Integrative genomic approaches to identify biomarkers and targets in NF1

Integrative genomic approaches to identify
biomarkers and therapeutic targets in NF1

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

Walter Jessen Integrative genomic approaches to identify
Walter@WalterJessen.com biomarkers and therapeutic targets in NF1

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

Max Delbrück, theoretical physicist


Principle
the 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


% patients affected
NF1 tumor subtypes
Dermal neurofibromas (dNF)
‣ Tumors that appear as multiple, firm rubbery 95%
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 25%
surrounding tissue
‣ Capable of becoming malignant
Malignant peripheral nerve sheath tumors (MPNST)
‣ Highly aggressive soft tissue sarcomas 10 – 13%
‣ Localized recurrence, chemo-resistance, frequent metastasis
‣ Median age of onset: 26, five year survival: 34%
No effective treatments exist for either neurofibroma or MPNST


NF1 tumor composition is complex

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

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

Lines of evidence

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

Schwann cells are the pathogenic cell type in peripheral nerve tumors


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

A. Myelinated Axon B. Unmyelinated Axons
Ax

S S Schwann cell
nucleus
S
Ax Axon

Ax

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


NF1 encodes neurofibromin, a GTPase activating protein
RAS activation stimulates downstream signaling pathways

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.


Very little is known about the pathways that drive NF1
tumor progression
Benign Malignant
Dermal neurofibroma

Normal Schwann cell
NF1-associated
Plexiform neurofibroma MPNST
Transformation
EGFR p53
PDGFRA Growth factor
Rb
NF1 mutation receptors
KIT Tumor CDKN2A
suppressor (p16)
S6kinase genes
H-, K-, N-Ras Signaling
cAMP CDKN2D
(p19)


Study: Biological pathways that drive NF1 tumor progression

Research objectives:
1. Gain insight into the biological pathways and processes that drive
NF1 tumor formation and transformation
2. 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

INTEGRATE
Human cell culture, human tumor,
mouse Schwann cell development


Profiling gene expression using Affymetrix DNA microarrays

DNA microarray
technology:
Enables researchers
to simultaneously
survey the expression
of a large number of
genes.

Microarray or GeneChip:
A tool used to analyze
gene expression,
consisting of a small
glass slide containing
samples of many
genes arranged in
a regular pattern.

Samples:
Sets of probes which
represent gene
transcripts


Analyze gene expression data from DNA microarrays by
clustering

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


Two common types of clustering methods
‣ Hierarchical clustering: subdivides each cluster into smaller clusters,
forming a dendrogram (tree-shaped data structure)
Algorithm summary
1. Place all points into their own clusters
2. 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 random
2. Repeat until stable:
a. Compute the centroid for each cluster
b. 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

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

Boxplot and histogram of signal Boxplot and histogram of signal
intensities before RMA pre-processing intensities after RMA pre-processing

R: language and environment for
Walter Jessen computing and graphics
statistical Integrative genomic al. Evolving gene/transcript definitions significantly alter
Dai et approaches to identify the
Bioconductor: open source software interpretation of GeneChip data. Nucleic Acids Res. 2005 Nov 10;33(20):e175.
Walter@WalterJessen.com analysisbiomarkers and therapeutic targets in NF1
project for genomic data

Affymetrix probe specificity and annotation issues

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 sequence
2. 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 probes
5. Transcript annotation is based on updated reference sequences (RefSeq)

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

R: language and environment for
Walter Jessen computing and graphics
statistical Integrative genomic al. Evolving gene/transcript definitions significantly alter
Dai et approaches to identify the
Bioconductor: open source software interpretation of GeneChip data. Nucleic Acids Res. 2005 Nov 10;33(20):e175.
Walter@WalterJessen.com analysisbiomarkers and therapeutic targets in NF1
project for genomic data

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 independent
2. dependent variable is normally distributed
3. 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 genes
2. distribution of the true differences
3. measurement variability
4. sample size
➡ Benjamini and Hochberg FDR
Walter Jessen Integrative Benjamini and Hochberg, Controlling the false discovery rate: a practical and
genomic approaches to identify
Walter@WalterJessen.com biomarkerspowerful approach to multiple testing. J. Roy. Statist. Soc. Ser. 1995, B 57 289-300.
and therapeutic targets in NF1

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

Normal and NF-derived Schwann cells Human tumors
# samples # samples

NHSC Normal human Schwann cells 10 dNF Dermal NF 13

dNFSC Dermal NF Schwann cells 11 pNF Plexiform NF 13

pNFSC Plexiform NF Schwann cells 11 MPNST MPNST 6

MPNST cell MPNST cell lines 13

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


Genes differentially expressed in cultured Schwann cells
Principle patterns
Statistical test:
ANOVA, FDR ≤ 0.001 Genes upregluated
in MPNST cell lines

Genes upregluated in NFSC

Genes upregluated in all

Genes downregulated
in MPNST cell lines

Genes downregulated
in MPNST cell lines and
class 2 NFSC
Two classes of NFSC

Walter@WalterJessen.com 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.

Genes similarly deregulated in NF1 cell cultures & human tumors

Normal and benign Schwann cells dNF and pNF MPNST Functional enrichment
‣ Cytoskeletal organization and
Cell culture Tumors biogenesis
‣Glycoprotein metabolism
‣Nervous system development
‣ Neurogenesis
‣ Sphingolipid metabolism

‣ Cell adhesion
‣Nervous system development
‣ Chromosome organization and
biogenesis
‣ Extracellular matrix organization
and biogenesis
Dermal and plexiform ‣Nervous system development
neurofibromas mix together ‣ Cell adhesion
‣ JAK-STAT cascade
‣ Skeletal development
‣ Cell adhesion
‣Morphogenesis
‣ WNT receptor signaling pathway
Walter@WalterJessen.com Miller*, Jessen* et al., Integrative genomic analyses of neurofibromatosis tumors identify

Four stages of Schwann cell
development

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.


Three activated
gene signatures
Immature Schwann cell
of Schwann cell gene signature

development

4,750 probe sets

Neural crest cell
gene signature

Schwann cell precursor
gene signature

E9 E12 E14 E16 E18 P0
E12 E14 E16

Statistical test: Compare genes to
ANOVA, FDR ≤ 0.2 clusters C6 – C11
Stage 1 Stage 2 Stage 3

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

Genes expressed in NF1 cell cultures and tumors

Boxes in red (up-regulated) or blue (down-regulated) are statistically significant
Miller*, Jessen* et al., Integrative genomic analyses of neurofibromatosis tumors identify
Walter@WalterJessen.com biomarkers and therapeutic targets inEMBO Mol Med 2009 July, 1(4);236-248.
SOX9 as biomarker and survival gene. NF1

Results are consistent with recently published data
Developed a mouse model:
DhhCre; Nf1 flox/flox E18.5
Jianqiang Wu

E12.5

E8.5

Stage 1 Stage 2 Stage 3 Stage 4


DhhCre; Nf1 flox/flox mice die by 13 months

100

75
Percent survival

50
Nf1 flox/flox; DhhCre (n=28)
Nf1 flox/+; DhhCre (n=22)
Nf1 flox/flox (n=10)
25 Nf1 flox/+ (n=8)

0
0 2 4 6 8 10 12 14
Months Jianqiang Wu


Mice have dermal- and plexiform-like neurofibromas
Human tumors DhhCre; Nf1 fl/fl mouse model

Dermal neurofibromas
DhhCre; Nf1 flox/flox
mice show symptoms
of tumor development
as early as 5½ months
of age
Normal mouse

Plexiform neurofibromas

Bioinformatics and biology suggest NF1 loss later in
Schwann cell development gives rise to neurofibromas Jianqiang Wu
Wu et al., Plexiform and dermal neurofibromas and pigmentation are caused by Nf1
Walter@WalterJessen.com biomarkers and desert hedgehog-expressing cells. Cancer Cell. 2008 Feb;13(2):105-16.
loss in therapeutic targets in NF1

SOX9
‣ 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 culture Human tumor
Fold change Fold change Fold change Fold change Fold change Fold change
NHSC to dNFSC NHSC to pNFSC NHSC to MPNST cell lines NHSC to dNF NHSC to pNF NHSC to MPNST

9.72 7.76 46.46 27.97 28.08 63.06

Perform immunohistochemistry on tumor
sections to evaluate protein expression

Immunohistochemical analysis of SOX9 protein expression

42 NF1 tumor sections (10 independent)

SOX9 is a biomarker for NF1 Brown = SOX9+

Miller*, Jessen* et al., Integrative genomic analyses of neurofibromatosis tumors identify Anat Stemmer-Rachamimov


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
Neurofibroma Schwann cells Corresponding phase MPNST cells Corresponding phase
contrast images contrast images

p ≤ 0.05

*

Miller*, Jessen* et al., Integrative genomic analyses of neurofibromatosis tumors identify Shyra Miller


Test MPNST cells for survival
‣ 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
MPNST cells
p ≤ 0.002

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


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 development
Identify enrichment of developmental programs in NF1 tumors



Model of NF1 tumor formation

Mature Schwann cells
Neural Crest Schwann cell Immature
Stage 4
Cell precursors Schwann cells




Study: Leverage mouse NF1 models for translation to
human therapeutics

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

INTEGRATE Mouse tumor, human tumor

Walter Jessen Integrative genomic approaches
Walter.Jessen@cchmc.org to peripheral nerve tumorigenesis

Evaluate three classes of transgenic mouse and human
samples

Transgenic mice Human
# samples of # samples
each genotype

Control nerve 5–5–5 Normal nerve 3

Neurofibroma 4–7–4 Neurofibroma 26

MPNST 3–3–5–3–4 MPNST 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
Human Mouse
Identify genes
ANOVA (FDR ≤ 0.05) ANOVA (FDR ≤ 0.05)
statistically different Nerve vs. NF vs. MPNST Control nerve vs. NF vs. MPNST

integrate, identify orthologous genes
Human Mouse Human Mouse

Filter for genes 398 414
similar in
neurofibroma UP UP DOWN DOWN
in 80% of samples >1.2 in 80% of samples >1.2 in 80% of samples <0.8 in 80% of samples <0.8

Human Mouse Human Mouse
Filter for genes
1,016 758
similar in
MPNST UP UP DOWN DOWN
in 80% of samples >1.2 in 80% of samples >1.2 in 80% of samples <0.8 in 80% of samples <0.8

2,212 orthologs similarly expressed


Gene orthologs similarly expressed between mouse and human tumors
Functional enrichment
Neurofibroma
‣ Axonogenesis
‣ Induction of apoptosis
‣ Negative regulation of
MAP kinase activity
‣ Regulation of
neurotransmitter levels

‣ Actomyosin structure
and organization
2,212 Transcripts

cell cycle progression
‣ Peripheral nervous
system development

MPNST
‣ Apoptosis
MAP kinase activity
‣ Phosphoinositide-
mediated signaling
‣ Regulation of mitosis
‣ Axon ensheathment
‣ Axonogenesis
‣ Catecholamine
metabolism
C1 C2 C3 C4 ‣ Peripheral nervous
Statistical test: system development

ANOVA, FDR ≤ 0.05


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)

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/
17 clusters C11 – C27
ToppGene: FDR ≤ 0.05

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


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

Clusters
Gene
Ontology
Pathway
TFBS
Cytoband
Protein domain
Drug
Gene sets
(disease associations)

Nodes: 2,653
Edges: 7,938


HS unchanged, MM activated
HS activated, MM repressed Four “Feature Domains”
Cell-cell signaling
Glutathione metabolism
GABA-B receptor signaling
Potassium/calcium transport Shared repression
Small GTPase mediated signal Cell-cell signaling
transduction Lipid metabolism
Synaptic vesicle trafficking Myelination
Nervous system development

Shared activation
Apoptosis
Cell cycle control
Cell proliferation
Regulation of MAP kinase activity
HS unchanged/repressed, MM activated
Angiogenesis, Apoptosis, Immune response,
Walter@WalterJessen.com biomarkers and therapeutic targets in NF1 transduction
Note: only ontologies and pathways are listed Ras protein signal

Gene orthologs and biological themes shared between mouse and human
Neurofibroma
‣ Axonogenesis
‣ Induction of apoptosis
MAP kinase activity
‣ Regulation of
neurotransmitter levels

‣ Actomyosin structure
and organization
2,212 Transcripts

cell cycle progression
‣ Peripheral nervous
system development

MPNST
‣ Apoptosis
MAP kinase activity
‣ Phosphoinositide-
mediated signaling
‣ Regulation of mitosis
‣ Axon ensheathment
‣ Axonogenesis
‣ Catecholamine
metabolism
C1 C2 C3 C4 ‣ Peripheral nervous
Statistical test: system development

ANOVA, FDR ≤ 0.05


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)

Fold change Fold change Fold change Fold change
Controls to NF Controls to MPNST Nerve to NF 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
Walter Jessen expression profile and evaluate genetic interactions
Integrative genomic approaches to identify

Genetic interaction network analysis
RAS activation stimulates downstream signaling pathways

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 PTPRZ1
2. 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.



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

Orange:
ERK/MAP kinase
genes
Direct interaction

Indirect interaction


All direct interactions
‣ Up-regulated gene targeted by a up-regulated in neurofibroma

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)-
Genes associated
activated kinase 2 with cell death,
- arrestin beta 1 neurological
disorders, cell
proliferation and
survival


All direct interactions
Genetic interaction network analysis 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
Pivotal genes in
critical signaling
pathways
Akt
CDKN2A (p16)
HIF1A
NFkB1
VEGFA


Gene is over-expressed in a subset of mesenchymal tumors
that are aggressive
MPNST (4)
Dedifferentiated Chondrosarcoma (3)

Myxoid Liposarcoma (6)
Alveolar Rhabdomyosarcoma (4)

Desmoid Fibromatosis (5)

Embryonal Rhabdomyosarcoma (3)

Monophasic Synovial Sarcoma (10)

Neurofibroma (4)
‣
Walter mesenchymal
19 Jessen tumor subtypes approaches to identifyal., A molecular map of mesenchymal tumors.
Integrative genomic Henderson et
Walter@WalterJessen.com biomarkers and therapeutic targets in NF1 2005;6(9):R76. Epub 2005 Aug 26.
Genome Biol.

Expression signature for the gene in human tumors


‣ DNA repair
‣Ensheathment of neurons
‣Integrin-mediated
signaling
‣ Mitotic cell cycle
854 Transcripts

‣Ras protein signal
transduction
‣ Vesicle-mediated transport

Statistical test:
ANOVA, FDR ≤ 0.05
7,174
Gene
signature Neurofibroma MPNST
854

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

[drug] nM


Summary
‣ 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 tumor
Cross-species profiling, genetic network analysis


Mouse models Human
Cincinnati, OH – CCHMC Cincinnati, OH – CCHMC
Nancy Ratner, Jianqiang Wu, Tilat Rizvi Nancy Ratner, Shrya Miller, Atira Hardiman

Paris, France – Fondation Jean Dausset Boston, MA – MGH/Harvard
Marco Giovannini, Jan Manent Anat Stemmer-Rachamimov

Gainseville, FL – University of Florida
Bioinformatics/Biostatistics Margaret Wallace
Cincinnati, OH – CCHMC
Bruce Aronow, Walter Jessen Barcelona, Spain – LʼHospitalet de Llobregat
Concepcion Lazaro, Eduard Serra
Birmingham, AL – University of Alabama
Grier Page, Tapan Mehta
Funding
DOD: W81XWH-04-1-0273
NIH: T32 HL07382-30


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