A List of Candidate Cancer Biomarkers for Targeted Proteomics

1

ORIGINAL RESEARCH

A List of Candidate Cancer Biomarkersfor Targeted ProteomicsMalu Polanski and N. Leigh AndersonThe Plasma Proteome Institute, P.O. Box: 53450, Washington DC, 20009-3450, USA.

Abstract: We have compiled from literature and other sources a list of 1261 proteins believed to be differentially expressed in human cancer. These proteins, only some of which have been detected in plasma to date, represent a population of can-didate plasma biomarkers that could be useful in early cancer detection and monitoring given suffi ciently sensitive specifi c assays. We have begun to prioritize these markers for future validation by frequency of literature citations, both total and as a function of time. The candidates include proteins involved in oncogenesis, angiogenesis, development, differentiation, proliferation, apoptosis, hematopoiesis, immune and hormonal responses, cell signaling, nucleotide function, hydrolysis, cellular homing, cell cycle and structure, the acute phase response and hormonal control. Many have been detected in studies of tissue or nuclear components; nevertheless we hypothesize that most if not all should be present in plasma at some level. Of the 1261 candidates only 9 have been approved as “tumor associated antigens” by the FDA. We propose that systematic collection and large-scale validation of candidate biomarkers would fi ll the gap currently existing between basic research and clinical use of advanced diagnostics.

Keywords: cancer, biomarkers, targeted proteomics, validation.

Abbreviations: MS mass spectrometry; GO Genome Ontology.

IntroductionThe study of cancer biomarker proteins began in 1847 with the discovery by Henry Bence-Jones of what turned out, more than 100 years later, to be a tumor-produced free antibody light chain “Bence Jones protein” in the urine of a multiple myeloma patient (Bence-Jones 1847; Kyle 1994) where it was present in large quantities and could be revealed by simple heat denaturation. One hundred and 40 years later this protein was demonstrated to be present also in the serum (Sinclair et al. 1986), and in 1998 a routine immunodiagnostic test was approved by the FDA. Hormones produced by tumors were also detected early on (Chan and Sell 1999): adrenocorticotropic hormone (ACTH), calcitonin, and chorionic gonadotropin (hCG), for example, are elevated in specifi c cancer types, though not with the tumor specifi city of Bence-Jones proteins.

Unfortunately, the paradigm in which an overproduced tumor-specifi c protein can be easily detected as a marker of cancer has turned out to be the exception rather than the rule: in the nearly 160 years since Bence-Jones’ discovery, less than 10 proteins have progressed to the level of FDA-approved cancer diagnostic tests, and most of these lack ideal sensitivity and specifi city for cancer.

In recent years “… the emerging science of genomics and proteomics have generated a plethora of candidate cancer biomarkers” (Pritzker 2002). Unfortunately few of these markers immediately stand out as superior prognostic or diagnostic tools, and even fewer have been validated and approved. Several factors might account for the slow pace of advance in cancer biomarkers. On the one hand, available proteomics technology has limited power to detect low-abundance cancer biomarkers against the background of high-abundance plasma proteins, and many of the best markers may thus be missed until discovery technology improves. On the other hand, the capacity to verify and validate existing candidate markers (through rigorous testing in large sample sets from many diseases) is limited, and it is therefore possible that the required biomarkers have already been “discovered” but not yet validated. In this paper, we are concerned with the latter possibility, and specifi cally with the problem of selecting among the existing candidates those that are most promising for systematic validation.

This line of enquiry immediately raises the question: where is the list of known candidate cancer biomarkers? While a number of useful reviews and books discuss specifi c cancer markers with clinical

Correspondence: N. Leigh Anderson, P.O. Box: 53450, Washington DC, 20009-3450,Tel: (301) 728-1451; Fax: (202) 234-9175; Email: [email protected].

Biomarker Insights 2006:1 1– 48

2

Malu Polanski and N. Leigh Anderson

promise, these generally concentrate on proven, or at least well-developed, markers or specifi c disease states. We were unable to fi nd a list that draws together a large population of candidates at all stages of development from multiple discovery sources, and thus our fi rst step has been to create one through a combination of literature search and other methods.

The value of a list of existing candidates could be limited by the general lack of sensitivity and specifi city exhibited by most of the cancer markers found to date, a factor that may have discouraged others from undertaking this task previously. Most candidates that have been followed up in larger studies have shown poor diagnostic value (Table 1), and even those that have been approved for clinical use exhibit lower sensitivity and specifi city than the well-known markers of, eg, acute cardiovas-cular events (ie, troponin in myocardial infarction

or B-type natriuretic peptide in congestive heart failure, Table 1).

On the other hand, there seems to be a growing consensus that panels of markers may be able to supply the specifi city and sensitivity that individual markers lack. For example a panel combining four known biomarkers (leptin, prolactin, osteopontin, insulin-like growth factor II), none of which used alone could distinguish patients from the controls, achieved a sensitivity and specifi city of 95% for the diagnosis of ovarian cancer (Mor et al. 2005). In this case a combination of known proteins in a novel panel provided a signifi cant advance. Xiaoet al. identifi ed 299 proteins in tissue culture by 1-D page and nano-ESI-MS/MS but then used ELISA to test 13 of the most interesting in serum. They reported that CD98, fascin, the secreted chain of the polymeric immunoglobulin receptor and 14-3-3 eta provide greater sensitivity when used together

Table 1. Example sensitivities and specifi cities for the nine FDA approved cancer biomarkers.

Marker Disease Cut Off Sensitivity Specifi city ReferenceCEA malignant pleural effusion NA1 57.5% 78.6% (Li et al. 2003)CEA peritoneal cancer 0.5 ng/ml 75.8% 90.8% (Yamamto dissemination et al. 2004)Her-2/neu stage IV breast cancer 15 ng/mL 40% 98%2 (Cook et al. 2001)Bladder Tumor Antigen urothelial cell carcinoma NA 52.8% 70% (Mian et al. 2000)Thyro-globulin thyroid cancer metastasis 2.3 ng/ml3 74.5% 95% (Lima et al. 2002)Alpha-fetoprotein hepatocellular carcinoma 20 ng/ml 50% 70% (De Masi et al.2005)PSA prostate cancer 4.0 ng/mL 46% 91% (Gann et al. 1995)CA 125 non-small cell lung cancer 95 IU/mL 84% 80% (Dabrowska et al. 2004)CA19.9 pancreatic cancer NA 75% 80% (Yamaguchi et al. 2004)CA 15.3 breast cancer 40 U/ml 58.2% 96.0% (Ciambellotti et al. 1993)leptin, prolactin, ovarian cancer NA 95% 95% (Mor et al.osteopontin, and IGF-II 2005)CD98, fascin, sPIgR4, lung cancer NA 96% 77% (Xiao et al.and 14-3-3 eta 2005) Troponin I myocardial infarction 0.1 microg/L 93% 81% (Eggers et al.2004)B-type natriuretic Congestive heart failure 8 pg/mL 98% 92% (Dao et al.peptide 2001)1. Not available2. vs benign breast diseases 3. vs 3rd week post surgery4. Secreted chain of the polymeric immunoglobulin receptor

Biomarker Insights 2006:1

3

Candidate Cancer Biomarkers

as a panel than any of the markers used alone (Xiao et al. 2005).

If, as we and others (Conrads et al. 2003) believe, panels of proteins provide the most promising avenue towards early and accurate cancer detection, then a re-examination of known candidates provides a logical approach to panel generation, with the expectation that a stream of new markers can be added as they are identifi ed by marker discovery studies. This candidate-based, or targeted, approach will require a comprehensive list of prioritized candidates coupled with a tech-nology able to assay these in large sets of plasma and serum samples from clinical and epidemio-logical studies (together a “biomarker pipeline” (Anderson 2005b)).

Here we have begun to compile and prioritize a database of candidate biomarkers reported to be differentially expressed in studies of human cancer. We have included changes observed either at the protein (plasma or tissue) or nucleic acid (tissue DNA or RNA) level for any cancer, and excluded results restricted to animal, cell culture systems, or single case report studies in hopes of focusing on the most promising clinical biomarker candidates. We hypothesize that the protein version of most, if not all of these markers should be detectable in blood plasma at some level, irrespective of the tissue source, ultimately allowing for their use in patient screening, diagnosis or follow-up.

Experimental Procedures

Search strategy The principal strategy for creation of our listinvolved compilation of designated cancer related proteins from: our previously published work(Anderson et al. 2004), PubMed literature searches, cancer microarrays (868 proteins from 111 hu-man cancer Superarrays (http://www.superarry.com and in supplemental material), CirculatingTumor Markers of the New Millennium (Wu 2002), American Association for Clinical Chemistry abstracts and general literature perusal. PubMed searches included de novo PubMed literature searches: [plasma (Title/Abstract) NOT membrane (Title/Abstract) NOT stimulation (Title/Abstract) NOT drug (Title/Abstract) NOT dose (Title/Ab-stract) AND protein (Title/Abstract) AND cancer] and [“cancer antigen” AND human], as well as the PubMed literature search used for proteins

from other sources [“protein name” AND cancer AND human AND (where necessary) diagnostic AND (where necessary) expression] and PubMed “related article” searches. Only proteins for which we found at least one published study on human cancer utilizing primary samples were retained (639 of the array proteins). Each biomarker refer-ence was then manually tabulated and curated as to disease and tissue (including plasma). Single case studies were excluded.

Clinical use dataFDA approval dates for tests were obtained from the FDA Center for Devices and Radiological Health database. Proteins designated here as clinical markers are those offered commercially by ARUP or by Mayo Medical Laboratories, or else offered for internal use by either NIH or the Fred Hutchinson Cancer Research Center.

Citation analysis Each documented protein on the resulting list was searched against the literature (via PubMed) us-ing the query [“protein name” AND human AND cancer AND diagnostic]. This is admittedly a crude metric of research interest in a biomarker, but provides a useful method of relative prioritization among markers. In tabulating citation frequen-cies we did not exclude those categories ruled out in compiling the list initially: studies of animal systems, single clinical cases, or cell lines. If the “protein name” was not found by this search strategy it was counted as zero. It must be noted that PubMed is not a static archive but rather con-stantly changing both by additions, subtractions, and redefinition of MeSH headings. Still this exercise allowed some relative ranking of interest and therefore importance. Total cancer citations per year were determined using the query [human AND cancer AND diagnostic] limited to a specifi c publication year.

Annotation Swiss-Prot/Uniprot accession numbers were obtained where possible. Most of the TrEMBL annotations were done prior to the addition of species information to the annotation number and so this form of the annotation was maintained. Candidate cancer biomarkers were annotated with GO numbers and IDs from EBI’s human GOA 30.0


4


(gene_association.goa_human, ftp://ftp.ebi.ac.uk/pub/databases) and the Gene Ontology’s GO.def version 1.213 (http://www.geneontology.org/ontol-ogy/GO.defs) respectively. Similar ID groupings were then combined. The entire Human GO fi le was treated in an identical fashion for comparison with the candidates.

Protein concentrationsWhere possible, normal or control values for the plasma concentration of each protein were obtained by literature search. Unless specifi cally noted, protein concentrations are for the intact protein not individual subunits.

ResultsA search strategy combining literature search, extraction from microarray data, and a review of existing clinical tests, followed by manual cura-tion, provided a list of 1261 candidate protein biomarkers (supplemental material) for which we found evidence of a quantitative change in some human cancer. As shown in Table 2, the candidates included proteins known to occur in plasma (274), proteins detected in tissue samples (542), and pro-teins whose corresponding mRNA or DNA levels were differentially expressed between cancerous and normal samples (656). These categories are non-exclusive in that a signifi cant number of the candidates were found in more than one type of study. Proteins detected in the plasma represent 22% of the total proteins documented to date.

Citation frequencyCitation frequency analysis was used as one method of prioritizing the biomarkers, on the assumption that proteins most widely studied in the context of cancer had more promise as biomarkers. Citation frequency was determined using a PubMed query intended to count citations in which the authors considered the proteins to have diagnostic value

(Figure 1, Table 3). When this is done, 29% of the 1,261 biomarkers have no such citations, 67% have fewer than 10, and 74% fewer than 20. Likewise only a very limited number of biomarkers have extensive citations, 62 proteins or only 5% of the total number of biomarkers were found to have greater than 500 citations.

Biomarkers with greater than 500 citationsOf the 34 biomarkers with more than 1000 citations (Figure 2, Table 3) 79% are found in the plasma and 56% are presently used clinically (89% of which are reported in plasma). Of the 28 markers with between 500 and 1000 citations (Figure 3) 57% are plasma proteins but only 7% are used clinically. Both of the markers used clinically are plasma proteins. Some proteins with high citation frequency (eg, albumin) are somewhat surprising to see in the context of cancer biomarkers; these have been retained nevertheless because they appear to have reasonable relevance (low serum albumin

Figure 1. Biomarker Citation Frequency. Citation Frequency for each protein was determined using the PubMed query [“protein name” AND human AND cancer AND diagnostic]. Proteins were then histogrammed in bins of 10, 100 and 1000 citations (for frequencies of n<100, 100<n<1000, and n>1000, respectively) and each bin’s count normalized through division by bin size (eg the count of proteins falling in the 11-20 citations bin was divided by 10).

Table 2. Distribution of cancer biomarkers. Other = amniotic, bile, cerebrospinal fl uid, follicular fl uid, milk of lactating women, pancreatic fl uid, seminal plasma, sputum, stools and urine.

1261 Unique proteins Proteins in plasma Tissue proteins DNA & RNA data OtherProteins in plasma 274 60 24 6Tissue proteins 60 542 152 6DNA & RNA data 24 152 656 3Other 6 6 3 15


5


Concentration Reference

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hose

tiss

ue e

xpre

ssio

n an

d le

vels

of t

he s

hed

prot

ein

in s

erum

hav

e be

en

show

n to

cor

rela

te w

ith tu

mor

sta

ge in

a ra

nge

of

aden

ocar

cino

mas

(Tsi

gris

et a

l. 20

02).

Ant

igen

iden

tifi e

d by

√

√

2 N

F P

4601

3

A pr

olife

ratio

n-as

soci

ated

ant

igen

that

ism

onoc

lona

l ant

ibod

y

incr

ease

d in

sm

all c

ell l

ung

canc

er p

atie

nts

Ki-6

7

(G

refte

et a

l. 20

04).

B-c

ell

√ √

2

yes

P10

415

A

n in

hibi

tor o

f apo

ptos

is B

cl-2

mai

ntai

nsC

LL/ly

mph

oma

2

ho

meo

stas

is in

the

imm

une

syst

em T

he d

iffer

ing

ef-

fect

s of

Bcl

-2 e

xpre

ssio

n on

pro

gnos

is m

ay b

e du

e to

whi

ch c

ells

are

exp

ress

ing

the

Bcl

-2, i

mm

une

cells

or t

umor

s. H

igh

expr

essi

on in

ova

rian

canc

er

(Her

od e

t al.

1996

) and

non

sm

all l

ung

canc

er (S

hi-

bata

et a

l. 20

04) a

re a

ssoc

iate

d w

ith b

ette

r pro

gno-

sis

whe

reas

wel

l diff

eren

tiate

d tu

mor

s m

ore

likel

y to

be

Bcl

-2 p

ositi

ve (S

oda

et a

l. 19

99).

BC

L2-a

ssoc

iate

d √

√

2 ye

s Q

0781

2

B

ax is

an

apop

tosi

s in

hibi

tor h

ighl

yX

pro

tein

Q07

814

expr

esse

d in

Hod

gkin

’s d

isea

se

Q

0781

5

(S

chla

ifer e

t al.

1995

).

(Con

tinue

d)


8


Bet

a-2-

√

√

2 ye

s P

6176

9

2.1E

+06

(Bie

n et

al.

Th

e no

npol

ymor

phic

cha

in o

f MH

C c

lass

mic

rogl

obul

in

20

04)

I mol

ecul

es. I

t is

slig

htly

incr

ease

d in

chi

ldre

n w

ith

acut

e le

ukem

ias

and

lym

phom

as b

ut n

ot in

sol

id

tum

or d

isor

ders

.

Bre

ast c

ance

r 1

√ √

2

yes

P38

398

Th

e B

RC

A1

prot

ein

is a

tum

or s

uppr

esso

rea

rly o

nset

th

at m

edia

tes

DN

A da

mag

e an

d re

pair,

tran

scrip

-tio

nal a

ctiv

ity a

nd c

hrom

osom

al s

tabi

lity.

How

ever

, w

hile

inhe

rited

mut

atio

ns o

f BR

CA

1 ar

e re

spon

sibl

e fo

r abo

ut 4

0-45

% o

f her

edita

ry b

reas

t can

cers

, th

ese

mut

atio

ns a

ccou

nt fo

r onl

y 2-

3% o

f all

brea

st

canc

ers

(Ros

en e

t al.

2003

).

CA

15.3

√

√

2 ye

s x

A m

onoc

lona

l ant

ibod

y id

entifi

ed

canc

er a

ntig

en

incr

ease

d in

pat

ient

s w

ith m

etas

tatic

bre

ast c

ance

r (L

ockh

art e

t al.

1999

).

CA

19.9

√

√

2 N

F x

A m

onoc

lona

l ant

ibod

y id

entifi

ed

canc

er a

ntig

en

incr

ease

d in

col

orec

tal c

ance

r pat

ient

s (L

ockh

art

et a

l. 19

99).

Cad

herin

1 ty

pe 1

√

√

2 ye

s P

1283

0

7.0E

+06

(Cha

n et

al.

E-c

adhe

rin, a

cel

l adh

esio

n pr

otei

n, p

lays

a ro

leE

-cad

herin

2001

) in

tiss

ue fo

rmat

ion

and

arch

itect

ure.

Ele

vate

d(e

pith

elia

l)

le

vels

of s

E-c

adhe

rin a

re fo

und

in s

era

of p

atie

nts

with

bla

dder

can

cer a

nd c

orre

late

with

kno

wn

prog

-no

stic

fact

ors.

(Griffi t

hs e

t al.

1996

).

Cas

pase

3

√ √

2

yes

P42

574

C

aspa

se 3

is in

volv

ed in

not

onl

y ap

opto

sis

exec

u-tio

n bu

t als

o pr

olife

ratio

n. It

has

bee

n sh

own

to

be d

ownr

egul

ated

in g

astri

c ly

mph

oma

tissu

e bu

t ne

gativ

ely

asso

ciat

ed w

ith ly

mph

nod

e m

etas

tase

s in

gas

tric

carc

inom

a (S

un e

t al.

2004

).

CD

44 a

ntig

en

√

√

2

yes

P16

070

2.

2E+0

5 (L

ockh

art

Cer

tain

CD

44 is

ofor

ms

that

regu

late

act

ivat

ion

et a

l. 19

99)

and

mig

ratio

n of

lym

phoc

ytes

and

mac

roph

ages

m

ay a

lso

enha

nce

loca

l gro

wth

and

met

asta

tic

spre

ad o

f tum

or c

ells

. Pre

sent

in s

erum

of n

orm

al

indi

vidu

als

it is

ele

vate

d in

the

seru

m fr

om g

as-

tric

and

colo

n ca

ncer

pat

ient

s, (G

uo e

t al.

1994

), H

odgk

in’s

lym

phom

a pa

tient

s(Lo

ckha

rt et

al.

1999

), an

d ac

ute

leuk

emia

pat

ient

s (Y

okot

a et

al.

1999

).

Cel

lula

r tum

or

√ √

2

yes

P04

637

Th

e p5

3 tu

mor

sup

pres

sor p

rote

in re

gula

tes

antig

en p

53

prol

ifera

tion,

cel

l cyc

le c

heck

poin

ts, a

nd a

popt

osis

. M

ore

than

one

hal

f of a

ll lu

ng c

ance

rs c

onta

in a

m

utat

ion

of th

e p5

3 tu

mor

sup

pres

sor g

ene

(Joh

nson

and

Kel

ley

1993

).


Documents

A List of Candidate Cancer Biomarkers for Targeted Proteomics