A

map

of

human

cancer

signaling

We

conducted

a

comprehensive

analysis

of

a

manually

curated

human

signaling

network

containing

nodes

and

signaling

regulatory

relations

by

integrating

cancer-associated

genetically

and

epigenetically

altered

genes

We

find

that

cancer

mutated

genes

are

enriched

in

positive

signaling

regulatory

loops

whereas

the

cancer-associated

methylated

genes

are

enriched

in

negative

signaling

regulatory

loops

We

further

characterized

an

overall

picture

of

the

cancer-signaling

architectural

and

functional

organization

From

the

network

we

extracted

an

oncogene-signaling

map

which

contains

nodes

links

and

the

interconnections

of

mutated

and

methylated

genes

The

map

can

be

decomposed

into

topological

regions

or

oncogene-signaling

blocks

including

a

few

oncogene-signaling-dependent

blocks

in

which

frequently

used

oncogene-signaling

events

are

enriched

One

such

block

in

which

the

genes

are

highly

mutated

and

methylated

appears

in

most

tumors

and

thus

plays

a

central

role

in

cancer

signaling

Functional

collaborations

between

two

oncogene-signaling-dependent

blocks

occur

in

most

tumors

although

breast

and

lung

tumors

exhibit

more

complex

collaborative

patterns

between

multiple

blocks

than

other

cancer

types

Benchmarking

two

data

sets

derived

from

systematic

screening

of

mutations

in

tumors

further

reinforced

our

findings

that

although

the

mutations

are

tremendously

diverse

and

complex

at

the

gene

level

clear

patterns

of

oncogene-signaling

collaborations

emerge

recurrently

at

the

network

level

Finally

the

mutated

genes

in

the

network

could

be

used

to

discover

novel

cancer-associated

genes

and

biomarkers

Introduction

Cells

use

sophisticated

communication

between

proteins

in

order

to

initiate

and

maintain

basic

cellular

functions

such

as

growth

survival

proliferation

and

development

Traditionally

cell

signaling

is

described

via

linear

diagrams

and

signaling

pathways

As

many

more

cross-talks

between

signaling

pathways

have

been

identified

-LRB-

Natarajan

et

al

-RRB-

a

network

view

of

cell

signaling

emerged

the

signaling

proteins

rarely

operate

in

isolation

through

linear

pathways

but

rather

through

a

large

and

complex

network

As

cell

signaling

is

crucial

to

affect

cell

responses

such

as

growth

and

survival

alterations

of

cellular

signaling

events

such

as

those

that

arise

by

mutations

can

result

in

tumor

development

Indeed

cancer

is

largely

a

genetic

disease

that

is

caused

by

acquiring

genomic

alterations

in

somatic

cells

Alterations

to

the

genes

that

encode

key

signaling

proteins

such

as

RAS

and

are

commonly

observed

in

many

types

of

cancers

During

tumor

progression

it

is

proposed

that

a

malignant

tumor

arises

from

a

single

cell

which

undergoes

a

series

of

evolutionary

processes

of

genetic

or

epigenetic

changes

and

selections

so

that

a

cell

within

the

population

can

acquire

additional

selective

advantages

for

cellular

growth

or

survival

resulting

in

progressive

clonal

expansion

-LRB-

Nowell

-RRB-

Genetic

mutations

of

the

signaling

proteins

might

overactivate

key

cell-signaling

properties

such

as

cell

proliferation

or

survival

and

then

give

rise

to

the

cell

with

selective

advantages

for

uncontrolled

cellular

growth

and

promoting

tumor

progression

In

addition

mutations

may

also

inhibit

the

function

of

tumor-suppressor

proteins

resulting

in

a

relief

from

normal

constraints

on

growth

Furthermore

epigenetic

alterations

by

promoter

methylation

resulting

in

transcriptional

repression

of

genes

controlling

tumor

malignancy

is

another

important

mechanism

for

the

loss

of

gene

function

that

can

provide

a

selective

advantage

to

tumor

cells

Enormous

efforts

have

been

made

over

the

past

few

decades

to

identify

mutated

genes

that

are

causally

implicated

in

human

cancer

Furthermore

a

genome-wide

or

large-scale

sequencing

of

tumor

samples

across

many

kinds

of

cancers

represents

a

largely

unbiased

overview

of

the

spectrum

of

mutations

in

human

cancers

-LRB-

Stephens

et

al

Sjoblom

et

al

Greenman

et

al

Thomas

et

al

-RRB-

Most

of

these

efforts

have

been

made

by

the

Cancer

Genome

Project

-LRB-

CGP

-RRB-

which

aims

to

identify

cancer-mutated

genes

using

genome-wide

mutation-detection

approaches

Similarly

genome-wide

identification

of

epigenetic

changes

in

cancer

cells

has

been

conducted

recently

-LRB-

Ohm

et

al

Schlesinger

et

al

Widschwendter

et

al

-RRB-

These

studies

showed

that

a

substantial

fraction

of

the

cancer-associated

mutated

and

methylated

genes

is

involved

in

cell

signaling

which

is

in

agreement

with

the

previous

finding

that

the

most

common

domain

encoded

by

cancer

genes

is

the

protein

kinase

domain

-LRB-

Futreal

et

al

-RRB-

Although

there

is

a

wealth

of

knowledge

regarding

molecular

signaling

in

cancer

the

complexity

of

human

cancer

prevents

us

from

gaining

an

overall

picture

of

the

mechanisms

by

which

these

genetic

and

epigenetic

events

affect

cancer

cell

signaling

and

tumor

progression

Where

are

the

oncogenic

stimuli

embedded

in

the

network

architecture

What

are

the

principles

by

which

genetic

and

epigenetic

alterations

trigger

oncogene-signaling

events

Given

that

so

many

genes

have

genetic

and

epigenetic

aberrations

in

cancer

signaling

what

is

the

architecture

of

cancer

signaling

Do

any

tumor-driven

signaling

events

represent

oncogenic

dependence

-LRB-

the

phenomenon

by

which

certain

cancer

cells

become

dependent

on

certain

signaling

cascades

for

growth

or

survival

-RRB-

Who

are

the

central

players

in

oncogene

signaling

Are

there

any

signaling

partnerships

generally

used

to

generate

tumor

phenotypes

To

answer

these

questions

we

conducted

a

comprehensive

analysis

of

cancer

mutated

and

methylated

genes

on

a

human

signaling

network

focusing

on

network

structural

aspects

and

quantitative

analysis

of

gene

mutations

on

the

network

Results

and

discussion

The

architecture

and

the

relationships

among

the

proteins

of

a

signaling

network

are

important

for

determining

the

sites

at

which

oncogenic

stimuli

occur

and

through

which

oncogenic

stimuli

are

transduced

Extensive

signaling

studies

during

the

past

decades

have

yielded

an

enormous

amount

of

information

regarding

regulation

of

signaling

proteins

for

more

than

signaling

pathways

most

of

which

have

been

assembled

and

collected

in

public

databases

in

diagrams

We

manually

curated

the

data

on

signaling

proteins

and

their

relations

-LRB-

activation

and

inhibitory

and

physical

interactions

-RRB-

from

the

BioCarta

database

and

the

Cancer

Cell

Map

database

-LRB-

see

Materials

and

methods

-RRB-

We

merged

the

curated

data

with

another

literature-mined

signaling

network

that

contains

proteins

-LRB-

Ma'ayan

et

al

-RRB-

As

a

result

we

have

built

a

human

signaling

network

containing

nodes

and

links

Integrative

network

analyses

have

provided

numerous

biological

insights

-LRB-

Wuchty

et

al

Han

et

al

Ihmels

et

al

Luscombe

et

al

Kharchenko

et

al

Wang

and

Purisima

Cui

et

al

-RRB-

Thus

the

integration

of

the

data

on

mutated

and

methylated

cancer-associated

genes

onto

the

network

will

help

us

to

identify

critical

sites

involved

in

tumorigenesis

and

increase

our

understanding

of

the

underlying

mechanisms

in

cancer

signaling

To

integrate

mutated

and

methylated

genes

onto

the

network

we

first

collected

the

cancer

mutated

genes

from

the

Catalogue

Of

Somatic

Mutations

In

Cancer

-LRB-

COSMIC

-RRB-

database

which

collects

the

cancer

mutated

genes

through

literature

curation

and

large-scale

sequencing

of

tumor

samples

in

the

CGP

We

then

combined

these

data

with

the

cancer

mutated

genes

derived

from

other

genome-wide

and

high-throughput

sequencing

of

tumor

samples

-LRB-

Stephens

et

al

Sjoblom

et

al

Greenman

et

al

Thomas

et

al

-RRB-

The

merged

gene

set

represents

a

mixture

of

the

past

directed

approach

and

current

systematic

screening

of

cancer

mutations

The

cancer-associated

methylated

genes

were

taken

from

the

genome-wide

identification

of

the

DNA

methylated

genes

in

cancer

stem

cells

-LRB-

Ohm

et

al

Schlesinger

et

al

Widschwendter

et

al

-RRB-

Finally

cancer

mutated

genes

and

DNA

methylated

genes

were

mapped

onto

the

network

Among

the

cancer

mutated

genes

-LRB-

-RRB-

and

-LRB-

-RRB-

genes

were

derived

from

large-scale

gene

sequencing

of

tumors

and

literature

curation

respectively

-LRB-

see

Materials

and

methods

Figure

-RRB-

In

general

cancer

genes

can

be

divided

into

two

groups

positive

regulators

-LRB-

oncogenes

-RRB-

that

promote

cancer

cell

proliferation

and

the

negative

regulators

-LRB-

tumor

suppressors

-RRB-

that

restrain

it

By

comparing

the

mutated

genes

with

the

known

tumor

suppressors

we

found

that

only

-LRB-

genes

-RRB-

of

the

mutated

genes

are

known

tumor

suppressors

and

that

the

majority

of

the

mutated

genes

are

oncogenes

-LRB-

Supplementary

Figure

-RRB-

On

the

other

hand

methylated

genes

are

mainly

found

to

encode

tumor

suppressors

in

cancer

cells

-LRB-

Supplementary

Figure

-RRB-

-LRB-

Ohm

et

al

Widschwendter

et

al

-RRB-

Cancer

mutated

genes

are

enriched

in

signaling

hubs

but

not

in

neutral

hubs

Genes

that

when

mutated

or

silenced

result

in

tumorigenesis

often

lead

to

the

aberrant

activation

of

certain

downstream

signaling

nodes

resulting

in

dysregulated

growth

survival

and/or

differentiation

The

architecture

of

a

signaling

network

is

important

for

determining

the

site

at

which

a

genetic

defect

is

involved

in

cancer

To

discover

where

the

critical

tumor

signaling

stimuli

occur

on

the

network

we

explored

the

network

characteristics

of

the

mutated

and

methylated

genes

The

signaling

network

is

presented

as

a

graph

in

which

nodes

represent

proteins

Directed

links

are

operationally

defined

to

represent

effector

actions

such

as

activation

or

inhibition

whereas

undirected

links

represent

protein

physical

interactions

that

are

not

characterized

as

either

activating

or

inhibitory

For

example

scaffold

proteins

do

not

directly

activate

or

inhibit

other

proteins

but

provide

regional

organization

for

activation

or

inhibition

between

other

proteins

through

protein

interactions

In

this

case

undirected

links

are

used

to

represent

the

interactions

between

scaffold

proteins

and

others

On

the

other

hand

adaptor

proteins

are

able

to

activate

or

inhibit

other

proteins

through

direct

protein

interactions

In

this

situation

directed

links

are

used

to

represent

these

relations

There

are

two

kinds

of

directed

links

An

incoming

link

represents

a

signal

from

another

node

The

sum

of

the

number

of

incoming

links

of

a

node

is

called

the

indegree

of

that

node

An

outgoing

link

represents

a

signal

to

another

node

The

sum

of

the

number

of

outgoing

links

of

a

node

is

called

the

outdegree

of

that

node

We

call

incoming

and

outgoing

links

as

signal

links

whereas

the

physical

links

are

neutral

links

We

first

examined

the

characteristics

of

the

nodes

that

represent

mutated

genes

on

the

network

We

compared

the

average

indegree

of

the

mutated

genes

with

that

of

the

nodes

in

the

whole

network

We

found

that

the

average

indegree

of

the

mutated

nodes

is

significantly

higher

than

that

of

the

network

nodes

-LRB-

P

Wilcoxon

test

Supplementary

Figure

-RRB-

A

similar

result

was

obtained

for

the

average

outdegree

of

the

mutated

nodes

-LRB-

P

Wilcoxon

test

Supplementary

Figure

-RRB-

In

contrast

there

is

no

difference

of

the

average

neutral

degrees

between

the

mutated

nodes

and

other

nodes

in

the

network

To

refine

these

results

further

we

calculated

the

correlations

between

the

indegree

outdegree

and

neutral

degree

of

the

network

nodes

We

found

a

significant

correlation

between

the

indegree

and

the

outdegree

of

the

network

nodes

-LRB-

R

P

Spearman

's

correlation

-RRB-

but

no

correlation

between

the

indegree

and

neutral

degree

of

the

nodes

-LRB-

R

P

Spearman

's

correlation

-RRB-

Taken

together

these

results

suggest

that

cancer

mutations

most

likely

occur

in

signaling

proteins

that

are

acting

as

signaling

hubs

-LRB-

i.e.

RAS

-RRB-

actively

sending

or

receiving

signals

rather

than

in

nodes

that

are

simply

involved

in

passive

physical

interactions

with

other

proteins

As

these

hubs

are

focal

nodes

that

are

shared

by

and/or

are

central

in

many

signaling

pathways

alterations

of

these

nodes

or

signaling

hubs

are

predicted

to

affect

more

signaling

events

resulting

in

cancer

or

other

diseases

In

previous

studies

we

found

that

cancer-associated

genes

are

enriched

in

hubs

-LRB-

Awan

et

al

-RRB-

However

these

results

indicate

that

cancer-associated

genes

are

enriched

in

signaling

hubs

but

not

neutral

hubs

We

also

investigated

the

relations

between

the

node

degree

and

the

methylated

genes

in

the

network

Methylated

gene

nodes

do

not

appear

to

differ

significantly

from

the

network

nodes

with

regard

to

their

indegree

outdegree

and

neutral

degree

respectively

-LRB-

P

P

P

Supplementary

Figure

-RRB-

These

results

suggest

that

cancer

mutated

genes

and

methylation-silenced

genes

have

different

regulatory

mechanisms

in

oncogene

signaling

Activating

and

inhibitory

signals

enhance

and

alleviate

oncogene-signaling

flows

respectively

Signaling

flow

branching

represents

the

splitting

of

one

signal

at

a

source

node

-LRB-

Figure

-RRB-

whereas

signaling

flow

convergence

represents

the

consolidation

of

the

signals

at

a

target

node

from

two

source

nodes

-LRB-

Figure

-RRB-

Both

types

of

the

signaling

flows

are

the

basic

elements

of

the

network

architectural

organization

In

the

network

when

the

upstream

and

downstream

nodes

of

a

particular

signal

transduction

event

get

altered

either

genetically

or

epigenetically

we

considered

the

transduction

event

-LRB-

link

-RRB-

to

be

most

likely

selected

and

used

in

cancer

signaling

and

defined

it

as

an

oncogenic

signal

transduction

event

-LRB-

Figure

-RRB-

If

a

particular

oncogenic

signal

transduction

event

is

frequently

found

in

many

tumor

samples

we

infer

that

the

tumor

cells

are

dependent

on

this

highly

used

signaling

event

and

call

it

oncogene-signaling-dependent

event

-LRB-

Figure

-RRB-

To

investigate

how

cancer

signaling

is

distributed

on

these

signal

transduction

routes

we

extracted

all

the

branching

and

convergent

signaling

flow

units

that

contain

at

least

one

oncogenic

signal

transduction

event

and

conducted

a

quantitative

analysis

by

overlaying

the

gene

mutation

frequency

onto

these

units

The

mutation

frequency

of

a

gene

was

defined

as

the

number

of

tumor

samples

that

contain

that

mutated

gene

divided

by

the

total

number

of

the

tumor

samples

that

are

used

to

screen

the

mutations

for

that

gene

The

mutation

frequency

of

each

mutated

gene

was

obtained

by

using

the

COSMIC

database

which

contains

the

data

on

more

than

tumor

samples

screened

for

cancer

gene

mutations

For

the

signaling

branching

units

we

divided

the

signaling

flows

into

two

groups

activating

and

inhibitory

group

-LRB-

Figure

-RRB-

and

compared

the

gene

mutation

frequencies

of

the

upstream

nodes

with

those

of

the

downstream

nodes

in

each

group

Interestingly

in

the

activating

group

the

upstream

nodes

often

have

lower

mutation

frequencies

than

those

of

the

downstream

nodes

In

contrast

in

the

inhibitory

group

the

upstream

nodes

often

have

higher

mutation

frequencies

than

those

of

the

downstream

nodes

-LRB-

Table

I

-RRB-

Statistical

tests

confirmed

that

these

observations

are

statistically

significant

-LRB-

Table

I

-RRB-

Similar

results

were

obtained

for

the

signaling

convergent

units

as

well

-LRB-

Figure

Table

I

-RRB-

These

results

suggest

that

the

oncogene-signaling

event

triggered

by

mutations

is

preferentially

associated

with

activating

downstream

signaling

paths

or

conduits

Conversely

oncogene-signaling

event

triggered

by

mutations

are

less

likely

to

be

associated

with

downstream

inhibitory

signaling

paths

In

general

there

are

far

more

activating

signaling

flows

than

inhibitory

ones

in

the

network

Thus

we

hypothesized

that

the

downstream

genes

of

the

network

especially

the

genes

of

the

output

layer

of

the

network

would

have

a

higher

mutation

frequency

To

test

this

possibility

we

compared

the

average

gene

mutation

frequency

of

the

nuclear

proteins

which

represent

the

output

layer

members

of

the

network

with

that

of

the

other

network

genes

Indeed

the

nuclear

genes

have

higher

mutation

frequency

than

others

-LRB-

P

Wilcoxon

test

-RRB-

which

complements

with

our

previous

finding

that

cancer-associated

genes

are

enriched

in

the

nuclear

proteins

-LRB-

Awan

et

al

-RRB-

In

contrast

the

distributions

of

the

methylated

genes

have

no

such

preference

suggesting

that

DNA

methylated

genes

do

not

tend

to

directly

affect

the

output

layer

of

the

network

These

results

strongly

suggest

that

the

genes

in

the

output

layer

of

the

network

which

play

direct

and

important

roles

in

determining

phenotypic

outputs

are

frequent

targets

for

activating

mutations

The

importance

of

this

output

layer

is

reinforced

by

our

previous

observations

that

the

expression

of

the

output

layer

genes

of

the

signaling

network

is

heavily

regulated

by

microRNAs

-LRB-

Cui

et

al

-RRB-

Mutated

and

methylated

genes

are

enriched

in

positive

and

negative

regulatory

loops

respectively

The

complex

architecture

of

signaling

networks

can

be

regarded

as

consisting

of

interacting

network

motifs

which

are

statistically

overrepresented

subgraphs

that

appear

recurrently

in

networks

A

signaling

network

motif

also

known

as

regulatory

loops

in

biology

is

a

group

of

interacting

proteins

capable

of

signal

processing

They

bear

specific

regulatory

properties

and

mechanisms

-LRB-

Babu

et

al

Wang

and

Purisima

-RRB-

The

structure

and

the

intrinsic

properties

of

the

frequently

occurring

network

regulatory

motifs

give

us

a

functional

view

of

the

organization

of

signaling

networks

Thus

the

study

of

the

distributions

of

the

mutated

and

methylated

genes

in

the

network

motifs

will

provide

insights

into

cancer-signaling

regulatory

mechanisms

We

first

examined

the

mutated

genes

on

the

feed-forward

loops

in

which

the

first

protein

regulates

the

second

protein

and

both

proteins

regulate

the

third

protein

We

classified

the

feed-forward

loops

into

four

subgroups

-LRB-

labeled

-RRB-

based

on

the

number

of

nodes

that

are

mutated

genes

We

calculated

the

ratio

-LRB-

Ra

-RRB-

of

positive

-LRB-

activating

-RRB-

links

to

the

total

directed

-LRB-

positive

and

negative

-RRB-

links

in

each

subgroup

and

compared

it

with

the

average

Ra

in

all

the

feed-forward

loops

which

is

shown

as

a

horizontal

line

in

Supplementary

Figure

The

Ra

-LRB-

-RRB-

in

subgroup

is

less

than

the

average

Ra

-LRB-

-RRB-

of

all

the

feed-forward

loops

-LRB-

P

Fisher

's

test

-RRB-

However

as

the

number

of

mutated

nodes

rises

the

Ra

for

the

corresponding

group

increases

to

a

maximum

of

-LRB-

Supplementary

Figure

Supplementary

Table

-RRB-

We

obtained

similar

results

when

we

extended

the

same

analysis

to

all

the

and

network

motifs

-LRB-

Figure

Supplementary

Table

-RRB-

These

motifs

show

a

clear

positive

correlation

between

positive

link

ratio

and

the

number

of

mutated

genes

in

the

motifs

These

results

suggest

that

cancer

gene

mutations

occur

preferentially

in

positive

regulatory

motifs

In

contrast

all

the

and

size

motifs

show

an

obviously

negative

correlation

between

positive

link

ratio

and

the

number

of

methylated

genes

in

the

motifs

-LRB-

Figure

Supplementary

Table

-RRB-

These

results

suggest

that

cancer

gene

methylation

preferentially

occurs

in

negative

regulatory

motifs

A

similar

trend

was

found

for

the

known

tumor

suppressors

-LRB-

Supplementary

Figure

d

-RRB-

which

is

in

agreement

with

the

notion

that

cancer-associated

methylated

genes

play

roles

as

tumor

suppressors

Collectively

these

facts

suggest

that

mutated

and

methylated

genes

have

different

regulatory

mechanisms

in

cancer

signaling

and

support

the

notion

that

gene

mutations

and

methylations

are

strongly

selected

in

tumor

samples

Signaling

information

propagates

through

a

series

of

built-in

regulatory

motifs

to

contribute

to

cellular

phenotypic

functions

-LRB-

Ma'ayan

et

al

-RRB-

The

transition

from

a

normal

cellular

state

into

a

long-term

deregulated

state

such

as

cancer

is

often

driven

by

prolonged

activation

of

downstream

proteins

which

are

regulated

by

upstream

proteins

or

regulatory

motifs

or

circuits

Positive

regulatory

loops

-LRB-

Ferrell

-RRB-

could

amplify

signals

promote

the

persistence

of

signals

serve

as

information

storage

and

evoke

biological

responses

to

generate

phenotypes

such

as

cancer

In

cancer

cells

constitutive

activation

of

the

oncogene

signaling

is

necessary

Neutral

mutations

do

not

affect

protein

function

whereas

missense

mutations

may

have

positive

or

negative

effects

on

protein

activity

The

enrichment

of

gene

mutations

in

positive

regulatory

loops

suggests

that

the

mutants

in

the

motifs

must

have

gain

of

function

or

increase

their

biochemical

activities

compared

to

the

wild-type

genes

in

order

to

constitutively

activate

downstream

proteins

Indeed

a

recent

survey

showed

that

out

of

the

mutants

in

tumors

have

gain

of

function

-LRB-

Gymnopoulos

et

al

-RRB-

Gain-of-function

mutants

in

a

positive

regulatory

loop

afford

the

amplification

of

weak

input

stimuli

and

serve

as

information

storage

to

extend

the

duration

of

activation

of

the

affected

downstream

proteins

This

might

allow

the

downstream

signaling

cascades

to

persistently

hold

and

transfer

information

leading

to

tumor

phenotypes

Promoter

gene

methylation

is

a

known

mechanism

of

inducing

loss

of

function

by

inhibiting

the

expression

of

genes

-LRB-

Ohm

et

al

Widschwendter

et

al

-RRB-

Negative

regulatory

loops

controlled

by

tumor-suppressor

proteins

repress

positive

signals

and

play

an

important

role

in

maintaining

cellular

homeostasis

and

restraining

the

cellular

state

transitions

-LRB-

Ma'ayan

et

al

-RRB-

A

loss

of

function

of

gene

methylation

in

a

negative

regulatory

loop

could

break

the

negative

feedbacks

thereby

releasing

the

restrained

activation

signals

and

promoting

oncogenic

state

transitions

Homeostasis

relies

on

the

balance

between

positive

and

negative

signals

in

crucial

components

of

the

network

Both

the

gain-of-function

mutated

genes

in

positive

regulatory

loops

and

the

loss-of-function

methylated

genes

in

negative

regulatory

loops

could

break

this

delicate

balance

thus

promoting

state

transitions

and

generating

tumor

phenotypes

Therefore

both

mutated

and

methylated

genes

and

their

regulatory

loops

-LRB-

oncogenic

regulatory

loops

-RRB-

are

critical

components

of

the

network

where

the

oncogenic

stimuli

occur

An

oncogene-signaling

map

emerges

from

the

network

In

the

language

of

networks

genes

whose

mutations

or

epigenetic

silencing

are

crucial

to

trigger

oncogene

signaling

might

link

together

as

components

in

the

network

Identification

of

such

components

will

help

us

to

discover

the

relationships

and

structural

organizations

of

the

oncogenic

proteins

To

uncover

the

architecture

of

cancer

signaling

and

to

gain

insights

into

the

higher-order

regulatory

relationships

among

signaling

proteins

that

govern

oncogenic

signal

stimuli

we

mapped

all

genetic

mutations

and

epigenetically

silenced

genes

onto

the

network

We

found

that

most

of

these

genes

-LRB-

-RRB-

are

connected

together

to

form

a

giant

linked

network

component

Randomization

tests

confirmed

that

such

a

component

is

unlikely

to

be

formed

by

chance

-LRB-

P

-RRB-

To

build

an

oncogenic

map

we

included

other

mutated

and

methylated

genes

that

are

not

present

in

the

composition

of

the

component

into

the

giant

network

component

based

on

node

connectivity

-LRB-

see

Materials

and

methods

-RRB-

The

resulting

oncogene-signaling

map

consists

of

approximately

of

the

signaling

network

nodes

-LRB-

nodes

links

-RRB-

and

includes

almost

of

the

mutated

and

methylated

genes

-LRB-

Figure

-RRB-

The

map

showed

different

network

topological

characteristics

from

the

signaling

network

For

example

the

average

length

of

the

map

is

less

than

that

of

the

signaling

network

-LRB-

versus

P

Wilcoxon

test

-RRB-

On

the

other

hand

the

average

clustering

coefficient

of

the

map

is

greater

than

that

of

the

signaling

network

-LRB-

versus

P

Wilcoxon

test

-RRB-

These

results

suggest

that

oncogenic

proteins

tend

to

have

more

interactions

and

signaling

regulatory

relationships

The

emerging

oncogene-signaling

map

represents

a

hot

area

where

extensive

oncogene-signaling

events

might

occur

As

a

proof

of

concept

we

found

that

the

MAPK

kinase

and

TGFβ

pathways

which

are

well-known

cancer-signaling

pathways

are

embedded

in

the

map

For

example

out

of

proteins

in

the

MAPK

kinase

pathway

-LRB-

Supplementary

Table

-RRB-

and

out

of

proteins

in

the

TGFβ

pathway

-LRB-

Supplementary

Table

-RRB-

respectively

are

included

in

the

map

More

importantly

in

addition

to

known

oncogenic

pathways

there

are

many

other

novel

candidate

cancer-signaling

cascades

present

in

the

map

For

a

given

gene

mutation

in

a

tumor

one

could

use

this

map

to

generate

testable

hypotheses

to

discover

the

underlying

oncogene-signaling

cascades

in

that

tumor

As

mentioned

above

oncogene-signaling-dependent

events

which

we

define

as

the

interactions

between

the

cancer

mutated

or

methylated

genes

are

frequently

found

in

tumor

samples

and

represent

various

oncogene-driving

events

that

could

play

more

critical

roles

in

generating

tumor

phenotypes

To

systematically

identify

such

events

and

discover

how

they

are

organized

in

the

map

we

charted

the

gene

mutation

frequency

onto

the

map

and

highlighted

the

signaling

links

between

any

two

genes

that

have

high

mutation

frequencies

Most

genes

have

mutation

frequencies

lower

than

whereas

a

handful

of

genes

have

very

high

mutation

frequencies

such

as

-LRB-

-RRB-

-LRB-

-RRB-

and

RAS

-LRB-

-RRB-

-LRB-

see

Materials

and

methods

-RRB-

Therefore

a

gene

mutation

frequency

equal

to

or

greater

than

was

considered

as

high

Interestingly

nearly

of

the

links

in

the

map

are

oncogene-signaling-dependent

events

Certain

signaling

events

such

as

and

in

the

map

are

well-known

oncogene-signaling-dependent

events/cascades

that

are

frequently

used

in

various

cancers

As

shown

in

Figure

most

oncogene-signaling-dependent

events

are

connected

and

three

major

regions

that

contain

densely

connected

oncogene-signaling-dependent

events

emerge

in

the

map

the

first

region

-LRB-

region

-RRB-

contains

mainly

tumor

suppressors

such

as

Rb

and

-LRB-

-RRB-

etc.

the

second

region

-LRB-

RAS

region

-RRB-

contains

mainly

well-known

oncogenes

such

as

RAS

EGFR

and

etc.

and

the

third

region

-LRB-

TGFβ

region

-RRB-

contains

and

a

few

other

TGFβ-signaling

proteins

Interestingly

genes

in

the

and

TGFβ

regions

are

also

heavily

methylated

in

cancer

stem

cells

suggesting

that

these

regions

are

involved

in

the

early

stage

of

oncogenesis

Other

methylated

genes

are

intertwined

with

the

mutated

genes

in

the

map

suggesting

that

they

share

some

oncogene-signaling

cascades

and

might

be

regulated

to

cooperate

in

cancer

signaling

via

gene

mutation

and/or

methylation

Notably

it

seems

that

in

cancer

stem

cells

TGFβ-signaling

pathway

is

shut

down

supporting

its

known

role

as

a

tumor

suppressor

in

the

early

stages

of

tumorigenesis

-LRB-

Hanahan

and

Weinberg

Siegel

and

Massague

-RRB-

These

results

suggest

that

the

crucial

players

of

oncogene

signaling

tend

to

be

closely

clustered

and

regionalized

This

map

uncovers

the

architectural

structure

of

the

basic

oncogene

signaling

and

highlights

the

signaling

events

that

are

highly

conserved

in

generating

tumor

phenotypes

Functional

collaboration

of

genes

between

oncogene-signaling

blocks

The

oncogene-signaling

map

can

be

decomposed

into

several

network

communities

or

network

themes

-LRB-

Zhang

et

al

-RRB-

in

which

each

network

community

contains

a

set

of

more

closely

linked

nodes

and

ties

to

particular

biological

functions

To

discover

such

network

communities

we

implemented

and

applied

an

algorithm

that

detects

network

communities

to

the

map

As

a

result

network

communities

ranging

in

size

from

nodes

-LRB-

Supplementary

Table

-RRB-

called

oncogene-signaling

blocks

were

found

in

the

map

Structurally

the

nodes

within

each

block

have

more

links

and

signaling

regulatory

relations

among

themselves

than

others

The

genes

in

each

block

share

similar

biological

functions

such

as

cell

proliferation

development

and

apoptosis

-LRB-

Supplementary

Table

-RRB-

We

further

performed

Gene

Ontology

-LRB-

GO

-RRB-

enrichment

analysis

for

each

oncogene-signaling

block

using

DAVID

Tools

-LRB-

-RRB-

Most

of

the

oncogene-signaling

blocks

are

enriched

with

protein

serine/threonine

kinase

activity

-LRB-

Supplementary

Table

-RRB-

which

is

well

known

to

take

part

in

tumorigenesis

Notably

Block

is

enriched

with

cell

surface

receptor-linked

signaling

whereas

Block

is

enriched

with

intracellular

signaling

cascades

Block

is

enriched

with

tumor

suppressors

and

biological

processes

such

as

apoptosis

and

cell

cycle

These

results

suggest

that

certain

blocks

are

taking

part

in

different

parts/kinds

of

signaling

that

is

cell

surface

receptor-related

signaling

intracellular

signaling

cascade

signaling

and

apoptotic

signaling

However

three

oncogene-signaling

blocks

have

no

GO

enrichment

detected

One

of

the

reasons

is

that

a

fraction

of

the

genes

in

these

blocks

is

not

well

annotated

yet

For

example

about

one-third

of

genes

in

Block

have

no

GO

term

associated

We

asked

if

the

genes

in

each

block

could

operate

in

a

compensatory

or

concerted

manner

to

govern

a

set

of

similar

functions

Toward

this

end

we

surveyed

the

gene

mutations

in

tumor

samples

where

at

least

two

genes

are

screened

for

mutations

As

a

result

the

co-occurrence

in

tumor

samples

of

mutated

gene

pairs

is

found

to

be

statistically

significant

-LRB-

Supplementary

Table

-RRB-

Significantly

only

three

collaborative

gene

pairs

came

from

the

same

block

whereas

other

collaborative

gene

pairs

came

from

two

different

blocks

with

predominantly

one

of

them

arising

from

Block

-LRB-

defined

as

block

-RRB-

which

contains

Rb

and

several

other

genes

involved

in

control

of

DNA

damage

repair

and

cell

division

Collectively

these

results

suggest

that

the

signaling

genes

from

different

blocks

most

likely

work

together

in

a

complementary

way

to

generate

tumor

phenotypes

We

further

asked

which

oncogene-signaling

blocks

work

together

to

produce

a

tumor

phenotype

To

address

this

question

we

surveyed

the

gene

mutations

in

the

tumor

samples

where

at

least

two

gene

mutations

are

found

In

total

tumor

samples

fit

this

criterion

We

used

the

samples

to

build

a

matrix

-LRB-

M

-RRB-

where

samples

are

rows

and

the

signaling

blocks

are

columns

If

a

gene

of

a

particular

signaling

block

-LRB-

b

-RRB-

gets

mutated

in

a

tumor

sample

-LRB-

s

-RRB-

we

set

Ms

b

to

otherwise

we

set

Ms

b

to

A

heatmap

was

generated

using

the

matrix

-LRB-

Figure

-RRB-

If

a

sample

contains

statistically

significant

co-occurring

mutated

gene

pairs

-LRB-

see

Supplementary

Table

-RRB-

these

pairs

were

highlighted

in

the

heatmap

Samples

were

organized

based

on

the

cancer

types

they

belong

to

Several

cancer

types

such

as

breast

central

nervous

system

blood

lung

pancreas

and

skin

tumors

that

have

relatively

more

samples

were

also

highlighted

in

the

heatmap

As

shown

in

Figure

two

signaling

blocks

have

statistically

significant

enrichment

of

gene

mutations

-LRB-

P

randomization

tests

-RRB-

suggesting

that

genes

in

these

two

signaling

blocks

are

predominantly

used

to

generate

tumor

phenotypes

One

oncogene-signaling

block

-LRB-

Block

defined

as

RAS

block

-RRB-

contains

genes

like

RAS

EGFR

and

etc.

which

share

similar

biological

functions

such

as

cell

proliferation

cell

survival

and

cell

growth

whereas

the

other

is

the

block

which

share

similar

biological

functions

such

as

cell

cycle

checkpoint

control

apoptosis

and

affecting

genomic

instability

-LRB-

Supplementary

Table

-RRB-

These

two

blocks

also

represent

the

two

oncogene-signaling-dependent

regions

-LRB-

and

RAS

regions

-RRB-

in

Figure

respectively

When

a

tumor

sample

has

a

mutation

in

a

gene

from

the

RAS-signaling

block

it

is

also

most

likely

to

contain

a

mutation

in

a

gene

from

the

block

-LRB-

P

-RRB-

To

check

if

this

phenomenon

is

primarily

due

to

a

particular

pair

of

genes

we

calculated

the

likelihood

of

co-occurrence

for

each

pair

of

the

genes

of

which

one

gene

is

mutated

in

one

block

and

the

other

gene

is

mutated

in

the

other

block

We

found

that

the

P-values

for

gene

pairs

are

always

significantly

greater

than

that

for

the

pair

of

Blocks

RAS

and

For

example

the

P-value

of

co-occurrence

of

RAS

-LRB-

in

Block

RAS

-RRB-

and

-LRB-

in

Block

-RRB-

mutations

is

which

is

greater

than

that

of

the

two

blocks

-LRB-

P

-RRB-

This

indicates

that

these

two

oncogene-signaling

blocks

collaborate

to

generate

tumor

phenotypes

for

most

tumors

Experimental

examples

have

shown

similar

gene

collaboration

in

tumorigenesis

activation

of

RAS

-LRB-

RAS

block

-RRB-

and

inactivation

of

-LRB-

block

-RRB-

induce

lung

tumors

-LRB-

Meuwissen

and

Berns

-RRB-

whereas

activation

of

RAS

-LRB-

RAS

block

-RRB-

and

inactivation

of

-LRB-

block

-RRB-

induce

pancreatic

tumors

-LRB-

Obata

et

al

-RRB-

In

general

tumor

cells

exhibit

either

elevated

cell

proliferation

or

reduced

differentiation

or

apoptosis

relative

to

normal

cells

The

oncogenic

blocks

we

have

identified

especially

the

RAS

and

blocks

encode

functions

that

are

tumor-related

such

as

cell

cycle

control

cell

proliferation

and

apoptosis

-LRB-

Supplementary

Figure

-RRB-

Activation

of

genes

in

the

RAS

block

promotes

the

cell

proliferation

whereas

inactivation

of

genes

in

the

block

prevents

apoptosis

Thus

a

functional

collaboration

between

the

genes

in

these

two

blocks

would

promote

synergistic

cancer

signaling

and

foster

tumorigenesis

Notably

we

found

that

at

least

one

gene

mutation

in

the

block

had

occurred

in

the

tumor

samples

we

examined

In

other

words

the

block

is

involved

in

generating

tumors

for

most

cancers

This

result

suggests

that

the

block

is

a

central

oncogene-signaling

player

and

essential

in

tumorigenesis

This

finding

is

further

supported

by

the

following

observations

-LRB-

a

-RRB-

To

become

oncogenic

tumor

suppressors

require

loss-of-function

mutations

which

occurs

more

often

than

gain-of-function

mutations

-LRB-

Gymnopoulos

et

al

-RRB-

Indeed

the

average

gene

mutation

frequency

in

the

block

is

higher

than

that

of

other

signaling

blocks

including

the

RAS

block

-LRB-

b

-RRB-

The

methylation

of

genes

in

the

cancer

stem

cells

resulting

in

long-term

loss

of

expression

represents

the

early

stage

of

the

tumorigenesis

In

fact

most

of

the

members

of

the

block

are

methylated

in

cancer

stem

cells

These

facts

further

support

that

the

block

might

play

an

important

role

in

the

earlier

stages

of

oncogenesis

-LRB-

c

-RRB-

Gene

methylation

or

inactivating

mutations

of

DNA

damage

checkpoint

genes

such

as

induce

genome

instability

and

thus

increase

the

chance

of

other

gene

mutations

including

the

genes

of

other

oncogene-signaling

blocks

that

could

functionally

collaborate

with

the

block

genes

to

generate

tumor

phenotypes

Using

the

map

as

a

framework

we

benchmarked

the

mutated

genes

in

the

cell

lines

which

represent

a

panel

of

well

characterized

cancer

cell

lines

and

various

cancer

types

A

systematic

mutation

analysis

of

known

cancer

genes

showed

that

most

cell

lines

have

at

least

two

mutations

among

the

cancer

genes

examined

-LRB-

Ikediobi

et

al

-RRB-

We

built

a

matrix

and

constructed

a

heatmap

using

these

cell

lines

and

their

mutated

genes

as

described

above

-LRB-

Figure

-RRB-

Overall

the

pattern

obtained

from

the

panel

resembles

that

of

the

tumor

panel

with

both

RAS

and

blocks

enriched

with

gene

mutations

and

exhibiting

statistically

significant

collaborations

in

these

cell

lines

-LRB-

Figure

P

-RRB-

which

is

in

agreement

with

the

earlier

observations

We

also

benchmarked

the

mutated

genes

derived

from

a

genome-wide

sequencing

of

tumor

samples

-LRB-

Sjoblom

et

al

-RRB-

Among

these

samples

breast

and

colon

tumor

samples

have

at

least

two

gene

mutations

in

the

map

As

shown

in

Figure

and

D

the

block

is

enriched

with

gene

mutations

For

the

colon

tumor

samples

collaboration

between

Block

and

Block

is

established

but

for

the

breast

tumors

collaborative

patterns

between

multiple

blocks

emerged

To

further

examine

the

block

collaborative

patterns

in

individual

tumor

types

in

higher

resolutions

from

the

heatmap

-LRB-

Figure

-RRB-

we

extracted

the

sub-heatmap

for

several

tumor

types

that

are

better

represented

among

the

tumor

samples

that

is

they

have

relatively

more

samples

within

the

samples

-LRB-

Figure

-RRB-

As

shown

in

Figure

signaling

block

collaborative

patterns

are

tissue

dependent

and

are

classified

into

two

groups

One

group

contains

pancreas

skin

central

nervous

system

and

blood

tumors

that

have

simple

block

collaborative

patterns

In

these

tumors

signaling

collaborations

are

mainly

between

Block

Block

RAS

with

some

minor

contributions

from

Blocks

or

suggesting

that

they

predominantly

use

these

oncogene-signaling

routes

to

generate

tumors

resulting

in

relatively

homogenous

cancer

cell

types

The

other

group

contains

breast

and

lung

tumors

that

also

contain

large

proportions

of

mutations

from

the

block

but

also

have

complex

patterns

of

collaborations

between

assortments

of

multiple

blocks

suggesting

that

these

tumors

may

have

a

larger

variety

of

oncogene-signaling

routes

which

may

explain

in

part

the

heterogeneous

nature

of

the

tumor

subtypes

in

this

category

These

results

might

also

explain

why

both

lung

and

breast

cancers

are

the

most

common

types

of

human

tumors

In

this

study

the

cancer

mutated

genes

were

collected

from

a

directed

approach

-LRB-

i.e.

mutational

analysis

of

specific

genes

such

as

-RRB-

and

a

large-scale

approach

-LRB-

i.e.

large-scale

sequencing

of

tumor

samples

-RRB-

We

tested

whether

the

mutated

genes

from

the

directed

approach

introduce

bias

to

our

analysis

Literature-curated

cancer

mutated

genes

-LRB-

directed

approach

-RRB-

have

been

assembled

in

the

Cancer

Gene

Census

-LRB-

Futreal

et

al

-RRB-

of

which

genes

were

found

in

the

human

signaling

network

As

of

November

among

the

Cancer

Gene

Census

genes

mutations

in

of

them

have

been

further

validated

by

additional

experimental

evidence

-LRB-

i.e.

other

independent

experiments

confirming

the

mutation

of

these

specific

genes

in

cancer

samples

have

been

documented

in

the

COSMIC

database

-RRB-

whereas

of

them

have

no

such

evidence

in

the

COSMIC

database

-LRB-

see

Materials

and

methods

-RRB-

In

fact

we

included

only

these

literature-curated

genes

in

the

cancer

mutated

gene

set

-LRB-

genes

-RRB-

used

in

all

of

our

analyses

above

-LRB-

see

Materials

and

methods

-RRB-

Of

the

literature-curated

genes

only

were

not

already

present

in

the

output

of

large-scale

sequencing

of

tumor

samples

-LRB-

Figure

-RRB-

We

removed

these

genes

from

the

cancer

mutated

gene

set

-LRB-

genes

-RRB-

mapped

rest

of

the

genes

onto

the

human

signaling

network

and

rebuilt

an

oncogene-signaling

map

oncogene-signaling

blocks

and

a

heatmap

On

the

other

hand

we

added

the

literature-curated

genes

which

have

no

independent

supporting

evidence

in

the

COSMIC

database

to

the

set

and

obtained

genes

Using

these

genes

we

reran

the

analyses

mentioned

above

In

these

two

analyses

although

the

gene

members

of

each

oncogene-signaling

block

have

some

minor

differences

with

those

of

the

original

blocks

the

major

collaboration

patterns

of

oncogene-signaling

blocks

remain

largely

unchanged

-LRB-

Supplementary

Figure

and

b

-RRB-

suggesting

that

our

findings

are

robust

to

addition

or

removal

of

the

cancer

mutated

genes

derived

from

the

directed

approach

The

mutated

genes

in

the

network

provide

a

predictive

power

A

substantial

number

-LRB-

-RRB-

of

mutated

genes

were

found

in

the

network

We

asked

if

a

gene

that

has

more

links

to

mutated

genes

in

the

network

is

most

likely

to

be

cancer

associated

To

answer

this

question

we

extracted

the

nonmutated

genes

that

have

more

than

one

link

to

the

mutated

genes

and

then

grouped

them

based

on

their

link

numbers

to

the

mutated

genes

We

interrogated

a

cancer-associated

gene

set

-LRB-

Supplementary

Table

-RRB-

compiled

from

literature

mining

-LRB-

see

Materials

and

methods

-RRB-

to

find

out

how

many

genes

in

each

group

are

cancer

associated

As

shown

in

Figure

the

more

mutated

genes

a

gene

links

to

the

more

probably

it

is

cancer

associated

When

the

link

number

of

the

network

genes

is

more

than

six

of

them

are

cancer-associated

genes

For

example

SHC

a

gene

that

has

been

implicated

in

cancer

metastasis

-LRB-

Jackson

et

al

-RRB-

has

numerous

links

to

the

mutated

genes

in

the

network

To

further

investigate

the

predictive

power

of

the

mutated

genes

in

the

network

we

took

the

network

genes

which

not

only

have

at

least

four

links

to

the

mutated

genes

but

also

are

not

implicated

in

cancer

in

the

literature

to

perform

a

survival

analysis

using

a

microarray

data

set

that

contains

the

gene

expression

profiles

and

survival

information

for

breast

tumor

samples

As

a

result

the

expression

profiles

of

out

of

the

genes

-LRB-

-RRB-

are

able

to

discriminate

good

and

bad

tumors

-LRB-

i.e.

patients

having

bad

tumors

have

higher

chance

of

tumor

recurrence

and

short

survival

time

-RRB-

Therefore

these

genes

are

potentially

novel

biomarkers

In

contrast

less

than

of

the

nonmutated

network

genes

have

similar

discriminatory

power

These

results

suggest

that

the

network

genes

which

have

more

links

to

the

mutated

genes

have

more

chance

to

be

perturbed

in

tumorigenesis

and

be

associated

with

cancer

Practically

the

mutated

genes

in

the

network

provide

a

predictive

power

that

can

be

used

to

discover

novel

biomarkers

of

tumors

Concluding

remarks

Although

a

wide

variety

of

genetic

and

epigenetic

events

contribute

to

the

signaling

of

tumorigenesis

it

has

been

challenging

to

gain

a

global

view

of

where

and

how

they

affect

the

signaling

alterations

to

generate

tumors

on

the

entire

signaling

network

By

integrative

analysis

of

the

human

signaling

network

with

cancer-associated

mutated

and

methylated

genes

we

uncovered

an

overall

picture

of

the

network

architecture

where

the

oncogenic

stimuli

occur

and

the

regulatory

mechanisms

involving

mutated

and

methylated

genes

Mutations

the

majority

of

which

are

activating

preferentially

occur

in

the

signaling

hub

genes

-LRB-

but

not

neutral

hubs

-RRB-

and

the

genes

of

the

positive

regulatory

loops

whereas

methylated

alterations

tend

to

occur

in

the

genes

of

the

negative

regulatory

loops

Cancer

and

cell

signaling

have

been

well

established

and

extensive

efforts

have

been

made

to

illustrate

cancer

signaling

during

the

past

few

decades

However

it

has

been

a

struggle

to

get

clues

of

how

the

oncogene

signaling

is

structurally

and

functionally

organized

In

this

analysis

we

extracted

an

oncogene-signaling

map

which

provides

a

blueprint

of

the

oncogene

signaling

in

cancer

cells

From

the

map

we

discerned

that

the

oncogene-signaling-dependent

events

form

three

highly

connected

regions

that

resemble

oncogene-signaling

superhighways

frequently

used

in

tumorigenesis

Topologically

the

map

has

been

divided

into

oncogene-signaling

blocks

Functional

collaborations

between

subsets

of

these

blocks

are

underlying

tumorigenesis

In

most

tumors

genes

in

both

and

RAS

blocks

often

get

mutated

although

the

combinations

of

with

other

signaling

blocks

are

also

found

in

a

small

fraction

of

tumors

Analysis

of

the

cell

line

panel

mutations

showed

the

enrichment

of

gene

mutations

in

and

RAS

blocks

which

is

similar

to

the

patterns

found

in

the

samples

Furthermore

we

can

dissect

some

of

this

functional

collaboration

among

different

tumor

types

These

facts

indicate

that

at

least

two

signaling

gene

mutations

one

from

the

block

and

the

other

from

another

block

are

necessary

for

tumorigenesis

This

fact

supports

the

notion

that

both

the

prevention

of

cell

death

-LRB-

block

-RRB-

and

the

promotion

of

cell

proliferation

-LRB-

RAS

or

other

blocks

-RRB-

are

necessary

to

generate

most

tumors

At

present

a

number

of

researchers

doubt

or

even

argue

against

the

value

of

large-scale

human

cancer

genome

sequencing

as

a

meaningful

or

efficient

strategy

in

cancer

research

Their

arguments

are

based

on

the

following

observations

-LRB-

Chng

-RRB-

-LRB-

a

-RRB-

previous

large-scale

human

cancer

genome

sequencing

revealed

that

each

tumor

has

a

different

mutation

pattern

and

the

prevalence

and

patterns

of

somatic

mutations

in

human

cancers

are

tremendously

diverse

and

complex

-LRB-

Kaiser

Sjoblom

et

al

Greenman

et

al

-RRB-

-LRB-

b

-RRB-

the

interpretation

of

such

complex

somatic

alterations

is

a

formidable

challenge

-LRB-

Chanock

and

Thomas

Thomas

et

al

-RRB-

We

mapped

the

mutation

data

from

the

genome-wide

sequencing

tumor

samples

-LRB-

Sjoblom

et

al

-RRB-

using

the

oncogene-signaling

map

as

a

framework

Although

the

number

of

mutated

genes

is

impressive

in

toto

most

signaling

gene

mutations

are

limited

to

critical

mutations

divided

among

several

signaling

blocks

per

individual

tumor

This

result

suggests

that

the

mutations

in

the

samples

of

the

same

tumor

type

might

share

a

similar

underlying

signaling

mechanism

because

each

oncogene-signaling

block

contains

a

set

of

genes

linked

together

through

shared

regulatory

relations

and

key

input

and/or

output

signaling

nodes

that

are

involved

in

tumorigenesis

These

findings

imply

that

although

the

mutations

seem

tremendously

diverse

and

complex

at

the

gene

level

clear

patterns

emerge

recurrently

at

the

network

level

in

most

tumors

Therefore

with

proper

bioinformatics

analysis

large-scale

cancer

genome

sequencing

efforts

would

be

fruitful

in

finding

appropriate

combinations

of

biological

targets

for

cancer

diagnostic

and

therapeutics

In

summary

this

work

revealed

novel

insights

into

the

oncogenic

regulatory

mechanisms

oncogene-signaling

network

architecture

and

oncogene-signaling

cooperative

relationships

that

drive

cancer

development

and

progression

It

also

highlights

the

emergence

of

the

central

players

in

cancer

signaling

Cancer

studies

have

integrated

microarray

knowledge

pathways

and

networks

-LRB-

Liu

and

Lemberger

-RRB-

but

not

genetic

and

epigenetic

data

yet

However

as

the

next

generation

of

genome

sequencing

technology

becomes

more

accessible

and

affordable

much

more

efforts

involving

genome-wide

sequencing

of

large

number

of

tumor

genomes

will

be

conducted

Our

work

provides

a

conceptual

and

technical

framework

for

incorporating

the

genome

sequencing

outputs

and

other

types

of

data

such

as

microarray

profiles

to

get

more

insights

into

the

cancer-signaling

mechanisms

that

will

facilitate

the

identification

of

key

genes

for

biomarkers

and

drug

development

Materials

and

methods

Data

sets

used

in

this

study

Human

signaling

network

To

build

up

the

human

signaling

network

we

manually

curated

the

signaling

molecules

-LRB-

most

of

them

are

proteins

-RRB-

and

the

interactions

between

these

molecules

from

the

most

comprehensive

signaling

pathway

database

BioCarta

-LRB-

-RRB-

The

pathways

in

the

database

are

illustrated

as

diagrams

We

manually

recorded

the

names

functions

cellular

locations

biochemical

classifications

and

the

regulatory

-LRB-

including

activating

and

inhibitory

-RRB-

and

interaction

relations

of

the

signaling

molecules

for

each

signaling

pathway

To

ensure

the

accuracy

of

the

curation

all

the

data

have

been

crosschecked

four

times

by

different

researchers

After

combining

the

curated

information

with

another

literature-mined

signaling

network

that

contains

signaling

molecules

-LRB-

Ma'ayan

et

al

-RRB-

we

obtained

a

signaling

network

containing

proteins

-LRB-

Awan

et

al

-RRB-

We

further

extended

this

network

by

extracting

and

adding

the

signaling

molecules

and

their

relations

from

the

Cancer

Cell

Map

-LRB-

-RRB-

a

database

that

contains

manually

curated

signaling

pathways

for

cancer

As

a

result

the

network

contains

nodes

and

links

that

include

activation

links

-LRB-

positive

links

-RRB-

inhibitory

links

-LRB-

negative

links

-RRB-

physical

links

-LRB-

neutral

links

-RRB-

and

links

whose

types

are

unknown

-LRB-

Supplementary

Table

-RRB-

To

our

knowledge

this

network

is

the

biggest

cellular

signaling

network

at

present

Cancer

mutated

genes

The

cancer

mutated

genes

were

taken

from

the

COSMIC

database

-LRB-

-RRB-

and

other

large-scale

or

genome-wide

sequencing

of

tumor

samples

-LRB-

Sjoblom

et

al

Greenman

et

al

Thomas

et

al

-RRB-

COSMIC

database

contains

manually

curated

cancer

mutated

genes

and

the

information

of

tumor

samples

mutated

sequences

from

literature

and

the

output

from

the

CGP

's

large-scale

sequencing

of

tumor

samples

-LRB-

Davies

et

al

Stephens

et

al

Greenman

et

al

-RRB-

The

literature-curated

genes

were

compiled

as

the

Cancer

Gene

Census

-LRB-

Futreal

et

al

-RRB-

which

is

accessible

in

COSMIC

database

The

CGP

is

using

human

genome

sequences

and

high-throughput

mutation

detection

techniques

to

identify

somatically

acquired

sequence

mutations

and

hence

to

identify

genes

critical

in

the

development

of

human

cancers

A

few

recent

publications

-LRB-

Davies

et

al

Stephens

et

al

Greenman

et

al

-RRB-

represent

a

small

fraction

of

the

CGP

output

In

addition

COSMIC

database

has

provided

mutation

frequencies

for

most

of

the

cancer

mutated

genes

The

cancer

gene

mutation

frequency

of

a

gene

is

defined

as

the

ratio

of

samples

containing

the

mutated

gene

to

the

total

samples

screened

for

that

gene

In

the

database

about

one-third

of

the

literature-curated

mutated

genes

-LRB-

Cancer

Gene

Census

genes

-RRB-

have

nonzero

mutation

frequencies

suggesting

that

the

literature

curation

of

these

genes

-LRB-

i.e.

included

them

into

the

Cancer

Gene

Census

-RRB-

has

been

supported

by

one

or

more

other

independent

experiments

For

the

network

analysis

in

this

study

we

first

intersected

the

network

genes

with

the

literature-curated

mutated

genes

As

a

result

we

obtained

genes

-LRB-

Supplementary

Table

-RRB-

of

which

genes

-LRB-

Supplementary

Table

-RRB-

have

nonzero

mutation

frequencies

Meanwhile

we

intersected

the

network

genes

with

the

mutated

genes

derived

from

the

CGP

large-scale

sequencing

output

and

several

other

genome-wide

and

high-throughput

sequencing

of

tumor

samples

-LRB-

Stephens

et

al

Sjoblom

et

al

Greenman

et

al

Thomas

et

al

-RRB-

As

a

result

we

obtained

another

gene

set

containing

genes

Finally

we

obtained

genes

by

merging

the

genes

and

the

genes

mentioned

above

Among

these

genes

-LRB-

-RRB-

and

-LRB-

-RRB-

genes

were

collected

from

the

large-scale

sequencing

of

tumors

and

literature

curation

respectively

-LRB-

Figure

-RRB-

Notably

genes

-LRB-

-RRB-

of

the

literature-curated

genes

were

overlapped

with

the

mutated

genes

derived

from

the

large-scale

gene

sequencing

of

tumors

The

genes

and

their

mutation

frequencies

from

sequencing

of

tumors

and

literature

were

collected

in

Supplementary

Table

Methylated

genes

in

cancer

stem

cells

We

obtained

DNA-methylated

genes

from

the

three

recent

genome-wide

determinations

of

the

methylated

genes

from

cancer

stem

cells

-LRB-

Ohm

et

al

Schlesinger

et

al

Widschwendter

et

al

-RRB-

Out

of

the

genes

were

mapped

onto

the

human

signaling

network

-LRB-

Supplementary

Table

-RRB-

Cancer-associated

gene

set

The

cancer-associated

gene

set

contains

the

following

data

sources

-LRB-

a

-RRB-

the

cancer

mutated

genes

we

mentioned

above

-LRB-

b

-RRB-

a

literature-mined

breast

cancer

gene

set

from

plasmID

database

-LRB-

-RRB-

-LRB-

c

-RRB-

the

genes

extracted

from

the

NCBI

's

Online

Mendelian

Inheritance

in

Man

-LRB-

OMIM

-RRB-

data

set

using

the

keywords

such

as

cancer

tumor

and

onco

etc.

The

cancer-associated

gene

list

contains

genes

-LRB-

Supplementary

Table

-RRB-

Microarray

data

Gene

expression

profiles

and

the

patients

survival

data

for

the

breast

tumor

samples

were

obtained

from

Chang

et

al

-LRB-

-RRB-

Oncogenic

map

extraction

To

extract

an

oncogenic

map

from

the

human

signaling

network

we

mapped

all

the

mutated

and

methylated

genes

onto

the

network

As

a

result

of

these

genes

are

connected

together

to

form

a

giant

linked

network

component

To

include

the

mutated

and

methylated

genes

that

are

not

present

in

this

network

component

we

first

found

one

shortest

path

between

such

a

gene

and

a

component

node

If

the

length

of

the

shortest

path

is

-LRB-

i.e.

the

gene

reaches

one

of

the

component

nodes

via

a

nonmutated

network

node

-RRB-

we

linked

that

gene

and

the

node

on

the

shortest

path

into

the

component

A

Java

program

had

been

written

to

implement

this

procedure

-LRB-

Supplementary

File

-RRB-

Network

analysis

To

extract

the

members

of

the

branching

and

convergent

units

and

and

network

motifs

mfinder

program

-LRB-

Kashtan

et

al

-RRB-

was

used

To

detect

the

signaling

network

communities

from

the

oncogene-signaling

map

we

applied

a

network

community

algorithm

-LRB-

Newman

-RRB-

Analyzing

the

enrichment

of

the

mutated

and

methylated

genes

in

the

network

motifs

We

mapped

the

mutated

and

methylated

genes

onto

each

type

of

the

motifs

We

then

counted

the

number

of

mutated

or

methylated

genes

in

each

motif

and

classified

each

type

of

motif

into

several

subgroups

based

on

the

number

of

nodes

that

are

mutated

or

methylated

genes

We

then

calculated

the

ratio

-LRB-

Ra

-RRB-

of

the

activation

links

to

the

total

activation

and

inhibitory

links

in

each

subgroup

Randomization

tests

We

performed

randomization

tests

to

evaluate

the

statistical

significance

of

the

observations

A

more

detailed

explanation

of

the

randomization

tests

was

described

previously

by

Wang

and

Purisima

-LRB-

-RRB-

Survival

analysis

To

evaluate

the

prognostic

value

of

a

gene

based

on

the

gene

expression

profiles

and

the

survival

information

of

the

tumor

samples

we

performed

Kaplan

Meier

analysis

by

implementing

the

Cox

Mantel

log-rank

test

using

R

a

statistical

computing

language

-LRB-

-RRB-

If

the

P-value

is

less

than

the

gene

was

thought

as

statistically

significant

to

classify

the

tumor

samples

into

good

and

bad

groups

Supplementary

Material

Supplementary

Figures

Supplementary

Table

Supplementary

Table

Supplementary

Table

Supplementary

Table

Supplementary

Table

Supplementary

Table

Supplementary

Table

Supplementary

Table

Supplementary

Table

Supplementary

Table

Supplementary

Table

Supplementary

Java

File

Supplementary

Tables

and

Java

File

Legends

O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

cancer signaling
gene mutation
signaling network
tumorigenesis

cancer-signaling map
dna methylation
oncogene-signaling dependence


Preconception Care and Treatment with Assisted Reproductive Technologies Couples with fertility problems seeking treatment with assisted reproductive technologies -LRB- ART -RRB- such as in vitro fertilization should receive preconception counseling on all factors that are provided when counseling patients without fertility problems . Additional counseling should address success rates and possible risks from ART therapies . Success rates from ART are improving , with the highest live birth rates averaging about 40 % per cycle among women less than 35 years old . A woman 's age lowers the chance of achieving a live birth , as do smoking , obesity , and infertility diagnoses such as hydrosalpinx , uterine leiomyoma , or male factor infertility . Singletons conceived with ART may have lower birth weights . Animal studies suggest that genetic imprinting disorders may be induced by certain embryo culture conditions . The major risk from ovarian stimulation is multiple gestation . About one-third of live-birth deliveries from ART have more than one infant , and twins represent 85 % of these multiple-birth children . There are more complications in multiple gestation pregnancies , infants are more likely to be born preterm and with other health problems , and families caring for multiples experience more stress . Transferring fewer embryos per cycle reduces the multiple birth rate from ART , but the patient may have to pay for additional cycles of ART because of a lower likelihood of pregnancy . Current knowledge Fertility problems affect about 10 % of couples in developed countries -LSB- 1 -- 3 -RSB- . These couples should receive preconception counseling on all factors that are discussed during preconception counseling for patients without fertility problems . Additional counseling should address success rates and possible risks from the specific fertility treatments being considered because of the couple 's diagnoses and preferences . To provide an example of an approach to preconception counseling for the infertile couple , treatment with assisted reproductive technologies -LRB- ART -RRB- such as in vitro fertilization -LRB- IVF -RRB- is reviewed here . About 1 % of births in the United States are the result of ART -LSB- 4 -RSB- . ART care represents a unique opportunity for preconception counseling because the moment of conception is easily discernable . Counseling should address expected success rates from ART based on the woman and the man 's risk profile . Older maternal age is the strongest risk factor for not achieving a successful outcome from ART . Live birth rates average about 40 % per cycle among women less than 35 years old , and gradually decrease to about 6 % per cycle among women over age 42 who use their own oocytes -LSB- 4 -RSB- . Older women who accept donor oocytes increase their chance of a live birth to approximately the rate seen in the age group of the donor woman -LSB- 5 -RSB- . Cumulative live birth rates account for the fact that many couples return for further treatment if the first cycle of ART is unsuccessful . For couples who complete two to three ART cycles , cumulative live birth rates are about 50 -- 60 % for women 35 years old or younger , declining to about 30 % by age 40 -LSB- 6 -RSB- . As men 's age increases , the time required for a couple to conceive lengthens , even after controlling for the woman 's age and other risk factors for reduced fertility -LSB- 7 -RSB- . The chance of having a live birth from ART therapy is influenced by the health habits and the infertility diagnoses of the couple . Current tobacco smoking by women decreases ovarian function and is manifested by increased basal levels of follicle stimulating hormone . Such women produce fewer oocytes during ART and have lower pregnancy rates -LSB- 8 , 9 -RSB- . Current smoking by the male partner also decreases pregnancy rates through direct effects on sperm and by exposing the woman partner to side-stream smoke . Increasing body mass index -LRB- BMI -RRB- is associated with decreasing ovarian response to follicle stimulating hormone , resulting in fewer oocytes collected in a given IVF cycle , as well as increasing rates of early pregnancy loss -LSB- 10 -RSB- . Although no strong evidence associates underweight with adverse ART outcomes , maternal underweight -LRB- BMI < 18.5 kg/m2 -RRB- is risk factor for low birth weight in the general population . As the number of specific infertility diagnoses increases for a couple , pregnancy and live birth rates from ART decrease . Moreover , certain infertility diagnoses are somewhat more difficult to overcome than others during treatment . Live birth rates after ART tend to be slightly higher if the only diagnosis is endometriosis , and slightly lower if the only diagnosis is uterine leiomyoma or hydrosalpinx -LSB- 4 , 6 , 11 -RSB- . Leiomyoma may decrease uterine receptivity , mainly when an intramural or submucosal fibroid distorts the endometrial cavity . The mechanism by which hydrosalpinx worsens the prognosis in ART is not understood , but tubal fluid in hydrosalpinx has embryotoxic properties and salpingectomy improves live birth rates -LSB- 12 -RSB- . Male factor infertility is present in about 40 % of couples presenting for ART , and may be a barrier to achieving pregnancy unless intracytoplasmic sperm injection -LRB- ICSI -RRB- is used -LSB- 4 , 6 , 11 -RSB- . The most common adverse outcome from infertility treatment is multiple gestation . ART produced 11,544 multiple birth deliveries in the United States during 2002 -LSB- 4 -RSB- . Of all the ART infants born that year , 45.7 % were twins and 7.6 % were triplets or higher order multiples . Although health outcomes are good to excellent in many twin pregnancies , mothers of twins experience more pregnancy complications , lasting infant neurologic impairment may occur from very preterm birth , and the neonatal death rate of twins is 5 -- 10 times greater than of singletons . For triplets , 90 % of births are preterm and the relative risk of death in the first month of life is increased 20-fold -LSB- 13 -RSB- . To place the risk of multiple gestation from ART in perspective , estimates are available on the proportion of multiple births from unassisted conception and from treatment with ovulation medications without ART . These estimates have been made by subtracting the rate of multiple births during the era before ovulation-inducing drugs -LRB- unassisted conception -RRB- and the rate of multiple births resulting from ART from the total rate of multiple births , adjusted for trends in maternal age -LSB- 4 , 14 -RSB- . Such an analysis shows that about 65 % of twins and 15 -- 18 % of triplets are from natural conception each year . About 20 % of twins and 35 -- 40 % of triplets are probably from ovulation induction without ART , and about 15 % of twins and 40 -- 45 % of triplet births result from ART -LSB- 14 -RSB- . In general , women with the best prognosis for achieving a live birth from ART are younger than 35 years , use freshly fertilized embryos from their own eggs , and have good quality embryos -- a marker of the latter is having more embryos available than are transferred . For this group , recent surveillance statistics showed that transferring one , two , three or four embryos resulted in live birth rates of 47.4 , 51.8 , 49.6 and 45.5 % , respectively ; the singleton rates among these births were 100.0 , 60.4 , 52.8 and 40.7 % , respectively -LSB- 4 -RSB- . By age 38 -- 40 years the prognosis is poorer among all women who use freshly fertilized embryos from their own eggs . Transferring one , two , three or four embryos in this group resulted in live birth rates of 10.4 , 23.3 , 28.9 and 29.9 % respectively ; the singleton rates among these births were 97.4 , 81.2 , 79.2 and 70.4 % , respectively . Thus , for women with the best prognosis , transferring more embryos provides little improvement in live birth rates with an increasing risk of multiples . For women with a poorer prognosis , additional embryos may improve live birth rates up to a point , and the risk for multiples may increase less dramatically -LSB- 4 -RSB- . During ART , ovarian hyperstimulation syndrome is associated with production of a large number of follicles . Usually the only symptoms are modest abdominal pain . More severely affected women , however , experience increased capillary permeability that leads to ascites and in rare cases , death . Symptoms requiring hospitalization occur in 0 -- 3 % of IVF cycles -LSB- 15 -RSB- . Ovarian cancer risk from medications used to stimulate folliculogenesis has been studied because of concerns that this treatment may trigger growth of ovarian cortical cells through the process of healing at the sites of ovulation . Although studies show that women treated with ART have about a 50 % increase in ovarian cancer risk compared to the general population , when compared to control women with infertility who did not receive ART , ovarian cancer risk is not elevated -LSB- 16 -RSB- . Thus it appears that infertility itself may be the risk factor for ovarian cancer , rather than ART . Singletons conceived with ART may be at greater risk of being born prematurely , or to be small for gestational age -LSB- 17 -RSB- . Available studies have been limited by lack of data on key confounders . When matching is used to control for the most important confounders , the risk difference between spontaneously conceived singletons and singletons conceived with ART remains , but appears to be relatively small . The rates of fetal growth restriction differ by about 3.7 % , with an overall rate of growth restriction of 8.6 % among spontaneous singletons compared to a rate of 12.3 % among singletons conceived with ART . The rates of very preterm birth differ by about 1.3 % , with an overall rate of very preterm birth of 0.7 % for spontaneous singletons compared to 2.0 % for singletons conceived with ART -LSB- 17 -RSB- . In some studies , an increased risk of birth defects has been noted among IVF children compared to the general population , but in other studies the difference tends to disappear when risk factors for both infertility and birth defects such as older parental age and multiple gestation are controlled -LSB- 18 -RSB- . Infertility is sometimes caused by sex chromosome aberrations , such as Y chromosome deletions that lead to reduced sperm counts in men -LSB- 18 , 19 -RSB- . Sperm of men with reduced fertility has more aneuploidy -LSB- 20 -RSB- . Whether treatment of male factor infertility by ICSI will cause fertility problems in male offspring can not be known until long-term follow-up is available . Adverse embryo culture conditions can cause imprinting disorders in mice during a procedure similar to ART . This lends biologic plausibility to imprinting disorders as a possible adverse effect of IVF -LSB- 18 , 19 , 21 -RSB- . Imprinting is the mechanism by which congenital disorders such as Beckwith-Wiedemann syndrome , Angelman syndrome and Prader-Willi syndrome occur . These syndromes have been reported in children conceived with ART , although the rarity of the syndromes and problems generalizing from animal experiments to humans makes a causal relationship difficult to establish . In an Australian study that used data from a national genetics testing laboratory , 37 cases of Beckwith-Wiedemann syndrome occurred in 1.3 million live births , a rate of one in every 35,580 births -LSB- 22 -RSB- . Four of the cases were associated with IVF compared to a matched control group in which one mother had IVF -LRB- P < 0.05 -RRB- . In a cohort study from the Danish national registries for IVF and for all births , however , no increase in imprinting diseases was linked to ART -LSB- 23 -RSB- . Recommendations for action Reproductive endocrinologists use a combination of clinical factors to advise couples of the likelihood of success from infertility treatment , and they use clinical tests such basal follicle stimulating hormone levels to further characterize prognosis . Professional practice guidelines are available that recommend transferring fewer embryos among the couples with greatest risk for multiple gestation -LSB- 24 -RSB- . Professional guidelines note the elements to be considered when educating patients during the informed consent process for ART -LSB- 25 -RSB- . Reproductive endocrinologists routinely counsel patients about the risks from ART , including multiple gestation , ovarian hyperstimulation syndrome , physical injury during procedures , and success rates -LSB- 26 -RSB- . It may be prudent to counsel patients about emerging scientific issues as new research is published , such as concerns about embryo culture and ICSI . This would be similar to the approach used for other potential concerns . That is , even though a causal relationship between ART treatment methods and development of ovarian cancer has not been substantiated , the majority of reproductive endocrinologists inform patients of the issue -LSB- 26 -RSB- . Effectiveness of interventions Little research exists on comprehensive preconception counseling for infertility patients . Studies that have examined counseling about the most common adverse outcome , multiple births , have found that many infertility patients prefer to have twins and sometimes triplets rather than a single infant -LSB- 27 -- 29 -RSB- . A randomized trial of a new educational intervention was ineffective at reducing this preference , although other research has found that raising awareness of the risks of multiple births reduces patient preference for multiples -LSB- 27 , 28 -RSB- . Integration with other preconception services Routine preconception counseling for women and men should include information on the many well-documented risk factors for experiencing infertility later in life . When patients who have trouble conceiving present for ART , reinforcing these concepts and combining preconception care at the key decision-making points should help maximize health outcomes for parents and their future children .

O O O O O B I I O O O O O O O B I I O O O O O B I I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I I O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

infertility
preconception counseling
pregnancy complications
pregnancy
multiple
in vitro fertilization
assisted reproductive technologies