

mediated signaling

‣ Regulation of mitosis

‣ Axon ensheathment

‣Axonogenesis‣ Catecholamine

metabolism‣ Peripheral nervous

system development



HumanTransgenic mice HumanTransgenic mice Neurofibroma

MPNST

C1 C2 C3 C4

2,21

2 Tr

ansc

ripts


PTPRZ1‣ Encodes a protein tyrosine phosphatase (receptor type Z)

‣ Expression is restricted to the nervous system (Levy et al., 1993)

‣ Plays a critical role in functional recovery from demyelinating lesions (Harroch et al., 2002)

‣ In the top 100 genes discriminating MPNST from 13 other soft tissue sarcomas (Francis et al., 2007)

Transgenic miceTransgenic mice HumanFold change Controls to NF

Fold change Controls to MPNST

Fold changeNerve to NF

Fold change Nerve to MPNST

13.22 7.77 2.56 -2.26

Expression profile suggests PTPRZ1 could be important for tumorigenesis

Use PTPRZ1 to select orthologs that have a similar expression profile and evaluate genetic interactions


Genetic interaction network analysis

Hypothesis: transcripts having a similar pattern of expression as PTPRZ1 and interacting with genes in the MAP kinase pathway will include critical regulators of survival in NF1

Analysis strategy: 1. Identify the top 100 gene orthologs that

correlate and anti-correlate with PTPRZ12. Add transcripts from clusters C1 and C3

associated with Negative regulation of MAP kinase activity

3. Add ERK genes (MAPK1, MAPK3, MAPK6, MAPK7, MAPK12)

4. Identify genetic interactions, removing those entities that don’t have connections Downward J, Cancer: A tumour gene's fatal

flaws. Nature. 2009 Nov 5;462(7269):44-5.

RAS activation stimulates downstream signaling pathways


Orange: ERK/MAP kinase genes

Direct interaction

Indirect interaction


Nodes are colored according to the degree of fold change from human nerve to neurofibroma


‣ Up-regulated gene targeted by a currently used cancer drug

‣ Directly interacts with a number of genes highly up-regulated in human neurofibroma

- c-Kit- beta-catenin- breast cancer anti-estrogen resistance 1- p21 protein (Cdc42/Rac)- activated kinase 2- arrestin beta 1

Genes associated with cell death, neurological disorders, cell proliferation and survival

All direct interactions up-regulated in neurofibroma




Pivotal genes in critical signaling pathwaysAktCDKN2A (p16)HIF1ANFkB1VEGFA

All direct interactions up-regulated in neurofibroma

‣ Move one step further down the interaction pathway, number of pivotal genes in critical signaling pathways

‣ Two genes have been targeted therapeutically: KIT and EGFR

‣ Data suggests that the gene is a potential promoter of malignant transformation in NF1


Dedifferentiated Chondrosarcoma (3)MPNST (4)

Neurofibroma (4)

Gene is over-expressed in a subset of mesenchymal tumors that are aggressive

Myxoid Liposarcoma (6)

Monophasic Synovial Sarcoma (10)

Alveolar Rhabdomyosarcoma (4)

Desmoid Fibromatosis (5)

Embryonal Rhabdomyosarcoma (3)

Henderson et al., A molecular map of mesenchymal tumors. Genome Biol. 2005;6(9):R76. Epub 2005 Aug 26.

‣19 mesenchymal tumor subtypes


Neurofibroma MPNST

‣DNA repair‣Ensheathment of neurons‣Integrin-mediated

signaling‣Mitotic cell cycle

‣Ras protein signal transduction‣Vesicle-mediated transport


Expression signature for the gene in human tumors

854

Tran

scrip

ts

Gene signature

854

7,174



Cancer drug is cytotoxic against 5 MPNST cell lines (dose at days 2 and 4 relative to day 0)

% C

ontro

l

[drug] nM


‣ Effective method for simultaneous comparison of transcriptional programs between mouse models and human tumors

‣ Human NF1 tumors and mouse NF1 model tumors share activation of genes associated with negative regulation of MAP kinase activity and repression of genes associated with peripheral nervous system development

‣ Genes down-regulated in human NF1 tumors but up-regulated in mouse NF1 models are associated with Ras protein signal transduction and immune response

‣ Use gene interaction network analysis to identify a gene that is a potential promoter of malignant progression in NF1 and a potential therapeutic target

Mouse tumor, human tumorCross-species profiling, genetic network analysis

Summary


HumanCincinnati, OH – CCHMC Nancy Ratner, Shrya Miller, Atira Hardiman

Boston, MA – MGH/Harvard Anat Stemmer-Rachamimov

Gainseville, FL – University of Florida Margaret Wallace

Barcelona, Spain – LʼHospitalet de Llobregat Concepcion Lazaro, Eduard Serra

Mouse modelsCincinnati, OH – CCHMC Nancy Ratner, Jianqiang Wu, Tilat Rizvi

Paris, France – Fondation Jean Dausset Marco Giovannini, Jan Manent

Bioinformatics/BiostatisticsCincinnati, OH – CCHMC Bruce Aronow, Walter Jessen

Birmingham, AL – University of Alabama Grier Page, Tapan Mehta

FundingDOD: W81XWH-04-1-0273NIH: T32 HL07382-30

Integrative Genomic Approaches to Identify Biomarkers and Therapeutic Targets in NF1

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