letters

to

nature

Chorvatova

A.

Gendron

L.

Bilodeau

L.

Gallo-Payet

N.

Payet

M.

D

A

Ras-dependent

chloride

current

activated

by

adrenocorticotropin

in

rat

adrenal

zona

glomerulosa

cells

Endocrinology

-LRB-

-RRB-

Tong

J.

et

al.

adenylyl

cyclase

pathway

Soc

Neurosci

Abstr

abstract

no.

-LRB-

Society

for

Neuroscience

New

Orleans

-RRB-

Ingram

D.

A.

et

al.

Hyperactivation

of

-LRB-

ras

-RRB-

and

the

hematopoietic-speci

c

Rho

GTPase

cooperate

to

alter

the

proliferation

of

neuro

bromin-de

cient

mast

cells

in

vivo

and

in

vitro

J.

Exp

Med

-LRB-

-RRB-

Jacks

T.

et

al.

Tumour

predisposition

in

mice

heterozygous

for

a

targeted

mutation

in

Nature

Genet

-LRB-

-RRB-

Umanoff

H.

Edelmann

W.

Pellicer

A.

Kucherlapati

R

The

murine

N-ras

gene

is

not

essential

for

growth

and

development

Proc

Natl

Acad

Sci

USA

-LRB-

-RRB-

Voikar

V.

Koks

S.

Vasar

E.

Rauvala

H.

Strain

and

gender

differences

in

the

behaviour

of

mouse

lines

commonly

used

in

transgenic

studies

Physiol

Behav

-LRB-

-RRB-

Blanton

M.

G.

Lo

Turco

J.

J.

Kriegstein

A.

R.

Whole

cell

recording

from

neurons

in

slices

of

reptilian

and

mammalian

cerebral

cortex

J.

Neurosci

Methods

-LRB-

-RRB-

Acknowledgements

We

thank

V.

Manne

for

the

and

E.

Friedman

for

technical

assistance

in

earlier

experiments

We

are

grateful

to

M.

Barad

D.

Buonomano

T.

Cannon

J.

Colicelli

P.

Frankland

L.

Kaczmarek

A.

Matynia

M.

Sanders

and

D.

Smith

for

discussions

and

to

C.

Brannan

and

S.

Schlussel

for

encouragement

R.M.C.

received

support

from

the

Graduated

Program

in

Basic

and

Applied

Biology

-LRB-

GABBA

-RRB-

of

the

University

of

Oporto

the

Portuguese

Foundation

for

Science

and

Technology

-LRB-

FCT

-RRB-

and

the

National

Neuro

bromatosis

Foundation

-LRB-

NNF

-RRB-

This

work

was

also

supported

by

a

generous

donation

from

K.

M.

Spivak

and

by

grants

from

the

NIH

-LRB-

-RRB-

Neuro

bromatosis

Inc.

-LRB-

National

Illinois

Mass

Bay

Area

Minnesota

Arizona

Kansas

and

Central

Plains

Mid-Atlantic

and

Texas

chapters

-RRB-

the

Merck

and

the

NNF

foundations

to

A.J.S.

Competing

interests

statement

The

authors

declare

that

they

have

no

competing

nancial

interests

Correspondence

and

requests

for

materials

should

be

addressed

to

A.J.S.

-LRB-

e-mail

Silvaa@mednet.ucla.edu

-RRB-

Gene

expression

pro

ling

predicts

clinical

outcome

of

breast

cancer

Laura

J.

van

t

Veer

Hongyue

Dai

Marc

J.

van

de

Vijver

Yudong

D.

He

Augustinus

A.

M.

Hart

Mao

Mao

Hans

L.

Peterse

Karin

van

der

Kooy

Matthew

J.

Marton

Anke

T.

Witteveen

George

J.

Schreiber

Ron

M.

Kerkhoven

Chris

Roberts

Peter

S.

Linsley

Rene

Bernards

Stephen

H.

Friend

Divisions

of

Diagnostic

Oncology

Radiotherapy

and

Molecular

Carcinogenesis

and

Center

for

Biomedical

Genetics

The

Netherlands

Cancer

Institute

Plesmanlaan

CX

Amsterdam

The

Netherlands

Rosetta

Inpharmatics

Avenue

NE

Kirkland

Washington

USA

These

authors

contributed

equally

to

this

work

Breast

cancer

patients

with

the

same

stage

of

disease

can

have

markedly

different

treatment

responses

and

overall

outcome

The

strongest

predictors

for

metastases

-LRB-

for

example

lymph

node

status

and

histological

grade

-RRB-

fail

to

classify

accurately

breast

tumours

according

to

their

clinical

Chemotherapy

or

hormonal

therapy

reduces

the

risk

of

distant

metastases

by

approximately

one-third

however

of

patients

receiving

this

treatment

would

have

survived

without

of

the

signatures

of

breast

cancer

gene

expression

reported

to

allow

for

patient-tailored

therapy

strategies

Here

we

used

DNA

microarray

analysis

on

primary

breast

tumours

of

young

patients

and

applied

supervised

classi

cation

to

identify

a

gene

expression

signature

strongly

predictive

of

a

short

interval

to

distant

metastases

-LRB-

poor

prognosis

signature

-RRB-

in

patients

without

tumour

cells

in

local

lymph

nodes

at

diagnosis

-LRB-

lymph

node

negative

-RRB-

In

addition

we

established

a

signature

that

identi

es

tumours

of

carriers

The

poor

prognosis

signature

consists

of

genes

regulating

cell

cycle

invasion

metastasis

and

angiogenesis

This

gene

expression

pro

le

will

outperform

all

currently

used

clinical

parameters

in

predicting

disease

outcome

Our

ndings

provide

a

strategy

to

select

patients

who

would

bene

t

from

adjuvant

therapy

We

selected

primary

breast

cancers

from

patients

who

developed

distant

metastases

within

years

from

patients

who

continued

to

be

disease-free

after

a

period

of

at

least

years

from

patients

with

germline

mutations

and

from

carriers

All

sporadic

patients

were

lymph

node

negative

and

under

years

of

age

at

diagnosis

From

each

patient

mg

total

RNA

was

isolated

from

snap-frozen

tumour

material

and

used

to

derive

complementary

RNA

-LRB-

cRNA

-RRB-

A

reference

cRNA

pool

was

made

by

pooling

equal

amounts

of

cRNA

from

each

of

the

sporadic

carcinomas

Two

hybridizations

were

carried

out

for

each

tumour

using

a

uorescent

dye

reversal

technique

on

microarrays

containing

approximately

human

genes

synthesized

by

inkjet

Fluorescence

intensities

of

scanned

images

were

quanti

ed

normalized

and

corrected

to

yield

the

transcript

abundance

of

a

gene

as

an

intensity

ratio

with

respect

to

that

of

the

signal

of

the

reference

Some

genes

were

signi

cantly

regulated

across

the

group

of

samples

-LRB-

that

is

at

least

a

twofold

difference

and

a

P-value

of

less

than

in

more

than

ve

tumours

-RRB-

An

unsupervised

hierarchical

clustering

algorithm

allowed

us

to

cluster

the

tumours

on

the

basis

of

their

similarities

measured

over

these

approximately

signi

cant

genes

Similarly

the

genes

were

clustered

on

the

basis

of

their

similarities

measured

over

the

group

of

tumours

-LRB-

Fig.

-RRB-

In

the

dendrograms

shown

in

Fig.

-LRB-

left

and

top

-RRB-

the

length

and

the

subdivision

of

the

branches

displays

the

relatedness

of

the

breast

tumours

-LRB-

left

-RRB-

and

the

expression

of

the

genes

-LRB-

top

-RRB-

Two

distinct

groups

of

tumours

are

the

dominant

feature

in

this

two-dimensional

display

-LRB-

top

and

bottom

of

plot

representing

and

tumours

respectively

-RRB-

suggesting

that

the

tumours

can

be

divided

into

two

types

on

the

basis

of

this

set

of

signi

cant

genes

Notably

in

the

upper

group

only

of

the

sporadic

patients

were

from

the

group

who

developed

distant

metastases

within

years

whereas

in

the

lower

group

of

the

sporadic

patients

had

progressive

disease

-LRB-

Fig.

-RRB-

Thus

using

unsupervised

clustering

we

can

already

to

some

extent

distinguish

between

good

prognosis

and

poor

prognosis

tumours

To

gain

insight

into

the

genes

of

the

dominant

expression

signatures

we

associated

them

with

histopathological

data

for

example

oestrogen

receptor

-LRB-

ER

-RRB-

a

expression

as

determined

by

immunohistochemical

-LRB-

IHC

-RRB-

staining

-LRB-

Fig.

-RRB-

Out

of

IHCstained

tumours

negative

for

ER-a

expression

-LRB-

ER

negative

-RRB-

clustered

together

in

the

bottom

branch

of

the

tumour

dendrogram

In

the

enlargement

shown

in

Fig.

a

group

of

downregulated

genes

is

represented

containing

both

the

ER-a

gene

-LRB-

-RRB-

and

genes

that

are

apparently

co-regulated

with

ER

some

of

which

are

known

ER

target

genes

A

second

dominant

gene

cluster

is

associated

with

lymphocytic

in

ltrate

and

includes

several

genes

expressed

primarily

by

B

and

T

cells

-LRB-

Fig.

-RRB-

Sixteen

out

of

eighteen

tumours

of

carriers

are

found

in

the

bottom

branch

intermingled

with

sporadic

tumours

This

is

consistent

with

the

idea

that

most

mutant

tumours

are

ER

negative

and

manifest

a

higher

amount

of

lymphocytic

in

The

two

tumours

of

carriers

are

part

of

the

upper

cluster

of

tumours

and

do

not

show

similarity

with

tumours

Neither

high

histological

grade

nor

angioinvasion

is

a

speci

c

feature

of

either

of

the

clusters

-LRB-

Fig.

-RRB-

We

conclude

that

unsupervised

clustering

detects

two

subgroups

of

breast

cancers

which

differ

in

ER

status

and

lymphocytic

in

ltration

A

similar

conclusion

has

also

been

reported

The

sporadic

lymph-node-negative

patients

were

selected

speci

cally

to

search

for

a

prognostic

signature

in

their

gene

expression

pro

les

Forty-four

patients

remained

free

of

disease

NATURE

VOL

JANUARY

Macmillan

Magazines

Ltd

letters

to

nature

Grade

Lymphocytic

infiltrate

a

Clustering

of

significant

genes

b

Agioinvasion

-LRB-

expression

ratio

-RRB-

c

Contig

MYB

Contig

Contig

UGCG

Contig

Contig

Contig

Contig

Contig

Contig

Contig

TEGT

Contig

Clustering

of

breast

tumours

d

Contig

Contig

IGL

IGL

Contig

Contig

IGL

IGKC

Contig

Contig

IGHM

TRD

Contig

Figure

Unsupervised

two-dimensional

cluster

analysis

of

breast

tumours

a

Twodimensional

presentation

of

transcript

ratios

for

breast

tumours

There

were

signi

cant

genes

across

the

group

Each

row

represents

a

tumour

and

each

column

a

single

gene

As

shown

in

the

colour

bar

red

indicates

upregulation

green

downregulation

black

no

change

and

grey

no

data

available

The

yellow

line

marks

the

subdivision

into

two

dominant

tumour

clusters

b

Selected

clinical

data

for

the

patients

in

a

germline

mutation

carrier

-LRB-

or

sporadic

patient

-RRB-

ER

expression

tumour

grade

-LRB-

versus

grade

and

-RRB-

lymphocytic

in

ltrate

angioinvasion

and

metastasis

status

White

indicates

positive

black

negative

and

grey

denotes

tumours

derived

from

NATURE

VOL

JANUARY

germline

carriers

who

were

excluded

from

the

metastasis

evaluation

The

cluster

below

the

yellow

line

consists

of

tumours

of

which

are

ER

negative

-LRB-

total

ER-negative

-RRB-

and

are

carriers

of

the

mutation

-LRB-

total

-RRB-

c

Enlarged

portion

from

a

containing

a

group

of

genes

that

co-regulate

with

the

ER-a

gene

-LRB-

-RRB-

Each

gene

is

labelled

by

its

gene

name

or

accession

number

from

GenBank

Contig

ESTs

ending

with

RC

are

reverse-complementary

of

the

named

contig

EST.

d

Enlarged

portion

from

a

containing

a

group

of

co-regulated

genes

that

are

the

molecular

re

ection

of

extensive

lymphocytic

in

ltrate

and

comprise

a

set

of

genes

expressed

in

T

and

B

cells

-LRB-

Gene

annotation

as

in

c.

-RRB-

Macmillan

Magazines

Ltd

ER

Metastases

letters

to

nature

after

their

initial

diagnosis

for

an

interval

of

at

least

years

-LRB-

good

prognosis

group

mean

follow-up

of

years

-RRB-

and

patients

had

developed

distant

metastases

within

years

-LRB-

poor

prognosis

group

mean

time

to

metastases

years

-RRB-

-LRB-

Fig.

-RRB-

To

identify

reliably

good

and

poor

prognostic

tumours

we

used

a

powerful

three-step

supervised

classi

cation

method

similar

to

those

used

In

brief

approximately

genes

-LRB-

signi

cantly

regulated

in

more

than

tumours

out

of

-RRB-

were

selected

from

the

genes

on

the

microarray

The

correlation

coef

cient

of

the

expression

for

each

gene

with

disease

outcome

was

calculated

and

genes

were

found

to

be

signi

cantly

associated

with

disease

outcome

-LRB-

correlation

coef

cient

or

-RRB-

In

the

second

step

these

genes

were

rank-ordered

on

the

basis

of

the

magnitude

of

the

correlation

coef

cient

Third

the

number

of

genes

in

the

prognosis

classi

er

was

optimized

by

sequentially

adding

subsets

of

genes

from

the

top

of

this

rank-ordered

list

and

a

Prognosis

reporter

genes

Distant

metastases

years

b

No

distant

metastases

years

Correlation

to

average

good

prognosis

profile

Sporadic

breast

tumours

patients

years

tumour

size

cm

lymph

node

negative

-LRB-

-RRB-

Tumours

c

Tumours

Figure

Supervised

classi

cation

on

prognosis

signatures

a

Use

of

prognostic

reporter

genes

to

identify

optimally

two

types

of

disease

outcome

from

sporadic

breast

tumours

into

a

poor

prognosis

and

good

prognosis

group

-LRB-

for

patient

data

see

Supplementary

Information

Table

-RRB-

b

Expression

data

matrix

of

prognostic

marker

genes

from

tumours

of

breast

cancer

patients

-LRB-

left

panel

-RRB-

Each

row

represents

a

tumour

and

each

column

a

gene

whose

name

is

labelled

between

b

and

c.

Genes

are

ordered

according

to

their

correlation

coef

cient

with

the

two

prognostic

groups

Tumours

are

ordered

by

the

correlation

to

the

average

pro

le

of

the

good

prognosis

group

-LRB-

middle

panel

-RRB-

Solid

line

Contig

Contig

Contig

Contig

HEC

GMPS

Contig

Contig

GNAZ

OXCT

Contig

DCK

CENPA

Contig

Contig

Contig

Contig

Contig

NMU

Contig

PECI

PECI

Contig

Contig

Contig

Contig

prognostic

classi

er

with

optimal

accuracy

dashed

line

with

optimized

sensitivity

Above

the

dashed

line

patients

have

a

good

prognosis

signature

below

the

dashed

line

the

prognosis

signature

is

poor

The

metastasis

status

for

each

patient

is

shown

in

the

right

panel

white

indicates

patients

who

developed

distant

metastases

within

years

after

the

primary

diagnosis

black

indicates

patients

who

continued

to

be

disease-free

for

at

least

years

c

Same

as

for

b

but

the

expression

data

matrix

is

for

tumours

of

additional

breast

cancer

patients

using

the

same

optimal

prognostic

marker

genes

Thresholds

in

the

classi

er

-LRB-

solid

and

dashed

line

-RRB-

are

the

same

as

b.

-LRB-

See

Fig.

for

colour

scheme

-RRB-

NATURE

VOL

JANUARY

Macmillan

Magazines

Ltd

Metastases

letters

to

nature

evaluating

its

power

for

correct

classi

cation

using

the

leave-oneout

method

for

cross-validation

-LRB-

see

Supplementary

Information

-RRB-

Classi

cation

was

made

on

the

basis

of

the

correlations

of

the

expression

pro

le

of

the

leave-one-out

sample

with

the

mean

expression

levels

of

the

remaining

samples

from

the

good

and

the

poor

prognosis

patients

respectively

The

accuracy

improved

until

the

optimal

number

of

marker

genes

was

reached

-LRB-

genes

-RRB-

The

expression

pattern

of

the

genes

in

the

samples

is

shown

in

the

colour

plot

of

Fig.

-LRB-

left

panel

-RRB-

where

tumours

were

ordered

by

rank

according

to

their

correlation

coef

cients

with

the

average

good

prognosis

pro

le

-LRB-

Fig.

middle

panel

-RRB-

The

classi

er

predicted

correctly

the

actual

outcome

of

disease

for

out

of

the

patients

-LRB-

-RRB-

with

respectively

poor

prognosis

and

good

prognosis

patients

assigned

to

the

opposite

category

-LRB-

Fig.

threshold

optimal

accuracy

solid

line

-RRB-

However

for

the

selection

of

patients

eligible

for

adjuvant

systemic

therapy

a

lower

number

of

poor

prognosis

patients

assigned

to

the

good

prognosis

category

should

be

attained

For

this

purpose

we

set

a

threshold

that

resulted

in

misclassi

cation

of

no

more

than

of

the

poor

prognosis

patients

-LRB-

patients

out

of

of

the

poor

prognosis

group

-RRB-

This

optimized

sensitivity

threshold

resulted

in

a

total

of

misclassi

cations

poor

prognosis

tumours

were

classi

ed

as

good

prognosis

and

good

prognosis

tumours

were

classi

ed

as

poor

prognosis

-LRB-

Fig.

dashed

line

-RRB-

We

classi

ed

tumours

having

a

gene

expression

pro

le

with

a

correlation

coef

cient

above

the

optimized

sensitivity

threshold

-LRB-

dashed

line

-RRB-

as

a

good

prognosis

Figure

Supervised

classi

cation

on

ER

and

signatures

a

Outline

of

a

two-level

classi

cation

system

breast

tumours

are

rst

classi

ed

into

an

ER-positive

group

and

an

ER-negative

group

which

is

further

divided

into

mutation

and

sporadic

tumours

b

Expression

data

matrix

of

the

sporadic

tumours

across

optimal

ER

reporter

genes

The

contrasting

patterns

discriminate

between

tumours

with

an

ERnegative

signature

-LRB-

below

solid

line

-RRB-

and

an

ER-positive

signature

-LRB-

above

solid

line

-RRB-

The

reporter

genes

were

ordered

on

the

basis

of

their

level

of

contribution

to

the

classi

ers

Tumours

are

arranged

according

to

the

leave-one-out

correlation

coef

cients

to

the

NATURE

VOL

JANUARY

average

signatures

of

the

classi

er

The

ER

status

as

determined

by

IHC

and

microarray

are

indicated

in

the

two

right

panels

c

Expression

data

matrix

of

ER-negative

tumours

de

ned

by

the

ER

classi

er

over

the

optimal

reporter

genes

The

degree

of

the

patterns

divides

the

tumours

in

the

ER-negative

group

into

two

subgroups

and

sporadic-like

Patients

above

the

solid

line

are

characterized

by

a

signature

The

classi

cation

for

each

tumour

was

based

on

the

leave-one-out

procedure

The

germline

mutation

status

is

indicated

in

the

right

panel

-LRB-

white

indicates

mutation

-RRB-

-LRB-

See

Fig.

for

colour

scheme

-RRB-

Macmillan

Magazines

Ltd

letters

to

nature

signature

and

below

this

threshold

as

a

poor

prognosis

signature

Even

small

primary

tumours

without

lymph

node

metastases

can

display

the

poor

prognosis

signature

indicating

that

they

are

already

programmed

for

this

metastatic

phenotype

The

functional

annotation

for

the

genes

provides

insight

into

the

underlying

biological

mechanism

leading

to

rapid

metastases

Genes

involved

in

cell

cycle

invasion

and

metastasis

angiogenesis

and

signal

transduction

are

signi

cantly

upregulated

in

the

poor

prognosis

signature

-LRB-

for

example

cyclin

metalloproteinases

and

and

the

VEGF

receptor

see

Fig.

-RRB-

If

we

evaluate

all

prognostic

reporter

genes

more

genes

belonging

to

these

functional

categories

become

apparent

-LRB-

for

example

cyclin

PCTAIRE

CENPF

VEGF

-RRB-

-LRB-

for

a

complete

list

see

Supplementary

Information

Table

-RRB-

Many

clinical

studies

have

correlated

alterations

in

expression

of

individual

genes

with

breast

cancer

disease

outcome

often

with

contradictory

results

Examples

include

cyclin

ER-a

UPA

and

Surprisingly

of

these

genes

are

present

in

our

set

of

marker

genes

This

could

be

due

to

the

fact

that

here

we

determine

gene

expression

at

the

level

of

transcription

whereas

most

previous

studies

measured

protein

levels

However

it

is

more

likely

that

these

genes

in

isolation

have

only

limited

predictive

power

which

highlights

the

need

for

an

approach

based

on

many

genes

To

validate

the

prognosis

classi

er

an

additional

independent

set

of

primary

tumours

from

young

lymph-node-negative

breast

cancer

patients

was

selected

This

group

consisted

of

patients

who

remained

metastasis

free

for

at

least

ve

years

and

patients

who

developed

distant

metastases

within

ve

years

The

disease

outcome

was

predicted

by

the

classi

er

and

resulted

in

out

of

incorrect

classi

cations

using

both

the

optimal

accuracy

threshold

-LRB-

Fig.

solid

line

-RRB-

and

the

optimized

sensitivity

threshold

-LRB-

Fig.

dashed

line

-RRB-

Thus

the

classi

er

showed

a

comparable

performance

on

the

validation

set

of

independent

sporadic

tumours

and

con

rmed

the

predictive

power

and

robustness

of

prognosis

classi

cation

using

the

optimal

marker

genes

-LRB-

Fisher

's

exact

test

for

association

P

-RRB-

The

prediction

of

the

classi

er

presented

in

Fig.

would

indicate

that

women

under

years

of

age

who

are

diagnosed

with

lymphnode-negative

breast

cancer

that

has

a

poor

prognosis

signature

have

a

odds

ratio

-LRB-

OR

-RRB-

-LRB-

con

dence

interval

CI

P

-RRB-

to

develop

a

distant

metastasis

within

years

compared

with

those

that

have

the

good

prognosis

signature

-LRB-

see

Methods

for

odds

ratio

de

nition

-RRB-

This

estimate

however

is

based

on

the

same

series

of

patients

that

the

classi

er

was

derived

from

and

therefore

this

odds

ratio

represents

an

upper

limit

A

performance

cross-validation

procedure

in

which

the

leave-one-out

sample

is

not

involved

in

selecting

the

prognosis

reporter

genes

and

the

number

of

reporter

genes

is

not

optimized

results

in

an

odds

ratio

of

for

a

short

interval

to

metastases

-LRB-

CI

P

-RRB-

-LRB-

see

Supplementary

Information

-RRB-

This

crossvalidated

predictive

value

of

our

classi

er

is

superior

to

the

currently

available

clinical

and

histopathological

prognostic

factors

high

grade

-LRB-

odds

ratio

OR

-LRB-

CI

-RRB-

P

-RRB-

tumour

size

greater

than

cm

-LRB-

OR

-LRB-

CI

-RRB-

P

-RRB-

angioinvasion

-LRB-

OR

-LRB-

CI

-RRB-

P

-RRB-

age

-LRB-

OR

-LRB-

CI

-RRB-

P

-RRB-

and

ER

negative

-LRB-

OR

-LRB-

CI

-RRB-

P

-RRB-

Furthermore

the

evaluation

of

the

cross-validated

classi

er

in

a

multivariate

model

that

includes

all

classical

prognostic

factors

indicates

that

it

is

an

independent

factor

in

predicting

outcome

of

disease

-LRB-

logistic

regression

OR

-LRB-

-RRB-

P-value

of

likelihood

ratio

test

-RRB-

Studying

a

large

and

unselected

cohort

of

breast

cancer

patients

is

required

to

provide

a

more

accurate

estimate

of

the

metastatic

risk

associated

with

the

prognosis

signature

Unsupervised

cluster

analysis

distinguishes

between

ER-positive

and

ER-negative

tumours

-LRB-

Fig.

-RRB-

To

investigate

the

expression

patterns

associated

with

the

immunohistochemical

staining

of

ER

and

to

explore

the

differences

between

the

sporadic

and

tumours

that

fall

into

the

ER-negative

cluster

-LRB-

Fig.

-RRB-

a

supervised

two-layer

classi

cation

was

performed

-LRB-

Fig.

-RRB-

Figure

shows

that

genes

optimally

report

the

dominant

pattern

associated

with

ER

status

including

genes

such

as

keratin

and

-LRB-

see

Supplementary

Information

Table

-RRB-

The

leaveone-out

analysis

shows

that

only

two

ER-positive

and

three

ERnegative

tumours

-LRB-

as

determined

by

IHC

-RRB-

were

classi

ed

in

the

opposite

gene

expression

group

-LRB-

correct

classi

cation

Fig.

middle

panel

-RRB-

However

in

all

ve

discordant

cases

the

abundance

of

ER

messenger

RNA

measured

by

the

microarray

agrees

with

the

classi

cation

-LRB-

Fig.

right

panel

-RRB-

An

ER

status

reporter

signature

was

also

determined

by

others

using

a

similar

classi

cation

and

their

ER

signature

gene

set

overlaps

with

ours

-LRB-

out

of

their

ER

status

reporter

genes

are

present

in

our

set

of

ER

reporters

-RRB-

Our

observation

in

the

unsupervised

analysis

that

ER

clustering

has

predictive

power

for

prognosis

is

also

valid

for

the

ER

supervised

classi

cation

although

it

does

not

reach

the

level

of

signi

cance

of

the

prognosis

classi

er

-LRB-

ER

signature

prediction

for

prognosis

OR

-LRB-

CI

-RRB-

P

data

not

shown

-RRB-

Figure

shows

the

leave-one-out

classi

cation

of

the

ERnegative

tumours

into

sporadic

cases

and

cases

based

on

an

optimal

set

of

genes

This

set

is

enriched

in

lymphocyte-speci

c

genes

-LRB-

see

Supplementary

Information

Table

-RRB-

The

classi

cation

into

sporadic

and

tumours

was

caused

mainly

by

the

differences

in

levels

of

gene

expression

-LRB-

amplitude

-RRB-

in

concordance

with

recent

ndings

that

mediates

ligand-independent

transcriptional

repression

of

the

-LRB-

accuracy

misclassi

ed

Fig.

-RRB-

The

one

sporadic

tumour

that

was

classi

ed

as

a

tumour

was

shown

to

contain

methylation

of

the

promoter

indicating

an

epigenetic

modi

cation

of

-LRB-

data

not

shown

-RRB-

Notably

the

discordant

tumour

is

from

a

patient

where

the

germline

mutation

has

only

altered

the

last

amino

acids

of

the

protein

-LRB-

mutation

-RRB-

which

abolishes

transcriptional

activation

by

-RRB-

One

previous

study

de

ned

a

gene

expression

signature

associated

with

germline

mutations

using

a

panel

of

seven

however

the

study

was

unable

to

appreciate

the

overlap

in

signatures

between

the

ER-negative

and

tumours

Furthermore

the

nine

status

reporter

were

not

present

in

our

set

of

optimal

reporter

genes

The

two-layer

cluster

analysis

that

we

have

used

and

the

larger

number

of

tumours

we

analysed

may

account

for

these

differences

Our

results

indicate

that

breast

cancer

prognosis

can

already

be

derived

from

the

gene

expression

pro

le

of

the

primary

tumour

Recent

consensus

conferences

on

treatment

of

breast

cancer

in

Europe

and

the

USA

-LRB-

St.

and

NIH

-RRB-

have

developed

guidelines

for

the

eligibility

of

adjuvant

chemotherapy

based

on

histological

and

clinical

characteristics

Following

these

Table

Breast

cancer

patients

eligible

for

adjuvant

systemic

therapy

Patient

group

Consensus

St

Gallen

NIH

Prognosis

pro

le

Total

patient

group

-LRB-

n

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

Metastatic

disease

at

yr

-LRB-

n

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

Disease

free

at

yr

-LRB-

n

-RRB-

The

conventional

consensus

criteria

are

tumour

cm

ER

negative

grade

patient

yr

-LRB-

either

one

of

these

criteria

St

Gallen

consensus

-RRB-

tumour

cm

-LRB-

NIH

consensus

-RRB-

Number

of

tumours

having

a

poor

prognosis

signature

using

our

microarray

pro

le

de

ned

by

the

optimized

sensitivity

threshold

in

the

classi

er

-LRB-

see

Fig.

-RRB-

Number

of

tumours

with

a

poor

prognosis

signature

in

the

group

of

disease-free

patients

when

the

cross-validated

classi

er

is

applied

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-LRB-

-RRB-

-RRB-

Macmillan

Magazines

Ltd

NATURE

VOL

JANUARY

letters

to

nature

guidelines

up

to

of

lymph-node-negative

young

breast

cancer

patients

are

candidates

for

adjuvant

systemic

treatment

As

of

these

patients

would

not

have

developed

distant

metastases

without

adjuvant

treatment

these

patients

may

not

bene

t

from

the

treatment

and

may

potentially

suffer

from

the

side

effects

We

applied

the

St

Gallen

and

NIH

consensus

criteria

on

our

patient

group

to

compare

the

ef

cacy

of

the

microarray

classi

er

for

the

selection

of

patients

for

adjuvant

systemic

treatment

Table

shows

that

the

prognosis

classi

er

selects

just

as

effectively

those

high-risk

patients

that

would

bene

t

from

adjuvant

therapy

but

signi

cantly

reduces

the

number

of

patients

that

receive

unnecessary

treatment

Thus

the

prognostic

pro

le

potentially

provides

a

powerful

tool

to

tailor

adjuvant

systemic

treatment

that

could

greatly

reduce

the

cost

of

breast

cancer

treatment

both

in

terms

of

adverse

side

effects

and

health

care

expenditure

Furthermore

the

signature

that

de

nes

ER

status

can

be

used

to

decide

on

adjuvant

hormonal

therapy

and

the

signature

that

reveals

status

may

further

improve

the

diagnosis

of

hereditary

breast

cancer

Finally

genes

that

are

overexpressed

in

tumours

with

a

poor

prognosis

pro

le

are

potential

targets

for

the

rational

development

of

new

cancer

drugs

Identi

cation

of

such

targets

may

improve

the

ef

ciency

of

developing

M

therapeutics

for

many

tumour

types

oligonucleotide

chosen

by

the

oligonucleotide

probe

design

After

hybridization

slides

were

washed

and

scanned

using

a

confocal

laser

scanner

-LRB-

Agilent

Technologies

-RRB-

Fluorescence

intensities

on

scanned

images

were

quanti

ed

corrected

for

background

noise

and

Microarray

data

are

available

at

publications/default.htm

Method

of

unsupervised

two-dimensional

clustering

In

the

two-dimensional

cluster

analysis

gene

clustering

and

tumour

clustering

were

performed

independently

using

an

agglomerative

hierarchical

clustering

algorithm

For

gene

clustering

pairwise

similarity

metrics

among

genes

are

calculated

on

the

basis

of

expression

ratio

measurements

across

all

tumours

Similarly

for

tumour

clustering

pairwise

similarity

measures

among

tumours

are

calculated

based

on

expression

ratio

measurements

across

all

signi

cant

genes

-LRB-

for

details

see

Supplementary

Information

-RRB-

Method

of

supervised

classi

cation

We

developed

a

method

for

classifying

breast

tumours

into

prognostic

or

diagnostic

categories

based

on

gene

expression

pro

les

This

method

includes

the

following

three

steps

-LRB-

-RRB-

selection

of

discriminating

candidate

genes

by

their

correlation

with

the

category

-LRB-

-RRB-

determination

of

the

optimal

set

of

reporter

genes

using

a

leave-one-out

cross

validation

procedure

-LRB-

-RRB-

prognostic

or

diagnostic

prediction

based

on

the

gene

expression

of

the

optimal

set

of

reporter

genes

-LRB-

for

details

see

Supplementary

Information

-RRB-

Statistical

analysis

The

odds

ratio

is

the

ratio

of

the

odds

in

favour

of

developing

distant

metastases

within

years

for

a

patient

in

this

study

with

a

tumour

characterized

by

the

poor

prognosis

signature

to

the

odds

in

favour

of

developing

metastases

without

this

signature

-LRB-

table

-RRB-

P-values

associated

with

odds

ratios

are

calculated

by

Fisher

's

exact

test

In

the

multivariate

analysis

a

logistic

model

was

applied

with

outcome

of

disease

as

the

dependent

variable

and

the

P-value

for

the

relevant

parameter

is

derived

from

the

likelihood

ratio

test

in

the

model

-LRB-

see

Supplementary

Information

-RRB-

Received

August

accepted

November

McGuire

W.

L.

Breast

cancer

prognostic

factors

evaluation

guidelines

J.

Natl

Cancer

Inst

-LRB-

-RRB-

Goldhirsch

A.

Glick

J.

H.

Gelber

R.

D.

Senn

H.

J.

Meeting

highlights

international

consensus

panel

on

the

treatment

of

primary

breast

cancer

J.

Natl

Cancer

Inst

-LRB-

-RRB-

Eifel

P.

et

al.

National

institutes

of

health

consensus

development

conference

statement

adjuvant

therapy

for

breast

cancer

November

J.

Natl

Cancer

Inst

-LRB-

-RRB-

Early

Breast

Cancer

Trialists

Collaborative

Group

Polychemotherapy

for

early

breast

cancer

an

overview

of

the

randomised

trials

Lancet

-LRB-

-RRB-

Early

Breast

Cancer

Trialists

Collaborative

Group

Tamoxifen

for

early

breast

cancer

an

overview

of

the

randomised

trials

Lancet

-LRB-

-RRB-

Perou

C.

M.

et

al.

Distinctive

gene

expression

patterns

in

human

mammary

epithelial

cells

and

breast

cancers

Proc

Natl

Acad

Sci

USA

-LRB-

-RRB-

Perou

C.

M.

et

al.

Molecular

portraits

of

human

breast

tumours

Nature

-LRB-

-RRB-

Gruvberger

S.

et

al.

Estrogen

receptor

status

in

breast

cancer

is

associated

with

remarkably

distinct

gene

expression

patterns

Cancer

Res

-LRB-

-RRB-

Martin

K.

J.

et

al.

Linking

gene

expression

patterns

to

therapeutic

groups

in

breast

cancer

Cancer

Res

-LRB-

-RRB-

Zajchowski

D.

A.

et

al.

Identi

cation

of

gene

expression

pro

les

that

predict

the

aggressive

behavior

of

breast

cancer

cells

Cancer

Res

-LRB-

-RRB-

Sorlie

T.

et

al.

Gene

expression

patterns

of

breast

carcinomas

distinguish

tumor

subclasses

with

clinical

implications

Proc

Natl

Acad

Sci

USA

-LRB-

-RRB-

West

M.

et

al.

Predicting

the

clinical

status

of

human

breast

cancer

by

using

gene

expression

pro

les

Proc

Natl

Acad

Sci

USA

-LRB-

-RRB-

Hughes

T.

R.

et

al.

Expression

pro

ling

using

microarrays

fabricated

by

an

ink-jet

oligonucleotide

synthesizer

Nature

Biotechnol

-LRB-

-RRB-

Roberts

C.

J.

et

al.

Signaling

and

circuitry

of

multiple

MAPK

pathways

revealed

by

a

matrix

of

global

gene

expression

pro

les

Science

-LRB-

-RRB-

Lakhani

S.

R.

et

al.

Multifactorial

analysis

of

differences

between

sporadic

breast

cancers

and

cancers

involving

and

mutations

J.

Natl

Cancer

Inst

-LRB-

-RRB-

Brenton

J.

D.

Aparicio

S.

A.

Caldas

C.

Molecular

pro

ling

of

breast

cancer

portraits

but

not

physiognomy

Breast

Cancer

Res

-LRB-

-RRB-

Khan

J.

et

al.

Classi

cation

and

diagnostic

prediction

of

cancers

using

gene

expression

pro

ling

and

arti

cial

neural

networks

Naure

Med

-LRB-

-RRB-

He

Y.

D.

Friend

S.

H.

Microarrays

the

century

divining

rod

Nature

Med

-LRB-

-RRB-

Bieche

I.

et

al.

Genetic

alterations

in

breast

cancer

Genes

Chromosomes

Cancer

-LRB-

-RRB-

Steeg

P.

S.

Zhou

Q.

Cyclins

and

breast

cancer

Breast

Cancer

Res

Treat

-LRB-

-RRB-

Janicke

F.

et

al.

Randomized

adjuvant

chemotherapy

trial

in

high-risk

lymph

node-negative

breast

cancer

patients

identi

ed

by

urokinase-type

plasminogen

activator

and

plasminogen

activator

inhibitor

type

J.

Natl

Cancer

Inst

-LRB-

-RRB-

van

Diest

P.

J.

et

al.

Cyclin

expression

in

invasive

breast

cancer

Correlations

and

prognostic

value

Am

J.

Pathol

-LRB-

-RRB-

Zheng

L.

Annab

L.

A.

Afshari

C.

A.

Lee

W.

H.

Boyer

T.

G.

mediates

ligand-independent

transcriptional

repression

of

the

estrogen

receptor

Proc

Natl

Acad

Sci

USA

-LRB-

-RRB-

Esteller

M.

et

al.

Promoter

hypermethylation

and

inactivation

in

sporadic

breast

and

ovarian

tumors

J.

Natl

Cancer

Inst

-LRB-

-RRB-

Chapman

M.

S.

Verma

I.

M.

Transcriptional

activation

by

Nature

-LRB-

-RRB-

Hedenfalk

I.

et

al.

Gene-expression

pro

les

in

hereditary

breast

cancer

N.

Engl

J.

Med

-LRB-

-RRB-

Methods

Breast

tumour

selection

criteria

The

criteria

for

the

sporadic

patients

-LRB-

n

-RRB-

were

primary

invasive

breast

carcinoma

less

than

cm

-LRB-

or

-RRB-

no

axillary

metastases

-LRB-

-RRB-

age

at

diagnosis

less

than

years

calendar

year

of

diagnosis

no

previous

malignancies

all

patients

were

treated

by

modi

ed

radical

mastectomy

-LRB-

n

-RRB-

or

breast-conserving

treatment

-LRB-

n

-RRB-

including

axillary

lymph

node

dissection

followed

by

radiotherapy

Five

patients

of

the

metastases

group

received

adjuvant

systemic

therapy

consisting

of

chemotherapy

-LRB-

n

-RRB-

or

hormonal

therapy

-LRB-

n

-RRB-

all

other

patients

did

not

receive

additional

treatment

All

patients

were

followed

at

least

annually

for

a

period

of

at

least

years

The

criteria

for

hereditary

patients

-LRB-

n

-RRB-

were

carriers

of

a

germline

mutation

in

or

and

primary

invasive

breast

carcinoma

no

other

selection

criterion

was

applied

This

study

was

approved

by

the

Medical

Ethical

Committee

of

the

Netherlands

Cancer

Institute

For

complete

patient

data

see

Table

in

Supplementary

Information

Clinical

parameters

of

breast

tumours

Tumour

material

was

snap-frozen

in

liquid

nitrogen

within

h

after

surgery

A

haematoxylin

and

eosin

stained

section

was

prepared

before

and

after

cutting

slides

for

RNA

isolation

for

assessment

of

the

percentage

of

tumour

cells

Only

samples

with

greater

than

tumour

cells

were

selected

mean

and

median

for

all

groups

studied

Formalin

xed

paraf

n-embedded

tumour

tissue

was

used

to

evaluate

the

following

tumour

type

-LRB-

according

to

the

World

Health

Organisation

classi

cation

-RRB-

histological

grade

-LRB-

grade

-RRB-

and

the

presence

of

angioinvasive

growth

and

extensive

lymphocytic

in

ltrate

ER

expression

was

determined

by

immunohistochemical

staining

-LRB-

negative

when

less

than

of

the

nuclei

showed

staining

all

others

ER

positive

-RRB-

RNA

isolation

We

used

sections

of

thickness

for

total

RNA

isolation

Total

RNA

was

isolated

with

RNAzolB

and

nally

dissolved

in

RNase-free

Twenty

ve

micrograms

of

total

RNA

was

treated

with

DNase

using

the

Qiagen

RNase-free

DNase

kit

and

RNeasy

spin

columns

Total

RNA

treated

with

DNase

was

dissolved

in

RNase-free

to

a

nal

concentration

of

mg

cRNA

labelling

cRNA

was

generated

by

in

vitro

transcription

using

RNA

polymerase

on

mg

total

RNA

and

labelled

with

or

-LRB-

CyDye

Amersham

Pharmacia

Biotech

-RRB-

Five

micrograms

of

Cy-labelled

cRNA

from

one

breast

cancer

tumour

was

mixed

with

the

same

amount

of

reverse

colour

Cy-labelled

product

from

a

pool

which

consisted

of

an

equal

amount

of

cRNA

from

each

individual

sporadic

patient

Expression

pro

ling

using

microarray

Labelled

cRNAs

were

fragmented

to

an

average

size

of

approximately

nucleotides

by

heating

at

in

the

presence

of

mM

added

to

a

hybridization

buffer

containing

M

NaCl

sodium

sarcosine

mM

MES

pH

and

formamide

to

a

nal

concentration

of

nal

volume

ml

at

microarrays

represented

the

biological

oligonucleotides

plus

control

probes

Sequences

for

microarrays

were

selected

from

RefSeq

-LRB-

a

collection

of

non-redundant

mRNA

sequences

http

-RRB-

and

from

expressed

sequence

tag

-LRB-

EST

-RRB-

contigs

-LRB-

-RRB-

Each

mRNA

or

EST

contig

was

represented

on

the

microarray

by

a

single

NATURE

VOL

JANUARY

Macmillan

Magazines

Ltd

letters

to

nature

Supplementary

Information

accompanies

the

paper

on

Nature

's

website

-LRB-

-RRB-

a

DO

Acknowledgements

We

thank

D.

Atsma

and

D.

Majoor

for

assistance

with

the

histological

analyses

and

the

preparation

of

tumour

RNA

T.

van

der

Velde

W.

van

Waardenburg

and

O.

Dalesio

for

medical

record

data

extraction

D.

Slade

J.

McDonald

J.

Koch

T.

Erkkila

M.

Parrish

and

others

at

Rosetta

's

High

Throughput

Gene

Expression

Pro

ling

Facility

for

microarray

experiments

R.

Stoughton

F.

van

Leeuwen

M.

Rookus

P.

Nederlof

F.

Hogervorst

and

D.

Voskuil

for

suggestions

and

A.

Berns

L.

Hartwell

J.

Radich

and

S.

Rodenhuis

for

support

and

reading

of

the

manuscript

This

work

was

supported

by

a

grant

from

the

Center

for

Biomedical

Genetics

DO

Competing

interests

statement

The

authors

declare

competing

nancial

interests

details

accompany

the

paper

on

Nature

's

website

-LRB-

-RRB-

Correspondence

and

requests

for

materials

should

be

addressed

to

S.H.F.

-LRB-

e-mail

-RRB-

b

Signal

peptide

MFSGLTLNCVLLLLQLLLARSLEDGYKVEVGKNAYLPCSYTLPTSGTLVPMCWGKGFCPW

MFSHLPFDCVLLLLLLLLTRSSEVEYRAEVGQNAYLPCFYTPAAPGNLVPVCWGKGACPV

IgV

domain

SQCTNELLRTDERNVTYQKSSRYQLKGDLNKGDVSLIIKNVTLDDHGTYCCRIQFPGLMN

FECGNVVLRTDERDVNYWTS-RYWLNGDFRKGDVSLTIENVTLADSGIYCCRIQIPGIMN

Mucin

domain

c

cell

surface

protein

regulates

macrophage

activation

and

severity

of

an

autoimmune

disease

DKKLELKLDIKAAKVTPAQTAHGDSTTASPRTLTTERNG-SETQTLVTLHNNNGTKISTW

DEKFNLKLVIKPAKVTPAPTLQRDFTAAFPRMLTTRGHGPAETQTLGSLPDINLTQISTL

Transmembrane

region

ADEIKDS

GETIRTAIHIGVGVSAGLTLALIIGVLILKWYSCKKKKLSSLSL

ANELRDSRLANDLRDSGATIRIGIYIGAGICAGLALALIFGALIFKWYSHSKEKIQNLSL

Cytoplasmic

region

ITLANLPPGGLANAGAVRIRSEENIYTIEENVYEVENSNEYYCYVNS-QQPS

ISLANLPPSGLANAVAEGIRSEENIYTIEENVYEVEEPNEYYCYVSSRQQPSQPLGCRFA

MP

c

CHO

mock

d

Relative

expression

Laurent

Monney

Catherine

A.

Sabatos

Jason

L.

Gaglia

Akemi

Ryu

Hanspeter

Waldner

Tatyana

Chernova

Stephen

Manning

Edward

A.

Green

eld

Anthony

J.

Coyle

Raymond

A.

Sobel

Gordon

J.

Freeman

Vijay

K.

Kuchroo

Center

for

Neurologic

Diseases

Department

of

Neurology

Brigham

and

Women

's

Hospital

and

Harvard

Medical

School

and

Department

of

Adult

Oncology

Dana-Farber

Cancer

Institute

and

Department

of

Medicine

Harvard

Medical

School

Boston

Massachusetts

USA

Millennium

Pharmaceuticals

Memorial

Drive

Cambridge

Massachusetts

USA

VA

Health

Care

System

Palo

Alto

and

Department

of

Pathology

Stanford

University

School

of

Medicine

Stanford

California

USA

CHO

No

of

cells

-RRB-

-RRB-

ck

-RRB-

-RRB-

-RRB-

ells

ells

ells

-LRB-

AE

Tim

mo

SC

T

c

c

c

lls

s

-LRB-

D

O

m

HO

aw

-LRB-

LS

ive

B

e

-LRB-

c

ell

H

C

s

-LRB-

R

cells

ells

CD

e

Th

c

C

ive

ag

ritic

B

c

T

h

rop

end

c

D

Ma

Activation

of

naive

T-helper

cells

results

in

the

development

of

at

least

two

distinct

effector

populations

and

cells

produce

cytokines

-LRB-

interferon

-LRB-

IFN

-RRB-

g

interleukin

-LRB-

IL

-RRB-

tumour-necrosis

factor

-LRB-

TNF

-RRB-

a

and

lymphotoxin

-RRB-

that

are

commonly

associated

with

cell-mediated

immune

responses

against

intracellular

pathogens

delayed-type

hypersensitivity

and

induction

of

organ-speci

c

autoimmune

cells

produce

cytokines

-LRB-

and

-RRB-

that

are

crucial

for

control

of

extracellular

helminthic

infections

and

promote

atopic

and

allergic

Although

much

is

known

about

the

functions

of

these

two

subsets

of

T-helper

cells

there

are

few

known

surface

molecules

that

distinguish

between

We

report

here

the

identi

cation

and

characterization

of

a

transmembrane

protein

which

contains

an

immunoglobulin

and

a

mucin-like

domain

and

is

expressed

on

differentiated

cells

In

vivo

administration

of

antibody

to

enhances

the

clinical

and

pathological

severity

of

experimental

autoimmune

encephalomyelitis

-LRB-

EAE

-RRB-

a

autoimmune

disease

and

increases

the

number

and

activation

level

of

macrophages

may

have

an

important

role

in

the

induction

of

autoimmune

diseases

by

regulating

macrophage

activation

and/or

function

In

addition

to

their

distinct

roles

in

disease

and

cells

cross-regulate

each

other

's

expansion

and

functions

Thus

preferential

induction

of

cells

inhibits

autoimmune

and

Figure

Cloning

of

a

c

cell

surface

protein

a

and

cells

were

stained

with

monoclonal

antibody

to

-LRB-

solid

line

-RRB-

or

rat

IgG

isotype

control

-LRB-

dotted

line

-RRB-

b

Deduced

amino-acid

sequence

of

murine

and

human

Shading

indicates

regions

of

homology

IgV

variable

region

of

immunoglobulin

c

CHO

cells

transfected

using

either

cDNA

-LRB-

CHO

-RRB-

or

vector

alone

-LRB-

CHO

mock

-RRB-

Stable

puromycin-resistant

cells

were

stained

with

monoclonal

antibody

to

-LRB-

solid

line

-RRB-

or

rat

IgG

isotype

control

-LRB-

dotted

line

-RRB-

d

Total

RNA

from

various

cell

lines

and

cells

puri

ed

from

SJL

mice

was

isolated

and

transcribed

to

cDNA

by

reverse

transcription

and

cDNA

was

used

for

Taqman

PCR

The

gure

shows

expression

of

RNA

relative

to

control

GAPDH

expression

predominant

induction

of

cells

can

regulate

induction

of

asthma

atopy

and

Several

groups

have

reported

the

association

of

chemokine

and

co-stimulatory

receptors

with

-LRB-

refs

-RRB-

and

-LRB-

refs

-RRB-

cells

however

the

nature

of

the

differences

in

expression

of

most

of

these

molecules

is

quantitative

To

identify

new

c

cell

surface

proteins

we

immunized

Lewis

and

Lou/M

rats

with

T-cell

clones

and

lines

including

the

established

c

clone

and

in

vitro

differentiated

cell

lines

derived

from

-LRB-

ref

-RRB-

and

T-cell

receptor

-LRB-

TCR

-RRB-

transgenic

mice

A

panel

of

approximately

monoclonal

antibodies

was

generated

and

screened

on

and

cells

Two

of

the

monoclonal

antibodies

-LRB-

and

-RRB-

that

selectively

stained

cells

were

further

characterized

These

NATURE

VOL

JANUARY

Macmillan

Magazines

Ltd

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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 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 B B O O O O O O O O O O O O O O O O B 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 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 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 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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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 B 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 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 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 B 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 B 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 B 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 B 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 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 B B O O O O O O O O O O O O O O O O O O O O O O B 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 B 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 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 B 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 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 B 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 B 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 B B 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 B 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 B O O O O O O O O O B O O O O O O O O O O O O O B B O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O B 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 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 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 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 B B 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 B B 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 B 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 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 B B O O O O O O B 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 B B 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 B O B 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 B B 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 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 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 O O B B 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B 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 B 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O