Prognostic Implications of Cell Cycle Apoptosis and Angiogenesis Biomarkers

7/30/2019 Prognostic Implications of Cell Cycle Apoptosis and Angiogenesis Biomarkers

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Prognostic Implications of Cell Cycle, Apoptosis, and Angiogenesis

Biomarkers in Non ^ Small Cell Lung Cancer: A Review

Sunil Singhal,1Anil Vachani,2 Danielle Antin-Ozerkis,2 Larry R. Kaiser,1 and Steven M. Albelda2

Abstract Lung cancer is the leading cause of cancer death in the U.S. with survival restricted to a subset ofthose patients able to undergo surgical resection. However, even with surgery, recurrence rates

range from 30% to 60%, depending onthe pathologic stage. With the advent of partially effective,

but potentially toxic adjuvant chemotherapy, it has become increasingly important to discover

biomarkers that will identify those patients who have the highest likelihood of recurrence and

who thus might benefit most from adjuvant chemotherapy. Hundreds of papers have appeared

over the past several decades proposing a variety of molecular markers or proteins that may have

prognostic significance in non ^ small cell lung cancer. This review analyzes the largest and most

rigorous of these studies with the aim of compiling the mostimportant prognostic markers inearly

stage non ^ small cell lung cancer. In this review, we focused on biomarkers primarily involved in

one of three major pathways: cell cycle regulation, apoptosis, and angiogenesis. Althoughno sin-

gle marker has yet been shownto be perfect in predicting patient outcome, a profile based on the

best of thesemarkers mayprove usefulin directingpatient therapy. Themarkers withthe strongest

evidence as independent predictors of patient outcome include cyclin E, cyclin B1, p21, p27, p16,

survivin, collagen XVIII, and vascular endothelial cell growth factor.

Lung cancer is the leading cause of cancer death in the U.S. TheAmerican Cancer Society estimates that in 2003, there were

>160,000 deaths from lung cancer and >165,000 new casesdiagnosed. Of these, non small cell lung cancer (NSCLC)accounted forf75%. The most important prognostic variable

for survival in NSCLC patients is tumor stage, primarily because

early stage disease is amenable to complete surgical resection,

hopefully before the tumor cells have acquired the ability tometastasize (1). Only patients who undergo curative surgeryhave a significant potential for cure (2).

Despite the fact that early stage disease may be cured with

surgery, recurrence rates remain high. The 5-year survival rates

range from f70% for stage IA disease to 40% for stage IIBtumors. It is thus likely that many cancers diagnosed as earlystage disease have already spread at the microscopic level. In

addition, tumors vary in their biological behavior. Some smallneoplasms are quite aggressive and, although found at a clin-

ically favorable stage, will progress to widespread, fatal disease.

Given that adjuvant therapies with efficacy in some patientsare now available (3 5), the ability to predict survival after lungcancer surgery is even more important because this information

could help target therapies to those patients which would obtainthe most benefit. An underlying hypothesis of the modern era of

cancer research has been that prediction of a patients prognosisor response to therapy could be improved by combiningstandard clinical variables (i.e., tumor size, differentiation, or

stage), with intrinsic genetic or biochemical characteristics of the

tumors. These characteristics have been defined by evaluating

the gene expression (by Northern blot and PCR) or protein(using immunoblot or immunohistochemical techniques)levels of selected candidate molecules. Hundreds of studieshave evaluated prognostic factors in lung cancer.

We have chosen to focus this review on three important

pathways in lung cancer: cell cycle regulation, apoptosis, andangiogenesis. Many of the genes and proteins involved in thesepathways have been defined and relevant marker studies

focusing on clinical outcome in NSCLC have been done. For aparticular marker to potentially have clinical utility, it must

provide independent prognostic information. Therefore, in our

analysis, particular attention was paid to the analytic methodsused to control for the effect of important clinical variables onsurvival, especially the impact of disease stage. Additionally,

results from prospective studies have been highlighted for thefew markers for which these studies have been done. We felt that

a consolidation of the information available would be helpful inguiding future research. This information may be particularly

useful as it will need to be compared with new candidatemarkers generated by high-throughput measures of gene or

protein expression using genomic and proteomic approaches.

Materials and Methods

In order to review the literature for prognostic biomarkers in lung

cancer, we did a search on PubMed, a service of the National Library of

Medicine, which includes over 14 million citations for biomedical

www.aacrjournals.orgClin Cancer Res 2005;11(11) June1, 2005 3974

Authors Affiliations: 1Section of Thoracic Surgery, Department of Surgery,

and 2Pulmonary, Allergy, and Critical Care Division, Department of Medicine,

University of Pennsylvania, Philadelphia, Pennsylvania

Received 12/23/04; revised 3/10/05; accepted 3/15/05.

The costs of publication of this article were defrayed in part by the payment

of page charges. This article must therefore be hereby marked advertisement in

accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Note: S. Singhal and A.Vachani contributedequally to this work.

Requests for reprints: Steven M. Albelda, Pulmonary, Allergy, and Critical Care

Division, Department of Medicine, University of Pennsylvania, 8th Floor, BRB II/III,

421 Curie Boulevard, Philadelphia, PA 19104. Phone: 215-573-9933; Fax : 215-

573-4469; E-mail: [email protected].

F2005 American Association for Cancer Research.

Review


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articles dating back to the 1950s (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi). We used the key phrases lung cancer prognosis protein

expression and lung cancer prognosis gene expression. This search

produced approximately 1,400 papers between 1960 and 2005. In the

process of extracting these papers and reviewing them, we screened the

references to select other papers that appeared to be appropriate for thisreview. We used the following criteria to select papers for more detailed

reviews: (a) the marker was involved in cell cycle regulation, apoptosis,

or angiogenesis, (b) a minimum of 50 patients were studied, (c) the

study included stage I and II patients, (d) an attempt was made to

quantify the biomarker, and (e) an attempt was made to control for

known clinical factors that are associated with outcome.

The main method used to evaluate the effect of a marker on clinical

outcome is the analysis of survival curves. This generally includes Cox

proportional hazards regression modeling (allowing the comparison of

survival in two or more groups while controlling for other variables) orKaplan-Meier analyses. Proportional hazards regression models allow

for the calculation of mortality hazard ratios (a measure of relative risk)

associated with a particular risk factor (i.e., biomarker) after controlling

for other important variables, particularly disease stage. Kaplan-Meier

analysis stratified by disease stage also allows for the assessment of a

markers independent prognostic value. Only studies that used survival

curve methods to evaluate the impact of the marker on patient outcome

were selected. Studies that controlled for disease stage have been

emphasized in the discussion and the associated tables, although

studies with important findings are included, even if disease stage was

not included in the evaluation.The tables provide a brief overview of markers evaluated in at least

two separate studies and describe the primary method of analysis. If

Cox regression modeling was done, hazard ratios and the associated Pvalues are presented. A hazard ratio 1

suggests high expression leads to decreased survival. In studies where a

stratified Kaplan-Meier analysis was done, the direction of effect

(improved survival versus poorer survival) and the associated P valueis presented.

The majority of the markers discussed in this review are illustrated in

the figures of the three major pathways (cell cycle, apoptosis, and

angiogenesis) that were evaluated. Markers were categorized based on

the level of evidence as follows: strong evidence as a prognostic

marker required at least two studies showing a statistically significanteffect on survival, with no studies showing the opposite effect on

survival; weak evidence required one statistically significant study

with no studies showing the opposite effect on survival; a controversial

marker had at least two studies showing opposite effects on survival.

Results

Cell cycle regulation. In nontransformed lung epithelialcells, cellular division is an ordered, tightly regulated process

involving multiple checkpoints that assess extracellular growthsignals, cell size, and DNA integrity. The replication of DNA

occurs in S phase and segregation of the chromosomes intodaughter progeny occurs in mitosis (M phase). The two gap

phases include G1, during which the cell prepares for DNAsynthesis, and G2 during which the cell prepares for mitosis

(Fig. 1). Cyclins and their associated cyclin-dependent kinases

www.aacrjournals.org Clin Cancer Res 2005;11(11) June 1, 20053975

Fig.1. Cell cycle pathway.The impact of theexpressionof selected cell cycle genes on NSCLCprognosiswas classified as favorable, unfavorable,or controversial.The level of evidence was furtherevaluated as strong or weak.Uncolored boxesindicate genes that have notbeen evaluated. ,strong evidence forhighexpression = favorableprognosis; , weakevidence forhigh expression =favorableprognosis; , strong evidence forhigh expression = unfavorable prognosis; , weakevidencefor high expression= unfavorableprognosis; , controversial prognostic marker; ,not enough data.

PrognosticMarkers inNSCLC


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(CDK) are the central machinery that control cell cycleprogression. Once activated, the cyclin/CDKs form complexes

that initiate phosphorylation of other proteins and downstream

cyclin/CDK complexes (see below). Alterations in theseproteins, which lead to failure of cell cycle arrest, may thusserve as markers of a more malignant phenotype.

G1-S transition. Failure of cell cycle arrest at the G1-Stransition can cause uncontrolled cellular proliferation (Fig. 1).

The product of the retinoblastoma susceptibility gene, Rb,plays a central role in the G1-S transition. In its unphos-phorylated state, Rb prevents progression from G1 to S phaseby binding the key transcription factor, E2F/DP-1. Once the

Rb protein is phosphorylated by the cyclin D/CDK complex,or if Rb is mutated or not expressed, E2F is released allowing

transcription of a battery of genes that regulate DNAmetabolism. In addition, other CDKs and cyclin molecules

are activated. This then enables the cell to pass through arestriction point from which the cell proceeds through the

remainder of the cycle.

Although abnormalities of Rb expression in NSCLC arecommon, studies have not consistently showed a difference in

clinical outcome related to Rb status (Table 1). In the largeststudy, which evaluated multiple markers in 408 stage I patients,

high Rb expression was associated with an improvement in 5-year survival, although the results did not achieve statistical

significance (6). However, the one prospective study of Rbexpression found no effect on survival (7).

Up-regulation of the cyclin D1 proto-oncogene is known tobe important in the regulation of the cell cycle pathway. An

increase in this genes expression permits loss of G1 restrictionpoint integrity. Of the four main studies of cyclin D1 in NSCLC,

two show improved survival, whereas the other two showpoorer survival. In a study limited to stages I and II, cyclin D1

expression was associated with shorter survival and the worstprognosis was observed in tumors with a combination of high

cyclin D1 expression with loss of p16 expression (8).Cyclin E/CDK2 complex can also phosphorylate Rb, as well as

other substrates, and is an important regulator of entry into the S


Table 1. Cell cycle markers

Author Year

Study

size

Study

population

Diagnostic

technique

Hazard

ratio* P

Analytic methods and

additional results

Retinoblastoma

DAmico et al. (6) 1999 40 8 s tage I NSCLC IHC 0.74 0.083 Cox p roportional hazards m odel

Reissmanncet al. (7) 1993 219 resectable NSCLC IHC, Northern ND NS Cox proportionalhazards model

Haga et al. (60) 2003 187 stage I NSCLC IHC ND ND Kaplan-Meier analysisno effect

on survival in adenocarcinoma

(P= 0.887) orSCC (P= 0.137)

Jin et al. (8) 2001 106 stage I-II NSCLC IHC 0.59 0.28 Cox proportional hazards model

Akin e t a l. (61) 20 02 10 4 stage I-III NSCLC IHC 0.26 0.0 009 Cox p ropor tional h azards model (SCC)

Cyclin D1

Nishio et a l. (62) 19 97 208 stage I-III NSCLC IHC 0.62 0.03 Cox p roportional hazards m odelJin et al. (8) 2001 106 s tage I-II NSCLC IHC 3.93 0.002 Cox proportional hazards model

Gugger e t al. (63) 2001 92 stage I-IV NSCLC IHC 0.21 0.02 Cox p roportional hazards m odel

Keum et al. (64) 1999 69 stage I-IIIA NSCLC IHC 1.32 0.39 Cox p roportional hazards m odel

Cyclin E

Fukuse et al. (65) 20 00 242 stage I-IIIA NSCLC IHC 1.92 0.0 08 Cox p ropor tional h azards model

Mishina e t a l. (66) 20 00 151 resectable NSCLC IHC 1.79 0.0 1 Cox p ropor tional h azards model

Dobashi e t al. (67) 2003 115 stage I-IIIA NSCLC IHC 1.43


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phase of the cell cycle. In contrast to cyclin D, cyclin E expressionhas been consistently associated with shorter survival among

stage I to IIIa NSCLC patients undergoing curative resection(Table 1). All studies showed an association between highercyclin E expression and poorer survival, although the two

smallest studies did not achieve statistical significance.

An important mecha nism for regula ting CDK activ ityinvolves the CDK inhibitors, a diverse class of proteins that

bind to and inactivate CDKs. These inhibitors are organizedinto two families based on structure and function: the Cip/Kipfamily (p21, p27, p57) and the INK4 family (p16, p18, p19).

Although the functions of CDK inhibitors are complex, they are

generally believed to regulate the cell cycle in response togrowth-inhibitory signals, such as DNA damage, hypoxia,

serum starvation, and transforming growth factor-h (TGF-h).The CDK inhibitor p21 (also known as WAF1, CIP1) inhibitsprogression through the cell cycle via several mechanisms. It

can inhibit the cyclin D/CDK4 and cyclin E/CDK2 complexes

early in G1 and it can also inhibit the cyclin A/CDK2 complexlater, prior to the S phase/G2 phase transition. In two studiesthat adequately controlled for disease stage, p21 expression was

associated with improved survival (Table 2). Slightly differentconclusions were reached by Bennett et al. (9). In an analysis

combining p21 and TGF-h, improved survival was observed in

early stage NSCLC when the p21 and TGF-h were in

concordance (i.e., either both high or both low expression).A 70% disease-free survival rate was observed in patients with

concordant p21 and TGF-h expression, whereas discordant p21and TGF-h expression yielded a disease-free survival rate of 35%(P = 0.0003; ref. 9).

Studies evaluating the effect of p27 expression have also

suggested a beneficial effect on lung cancer survival (Table 2).In three studies, p27 expression was an independent

prognostic factor for improved outcome (10 12), howeverEsposito et al. (13) found no effect on survival when results

were stratified by disease stage. Similarly, studies of p16 have

consistently shown an improved survival with higher expres-

sion, although not all studies have reached statisticalsignificance (Table 2).

S and G2. Progression through the S phase is principallyregulated by the expression and kinase activity of the cyclin A/CDK2 complex. Both studies that have evaluated cyclin A in

NSCLC suggest that increased expression is associated with a

poorer outcome (Table 1).G2-M transition. The second major checkpoint in the cell

cycle occurs at the transition from G2 into M. Cyclin B1/

CDC2 is the classic M phase promoting factor that drivesentry into mitosis. The association of cyclin B with the active

form of CDC2 initiates chromosome condensation, destruc-

tion of the nuclear membrane, and assembly of the mitotic


Table 2. Cyclin kinase inhibitors

Author Year

Study

size Study population

Diagnostic

technique

Hazard

ratio*

P

value

Analytic methods &

additional results

p21

Shoji e t al. (70) 2002 233 stage I-IIIA NSCLC IHC 0.59 0.0 4 Cox p roportional hazards m odel

Komiya e t al. (71) 1997 137 stage I-IIIA NSCLC IHC NP NS Cox p roportional hazards m odel

no details provided

Esposito et al. (72) 20 04 6 8 stage I-III NSCLC IHC 0.55 0.0 06 Cox propor tional h azards model

p27

Tsukamoto et al. (12) 20 01 161 stage I-IV NSCLC IHC 0.55 0.02 Cox p roportional h azards model

Esposito et al. (13) 1997 10 8 stage I-III NSCLC IHC ND ND K aplan-Meier a nalysis (stratified b y s tage)

no effect on survival

Hommura et al. (11) 20 00 107 stage I-II NSCLC IHC 0.23 0.0 1 Cox propor tional h azards model (SCC);

not significant in adenocarcinoma

Hayashi et al. (10) 2001 98 resectable adenocarcinoma IHC 0.59 0.13 Cox proportional hazardsmodel

p16

Huang e t a l. (73) 20 00 171 stage I-III NSCLC IHC 0.26 0.02 Cox propor tional h azards model (SCC).

HR 0.76 (P= 0.413) in adenocarcinoma

Groegeretal. (74) 1999 135 stage I-IV NSCLC Western, IHC 0.03 0.001 Cox proportional hazardsmodel

Taga et al. (75) 1997 115 stage I-IIIB NSCLC IHC ND ND Kaplan-Meier a nalysis ; i mproved sur vivalin stage I/II (P= 0.021) but not stage III

Jin et al. (8) 2001 106 stage I-II NSCLC IHC 0.52 0.18 Cox p roportional hazards m odel

Krat zke et al. (76) 199 6 10 0 stage I-IV NSCLC IHC NP 0.03 17 Cox propor tional h azards model

Gonzalez-Quevedo

et al. (77)

20 02 9 8 stage I-IIIA NSCLC Western 0.21 0 .01 Cox propor tional h azards model

Esposito et al. (72) 20 04 6 8 stage I-III NSCLC IHC 0.32 0.0 00 6 Cox propor tional h azards model

Kawabuchi etal. (78) 1999 51 stageI-IIIA adenocarcinoma IHC 0.53 0.16 Cox proportional hazards model

Abbreviations: IHC, immunohistochemistry; ND, no data; NS, not significant; NP, not provided in study; HR, hazard ratio; RT-PCR, reverse transcriptase PCR; SCC,

squamous cell cancer.

*HR >1suggests thathigh expressionleads to decreased survival. HR


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spindle. Regulators of CDC2 play a central role in the DNAdamage induced G2 checkpoint, a cellular response to DNA

damage that allows time for repair and prevents mitosis of

damaged cells. High cyclin B1 expression in stage I NSCLC isassociated with a significantly shorter survival time; this effectis seen primarily in squamous cell cancers (14). A more recent

study yielded similar results, however, the effect of histologywas not evaluated (15).

In summary, cell cycle markers are some of the mostpowerful predictors of survival. As summarized in Fig. 1, lossof expression of the inhibitors p16, p27, and p21 and/or up-regulation of the cyclins A, E, and B1 all predict a poor

prognosis after surgery. The prognostic data for expression ofRb protein and cyclin D are not convincing in NSCLC.

Apoptosis. One of the hallmark features of cancer cells istheir ability to evade apoptosis. There are two fundamental

pathways in apoptosis: the death receptor pathway and themitochondrial pathway (Fig. 2 shows a very simplified schema

of apoptosis). These pathways are intimately connected viacaspase 8 and Bid. The first pathway is initiated by cell surface

receptormediated activation of caspases, a family of cysteine

proteases. There are two sets of caspases: initiator caspases andeffector caspases. Initiator caspases (such as caspases 8, 9, and10) transmit apoptotic signals and activate effector caspases

(such as caspases 3, 6, and 7) that can then activate degradationenzymes that destroy the cell.

Death receptors. Initiation of the death receptor cascadedepends on cleavage of the initiator caspases by the cellsurface death-receptors (Fig. 2), which include Fas, tumor

necrosis factor receptor-1 (TNFR-1) and TNFR-2. Few studies

evaluating the impact of death receptor pathway markers andlung cancer survival have been done (Table 3). In two large

studies, expression of Fas was associated with improvedsurvival in NSCLC (16, 17), although the effect was limitedto stage III disease in one of the studies (16). Single studies

of TNFR-1, TNFR-2, and TNF-a show that these markers arealso associated with improved survival rates in NSCLC

patients.Caspases. Studies of caspases in lung cancer outcome are

also limited. The two studies of capase-3 expression have shownconflicting effects on survival (18, 19). Caspase-3 can also be

regulated by inhibitor of apoptosis genes, such as survivin.The survivin gene is a novel apoptosis inhibitor, related to a

baculovirus gene. Survivin expression is more frequentlyincreased in lung adenocarcinomas as compared with squa-

mous cell tumors and studies show that high expression wasassociated with decreased survival in NSCLC (20, 21). Expres-sion of another recently identified gene, antiapoptosis clone 11,

was associated with poorer survival (22); however, this result

has not been confirmed in other studies.Death-associated protein. Expression of the death-associated

protein kinase is also involved in TNF-a- and Fas-mediatedapoptosis. Lung cancer cells lacking death-associated proteinkinase activity seem to be more invasive and have more

metastatic potential. In one study of 135 patients with stage I

NSCLC, hypermethylation of the death-associated protein

kinase promoter was found in 44% of the tumors and was asignificant independent factor predicting poorer disease-specificsurvival (23).

In summary, some mediators of apoptosis may be predictors

of survival in lung cancer. As shown in Fig. 2, high expression

of apoptosis inhibitors, such as survivin, predicts unfavorablesurvival, whereas up-regulation of the TNF-receptor, TNF-a,and Fas, may signal a favorable prognosis.

Bcl-2 family. The mitochondrial pathway is composed ofmembers of the Bcl-2 family of proteins. The Bcl-2 family has

both proapoptotic factors (Bax, Bak, Bcl-xs, Bad, and Bid) and

antiapoptotic factors (Bcl-2, Bcl-xL, and Bcl-w; Fig. 2). Whencells are exposed to apoptotic stimulation, proapoptoticproteins are activated through posttranslational modifications


Fig. 2. Apoptosis pathway. The impact ofthe expressionof selected apoptosis geneson NSCLC prognosis was classified asfavorable, unfavorable, or controversial.The level of evidence was further evaluatedas strong or weak. Uncolored boxes

indicate genes that have not beenevaluated. , strong evidence for highexpression = favorable prognosis; , weakevidence for high expression= favorableprognosis; , strong evidencefor highexpression = unfavorable prognosis; ,weakevidence forhigh expression =unfavorable prognosis; , controversialprognostic marker; , not enoughdata.

Review


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or changes in their conformation. The critical site of actionseems to be the mitochondria, where these proteins increase the

permeability of the outer membrane resulting in the release ofproteins, including cytochrome c, from the intermembranespace. In the cytosol, cytochrome c activates caspase cascades

that ultimately lead to cell death.

Studies of Bcl-2 expression and lung cancer outcome have

yielded conflicting results. Although one small study showed

that high bcl-2 expression adversely affects prognosis (24),

several other well-done studies suggest that bcl-2 is an

independent prognostic marker of improved survival (25 27),

a finding that seems counterintuitive.

Overexpression of Bcl-2 and Bcl-xL are known to inhibitthe proapoptotic activity of Bax. In normal cells or tissues,

Bax is predominantly located in the cytosol. After apoptoticstimulation, Bax translocates to the mitochondria and forms

channels in the mitochondrial membrane. Cells that mutate

Bax are relatively resistant to some types of chemotherapy.The impact of Bax expression on lung cancer outcome inearly stage disease has not been studied. In one small study

of advanced disease, Bax expression was associated withimproved median survival in stage IV NSCLC (6 versus 3

months, P < 0.017; ref. 28).

p53. There are abnormalities of the tumor-suppressor genep53 in more than half of all human malignancies. In addition

to its effects on cell-cycle arrest, p53 can induce the apoptosispathway through induction of Bax. The importance of p53

mutations in the pathogenesis of human lung carcinoma is wellestablished, but it is still controversial whether the presence ofp53 mutations or overexpression of p53 protein adversely

affects an individual patients chances of survival. The

controversy may be partially due to the methodologic differ-ences in examination for p53 alterations: gene analysis or

immunohistochemical staining. Most of the studies focus onp53 expression, whereas others evaluated specific mutations ofthe p53 gene. These are sometimes related because wild-type

p53 has a very short half-life and is not usually visualized by

immunostaining of normal cells. In contrast, many p53mutations markedly prolong its half-life allowing visualizationby staining techniques.

The impact of p53 expression on lung cancer prognosis hasbeen studied by multiple groups (Table 4). The four largest

studies were limited to stage I patients and have yielded

conflicting results (6, 2931). In the largest study, increased

p53 expression had no effect on outcome (31), whereas the

other three studies found a slightly increased risk of poorer

overall survival (6, 29, 30). The other studies of p53 expression

have shown similar conflicting results with hazard ratios

varying from 0.5 to 4.5. In contrast to the experience with

p53 protein expression, multiple studies of mutational analysis


Table 3. Apoptosis

Author Year

Study

size

Study

population

Diagnostic

technique

Hazard

ratio*

P

value


additional results

FAS

Uramoto et al. (16) 1999 220 stage I-IIIANSCLC IHC ND ND Kaplan-Meier analysisimproved survival

in stage III (P= 0.026) but not stage I/II

Koomagi andVolm (17) 1999 164 stage I-IIINSCLC IHC NP 0.01 Cox proportionalhazards model

(improved survivalHR not provided)

Caspase3

Koomagi andVolm (18) 2000 135 stage I-IIIA NSCLC IHC ND ND Kaplan-Meieranalysisimproved

survival (P= 0.038)

Takata et a l. (19) 2001 118 stage I NSCLC IHC 2.1 0.004 Cox proportional hazards m odel

BCL-2

DAmico et al. (6) 1999 408 stage I NSCLC IHC NP 0.5 Univariate analysis

Silvestrini et al. (25) 1998 229 stage I-IIIANSCLC IHC 0.19 0.065 Cox proportionalhazards model

Higashiyama et al. (79) 1997 182 stage I-IIINSCLC IHC NP 0.159 Cox proportional hazards model

Cox et al. (26) 2001 167 stage I-IIIA NSCLC IHC 0.54 0.01 Cox p roportional hazards model

Fontanini et al. (8 0) 199 8 107 stage I-III NSCLC IHC 0.76 0.73 Cox p ropor tional h azards model

Ohsaki e t al. (27) 1996 99 stage I-IV NSCLC IHC 0.4 4 0.054 Cox p roportional hazards model

Han et al. (81) 2002 85 stage I NSCLC IHC ND ND Kaplan-Meier analysisno effecton survival (P= 0.56)

Laudanski et al. (82) 199 9 8 4 s tage I-IIIA NSCLC IHC 0.64 0.19 Cox p ropor tional h azards model

Poleri et al. (24) 2003 53 stage I NSCLC IHC 7.14 0.007 Cox p roportional hazards model

Survivin

Kren et al. (20) 2004 102 stage I-IIIA NSCLC IHC NP 0.05 Cox proportional hazards

modelpoorer survival

Oshita e t al. (21) 20 04 72 s tage I a denocarcinoma IHC ND ND Kaplan-Meier a nalysis

poorer survival (P= 0.014)

Abbreviations: IHC, immunohistochemistry; ND, no data; NS, not significant; NP, not provided in study; HR, hazard ratio; RT-PCR, reverse transcriptase PCR; SCC,

squamous cell cancer.

*HR >1suggests thathigh expressionleads to decreased survival. HR


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Table 4. p53 tumor suppressor gene

Author Year

Study

size Tumor

Hazard

ratio*

P

value


additional results

Expression level (Immunohistochemistry)

Pastorino et al. (31) 1997 515 stage I NSCLC 0.98 0.83 Univariate analysis

DAmico et al. (6) 1999 408 stage I NSCLC 1.63 0.037 Cox proportionalhazards model

Scagliotticet al. (33) 2003 387 stage I-IIIA NSCLC 1.03 0.30 Cox proportionalhazards model

Harpole et al. (29) 1995 271 stage I NSCLC 1.47


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have consistently found mutated p53 to adversely affect

survival (Table 4). However, two recent clinical trials of

adjuvant therapy following surgical resection analyzed p53

and K-ras abnormalities and found that neither marker added

prognostic information (32, 33). Thus, evaluation of p53,

either by protein expression or mutational status, is unlikely to

be incorporated into clinical practice.

Angiogenesis

Once malignant transformation has occurred, tumor cells,

like all cells, require oxygen and nutrients for expansion. When

tumors are small (


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the presence of tumor-infiltrating macrophages and tumor IL-8 expression, suggesting a mechanism forhow macrophages may

adversely affect outcome in NSCLC (39).Although VEGF, PDGF, bFGF, and IL-8 are the primary

growth factors involved in the angiogenic process, several othergrowth factors also play a role in the development of tumoral

blood supply. Hepatocyte growth factor, a cytokine producedby mesenchymal cells, impacts epithelial and endothelial cells

through its receptor, the c-met protein. Tissue factor, depositedearly in the coagulation cascade, likely plays a role in

angiogenesis as well. Limited studies of hepatocyte growthfactor, c-met, and tissue factor suggest that these markers may

be important prognostic markers in NSCLC (4043).Inhibitors of angiogenesis. Tumors may activate angiogenic

inhibitors such as angiostatin and endostatin which control

growth by suppressing endothelial cell proliferation and

angiogenesis and by indirectly increasing apoptosis in tumor

cells. Expression of angiostatin has been associated withimproved survival (44); however, this study did not control

for clinical risk factors. Collagen XVIII, a precursor of endo-statin, is associated with a poorer outcome in NSCLC (45, 46),

although the reasons for this association are unclear given thathigher expression of endostatin would be expected to improve

survival.Markers of angiogenic activity. Angiogenesis is frequently

assessed in tumors by evaluating the microvessel count oftenusing antibodies to factor VIII, CD31, or CD34. Although a

recent metaanalysis found microvessel density to be associatedwith poorer survival in NSCLC (47), the impact of microvessel

density on clinical outcome remains controversial. This ishighlighted by two large studies of stage I disease (6, 31). Eachof these studies included >400 patients and evaluated the

association of multiple immunohistochemical markers with

overall survival. In one study, factor VIII expression was an


Table 5. Angiogenesis

Author Year

Study

size

Study

population

Diagnostic

technique

Hazard

ratio*

P

value


additional results

VEGF

OByrne et al. (105) 2000 183 stage I-IIIANSCLC IHC 1.5 0.128 Cox proportionalhazards model

Kojima e t a l. (35) 20 02 132 stage I NSCLC IHC 4.71 0.0 11 Univariate analysis for a denocarcinoma ;

HR1.54 in SCC (P= 0.437)

Liao et al. (10 6) 20 01 127 stage I-III NSCLC IHC ND ND Kaplan-Meier a nalysis (stratified b y s tage)

no effect on survival

Ohta et al. (107) 2000 122 s tage I NSCLC IHC 2.3 0.02 Cox p roportional hazards model

Volm (108) 1998 121 stage I-III SCC IHC 1.8 0.08 Cox p roportional hazards model

Giatromanolaki

et al. (109)

199 8 114 stage I-II NSCLC IHC ND ND Kaplan-Meier analysis

poorer survival (P= 0.04)

Volm et al. (110) 1997 10 9 stage I-IIIA SCC IHC NP 0.0 86 C ox p roportional h azards model

Fontanini et al. (80) 1998 107 resectable NSCLC IHC 7.09 0.01 Cox proportionalhazards model

Imoto et al. (111) 199 8 91 stage I-III NSCLC IHC 2.59 0.0 04 Cox p ropor tional h azards model

Koukourakis

et al. (112)

2000 93 stage I-IINSCLC IHC 3.28 0.001 Cox proportionalhazards model (for antibody

recognizingVEGF/VEGFR2 complex)

Han et al. (113) 2001 85 s tage I NSCLC IHC 1.82 0.006 Cox p roportional hazards m odel

Yuan et al. (114) 2000 72 stage I-IIIA NSCLC IHC 3.74 1suggests thathigh expressionleads to decreased survival. HR


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independent predictor of survival, whereas the other found noeffect. There are several likely reasons for this discrepancy. First,

these methods assume that the area with the most angiogenic

activity reflects the activity of the tumor as a whole.Additionally, these markers do not distinguish between newand old vessels or whether there is actual blood flow through

these neovessels (34). High vessel count may solely reflecttumor size and nodal status and therefore may be an unreliable

marker in early stage disease.Multiple marker studies. Although most studies to date have

studied single markers, interest has also focused on combining

the results of two or more markers together to provideprognostic information. In the case of p53, most studies have

focused on bcl-2 as the second marker. Two studies suggest thatthe combination with increased p53 expression and decreased

bcl-2 expression portends the poorest survival (27, 48). Similarresults have been shown for the combination of p53 and Rbexpression (49, 50). The interrelationship between these

proteins in the cell cycle and apoptotic pathways may explain

this potentially synergistic effect of p53 and bcl-2 or Rb.More recent studies have tried to evaluate multiple markers

simultaneously. Summarizing this literature is difficult giventhe differences in study populations and the specific markersconsidered. However, two main conclusions can be made.

First, multiple marker studies have not been more successful

in identifying promising groups of markers for prognosis inNSCLC. This is highlighted by the experience with p53. Ofsix studies that have evaluated four or more markers

(including p53) simultaneously, three studies found anindependent effect of p53 (6, 30, 51), whereas the remaining

three did not (24, 31, 52). Second, most of these studies arelimited by the large number of hypotheses tested in one

group of patients, without confirmation of their findings inany validation cohorts. For example, Volm et al. evaluated 21

markers in 216 NSCLC patients using cluster analysis andfound a combination of 9 markers (not including p53)

which was associated with long-term survival (53). Althoughpromising, these results may be due to overfitting of the

data and require confirmation in an external group ofpatients.

Conclusions

This review of the literature summarizes a portion of the largenumber of tumor marker studies that have been reported to

predict outcome in patients with resectable NSCLC. It shouldbe acknowledged that this overview is not comprehensive.

Although we focused on genes involved in cell cycle regulation,

apoptosis, and angiogenesis, there are several other importantpathways (e.g., growth factors, DNA repair, cell motility,coagulation) that we did not address. Additionally, we have

not discussed evidence to suggest that augmented immuneresponses to the tumor (i.e., lymphocyte infiltration) can be

associated with improved survival.Several limitations to this review must be acknowledged.

There is significant heterogeneity in patient populations (many

of these studies include various stages of NSCLC) and in

treatments employed. The majority of these studies wereretrospective and bias may have been introduced in patient

selection. In addition, the length of follow-up in these studieswas not constant, and methods of gene expression analysis

varied widely and were often not precisely quantitated. Many of

the referenced studies failed to categorize their patients diseaseas well, moderate, or poorly differentiated NSCLC. It is

therefore possible that the markers were influenced heavily by

the differentiation state.Given these limitations in the currently available literature,

however, we did identify several markers that seemed to

independently predict patient outcome. Table 6 reflects asummary of some of those genes and proteins that seem to

have the strongest evidence (i.e., consistent conclusions inmultiple, large, well-done studies) to support their potential

use in predicting patient outcome. Whether any of thesemarkers can be used to select patients prospectively for different

treatment modalities will need to be evaluated in future studies.Up-regulation of cyclin E expression has been clearly shown

to be a marker of poor prognosis in resectable NSCLC and is

associated with an increased tendency for NSCLC to invade

local structures and with poorer survival overall. In contrast,up-regulation of cyclin kinase inhibitors, such as p21, p27, and

p16 may prevent tumor cell expansion and lead to improvedpatient survival.

Expression of apoptotic genes was also associated with

prognosis in several studies. Survivin expression was linked

to improved survival in NSCLC, whereas studies of FAS,TNFR-1, TNFR-2, TNF-a, and antiapoptosis clone 11 are alsosuggestive. The data on caspase 3 and Bcl-2 do not clearly

implicate an effect of these two genes on lung canceroutcome.

Angiogenesis also seems to be a critical factor in predicting

which patients with resectable NSCLC were likely to recur andeventually die from their disease. High VEGF expression was

consistently shown to predict poor outcome. Increasinglyconvincing studies showing the role of IL-8 in angiogenesisare starting to appear, and one small study suggests an effect

of IL-8 expression on clinical outcome. High expression of

collagen XVIII, a precursor of the angiogenesis inhibitorendostatin, seems to adversely impact prognosis.

Interestingly, genes known to have prognostic significance in

other solid organ tumors, such as Rb, failed to showimportance in predicting outcome in NSCLC. Other genes

(i.e., cyclin D1, bcl-2) were equivocal in determining patientoutcome. This variability may reflect studies that fail to

compare equivalent patient populations or studies that mayreflect regional influences.


Table 6. Best prognostic markers in NSCLC

Gene

Molecular

function

Number of

significant studies

Favorable prognosis

p16 cell cycle 6

p21 cell cycle 2

p27 cell cycle 2

Unfavorable prognosis

Cyclin B1 cell cycle 2

Cyclin E cell cycle 4

Survivin apoptosis 2

VEGF angiogenesis 9

Collagen XVIII angiogenesis 2



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It is relevant to compare the results of this survey to recentstudies that have used genomic approaches. Gene expression

profiling provides the opportunity to examine thousands of

genes simultaneously to discover markers that correlate withpatient outcome. Although several studies have begun toevaluate resectable NSCLC and patient outcome (54, 55), there

are a number of problems with the genomic data. First, themajority of the genes identified as significant in one study do

not match those in other studies. Second, validation in otherdata sets has proven difficult. For example, when we examinedour own gene array data and the data of others, genesidentified as important in this review very rarely seem to be

associated with prognosis in the genomic studies. Similarly,Beer et al. (54) studied the genetic profile of 86 patients with

resectable NSCLC and saw no clear associations with any of thegenes mentioned in Table 6, except that VEGF expression was

graded according to patient survival. Instead, they identifiedseveral novel genes that may be associated with patient

outcome such as S100P and crk oncogene. The reasons forthese discrepancies are not yet clear. Sample preparation,

technical factors, array platforms, and accuracy of clinical

information may all be important. Gene expression levels maynot always correlate well with protein levels observed usingimmunohistochemistry. Genomics studies are in their infancy

and may reveal new biomarkers to predict patient outcome,

however, to date, we are unaware of any validated andreproducible markers.

As noted earlier, most studies to date have focused on onlyone or a few genes within a certain biological pathway,

although studies have begun to look at panels of markers.Given the complexity of the malignant process, we agree that

this more comprehensive approach may be helpful. This willlikely require the prospective application and quantitative

analysis of a carefully selected panel of antibodies used onhigh-quality NSCLC sections that have been coupled to detailed

clinical and pathologic information. Histologic analysis canthen be subjected to the appropriate statistical interpretations.

The production of tissue microarrays should greatly facilitatethis process and allow validation of key antibodies (5658). As

genomic and proteomic technologies improve, it may bepossible to define a genetic or protein biomarker expression

pattern (in tumor tissues or in serum) using panels of genes orproteins that will allow the physician to map out a specific,

individualized therapy for each patient. Given the large amountof data available and the increasing importance of predicting

outcome after surgical resection, it is hoped that existing

biomarker information will soon be used to help predictpatient outcome and direct future patient therapy. Thisapproach has begun to show promise in other types ofmalignancy, such as breast cancer (59).


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