Section name 1

National Cancer Institute • Division of Cancer Prevention

Second Report • October 2002 

The Early Detection

Research Network

Translational Research to Identify

Early Cancer and Cancer Risk

5 Foreword 

6 Introduction 

15 Sustaining the Scientific Direction 

Discovery, Evaluation, and Validation through Collaboration

Guiding Principles for Biomarker Research 

Progress in Discovery by Organ Site 

30 Meeting the Challenges 

Creating Novel Approaches to Validation Studies 

Improving Informatics and Information Flow 

Standardization of Data Reporting 

Statistical Methodologies and Computational Tools

Standardization of Reagents and Assays

44 Encouraging Collaborations with the Network 

Associate Memberships 

Shared Technology and Resources

Partnerships with Private Sectors

Dissemination and Diffusion of Research Results

55 Network Evaluation and Next Steps 

57 Appendix 

I Key Publications by Network Investigators

II NCI Programmatic Publications Describing the Network 

III Metrics for Programmatic Evaluation

IV Institutional Review Board Guiding Documents

74 Acknowledgments 

Contents

Foreword 5

Foreword

We are in the midst of a biomedical revolution, where multidisciplinary collaboration

will translate the breakthroughs of basic

research swiftly from the laboratory to 

the clinic. Discovery of the molecular 

signatures of cancer will make possible 

powerful, new tools for detecting cancer

and cancer risk–this is the focus of the

National Cancer Institute’s Early Detection

Research Network.

The Network has a straightforward mission:

to translate newly emerging molecular

knowledge into practical clinical tests to

detect cancer and cancer risk. For most

cancers, successful treatment depends on

early detection and successful prevention

depends on the accurate evaluation of risk.

The Early Detection Research Network

seeks to give treatments the opportunity 

to work and to make prevention possible.

The Network is using cutting-edge 

technologies to identify the changes 

that occur in the earliest stages of a cell’s

transformation onto the road of cancer.

Scientific expertise from leading national

and international institutions has been 

harnessed to first identify, and then validate,

crucial molecular markers to detect cancer

and to assess cancer risk. The Network is

an investigator-initiated consortium for the

collaborative research to link the discovery

of biologic markers directly to the next

steps in the process of developing early

detection tests.

I am pleased to introduce this Second

Report on the Early Detection Research

Network, a flagship program of the

National Cancer Institute’s Division of

Cancer Prevention. In the two years since

the Initial Report, the Network has made

critical progress, discovering many promising biomarkers and moving several into

crucial validation studies. The power 

of bioinformatics and computer-assisted

programs are being put to full use to analyze Network data and to facilitate faster

answers to key questions. Collaborations

and partnerships that are necessary for our

ultimate success have been put into place.

The Network is an opportunity and a 

challenge for the scientific community – 

an opportunity to make science work 

for people and a challenge to make this

new-found model of collaboration a 

productive scientific construct. This type 

of translational research is our first step

toward transforming cancer medicine and

improving public health in the 21st century.

Andrew C. von Eschenbach, M.D.

Director

National Cancer Institute
Detecting cancer in its initial stages presents the opportunity to treat disease

before it spreads. Further, the ability to reduce a person’s risk of developing 

cancer opens the way for optimum prevention strategies. The National Cancer

Institute (NCI) is committed to progress in cancer detection and risk assessment that allows interventions to focus on the earliest stages of disease.

The burden of cancer has been significantly reduced since enactment of the

1971 National Cancer Act, but sustained research and clinical efforts are 

necessary to continue the trend. While cancer incidence and mortality rates

declined overall by about 1.1 percent per year from 1992-1998, cancer 

remains a leading cause of death in the United States.

The Early Detection Research Network is a scientific consortium funded 

and coordinated by NCI’s Division of Cancer Prevention. In its third year, 

the Network is the nation’s premier program for systematic identification, 

development, and validation of novel biological markers, known as biomarkers,

which distinguish the characteristics of cancer cells. The Network has produced

a system for evaluating biomarkers as tools to clinically detect cancer before

symptoms appear, and to identify people at risk. 

6 The Early Detection Research Network

Introduction

He is a better physician that keeps diseases off us, than he that cures them being on us; prevention 

is so much better than healing because it saves the labour of being sick.

THOMAS ADAMS, 1618
The Network’s Initial Report, published in September 2000, described the

strategic design that enables researchers across disciplines and institutions 

to develop and validate biomarkers as a team within a streamlined process.

Guiding principles for the forward movement of biomarkers from the 

laboratory to clinical testing were also detailed. 

The highlights described in this Second Report focus on the progress made 

by scientists within the Network’s research infrastructure; the rigorous work

needed to prove biomarkers can strengthen health outcomes; and the research

milestones of novel biomarkers currently in development or in validation.

NCI’s Division of Cancer Prevention has a core mission to conduct and 

support research to improve the health of the public by decreasing the 

incidence, mortality, and morbidity of cancer. The division is the primary 

NCI unit devoted to cancer prevention research and is directed by Peter

Greenwald, M.D., Dr.P.H.

More specifically, the Division of Cancer Prevention:

• Plans and directs an extramural program of cancer prevention 

research, including chemoprevention, nutritional science, genetic 

and infectious agents, biometry, and early detection including

biomarker development and validation;

• Develops and supports research training and career development 

in cancer prevention and early detection;

• Coordinates program activities with other Divisions, Institutes, 

or federal and state agencies, and establishes liaison with professional

and voluntary health agencies, cancer centers, labor organizations, 

cancer organizations, health care delivery and managed-care 

organizations, and trade associations; and

Introduction 7
• Coordinates community-based clinical research in cancer 

prevention, and dissemination of cancer treatment practices 

through a consortium of community clinical centers.

Within the Division of Cancer Prevention, the mission of the Cancer

Biomarkers Research Group is to engage basic and clinical scientists as 

well as epidemiologists and statisticians in a search for and validation 

of promising early cancer biomarkers. To do so, the Cancer Biomarkers

Research Group supports and facilitates a broad spectrum of national 

and international research activities in molecular biology and genetics, 

particularly for the discovery of biomarkers for risk prediction and 

early detection.

The Cancer Biomarkers Research Group also supports the development 

of databases and informatics systems to optimize tracking and assessment 

of biomarker utility and expression patterns. By facilitating, promoting, 

and coordinating cutting edge research with the latest discoveries in technology and molecular circuitry of preneoplastic cells, the Cancer Biomarkers

Research Group hopes to provide a mechanistic picture of preneoplastic 

progression and tools for effective cancer prevention and clinical management. The mission will be accomplished through a mix of program

portfolios, including grants, contracts, and program-initiated research.

Sudhir Srivastava, Ph.D., M.P.H., is chief of the Cancer Biomarkers Research

Group and program director for the Early Detection Research Network. 

8 The Early Detection Research Network
Introduction 9

November 13, 1998 Early Detection Implementation Group proposes concept for

Early Detection Research Network to NCI Board of Scientific

Advisors. Concept is approved.

October 6, 1999 Eighteen Biomarkers Developmental Laboratories’

funds awarded.

April 2000 Early Detection Research Network fully launched and First

Steering Committee meeting is held in St. Petersburg, Fla.

May 2000 Funds awarded for the three Biomarkers Validation

Laboratories, nine Clinical and Epidemiological Centers, 

and the Data Management and Coordinating Center.

June 2000 Task Force for molecular taxonomy of preneoplasia convened.

September 2000 First round of proposals for collaborative studies approved.

September 25-27, 2000 Second Steering Committee meeting and first Scientific

Conference held in Chicago, Ill.

October 2000 Initial Report of Early Detection Research Network published.

January 21-22, 2001 Third Steering Committee meeting convened 

in San Antonio, Texas.

May 22, 2001 Progress Report presented to the National Cancer 

Advisory Board.

June 22-23, 2001 Fourth Steering Committee and the First Network Consulting

Committee meetings convened in Washington, DC.

June 26, 2001 Progress Report presented to the Board of Scientific Advisors.

October 13-15, 2001 Second EDRN Workshop convened in Seattle, WA.

February 3-5, 2002 Fifth Steering Committee and Second Network Advisory

Committee meetings convened in Houston, Texas.

March 10-15, 2002 Gordon Research Conference on New Frontiers in Cancer

Detection and Diagnosis, held in Ventura, CA.

September 3-6, 2002 Sixth Steering Committee Meeting convened in Ann Arbor, MI.

October 3-4, 2002 Third Network Consulting Committee Meeting, 

Washington, DC.

November 14-15, 2002 Progress Report presented to NCI Board of Scientific Advisers.

January 29-31, 2003 Seventh Steering Committee meeting, held in Birmingham, AL.

Timeline
10 The Early Detection Research Network

The Early Detection Research Network 

was conceived on the premise that a 

“vertical” approach to biomarker research—

that is, an established integrated, multidisciplinary environment—will facilitate

collaboration among technology developers,

basic scientists, clinicians, epidemiologists, 

biostatisticians, and other health professionals, and therefore expedite clinical

applications of the molecular knowledge

that has burgeoned in recent years.

Biomarker research occurs in phases: initial

discovery of biological markers, evaluation

of the most promising biomarkers, and 

validation to determine that they can work

in a clinical setting. The consortium of 

collaborating investigators is dedicated 

to working in a systematic and concerted 

fashion to speed translations of basic 

scientific discoveries in genomics and 

proteomics into medical benefits. 

Structured around four main components,

the Network comprises a group of 18

Biomarkers Developmental Laboratories,

three Biomarkers Validation Laboratories,

nine Clinical and Epidemiology Centers,

and a single Data Management and

Coordinating Center.

• Biomarkers Developmental Laboratories

develop and characterize new biomarkers and cancer signatures. 

• Biomarkers Validation Laboratories

serve as a resource for clinical and 

laboratory validation of biomarkers,

including technological development,

standardization of assay methods, 

and refinement. 

• Clinical/Epidemiology Centers

conduct the early phases of clinical

and epidemiological research on 

the application of biomarkers. 

• Data Management and Coordinating

Center provides statistical, logistics 

and informatics support, and develops

the theoretical statistical approaches 

to pattern analysis of multiple markers

simultaneously.

A Steering Committee, comprised of 

the Network’s Principal Investigators and 

NCI staff, coordinates the work of the 

consortium and provides major scientific

management oversight. The group is

responsible for developing and implementing protocols, designs, and operations. 

An Executive Committee of the Steering

Committee meets monthly; it is comprised

of chairs for the Collaborative Groups, 

the NCI program director, the Steering

Committee chair and co-chair. Five 

subcommittees and one working group

report to the full committee. 

Additional collaborations are encouraged

through the Associate Membership 

program, which supports one-time pilot

projects, ongoing resource sharing projects,

and open participation in meetings, workshops, and conferences by non-Network

professionals with a strong interest in early

detection of cancer.

Within the Network structure,

Collaborative Groups, open to any 

investigator, focus on specific organ sites 

to exchange information and facilitate

research on organ-related biomarkers.

Network Principal Investigators are elected

by group members to serve as chairs. 

Chairs frequently invite outside experts 

to talk about research related to potential

collaborations. The Groups meet in conjunction with the Steering Committee

meeting to discuss and prioritize the

biomarker research agenda for their 

respective organ sites:

Components of the Early Detection Research Network
breast and gynecologic cancers; G.I. and

other associated cancers; lung and upper

aerodigestive cancers; and prostate and 

urologic cancers. Chairs report on the

deliberations to the Steering Committee.

Members of the Consulting Committee 

are not investigators in the Early Detection

Research Network. Their role is to review

the Network’s progress, recommend 

new research initiatives, and ensure 

the Network is responsive to promising 

opportunities.

Introduction 11

Biomarkers 

Validation 

Laboratories

Biomarkers

Developmental 

Laboratories

Clinical and

Epidemiologic

Centers 

Network Consulting

Committee

Chair: Bernard Levin, M.D.

Subcommittees

Steering Committee

Chair: David Sidransky, M.D.

Task Forces

(Working Groups)

Associate Members

Data Management

and Coordinating Center

Director: Ziding Feng, Ph.D.

Collaborative Groups

Chairs:

Breast and Gynecologic,

Daniel Cramer, M.D.

Gastrointestinal & Other,

Dean Brenner, M.D.

Lung and Upper 

Aerodigestive Tract,

Mel Tockman, M.D.

Prostate and Other 

Genitourinary,

Ian Thompson, M.D.

3 18

9

Components of the Early Detection Research Network
12 The Early Detection Research Network

Clinical and Epidemiologic Centers

Principal Investigator Institute Organ Focus

Elizabeth R. Unger, M.D., Ph.D. Centers for Disease Control and Prevention, Atlanta, GA Cervix

Kathy Helzlsouer, M.D. Johns Hopkins University, Baltimore, MD Breast

Alan Partin, M.D., Ph.D. Johns Hopkins University, Baltimore, MD Prostate

Daniel W. Cramer, M.D. Brigham and Women’s Hospital, Boston, MA Ovary

Dean E. Brenner, M.D. University of Michigan, Ann Arbor, MI Colon

Henry T. Lynch, M.D. Creighton University, Omaha, NE Pancreas

William N. Rom, M.D. New York University School of Medicine, New York, NY Lung

Margaret R. Spitz, M.D., M.P.H. University of Texas, M.D. Anderson, Houston, TX Head & Neck

Ian M. Thompson, M.D. University of Texas Health Science Center, San Antonio, TX Prostate

Biomarkers Developmental Laboratories 

Principal Investigator Institute Organ Focus

Wilbur Franklin, M.D. University of Colorado Health Science Center, Denver, CO Lung

Jose Costa, M.D. Yale University, New Haven, CT Breast, Colon, Pancreas

Melvin Tockman, M.D., Ph.D. University of South Florida, Tampa, FL Lung

David Fishman, M.D. Northwestern University, Evanston, IL Ovary

David Sidransky, M.D. Johns Hopkins University, Baltimore, MD Lung

Bruce Trock, Ph.D. Johns Hopkins University, Baltimore, MD Breast

Edward Highsmith, Jr., Ph.D. University of Maryland, Baltimore, MD Variety of sites

Stephen Meltzer, M.D. University of Maryland, Baltimore, MD Esophagus

Principal Investigators
Introduction 13

David Beach, Ph.D. Genetica, Inc., Cambridge, MA Breast, Colon

Samir Hanash, M.D., Ph.D. University of Michigan, Ann Arbor, MI Variety of sites

Jeffery Marks, Ph.D. Duke Medical Center, Durham, NC Breast

Timothy Block, Ph.D. Thomas Jefferson University, Doylestown, PA Liver

William L. Bigbee, Ph.D. University of Pittsburgh, Pittsburgh, PA Bladder, Colon, Lung

Adi Gazdar, M.D. University of Texas, Southwestern Medical Center, Dallas, TX Lung

Yingming Zhao, Ph.D. University of Texas, Southwestern Medical Center, Dallas, TX Ovary, Prostate

Bogdan Czeriniak, M.D., Ph.D. University of Texas, M.D. Anderson Cancer Center, Houston, TX Bladder

John O. Semmes, Ph.D. Eastern Virginia Medical School, Norfolk, VA Prostate

Nancy Kiviat, M.D. University of Washington, Seattle, WA Ovary, Prostate

Biomarkers Validation Laboratories 

Principal Investigator Institute Organ Focus

William E. Grizzle, M.D., Ph.D. University of Alabama, Birmingham, AL All sites

David Chia, Ph.D. University of California, Los Angeles, CA All sites

Peter E. Barker, Ph.D. National Institute of Standards and Technology, Gaithersburg, MD All sites

Data Management and Coordinating Center 

Principal Investigator Institute Organ Focus

Ziding Feng, Ph.D. Fred Hutchinson Cancer Research Center, Seattle, WA All sites

Dan Crichton, M.S. National Aeronautics and Space Administration, All sites

California Institute of Technology, Pasadena, CA
14 The Early Detection Research Network

Network Consulting Committee

Chair

Bernard Levin, M.D.

University of Texas M.D. 

Anderson Cancer Center 

Houston, TX

Hoda Anton-Culver, Ph.D.

University of California Irvine

Irvine, CA

Mary Daly, M.D.

Fox Chase Cancer Center 

Cheltenham, PA

Judy Ellen Garber, M.D.

Dana-Farber Cancer Institute 

Boston, MA

Robert Jaffee, M.D.

University of California San Francisco

San Francisco, CA

David Johnson, M.D.

Vanderbilt Medical School 

Nashville, TN

Peter Jones, Ph.D.

University of Southern California

Norris Comprehensive Cancer Center

Los Angeles, CA

Heidi Malm, Ph.D. (Ethicist )

Loyola University 

Chicago, IL

Larry Norton, M.D.

Memorial Sloan-Kettering Cancer Center

New York, NY

David Parkinson, M.D.

Novartis Pharmaceuticals 

East Hanover, NJ

Richard Pazdur, M.D.

U.S. Food and Drug Administration

Center for Drug Evaluation and Research

Rockville, MD

Jeffery Trent, Ph.D.

National Human Genome 

Research Institute

U.S. National Institutes of Health

Bethesda, MD 

Jane Beth William (Cancer Survivor)

Houston, TX
In recent decades, scientific research has

deepened our insights into numerous 

biological processes. The draft map of 

the human genome was presented just 

two years ago. While such research proceeds at a rapid pace, the translation of

these discoveries into medical benefits 

has yet to catch up. The Early Detection

Research Network seeks to harness the

emerging scientific knowledge into 

practical clinical tests.

Sustaining the Scientific Direction 15

Sustaining the 

Scientific Direction

Discovery, Evaluation, and Validation through Collaboration

The Network promotes collaboration

among researchers by creating an 

investigator-driven environment of 

“cross-fertilization,” that is, teamwork

across disciplines and laboratories to 

achieve common goals. These objectives 

are to:

• Develop and test promising biomarkers or technologies in institutions with

the scientific and clinical expertise to

obtain preliminary information that

will guide further testing;

• Efficiently evaluate promising, 

analytically proven biomarkers or 

technologies, including measures of

diagnostic predictive accuracy, sensitivity, specificity, and, whenever possible,

medical benefits as predictors of clinical outcome or surrogate endpoints for

early detection and for prevention

intervention clinical trials; 

• Analyze biomarkers and their expression patterns, including simultaneous

analyses of multiple markers which

will serve as background information

for subsequent large definitive validation studies in the field of cancer

detection and screening;

• Collaborate among academic and

industrial leaders in molecular biology,

molecular genetics, clinical oncology,

computer science, public health, and

other disciplines, for the development

of high-throughput, sensitive assay

methods for biomarkers for 

early detection and risk assessment;

• Conduct early phases of clinical 

and epidemiological studies; and

• Encourage collaboration and rapid 

dissemination of information among

grantees to ensure progress and avoid

fragmentation of effort.
As the integration of disparate groups and

work elements proceeds in a collaborative

project, a major role for the National

Cancer Institute is to motivate all 

stakeholders toward a common goal. 

The Network’s vertical approach 

encourages researchers to work in teams 

as collaborators rather than as individual

competitors. Efforts focus on ensuring a

smooth flow between laboratory discoveries

and clinical translation of biomarkers to

provide timely, cost-effective tests for early

detection of cancer and identification of

high-risk individuals. 

Successful teamwork is predicated by 

well-defined workflow, collaboration, and

communication. People with similar areas

of research (such as in the Organ Site

Collaborative Groups), and people who

have roughly similar time horizons for their

output need to work together. Laboratories

that are developing biomarkers gain much

by sharing information on both successes

and failures.

16 The Early Detection Research Network

Guiding Principles for Biomarker Research

The Network has developed and 

implemented systematic, comprehensive

guidelines to develop, evaluate, and validate

biomarkers. This five-phase approach 

establishes both a standard and a road map

for successfully translating research on

biomarker applications from the laboratory

to the bedside.

Phase 1 includes exploratory study to 

identify potentially useful biomarkers–

this is called the “discovery” phase. 

Phase 2 is where biomarkers are studied to

determine their capacity for distinguishing

between people with cancer and those 

without–the validation phase. 

Phase 3 determines the capacity of a

biomarker to detect preclinical disease by

testing the marker against tissues collected

longitudinally from research cohorts. 

Phase 4 includes prospective screening 

studies.

Phase 5 is when large-scale population 

studies evaluate not just the role of the

biomarker for detection of cancer, but 

the overall impact of screening on the 

population.

Although the Network’s focus is projected

mainly on phases 1 through 3, researchers

have welcomed the five-phase structure

because it provides an orderly series of 

studies that build upon each other to yield

an efficient and thorough approach to

biomarker development. The key aspects 

of study designs for each of the phases 

has been discussed and published. (See

Appendix II.) 

In contrast to the development structure,

literature related to the statistical evaluation

of biomarkers is both limited and scattered.

The Data Management and Coordinating

Center and Fred Hutchinson Cancer

Center are advancing several concepts and

methods to fill a major gap in research

methodology for studies in phases 2 to 5.

This is already proving to be a valuable

resource. 
Specifically, these investigators have used

“Boosting Tree” algorithms, developed 

in-house, to analyze data from tests of 

tissues from patients with prostate cancer,

benign prostatic hyperplasia, and healthy

cells at the Eastern Virginia Medical

School. The tests, known as SurfaceEnhanced Laser Desorption/Ionization or

SELDI, enable the detection of a specific

protein pattern for each of the three groups

with sensitivity and specificity of 97%. 

Based on these results, a validation study

using samples from NCI’s Prostate Cancer

Prevention Trial was proposed. In this 

clinical trial, 18,000 men took either the

drug finasteride or a placebo for seven years

and then received a prostate biopsy. In 

the Network proposal, testing these biopsy

samples would be the phase 3 study that

moves forward based on data provided by 

a phase 2 study–the study using samples

from Eastern Virginia Medical School. 

The phase 3 sample size was calculated

based on joint confidence regions of 

sensitivity and specificity, a novel approach

developed by Data Management and

Coordinating Center investigators. 

Sustaining the Scientific Direction 17

Phases of Early Detection

The Network focus on the first three phases of early detection has established 

a well documented scientific foundation for research to follow in Phases 4 and 5.

Preclinical Exploratory PHASE 1 Promising directions identified

Clinical Assay and Validation PHASE 2 Clinical assay detects established disease

Retrospective Longitudinal PHASE 3 Biomarker detects preclinical disease 

and a “screen positive” rule defined

Prospective Screening PHASE 4 Extent and characteristices of disease detected by the test and the false referral rate are

identified

Cancer Control PHASE 5 Impact of screening on reducing burden of

disease on population is quantified

Source: Journal of the National Cancer Institute, Vol. 93, No. 14, 1054-1061, July 18, 2001
18 The Early Detection Research Network

Measuring the Predictive Power of a Biomarker

NCI investigators have developed guidelines

for statistical design and analysis of nested

case-control studies on serially collected

blood or tissue specimens. These guidelines, listed below, will be used by Network

researchers designing studies to measure

the predictive power of a biomarker:

• For clearest interpretation, statistics

should be based on false and true positive

rates, not odds ratios or relative risk.

• To avoid over-diagnosis bias, cases should

be diagnosed as a result of symptoms

rather than on screening. 

• To minimize selection bias, the spectrum

of control conditions should be the 

same in the study and target screening

populations. 

• To extract additional information, criteria

for a positive test should be based on

combination of individual markers and

changes in marker levels over time. 

• To avoid over-fitting, the criteria for a positive marker combination developed in a

training sample should be evaluated in a

random test sample from the same study

and, if possible, a validation sample from

another study. 

• To identify biomarkers with true and false

positive rates similar to mammography,

the training test and validation samples

should each include at least 110 randomly

selected subjects without cancer and 70

subjects with cancer. 

Source: BMC Medical Research Methodology 2:4, 2002, 

online BioMed Central, http://www.biomedcentral.com/1471-2288/2/4
Sustaining the Scientific Direction 19

Progress in Discovery by Organ Site

The Early Detection Research Network is

actively testing and evaluating numerous

biomarkers to detect cancer or cancer risk

in a variety of organ sites. The initial phase

of the process encompasses discovery–

identifying potential biomarkers and 

making initial inroads in evaluating and

determining whether or not they might 

be a useful test. 

The following pages detail some of the

most promising results of this discovery

phase. The sections are organized by the

organ-specific collaborative groups used in

the Early Detection Research Network to

facilitate collaboration and information

exchange. 

Breast and Gynecologic Cancers

BREAST CANCER

Breast cancer is the most frequently diagnosed cancer and the second most frequent

cause of cancer death for women in the

United States. More than 203,000 women

are diagnosed with breast cancer each year.

Mammography is an important and useful

early detection test for breast cancer, but it

is not the final answer. While nearly twothirds of breast cancers are diagnosed while

localized, more than one-third are not.

Women diagnosed with breast cancer at the

most advanced stages have less than a 25%

chance of living for five years. Biomarkers

to detect breast cancer and breast cancer

risk will play a crucial role in decreasing 

the number of women who are diagnosed

with or die from this disease.

• Investigators at Duke/Abbott Developmental Laboratory analyzed the expression

of two promising markers for the detection of breast cancer, BS106 and BU101

in tissues from early stage and metastatic

breast cancers, along with the expression

of two known markers, cytokeratin 19

and mammaglobin. The data showed that

cytokeratin-19 is as good as or better than

all other markers in detecting a breast 

cancer when it is present, but is not good

at detecting risk when a woman does 

not have the disease. Of the breastspecific markers, BS106 appears to be 

the best marker to move forward into

more research.

• Collaborative studies between Duke

University and Eastern Virginia Medical

School are testing whether SELDI can

detect circulating levels of the protein

markers, BU101 and BS106. These 

investigators have successfully used 

the SELDI approach for analyzing antibody-mediated prostate-specific antigen

(PSA) detection. A second study will

explore the possibility of early detection 

of breast cancer using artificial intelligence

to detect SELDI profiles. A series of 

blood serum specimens from benign 

and malignant breast patients are being

analyzed to see if this technique can 

successfully distinguish between cancer

and benign disease.
What is SELDI?

Surface-Enhanced Laser Desorption/

Ionization or SELDI is a process that

enables the exploration, mapping, and 

discovery of molecular actions. It is a chipbased molecular imaging process where

silicon wafers used in computers are coated

with an active layer of molecular “bait”

designed to capture DNA or proteins. These

chips are exposed to the materials under

study. The image of molecules captured on

the chip’s surface is developed and can be

“read” with laser energy to create a digital

image or molecular map of proteins. Within

the Network, investigators are looking to

see how the molecular map of proteins

changes between healthy tissues, 

precancerous tissues, and cancer.

• Eastern Virginia Medical School investigators in collaboration with Duke/Abbott

Developmental Laboratory and other

Network members are also evaluating 

the SELDI protein-profiling assay as a

serum-based diagnostic tool for breast

cancer. A pilot study involving the 

analysis of serum samples from 40 newly

diagnosed patients with breast cancer 

and 40 patients with benign breast 

diseases showed breast cancer was 

detected with 92% sensitivity and 

70% specificity. A larger-scale study

including the analysis of 300 serum 

samples from the collaborating 

universities is in progress. 

20 The Early Detection Research Network

Data Summary of Promising Breast Cancer Biomarkers

Marker Cytokeratin-19 Mammaglobin BU101 BS106

Sensitivity (cancer) 95/95 (100%) 90/95 (95%) 82/95 (86%) 94/95 (99%)

Units (SKBR3 eq) 0.001-100 0.0008-130 0.017-1800 0.06-5900

Range 1 x 105 1.6 x 105 1.06 x 105 9.8 x 104

• Investigators at the Johns Hopkins

University, Food and Drug

Administration, National Cancer

Institute, and Eastern Virginia Medical

School are jointly developing a “proteomic

profile” using SELDI for early detection

of breast cancer. By comparing samples 

of breast fluid (or blood) from women

with early, curable cancers to samples

from women with normal breasts, they

were able to identify specific protein 

patterns that consistently appeared in 

one group but not the other. 

Source: LumiCyte, Inc., 

http://www.lumicyte.com/html/technology/2/.
OVARIAN CANCER

Ovarian cancer is the leading cause of

death from gynecological malignancies 

and is the fifth most common female 

cancer in the United States. Network 

and NCI investigators are developing a

novel approach in their quest for identifying ovarian cancer at the earliest stage.

Unfortunately, only 25% of patients are

diagnosed when ovarian cancer is still

localized to the ovary. Up to 90% of 

these very early cancers can be successfully

treated, while only 30% of the patients

with more advanced cancers will survive

five years.

• Investigators from the Brigham and

Women’s Hospital in Boston have 

successfully used cDNA microarray 

analysis to compare ovarian cancer

epithelial cell lines and normal surface

epithelial cells to identify genes that are

upregulated in cancer. Two genes,

prostasin and osteopointin, are the newest

candidate markers for ovarian cancer and

may prove complementary to CA125, 

an ovarian cancer biomarker that is used

primarily in managing treatment of the

disease, but is also being evaluated as a

cancer screening test.

Prostasin is normally secreted by the

prostate gland, but has been found 

to be elevated in epithelial ovarian 

cancers; tissues from 64 cases of ovarian

cancer showed nearly twice the level of

prostasin compared to normal tissues. 

Osteopontin is found in body fluids 

and extracellular matrix components. 

In 51 ovarian cancer cases, the levels of

osteopontin were three times the average

level in 107 healthy control subjects. 

What is microarray 

analysis? 

Significant information about potential

biomarkers is being gathered by microarray

analysis of gene and protein expression.

Microarray technology is a powerful, but

technically challenging, new research tool

that allows scientists to assess the level of

expression of a large subset of the 60,000

human genes in a cell or tissue. This technology can quickly produce a snapshot of

the genes that are active or inactive in

tumor cells, preneoplastic cells, or healthy

cells, providing critical information in 

narrowing down the precise molecular 

triggers of the cancer process.

• Investigators at Northwestern 

University, Chicago, have found that

lysophosphatidic acid (LPA) is elevated 

in the plasma of women with ovarian

cancer, including 90% of women with

stage I disease. The presence of LPA in

early stage disease may suggest that it is

produced by the cancer itself and plays 

a role in allowing the cancer to spread.

In a collaborative study with Atairgin

Technologies Inc., plasma levels of LPA

were found to be significantly higher in

ovarian cancer patients when compared

to healthy female controls. In comparison, CA125 was elevated in only 43% of

women with early stage ovarian cancer

using the same patient population. 

A trial evaluating 1,600 women will 

determine the sensitivity and specificity

of a high-throughput version of this

assay. Analysis of the data is under way.

Sustaining the Scientific Direction 21
LPA may also be involved in matrixmetalloproteinase (MMP)-mediated 

invasion and tumor spread. A recent

study demonstrated that LPA added 

to ovarian cancer cells up-regulated 

activation of certain MMPs involved in

the migration of cancer cells and their

invasion into healthy tissues. 

All three of these prospective markers,

prostatin, osteopontin, and LPA, will 

be assessed using a joint set of specimens

from women with ovarian cancer and

healthy controls to compare their 

potential.

• Several EDRN laboratories and centers 

in collaboration with the Food and Drug

Administration and NCI are conducting

studies to validate the accuracy, and

reproducibility of SELDI technology

with the Standard Reference Materials

(SRM) 1951A for serum protein provided by the National Institute of Standards

and Technology. Upon completion of 

this analytical validation, investigators are

proposing to assess proteomic patterns 

as a screening tool for earlier stages of

ovarian cancer in women at increased 

risk of developing ovarian cancer.

• A Network study led by investigators 

at the National Institutes of Health

described the application of an artificial

intelligence algorithm to analyze 

proteomic data to identify diagnostic 

patterns that could distinguish blood

from women with and without ovarian

cancer. Rapid, high-throughput molecular weight profiles consisting of tens of

thousands of data points were generated

by SELDI and analyzed by a novel,

heuristic pattern recognition algorithm.

The trained algorithm was accurate in

predicting the presence of ovarian cancer

in 260 of 260 women, including 60 of

60 from sera of women with stage 1 cancer. Validation of this new tool is ongoing 

in larger study sets of serums for diagnostic

utility for early detection of prostate, 

ovarian, breast, and colon cancer.

• Ovarian cancer may be due to a mutation

in either the BRCA1 or BRCA2 genes,

which are more commonly associated

with risk of breast cancer. Two mutations

in BRCA1 (185delAG and 5382insC) and

one in BRCA2 (6174delT) are common

in the Ashkenazi Jewish population 

and each mutation is associated with 

an increased risk of ovarian cancer. 

A significant proportion of Jewish

women with ovarian cancer may 

carry one of these mutations.

Recent publication by Northwestern

University investigators reported that

approximately 40% of ovarian cancers 

in Ashkenazi Jewish women with no 

family history of breast or ovarian cancer

showed the presence of a founder BRCA1

or BRCA2 mutation (an inherited

germline mutation). Also, the investigators revealed that about 20% of breast

cancer patients in this group had a

founder mutation in BRCA1 and or

BRCA2 genes. Screening for a founder

mutation in BRCA1 and BRCA2 might

greatly benefit early detection of ovarian

and breast cancer in high-risk women.

• Investigators at the Brigham and

Women’s Hospital reported that the

application of SELDI to profile proteins,

which are detected in blood sera of 

ovarian cancer patients as compared 

to controls, led to the identification 

of a marker. The protein was purified 

and identified as the alpha chain of 

hepatoglobin and was found to be 

significantly elevated in 94 cases of 

ovarian compared to 99 healthy controls.

As an acute-phase reactant protein, 

alpha hepatoglobin may be a general

marker for cancer and therefore have

applicability in the detection of 

disease in several organs.

22 The Early Detection Research Network
• Investigators at the Mayo Clinic have

identified a novel isoform of EGFR,

referred to as sErbB1 p110. Pre-operative

levels of this protein were significantly

lower in women with stage III or IV

epithelial ovarian cancer than in healthy

women of similar ages. To determine if

serum sErbB1 may be a useful (negative)

biomarker for ovarian cancer, sErbB1

p110 levels have been compared in

healthy women, women with benign

masses in organs near the ovaries, other

benign gynecologic conditions, and

women with stage I-IV ovarian cancers.

Women with ovarian cancer have a lower

median serum sErbB1 p110 level than

the control groups of healthy women and

those with benign masses in organs near

their ovaries or other benign gynecologic

conditions. A subset of ovarian cancer

patients had a higher median CA125

compared to women with benign diseases, suggesting that serum sErbB1 p110

levels, in combination with CA125 levels,

may be useful for differentiating between

benign and malignant pelvic masses.

Gastrointestinal and Other Cancers 

Investigators of the Gastrointestinal and

Other Cancers Collaborative Group focus

on the discovery and validation of surrogate biomarkers of cancers of the colon,

esophagus, liver, pancreas, stomach, and

gall bladder. 

COLON CANCER

Colon cancer is the third most frequently

diagnosed cancer in men and women 

in the United States and the third most 

frequent cause of cancer death. More

widespread use of current screening 

technologies (fecal occult blood test, 

sigmoidoscopy, colonoscopy, and barium

enema) could reduce deaths from the 

disease, but many people avoid the tests

due to their discomfort. Alternate strategies

to screen for colorectal cancer could identify those at greatest risk or likelihood of

disease versus those who need not submit

to an invasive test.

• Scientists at the Great Lakes-New

England Clinical Epidemiology Center

Consortium identified a ligand, or binding protein, for galectin-3 in circulating

blood; galectin-3 is a protein related to

tumor progression and spread. The new

ligand was found to be a hepatoglobinrelated protein, and is present in higher

concentrations in patients with colon

cancer than in those with adenomas 

(precancerous polyps) or in normal 

subjects. There was also an increase in

serum galectin-3 itself in patients with

colon cancer as compared to those with

adenomas or to normal, but no correlation between the ligand and galectin-3. 

In the blood of people with colon cancer,

there is twice as much of any haptoglobin

than in the blood of normal people, and

30 times more of the ligand. This difference in concentration suggests that

cancer causes alterations in how 

haptoglobins work in the body and 

may mean that haptoglobins are good

biomarkers for early cancer detection.

• Network scientists at Creighton

University, in collaboration with Exact

Laboratories, are examining whether a

stool-based assay for mutated BAT-26, 

a marker of microsatellite instability,

could be used to diagnose early stage 

colorectal cancer in people with symptomatic Hereditary Non-Polyposis

Colorectal Cancer (HNPCC). People

with HNPCC do not have an unusual

number of polyps (which would signal

cancer risk) and usually develop a 

solitary colorectal tumor, often in the

right (proximal) side of the colon (most 

Sustaining the Scientific Direction 23
cancers occur on the left or distal side).

Since many proximal cancers exhibit

microsatellite instability, it was hypothesized that detection of a specific one

might complement colorectal cancer

screening, especially if the marker reflected specifically proximal cancers. Of 78

cases, 70 had negative BAT-26 stool tests

and, in cases where abnormal tissue was

available to test, negative BAT-26 tissue

tests. In the eight positive BAT-26 tests,

those patients had a significant pathology,

i.e. had a cancer or precancerous tissues

in the proximal colon. Current data 

suggest that stool BAT-26 testing may

detect early stage colorectal neoplasia 

in high-risk individuals with HNPCC.

PANCREATIC CANCER

Currently there is no successful treatment

for pancreatic cancer, making it one of 

the most lethal of all human cancers.

Pancreatic cancer can be successfully 

treated when diagnosed at its earliest stage,

however only about 8% of cases are 

diagnosed this early. Families have been

identified where pancreatic cancer occurs

frequently and researchers have identified

several genes linked to the inherited risk

for the disease.

• Investigators at Creighton University

established a cohort containing 159 

pancreatic cancer families. Hereditary

pancreatic cancer is a very diverse disease

which is frequently associated with 

inherited cancer syndromes, including

familial atypical multiple mole melanoma

syndrome, also known as FAMMM. 

In collaboration with Johns Hopkins

University and the University of Toronto,

Creighton investigators have discovered

the CDKN2A (p16) germline mutation 

in eight of 20 FAMMM families in their

cohort.Testing for CDKN2A mutations 

is still ongoing, but given the known

genotypic and phenotypic diversity in 

the FAMMM syndrome, the investigators

anticipate that germline mutations 

in additional genes will one day be 

identified as causing pancreatic cancer 

in patients without CDKN2A (p16)

mutations.

Up to 10% of malignant melanomaaffected families showed patterns of

transmission consistent with a hereditary

(autosomal dominant) predisposition. 

An unknown portion of these carry the

CDKN2A mutation of the FAMMM-PC

syndrome. People who carry the

CDKN2A mutation show a low risk 

of developing pancreatic cancer (about

17%) compared to as much as an 80%

likelihood for developing melanoma.

Thus, this FAMMM-PC model will

allow cancer prevention activities for

melanoma through identification of 

the FAMMM phenotype, defined by 

the presence of many moles or atypical

moles. The CDKN2A germline mutation

predisposing to pancreatic cancer will

provide an opportunity for development

of a variety of putative biomarkers that

correlate with the CDKN2A mutation.

This research is expanding to other

hereditary syndromes where pancreatic

cancer can appear, including: hereditary

non-polyposis colon cancer of the Lynch

syndrome II variant (MLH1 or MSH2

mutation), Familial Adenomatous

Polyposis with colorectal cancer and 

pancreatic cancer (APC mutation), 

and hereditary breast-ovarian cancer 

syndrome where germline markers for

BRCA1 and BRCA2 appear to predispose

to pancreatic cancer.

24 The Early Detection Research Network
ESOPHAGEAL CANCER

Esophageal cancer rates are rising, mostly

due to increases in adenocarcinoma of the

esophagus. More than 13,000 people are

diagnosed with esophageal cancer each year

and 12,600 die of the disease. Five-year

survival is about 13%. The only known

precancerous condition that signals risk for

esophageal cancer is Barrett’s Esophagus or

Barrett’s dysplasia. 

A collaborative effort between scientists 

at University of Michigan Biomarker

Developmental Laboratory and the Great

Lakes-New England Clinical Epidemiology

Center focused on gene amplification 

in esophageal adenocarcinomas and 

in Barrett’s metaplasia with high-grade 

dysplasia. A cohort of 87 esophageal adenocarcinomas and 22 cases of Barrett’s

metaplasia with high-grade dysplasia and 

3 Barrett’s adenocarcinoma cell lines were

examined. One or more gene amplification

events were present in 50/87 (57%) of 

adenocarcinomas. The researchers will 

look further at these events for potential

biomarkers. See the Web at:

https://glnecec.dartmouth.edu/.

LIVER CANCER

Liver cancer rarely causes any symptoms

until it has spread beyond the liver itself.

The strongest risk factor for the disease is

chronic infection with hepatitis viruses.

About 16,600 people are diagnosed with

liver cancer in the United States each year

and 14,100 die of the disease.

• A collaboration between Thomas

Jefferson University and Great LakesNew England Clinical Epidemiology

Center investigators focused on early

detection of liver cancer and hepatitis 

B in a patient population with a background of hepatitis B infection. Evidence

of an inverse relationship between the

abundance of serum C3 fragment and 

an isoform of apolipoprotein A1 (ApoA1)

and the risk of liver cancer has been 

identified in a small set of samples examined thus far. The average group intensity

of C3 fragment in patients with no liver

disease or with a symptomatic hepatitis B

infection is greater than in the groups of

those with active hepatitis of liver cancer.

Overall, however, consistently low levels

of both of these proteins are seen in

patients with a diagnosis of liver cancer. 

Lung and Upper Aerodigestive Cancers

LUNG CANCER

Lung cancer is the greatest cause of cancer

death in the United States. More than

150,000 people die of lung cancer each

year. There is no established early detection

test for the disease, and only 15% of lung

cancer cases are diagnosed when the disease

is localized. 

• Abnormal DNA methylation patterns are

a hallmark of most cancers. Gene-specific

methylation changes in tumor cells and

sputum are being evaluated as promising

markers of early lung cancer. Different

laboratories have optimized different

assays of gene-specific promoter hypermethylation. Investigators from Johns

Hopkins University, the University of

Texas M. D. Anderson Cancer Center,

The University of Texas Southwestern

Medical Center, Moffitt Cancer Center

and Lovelace Respiratory Research

Institute (Albuquerque), are each using

their own methylation assay to determine

accuracy for cancer endpoints on blinded

lung cancer and control specimens. This

collaborative study will determine the

methylation assay that most accurately

reflects the presence of lung cancer and

thereby the assay to be proposed for subsequent validation. 

Sustaining the Scientific Direction 25
• Investigators at the University of

Michigan have compiled a database of

protein expression in lung cancer that

integrates two-dimensional gel profiles,

mass spectrometry data, quantitative 

protein data, and gene expression data 

to serve as a resource for identification 

of protein markers in lung cancer.

Investigators at the Moffitt Cancer

Center have found specific protein 

profiles for microdissected lung tumors

and pre-malignant lesions using SELDI.

• Investigators at the M. D. Anderson

Cancer Center are examining DNA

repair gene polymorphisms that may 

predict DNA repair capacity.

Polymorphisms in the nucleotide excision

repair gene, XPD, could have an effect on

host capacity for removing bulky adducts

induced by exposure to carcinogens. If

true, detection of the differences in DNA

repair capacity in individuals of different

XPD genotypes would be possible. The

investigators report that the variant

Gln751Gln and Asp312Asn genotypes

were associated with less optimal DNA

repair capacity. Both cases and controls

with the wild type genotypes exhibited

the most proficient DNA repair capacity.

The risk for suboptimal DNA repair ability (defined as less than the median value

among the controls) was 1.57 for those

with the Gln/Gln751 genotype. For cases

with the Asn/Asn312 genotype, the risk

was 3.50. For cases who were homozygous at either locus, the risk was 2.29.

The ability to identify smokers with 

the highest risks of developing tobaccorelated cancers has substantial

implications for early detection.The 

challenge in such risk assessment is to

account for inter-individual variation in

susceptibility to tobacco carcinogenesis.

One such measure is the in vitro

mutagens (e.g., BPDE and bleomycin)

challenge assay that reflects the combined

effects of the extent of mutagen induced

initial chromosomal damage and the

effectiveness of subsequent DNA repair.

In a series of published studies, this assay

has been shown to be a strong, statistically significant, and independent risk factor

for a variety of cancers. 

26 The Early Detection Research Network

No 

9

7

5

3

1

No 

Yes 

Yes 

8.5*

4.0*

3.0*

1.0

Odds Ratio

For Lung Cancer*

BPDE Sensitivity

* adjusted for age, gender, ethnicity and smoking status

* P value < 0.05

Bleomycin

Sensitivity

Risk Estimates for Bleomycin and BPDE Sensitivity
Sustaining the Scientific Direction 27

• Investigators from the Johns Hopkins

University compared expressed genes of

non-small cell lung cancer with those of

normal lung using serial analysis of gene

expression (SAGE) to describe a molecular

signature for lung cancer. Further work is

under way on this signature.

• Investigators at the University of Texas

Southwestern Medical Center report a

new tumor suppressor gene at 3p21.3,

SEMA3B. The expression of this gene 

is frequently inactivated in lung cancers.

• University of Colorado is studying gene

regulation pathways involving HOX,

WNT, and fibroblast growth factor

(FGFR), all of which are frequently altered

in lung cancer, as potential biomarkers.

• Investigators at the University of

Pittsburgh report that estrogen signaling

plays a biological role in both the epithelium and the mesenchyme in the lung.

Estrogens could potentially promote lung

cancer, either through direct actions on

precancerous or cancerous cells or through

indirect actions on lung fibroblasts. 

• Investigators at New York University have

found that p38, a mitogen-activated protein kinase usually associated with stress

responses, growth arrest, and apoptosis, 

is activated in all of their human lung

cancer samples. This activation suggests

an additional role for this pathway in

malignant cell growth or transformation.

Prostate and Other 

Genitourinary Cancers

PROSTATE CANCER

Prostate cancer is the most frequently 

diagnosed cancer in American men, with

nearly 190,000 men diagnosed each year

and 30,200 dying of the disease. While

prostate cancer is frequently present in men

over age 50, many of these cases will never

be lethal or even clinically detectable. The

challenge for early detection is to discern

latent from lethal disease.

• Researchers at the University of Texas at

San Antonio are evaluating insulin-like

growth factor (IGF)-1, IGF-binding 

proteins 3 and 4, and genetic polymorphisms of the androgen receptor for 

their potential to predict risk for prostate

cancer in an ethnically varied population.

These patients are also screened for

prostate cancer by the PSA test and 

digital rectal exam. 

• Scientists at the Johns Hopkins University

are evaluating the clinical utility of PSA,

human kallikrein-2, B23 (a nuclear matrix

protein), p27 (a cell cycle regulator), 

and Ki67 (a cell proliferation marker) 

as biomarkers for prostate cancer.

• Researchers at the University of

Washington and Eastern Virginia Medical

School are developing highly sensitive and

specific assays in seminal plasma/prostatic

fluid, serum, and urine that signal an early

transformation event in prostate cancer. 

• The University of Texas Southwestern

Medical Center is utilizing analytical 

spectrometric and chromatographic 

technology to detect differences in 

protein expression between prostate 

cancer patients and normal controls,

which can be used to develop biomarkers

for the disease.
28 The Early Detection Research Network

• A Network associate at Massachusetts

General Hospital is developing and 

refining a serum-based, high-throughput

assay that can detect glycovariants of 

PSA through a capillary electrophoresis

approach. Bioinformatic tools are being

applied to enhance the interpretation of

existing and new assays. It is anticipated

that this study will lead to the development of new methods for analyzing and

managing clinical information associated

with multiple biomarkers. 

• Researchers at the University of Alabama

at Birmingham are characterizing 

molecular markers in high grade prostate

intraepithelial neoplasia (PIN) lesions

using tissue matrix technology. Scientists

at this site have the capability to validate

protein biomarkers, like those identified

in seminal plasma and sera. 

• Investigators at the University of Texas

Health Science Center in San Antonio

using the San Antonio Center of

Biomarkers of Risk (SABOR) cohort

found that androgen receptor (AR)-length

polymorphism is associated with prostate

cancer risk in Hispanics. The shorter

CAG repeat (< 18 ) in exon 1 of the 

AR gene confers a significantly increased

risk of prostate cancer among Hispanic

men compared to those with longer 

CAG repeats ( > 18). 

• The Biomarker Development Laboratory

at Eastern Virginia Medical School 

is applying Ciphergen Biosystems

ProteinChip® SELDI-TOF-MS system

for discovery of the signature proteins that

distinguish prostate cancer from benign

prostate disorders, and healthy prostate

tissue. SELDI analyses of cell lysates 

prepared from microdissected cells have

identified clusters of small polypeptides

that are overexpressed in the preneoplastic

lesion PIN (prostate intraepithelial neoplasia) and continue to be overexpressed

in primary and metastatic cancer cells.

These small and low abundance proteins

may represent significant and potential

biomarkers associated with the early

development of cancer. 

• Coupling SELDI protein profiling of 

seminal plasma and serum with learning

algorithms developed at the EDRN Data

Management Coordinating Center at 

Fred Hutchinson Cancer Research Center,

have achieved better assay sensitivities 

and specificities (i.e., >95%) than PSA

in identifying prostate cancer.

• Using the same biochip platform, SELDI

multiplex immunoassays have been 

constructed to quantify the prostate 

cancer biomarkers PSA and PSMA

(prostate specific membrane antigen) 

in sera. A significant observation was 

finding serum PSMA superior to PSA

in distinguishing between benign prostate

hyperplasia and prostate cancer. These

early results suggest the SELDI system 

is a promising proteomic approach for

biomarker discovery and as a clinical 

platform for development of innovative

diagnostic assays.

• Distinct molecular changes occur at each

progressive stage of prostate cancer that

can be studied using gene microarray

technology. University of Texas Health

Science Center-San Antonio investigators

in collaboration with Gene Logic Inc.,

determined gene expression profile in

prostate cancer. Analysis was performed

on the Human U95 Affymetrix

GeneChip7 high density oligonucleotide

microrarray platform for simultaneous

analysis of 60,000 fragments. RNA were
Sustaining the Scientific Direction 29

extracted from fresh prostates from areas

highly enriched in tumor (>90%) and

adjacent areas from the same prostates

that are free of tumor. The expression data

from these samples was compared to other

human tissue samples using existing software tools. Hierarchal Clustering Analysis

resulted in patterns that corroborates 

with the Principal Component Analysis.

Expression profiles of these genes in over

3,000 human normal, diseased, benign,

and tumor were evaluated to determine

whether they are selectively up-regulated

or down-regulated in prostate cancer 

tissues. These prostate cancer-regulated

genes provide novel markers for prostate

cancer, new targets for treatment, and

patient stratification. Interestingly, among

the upregulated genes in the Principal

Component Analysis was the alphaMethylacyl Coenzyme A racemase, 

which was recently reported in several

publications as a tissue biomarker for

prostate cancer.

BLADDER CANCER

At present, mortality from bladder cancers

is high and survival is low. Techniques for

early diagnosis of these cancers are extremely limited and treatment fails in 95 percent

of patients with advanced disease. Bladder

cancer is the fourth most frequently 

diagnosed cancer in men and the tenth 

in women.

• University of Texas M. D. Anderson

Cancer Center investigators developed 

a progression model for bladder cancer.

The investigators determined a pattern 

of loss of heterozygosity in multiple loci,

on multiple chromosomes, analyzing large

number of neoplastic and pre-neoplastic

lesions obtained from 5 cystectomies.

Using this approach they attempted to

identify chromosomal regions which were

involved in early occult phases of bladder

neoplasia and those that could be relevant

for the development of more advanced

disease such as severe dysplasia or 

carcinoma in situ progressing to clinically

aggressive invasive cancer. In preliminary

studies, a panel of 20 markers located in

chromosomal regions involved in early

phases of bladder neoplasia was used to

profile the pattern of loss of heterozygosity on voided urine samples of patients

with a history of bladder cancer that were

clinically cancer free. Using this approach

they were able to identify nearly 80% 

of patients clinically occult phases of 

bladder neoplasia. These markers might

be used for detection and monitoring 

of bladder cancer. 
30 The Early Detection Research Network

Meeting the Challenges

Creating Novel Approaches to Validation Studies

By following the principles and theories 

of systems biology, in which disciplines 

like biology, chemistry, computational 

science, and clinical sciences are integrated

seamlessly, the Network is meeting the 

scientific challenges of biomarker research.

Interdisciplinary teams of investigators 

are tackling these critical areas:

• Creating novel approaches to validation

studies;

• Improving informatics and information

flow;

• Standardizing data reporting; and 

• Creating general statistical and 

computational tools and standardizing

reagents and assays.

A fundamental goal of the Network is 

to find methods other than randomized

clinical trials to evaluate and validate

biomarkers for clinical application during

the early stages of investigation. Validation

of new biomarkers by traditional clinical

trials will be unfeasible due to cost, time

required for follow-up, rapidly changing

technologies, variations in treatment, and

the large numbers of biomarkers expected

to be discovered. 

The Network’s three validation laboratories

remain integral to the search. A resource 

for all investigators in the consortium, the

laboratories are housed at: the National

Institutes of Standards and Technologies

(NIST); University of California at Los

Angeles (UCLA); and University of

Alabama at Birmingham (UAB). The 

following studies are at different stages 

of validation.

Detecting Promoter Methylation 

as a Risk Marker

Epigenetic changes, such as methylation of

the promoter regions of some key genes like

p16, APC, RAR-beta, DAPK, and MGMT,

commence early during the lengthy preneoplastic process, and thus may be useful

markers for risk assessment. These markers

are undergoing the Phase II validation process. Methylation is a way that a cell may 

temporarily or reversibly silence the activity

of a gene. Many CpG regions within the

human genome help regulate gene function.

This preliminary study will identify an 

optimal approach for the detection of 

promoter CpG methylation that can be 

recommended to a Biomarkers Validation

Laboratory for a full scale study. The optimum approach will be selected based upon

assay accuracy (sensitivity and specificity)

relative to the presence or absence of cancer

at the cellular level.
Meeting the Challenges 31

In lung cancers, methylation is present

more frequently in non-small cell lung 

cancer tumors (43%) and cell lines (50%)

than in small cell lung cancer cell lines

(20%). To avoid inter-lab and intra-lab

variation in promoter hypermethylation in

non-small cell lung carcinoma, six Network

investigators are involved in testing blinded

lung tumor and control specimens to 

evaluate for promoter hypermethylation. 

A minimum of four genes will be tested 

and hypermethylation frequency of each

gene will be measured in cell-type specific

tumor specimens compared to normals to

determine whether methylation detection

differs by method, primers, fixation, or

plasma as compared to tumor tissue.

“FISH”ing for Chromosomal Breakage

Non-random chromosomal rearrangements,

including translocations, inversions, deletions and chromosomal breaks, are highly

associated with cancer development. 

A group of MD Anderson Cancer Center

investigators has proposed to use the cancer

treatment antibiotic bleomycin to induce

chromosome breakage as a susceptibility

marker for lung cancer. To further refine

this assay, they have focused on two areas 

of the karyotype, or chromosomal 

complement of an individual, where 

specific chromosome damage is induced:

chromosome band 3p14 and chromosome

5 (5p14 through 5q34). 

NIST is validating the chromosomal 

breakage as a marker of lung cancer susceptibility and early lung cancer detection

using Fluorescence In Situ Hybridization

(FISH). Although FISH technology was

reproducible, criteria for cell selection and

classification of aberrations varied among

observers. To standardize and evaluate 

criteria, a Web-based validation study was

performed. Two hundred FISH images were

collected in CD-R format and assigned 

random identification numbers. Detailed

criteria were developed for cell selection

from the test image collection and for cell

classification of un-scored, normal, or

abnormal subsets. 

Eight observers scored NIST FISH Image

Set 02-01 based on criteria from NIST 

and MD Anderson. All observers were 

certified. Half were doctoral-level experienced cytogeneticists. All participants had 

5 years of experience. Questions addressed

were: (1) given standard criteria, how 

similarly did cytogeneticists choose cells and

score chromosome damage; (2) did concordance vary with educational level, years

experience or professional certification? 

Results indicate a greater degree of consensus on population chromosome damage

rates than on selection of individual cells

judged adequate for analysis. These results

will be used to estimate the components 

of assay variance that can be attributed to

definitions, technical variables or biological

variables. Once the standard cell selection

and scoring criteria are established, work 

on the analytical and clinical validation 

will proceed. 
32 The Early Detection Research Network

Mutations in Mitochondrial DNA 

as an Early Detection Marker

The mitochondrion structures within cells

generate energy for the cell and contain

genetic material and enzymes important 

for cell metabolism. Investigators at Johns

Hopkins University have shown various

DNA sequence mutations in cancer mitochondrial cells from lung cancer. The cancer

samples also yielded a higher proportion of

homoplasmic cells, that is, cells containing

a single mitochondrial DNA (mtDNA)

sequence species. 

To validate mutations and homoplasmy,

NIST was directed to sequence completely

the normal mtDNA and paired cancer 

cell mtDNA from 200 patients. Reference

threshold concentrations for mtDNA 

below which mtDNA cannot be measured

and sequenced are being established for

mutational analysis in the D-loop of

mtDNA, which is known to be mutated 

in early stages of lung, head and neck, 

and bladder cancer. 

NIST designed a set of mtDNA amplification primers and sequencing primers, and

established conditions suitable for limited

(10 - 20 ng) samples, including procedures

for fluorescence sequencing that incorporate

robotic workstations. Because of ethnic

group-specific sequence differences, 

primers had to be adapted for use on five

cell line controls representing both sexes

and various ethnic groups reflecting the

diversity of the U.S. population. 

To date, background studies have refined

the system and clinical work on 200 paired

samples has begun. It is expected NIST 

will do primary amplifications and generate

sequencing templates and that a contract

with an off-site DNA sequencing company

will perform automated sequencing. NIST

will then accept, process, and analyze raw

data using informatics. 

Microsatellite Instability (MSI) 

as an Early Detection Marker 

Investigators at Johns Hopkins University

have developed an assay system to detect

instability in microsatellites, repetitive

stretches of short sequences of DNA.

Microsatellite Instability (MSI) testing

offers potential for improving the early

detection of bladder cancer.

Bladder cancer is a common malignancy 

in the United States with more than 

50,000 new cases in 2001. It particularly

affects smokers and often follows a chronic, 

relapsing clinical course. Current methodologies for detecting primary or recurrent

bladder cancer are not optimal in terms 

of sensitivity, specificity, and cost. Because

patients at high risk for initial or recurrent

bladder cancer require frequent surveillance,

there is a great need for additional methods 

of early detection. Early detection may 

also increase the proportion of patients 

for whom preservation of the bladder 

is possible. 
Meeting the Challenges 33

Validation of MSI tests for early detection

of primary or recurrent bladder cancer will

pave the way for its acceptance in the 

clinical setting. More than 10 institutions,

including Johns Hopkins, MD Anderson

Cancer Center, and NIST, are participating

in this validation study.

Telomerase Activity as an Early

Detection Marker 

Telomerase is an enzyme found at elevated

levels in malignant cells that allows cells to

proliferate indefinitely. This activity occurs

in a variety of tumors and in nearly 90% 

of all tumor types tested to date. 

University of Maryland investigators 

presented preliminary data suggesting that

serum telomerase levels might be a useful

marker for early cancer detection. All 

previous measurements of telomerase 

activity have used cellular material, making

the current assay cumbersome and reliant

on slab gels and radioisotopes. NIST 

developed an assay based on the use of 

fluorescence marker and capillary gel 

electrophoresis. These changes considerably

increased sensitivity of the assay. 

The next step is to adapt the assay for high

throughput analysis by incorporating the

NIST robotics workstation in sample 

preparation and by adapting the current

capillary electrophoresis protocol for high

throughput on a 16 capillary ABI 3100

DNA sequencing instrument.

Improving Informatics and Information Flow

Electronic communications are making possible unprecedented research opportunities

by increasing the efficiency of knowledge

dissemination. With investigators distributed across the country and abroad, effective

information sharing about the resources,

tools and reagents available is critical 

to the success of the Early Detection

Research Network. 

By collaborating with the National

Aeronautics and Space Administration’s

(NASA) Jet Propulsion Laboratory (JPL),

the Network has launched informatics

infrastructures aimed at speeding the 

discovery process, translating the best 

discoveries into clinical practice, and 

facilitating communications. 

Network Web Sites

The public Web site (http://www.cancer.

gov/prevention/cbrg/edrn/) provides 

information and news to scientists and 

non-scientists about the Network generally,

recent publications, and contact information. This site, hosted by NCI, also offers

non-Network investigators information

about the program, such as application 

processes and deadlines for Associate

Membership, recent Steering and 

Advisory Committee meetings, and 

scientific workshops.
34 The Early Detection Research Network

Public Web Site

The public site provides information and news to scientists and non-scientists.
Meeting the Challenges 35

The successful communication at the heart

of the Network’s collaborative efforts stem

from the information management system

implemented and maintained by the Data

Management and Coordinating Center 

via the Network’s secure Web site.

Secure Web Site

Approved applicants can use the secure site.
36 The Early Detection Research Network

The secure Web site, hosted by the Data

Management and Coordinating Center, 

is only accessible to approved applicants.

Security is maintained in many ways, such

as a procedure for deactivating and deleting

accounts that have been unused for six or

12 months. Features of the site include: 

• A database that allows investigators 

to obtain a protocol ID, enter their 

protocol abstract, and track and search

Network protocols, collaborations, 

and IRB approval; 

• Contact information for all Network 

sites that can be searched or updated; 

• Listserv utilities to view archived emails

and all subscribers of a list; 

• Search capability by research interests,

keywords, and Network site; 

• A review system for the Standing Review

Group to oversee proposals for associate

Members and other reviews; 

• Registration for Steering Committee

meetings; and

• A reminder system for tasks that need to

be competed (such as entering protocols

on the Web site to obtain a protocol ID,

entering research interests, or completing

a survey).

Standardization of Data Reporting

The Network is one of the pioneers in

developing common data elements (CDEs)

to speed consistency in the way data 

are described across institutions. With

improved data classification, scientists have

the capability to use common search criteria

and common data elements to retrieve

information.

A complete set of CDEs is available to be

collected for all Network studies; drafts of

10 different sets of specimen-specific CDEs;

and five different sets of organ-specific

CDEs. Eight sets of the specimen-specific

CDEs and three sets of the organ-specific

CDEs are being piloted by Network sites. 

Coordinating the development of CDEs

included building a data dictionary 

compliant with ISO/IEC 11179 database

guidelines. Part of this effort focused on

developing an online database on the

Network’s secure Web site to facilitate 

use of CDEs by investigators to track 

modifications and version changes.

Early Detection Research

Network Exchange (ERNE)

NCI and NASA’s Jet Propulsion Laboratory

(JPL) entered into an interagency 

agreement to research and implement 

informatics solutions across the Early

Detection Research Network to enable data

sharing for geographically diverse science

collaborations. JPL, part of the California

Institute of Technology, is NASA’s lead 

center for robotic exploration of the solar

system, and has put into practice data

archiving and distribution systems to support planetary science for the past 15 years. 

JPL and the Data Management and

Coordinating Center lead the development

of the Early Detection Research Network

Exchange (ERNE). This system is both a

distribution and computing network that

allows remote access to live databases at

each Network site via the secured Web site.

There is minimal impact on the local site. 
Meeting the Challenges 37

Potential application of the system is broad.

The NCI/JPL collaboration enables 

common approaches to the establishment 

of data architectures that will benefit both

biomedical and space science research.

These architectures provide tools to locate,

access, and exchange information across

widely distributed databases. As such,

opportunities arise for new science analysis

and discoveries through the increased 

volume and diversity of data that is 

captured and shared. 

The JPL architecture links existing 

institutional databases containing specimen

information into a single Web-enabled

tracking system, which complies with 

data security requirements in the 1996

Health Insurance Portability and

Accountability Act.

Users can search databases through the 

system for a variety of different types of

specimens based on specific epidemiological

and clinical characteristics of the specimen

donor. This specimen identification system

successfully connected to live databases

from two Network sites during the

feasibility phase via a query user-interface

on the secure Web site. 

The pilot phase, scheduled to be completed

in Fall 2002, involves adding databases

from four more Network sites; assessing 

the ability of the system to expand to 

additional sites; improving the query 

user-interface; and developing tools to 

facilitate additional sites. 

ERNE allows Network investigators to 

create detailed epidemiological queries of

tissue specimens, bodily fluids, purified

nucleic acids, and whole cell lysates from

specimen collections/repositories across 

the Network for biomarker discovery and

validation studies. The epidemiological 

and clinical data elements selected to 

create queries were based on the Network’s

common data elements. 

Creighton University

DMCC

University of Colorado

UT San Antonio H. Lee Moffitt

University of Michigan

University of Pittsburgh

Network Sites Participating in ERNE Pilot Project
38 The Early Detection Research Network

ERNE Informatics Pilot Project Query Request
ERNE Informatics Pilot Project Query Result

Meeting the Challenges 39
40 The Early Detection Research Network

Generating Statistical Methodologies and Computational Tools

Vital to successful biomarker discovery are

analytic approaches that enable thorough,

yet robust, analysis of massive databases

generated by modern biotechnologies, 

such as microarrays for genetic markers 

and time-of-flight mass spectrometry

(TOF-MS) for proteomic markers. 

The Network’s Data Management and

Coordinating Center is developing such

approaches for protein biomarker discovery

in collaboration with the Biomarkers

Development Laboratory at Eastern

Virginia Medical School. The principles

and methods developed are not limited 

to one type of data, but easily extend to

other proteomic and genomic data. Key

developments are summarized below. 

Pre-analysis Data Processing 

Prior to a statistical analysis of marker discovery, TOF-MS data requires pre-analysis

processing to extract relevant information,

standardize the data, and summarize the

data. Based on eminent properties of the

data, pre-analysis processing first identifies

all protein signals that are distinguishable

from noise, then calibrates mass (per

charge) values of proteins for potential 

measurement errors, and finally aggregates,

as a single signal, multiple protein signals

that are within the range of measurement

errors. The Data Management and

Coordinating Center continues to modify

this algorithm to improve performance. 

Disease Classification 

Collaborating researchers are developing

algorithms to use SELDI serum protein 

fingerprints to classify prostate cancer,

benign prostate hypertrophy (BPH), and

normal state, based on differential serum

protein patterns. The rationale is to directly

analyze the whole spectrum of data for 

disease classification without protein 

identification. Because the protein expression profile for each sample consists of

thousands of measurements (mass/charge

and intensity), conventional statistical

methods are not able to analyze such high

dimensional data. Data dimension reduction was achieved using discrete wavelet

transformation in discriminant analysis. 

Wavelet is a method used in computer 

science for signal compression, but had 

not been used for proteomic data analysis.

This method is useful for disease classification using data generated from SELDI if 

no peak identification and normalization

algorithm is available. The wavelet data 

is then analyzed by the Kullack-Leibler 

statistical discrimination measure, to 

identify the wavelet coefficients that 

will form the classifier. This biostatistical 

classification algorithm yielded a sensitivity

of 97% and specificity of 100% in detecting prostate cancer in a study set consisting

of 386 serum samples (96 age-matched 

normal men, 92 BPH, and 197 prostate

cancer).
Meeting the Challenges 41

Protein Biomarker Identification 

Though wavelet transformation gives 

excellent classification, back transforming

wavelet coefficients to identify proteins

important for classification is difficult.

When peaks are identified by the preprocessing analysis and the dimension is

reduced, it is then more desirable to use an

analytical method that can lead to protein

identification as well as good classification

of diseases. To achieve both good classification and interpretation, a modification of 

a boosting method, originally developed 

in the 1990s by computer scientists, was

used to develop a decision tree classifier for

the diagnosis of prostate cancer from the 

non-cancer controls. 

The key feature of boosting is the ability 

to select the signals in a robust manner but

that avoid overfitting (i.e., selecting noise 

as signal), which is a major challenge in

analyzing high dimensional data. Boosting

first starts with a number of single protein

mass peaks, each of them usually classifies

poorly. However, by iteratively reweighting

the importance of each promising protein

mass peak, a combination of these single 

protein mass peaks can achieve a much 

better classifier.

Using the same serum study set described

for the biostatistical classification algorithm

analysis, a single decision tree classifier was

developed that was 90% accurate yielding 

a sensitivity of 83%, a specificity of 97%,

and a positive predictive value of 96% in

differentiating prostate cancer from the

non-cancer controls using 9 protein masses.

The boosting-logistic regression decision

tree classifier used 21 protein peaks to 

distinguish normal vs. abnormal samples

(i.e., prostate cancer and benign prostate

hypertrophy) with 96.67% accuracy. A

nearest neighbor classifier using 14 peaks

selected by the boosted decision tree

method has 100% specificity and 93.33%

sensitivity for detecting prostate cancer. 

Currently, Data Management and

Coordinating Center investigators are

extending this methodology of boosting

learning to the situation in which the 

disease status of a sample is subject to

error—a common situation in biomedical

settings. For example, in the analysis of the

prostate cancer data there is a 20%-30%

possibility for a patient with a diagnosis of

BPH related to multiple negative biopsies,

to harbor cancer cells, thereby potentially

resulting in a false negative classification. 
42 The Early Detection Research Network

Algorithm Sensitivity Specificity Error Rate

BMCA (TOF data) 80% 93% 13%

Boosting BMCA* (TOF data) 97% 93% 5%

BSCA* (TOF data) 77% 90% 17%

SVM (SELDI processed data) 90% 93% 7%

Ciphergen Biosystems Pattern Matching 90% 97% 5%

Software: CART (SELDI processed data)

Decision Tree* (SELDI processed data) 83% 97% 10%

Boosting Decision Tree* 97% 97% 3%

(SELDI processed data)

Artificial Intelligence Learning Algorithms

Besides the biostatistical wavelet classification, decision tree, and boosting classifiers developed by the Data Management and Coordinating Center, the Eastern Virginia Medical School

laboratory has evaluated other artificial intelligence learning algorithms to process the SELDI

and develop a diagnostic classifier. 

A summary of the results to separate prostate cancer from the non-cancer controls follows:

The best accuracy in differentiating the prostate cancer from the benign and normal controls

was with the two-decision tree and boosting algorithms. However, these results suggest that it

will be important to evaluate SELDI profiling data sets by more than one bioinformatics tool in

an effort to find the diagnostic classifier that accurately separates the cancer population from

the non-cancer controls without over-fitting the data. Another important observation in these

studies is that accurate data analysis depends entirely on having an efficient and reproducible

peak selection and alignment algorithm with biostatistical confidence.

Genomic and Proteomic Data Mining 

Data are rapidly accumulating from 

genomic and proteomic analyses. A key

concern of Network statisticians is how to

develop algorithms to combine information

from multiple biomarkers to identify 

preclinical cancer. When gene expression

profiles or protein mass spectrometer 

profiles are the basis for identification, 

the number of potential markers (genes or

proteins) is enormous. Innovative analytic

tools are needed to provide the best metric

for biomarker evaluation.

The design and evaluation of exploratory

studies is perhaps the most promising and

difficult statistical issue in biomarker development. Consider the analysis of gene

expression array data and the problem 

of ranking genes in regards to their 

differential expression in cases and controls.

* Developed at DMCC

BMCA=Binary Markers

Combination Approach

BSCA= Biostatistical

Classification Algorithm

SVM=Surface Vector

Machine

CART=Classification 

and Regression Trees
Meeting the Challenges 43

Classical statistical measures, such as the 

t-statistic, do not necessarily identify the

most useful genes. 

The Data Management and Coordinating

Center is proposing several novel approaches to disease screening, including one that

has been successfully applied to an ovarian

cancer data set involving 30 cases and 23

controls. Researchers have developed a 

strategy for sample size calculations for

phase 1 studies that is based on having a

high probability of identifying the most 

differentially expressed genes. Again this is

different from and more appropriate than

traditional sample size calculations that are

based on statistical hypothesis tests.

System Screening 

One of the important potential uses of

markers for early cancer detection is to

monitor target populations via large public

screening programs. It is crucial to explore

and understand factors that influence the

accuracy (sensitivity and specificity) of 

such programs. 

Evaluating the accuracy of a single diagnostic test has gained much attention in the

statistical and applied literature, but little

attention has been paid to factors affecting

accuracy characteristics of a sequence of

tests, as would be used in routine screening.

With repeated marker measurements over

time in a target population, true cases and

false positives will be identified cumulatively, at each time of testing. In practice, when

a single marker is used repeatedly in routine

screening, the same screening threshold is

typically used to define positivity at each

screening visit. For instance, when PSA

is used to screen for prostate cancer, a 

common threshold is 4 ng/ml. 

One possible alternative is to adjust the

threshold at successive visits according to

individual-specific characteristics. By application to a prostate cancer screening setting

using PSA, the Data Management and

Coordinating Center has illustrated how

such ideas may guide choices in designing 

a screening program. In the setting 

considered, it has been shown that adaptive

thresholds may perform better than 

constant thresholds (such as 4 ng/ml) and

that screening biannually has accuracy 

characteristics almost as good as annual

screens, with very little difference between

annual and biannual screening designs in

the mean time prior to clinical diagnosis 

at which cases are identified.

Standardization of Reagents and Assays

Accurate early detection screening tests

must be accompanied by high-throughput

assays/technologies that are reproducible

and affordable. In collaboration with NIST,

Network investigators continue to standardize methodologies, refine assays, and

establish standard reference materials for

biochemical, molecular and cytologic assays. 

As described earlier, the scoring criteria 

and image standards for measuring 

chromosomal breakage as a measure of 

susceptibility for lung and upper respiratory

tract cancers are being developed at NIST.

In addition, reference threshold concentrations for mtDNA below which mtDNA

cannot be measured and sequenced are

being established for mutational analysis 

in the D-loop of mtDNA, which is known

to be mutated in early stages of lung, head

and neck, and bladder cancer (described on

page 32). 
44 The Early Detection Research Network

At the heart of cancer prevention research is the overlap of efforts in bioinformatics, basic 

science, and clinical science by collaborating professionals in government, industry, and academia.

The forum provided by the Network

encourages interaction and collaboration

among leading cancer researchers around

the world. Investigators and liaisons meet

regularly to discuss how to better decipher

the molecular circuitry of cells and to apply

this understanding to the earlier detection

of cancer. Two primary collaborative activities take place: sharing research materials,

including specimens, and sharing and

exchanging data and research results.

Using both the unconventional approach 

to biomarker research through scientific

consortia, and facilitating the regular

research-intensive interaction of leading

experts from multiple disciplines, the

Network is pursing biomarker discovery,

development and validation at an unprecedented pace for both common and rare

cancers.

Encouraging Collaborations

with the Network

Clinical 

Science

Basic 

Science

Bioinformatics

Cancer

Prevention

Industry Academia

Government

Encouraging Collaborations with the Network
Encourging Collaborations with the Network 45

Collaborations between Network investigators and investigators from other U.S. 

and foreign institutes and industries 

are supported through the Associate

Membership program. 

Associate Members are non-Network investigators who propose collaborative studies

within the scope and objectives of the

Network, and often contribute by sharing

available technologies, specimens, high-risk

registries and cohorts, and other resources.

Applications are accepted three times per

year for studies that could expand resources

within the Network or enable investigators

to collect data to support applications for

future independent funding.

Investigators planning to become Associate

Members can join one of the Network’s

four Collaborative Groups according to

their expertise and interest. The group’s

chairs are Network principal investigators

who serve as the primary contact for nonaffiliated investigators. Interested researchers

can also contact any Network principal

investigator about collaboration. 

The Collaborative Groups and chairs are:

• Breast and Gynecologic Cancer, 

Daniel Cramer, M.D., Brigham 

and Women’s Hospital

• Gastrointestinal and Other Cancers, Dean

E. Brenner, M.D., University of Michigan

• Lung and Upper Aerodigestive Tract

Cancer, Mel Tockman, M.D., 

H. Lee Moffitt Cancer Center

• Prostate and Other Urologic Cancers, 

Ian Thompson, M.D., University of Texas

Health Science Center

The Associate Membership program is

soliciting proposals to study the following

cancers: breast, mesothelioma, nasopharyngeal carcinoma, gastric cancer, pancreatic

cancer, liver cancer, esophageal cancer,

endometrial cancer, kidney cancers, germ

cell cancers of the testis, and melanoma. 

In the past three years, NCI has funded 

12 awards to nonaffiliated investigators for

studies that span the collaborative groups,

including the activities listed here.

Breast and gynecologic cancer collaborative groups are evaluating human kallikrein

6 (hk6) as a biomarker for the early 

detection of epithelial ovarian cancer. The

research screens the pre- and post-operative

sera of over 400 women diagnosed with

stage I ovarian cancer matched against 

controls using immunoassays to this 

candidate biomarker. The results are being

compared against serum cancer antigen 

125 (CA125) levels from the same patients. 

In another effort, researchers are developing

molecular markers for cervical carcinogenesis for use in translational research. The

overall goal is to develop a molecular assay

to be performed on routinely obtained 

Pap smears that will predict the risk of

aggressive cervical dysplasias that may

rapidly progress to invasive cervical cancer.

Aberrantly methylated genes present in

squamous cell and adenocarcinomas of the

cervix are profiled assessing the methylator

phenotype and key methylated genes in

high-grade cervical intraepithelial lesions 

as a predictor of recurrent or persistent 

cervical dysplasia. This study will analyze

associations between the methylator 

phenotype and endogenous or exogenous

epidemiological risk factors.

Associate Memberships
46 The Early Detection Research Network

New insights on rare tumors are emerging.

An associate investigator at Wayne State

University is studying development and

progression of malignant pleural mesothelioma (MPM) by: (1) gene profiling 

for prognostication in mesothelioma, 

(2) methylation studies in mesothelioma

and relation to SV40, and (3) SELDI 

analyses of mesothelioma pleural effusions.

The gene array data have revealed obvious

differences in gene expression of PIG3, Cell

adhesion kinase B, and natriuretic receptor

A between four short-term survivors and 

10 long-term survivors. For the methylation

studies, they have developed a novel

demethylating assay in which DAC was

added to SV40 infected and transformed

mesothelial cells. These data have revealed

that HMG1 and MIC1 are methylated in

these transformants, and have been verified

by re-expression verification assays using

HMG antibodies. Finally, in a comparison

of 30 pleural effusions to 28 control effusions, SELDI protein chip proteomics has

revealed three biomarkers, which are 100%

specific and sensitive for mesothelioma.

Validation studies are in progress.

Novel technologies amenable to high

throughput assay are under development

by investigators from Van Andel Research

Institute. The research is aimed at an antibody array to identify markers in the serum

or pancreatic juice that would lead to the

development of more sensitive, specific and

minimally invasive diagnostic tests for 

pancreatic cancer. Microarrays comprised 

of antibodies to putative markers, cancerrelated genes, cytokines and serum markers

from other cancers are being tested to 

measure proteins in the sera and pancreatic

juice of patients. Bioinformatic analyses 

of protein microarray data are being done

initially through hierarchical clustering

between arrays from sera of cancer patients

as compared to control. These results are

assessed to determine how well their clusters

perform against software-derived groupings

in observed samples as compared to random

permutations.

Lung and upper aerodigestive tract 

cancers studies are testing the hypothesis

that newly identified genetic polymorphisms in the Epoxide Hydrolase (EH) 

gene play an important role in risk for

tobacco-related cancers. Network 

investigators provided more than 100 

DNA samples from lung cancer and 

case controls. Samples from both AfricanAmerican and Caucasians using DNA

samples isolated from buccal specimens are

being screened by PCR and single-stranded

conformational polymorphism analysis 

of nine exons of the EH gene. 
Encourging Collaborations with the Network 47

Member/Institution Research Focus Network Sponsor

Brian B. Haab, Ph.D. Antibody Array Jose Costa, M.D. 

Van Andel Research Institute Pancreas

Grand Rapids, MI

Leonard S. Marks, M.D. PSA Isoforms Alan Partin, M.D., Ph.D. 

Department of Urology Prostate

Urological Sciences Research 

Foundation, Culver City, CA

Mai H. Nguyen, M.D. DNA Permutations in Urine David Chia, Ph.D.

University of California-Los Angeles Breast 

Los Angeles, CA

Carolyn Y. Muller, M.D. Methylated Genes Adi Gazdar, M.D.

University of Texas Southwestern Cervix

Dallas, TX

Jong Y. Park, Dr.P.H. Mutations in Epoxide Hydrolase Melvin Tockman, M.D., Ph.D.

Cancer Control H. Lee Lung

Moffitt Cancer Center

Tampa, FL

Harvey I. Pass, M.D. Mesothelioma DNA Array Adi Gazdar, M.D. 

Wayne State University Lung

Detroit, MI

Karen Smith-McCune, M.D., Ph.D. In situ hybridization Elizabeth Unger, M.D., Ph.D.

University of California Cervix 

School of Medicine

San Francisco, CA

Ying-Hsiu Su, Ph.D. Permutations in DNA from Urine Timothy Block, Ph.D. 

Thomas Jefferson University Liver, Colon, Rectum

Doylestown, PA

Eleftherios P. Diamandis, M.D., Ph.D. Human Kallikreins Sudhir Srivastava, Ph.D., M.P.H.

Mount Sinai Hospital Ovary

University of Toronto

Toronto, Canada

Holger Hebestreit Proteomics Timothy Block, Ph.D. 

Oxford Glycobiology Institute Liver

Oxford, UK

Edward F. Patz, M.D. MALDI-TOF Jeffrey Marks, Ph.D. 

Duke University Medical Center Lung Proteins

Durham, NC

William Grady, M.D. Methylated Genes Dean Brenner, M.D. 

Vanderbilt University Colon and Henry Lynch, M.D. 

Nashville, TN

Associate Members
48 The Early Detection Research Network

Shared Technology and Resources

The Network has initiated several collaborative projects with other NCI-supported

programs, such as the Cancer Genetics

Network and Specialized Programs for

Research Excellence, to better understand

biological risk factors for familial and

hereditary cancers. Accomplishments 

resulting from active collaborations follow. 

Tissue Microarrays

The UCLA Validation Laboratory (the

UCLA Defined Tumor-Marker Evaluation

Core, or D-TEC) has constructed 

high-density tissue microarrays for the

Network. Two microarrays have been 

completed, prostate and breast cancer, and

one is under construction for lung cancer.

Tissue microarray technology is used to

analyze several hundred tumor samples, 

as well as control/normal tissue from the

same organ, on a single slide. This approach

allows high throughput analysis of genes

and proteins on a large cohort. The method

consists of sampling core tissues from 

paraffin-embedded tissue donor blocks and

placing them into a single paraffin block. 

In spite of the low amount of tissue 

analyzed by tissue microarray, several 

independent studies have demonstrated 

a high concordance of protein expression

between this technique and the conventional tissue sections.

Implementation of high-throughput 

genetic technologies, such as cDNA and

oligonucleotide microarrays, generates 

myriad points of data. The identified 

cancer-associated candidate genes need 

to be further characterized and selected

using a large number of well-characterized

tumors and stringent criteria. The highthroughput expression profiling of tumor

samples provides key information at the

micro-anatomical level. 

The samples used to construct each tissue

microarray are from the vast archives of 

the UCLA Medical Center. Each tissuespecific microarray has more than 1,000

individual spots representing approximately

500 patients. Samples are chosen as to 

represent all stages and grades of tumor 

progression, and to include different 

subsets of tissue-specific tumors. Matched

histologically normal samples are also

included. A relational database is currently

being constructed to link pathology and

clinical information, images of each spot,

and the corresponding gene expression data. 

In addition, the UCLA D-TEC program 

in conjunction with the Tissue Array Core

Facility (UCLA Department of Pathology

and Laboratory Medicine) and the

Biostatistics Core (UCLA Department 

of Human Genetics) is optimizing 

conditions to construct, stain, and analyze

these high-density tissue microarrays.
Encourging Collaborations with the Network 49

High Risk Cohorts

One of the early charges of the Network

was to expand identification of high-risk

healthy populations based on genetic 

predispositions and the development of 

new molecular markers. Advancements

related to cancers of the head and neck,

lung, prostate, ovary, breast, gastrointestinal, and hereditary cancer syndromes are

highlighted below.

Head and Neck and Lung Cancers

University of Texas MD Anderson Cancer

Center investigators in collaboration with

several organizations established two 

collections of cohorts that mark an important resource for developing and validating

new biomarkers, and identifying subgroups

that can be enrolled into intensive screening

programs for early detection and cancer

prevention.

The first collection contains more than

1,300 cases of patients with stage I and

stage II upper digestive tract cancers.

Subjects were at least 18 years of age 

and free of cancer for up to three years 

after primary treatment. A second collection

includes 1,100 lung cancer patients equally

divided between non-smokers and smokers.

The epidemiological profiles for both

cohorts were established. The control 

pool includes more than 700 individuals

matched to the cohort by gender, age 

(within 5 years), ethnicity and smoking 

status. Specimens (serum, blood lymphocytes and paraffin blocks) are available. 

Prostate Cancer

Based on the knowledge that AfricanAmerican men develop prostate cancer

earlier in their lives and are more likely to

die from it than white, non-Hispanic men,

the University of Texas at San Antonio

Medical Center in collaboration with the

Audie Murphy VA Hospital, Brooke Army

Medical Center and Wilford Hall Medical

Center is conducting a prospective study of

a multiethnic populations in San Antonio

to determine the use of novel biomarkers in

prostate cancer.

Known as the San Antonio Center of

Biomarkers of Risk for Prostate Cancer

(SABOR), the program will enroll 10,000-

patients, including a large number of

minority patients (Mexican Americans and

African Americans). The registry contains

1,457 individuals of various races and 

ethnicity. Efforts are aimed at increasing

recruitment of ethnic minorities and 

under-served populations. A comprehensive

database established for patients participating in the study includes information

about: 

• Past medical history

• Current use of medications 

for prostate disease

• Current use of phytotherapy

• Family history of neoplastic disease

• Diet

• Ethnicity

• Anthromorphometry

• AUA urinary symptom score

• Digital rectal examination findings

• Blood pressure

• PSA
50 The Early Detection Research Network

The repository of biological materials from

SABOR features: serum, blood lymphocytes

and toenails from 362 individuals; 309

frozen prostatectomy specimens; and 1,590

paraffin embedded specimens of biopsies,

trans-urethral resection of the prostate 

and prostatectomies.

A separate group of investigators at 

Johns Hopkins University is accessing two 

different cohorts for prostate cancer studies.

The first is through collaboration with the

Baltimore Longitudinal Study of Aging.

This cohort was established in 1958 and

contains 1,500 men within the age range 

of prostate cancer. There is a complete 

epidemiological profile for each of the 

participants and serum samples. The second

cohort of about 3,000 individuals is a result

of the early detection screening program 

at the university’s departments of Urology

and Clinical Chemistry/Pathology. 

Hereditary Cancers

Creighton University has developed a 

registry of individuals at high risk for 

specific cancers who are willing to 

participate in biomarkers studies, known 

as the Early Detection Research Network

High Risk Registry. The registry includes

157 individuals who are carriers of germ

line mutations for hereditary cancer 

syndromes such as hereditary non-polyposis

colorectal cancer (HNPCC), hereditary

breast/ovarian Cancer (HBOC), familial

adenomatus polyposis (FAP), hereditary

hematologic cancers, familial pancreatic

cancer and others. Recruitment is ongoing. 

Creighton may also recruit participants 

for specific Network studies from their

Hereditary Cancer Family registry. This

registry contains 439 colon cancer families,

of which 129 families are HNPCC, 83 are

with HNPCC-associated mutation, 22 

families had FAP, and 4 families had APC

gene mutations. Another 114 families were

FAMMM/pancreatic cancer families, of

which eight have the p16 gene mutation.

Of 933 breast/ovarian families, 92 have

mutation in BRCA 1/2 genes. 

Ovarian Cancer

One of the Network’s Clinical and

Epidemiology Center’s, the Brigham 

and Women’s Hospital, Harvard Medical

School, is collaborating with Dr. Ian Jacobs

in London to collect serum from women

previously enrolled in prospective studies 

of screening for ovarian cancer. They have

collected more than 1,000 sera. The Center

maintains a repository of pre-operative

serum and plasma from 160 women 

with ovarian cancer, 300 women with

benign gynecologic diseases, and 238 

normal women selected from the general

population. 

Breast and GI Cancer 

Duke/Abbott Developmental Laboratory

has enrolled in a high-risk registry 220

breast patients and 100 GI patients. For 

the past year, more than 500 blood samples

were collected with CPT tubes and white

blood cell fractions were carefully cryopreserved for biomarkers recovery. All clinical

information on these subjects is recorded

and maintained in a password-protected

database. The laboratory initiated a new

protocol to collect and analyze nipple 

aspirates (NAF) and blood from women

who are being seen at the high-risk breast

clinic. NAF will be analyzed on SELDI 

or MALDI platforms for detection of 

premalignant disease. Most women entered

into the study will have known BRCA1

and BRCA2 mutational status.
Encourging Collaborations with the Network 51

NCI anticipated from the outset that

Network members would collaborate with

industry both in developing biomarkers

and/or reagents and providing a clinical

environment for the evaluation of new 

technologies. It is hoped that validated

biomarkers ultimately will be commercialized into diagnostic products for early

detection and risk assessment. 

Collaborations involving companies help

the research community explore possibilities

for new products and approaches, and 

permit private sectors to realize the value 

of their research investments. When such

interactions occur early, they foster coordinated research plans that can benefit all

partners.

However, concern about confidential 

information, ownership of and access 

to intellectual property and data that

emanate from research can constrain 

studies. NCI’s Technology Transfer Branch

and Division of Cancer Prevention’s Cancer

Biomarkers Research Group drafted 

industry collaboration guidelines after the

Network’s Technology Resources Sharing

Committee articulated a need to alleviate

potential barriers to collaboration. 

Feedback provided by Network members

during the February 2002 Steering

Committee meeting will be incorporated

into final guidelines to be presented 

for adoption at a future meeting. Final

guidelines will cover sharing reagents, 

biological materials, data and technology 

in Network-related studies between 

investigators and pharmaceutical and

biotechnology companies. Issues relating 

to publication, confidential treatment 

of proprietary information, intellectual 

property rights, and licensing will also 

be addressed. 

The guidelines recognize the rights and

obligations of grantee institutions under the

Bayh Dole Act (35 U.S.C. 200 et seq.) and

are founded on the National Institutes of

Health Grants Policy Statement, which

include “Principles and Guidelines for

Recipients of NIH Research Grants and

Contracts on Obtaining and Disseminating

Biomedical Research Resources.” 

Private collaborating organizations 

have included: Abbott Pharmaceuticals,

Advanced Biosciences, Atairgin

Technologies Inc., Bayer Pharmaceuticals,

Bristol Myers, Circon Corp., Ciphergen

Biosystems, Clenomics, COBE BCT, 

Inc., Corixa, Gene Logic, Genetica 

Inc., ORCA Biosciences Inc., Oxford

GlycoSciences, Proteome Sciences, Roche,

Research Genetics Inc., Spectral 

Imaging, Urosciences Group, and 

Zeneca Diagnostics. Recent endeavors 

are described below.

Michigan-Bayer Collaboration

The Great Lakes New England (GLNE)

Clinical Epidemiology Center launched a

collaboration with Bayer, which, with its

subsidiary, Chiron, Inc., has made major

scientific and financial commitment to 

discovery, validation, and commercialization

of surrogate endpoint biomarkers for 

early cancer diagnosis. Bayer employs 

proteomics, genomics, and nanotechnology

to discover promising new biomarkers.

Teams of molecular biologists, physicists,

and statisticians use state-of-the-art 

informatics to mine human genomic and

proteomic data commercially procured or

discovered in house to identify promising

biomarkers. Once identified, banks of 

tissue samples and matching sera have 

been used to provide preliminary decision

analysis data. 

Forging Partnerships with Private Sectors
52 The Early Detection Research Network

In collaboration with Network investigators

and the NCI Technology Transfer Branch, 

a single confidentiality agreement was 

executed by Bayer and the five GLNE 

institutions. This agreement allows GLNE

to develop and execute validation projects

for early diagnostic and carcinogenesis

biomarkers of products derived from Bayer

research under Network sponsorship. 

Duke University-Abbott Diagnostic

Laboratory Collaboration

This research aims to identify breast-specific

markers that will be useful for early detection and monitoring of disease progression.

By utilizing proprietary, large expression

databases from Abbott Diagnostic

Laboratory, investigators at Duke University

have found two new genes (BU101 and

BS106) that appear to be expressed 

primarily in breast cells (both benign 

and malignant). Since breast cells are not

found in the blood of healthy women, the

presence of these gene products may signal

a growth abnormality in the breast, most 

likely cancer or early pre-cancerous changes.

Investigators developed a series of reagents

and assays for the two new genes with 

the goal of detecting very low levels in 

the blood. 

This joint effort is a model for other 

academic-industrial partnerships in many

ways. Abbott continues to support the

research at Duke through transfer of

reagents, instrumentation, and most importantly, intellectual property. Duke scientists

have in turn, taken several of the lead markers identified by Abbott to the next stage 

of clinical development. Further, markers

identified at Duke have been tested, in 

silico, using Abbott resources. At the same

time, Abbott scientists have gained a 

position in the Network that allows them 

to keep abreast of current academic developments in biomarker research, contribute

to the practical aspects of the science from

its base of resources and experience, and

possibly negotiate early licensing arrangements to facilitate rapid development of

new markers. 

Network-Ciphergen Collaboration

The Eastern Virginia Medical School

Biomarkers Development Laboratory 

was the first to develop a partnership with

Ciphergen, and introduced the SELDI

technology to the Network as a new and

innovative technology to not only discover

novel cancer biomarkers but to use SELDI

protein profiles to discriminate cancer 

from non-cancer controls. The laboratory

provides training in SELDI use to Network

and non-Network investigators. A number

of Network laboratories are actively 

collaborating with Ciphergen for 

proteomic analysis. 

Both the investigators and the company

benefited from this collaboration.

Investigators received priority services from

the equipment maker in terms of delivery,

training and other enhancements. In return,

the company benefited from verification 

of the SELDI accuracy, precision, and 

sensitivity for serum and other body fluids

analyses. Precision testing and calibration 

of the SELDI technology and molecular

assays with SRMs provided by NIST 

will significantly further development of

diagnostic reagents and tools. Partnerships

with a number of biotechnology and diagnostic companies augment the precision

and capacity of SELDI technology for

sequencing, enhancing the ability to mine

data on protein and genomic expression

profiles, and increasing the accuracy of

diagnostic assays. 
Encourging Collaborations with the Network 53

Partnership with Standard Reference

Laboratories and Other Organizations

This governmental-industrial-university

relationship enhances the scientific 

opportunities for the Network by bringing

the resources of major industrial sponsors 

to the collaborating institutions through

mutually beneficial scientific collaboration.

Over the next year, prospective cross sectional and longitudinal validation projects

of promising biomarkers discovered by the

Network and industrial partners will begin.

Network investigators, working with the

Food and Drug Administration, plan 

to organize a meeting to solicit input from

the World Health Organization, the

National Committee for Clinical

Laboratory Standards (NCCLS), the

National Institute for Biological Standards

and Controls (NIBSC), the Red Cross, and

NIST 

on the needs and requirements for a 

proteomics standard like NIST SRM1951a.

These investigators are reaching out to a

wider scientific community, including 

such groups as the Human Proteomic

Organization, to gain the participation 

of researchers across the world in the 

effort to direct proteomics standards.

Con Edison Partnership

Con Edison, a utility firm serving the New

York area and part of the Northeastern

states, collaborates with the Lung Cancer

Biomarker Center, a Network Clinical and

epidemiological Center, under the leadership of Dr. William Rom. With the Utility

Worker’s Union, they are recruiting smokers

for a lung cancer screening project. They

have an NIH Certificate of Confidentiality

for participating workers where aggregate

data without any personal identifiers is 

supplied to Con Edison and the Utility

Workers Union. 

This group represents an important cohort

for lung cancer biomarkers validation 

studies because of their potential exposure

to asbestos and other substances. Con

Edison has had a contract for a collaborative relationship about asbestos exposed

workers since 1990 with the New York

University Division of Pulmonary and

Critical Care Medicine. The utility has

approximately 10,000 full-time employees

and 2,000 retirees, who have worked in

power plants using oil or natural gas

throughout the New York City region, 

and serviced power substations and the

steam and power conduits underground

and above ground in the New York Region.

Workers are exposed to asbestos in the

power plants where insulation is used in

boilers and hot water pipes. Asbestos, 

solvents, lead, and other exposures can also

occur in manholes and other worksites.
54 The Early Detection Research Network

Dissemination and Diffusion of Research Results

Along with its primary mission to support

research on cancer detection, prevention

and treatment is NCI’s support of the 

subsequent dissemination of findings. 

Published abstracts are one of the best 

testaments to the Early Detection Research

Network’s collaborative approach. These

documents show substantial barriers being

overcome and relationships being forged.

They demonstrate the continued pursuit 

of a better understanding of cancer cells 

and a Network well poised to find the 

missing pieces of the puzzle.

Several communications means convey the

progress in biomarker research to the lay

public and scientists. These include Web

sites, sponsoring meetings, workshops, 

conferences, and participating in other

national and international meetings. At 

the Network’s first and second scientific

workshops in September 2000 and October

2001, investigators presented research findings that detailed the latest advances and

emerging issues in biomarker research. 

The two-day meeting addressed topics such

as advances in organ specific molecular

detection; correlative clinical studies; 

computational analysis and statistics; informatics; emerging issues; technology for

biomarkers of risk; and technologies within

the Network. More than a dozen invited

speakers and over 60 abstracts fostered 

discussions on these and other issues. 

The abstracts were presented in the journal

Disease Markers. (See Appendix I).

A highlight of EDRN’s dissemination

efforts was the initiation of the March 

2002 scientific meeting as one of the 

prestigious Gordon Research Conferences.

By choosing to elevate New Frontiers 

in Cancer Detection and Diagnosis to 

a Gordon conference, the stature of the 

meeting was greatly increased and it was

promoted as an international forum for 

idea exchange and scientific discussion.

EDRN Investigators Disseminate Information Through Liaisons

American Association for Cancer Research (AACR): William Bigbee, Ph.D.

American College of Obstetricians and Gynecologists (ACOG): Daniel Cramer, M.D.

American Society for Investigative Pathology (ASIP): Elizabeth Unger, M.D., Ph.D.

American Society of Clinical Oncology (ASCO): L. Austin Doyle, M.D., 

American Society of Preventive Oncology (ASPO): Margaret Spitz, M.D., M.P.H.

American Urological Association (AUA): Alan Partin, M.D., Ph.D.

Cancer Genetics Network: Steven Skates, Ph.D.

Cooperative Family Registries: John Baron, M.D.

Director’s Challenge: Samir Hanash, M.D., Ph.D.

European Organization for Research and Treatment of Cancer (EORTC): 

Angelo Paradiso, M.D., Maria Diadone, Ph.D.

Mouse Models of Human Cancers Consortium: Jeffrey Marks, Ph.D. 

Other gynecology groups (not including ACOG): David Fishman, M.D.

Pharmaceutical and industrial relations: Greg Downing. D.O.

Specialized Programs of Research Excellence (SPORE) Groups: Adi Gazdar, M.D. 

Cooperative Groups: Ian Thompson, M.D.

Union Internationale Contre le Cancer (International Union Against Cancer) (UICC): Michles Bodo, M.D.
Evaluation and Next Steps 55

Network Evaluation 

and Next Steps

The Network will continue to build on its

existing resources and expertise to advance

biomarkers for validation studies. Engaging

scientists in translational research will occur

in several ways:

• Accelerated validation of newly discovered

biomarkers

• Expanded Network attention to additional organ sites for biomarker discovery and

clinical validation 

• Comprehensive development of genomic

and proteomic fingerprints of premalignant and preinvasive lesions

• Supported translational research on 

epigenetic changes in identifying cancer

risk, precursor lesions, and people at 

elevated risk 

• Development of high-yield technologies

for isolating exfoliated cells in body fluids,

particularly for noninvasive detection of

abnormal cells through, for example, 

seminal fluid and urine

• Using high risk cohorts for association

studies between polymorphism of 

candidate genes and cancer risk

• Expanded public-private partnerships for

validation studies by providing incentives

to industry, such as access to specimen

resources, while industry in return will

provide necessary technological platforms 

• Established international consortium 

on rare tumors, such as naso-pharangeal

carcinoma, and mesothelioma

In short, researchers need to identify

biomarkers that are predictive of clinical

outcomes. Surrogate endpoint biomarkers

may also provide biologic insights in 

the short term, and eventually provide 

a rationale for changes in the design of 

clinical trials. 

Metrics to evaluate the Network’s success

were fleshed out by the Steering

Committee. Detailed in Appendix III, 

these metrics define the goals of scientific

excellence, productivity, and collaboration. 

While the Network continues to improve,

investigators will gain empowerment as the

direct funding of their Centers places on

them the burden of scientific leadership,

research agenda, and collaboration. 
56 The Early Detection Research Network

At the beginning of the continuum, basic

scientists with robust bench research records

will be influenced to pool ideas, resources,

and tools through additional core funds 

and supplements. Then, translational and

epidemiology investigators with strong 

tools and publication track records will be

directly funded with a mandate to translate

concepts arising from the basic science labs.

Concurrently, analytical tools, laboratory,

statistical, and informatics activities will be

directly supported with a collaborative

mandate. 

It is expected that leadership of this whole

collaborative emanates from the grassroots

investigators and the Executive Leadership

will interact with this highly knowledgeable

group of scientists in a manner that

enhances collaboration and productivity. 

Finally, the impact of new technology on

reducing mortality is contingent on development of highly predictive biomarkers 

for earlier detection and risk assessment.

The success of this effort relies in large 

measure on exploring the concordance

between genetic or molecular markers 

and the morphologic changes associated

with premalignant and preinvasive lesions 

that have life threatening potential.

Overall, the Early Detection Research

Network represents a new paradigm 

of cooperative research that will fuel the

future clinical studies on which progress 

in prevention will ultimately depend. 
Appendix 57

Appendix

Adam BL, Vlahou A, Semmes OJ, Wright

GL Jr. Proteomic approaches to biomarker

discovery in prostate and bladder cancers.

Proteomics. 2001; 1:1264-70.

Adam BL, Qu Y, Davis J, Ward M,

Clements M, Cazares L, Semmes JO,

Schelhammer P, Dalmasso E, Yasui Y, Feng

Z, Wright G. Serum protein fingerprinting

coupled with a pattern matching algorithm

that distinguishes prostate cancer from

benign prostate hyperplasia and healthy

men. Cancer Res. 2002, 62: 3609-14.

Ahram M, Best C J M, Flaig M J, et al.

Proteomic analysis of human prostate 

cancer reveals marked heterogeneity of

tumor alterations. Molecular Carcinogenesis.

2001; 33: 9-15.

Akhtar M, Cheng Y, Magno RM,

Ashktorab H, Smoot DT, Meltzer SJ,

Wilson KT. Promoter methylation regulates

Helicobacter pylori-stimulated cyclooxygenase-2 expression in gastric epithelial cells.

Cancer Res. 2001; 61:2399-403.

Alonzo TA, Pepe, MS, Moskowitz C.

Sample size calculations for comparative

studies of medical tests for detecting 

presence of disease. Statistics in Medicine.

2002; 21:835-52. 

Balic I, Graham ST, Troyer DA, Higgins B,

Pollock BH, Johnson-Pais TL, Thompson

IM, Leach RJ. Androgen receptor length

polymorphism associated with prostate 

cancer risk in Hispanics. J Urology, 2002; 

(in press).

Barker PE, Watson MS, Ticehurst JR,

Colbert J C, O’Connell CD. NIST physical

standards for DNA-based medical testing. 

J Clin Lab Analysis. 2001; 16:5-10.

Belinsky SA, et al. Aberrant promoter

methylation in bronchial epithelium and

sputum from current and former smokers.

Cancer Res. 2002; 62:2370-7.

Brabender J, Usadel H, Danenberg KD,

Metzger R, Schneider PM, Lord RV,

Wickramasinghe K, Lum CE, Park JM,

Salonga D, Sidransky D, Hölscher AH,

Meltzer SJ, Danenberg PV. Adenomatous

Polyposis Coli gene promoter hypermethylation in non-small cell lung cancer is

associated with survival. Oncogene. 2001;

20:3528-32.

I Key Publications by Early Detection Research Network Investigators
58 The Early Detection Research Network

Bremnes RM, Veve R, Gabrielson E, 

Hirsch FR, Baron A, Bemis L, Gemmill

RM, Drabkin HA, Franklin WA. Highthroughput tissue microarray analysis used

to evaluate biology and prognostic significance of the E-cadherin pathway in

non-small-cell lung cancer. Clin Oncology.

2002; 20:2417-28.

Bremnes RM, Veve R, Hirsch FR, Franklin

WA. The E-cadherin cell-cell adhesion

complex and lung cancer invasion, 

metastasis, and prognosis. Lung Cancer.

2002; 36:115-24. 

Berezuk M, West J, Varella-Garcia M,

Franklin WA. Adjusting interphase FISH

results in epithelial tissue sections to 

whole cell complement. Analytical and

Quantitative Cytology and Histology. 2001;

23:93-100.

Brichory F, Beer D, Le Naour F, Giordano

T, Hanash S. Proteomics-based identification of protein gene product 9.5 as a tumor

antigen that induces a humoral immune

response in lung cancer. Cancer Res. 2001;

61:7908-12.

Brichory FM, Misek DE, Yim AM, Krause

MC, Giordano TJ, Beer DG, Hanash SM.

An immune response manifested by the

common occurrence of annexins I and II

autoantibodies and high circulating levels 

of IL-6 in lung cancer. Proc Natl Acad Sci

USA. 2001; 98:9824-9.

Brunagel G, Vietmeier BN, Bauer AJ,

Schoen RE, Getzenberg RH. Identification

of nuclear matrix protein alterations associated with human colon cancer. Cancer Res.

2002; 62:2437-42.

Calhoun EA, Chang C, Welshman EE,

Fishman DA, Lurain JR, Bennett CL.

Evaluating the total costs of chemotherapyinduced toxicity: results from a pilot study

with ovarian cancer patients. The Oncologist.

2001; 6:441-5.

Casey MJ, Bewtra C, Hoehne LL, Tatpati

AD, Lynch HT, Watson P. Histology of

prophylactically removed ovaries from

BRCA1 and BRCA2 mutation carriers 

compared with noncarriers in hereditary

breast ovarian cancer syndrome kindreds.

Gynecol Oncol. 2000; 78:278-87.

Calvo R, West J, Franklin W, Erickson P,

Bemis L, Li E, Helfich B, Bunn P, Roche J,

Brambilla E, Rosell R, Gemmill RM,

Drabkin HA. Altered HOX and WNT7A

expression in human lung cancer. Proc Natl

Acad Sci USA. 2000; 97:12776-81. 

Cazares LH, Adam BL, Ward MD, Nasim

S, Schellhammer PF, Semmes OJ, Wright

GL. Normal, benign, pre-neoplastic, 

and malignant prostate cells have distinct

protein expression profiles resolved by

SELDI mass spectrometry. Clin Cancer 

Res. 2002; 8:2541-52.

Chen, G, Gharib TG, Huang CC, Thomas

DG, Sheddon KA, Taylor JMG, Kardia

SLR, Misek DE, Giordano, TJ, Iannettonni

MD, Orringer MB, Hanash S, Beer DG.

Proteomic analysis of proteins overexpressed

in lung adenocarcinomas with correlation

to mRNA abundance. Clin Cancer Res.

2002, 8:2298-305.

Cheng L, Sturgis EM, Guan Y, Guo Z,

Honn S, Eicher SA, Spitz MR, Wei Q.

Expression of nucleotide excision repair

genes and the risk for squamous cell 

carcinoma of the head and neck. Cancer.

2002; 94:393-7.

Cohen LS, Escobar PF, Scharm C, Glimco

B, Fishman DA. Three-dimensional power

Doppler ultrasound improves the diagnostic

accuracy for ovarian cancer prediction.

Gynecol Oncol. 2001; 82:40-8.

Cohen L, Fishman DA. Ultrasound 

and ovarian cancer. Cancer Treatment 

and Research. 2002; 7:119-32.
Appendix 59

Czerniak B, Li L, Chaturvedi V,

Johnston DA, Ro JY, Logothetis C, von

Eschenbach AC. Genetic modeling of

human urinary bladder carcinogenesis.

Genes Chrom Cancer. 2002; 27:392-402.

Dolan K, Morris AI, Gosney JR, Meltzer

SJ, Field JK, Sutton R. Loss of heterozygosity on chromosome 17p predicts neoplastic

progression in Barrett’s esophagus.

Gastroenterol. 2002 (in press).

Esteller M, Sparks A, Toyota M, SanchezCespedes M, Capella G, Peinado MA,

Gonzalez S, Tarafa G, Sidransky D, Meltzer

SJ, Baylin SB, Herman JG. Analysis of

Adenomatous Polyposis Coli promoter

hypermethylation in human cancer. 

Cancer Res. 2000; 60:4366-71.

Esteller M, Cordon-Cardo C, Corn PG,

Meltzer SJ, Pohar KS, Watkins NG, Capella

G, Peinado MA, Matias-Guiu X, Prat J,

Baylin SB, Herman JG. p14 (ARF) silencing by promoter hypermethylation mediates

abnormal intracellular localization of

MDM2. Cancer Res. 2001; 61:2816-21.

Feng G, Xu XC, Youssef EM, Lotan R.

Diminished expression of S100A2, a 

putative tumor suppressor at early stage 

of human lung carcinogenesis. Cancer Res.

2001; 61:7999-8004.

Fishman DA, Liu Y, Ellerbrok S, and Stack

MS. Lysophosphatidic acid promotes matrix

metalloproteinase (MMP) activation and

MMP-dependent invasion in ovarian cancer

cells. Cancer Res. 2001; 61:3194-9.

Fleisher AS, Meltzer SJ, James SP. Colon

polyps in Beckwith-Wiedmann syndrome:

role of imprinted genes. Gastroenterology.

2000; 118:37.

Fleisher AS, Esteller M, Leytin A, Harpaz

N, Rashid A, Xu Y, Liang J, Stine OC, Yin

J, Zou T-T, Abraham JM, Kong D, Wilson

KT, James SP, Herman JG, Meltzer SJ.

Microsatellite instability in IBD-associated

neoplastic lesions is associated with hypermethylation and diminished expression of

the DNA mismatch repair gene, hMLH1.

Cancer Res. 2000; 60:4864-8.

Fleisher AS, Esteller M, Tamura G, 

Rashid A, Stine OC, Yin J, Zou TT,

Abraham JM, Kong D, Nishizuka S, James

SP, Wilson KT, Herman JG, Meltzer SJ.

Hypermethylation of the hMLH1 gene 

promoter is associated with microsatellite

instability in early human gastric neoplasia.

Oncogene. 2001; 20:329-35.

Le Naour F, Brichory F, Misek DE, Bréchot

C, Hanash SM, and Beretta L. A distinct

repertoire of autoantibodies in hepatocellular carcinoma identified by proteomic

analysis. Mol Cell Proteomics. 2002; 1: 

197-203.

Fusaro RM, Lynch HT. The FAMMM 

syndrome: epidemiology and surveillance

strategies. Cancer Invest. 2000; 18:670-80.

Franklin WA, Veve R, Hirsch FR and Bunn

PA. Epidermal growth factor receptor family in lung cancer and premalignancy.

Seminars in Oncology. 2002; 29:3-14.

Gharib TG, Chen G, Wang H, Huang CC,

Prescott MS, Sheddon KA, Misek DE,

Thomas DG, Giordano, TJ, Taylor JMG,

Yee, J, Orringer MB, Hanash S, Beer DG.

Proteomic analysis of cytokeratin isoforms

associated with survival in lung adenocarcinoma. Neoplasia. 2002 (in press).
60 The Early Detection Research Network

Chen G, Gharib TG, Huang C, Taylor

JMG, Misek DE, Kardia SLR, Giordano

TJ, Iannettoni MD, Orringer MB, Hanash

SM, and Beer DG. Discordant protein and

mRNA expression in lung adenocarcinomas. Mol Cell Proteomics. 2002; 1:304-13.

Gazdar A F, Czerniak B. Filling the void:

urinary markers for bladder cancer risk 

and diagnosis. J Natl Cancer Inst. 2001;

93:413-15.

Grady WM, Willis J, Guilford PJ, 

Dunbier AK, Toro TT, Lynch H, Wiesner

G, Ferguson K, Eng C, Park JG, Kim SJ,

Markowitz S. Methylation of the CDH1

promoter as the second genetic hit in 

hereditary diffuse gastric cancer. Nat Genet.

2000; 26:16-17.

Greenberg AK, et al. Selective p38 activation in human non-small cell lung cancer.

Am J Respir Cell Mol Biol. 2002; 26:558-64

Hanash S, Brichory F, Beer D. A proteomic

approach to the identification of lung 

cancer markers. Disease Markers.

2001;17:295-300.

Hirsch FR, Varella-Garcia M, Franklin WA,

Veve R, Chen L, Helfrich B, Zeng C, Baron

A, Bunn PA. Evaluation of HER-2/neu

amplification/expression in lung tumors 

by fluorescence in situ hybridization and

immunohistochemistry. British Cancer

Journal. 2002; 86:1449-56.

Hirsch FR, Franklin WA, Gazdar A, Bunn

PA. Early detection of lung cancer: clinical

perspectives of recent advances in biology

and radiology. Clin Cancer Res. 2001; 

7:5-22.

Hirsch FR, Prindiville SA, Miller YE,

Franklin WA, Dempsey E, Murphy J, 

Bunn PA, Kennedy TC. A randomized

study of autofluorescence bronchoscopy

versus white light bronchoscopy for early

detection lung cancer in high risk smokers.

J Natl Cancer Inst. 2001; 93:1385-92.

Jeronimo C, Usadel H, Henrique R,

Oliveira J, Lopes C, Nelson WG, Sidransky

D. Quantitation of GSTP1 methylation 

in non-neoplastic prostatic tissue and

organ-confined prostate adenocarcinoma. 

J Natl Cancer Inst. 2001; 93:1747-52.

Jones M B, Krutzsch H, Shu H, Zhao Y,

Liotta L A, Kohn E C, Petricoin EF.

Proteomic analysis and identification of

new biomarkers and therpeutic targets for

invasive ovarian cancer. Proteomics. 2002;

2:76-84.

Kawakami K, Brabender J, Lord RV,

Groshen S, Greenwald BD, Krasna MJ, 

Yin J, Fleisher AS, Abraham JM, Beer DG,

Sidransky D, Huss HT, DeMeester TR,

Eads CA, Laird PW, Ilson DH, Kelsen DP,

Harpole D, Moore MB, Danenberg KD,

Danenberg PV, Meltzer SJ. Hypermethylated APC promoter DNA in serum and

prognosis in esophageal adenocarcinoma

patients. J Natl Cancer Inst. 2000; 

92:1805-11.

Kim JH, Skates SJ, Uede T, Wong KK,

Schorge JO, Feltmate CM, Berkowitz RS,

Cramer DW, Mok SC. Osteopontin as a

potential diagnostic biomarker for ovarian

cancer. JAMA. 2002; 287:1671-9.

Kram A, Li L, Zhang RD, Yoon DS, 

Ro JY, Johnston D, Grossman HB, 

Scherer S, Czerniak B. Mapping and

genome sequence analysis of chromosome 

5 regions involved in bladder cancer 

progression. Lab Invest. 2001; 81:1039-48.

Le Naour F, Misek DE, Krause MC,

Deneux L, Giordano TJ, Scholl S, Hanash

SM. Proteomics-based identification of

RS/DJ-1 as a novel circulating tumor 

antigen in breast cancer. Clin Cancer Res.

2001; 7:3328-35.
Appendix 61

Lerman C, Hughes C, Croyle RT, Main D,

Durham C, Snyder C, Bonney A, Lynch JF,

Narod SA, Lynch HT. Prophylactic surgery

decisions and surveillance practices one year

following BRCA1/2 testing. Prev Med.

2000; 31:75-80.

Li D, Firozi PF, Wang LE, Xiong P, Eicher

SA, Spitz MR, Hong WK, Wei Q. In vitro

BPDE-indced DNA adducts in peripheral

lymphocytes as a risk factor for squamous

cell carcinoma of the head and neck. Int J

Cancer. 2001; 93:436-40.

Liede A, Metcalfe K, Hanna D, Hoodfar E,

Snyder C, Durham C, Lynch H, Narod S.

Evaluation of the needs of male carriers of

mutations in BRCA1 or BRCA2 who have

undergone genetic counseling. Am J Hum

Genet. 2000; 67:1494-504.

Lango MN, Dyer KF, Lui VW, Gooding

WE, Gubish C, Siegfried JM, Grandis JR.

Gastrin-releasing peptide receptor-mediated

autocrine growth in squamous cell carcinoma of the head and neck. J Natl Cancer

Inst. 2002; 94:375-83.

Lynch HT. Anticipation in familial

Hodgkin’s lymphoma (editorial). 

Hum Genet. 2000; 107:290-3.

Lynch HT. Family Information Service

(FIS) and hereditary cancer. Cancer. 2001;

91:625-8.

Lynch HT, Brand RE, Hogg D, Deters CA,

Fusaro RM, Lynch JF, Liu L, Knezetic J,

Lassam NJ, Goggins M, Kern S.

Phenotypic variation in eight extended

CDKN2A germline mutation familial 

atypical multiple mole melanomapancreatic carcinoma syndrome. Cancer.

2002; 94:84-96.

Lynch HT, Brand RE, Lynch JF, Fusaro

RM, Kern SE. Hereditary factors in 

pancreatic cancer. J Hepatobiliary Pancreat

Surg. 2002; 9:21-31.

Lynch H, Brand R, Lynch J, Fusaro R,

Smyrk T, Goggins M, Kern SE. Genetic

counseling and testing for germline 

p16 mutations in two pancreatic cancerprone families. Gastroenterology. 2000;

119:1756-60.

Lynch HT, Grady WM, Lynch JF, Tsuchiya

K, Wiesner GL, Markowitz S. E-cadherin

mutation-based genetic counseling and

hereditary diffuse gastric carcinoma. 

Cancer Genet Cytogenet. 2000; 122:1-6.

Lynch HT, Lynch J. Lynch syndrome:

genetics, natural history, genetic counseling,

and prevention. J Clin Oncol. 2000;

18:19S-31S.

Lynch HT, Sanger WG, Pirruccello S,

Quinn-Laquer B, Weisenburger DD.

Familial multiple myeloma: a family study

and review of the literature. J Natl Cancer

Inst. 2001; 93:1479-83.

Maitra A, Wistuba II, Washington C,

Virmani AK, Ashfaq R, Milchgrub S,

Gazdar AF, Minna JD. High-resolution

chromosome 3p allelotyping of breast 

carcinomas and precursor lesions demonstrates frequent loss of heterozygosity and 

a discontinuous pattern of allele loss. 

Am J Pathol. 2001; 159:119-30.

Maruyama R, Toyooka S, Toyooka KO,

Harada K, Virmani AK, Zochbauer-Muller

S,Farinas AJ, Vakar-Lopez F, Minna JD,

Sagalowsky A, Czerniak B, Gazdar AF.

Aberrant promoter methylation profile 

of bladder cancer and its relationship to

clinical pathological features. Cancer Res.

2001; 61:8659-63.

Madoz-Gurpide J, Wang H, Misek DE,

Brichory F, Hanash SM. Protein based

microarrays: a tool for probing the 

proteome of cancer cells and tissues.

Proteomics. 2001; 1:1279-87.
62 The Early Detection Research Network

Mok SC, Chao J, Skates S, Kwong-kwok

W, Yiu GK, Muto MG, Berkowitz RS,

Cramer DW. Prostasin, a potential serum

marker for ovarian cancer: identification

through microarray technology. J Natl

Cancer Inst. 2001; 93:1458-64.

Mori Y, Yin J, Rashid A, Leggett BA, Young

J, Kuehl PM, Langenberg P, Meltzer SJ,

Stine OC. Instabilotyping: comprehensive

identification of novel cancer-related genes

by large-scale probing for mutations in coding region microsatellites. Cancer Res. 2001;

61:6046-9.

Narod SA, Sun P, Ghadirian P, Lynch H,

Isaacs C, Garber J, Weber B, Karlan B,

Fishman DA, Rosen B, Tung N,

Neuhausen S. Tubal ligation and the risk 

of ovarian cancer in carriers of BRCA1 or

BRCA2 mutations: a case-control study.

Lancet. 2001; 357: 1467-70.

Nomoto S, Yamashita K, Koshikawa K,

Nakao A, Sidransky D. Mitochondrial 

D-loop mutations as clonal markers in 

multicentric hepatocellular carcinoma and

plasma. Clin Cancer Res. 2002; 8:481-7.

Oh JM, Brichory F, Puravs E, Kuick R,

Wood C, Rouillard JM, Tra J, Kardia S,

Beer D, Hanash S. A database of protein

expression in lung cancer. Proteomics. 2001;

1:1303-19.

Parrella P, Xiao Y, Fliss M, SanchezCespedes M, Mazzarelli P, Rinaldi M, 

Nicol T, Gabrielson E, Cuomo C, Cohen

D, Pandit S, Spencer M, Rabitti C, Fazio

VM, Sidransky D. Detection of mitochondrial DNA mutations in primary breast

cancer and fine-needle aspirates. Cancer Res.

2001; 61:7623-6.

Paweletz CP, Trock B, Pennanen M,

Tsangaris T, Magnant C, Liotta LA,

Petricoin EF. Proteomic patterns of nipple

aspirate fluids obtained by SELDI-TOF:

potential for new biomarkers to aid in the

diagnosis of breast cancer. Disease Markers.

2001; 17:301-7.

Pepe MS, Etzioni R, Feng Z, Potter JD,

Thompson M, Thornquist M, Winget M,

Yasui Y. Phases of biomarker development

for early detection of cancer. J Natl Cancer

Inst. 2001; 93:1054B61.

Pepe MS. Statistical evaluation of diagnostic

tests and markers. Oxford University Press,

London, 2002 (in press).

Petricoin EF, Ardekani AM, Hitt BA,

Levine PJ, Fusaro VA, Steinberg SM, 

Mills GB,Simone C, Fishman DA, Kohn

EC, Liotta LA. Use of proteomic patterns

in serum to identify ovarian cancer. Lancet.

2002; 359:572-7.

Porter D, Lahti-Domenici J, Torres-Arzayus

M, Chin L, Polyak K. Expression of high in

normal-1 (HIN-1) and uteroglobin related

protein-1 (UGRP-1) in adult and developing tissues. Mech Dev. 2002; 114:201-4.

Porter DA, Krop IE, Nasser S, Sgroi D,

Kaelin CM, Marks JR, Riggins G, Polyak K

A. SAGE (serial analysis of gene expression)

view of breast tumor progression. Cancer

Res. 2001; 61:5697-702.

Qu Y, Adam B, Yasui Y, Ward M, Cazares

L, Schellhammer P, Feng Z, Semmes J,

Wright G. Boosted decision tree analysis 

of SELDI mass spectral serum profiles discriminates prostate cancer from non-cancer

patients. Clinical Chemistry. 2002 (in press).

Qiao Y, Spitz MR, Shen H, Guo Z, Shete

S, Hedayati M, Grossman L, Mohrenweiser

H, Wei Q. Modulation of repair of 

ultraviolet damage in the host-cell 

reactivation assay by polymorphic XPC 

and XPD/ERCC2 genotypes.

Carcinogenesis. 2002; 23:295-9.
Appendix 63

Rebbeck TR, Lynch HT, Neuhausen SL,

Narod SA, van’t Veer L, Garber JE, Evans

G, Isaacs C, Daly MB, Matloff E, Olopade

OI, Weber BL, for the Prevention and

Observation of Surgical End Points Study

Group. Prophylactic oophorectomy in 

carriers of BRCA1 or BRCA2 mutations. 

N Engl J Med. 2002; 346:1616-22.

Rebbeck TR, Wang Y, Kantoff PW,

Krithivas K, Neuhausen SL, Godwin AK,

Daly MB, Narod SA, Brunet J-S, Vesprini

D, Garber JE, Lynch HT, Weber BL,

Brown M. Modification of BRCA1- and

BRCA2-associated breast cancer risk by

AIB1 genotype and reproductive history.

Cancer Res. 2001; 61:5420-4.

Russo J, Lynch HT, Russo I. Mammary

gland architecture as a determining factor 

in the susceptibility of the human breast to

cancer. The Breast Journal. 2001; 7:278-91.

Sato F, Harpaz N, Shibata D, Xu Y, Yin J,

Mori Y, Zou T-T, Wang S, Desai K, Leytin

A, Selaru F, Abraham JM, Meltzer SJ.

Hypermethylation of the p14ARF gene 

in ulcerative colitis-associated colorectal 

carcinogenesis. Cancer Res. 2002; 

62:1148-51.

Selaru F, Zou T, Shustova V, Xu Y, Yin J,

Mori Y, Sato F, Wang S, Shibata D,

Greenwald BD, Krasna MJ, Abraham JM,

Meltzer SJ. Global gene expression profiling

in Barrett’s esophagus and esophageal 

cancer: a comparative analysis using cDNA

microarrays. Oncogene. 2002; 21:475-8.

Selaru F, Xu Y, Yin J, Zou T, Liu TC, Mori

Y, Abraham JM, Sato F, Wang S, Twigg C,

Olaru A, Shustova V, Leytin A, Shibata D,

Harpaz N, Meltzer SJ. Artificial neural 

networks distinguish among subtypes of

neoplastic colorectal lesions. Gastroenterol.

2002; 122:606-13.

Sen S, Zhou H, Zhang R, Yoon D, VakarLopez F, Ito S, Jiang F, Johnston DA,

Grossman H, Ruifrok AC, Katz RL,

Brinkley W, Czerniak B. Amplification/

overexpression of a mitotic kinase gene is

associated with aneuploidy and aggressive

phenotype of human bladder cancer. 

J Natl Cancer Inst. 2002 (in press).

Shen H, Spitz MR, Qiao Y, Zheng Y, Hong

WK, Wei Q. Polymorphism of DNA ligase

I and risk of lung cancer; a case-control

analysis. Lung Cancer. 2002; 36:243-7.

Shen H, Spitz MR, Wang LE, Hong WK,

Wei Q. Polymorphisms of methylenetetrahydrofolate reductase and risk of 

lung cancer B a case-control study. 

Cancer Epidemiol Biomarkers Prev. 2001;

10:397-401.

Shen H, Sturgis EM, Khan SG, Qiao Y,

Shahlavi T, Eicher SA, Strom SS, Xu Y,

Wang X, Spitz MR, Kraemer KH, Wei Q.

An intronic Poly (AT) polymorphism of 

the DNA repair gene XPC and risk of 

squamous cell carcinoma of the head and

neck: a case-control study. Cancer Res.

2001; 61: 3321-5. 

Shivapurkar N, Harada K, Reddy J,

Scheuermann RH, Xu Y, McKenna RW,

Milchgrub S, Kroft SH, Feng Z, Gazdar AF.

Presence of simian virus 40 DNA sequences

in human lymphomas. Lancet. 2002;

359:851-2.

Sidransky D. Emerging molecular markers

of cancer. Nature Rev Cancer. 2002; 

2:210-9.

Simms LA, Young J, Wicking C, Meltzer

SJ, Jass JR, Leggett BA. The apoptotic 

regulatory gene, BCL10, is mutated in 

sporadic mismatch repair deficient colorectal cancers. Cell Death Diff. 2000; 7:236-7.
64 The Early Detection Research Network

Small W, Zeytinoglu M, Keh P, Porter G,

Bozorgi K, Liu D, Rademaker AW, Fishman

DA, Shetty R, Singh D, Lurain JR.

Endometrial adenocarcinoma invasive 

to less than or equal to one-half the

myometrial thickness: analysis of prognostic

variables for recurrence and survival. 

Int J Rad Onc Bio Physics. 2002 (in press).

Smolinski KN, Abraham JM, Souza RF, 

Yin J, Wang S, Zou T-T, Kong D, Fleisher

AS, Meltzer SJ. A functional promoter 

for the squamous differentiation marker

esophagin that is activated during 

differentiation of cultured esophageal cells.

Genomics. 2002 (in press).

Smyrk TC, Watson P, Kaul K, Lynch HT.

Tumor-infiltrating lymphocytes are a marker for microsatellite instability in colorectal

cancer. Cancer. 2001; 91:2417-22.

Spitz MR, Wu X, Wang Y, Wang LE, Shete

S, Amos CI, Guo Z, Lei L, Mohrenweiser

H, Wei Q. Modulation of nucleotide excision repair capacity by XPD polymorphisms

in lung cancer patients. Cancer Res. 2001;

61:1354-7.

Srinivas P, Barker PE, Sirvastava S.

Nanotechnology in early cancer detection.

Lab Invest. 2002; 82:1-5.

Steel LS, Mattu T, Heibersteit H, Dwek

RA, Block TM. A proteomic approach 

to the early detection of liver cancer.

Disease Markers. 2001; 17:179-89.

Steven A, Narod, P S, Ghadirian, P, Lynch

H, Isaacs C, Garber J, Weber B, Karlan B,

Fishman D, Rosen B, Tung N and

Neuhausen S. Tubal ligation and the risk 

of ovarian cancer in carriers of BRCA1 or

BRCA2 mutations: a case-control study.

Lancet. 2001;357:1467-70.

Sturgis EM, Dahlstrom KR, Guan Y, Eicher

SA, Strom SS, Spitz MR, Wei Q. Alcohol

dehydrogenase 3 genotype is not associated

with risk of squamous cell carcinoma of the

oral cavity and pharynx. Cancer Epidemiol

Biomarker Prev. 2001;10:273-5.

Sturgis EM, Zheng R, Li L, Castillo EJ,

Eicher SA, Minhui Chen, Strom SS, Spitz

MR, Wei Q. XPD/ERCC2 polymorphisms

and risk for the head and neck cancer: a

case-control analysis. Cancinogenesis. 2000;

21:2219-23.

Sugita M, Haney JL, Gemmill RM,

Franklin WA. Duplex RT-PCR for 

assessment of RNA degradation. 

Analytical Biochemistry. 2001; 295:113-6 

Sugita M, Gao B, Geraci M, Johnson G,

Lapdat R, Powell RL, Hirsch FR,

Gabrielson E, Bremnes R, Bunn PA,

Franklin WA. Combined use of oligonucleotide and tissue microarrays identifies

cancer/testis genes as biomarkers in lung

carcinoma. Cancer Res. 2002 (in press).

Tamura G, Yin J, Wang S, Fleisher AS, 

Zou T, Abraham JM, Kong D, Smolinski

KN, Wilson KT, James SP, Silverberg SG,

Nishizuka S, Motoyama T, Meltzer SJ. 

E-cadherin gene promoter hypermethylation in primary gastric carcinomas. 

J Natl Cancer Inst. 2000; 92:569-73.

Toretsky JA, Zitomersky NL, Eskenazi AE,

Voigt RW, Strauch ED, Sun CC, Huber R,

Meltzer SJ, Schlessinger D. Glypican-3

expression in Wilms tumor and hepatoblastoma. J Ped Hematol/Oncol 2001; 23:496-9.

Toyooka KO, Toyooka S, Virmani A K,

Sathyanarayana UG, Euhus DM, Gilcrease

M., Minna J D, Gazdar AF. Loss of 

expression and aberrant methylation of 

the CDH13 (H-cadherin) gene in breast and

lung carcinomas. Cancer Res. 2001;

61:4556-60.
Appendix 65

Toyooka S, Pass H I, Shivapurkar N,

Fukuyama Y, Maruyama R, Toyooka K O,

Gilcrease M, Farinas A, Minna J D, Gazdar

A F. Aberrant methylation and simian virus

40 tag sequences in malignant mesothelioma. Cancer Res. 2001; 61:5727-30.

Toyooka S, Toyooka O K, Maruyama R,

Virmani A K, Girard L, Miyajima K,

Harada K., Ariyoshi Y, Takahashi T, Sugio

K, Brambilla E, Gilcrease M, Minna J D

Gazdar A F. DNA methylation profiles of

lung tumors. Molecular Cancer Therapeutics.

2001; 1:61-7.

Toyooka K O, Toyooka S, Maitra A, Feng

Q, Kiviat N, Smith A, Minna J D, Ashfaq

R and Gazdar A F. Establishment and validation of real time PCR method for CDH1

promoter methylation. Am J Pathol. 2002

(in press).

Tsuchiya T, Tamura G, Sato K, Endoh Y,

Sakata K, Jin Z, Motoyama T, Usuba O,

Kimura W, Nishizuka S, Yin J, Fleisher AS,

Zou T, Kong D, Meltzer SJ. Distinct

methylation patterns of two APC gene 

promoters in normal and cancerous gastric

epithelium. Oncogene. 2000; 19:3642-6.

Usadel H, Brabender J, Danenberg KD,

Jeronimo C, Harden S, Engles J,

Danenberg PV, Yang S, Sidransky D.

Quantitative adenomatous polyposis coli

promoter methylation analysis in tumor 

tissue, serum, and plasma DNA of 

patients with lung cancer. Cancer Res. 

2002; 62:371-5.

Virmani A K, Muller C, Rathi A,

Zoechbauer-Mueller S, Mathis M, and

Gazdar A F. Aberrant methylation during

cervical carcinogenesis. Clin Cancer Res.

2001; 7:584-9.

Virmani AK, Tsou JA, Siegmund KD, 

Shen LY, Long TI, Laird PW, Gazdar AF,

Laird-Offringa IA. Hierarchical clustering

of lung cancer cell lines using DNA methylation markers. Cancer Epidemiol Biomarkers

Prev. 2002; 11:291-7.

Vlahou A, Schellhammer PF, Mendrinos S,

Patel K, Kondylis FI, Gong L.

Development of a novel proteomic

approach for the detection of transitional

cell carcinoma of the bladder in urine. 

Am J Pathol. 2001; 158:1491-1502.

Watson P, Bützow R, Lynch HT, Mecklin 

J-P, Järvinen HJ, Vasen HFA, Madlensky L,

Fidalgo P, Bernstein I, International

Collaborative Group on HNPCC. The 

clinical features of ovarian cancer in 

hereditary nonpolyposis colorectal cancer.

Gynecol Oncol. 2001; 82:223-8.

Watson P, Lynch HT. Cancer risk in 

mismatch repair gene mutation carriers.

Familial Cancer. 2001; 1:57-60.

West M, Blanchette C, Dressman H,

Huang E, Ishida S, Spang R, Zuzan H,

Marks JR, Nevins JR. Predicting the clinical

status of human breast cancer by using gene

expression profiles. Proc Natl Acad Sci USA.

2001; 98:11462-7.

Wu X, Lippman SM, Lee JJ, Zhu Y, Wei

QV, Thomas M, Hong WK, Spitz MR.

Chromosome Instability in lymphocytes: 

a potential indicator of predisposition to

oral premalignant lesions. Cancer Res. 2002;

62:2813-8. 

Wu X, Zhao H, Amos CI, Shete S, Makan

N, Hong WK, Kadlubar FF, Spitz MR.

p53 genotypes and haplotypes associated

with lung cancer susceptibility and ethnicity. J Natl Cancer Inst. 2002; 94:681-90. 
66 The Early Detection Research Network

Xiao Z, Adam BL, Cazares LH, Clements

MA, Davis JW, Schellhammer PF,

Dalmasso EA, Wright GL Jr. Quantitation

of serum prostate-specific membrane 

antigen by a novel protein biochip

immunoassay discriminates benign from

malignant prostate disease. Cancer Res.

2001; 61:6029-33. 

Xu Y, Selaru FM, Yin J, Zou TT, Shustova

V, Mori Y, Sato F, Liu TC, Olaru A, 

Wang S, Kimos MC, Perry K, Desai K,

Greenwald BD, Krasna MJ, Shibata D,

Abraham JM, Meltzer SJ. Artificial neural

networks and gene filtering distinguish

between global gene expression profiles of

Barrett’s esophagus and esophageal cancer.

Cancer Res. 2002, 62:3493-7. 

Yoon DS, Li L, Zhang RD, Kram A, Ro JY,

Johnson D, Grossman HB, Scherer S,

Czerniak B. Genetic mapping and DNA

sequence-based analysis of loci on chromosome 16 involved in progression of bladder

cancer from occult preneoplastic conditions

to invasive disease. Oncogene. 2001;

20:5005-14.

Zheng Y, Li L Shen H, Sturgis ER, 

Eicher SA, Strom SS, Spitz MR, Wei Q.

Polymorphic hchk2/hcds1 codon 84 allele

and risk of squamous cell carcinoma of the

head and neck, a case-control analysis.

Carcinogenesis. 2001; 22:2005-8.

Zheng Y, Shen H, Sturgis EM, Wang LE,

Eicher SA, Strom SS, Frazier ML, Spitz

MR, Wei Q. Cyclin D1 polymorphism 

and risk for squamous cell carcinoma of 

the head and neck a case-control study.

Carcinogenesis. 2001; 22:1195-9.

Zhou G, Li H, DeCamp D, Chen S, Shu

H, Gong Y, Flaig M, Gillespie J W, Hu N,

Taylor P R, Liotta L A, Emmert-Buck M R,

Petricoin III E F, Zhao Y. 2D Differential

In-gel Electrophoresis (DIGE) for the 

identification of esophageal squamous cell

cancer-specific protein markers. Mole Cell

Proteomics. 2001; 1:117-23.

Zhu Y, Spitz MR, Strom S, Tomlinson GE,

Minna JD, Wu X. Lymphocytic chromosome 9 aberrations associated with genetic

predisposition to lung cancer: a casecontrol study with family history analysis.

International Journal of Cancer. 2002 

(in press).

Zhu Y, Spitz MR, Zheng Y-L, Hong WK,

Wu X. BPDE-inducedd lymphocytic

3p.21.3 aberrations may predict head and

neck cancer risk. Cancer. 2002 (in press).

Zou T, Selaru F, Xu Y, Yin J, Shustova V,

Mori Y, Sato F, Liu TC, Abraham JM,

Meltzer SJ. Application of cDNA microarrays to generate a molecular taxonomy

capable of distinguishing between colon

cancer and normal colon. Oncogene. 2002

(in press).
Appendix 67

Baker S, Srivastava S, Kramer BS. Markers

for early detection of cancer: statistical

guidelines for retrospective longitudinal

studies. BMC Med Res Methodol. 2002; 

2:1-8.

Kutkat L, Srivastava S. The Early

Detection Research Network: a platform 

for communication and collaboration.

Disease Markers. 2001; 17: 3-4.

Mills GB, Bast RC, Srivastava S. Future of

ovarian cancer screening: from emerging

technology of transcriptional profiling and

proteomics. J Natl Cancer Inst. 2001; 93:

1437-9.

Negm R, Verma M, Srivastava S. The

promise of biomarkers in cancer screening

and detection. Trends in Molecular

Medicine. 2002; 8:288-93. 

Srinivas P, Kramer BS, Srivastava S. Trends

in biomarkers research for cancer detection.

Lancet Oncology. 2001; 2:698-704.

Srinivas P, Hanash S, Wright G, Srivastava,

S. Proteomic approaches in biomarkers 

discovery. Clinical Chemistry. 2001;

47:1894-1900.

Srinivas PR, Srivastava S, Hanash S, 

Wright GL Jr. Proteomics in early detection

of cancer. Clinical Chemistry. 2001;

47:1901-11.

Srinivas PR, Barker P, Srivastava S.

Nanotechnology in early detection 

of cancer. Lab Invest. 2002 82:657-62.

Srinivas PR, Verma M, Zhao Y, Srivastava

S. Proteomics for cancer biomarkers discovery. Clin Cancer Res. 2002; 132(85): 2471-5.

Srivastava S, Kramer BS. Early detection

cancer research network. Lab Invest. 2000;

80:1147-8.

Srivastava S, Verma M, Henson DE.

Biomarkers for early detection of colon 

cancer. Clin Cancer Res. 2001; 7:1118-26. 

Srivastava S , Hanash S. Global strategies

for disease detection and treatment. 

Disease Markers. 2001; 17: 203-4.

Srivastava S, Gopal-Srivastava R.

Biomarkers in cancer screening: a public

health perspective. J Nutrition. 2001; 

132(85): 2471-5.

Verma M, Wright GL Jr, Hanash SM,

Gopal-Srivastava R, Srivastava S. Proteomic

approaches within the NCI Early Detection

Research Network for the discovery and

identification of cancer biomarkers. 

Ann N Y Acad Sci. 2001; 945:103-15. 

Verma M, Lambert PF, Srivastava SK.

Meeting highlights: National Cancer

Institute workshop on molecular 

signatures of infectious agents. Disease

Marker. 2001;17(3):191-201

Verma M, Srivastava S. Molecular

Signatures of Infectious Agents in Human

Cancer. Disease Markers. 2001; 17:121-2.

II NCI Publications Describing the Early Detection Research Network
Excerpted from the Manual of Operations

Of the Early Detection Research Network

April 27, 2001

It is the responsibility of the awarding

agency, in this case the National Cancer

Institute (NCI), National Institutes 

of Health, to review progress achieved

towards scientific goals in original grant

applications over specified grant periods

and to provide scientific and logistical

input to grantees to enhance the quality 

of their scientific efforts. For details, see

HHS 45 CFR, Part 74. To review progress

towards achieving the objectives of the

EDRN and its investigators, EDRN 

program officials are charged as part of the

terms of the award to gather information

on the functioning of the network in order

to update the NCI leadership. This document describes the prospectively developed

metrics, rationale, and standards for evaluating the overall success of the EDRN. 

Introduction

Fair, rigorous peer review of investigatorinitiated scientific applications remains 

the cornerstone of scientific progress in the

United States. Peer review has ensured that

the best science is supported. The EDRN

was initiated with this concept in mind. 

By selecting scientific collaborators for the

EDRN on the basis of rigorous peer review

and fully funding the best applications, the

NCI has successfully obtained strong participation from the scientific community. 

The EDRN represents a major pioneering

effort in collaborative translational

research. It departs from prior Cancer

Cooperative Group models in many

important ways—through empowering

investigators by funding their Centers

directly and by placing the burden of 

scientific leadership, research agenda, 

and collaboration upon these directly 

funded Centers. Basic scientists with 

robust bench research records have been

funded to pool their ideas, resources, and

tools. Translational and epidemiological

investigators with strong tools and publication track records are directly funded with

a mandate to translate concepts arising

from basic science labs. Analytical tools,

laboratories, statistical methods, and 

informatics are also supported directly 

with a collaborative mandate. Leadership

of this collaborative must emanate from

the grass-root investigators, and the

Executive Leadership must communicate

with a highly knowledgeable group of 

scientists in a manner that enhances collaboration and productivity. This Network

represents a new paradigm of Cooperative

research. 

NCI Charge to the EDRN 

At the opening meeting of all of the 

funded EDRN units, NCI leadership 

and Program Staff provided the following

charge for this collaborative enterprise:

• Establish criteria for the discovery and

validation of biomarkers at all points 

of the integrated research scheme; 

• Establish a rigorous quality

assurance/quality control program 

for biomarkers; 

68 The Early Detection Research Network

III Metrics for Programmatic Evaluation
• Establish and deal with issues of

biorepositories—how the samples will be

obtained, stored and most importantly,

allocated; 

• Support Translational Research

Projects—both within and outside 

the EDRN—and establish policies and

procedures that are inclusive of investigators who wish to utilize the infrastructure

and facilities of the EDRN; 

• Establish and foster industrial collaborations which will be crucial to the ability

to rapidly translate the research effort

into products and to test innovative

biomarkers being developed by industry; 

• Establish and maintain effective and efficient communications, including the use

of EDRN Web sites (public and private),

email, and regularly scheduled meetings; 

• Develop and maintain an effective, 

efficient, and productive management

domain with minimal committee structure and maximal collaboration, with

financial rewards for collaboration; 

• Encourage inclusiveness by ensuring 

that scientists with promising research

ideas get the opportunity to collaborate

productively with the EDRN. 

Evaluation Metrics 

Since there are no prior models of such 

a cooperative research enterprise devoted 

to early detection biomarkers, it is very

important to carefully monitor and assess

progress from both macro and micro perspectives. This review will be particularly

important during the first grant period in

which substantial administrative effort

should be expended in order to build 

the new infrastructure. The following 

evaluation metrics are to be used: 

For the Individual Laboratory 

and Center: 

1. Scientific Excellence 

Quality of Questions: Has the EDRN site

clearly defined their objectives, hypotheses,

and scientific plan? Scientific Progress to

Date: Has the EDRN site made progress

towards meeting these objectives as 

specified in their originally funded research

plan? What pitfalls have been encountered

and how have they been managed? 

Innovation: How has the EDRN site used

innovation to overcome obstacles? Is the

site aware of new methods or approaches

that might be useful to or portable into 

the EDRN environment?

Future Plans: What does the site plan to do

over the coming two years? How will these

plans meet the original grant objectives? 

2. Productivity Metrics 

Publication productivity: Has the site 

published papers on the objectives funded

by the EDRN? How many and in what

Journals? If not, are there problems that

need to be addressed or require assistance? 

Grant funding: Has the site applied for

additional peer-reviewed grant or contract

funding? Has the site team been successful

in gaining additional funds? Has the

EDRN been helpful to the success of 

funding these new grants or contracts? 

Biomarkers identified (BDLs): Number of

new biomarkers pursued for evaluation?

Number of biomarkers sent forward to

CECs or BVLs for validation? Number of

biomarkers added to early detection or risk

assessment panels? Number of biomarkers

used in chemoprevention clinical trials? 

Appendix 69
Assays performed (BVLs): Numbers of assays

developed for EDRN projects? Numbers 

of samples processed? Types of samples

processed? Results reported? Quality 

control of samples assayed? Number and

type of development projects approved?

Use of CDEs? 

DMCC: Standards of informatics support?

Type of informatics, QC procedures,

patient privacy protection measures, data

storage, and retrieval systems for Validation

Studies? Development of Network-wide

communication systems? Development 

of Network-wide systems to promote data

and specimen sharing? Development of 

statistical methodology to meet the needs

of EDRN? 

Samples collected and provided (CECs):

Numbers of samples collected? Types of

samples collected? Sources of samples 

collected? Numbers of samples provided 

to EDRN BDLs or BVLs? Use of CDEs?

How many CECs have had their set-aside

funds released? How many CECs have

requested the release of Developmental

funds? 

3. Collaborative Metrics

EDRN collaborations: With whom is the

EDRN site collaborating? How many 

projects are collaborative? How many joint

papers have been published? Use of EDRN

resources: Has the EDRN site collaborated

with CECs, a BVL or BDL site? If so, how

many? Joint publications? Joint grants?

How many BDLs have requested release 

of their restricted funds for Network

Collaborative Studies?

Participation in EDRN Activities:

Attendance from the site at EDRN 

meetings. Participation on Committees,

working groups, and task forces? Special

EDRN projects completed. Did EDRN

site participate in developing the CDEs?

Did EDRN site help to standardize/

streamline the IRB approval process? 

Did EDRN site help develop systems for

streamlining data sharing and/or specimen

sharing? Did EDRN site help develop 

systems to standardize/streamline 

technology transfer issues?

EDRN outreach: Number of new Associate

Members from the outside? Amount of

Chair’s funds allocated to new Associate

Members? The number of applications for

Chair’s funding? Other outreach activities? 

Process for Evaluating Metrics: 

1. Annual written progress report 

Reviews should be based upon the yearly

progress report required for non-competitive renewal. Instructions for preparation 

of the non-competitive renewal should be

specific and emphasize progress towards

scientific goals of the original grant 

application and progress towards addressing EDRN’s mission. While scientific

quality and progress need to be recorded

and addressed, primarily, metrics should 

be required to allow NCI staff to report

data to NCI leadership.

70 The Early Detection Research Network
The review process should assess the

progress of each of the funded units

towards meeting the specific aims of 

their funded grant application and their

progress and contributions in meeting 

the above-described charges for the entire

group. While the review is structured to

provide NCI leadership and staff with data

to track the progress of the EDRN and its

components, equally important goals are 

to provide constructive feedback to 

EDRN Principal Investigators and their

collaborators. Reviews may be used by

EDRN leadership, NCI staff, and the

Network Consulting Committee to make 

mid-course changes or to encourage constructive changes in individual scientific

direction or focus. Initial reviews might

assist in building collaborations among

investigators and their groups. Reviews

may also be used to assess administrative

progress, to quantify publications and

grants, and to quantify numbers of 

subjects studied. 

2. Site Visits

Each Center/Laboratory should be site 

visited by a panel comprised of external

consultants (individual members of the

Network Consulting Committee), NCI

staff and other experts on an as-needed

basis. The site visit should be brief 

(preferably a half day or less) but enable 

a thorough review of scientific progress,

future scientific plans, performance 

metrics, facilities and staff in support 

of the EDRN charge. The site Principal

Investigator would provide a 2-3 hour 

presentation period to review scientific

progress, spell out new scientific initiatives

for EDRN research, and address required

metrics. The Principal Investigator should

be encouraged to share problems, concerns,

and questions to the site visit team so that

the process is interactive and collegial.

While an agenda and presentation should

be necessary, no scoring should be used. 

3. Frequency of Site Visits 

The frequency of the site visits will be

determined by the NCI. However, it is

anticipated that one initial site visit by

NCI program officials, in year one will

occur, and one mid-grant site visit (for a

five-year grant, it will be between the year

2 and year 3). Additional site visits may 

be required when deemed necessary by 

the NCI.

Deficient performance and remedies will

be conducted in accordance with HHS 

45 CFR, Part 74 and other pertinent 

regulations. 

Overall Evaluation of Early

Detection Research Network: 

It is the intention of the NCI that the

members of the Network Consulting

Committee and Chairs and Co-Chairs 

of the EDRN Steering Committee will 

discuss the overall performance of the

EDRN using the metrics presented in this

document and suggest changes/modifications in the working structure of EDRN

for the next five-year cycle. 

Cancer Center Review Model: Perhaps the

best model to evaluate the success of the

EDRN is a Cancer Center type review.

View the EDRN as a “Cancer Center” 

that is devoted to discovery and translation

of surrogate endpoints for early cancer

detection and risk assessment. In a Cancer

Center review, the Center’s leadership 

and Program Directors are asked to

demonstrate how the sum exceeds the 

total of its parts. This approach requires 

all program leaders, participating 

investigators, and Center leadership 

to demonstrate how the Core facilities,

administration of resources, and Core 

grant funding have enhanced scientific

progress. This review measures 

collaborative efficacy. 

Appendix 71
Excerpted from Manual of Operations

of the Early Detection Research

Network

M E M O R A N D U M (draft)

Date:

To: [ Name of Institutional IRB

Chairs & Administrators]

From: [Name of EDRN investigator]

Subject: Early Detection Research

Network (EDRN) proposed

IRB standardized review

This memo is to discuss with you the 

possibility of setting up a standardized 

IRB review system for the Early Detection

Research Network (EDRN). 

The EDRN is an NCI-sponsored, 

peer-reviewed, Institutional Review Board

(IRB) approved consortium of 31 investigator-initiated research projects organized

to develop and validate biomarkers of 

risk and early detection of human cancer.

Collaborations between EDRN investigators and investigators from the United

States, private industries, and foreign 

institutes are encouraged. Associate

Membership, as this collaboration is called,

can be sought through an established

EDRN investigator and approved through

the EDRN Steering Committee. EDRN

collaboration and validation studies may

include as few as two institutions or extend

to many EDRN institutions. EDRN

research studies will involve sharing of 

biological samples, data, and in some 

cases reviewing medical records. 

The following EDRN objectives include

human subject research activities requiring

IRB review: 

• develop and test promising biomarkers 

or technologies

• evaluate promising, analytically proven

biomarkers or technologies

• collaborate with academic and industrial

leaders in molecular biology, molecular

genetics, clinical oncology, computer 

science, public health and clinical 

application for early cancer detection

• collaborate and rapidly disseminate 

information among EDRN members 

and associate members

The NCI sponsored an EDRN IRB 

working group to provide a forum where

EDRN human subject issues could be 

discussed on a regular basis and common

IRB concerns could be addressed and

resolved. This group is comprised of 

six EDRN investigators and their IRB 

representatives (chairs and/or institutional

office Administrator) from across the 

country, NCI representatives, and George

Gasparis from the Office of Human

Research Protections (OHRP). Of 

specific interest to this group was finding 

a way to streamline the IRB review process

over the multiple EDRN sites, while 

maintaining the high level of protection 

of our human subjects. 

72 The Early Detection Research Network

IV Institutional Reveiw Board Guiding Documents
This letter is to propose setting up a 

standardized EDRN IRB review process

with EDRN local IRBs. This would 

consist of using a standardized IRB 

applications form and a consent form 

template that will be modified according 

to each collaborative project and each local

IRB requirements. We have discussed 

this with George Gasparis (OHRP) who

endorses the concept of an IRB standardized review process. 

Since January, 2001 this IRB working

group has been working on developing

these forms, which are attached for your

review. By using these forms, we assure

continuity in addressing questions that are

specific to EDRN and also present EDRN

collaborative study proposals to the IRBs

in a standardized format for ease of IRB

review. We welcome your feedback to

these forms.

After you have had an opportunity to

review this material, I welcome an 

opportunity to discuss this concept with

you further and answer any questions you

might have. Because I was not part of the

EDRN IRB working group, some of your

questions may best be answered by those

who were involved in the process. 

Please feel free to contact Cim Edelstein,

Study Administrator of the EDRN Data

Management and Coordinating Center 

at 206-667-4995; e-mail address

cedelste@fhcrc.org, or Karen Hansen,

Director of the Fred Hutchinson Cancer

Research Center Institutional Review

Board, at 206-667-4867; e-mail address

khansen@fhcrc.org. They have both

worked on developing similar systems with

other large multi-centered trials and can

provide you with detailed information.

Thank you in advance for considering 

this proposal. We believe that it will work

well for both EDRN and with the local

EDRN IRBs.

Appendix 73
74 The Early Detection Research Network

IRB Cover Sheet and Final Application

The goal of the application process is to ensure that the Institutional Review Board (IRB) is provided with sufficient documentation to determine that research studies are ethically sound. It is the intent of the application form to guide the investigator to

areas that must be addressed when designing the protocol in order to maximize the protection of subjects.

Date:______________________ Protocol #:___________________ 

EDRN Site:_______________________________________________ 

Title of Protocol/Activity: ___________________________________________________________________________________

Principal Investigator: ___________________________________________________________________________________

Institution: ___________________________________________________________________________________

Address: ___________________________________________________________________________________

Phone: ____________________________ E-mail:_________________________________________________

Other Investigators: ___________________________________________________________________________________

Type of Review Requested: 

❏ New IRB Protocol

❏ Full Review ❏ Expedited Review

❏ Cooperative Review ❏ Review of Exempt Status

❏ Revision to previously application, IR# __________

Funding Agency: ___________________________________________________________________________________

Title of funding proposal: ___________________________________________________________________________________

(if different than IRB study title)

Dates of Funding: from_____________________________________ to_______________________________________ 

Approvals:

Local EDRN Approvals:

Final Approvals:

Shaded Area for Review Office Only

IR#:__________________ Date Rec’d:____________

Principal Investigator (type)

Signature Date

Division /Department Head (type)

Signature Date

Date of EDRN scientific review:______________________________

Name (type)

Title

Dates of approval__________________________________________

Signature

Date

to_______________________________________________________
Appendix 75

Contents

1 Summary/Outline of Activity

2 Research Participant Profile

3 Populations Requiring Special Considerations

4 Recruitment

5 Informed Consent

6 Confidentiality/Autonomy

7 Research Procedures

8 Health and Safety Considerations

9 Risk vs. Benefit Analysis

1 Summary/Outline of Activity

1.1 Summarize the background and rationale for this activity.

1.2 Describe the objectives which are to be met.

2 Research Participant Profile

2.1 Summarize the criteria for selection and/or exclusion of participants.

2.2 What are approximate number and ages of the following?

NUMBER OF PARTICIPANTS Entire Study AGE RANGE OF PARTICIPANTS Entire Study

PARTICIPANTS/CASES

NORMAL/CONTROLS

OTHERS (specify)

2.3 Will participant population include equitable gender representation?

❏ Yes ❏ No

a. If no, please explain:

2.4 Will participant population include equitable minority representation?

❏ Yes ❏ No

a. If no, please explain:

10 Alternatives

11 Investigational New Drugs

12 Investigational Devices

13 Monoclonal Antibodies and Biologics

14 Conflict of Interest

15 Local EDRN Member Institution Requirements

16 Investigator’s Statement

17 Checklist – Order of Attachments
76 The Early Detection Research Network

3 Populations Requiring Special Considerations

3.1 Will illiterate or non-English speaking populations be included in the research?

❏ Yes ❏ No

a. If yes, please describe the procedures to be used to ensure there is informed consent in this population:

3.2 Will this study involve prisoners as research subjects?

❏ Yes ❏ No

a. If yes, please use the following definitions to check the involvement of the research participant for each category of

research in the table below: (Check all that apply.)

Category 1 – A study of the possible causes, effects, and processes of incarceration, and of criminal behavior, provided that

the study presents no more than minimal risk and no more than inconvenience to the subjects.

[45CFR46.306(a)(2)(A)]

Category 2 – A study of prisons as institutional structures or of prisoners as incarcerated persons, provided that the study

presents no more than minimal risk and no more than inconvenience to the subjects. [46CFR46.306(a)(2)(B)]

Category 3 – Research on conditions particularly affecting prisoners as a class, provided that the study may proceed only

after the Secretary has consulted with appropriate experts including experts in penology, medicine, and ethics,

and published notice, in the Federal Register, of his intent to approve such research. [45CFR46.306(a)(2)(C)]

Category 4 – Research on practices, both innovative and accepted, which have the intent and reasonable probability of

improving the health or well-being of the subject. In cases in which those studies require the assignment of

prisoners in a manner consistent with protocols approved by the IRB to control groups which may not benefit

from the research, the study many proceed only after ... [45CFR46.407]

Research with prisoners Cases Controls Other None of these

Category 1 (study of incarceration, minimal risk) ❏ ❏❏ ❏

Category 2 (study of institutional structures, minimal risk) ❏ ❏❏ ❏

Category 3 (research on conditions affecting prisoners) ❏ ❏❏ ❏

Category 4 (research on practices with intent of improving health of subject) ❏ ❏❏ ❏

3.3 Will this study involve minors (as defined by state law) as research subjects?

❏ Yes ❏ No

a. If yes, please use the following definitions to check the involvement of the research participant for each category of

research in the table below: (Check all that apply.)

Category 1 – Research not involving greater than minimal risk. [45CFR46.404]

Category 2 – Research involving greater than minimal risk, but presenting the prospect of direct benefit to the individual

subject. [46CFR46.405]

Category 3 – Research involving greater than minimal risk with no prospect of direct benefit to individual subjects, but likely

to yield generalizable knowledge about the subject’s disorder or condition. [45CFR46.406]

Category 4 – Research not otherwise approvable, but which presents an opportunity to understand, prevent, or alleviate a

serious problem affecting the health or welfare of children. [45CFR46.407]
Appendix 77

Research with minors Cases Controls Other None of these

Category 1 (not greater than minimal risk) ❏❏❏ ❏

Category 2 (greater risk, but with prospect of direct benefit) ❏❏❏ ❏

Category 3 (greater risk, with no prospect of direct benefit) ❏❏❏ ❏

Category 4 (not otherwise approvable) ❏❏❏ ❏

3.4 Will this study involve pregnant women as research subjects?

❏ Yes ❏ No

a. If yes, please use the following definitions to check the involvement of the research participant for each category of

research in the table below: (Check all that apply.)

Category 1 – The purpose of the activity is to meet the health needs of the mother and the fetus will be placed at risk only

to the minimum extent necessary to meet such needs. [45CFR46.207(a)(1)]

Category 2 – The risk to the fetus is minimal. [46CFR46.207(a)(2)]

Research with pregnant women Cases Controls Other None of these

Category 1 (minimum necessary risk to fetus) ❏ ❏ ❏❏

Category 2 (minimal risk to fetus) ❏ ❏ ❏❏

3.5 Will this study involve women of childbearing potential as research subjects? [If yes, consent requirements 

found at 45CFR46.116(b)(1)]

❏ Yes ❏ No

3.6 Will this study involve people not competent to provide informed consent? [If yes, 45CRF46111(b) states additional safeguards need to be taken as a criteria for IRB approval. Additional guidance may also be found at the NIH web site:

http://grants/nih/gov/grants/policy/questionablecapacity.htm]

❏ Yes ❏ No

4 Recruitment

4.1 Will you obtain names and addresses of individuals for potential study recruitment from a registry or repository containing

confidential information?

❏ Yes ❏ No

a. If yes, what is the source?

b. Provide a letter of support from the gatekeeper of the registry or repository authorizing your access to the identifiable data

for the purposes of your study been obtained?

4.2 When, how, and by whom will the participants be recruited?
78 The Early Detection Research Network

4.3 (Check all that apply.)

❏ Advertisements ❏ Brochures

❏ Contact letters ❏ Flyers

❏ Internet (other than Physicians’ Data Query) ❏ Radio

❏ Television ❏ Other (describe)___________________________________

❏ None of these _________________________________________________

5 Informed Consent

5.1 When, and by whom, will the participant or participant’s legal guardian initially be approached for consent?

5.2 Where will informed consent be obtained (e.g., in a clinic, investigator’s private office, participant’s home, etc.)?

5.3 If nurses, CRAs, social workers, etc., will be involved in the informed consent process, indicate what they will do and their

roles in the process.

5.4 In addition to the consent form, what method(s), if any, will be used to educate the potential research participant of the

research project and their rights as a participant? (Check all that apply.)

❏ Brochures ❏ Conference with participant and family member(s)

❏ Conference with interpreter ❏ Follow-up discussion

❏ Video ❏ Other (describe)

5.5 What method(s), if any, will be used to evaluate the understanding of the potential research participant of the research project and their rights as a participant? (Check all that apply.)

❏ Pre- and post-test

❏ Verbal feedback

❏ Other (describe)

5.6 What type(s) of informed consent will be used? (Check all that apply.)

❏ Written consent signed by the participant and/or the participant’s legal guardian [45CFR46.116(a)]

❏ Oral consent statements or written study overview not requiring documented signature by the participant 

or participant’s legal guardian [45CRF46.117(c)] 

❏ Waiver of consent [45CRF46.116(d) and 45CFR164.512]

6 Confidentiality/Autonomy

PLEASE NOTE:

Federal certificates of confidentiality may be issued for studies that could expose subjects and their families to adverse economic, psychological and social consequences were an investigator compelled to disclose identifying information in any civil,

criminal, administrative, legislative or other proceeding whether federal, state, or local. Contact the Office for Human Research

Protection for additional guidance about obtaining a certificate of confidentiality.
Appendix 79

6.1 Describe the steps taken to assure that the identities of participants will be kept confidential. What safeguards are used to

protect against identifying, directly or indirectly, any participant in any report of the research project?

6.2 Will data with participant identification be available to any of the following?

❏ Study staff ❏ IRB

❏ NIH/NCI ❏ FDA

❏ Drug company (sponsor) ❏ Other (specify): 

6.3 Is identifying information required for the purposes of another research project?

❏ Yes ❏ No

a. If yes, please describe this project:

7 Research Procedures

7.1 List, in sequence, a brief description of procedures that will be followed in this activity/protocol:

7.2 Will patients receive intervention or follow-up care outside of the local institution under the auspices of a non-local physician?

❏ Yes ❏ No

a. If yes, please explain:

7.3 Will your study involve collection and analysis of biological specimens for research purposes?

❏ Yes ❏ No

If yes, please respond to each of the following:

a. Are the results currently useful for predicting the [future] occurrence or prognosis of disease?

❏ Yes ❏ No

If yes, please explain:

b. Will the results be potentially useful for predicting the [future] occurrence or prognosis of disease?

❏ Yes ❏ No

If yes, please explain:

c. Will the results potentially be used for predicting disease risk/susceptibility in family members?

❏ Yes ❏ No

If yes, please explain:
80 The Early Detection Research Network

d. Will the research test results be provided to the research participant?

❏ Yes ❏ No

If no, please explain:

If yes, confirm that tests will be performed in a clinically certifiable manner (i.e., CLIA approved laboratory), and then

explain how such information will be provided to each participant and his/her health care provider, including pre- and posttest counseling, if applicable. Also describe what steps will be taken to assure access to this information does not

jeopardize the participant’s privacy or confidentiality and/or medical record.

❏ Yes, tests will be clinically certifiable ❏ No, tests will not be clinically certifiable

7.4 Will your study include collection/storage of biological specimens for future research analysis?

❏ Yes ❏ No

If yes, please respond to each of the following:

a. What types of specimens will be stored? 

❏ Blood fractions (e.g., whole blood, serum, plasma, buffy coat, blood spots)

❏ Tumor samples

❏ Bone marrow ❏ Saliva ❏ Urine

❏ Buccal swabs ❏ Sputum ❏ Extracted DNA/RNA

❏ Establishment of permanent cell lines

❏ Other specimen, specify: 

b. Can any of these specimen be used to derive DNA/RNA? ❏ Yes ❏ No

c. Does your informed consent address this future use?

❏ Yes ❏ No

If yes, be sure the consent document includes a clear description of

1) the operation of the cell repository,

2) the specific types of research to be conducted,

3) the conditions under which data and specimens will be released to recipient-investigators, and

4) procedures for protecting the privacy of subjects and maintaining the confidentiality of data.

If no, please explain:

d. If participant withdraws, what happens to the specimen and/or data?

7.5 Will HIV serostatus be evaluated or be an eligibility criteria for participants on this study?

❏ Yes ❏ No

If yes, please provide the rationale for testing and an outline of procedures to be followed:
Appendix 81

7.6 Will your activity involve the collection of family medical history information?

❏ Yes ❏ No

If yes, please respond to each of the following:

a. Are personal identifiers (i.e., first and last name, address, phone number) collected on family members?

❏ Yes ❏ No

b. Will you have access to personal identifiers?

❏ Yes ❏ No

If no, who does have access to the identifiers?

c. How will this family history information be used?

d. Is family history information (e.g., disease status of members) shared among family members as part of this study?

❏ Yes ❏ No

If yes, does the consent form describe this activity?

If no, what steps will be taken to assure participant confidentiality (including that of the relatives) be protected?

7.7 Are there specific components of this study that will be provided free of charge to the participant?

❏ Yes ❏ No

If yes, please explain and include details in the informed consent:

7.8 Are there additional expenses related to this protocol?

❏ Yes ❏ No

If yes, please explain and include details in the informed consent:

7.9 Will participants be paid or otherwise compensated for research participation?

❏ Yes ❏ No

If yes, please respond to the following:

a. What is the amount of compensation? $__________

b. If not monetary, what will be used for compensation?

c. What is the reason for compensation?

d. Who is responsible for preparing the compensation and how is the participant’s confidentiality protected?
82 The Early Detection Research Network

8 Health and Safety Considerations

8.1 Will materials with potential radiation risk be used either in vitro or in vivo?

❏ Yes ❏ No

If yes, please describe type of radiation below and provide the Memorandum of Understanding (MUA) approval date and

letter for each institutional site:

8.2 Will this study involve the use of recombinant DNA?

❏ Yes ❏ No

If yes, please provide EMUA approval date and letter for each institutional site:

8.3 Will this study involve use of a select carcinogen?

❏ Yes ❏ No

If yes, please provide CMUA approval date and letter for each institutional site:

8.4 Will this study involve direct gene transfer?

❏ Yes ❏ No

If yes, please provide approval date and letter for each institutional site:

9 Risk vs. Benefit Analysis

9.1 Summarize the nature and amount of risk (including side effects) or substantial stress or discomfort involved. Examples of

risk include physical risks, psychological risks (such as substantial stress, discomfort, or invasion of privacy), and social

risks (such as jeopardy to insurability or employability).

9.2 Summarize the overall plans for minimizing risks.

9.3 Describe the potential benefits of the research for the individual subjects.

9.4 Describe the potential benefits of the knowledge gained to others.

9.5 Explain how the potential benefits of the research outweigh the potential risks and how this is acceptable.
Appendix 83

10 Alternatives

10.1 If the study involves clinical interventions, please identify alternative procedures, if any, not proposed for this study that

might be advantageous to the participant.

11 Investigational New Drugs

11.1 Does this study involve an Investigational New Drug (IND)?

❏ Yes ❏ No

If yes, please provide the following:

a. Drug name______________________________________ 

b. IND#___________________________________________ 

c. Holder of IND____________________________________ 

d. FDA Status (o I, o II, o III, or o IV)

e. Dosage_________________________________________ 

f. Responsible PI at each institutional site:______________

________________________________________________

________________________________________________

________________________________________________

12 Investigational Devices

12.1 Does this study involve an Investigational Device (IDE)?

❏ Yes ❏ No

If yes, please provide the following:

a. Device name_____________________________________ 

b. IDE#____________________________________________ 

c. Holder of IDE_____________________________________ 

d. Responsible PI at each institutional site:______________

________________________________________________

________________________________________________

________________________________________________
84 The Early Detection Research Network

12.2 Is this a significant risk or non-significant risk device?

❏ Significant risk device ❏ Non-significant risk device

13 Monoclonal Antibodies and Biologics

13.1 If monoclonal antibodies and/or biologics are use, which ones?

14 Conflict of Interest

14.1 Does the investigator or any other person responsible for the design, conduct, or reporting of this research have an economic interest in or act as an officer or director of any outside entity whose financial interest would reasonably appear to

be affected by this research?

❏ Yes ❏ No

If yes, please answer the following:

a. Has the investigator’s involvement with the outside entity been approved by institutional compliance offices?

❏ Yes ❏ No

b. If the economic interest is significant, has a plan for eliminating or managing any conflict been approved?

❏ Yes ❏ No

If yes, please describe the plan and attach a copy of the approval:

15 Local EDRN Member Institution Requirements

15.1 Use this section to identify state laws, local policies, and requirements of your local IRB (for example, 1572 forms, genetic

testing laws, health care information laws).
Appendix 85

16 Investigator’s Statement

As Principal Investigator, I acknowledge:

a That I am responsible for reporting any emergent problems, any adverse effects or reactions, or proposed procedural modifications and that no modifications will be put into effect without prior Institutional Review Board (IRB) approval except where

necessary to eliminate apparent immediate hazards; 

b) That unless otherwise directed by the IRB Chairperson, I will renew this application with the IRB at least annually; 

c) That the research project is being conducted in compliance with the IRB’s understanding and recommendations; 

d) That the IRB is provided all the information on the research project necessary for its complete review; and

e) That this research project will not be put into effect until final IRB approval is received.

Signature of Principal Investigator Date

17 Checklist – Order of Attachments

17.1 Protocol/Activity Plan and Grant Application

❏ Yes ❏ N/A 

If N/A, please provide an explanation below:

17.2 Letters of Approach/Recruitment

❏ Yes ❏ N/A 

If yes, list below in sequential order:

17.3 Consent Forms

❏ Yes ❏ N/A 

If yes, list below in sequential order:

17.4 Questionnaires

❏ Yes ❏ N/A 

If yes, list below in sequential order:
86 The Early Detection Research Network

17.5 List of performance sites and/or off-site investigators participating in this protocol

❏ Yes ❏ N/A 

If yes, list below in sequential order:

17.6 Review and Approval through the Recombinant DNA Advisory Committee (RAC)

❏ Yes ❏ N/A 

If yes, please provide a copy of the completed "Points to Consider in the Design and Submission

of Human Somatic-Cell Gene Therapy Protocols" prepared for the RAC:

17.7 Relevant Health and Safety Office’s Approval Letters, including radiation safety and biologic

safety letters

❏ Yes ❏ N/A 

If yes, list below in sequential order:

17.8 Investigational New Drugs Investigator’s Brochure, drug booklet, or information sheet supplied

by the drug company (sponsor)

❏ Yes ❏ N/A 

17.9 Other pertinent information

❏ Yes ❏ N/A 

If yes, list below in sequential order:
Acknowledgments 87

Acknowledgments

Dr. Srivastava would like to acknowledge the 

following individuals for their assistance in the 

creation of this Second Report:

Barry Kramer, M.D.

Director

Office of Medical Applications of Research

National Institutes of Health

Kara Smigel Croker, M.S.

Communications Manager

Division of Cancer Prevention

National Cancer Institute

Gwen Moulton

Editorial Consultant

GM Authoring Services

Wendy Wang, Ph.D.

Program Director

Cancer Biomarkers Research Group

Division of Cancer Prevention

National Cancer Institute

Fidelia Acevedo

Administrative Program Assistant

Cancer Biomarkers Research Group

Division of Cancer Prevention

National Cancer Institute
88 The Early Detection Research Network