National Cancer Institute

The Early Detection

Research Network 

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

National Institutes of Health

Fourth Report • JANUARY 2008

Division of Cancer Prevention

Investing in Translational Research on 

Biomarkers of Early Cancer and Cancer Risk
2 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk
Contents

5 Foreword 

7 Introduction

8 Executive Summary 

Part I: Progress and Disease-Specific Developments

14 Chapter 1 Overview

26 Chapter 2 Breast and Gynecologic Cancers 

34 Chapter 3 Colorectal and Other Gastrointestinal Cancers

47 Chapter 4 Lung and Upper Aerodigestive Cancers

56 Chapter 5 Prostate and Other Urologic Cancers

Part II: Process and Collaboration 

66 Chapter 6 Validation Stages and Processes

77 Chapter 7 Enabling Technologies

Part III: Investing in Biomarker Research

91 Chapter 8 Business Model

99 Chapter 9 Evaluating Biomarker Progress in Translational Research 

104 Chapter 10 Investing in Biomarker Research for Early Detection

Appendix

115 I. Key Publications by Investigators

123 II. Publications Co-Authored by NCI Program Staff

124 Glossary

 3
4 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk
Foreword

January 2008

In 2000, NCI’s Division of Cancer Prevention created an investigatordriven network designed to conduct translational research that identified 

markers both for the early detection of cancer and of cancer risk. That 

program, the Early Detection Research Network (EDRN), focuses on the 

goal of creating validated biomarkers ready for large-scale clinical testing and eventual application. Without a doubt, real progress has been 

made—and is being made—by this consortium of more than 300 investigators and 40 private sector and academic institutions. These scientists 

represent divergent disciplines, including genomics, proteomics, metabolomics, bioinformatics and public health.

EDRN is at the forefront of technology-driven research on the use of 

biomarkers for the early detection of cancer. By identifying and validating biomarkers, such as novel proteins or changes in gene expression, it 

is possible to measure an individual’s disease risk, progression of disease, 

or response to therapy. Ultimately, EDRN research will aid in prevention 

and in early therapeutic intervention, based on early detection of disease.

Researchers with EDRN have been instrumental in identifying and 

validating markers for many major cancers, such as prostate (protein 

profiling of BPH, HPIN and IGFb3/br), colon (K-ras mutations in stool 

and urine) and breast (alpha catenin genes). They have also joined forces 

with clinical trial communities to accelerate biomarker validation. To 

take just one example, EDRN investigators work with investigators in 

the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening 

Trial and in the Specialized Programs of Research Excellence (SPORE) 

program, to test a panel of biomarkers for ovarian cancer in sera collected 

in the PLCO trial.

Early detection can dramatically improve outcomes. Finding breast and 

colon cancers when they remain localized results in 5-year survival rates 

of 90 percent or higher. EDRN is helping make that an achievable goal 

for more and more cancers.

John Niederhuber, M.D.

Director

National Cancer Institute

National Institutes of Health

Foreword 5

NCI’s Division of Cancer Prevention set out 7 years ago to create a 

strong, investigator-driven network to conduct translational research to 

identify tests for early cancer and cancer risk. In 2000, the Early Detection Research Network (EDRN) became a fully funded group of 28 

grantees focused on the overarching goal of creating validated biomarkers 

ready for large-scale clinical testing. 

Today, EDRN is a nationwide, interdisciplinary group of established 

partnerships among scores of institutions and hundreds of individuals 

working to advance the science for public benefit.

These research collaborations take place within an environment of teamwork across different disciplines and laboratories focused on achieving 

common goals, such as: 

• Developing and testing promising biomarkers and technologies to obtain preliminary information to guide further testing; 

• Evaluating promising, analytically proven biomarkers and technologies, 

such as measures of accuracy, sensitivity, specificity and, when possible, 

as potential predictors of outcomes or surrogate endpoints for clinical 

trials; 

• Analyzing biomarkers and their expression patterns to serve as background for large, definitive validation studies; 

• Collaborating with academic and industrial leaders to develop highthroughput, sensitive assay methods; 

• Conducting early phases of clinical and epidemiological biomarker 

studies; and

• Encouraging collaboration and dissemination of information to ensure 

progress and avoid fragmentation of effort. 

EDRN is a leader in defining and using criteria for the validation of 

biomarkers—an essential condition for scientific progress. While myriad 

proteins and genes have been linked with a variety of cancers, acceptable 

biomarkers must be: reliable and repeatable in testing; highly sensitive 

and specific; quantitative; readily obtained by non-invasive methods; part 

of the causal pathway for disease; capable of being modulated by the chemopreventive agent; and have high predictive value for clinical disease. 

EDRN is helping translate the discovery and validation of biomarkers to 

clinical use and we are delighted to be working toward that end. 

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

Division of Cancer Prevention, National Cancer Institute 

Assistant Surgeon General, U.S. Public Health Service 

Introduction

Introduction 7
The National Cancer Institute (NCI) is 

bringing visionary people together through 

research collaborations that inspire innovative 

approaches to early detection, prevention and 

treatment of cancer. 

NCI launched the Early Detection Research 

Network (EDRN) (http://edrn.nci.gov/) in 

2000 to identify biomarkers, substances found 

in blood, body fluids or tissue that show the 

risk or presence of disease before cancer has 

had the opportunity to progress in the body. 

EDRN is the only program focused directly 

on the discovery and validation of biomarkers 

for noninvasive, early detection of cancer. 

The Network unites clinical and basic 

scientists so that discovery is clinically driven, 

yet balanced with a systematic approach 

to validation.

Recent reductions in cancer mortality are 

due in part to risk reduction behaviors like 

smoking cessation and more strongly to early 

detection of cancer coupled with appropriate 

therapy. Yet, there are no validated molecular 

biomarker tests for the early detection of any 

cancer (see Table I). Among the list of Food 

and Drug Administration (FDA)-approved 

biomarkers, none have been approved for 

cancer early detection and screening. EDRN 

is studying more than 120 biomarkers for the 

major organ system groups (see Table 2), some 

of which are in Phase 3 testing, a retrospective 

longitudinal approach that determines how 

well biomarkers detect preclinical disease 

by testing them against tissues collected 

longitudinally from research cohorts. 

Investigators from more than 40 research 

institutions are part of the Network. All 

share a common belief that the integration 

of discovery, evaluation and clinical validation 

phases of medical research are more likely 

to succeed when they are carried out in a 

concerted and systematic fashion. A common 

problem is that after researchers discover 

biomarkers, the biomarkers are not produced 

for clinical use because they have not been 

validated in other laboratories. To address this, 

EDRN drew up and implemented standards 

to accelerate the progress for discovering 

and validating reproducible biomarkers that 

ultimately can be moved on to clinical use. 

Through cooperative agreement awards, NCI 

is closely involved in the EDRN projects 

to ensure the studies gather necessary data. 

EDRN welcomes other interested researchers 

to join the Network through smaller scale 

Table 1. Early Detection Tests 

for Cancer, Selected Organ Sites 

Organ Site Test

Bladder None

Breast Mammogram

Cervix Pap smear

Colorectal Fecal occult blood test, 

sigmoidoscopy, colonoscopy, 

double contrast barium 

enema, digital rectal exam

Esophageal None

Kidney None

Liver (primary) None, but two molecular 

tests are approved for risk 

assessment

Lung Imaging

Ovary None proven to decrease 

mortality

Pancreatic None

Prostate None proven to decrease 

mortality

Executive Summary

8 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk
projects. The Network is challenged to 

motivate scientists to offer their candidate 

biomarkers for testing and to educate 

scientists about the importance of rigorous 

prevalidation studies that prepare the way for 

successful biomarker validation.

This report, the fourth in a series, summarizes 

the major developments in the Network since 

its inception through a discussion of concepts 

and concrete examples, beginning with a 

historical and structural overview. It also 

shows how progress has occurred in the areas 

of:

• Disease-specific advancements across the 

major organ sites;

• Process and collaboration; and

• An adaptive business model approach that 

encourages public-private partnerships and 

team science. 

Disease-Specific Advancements

EDRN has active ongoing work in cancer 

sites that constitute nearly 1 million cancer 

diagnoses each year and more than 350,000 

deaths.

Biomarkers in development by EDRN 

address common malignancies as well as 

mesothelioma and hepatocellular cancer. 

The latter are of major worldwide importance 

and are increasing in incidence in the United 

States. EDRN Collaborative Groups, 

focused on breast and gynecologic cancers, 

gastrointestinal and other associated cancers, 

lung and upper aerodigestive cancers and 

prostate and urologic cancers, each have 

biomarkers in prevalidation and validation 

phases in which the accuracy of experimental 

results is confirmed. 

There are over 120 biomarkers in 

development, alone and in combinations, 

across the EDRN phases: 27 in Phase 2 

development (validating the capacity of 

biomarkers to distinguish between people with 

cancer and those without), of which, more 

than 15 are progressing toward Phase 3; and 

five in Phase 3 development (determining the 

capacity to detect preclinical disease).

Highlights of EDRN achievements include:

• Standard reference specimens and reagents, 

primarily plasma and serum (cases and 

matched controls) were developed for 

detection and evaluation of prostate cancer 

biomarkers; urine reference sets are being 

developed for bladder, prostate, colon and 

lung cancers.

• Recurrent non-random chromosomal 

translocations were discovered in prostate 

cancer along with some other potential 

markers, such as %proPSA, PCA3, 

AMACR and a panel of autoantibodies; 

panels of methylated DNA sequences and 

other biomarkers have been identified 

as promising biomarkers for bladder 

and prostate cancers; and mutations and 

deletions in mitochondrial DNA were 

detected in prostate and other cancers.

• Molecular tests for ovarian cancer are 

progressing towards validation; one of 

the tests included a panel of markers 

consisting of MIF-1, prolactin, osteopontin, 

IGF-2, leptin, HE-4 and others. Studies are 

underway targeting pre-cancers of the cervix 

to improve outcomes and reduce treatments; 

and novel strategies against breast cancer, 

including early detection using blood 

markers, will be tested in the next year. 

Table 2. Early Detection Biomarkers in 

Study for Selected Cancer Sites 2003 

to 2007 (partial list; see organ specific 

chapters for details) 

Site Number of Biomarkers *

Bladder 3

Breast 7

Cervical /Endometrial 2

Colorectal 21

Esophagus 7

Hepatocellular 9

Kidney 1

Lung 12

Mesothelium 2

Ovarian 5

Pancreatic 16

Prostate 15

* Panels including more than one biomarker were counted 

as one.

Executive Summary 9
• For each digestive cancer organ site (colon, 

rectum, esophagus, liver and pancreas), new 

biomarkers have been discovered and, in 

prevalidation studies, have been shown to be 

superior to current standards of care. Two 

of these biomarkers for colorectal cancer, 

CCSA-2 and CCSA-3 and two biomarkers 

for liver cancer, DCP and AFP-L3, are now 

in clinical validation. 

• Work is advancing to identify and validate 

non-invasive biomarkers in blood or sputum 

for the early detection of lung cancer, which 

could be combined with CT scanning of 

the lung or other imaging methods. In two 

preliminary blinded experiments, a panel 

of only two marker genes readily identified 

lung cancers at specificity and sensitivity 

values exceeding those of conventional 

cytology by two to three times. 

• Investigators supported through various 

funding mechanisms (e.g., EDRN, R01, 

P01 and Specialized Programs of Research 

Excellence (SPOREs) ) have formed a 

Lung Cancer Biomarkers Working Group. 

This group is developing and validating 

proteomics-based biomarkers for early 

detection of lung cancer and collaborating 

with other researchers by providing 

statistically powered specimen sets for rapid 

evaluation of emerging technologies and 

biomarkers.

Some biomarker discoveries are performed 

in tandem with prevalidation studies using 

high-quality specimens made available 

to investigators by other NIH supported 

programs, such as the Women’s Health 

Initiative (WHI) for a colon cancer project; 

the Carotene and Retinol Efficacy Trial 

(CARET) for a lung cancer and mesothelioma 

project; and the Prostate, Lung, Colorectal 

and Ovarian Cancer Screening Trial (PLCO) 

for an ovarian cancer project. Leads on other 

biomarkers from model systems are being 

tested in humans.

Process and Collaboration

Validation of biomarkers is a formidable task, 

which needs a consistent approach. EDRNsupported validation studies are, therefore, 

remarkable achievements. Few biomarkers 

and developmental laboratories ever achieve 

the requirements necessary to conduct such 

studies. But EDRN brings to the table 

both the scientific paradigm and the ability 

to effectively organize the resources. Five 

case-control studies described in this report 

illustrate this capacity. EDRN also adopted 

criteria to prioritize analytical and clinical 

validation studies. 

Quality assurance is integral to EDRN. The 

Network established five Biomarker Reference 

Laboratories (BRLs) to support clinical and 

analytical validation efforts: the University of 

California, Los Angeles (UCLA), University 

of Alabama, Birmingham (UAB), Johns 

Hopkins University (JHU), the University of 

Maryland (UM) and the National Institute 

of Standards and Technology (NIST). The 

BRLs are important resources for technology 

development, standardization of biomarkers 

and the refinement of existing methods. Some 

BRL projects include: 

• Validation of bleomycin-induced 

chromosomal breakage in lymphocytes as 

markers of lung cancer susceptibility; 

• Validation of mitochondrial DNA mutations 

as an early detection marker; 

• Development of high-density breast and 

prostate tissue microarrays; 

• Validation of saliva-based assay for oral 

cancer, refinement of ELISA-based assay for 

ovarian biomarker panel; 

• Validation of standard operating procedures, 

MSA assays, methylation assays; and

• Validation of several prostate-specific 

biomarkers, assays and proteomics-based 

discoveries.

EDRN develops and optimizes technologies 

for biomarker research. Innovative methods 

to identify gene alterations, gains and 

mutations and mitochondrial DNA mutations 

have been used. Proteomics, auto-antibodies, 

microsatellite analyses, immunohistochemical 

markers, polymerase chain amplification of 

RNA and glycobiology are also employed. 

Advances were made in deploying and 

expanding an informatics framework to 

support information management. Accessing 

the information includes specific annotations 

of markers, the capture of scientific data, 

management of the study-specific information 

10 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk
and a scientific portal. A major new release 

integrated with a scientific portal was 

deployed in 2007. 

One of the signature accomplishments of 

the informatics team is the development 

of common data elements (CDEs) for use 

among the EDRN Clinical Epidemiology and 

Validation Centers (CEVCs). CDEs capture 

and share data among centers. State-of-theart methods that previously did not exist 

have been established for data elements, e.g. 

acquisition and storage of biologicals, study 

design, outcome assessment and biomarker 

validation.

Each EDRN institution within the knowledge 

system uses CDEs to describe critical cancer 

data objects and to map their local data 

models to the Network’s knowledge system, 

in turn providing Network-wide semantic 

consistency. At the same time, the EDRN 

Network Exchange system (ERNE) unified 

search and retrieval of biospecimen data 

from all institutions regardless of their 

location, how it is stored, or the differences 

in the underlying data models. This enables 

a scientist, for example, to locate tissue 

specimens for breast cancer by searching data 

catalogs at participating EDRN institutions 

across the country. 

EDRN-supported statistical tools and 

informatics infrastructure make the sharing 

of samples, the developing of collaborations 

and the exchanging of information with the 

extramural community at-large, both feasible 

and productive. The EDRN informatics 

efforts were cited as a model in reports by 

the National Academy of Sciences Institute 

of Medicine, Developing Biomarker-Based 

Tools for Cancer Screening, Diagnosis and 

Therapy: The State of the Science, Evaluation, 

Implementation and Economics (Margie 

Patlak and Sharyl Nass, 2006) and Cancer 

Biomarkers: The Promises and Challenges of 

Improving Detection and Treatment, (Sharyl J. 

Nass and Harold L. Moses, Editors, 2007).

EDRN developed a secure, web-based 

system, the Validation Studies Information 

Management System (VSIMS), to manage 

the necessary components for capturing and 

preserving the metadata and data objects that 

integrate into the overall knowledge system 

architecture. These components include 

protocol management tools, communication 

tools, a data-collection and -processing system 

and a specimen-tracking system.

EDRN is establishing a science data 

warehouse, which will act as a distributed 

metadata-driven system to capture, track, 

process and retrieve scientific data from 

biomarker validation studies and to share 

across institutions. The EDRN Knowledge 

System promises to dramatically improve the 

capability for scientific research by enabling 

real-time access to a variety of information 

across research centers.

Adaptive Business Model

The core of EDRN’s achievements is 

the Vertical Adaptive Business Model. 

This structure encourages public-private 

partnerships and team science. EDRN 

promotes a vertical approach for conducting 

biomarker research, whereby biomarkers 

are developed in BDLs, refined and cross 

validated by Biomarker Reference Laboratories 

(BRLs) and validated in collaboration with 

CEVCs, all within one organization. The 

focus is on coordinating multiple resources 

with a goal of minimizing the barriers to 

the rapid and efficient “hand-off” between 

entities.

Five federal agencies—NIST, the Centers for 

Disease Control and Prevention, FDA, the 

Pacific Northwest National Laboratories of 

the Department of Energy and the National 

Aeronautics and Space Administration (NASA) 

Jet Propulsion Laboratory (JPL)—participate 

with EDRN through interagency agreements. 

Other intergovernmental collaborative 

partnerships include the National Heart, 

Lung and Blood Institute (NHLBI) on the 

use of the Women’s Health Initiative (WHI) 

biorepository for discovery and validation 

of biomarkers; the collaboration with the 

Consortium of Functional Glycomics, funded 

by National Institutes of Health’s (NIH) 

National Institute of General Medical Sciences 

(NIGMS) and four carbohydrate research 

centers funded by NIH’s National Center 

for Research Resources (NCRR).

Executive Summary 11
EDRN unites partners with different 

research foci, resulting in productive and 

stable alliances to expedite discovery and 

development of biomarkers and technologies. 

For instance, JPL, known for rocket 

launching, joined forces with EDRN to bring 

disparate groups of institutions together 

by creating virtual resources of specimens, 

biomarkers, tools and technologies, 

through innovative uses of their informatics 

infrastructures already validated and proven 

for the management of planetary data. 

Another unlikely alliance is NIST and EDRN. 

NIST is traditionally known for research 

on physical sciences and standards, not for 

diagnostics. By joining EDRN, NIST has 

taken an interest in developing standards for 

genomics- and proteomics-based diagnostics.

EDRN fosters collaborations with industry. 

During its inception, EDRN worked with 

NCI’s Technology Transfer Center to develop 

novel methods for sharing confidential 

information with industry and EDRN’s 

Technology Resources Sharing Committee 

developed guidelines for working with 

industry. EDRN also conducted a workshop 

on public-private partnerships. Collaborations 

with the Human Proteome Organization on 

proteomics and glycomics, the Lustgarten 

Foundation on pancreatic cancer biomarkers 

and the Canary Foundation on ovarian cancer 

markers are yielding results.

EDRN enables alliances of investigators 

with differing expertise, disciplines and 

organizational cultures to function as 

cohesive, integrated groups for the purpose 

of developing biomarker-based diagnostics. 

This Network of discovery, validation and 

epidemiologic centers that place collective 

goals above individual goals is without 

peer among academic institutions. Unlike 

previous approaches in the field, EDRN 

rewards collaboration and individual skills 

and thereby is likely to succeed in meeting the 

new research realities involved in translational 

research.

EDRN builds standards in study designs for 

the systematic evaluation of protein profiling 

for cancer. The Network developed standards 

of organization and collection for tissue 

procurement for biomarker studies. Aspects 

of the standards are recognized as best 

practices in the field for sharing and 

dissemination within an informatics network 

exchange system (National Biospecimen 

Network Blueprint from the Constella Group 

and the Case Studies of Human Tissues 

Repositories: “Best Practices” for Biospecimen 

Resource for Genomic and Proteomic Era

(Eiseman E., et.al., RAND Corporation)).

The number of peer-reviewed publications by 

EDRN-funded investigators is an important 

metric to illustrate progress toward the 

Network’s goals. More than 460 manuscripts 

have been published by EDRN investigators 

and program staff in the past 6 years. Seminal 

articles on proteomics, fusion genes in the 

prostate and methylation have received wide 

citations. 

When EDRN was created, NCI embarked 

on a new organizational structure unique 

to academic science. EDRN created a 

rigorous peer-review system that ensures 

that preliminary data—analytical, clinical 

and quantitative—are of excellent quality. 

Additionally, the Associate Membership 

Program is highly productive in offering new 

technologies and products.

Past, Present, Future

The progression of biomarkers from the 

discovery phase to the validation phase has 

been slow to date, reflecting initial challenges 

with cultural and infrastructural issues. 

Without EDRN, research into new 

biomarkers of early cancer detection and 

risk would have remained on the periphery 

of research with a strong, but fragmented 

laboratory presence and little translational 

interest among the academic scientific 

community. But with the Network, a new 

translational paradigm is defining the 

organization, approaches and standards by 

which biomarkers are developed and assessed. 

The Network’s publications, meetings, 

funding opportunities and infrastructure 

have fashioned a new environment for cancer 

prevention research. 

12 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk
Title of chapter goes here 13

Part One

Progress and DiseaseSpecific Development 
I

n its first 7 years, the Early 

Detection Research Network (EDRN) 

evolved from ground-breaking concept 

to operational success. With a primary 

mission to discover and scrupulously validate 

biological markers that signal the earliest 

stages of cancer (such as pre-malignant lesions, 

genetic variations and risk indicators) EDRN 

combines collaborative and multidisciplinary, 

investigator-initiated projects with a strong 

administrative and data infrastructure.

In making cancer biomarkers of early 

detection and screening ready for large-scale 

clinical testing, the Network requires and 

supports collaboration and information 

sharing across institutions. Key milestones 

from inception to the present are described in 

this chapter.

1997 through 2000: 

Inception and Inauguration

In 1997, a 20-member Cancer Prevention 

Program Review Group, seeking a means to 

revitalize the National Cancer Prevention and 

Control Program, recommended the concept 

of EDRN to NCI’s Board of Scientific 

Advisors (BSA) and the National Cancer 

Advisory Board (NCAB). (See EDRN Initial 

Report, Translational Research to Identify Early 

Cancer and Cancer Risk, October 2000, http://

edrn.nci.nih.gov/docs.)

The concept, developed by the Early 

Detection Implementation Group, was 

approved by the BSA on November 13, 

1998. A Network was envisioned that would 

discover and coordinate the evaluation of 

biomarkers and reagents for risk assessment 

Overview

“T he EDRN was designed with a very specific and tangible goal in mind. This 

has not changed since its inception. For this reason, the network is efficient and 

functions true to its origin. Further, since it is fully functional, there is little effort 

wasted on operational issues. The operations manual was adopted early and 

remains a viable document. Under any context, these are remarkable properties, 

that it was created by a governmental agency is nearly unimaginable. With 

academic scientists and clinicians working under cooperative agreements, not 

contracts, to specifically further the goals of the network, not just their personal 

goals, the arrangement becomes even more unlikely.” 

 Jeff Marks, Ph.D. 

 Principal Investigator, 

EDRN Biomarker Development Laboratory

Duke University Medical Center

14 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

C hapter O ne
Title of chapter goes here 15

Recent Milestones* 

March 2003 EDRN Approved for Second 5-Year Cycle

July 2003 Validation Study Launched: SELDI Profiling for Prostate Cancer

August 2003 Gordon Conference on New Frontiers in Cancer Detection and Diagnosis, Andover, NH

September 2003 Launch of the First Clinical Validation Study for Microsatellite Instability as a Biomarker for Bladder Cancer

March 2004 Training Workshop on the Analysis of Proteomic Spectral Data including SELDI/MALDI-TOF-MS Applications; 

Review of SELDI Phase 1, Seattle, WA

June 2004 Third Annual Scientific Workshop, Bethesda, MD

September 2004 EDRN Outreach Meetings:

 Breast/GYN Collaborative Group Meeting, New York, NY 

 GI Collaborative Group Meeting, Norfolk, VA

 GU Collaborative Group Meeting, Houston, TX

 Lung Collaborative Group Meeting, Denver, CO

January 2005 Gordon Conference on New Frontiers in Cancer Detection and Diagnosis, Santa Barbara, CA

March 2005 Tenth Steering Committee Meeting, Bethesda MD

September 2005 Eleventh Steering Committee Meeting, Seattle, WA 

August 2005 NIST-EDRN Workshop on Standards and Metrology for Cancer Diagnostics, Gaithersburg, MD

January 2006 EDRN Pancreatic Implementation Meeting, Denver, CO 

February 2006 EDRN Lung Implementation Team Meeting, Rockville, MD 

March 2006 Twelfth Steering Committee Meeting and 4th Annual Scientific Workshop, Philadelphia, PA

September 2006 Thirteenth Steering Committee Meeting, Pittsburgh, PA

October 2006 EDRN and Hepatitis B Foundation Workshop, Princeton, NJ

January 2007 Gordon Conference on New Frontiers in Cancer Detection and Diagnosis, Ventura, CA

February 2007 EDRN FDA Education Workshop Bethesda, MD

March 2007 Fourteenth Steering Committee Meeting, Denver, CO

April 2007 AACR Session on Novel Technologies and Validation Challenges, Los Angeles, CA

May 2007 NCI Division of Cancer Prevention Workshop on Cancer Stem Cells as Targets for Cancer Prevention 

and Early Detection, Bethesda, MD

* See previous reports for earlier milestones.
and early detection of cancer in primary 

organ systems, such as prostate, breast, lung, 

colorectal and upper aerodigestive tract. To 

accomplish this vision, the Network would:

• Develop and test promising biomarkers and 

technologies in institutions with outstanding 

scientific and clinical expertise;

• Evaluate promising biomarkers for 

diagnostic predictive accuracy, sensitivity, 

specificity and medical benefits;

• Develop molecular and expression markers 

to serve as background information for 

subsequent large definitive validation

studies of detection and screening 

biomarkers;

• Coordinate academic and industrial leaders 

in molecular biology, molecular genetics, 

clinical oncology, computer science, public 

health and other disciplines to develop highthroughput, sensitive assay methods;

• Conduct early phase clinical and 

epidemiological studies to evaluate the 

predictive value of biomarkers; and

• Encourage collaboration and rapid 

dissemination of information among 

participants to aid progress and avoid 

fragmentation of efforts.

A structure emerged (see Figure 1-1) 

with working components comprised of 

laboratories and validation centers and data 

management centers and two oversight 

components, a Steering Committee and a 

Network Consulting Team. The business 

model for this structure is discussed in 

Chapter 8.

Figure 1-1. Infrastructure of the Early Detection Research Network

This schematic outlines the EDRN infrastructure for supporting translational research on 

molecular biomarkers for cancer detection and risk assessment.

Working Groups

Associate

Members

Steering

Committee

Biomarker

Reference

Laboratories

Biomarker

Developmental

Laboratories

Clinical 

Epidemiology 

and Validation 

Centers

Informatics

Center

Data Management

And Coordinating

Center

Collaborative

Groups

Breast and Gynecologic

Subcommittees/ Taskforces

Colorectal and Other

Gastrointestinal Cancers 

Lung and Upper 

Prostate and Other Aerodigestive Tract

Genitourinary

Translational Research

16 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk
The Biomarker Developmental Laboratories 

(BDLs) were designed to develop and 

characterize new biomarkers, or refine 

existing biomarkers, by conducting active 

translational research in the biology of 

cancer formation. It was expected that 

discoveries would move from laboratory to 

clinical and population research settings and 

that observations from these settings would 

move back to the laboratory for further 

refinements as needed. 

The Biomarker Reference Laboratories 

(BRLs) were planned to serve as a resource 

for both laboratory and clinical validation 

of biomarkers, in the areas of technology 

development, standardized assays and 

methods, refinement and high-throughput 

operations. BRLs were also responsible for 

instituting quality control for reagents and 

technologies.

The Clinical Epidemiology and Validation 

Centers (CEVCs) were established to 

conduct and support early phases of clinical 

and epidemiological research on biomarker 

applications. Approved projects were soon 

started to look at a range of issues, including: 

resources and methods for rapid clinical 

evaluation of risk and disease biomarkers; 

defining molecular signatures predictive of 

neoplastic progression in cervical lesions; 

clinical utility of certain prostate cancer 

biomarkers; developing and maintaining a 

registry of individuals harboring germline 

mutations for hereditary cancer syndromes; 

and identifying preneoplastic lesions and 

early cancer in populations at risk due to 

environmental and occupational exposures.

To manage the flow of information across 

the network, the Data Management and 

Coordinating Center (DMCC) and an 

Informatics Center, managed by the Jet 

Propulsion Laboratory (JPL) at the National 

Aeronautics and Space Administration 

(NASA) were established. These entities were 

designed to support statistics, logistics and 

informatics and develop theoretical statistical 

approaches for pattern analysis of multiple 

biomarkers simultaneously. DMCC also 

coordinates network-wide meetings and 

conferences and serves as the Coordinating 

Center for validation studies. (See Margaret 

Sullivan Pepe, The Statistical Evaluation of 

Medical Tests for Classification and Prediction, 

Oxford Statistical Science Series Number 28, 

Oxford University Press, 2003.)

A Steering Committee, comprised of the 

Network’s Principal Investigators and 

NCI staff, was formed to coordinate the 

work of the consortium and provide major 

scientific and management oversight, such as 

developing and implementing protocols, study 

designs and general operations.

An ad hoc Network consulting team of 

non-EDRN investigators was instituted to 

recommend new research initiatives and to 

ensure Network responsiveness to promising 

research opportunities. Members of the group 

have reviewed EDRN as part of the external 

evaluation process.

Biomarker Reference Laboratories in 2008 

These laboratories serve as a Network resource for clinical and laboratory validation of biomarkers.

Principal Investigator Location

Dan Chan, Ph.D. Johns Hopkins Medical Institutions 

David Chia, Ph.D. University of California, Los Angeles 

Miral Dizdar, Ph.D. National Institute of Standards and Technology 

William E. Grizzle, M.D., Ph.D. University of Alabama at Birmingham 

Karin Rodland, Ph.D. Pacific Northwest National Laboratory 

Sanford Stass, M.D. University of Maryland School of Medicine 

Overview 17
18 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Early Challenges

Establishing and sustaining collaborations 

while ensuring a smooth flow of discoveries 

from the laboratory to the clinic were clearly 

key challenges to the nascent Network. 

Efforts focused on developing methods for:

• novel approaches to validation studies 

during the early stages of investigation;

• improved informatics and information flow 

using new systems for data organization and 

sharing;

• standardized data reporting by creating a 

dictionary of neoplastic and pre-neoplastic 

events and common data elements (CDE) 

for biomarkers;

• statistical and computational tools; and

• standardized reagents and assays.

Biomarker Development Principles 

The Network developed systematic, 

comprehensive guidelines to develop, evaluate 

and validate biomarkers. This five-phase 

approach established both a scientific standard 

and a roadmap for successfully translating 

biomarker research from the laboratory to the 

clinic. 

Phase 1 – discovery, involves exploratory 

study to identify potentially useful 

biomarkers. 

Phase 2 – validation, occurs where biomarkers 

are studied to determine their 

capacity for distinguishing between 

people with cancer and those 

without. 

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 on biomarker performance 

in large populations and determines 

its false referral rate. 

Phase 5 – suggests the penultimate period 

in which large-scale population 

studies evaluate both the role of the 

biomarker for cancer detection and 

its overall screening impact. 

Although the Network’s focus is mainly 

on Phases 1 through 3, researchers have 

welcomed the five-phase structure because 

it provides an orderly succession of studies 

that build upon each other to yield an 

efficient and thorough approach to biomarker 

development. 

Project Prioritization

The Network implemented guiding principles 

for biomarker validation and used criteria 

developed by the Review Group to prioritize 

the first round of proposals for collaborative 

projects. These principles were:

1. Biologic rationale/strength of hypothesis

2. Strength of design

3. Technical parameters

4. Clinical or scientific impact

5. Portfolio balance

6. Practicality

7. Collaborative strength/team effort

Individual grantees brought to the Network a 

diverse assortment of potential biomarkers for 

development. Projects ranged from biomarkers 

for lung carcinoma and pre-malignancy to 

cancer risk prediction by mutational load 

distribution. Some investigators were seeking 

to detect pre-clinical cancer across a range 

of organ sites (prostate, liver, ovarian, breast, 

lung, colorectal) by protein signatures in 

body fluids using novel technologies such 

as mass spectrometry and laser capture 

microdissection. The BRLs set out to validate 

molecular cytogenetic and automated 

cytometry assays involving slide-based analysis 

of chromosomes as a first step to further 

standards setting.

Collaborative Groups and Associate 

Memberships

To broaden the opportunities for scientific 

interactions and coordinated research, 

Collaborative Groups were formed. These 

organ-specific research groups were structured 

to promote information exchange on organrelated biomarkers and to identify research 

priorities within EDRN.
One major role of the Collaborative Groups 

was to serve as advisors/liaisons with Associate 

Members. The Associate Membership 

component was designed for investigators 

who are not affiliated with EDRN but wish to 

join the Network by proposing collaborative 

studies within its scope and objectives. 

Three categories for Associate Membership 

were established:

• Category A – domestic or foreign 

investigators who propose to conduct basic 

or translational research consistent with the 

priorities of EDRN;

• Category B – domestic or foreign 

members who contribute to the Network 

by sharing available technologies and 

supplying specimens, making available 

high-risk registries and cohorts and other 

complementary resources; 

• Category C – domestic or foreign 

corresponding members who are scientists, 

organizations, clinicians, patient advocates, 

or ethicists interested in participating in 

Collaborative Group meetings, workshops 

and conferences, without EDRN funding. 

Profile of the EDRN Associate 

Membership Program in 2008

• More than 151 applications received since 2000

• Approximately 40 applications approved

• More than 15 diagnostics firms joined as 

Category C members

• More than 45% of members are new 

investigators

• More than 60% of Category A members 

successfully competed for major grants

• Two Associate Members successfully proposed 

validation studies

2001 to 2003: Meeting the Scientific 

Challenges

Following the principles of systems biology, 

in which disciplines like biology, chemistry, 

computational science and clinical sciences 

are integrated seamlessly, the Network made 

strides in meeting the scientific challenges 

of biomarker research. The first round 

of proposals for collaborative studies was 

approved and Steering Committee meetings 

convened to continue managing the formation 

of the new Network. (See EDRN Second 

Report, Translational Research to Identify 

Early Cancer and Cancer Risk, October 2002, 

http://edrn.nci.nih.gov/docs.)

Discovery Phase

EDRN began actively identifying potential 

biomarkers and making inroads for testing 

and evaluating usability in early detection 

and risk indication. Promising results were 

attained, such as:

• Lysophosphatidic acid (LPA) was found to 

be promising as a biomarker and further 

studies were performed at the discovery 

laboratories. LPA is elevated in the plasma of 

women with ovarian cancer including 90% 

of women with stage I disease.

• A ligand or binding protein for Galectin3 was pursued at the Great Lakes New 

England Clinical Epidemiology and 

Validation Center, which identified the 

binding protein in circulating blood. 

Galectin-3 is a protein related to tumor 

progression and was found to be a 

hepatoglobin-related protein, present in 

higher concentrations in patients with 

colon cancer when compared to those with 

precancerous polyps or normal subjects.

• A positive finding that androgen receptorlength polymorphism is associated with 

prostate cancer risk in Hispanic men was 

made.

• A progression model for bladder cancer was 

developed.

• The result of an extensive search of gene 

and protein expression data generated 

through two-dimensional gel profiles, mass 

spectrometry, quantitative protein data and 

gene expression data, found two proteins, 

Overview 19
20 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Annexin-1 and Annexin-2, to be candidate 

biomarkers for lung cancer (Proc Natl Acad 

Sci USA. 2001 98:9824-9). Further validation 

studies are ongoing.

• Discussions concerning the informatics 

needs of EDRN were conducted and plans 

for building the infrastructure began. 

Prototypes of the EDRN Network Exchange 

system (ERNE), EDRN Task Management 

Software, EDRN Statistical Software and the 

EDRN secure site were produced and tested. 

Guidelines Set for Measuring Biomarker 

Predictive Power

To prepare for the next level of investigation, 

the Network developed guidelines for 

statistical design and analysis of nested casecontrol studies on serially collected blood 

or tissue specimens. These guidelines, listed 

below, are used by 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 a 

combination of individual markers and 

changes in marker levels over time. 

• To avoid over-fitting the data, the criteria for 

a positive marker combination developed in 

a training sample should be evaluated against 

random samples from the same study and, 

if possible, validation samples from another 

study.

Critical Challenges Faced

The interdisciplinary teams of investigators 

tackled the critical challenges identified at 

the beginning: novel approaches to validation 

studies; advanced informatics and information 

flow; standardization of reagents and assays 

and data reporting; and creation of standard 

statistical and computational tools (see Part II). 

New approaches to validation studies were set 

in motion with preliminary studies in:

• detecting promoter methylation as a risk 

marker; 

• chromosomal breakage as a marker of lung 

cancer susceptibility and early lung cancer 

detection using Fluorescence in Situ 

Hybridization (FISH);

• mutations in mitochondrial DNA and 

telomerase activity as early detection 

markers; and 

• microsatellite instability as an early 

detection marker for bladder cancer.

“T he EDRN’s goals are ambitious and admirably attempt to perform and 

deliver from both ends of the linear biomedical industries world: to discover 

new early disease biomarkers and deliver them to the public for use. As if this 

was not enough, this is to be done across a range of different cancers.” 

 Tim Block, Ph.D. 

 Principal Investigator 

EDRN Biomarker Development Laboratory 

Drexel University College of Medicine
EDRN Liaisons to Professional 

and Scientific Organizations

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): 

Dean Brenner, M.D.

American Society of Preventive Oncology (ASPO): 

Dean Brenner, M.D.

American Urological Association (AUA): 

Alan Partin, M.D., Ph.D.

Cooperative Family Registries: John Baron, M.D.

Human Proteome Organization (HUPO): 

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.

Pharmaceutical and industrial relations: 

Wendy Patterson, Esq.

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):

Michles Bodos, M.D.

2003 to 2004: Network Surges Ahead

NCI supported more than 100 collaborative 

projects that spanned the organ sites. BDLs 

investigated biomarker candidates for major 

organ sites while the first clinical validation 

study, microsatellite instability as a biomarker 

for bladder cancer, commenced in September 

2003. EDRN’s portfolio expanded, its 

collection of sample sets and reference data 

sets grew markedly and standard tools and 

resources were widely utilized. (See EDRN’s 

Infrastructures were built to improve 

informatics and information flow across the 

Network. A public web site and a secure 

web site contained general and specific 

information about upcoming events, contacts 

for institutions and committees, data from 

collaborative studies and approved validation 

proposals.

Standardization of data reporting came closer 

to reality with the development of CDEs 

required for use at Network sites. In addition, 

a distribution and computing network, known 

as the EDRN ERNE, which allows remote 

access to live databases at each Network site 

via the secured website, was developed by 

JPL and the DMCC. ERNE unifies search 

and retrieval of biospecimen data from all 

institutions regardless of their location, how 

data are stored, or the differences in the 

underlying data models. 

Exceptional analytical approaches and 

methods were developed to generate effective 

statistical methodologies and computational 

tools. These incorporated pre-analysis data 

processing; disease classification; protein 

biomarker identification; artificial intelligence 

learning algorithms; genomic and proteomic

data mining; and systems screening.

In collaboration with EDRN’s federal 

partner, NIST, NCI-supported investigators 

continued during this period to standardize 

methodologies, refine assays and establish 

standard reference materials for biochemical, 

molecular and cytologic assays.

EDRN forged partnerships with the private 

sector (see Part III). The Network initiated 

collaborative projects with other NCIsupported programs to leverage shared 

technology and resources; investigators 

published abstracts of their work; and liaisons 

to numerous professional organizations were 

established.

Overview 21
22 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Third Report, Translational Research to Identify 

Early Cancer and Cancer Risk, March 2005, 

http://edrn.nci.nih.gov/docs.)

To make resources available for validation 

research, a number of technologies were 

approved and clinical specimens collected and 

pronounced “open access” for collaborative 

efforts. In addition, the Network surged 

ahead in its partnerships with federal 

agencies through joint projects. Also, a series 

of workshops, meetings, conferences and 

collaborative group “town hall” gatherings 

were held to further cement alliances and 

share information. 

Another unique partnership emerged with 

the Plasma Proteome Project Initiative of the 

Human Proteome Organization (HUPO), 

to evaluate multiple technology platforms, 

develop bioinformatic tools and standards for 

protein identification and create a database 

of the plasma proteome (Proteomics August 

2005).

The Network-developed study design for a 

systematic evaluation of protein profiling, 

in this case using SELDI-TOF for cancer 

diagnosis, was published and became a model 

that can be applied to any other profilebased proteomics platforms. Accordingly, the 

model was extensively discussed and accepted 

throughout the research community (Disease 

Markers 2005). 

The ERNE knowledge system was deployed 

to 10 institutions in early 2003, providing a 

common web-based client interface. Creation 

of a robust framework called the Validation 

Study Information Management System 

(VSIMS) was created to allow multiple studies 

to be administered efficiently by minimizing 

development time with standardization of 

information and data management across 

multiple activities and research sites. 

2005 to 2007: An Investment 

in Prevention

The NCI’s Translational Research Working 

Group (TRWG) was established in 2005 

to evaluate the status of NCI’s investments 

in translational research and chart a vision 

for the future. TRWG defined translational 

research as “research that transforms 

scientific discoveries arising in the lab, clinic 

or population into new clinical tools and 

applications that reduce cancer incidence, 

morbidity and mortality” (see Figure 1-2). 

Figure 1-2. Translational Research Paradigm as defined by NCI’s Translational Research Working Group

Source: Translational Research Working Group Interim Report to the National Cancer Advisory Board, Envisioning the Future of NCI’s Investment in Translational 

Research, June 14, 2006 (http://www.cancer.gov/aboutnci/trwg/hawk-NCAB.pdf)

Lab

Clinic Population

New Tools & 

New Applications
Biomarker Development Laboratories in 2008 

These laboratories are responsible for development and characterization of new, 

or refinement of existing, biomarkers.

Principal Investigator Location

William L. Bigbee, Ph.D University of Pittsburgh Cancer Institute 

Timothy Block, Ph.D. Drexel University College of Medicine 

Paul Cairns, Ph.D. Fox Chase Cancer Center 

Arul M. Chinnaiyan, M.D., Ph.D. University of Michigan 

Bogdan Czerniak, M.D., Ph.D. University of Texas M. D. Anderson Cancer Center 

Laura J. Esserman, M.D., M.B.A. University of California, San Francisco 

Wilbur Alan Franklin, M.D. University of Colorado Health Science Center 

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

Samir Hanash, M.D., Ph.D. Fred Hutchinson Cancer Research Center 

Michael Hollingsworth, Ph.D. University of Nebraska Medical Center 

Ann M. Killary, Ph.D. University of Texas M. D. Anderson Cancer Center 

Joshua LaBaer, M.D., Ph.D. Harvard Institute of Proteomics 

Alvin Y. Liu, Ph.D. University of Washington 

Zvi Livneh, Ph.D. Weizmann Institute of Science 

Anna Lokshin, Ph.D. University of Pittsburgh Cancer Instititute 

Jeffrey Marks, Ph.D. Duke University Medical Center 

Martin McIntosh, Ph.D. Fred Hutchinson Cancer Research Center 

Stephen Meltzer, M.D. Johns Hopkins University 

Harvey Ira Pass, M.D. New York University School of Medicine 

Hemant K. Roy, M.D. Evaston Northwestern Healthcare Research Institute 

O. John Semmes, Ph.D. Eastern Virginia Medical School 

David Sidransky, M.D. Johns Hopkins University 

Michael A. Tainsky, Ph.D. Karmanos Cancer Institute 

Richard C. Zangar, Ph.D. Pacific Northwest National Laboratory 

Overview 23

Informatics Center in 2008 

The Informatics Center supports EDRN’s efforts through software systems development for information 

management and flow.

Principal Investigator Location

Daniel Crichton, M.S. NASA Jet Propulsion Laboratory at the California 

Institute of Technology 
24 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Clinical Epidemiology and Validation Centers in 2008 

The Centers conduct clinical and epidemiological research on the medical application of biomarkers. 

Principal Investigator Location

Steven Belinsky, Ph.D. Lovelace Respiratory Research Institute 

Dean Brenner, M.D. University of Michigan 

Daniel Cramer, M.D., Sc.D. Brigham and Women’s Hospital 

Paul Engstrom, M.D. Fox Chase Cancer Center 

Henry Lynch, M.D. Creighton University 

Alan W. Partin, M.D., Ph.D. Johns Hopkins University Department of Urology 

William Rom, M.D., M.P.H. New York University School of Medicine 

Martin Sanda, M.D. Beth Israel Deaconess Medical Center 

Ian M. Thompson, M.D. University of Texas at San Antonio 

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

Data Management and Coordinating Center in 2008 

The Center is responsible for coordinating EDRN activities by developing a common database for the Network, providing logistic support, conducting statistical and computational research and guiding statistical 

design and data analyses of validation studies. 

Principal Investigator Location

Ziding Feng, Ph.D. Fred Hutchinson Cancer Research Center 

Program for Rapid, Independent Diagnostic Evaluation (PRIDE):

• The program was modeled on the NCI Rapid Access to Intervention Development (RAID) program, 

which was designed to assist translation to the clinic of novel anticancer therapeutic interventions, 

either synthetic, natural product, or biologic, arising in the academic community.

• PRIDE is designed to assist extramural scientists in validating biomarkers and technologies following 

the device pathway developed by TRWG.

• Initiated in June 2006.

• More than 10 proposals received.

• Three applications are supported.

• Data is shared and analyzed by EDRN DMCC and investigators.
Overview 25

The EDRN has achieved several milestones. 

The operations manual was proven viable. 

Guidelines laying out the criteria and 

sequential study designs for justification 

of requested resources were provided 

to investigators. The fully characterized 

Network provides an unparalleled system of 

strong scientific collaborations that facilitate 

high-quality translational research. The 

infrastructure works to ensure that good 

biomarkers are promoted without regard 

to pecuniary interests. The Network’s 

emphasis on inclusiveness allows any scientist, 

from academia, industry or government to 

participate in EDRN activities, thus ensuring 

the best chance for promising markers to 

become future medical tools.

The Associate Membership Program, 

along with a newly established Program for 

Rapid, Independent Diagnostic Evaluation 

(PRIDE), continues to ensure inclusiveness 

of stakeholders, biomarkers, technologies 

and processes all along the EDRN business 

model. In late 2006, EDRN announced the 

PRIDE (http://grants.nih.gov/grants/guide/

notice-files/NOT-CA-07-003.html), as an 

administrative means to assist extramural 

investigators to successfully conduct 

cross-laboratory validation of biomarkers. 

Investigators from the diagnostic community 

were invited to partner with EDRN to 

develop new standards for methodologies, 

assays, reagents and tools. This initiative 

is expected to expand the capacity of 

existing resources and speed development 

of diagnostic markers. PRIDE will fill a gap 

between discovery and clinical application by 

providing independent evaluation of potential 

biomarkers developed through various 

technology platforms and the assays and 

reagents needed to accelerate them to 

clinical use.
Breast and gynecologic

cancers are a study in contrasts, from 

their incidences and detection strategies, 

to their associated biomarkers. 

Breast cancer is highly prevalent and has 

well-established early detection strategies 

available: mammograms and clinical breast 

exams. Molecular tests exist to help determine 

treatment options following breast cancer 

diagnoses, but the challenge of finding blood 

tests to detect the disease is daunting. Ovarian 

cancer is less prevalent than breast cancer, 

however, it is highly lethal and, thus far, has 

no approved or marketed early detection tests. 

(Transvaginal ultrasound and CA-125 are 

under study in large screening trials.) Recent 

work within the Early Detection Research 

Network (EDRN) is leading to a molecular 

test that is promising and may be widely 

available in the near future. 

Cervical cancer incidence and mortality 

have been reduced dramatically due to the 

introduction of the Papanicolaou (Pap) test. 

Some health care providers are also testing 

DNA from human papillomavirus (HPV) to 

determine risk for the disease and the new 

HPV vaccine may reduce the actual incidence 

of disease. For this cancer, EDRN targets 

pre-cancers to improve outcomes and reduce 

treatments.

Breast and 

Gynecologic Cancers

“E arly detection and surgical resection remain mainstays of cancer treatment and 

the EDRN was, and is, needed to develop the potential of early detection.” 

 Paul Cairns, Ph.D. 

 Principal Investigator

 EDRN Biomarker Development Laboratory 

 Fox Chase Cancer Center

26 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

C hapter TWO
EDRN Breast and Gynecologic Cancers Collaborative Group Members 

Jeffrey Marks, Ph.D., Chair

Duke University Medical Center

Daniel Cramer, M.D., Sc.D., Co-chair

Brigham and Women’s Hospital

Karen Anderson, M.D., Ph.D.

Harvard Institute of Proteomics

Paul Cairns, Ph.D.

Fox Chase Cancer Center

David Chia, Ph.D.

University of California, Los Angeles

Miral Dizdar, Ph.D.

National Institute of Standards and Technology

Richard Drake, Ph.D.

Eastern Virginia Medical School

Paul Engstrom, M.D.

Fox Chase Cancer Center

Laura J. Esserman, M.D., M.B.A.

University of California, San Francisco

Andrew K. Godwin

Fox Chase Cancer Center

Lee Goodglick, Ph.D.

University of California, Los Angeles

Joshua LaBaer, M.D., Ph.D.

Harvard Institute of Proteomics

Anna Lokshin, Ph.D.

University of Pittsburgh Cancer Institute

Martin McIntosh, Ph.D.

Fred Hutchinson Cancer Research Center

Gil Mor, M.D., Ph.D.

Yale University

Christos Patriotis, Ph.D.

National Cancer Institute

Margaret Sullivan Pepe, Ph.D.

Fred Hutchinson Cancer Research Center

Steven Skates, Ph.D.

Brigham and Women’s Hospital

Martin Steinau, Ph.D.

Centers for Disease Control and Prevention

Michael A. Tainsky, Ph.D.

Karmanos Cancer Institute

Elizabeth R. Unger, M.D., Ph.D.

Centers for Disease Control and Prevention

Wendy Wang, Ph.D.

National Cancer Institute

Patrice Watson, Ph.D.

Creighton University

Richard C. Zangar, Ph.D.

Pacific Northwest National Laboratory

Breast and Gynecologic Cancers 27
28 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Breast Cancer

Breast cancers can be detected at very early 

stages by imaging of the breast. The increase 

in breast cancer incidence observed over the 

last 20 years is almost entirely attributable to a 

detection of ductal carcinoma in situ (DCIS) 

and stage I disease by imaging. Current 

screening and diagnostic practices can detect 

lesions that are as small as 1 mm. While there 

are many cancers missed by these screening 

approaches, the standard for a successful 

test must focus on relatively small lesions or 

those that have yet to become invasive. The 

costs associated with annual mammogram 

screenings and tissue biopsies are significantly 

high and, therefore, the need for additional 

complementary blood testing strategies is 

urgent.

Due to the heterogeneous nature of breast 

cancer, it is unlikely that a single gene or gene 

product will be useful as a biomarker. This can 

hinder progress in development of a blood test 

for clinical application. In this context, several 

programs to move the field forward have been 

extensively discussed within EDRN. Novel 

approaches will be tested in the next year. 

Definition of breast cancer: Cancer that forms 

in tissues of the breast, usually the ducts (tubes 

that carry milk to the nipple) and lobules (glands 

that make milk). It occurs in both men and women, 

although male breast cancer is rare. 

Estimated new cases and deaths from breast 

cancer in the United States in 2007:

New cases: 178,480 (female) and 2,030 (male) 

Deaths: 40,460 female, 450 male

One promising approach is the analysis 

of autoimmunity to the disease using 

high-throughput methods developed by 

investigators from Karmanos Cancer Institute 

and Harvard Institute of Proteomics. This 

approach may provide a better handle on 

the heterogeneity of the disease and the host 

response to the disease than traditional serum 

markers.

The second promising area for detection is 

the creation of an antibody library for breast 

cancer-specific secreted glycoproteins. In 

a collaboration between Duke University 

Medical Center and Pacific Northwest 

National Laboratory, a yeast single-chain 

antibody library enriched for glycoproteins is 

being created. This library will greatly increase 

the number of potential biomarkers for breast 

cancer. 

Serum methylation markers, while holding 

promise for breast cancer detection, have 

significant methodologic and theoretical 

sensitivity issues that may limit their 

application in this context. 

Two other strategies for improving breast 

cancer detection are also actively being 

pursued. In both cases, the central concept 

is risk stratification. As with other cancers, 

there is a significant hereditary component 

to breast cancer. Alterations in several genes 

(BRCA1 and BCRA2) strongly predispose 

to the disease and the success of genetic 

testing and subsequent risk management is 

a paradigm in modern molecular genetics. 

There are likely to be a number of other 

genetic variants that are more prevalent in the 

population but less dramatic in conferring 

increased risk. Identification of these variants 

will be incorporated into risk models that 

should guide future screening and prevention 

strategies. 

Current Early Detection Tests 

for Breast Cancer

There are currently no biomarker tests to screen 

for or diagnose breast cancer. The best method 

of detecting breast cancer early is by regular 

high-quality mammogram screening and clinical 

breast exam by a health care provider. Similar to 

many tests, mammograms have both benefits and 

limitations. For example, some cancers cannot 

be detected by a mammogram, but may be found 

by breast examination. Breast self-exam (BSE) 

alone has not been shown to reduce the number 

of deaths from breast cancer. BSE should not take 

the place of routine clinical breast exams and 

mammograms. 
The second aspect of risk stratification is in 

the diagnostic setting. In the United States, 

more than 1 million breast biopsies are 

performed annually resulting in the diagnosis 

of about 200,000 cancers. For the frank 

cancers, subsequent therapeutic steps are 

reasonably well delineated. However, a large 

number of women are diagnosed with lesions 

that are not invasive cancer but may be an 

early indication of the cancer process. Tissuebased markers that can identify lesions that 

are likely to progress would be of immediate 

benefit. Several promising markers have been 

identified by the University of California, 

San Francisco and will be further tested and 

validated in collaboration with EDRN. These 

two risk-based approaches will integrate into 

current screening and diagnostic practices 

and could serve to focus resources on women 

at the highest risk of developing clinically 

relevant disease and reduce the morbidity in 

women with low risk.

EDRN Investigator Honored by DoD 

A Department of Defense (DoD) Innovator Award 

grant, totaling almost $8 million, was awarded 

in October 2007 to Joe Gray, Director of the Life 

Sciences Division of the Department of Energy’s 

Lawrence Berkeley National Laboratory (http://

www.lbl.gov), and an EDRN Co-P.I. with Dr. Laura 

Esserman (UCSF). 

One of the innovative technologies that is being 

advanced as part of the Innovator Award, was 

developed through Dr. Esserman’s EDRN ”Chair’s 

Challenge” Grant. Via the Chair’s Challenge 

mechanism, Dr. Gray has been collaborating 

with Dr. Esserman to find better ways to screen 

women for breast cancer, and to tailor screening 

to the type of tumors that might develop. 

Dr. Esserman and colleagues have recently 

shown data suggesting that current screening is 

not reducing the risk for women with the most 

aggressive breast cancers. Other collaborators 

on the project are from Lawrence Livermore 

National Laboratories, who are employing 

a time-of-flight secondary ion mass spectrometer

(TOF-SIMS), capable of chemical mapping of 

breast cells and tumor tissues.

Candidate Breast Cancer Biomarkers 

Candidate Biomarker Discovery Pre-validation Validation

Autoimmunity (Serum)

Glycoprotein Antibodies (Serum)

Candidate SNPs (Lymphocytes) 

Genetic Variants

Ki67, Cox-2, p16 expression 

(Tissue)

Methylation Markers (Plasma)

 Methylation Markers (Tissue)

Discovery

Predictive Analysis

Assay Refinement

Blinded Limited 

Cross-Sectional

Large Cross-Sectional

Breast and Gynecologic Cancers 29
30 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Ovarian Cancer 

Early detection of ovarian cancer has been an 

emphasis for EDRN since its inception. The 

absence of a useful screening test coupled with 

the lethality of the disease when it is typically 

diagnosed at an advanced stage strongly 

support the potential utility of biomarkers 

for early detection. The serum tumor marker 

CA-125 provides a strong foundation to build 

multiplexed tests for the disease. 

EDRN investigators have made substantial 

progress in building and testing marker 

panels for ovarian cancer. This year, EDRN 

in collaboration with Specialized Programs 

of Research Excellence (SPOREs) in 

ovarian cancer, will perform a head-to-head 

comparison of a series of blood-based assays 

that have demonstrated strong predictive 

value, even in early stage disease. The 

challenge from this approach will be to 

narrow the list of candidate markers to those 

that provide independent value and that can 

be combined for optimal sensitivity and 

specificity. Pre-diagnostic samples from 

NCI’s Prostate, Lung, Colorectal and Ovarian 

(PLCO) Cancer Screening Trial will yield 

information on the lead-time before clinical 

diagnosis of ovarian cancer that these markers 

will provide. Overall, these robust multidisciplinary activities should result in clear 

answers and potential applications for an 

ovarian cancer diagnostic test within the 

next two years.

Definition of ovarian cancer:

Cancer that forms in tissues of the ovary (one of 

a pair of female reproductive glands in which the 

ova, or eggs, are formed). Most ovarian cancers 

are either ovarian epithelial carcinomas (cancer 

that begins in the cells on the surface of the ovary) 

or malignant germ cell tumors (cancer that begins 

in egg cells).

Estimated new cases and deaths from 

ovarian cancer in the United States in 2007:

New cases: 22,430 

Deaths: 15,280

Three groups have developed panels of 

markers that achieve sensitivity (ability 

to accurately identify people with ovarian 

cancer) and specificity (ability to accurately 

identify people without ovarian cancer), in 

the high 90th percentile in the detection 

of ovarian cancer in newly diagnosed cases 

when compared to healthy controls. All three 

approaches measure the levels of known 

circulating proteins by either sandwich 

enzyme linked immunosorbent assay 

(ELISA) or a variation thereof with CA-125 

as a key component of each panel of markers. 

Testing of these panels in additional cohorts 

revealed a reduced ability to discriminate 

benign from malignant disease, although 

they are still highly promising. The next step 

in validating these marker panels is to test 

their ability to detect disease prior to clinical 

Candidate Ovarian Cancer Biomarkers 

Candidate Biomarker Discovery Pre-validation Validation

Marker Panels (Serum) – CA-125; 

MIF-1, prolactin, osteopontin, 

IGF-2, leptin

Discovery

Predictive Analysis

Assay Refinement

Blinded Limited 

Cross-Sectional

Large Cross-Sectional
diagnosis. Sera from the WHI and the United 

Kingdom’s Ovarian Cancer Screening Trial 

will be obtained by these groups to test the 

effectiveness of their markers and algorithms. 

Within the next 12-18 months, these panels 

will be tested on pre-diagnostic specimens to 

determine the sensitivity and specificity and 

timing of detection of ovarian cancer in a 

screening situation. These results could pave 

the way for the phased implementation of 

such a test in the general population. 

Current Early Detection Tests for 

Ovarian Cancer

To date, no biomarkers are available for screening 

of ovarian cancer. Studies are ongoing to 

determine whether routine screening for ovarian 

cancer with serum markers, such as CA-125, 

transvaginal ultrasound, or pelvic examinations 

would result in decreased mortality from ovarian 

cancer.

Cervical Cancer

Cervical cancer is a testament to the 

effectiveness of early detection in reducing 

cancer mortality. Since the introduction of Pap 

test screening, the incidence of cervical cancer 

in the United States decreased 70%. While 

early detection is not primary prevention of 

cancer, cervical cytology screening programs 

prevent invasive disease by detecting cancer 

precursors that can be surgically removed. In 

addition, the strong etiologic link of HPV 

with cervical cancer lends itself to a model 

system for understanding molecular features 

of other tumors related to infection.

Definition of cervical cancer: 

Cancer that forms in tissues of the cervix (the 

organ connecting the uterus and vagina). It is 

usually slow-growing and may not have symptoms 

but can be detected with regular Pap tests (a 

procedure in which cells are scraped from the 

cervix and evaluated under a microscope). 

Estimated new cases and deaths from 

cervical (uterine cervix) cancer in the 

United States in 2007:

New cases: 11,150 

Deaths: 3,670

There is a cost that arises from targeting precancers for therapy. Although relatively few 

of these lesions would progress to invasion, 

increasingly, even minor abnormalities are 

treated. Since individual risk cannot be 

determined, often clinicians and patients 

prefer to err on the side of over-treatment. 

In 2007, estimates of new cervical cancers 

will reach 11,150 with resulting deaths of 

3,670. HPV vaccines will not eliminate the 

need for screening as not all types of HPVassociated cancers are targeted by vaccines. 

It is estimated that the impact of vaccines 

on cancer incidence will not occur for 

10-15 years after implementation; therefore, 

screening for vaccine-missed cervical cancers 

will require even more cost-effective and 

specific screening tools. 

Breast and Gynecologic Cancers 31
32 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

SPOREs, PLCO and EDRN Collaborate 

to Validate Ovarian Cancer Markers

Three major NCI programs, the Specialized 

Program of Research Excellence (SPORE), the 

Prostate, Lung, Colorectal and Ovarian (PLCO) 

Cancer Screening Trial and the Early Detection 

Research Network (EDRN) are collaborating to 

validate ovarian cancer biomarkers. Five SPORE 

sites and two EDRN sites have come together for 

the first time under the leadership of Nicole Urban, 

M.D., (SPORE) and Dan Cramer, M.D., (EDRN) in an 

exemplary effort. 

The investigators have identified several putative 

biomarkers for the detection of ovarian cancers 

and plan to screen their markers for the ability to 

detect ovarian cancer in pre-diagnostic and early 

cases of ovarian cancer specimens collected from 

the PLCO trial. The hypothesis is that a panel of 

biomarkers combined into a composite marker 

will have a lead time sufficient to identify ovarian 

cancer cases two or more years prior to the time 

they might be clinically diagnosed. 

As a first step, investigators are collectively 

analyzing their putative ovarian cancer biomarkers 

in an independent set of ovarian cancer specimens 

to reconfirm the performance of the markers and 

to identify a consensus panel of markers. The 

consensus panel will comprise the biomarkers that 

are most informative on their own as well as those 

that are most complementary when used together. 

The panel will then be used to analyze specimens 

from women in the PLCO who developed ovarian 

cancer as well as those that did not. The baseline 

performance of each marker on its own and in 

combination will establish a screening rule using 

pre-diagnostic samples. The screening rule allows 

the lead-time to be determined.

This collaboration is being coordinated by the 

EDRN Data Management and Coordination Center 

(DMCC). If successful, the biomarker panel will 

likely lead to a clinically approved ovarian cancer 

screening test. 

(See Chapter 6, Validation Studies, Case 3 for 

more information.)

Current Early Detection Tests 

for Cervical Cancer

The current number of deaths from cervical 

cancer reflects an estimated 70% decline from 

the mid-20th century when the Pap test was first 

introduced as a screening tool. In addition to the 

Pap test, some health care providers also test for 

DNA from HPV, an infection that may increase risk 

for cervical cancer.

Based on their prior experience with cervical 

cancer biomarker discovery and validation, the 

EDRN Cancer Epidemiology and Validation 

Center modified their study design for 

biologic sample and data collection. The effort 

continues to focus on high-risk populations 

but includes a 2-year longitudinal study 

designed to provide the follow-up needed to 

assure precision in disease ascertainment and 

to evaluate biomarker change in response 

to therapy. Recruitment was expanded to 

include HIV-positive women, a more diverse 

ethnic group and additional women with 

invasive cervical disease. The biorepository 

is an EDRN-shared resource and is linked to 

digitized pathology images producing a virtual 

slide library permitting web-based pathology 

of the diagnostic material. Incorporating 

this new technology allows users of the 

biorepository to verify case ascertainment. 

Numerous molecular biomarkers have been 

suggested for early detection of cervical 

cancer but their utility in routinely collected 

exfoliated cells remains uncertain. EDRN 

investigators have used quantitative reverse 

transcriptase-polymerase chain reaction 

amplification (qRT-PCR) to evaluate the 

expression of 40 candidate genes as markers 

for high-grade cervical intraepithelial neoplasia 

(CIN) in exfoliated cervical cells collected at 

the time of colposcopy. Samples from the 93 

women with either CIN3 (the most advanced 

stage of CIN) or cancer were compared to 

those from 186 women without disease and 

matched (1:2) for age, race and high-risk 
HPV status. Their diagnostic performance 

was determined and six markers were found to 

be promising by exhibiting an area under the 

curve (AUC) greater than 0.6. (See Figure 3-1 

in Chapter 3 for an illustration of this type of 

diagnostic profile.)

This study supports the concept that 

exfoliated cervical cells reflect changes in gene 

transcription that are similar to those found in 

the biopsy tissue; and because these cells show 

similar sensitivity and specificity, they perhaps 

can replace the biopsy to detect CIN. The 

sensitivity for individual markers was relatively 

low and a five-gene panel resulted in 60% 

sensitivity with 76% specificity. Although the 

results did not indicate superiority of RNA 

markers for cervical cancer screening, their 

performance in detecting disease in women 

referred for colposcopy suggests that the 

genes and pathways they highlight could be 

useful in alternative detection formats, or in 

combination with other screening indicators. 

In guiding further work in biomarkers 

discovery and validation, investigation into the 

problems of low sensitivity and specificity are 

being explored using immunohistochemistry 

to evaluate expression of these markers. 

In a collaboration with NCI intramural 

scientists, the performance of a fluorescent 

in situ hybridization (FISH) assay for 

3q amplification is being evaluated on 

residual archived liquid Pap samples as those 

previously evaluated by qRT-PCR for the six 

promising marker genes. This will allow direct 

comparison of the assay results.

NCI Intramural/CDC Collaboration 

on Cervical Cancer Biomarkers

Investigators from the Centers for Disease 

Control and Prevention (CDC) and NCI Intramural 

research programs collaborate to validate 

cervical cancer biomarkers for predicting 

progression. NCI Intramural investigators have 

found chromosomal gains of 3q using FISH for 

detecting cervical cancer in an independent 

study. In the current collaborative study, the 

performance of chromosomal amplification of 3q 

by FISH and RNA expression markers (6 marker 

panel) will be employed in liquid Pap smear 

samples for predicting the progression of cervical 

cancer among women with abnormal test results. 

The specimens were collected in an Interagency 

Agreement with CDC. The current study, if 

successful, will reduce the need for repeated 

colposcopies and enable reduction in the costs 

for screening cervical cancer. 

Candidate Cervical Cancer Biomarkers 

Candidate Biomarker Discovery Pre-validation Validation

qRT-PCR (6 gene panel) (Exfoliated 

Cells): CLDN1, MCM5, MCM7, 

CDC6, MKI67, SHCBP1

Discovery

Predictive Analysis

Assay Refinement

Blinded Limited 

Cross-Sectional

Large Cross-Sectional

Breast and Gynecologic Cancers 33
Cancers in the gastrointestinal tract 

include one of the most prevalent cancers 

in the United States (colorectal cancer), 

a cancer with the fastest rising incidence 

(liver cancer) and two of the most deadly 

malignancies (esophageal and pancreatic 

cancers). 

The Early Detection Research Network 

(EDRN) Gastrointestinal Collaborative Group 

is working to identify candidate biomarkers

that will improve patient outcomes in these 

diseases, through both independent discovery 

and collaborative work. For each organ site, 

new biomarkers have been discovered and, in 

preliminary prevalidation studies, have been 

shown to be superior to current standards 

of care. In two circumstances, the newly 

discovered biomarkers have reached clinical 

validation, an important milestone in the 

delivery of a new biomarker into clinical use. 

Colorectal and Other 

Gastrointestinal Cancers

“T he concept of simultaneously testing multiple technologies is starting to be 

explored in several settings. The idea is to conduct a kind of bake-off in which 

identical sets of ingredients — strong unbiased specimens — are circulated 

to laboratories around the country or around the world. In the NCI’s Early 

Detection Research Network we are assessing whether four different serum 

proteomics technologies can diagnose colon cancer using the same specimens.”

 

 David Ransohoff, M.D.

 EDRN Associate Member 

University of North Carolina, Chapel Hill

34 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

C hapter T hree
EDRN Gastrointestinal Collaborative Group Members 

Timothy Block, Ph.D., Chair

Drexel University College of Medicine

Stephen Meltzer, M.D., Co-Chair

Johns Hopkins University

John A. Baron, M.D. 

Dartmouth Medical School

Surinder Batra, Ph.D.

University of Nebraska

William Bigbee, Ph.D.

University of Pittsburgh Cancer Institute

Dean Brenner, M.D.

University of Michigan

Robert Bresalier, M.D.

University of Texas M. D. Anderson Cancer 

Center

David Chia, Ph.D.

University of California, Los Angeles

Ziding Feng, Ph.D.

Fred Hutchinson Cancer Research Center

Radoslav Goldman, Ph.D.

Georgetown University

William Grady, M.D.

Fred Hutchinson Cancer Research Center

Michael Hollingsworth, Ph.D.

University of Nebraska Medical Center

Ann M. Killary, Ph.D.

University of Texas M. D. Anderson Cancer 

Center

Anna Lokshin, Ph.D.

University of Pittsburgh Cancer Institute

David Lubman, Ph.D.

University of Michigan

Henry Lynch, M.D.

Creighton University

Upender Manne, Ph.D.

University of Alabama, Birmingham

Norman Marcon, M.D.

University of Toronto

Jorge Marrero, M.D.

University of Michigan

Hermant K. Roy, M.D.

Evanston Northwestern Healthcare Research 

Institute

Robert E. Schoen, M.D., M.P.H.

University of Pittsburgh Cancer Institute

Ying Hsiu Su, Ph.D.

Drexel University College of Medicine

Sapna Syngal, M.D.

Dana-Farber Cancer Institute

Paul Wagner, Ph.D.

National Cancer Institute

Patrice Watson, Ph.D

Creighton University

Colorectal and Other Gastrointestinal Cancers 35
36 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Colorectal Cancer

Colon cancer is the third most frequently 

diagnosed cancer in the United States and 

the third most frequent cause of cancer 

death. Successful prevention of deaths 

from colorectal cancer depends on early 

detection. 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 these 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. 

EDRN investigators have identified genetic, 

epigenetic and protein biomarkers that 

correlate with the presence of colorectal cancer 

using serum, stool and urine. 

Definition of colon cancer:

Cancer that forms in the tissues of the colon 

(the longest part of the large intestine). Most 

colon cancers are adenocarcinomas (cancers 

that begin in cells that make and release mucus 

and other fluids). 

Definition of rectal cancer: 

Cancer that forms in the tissues of the rectum 

(the last several inches of the large intestine 

before the anus). 

Estimated new cases and deaths from 

colon and rectal cancer in the United 

States in 2007: 

New cases: 112,340 (colon); 41,420 (rectal) 

Deaths: 52,180 (colon and rectal combined)

The University of Pittsburgh/Johns Hopkins 

University EDRN Biomarker Development 

Laboratory (BDL) used proteomics focused 

on the nuclear matrix to identify several 

serum markers, known as CCSA-2, -3 and 

-4, that appear to be associated with colon 

cancer. Using antibodies produced against 

these markers, investigators are able to 

differentiate samples from individuals with 

cancer or advanced adenomas, from samples 

from normal individuals. Individuals with 

advanced adenomas are at increased risk for 

developing colon cancer. Preliminary data 

show that these biomarkers are more specific 

(able to accurately identify people with colon 

cancer) and more sensitive (able to accurately 

identify people without colon cancer) than 

current tests. A preliminary validation study 

is in progress using specimens from the Great 

Lakes New England Clinical Epidemiology 

and Validation Center of the EDRN. 

Current Early Detection 

Tests for Colorectal Cancer 

Health care providers may suggest one or more 

of the tests listed below for colorectal cancer 

screening.

A fecal occult blood test (FOBT) checks for 

hidden blood in the stool. Studies have proven 

that this test, when performed every 1 to 2 years 

in people ages 50 to 80, reduces the number of 

deaths due to colorectal cancer by as much as 

30%. 

A sigmoidoscopy is an examination 

of the rectum and lower colon using a 

lighted instrument called a sigmoidoscope. 

Sigmoidoscopy can find precancerous or 

cancerous growths in the rectum and lower 

colon. 

A colonoscopy is an examination of the rectum 

and entire colon using a lighted instrument 

called a colonoscope. Colonoscopy can find 

precancerous or cancerous growths throughout 

the colon, including the upper part of the colon, 

where they would be missed by sigmoidoscopy. 

A double contrast barium enema (DCBE)

is a series of x-rays of the entire colon and 

rectum. The x-rays are taken after the patient is 

given an enema with a barium solution and air 

is introduced into the colon. The barium and air 

help to outline the colon and rectum on the xrays. Research shows that DCBE may miss small 

polyps. 

A digital rectal exam (DRE) is often part of 

a routine physical examination. The health care 

provider inserts a lubricated, gloved finger into 

the rectum to feel for abnormal areas. DRE 

allows for examination of only the lower part of 

the rectum.
Investigators at Evanston Northwestern 

Healthcare Research Institute are using 

cutting edge optical technologies to create 

a device that evaluates the anatomical 

architecture of the cells lining the colon. 

Using this technology, a doctor would be 

able to assess whether or not changes in the 

overall structure of the cells indicate a risk of 

developing colorectal cancer. In many cancers, 

small changes occur in all the tissues exposed 

to potential cancer-causing compounds, a 

concept known as field carcinogenesis. They 

plan to develop a free-standing optical probe 

that will allow a primary care physician to 

determine the need for colonoscopy during 

a digital rectal exam. Spectral markers 

based on two optics technologies are being 

employed. In a study of more than 254 

people, the spectral-assisted approach was 

able to detect 100% of the people with cancer 

(sensitivity) and 88% of those without the 

disease (specificity). The test has positive

and negative predictive values of 71% and 

100%, respectively, highlighting the ability 

of this technique to accurately detect patients 

with adenomas or colon cancer.

The EDRN Great Lakes New England 

(GLNE) Clinical Epidemiology and 

Validation Center (CEVC) has a number 

of ongoing collaborations to discover and 

validate genomic and proteomic biomarkers 

for the early detection of colorectal cancer. 

For example, an EDRN Associate Member at 

Case Western Reserve University, discovered 

two proteins, called ColoUp 1 and 2, which 

can distinguish patients with colon cancer 

from healthy people without the disease. The 

GLNE CEVC will support the validation of 

these markers by supplying blinded specimens 

for additional testing.

The GLNE is also supporting several 

sophisticated approaches for the creation 

of new protein biomarkers or panels of 

biomarkers for colorectal cancer. The 

complexity and diversity of proteins derived 

from clinical specimens present a challenge to 

conventional proteomics. At the University 

of Michigan, an EDRN Associate Member 

developed a new method that simplifies 

profiles, prior to mass spectral analysis. 

Specimens from diseased and healthy tissue or 

sera are resolved, side-by-side, by sophisticated 

two-dimensional liquid separations (2-D 

Mass Map). Proteins of interest are then 

identified by using electrospray time-offlight mass spectrometry (TOF-MS). An 

analytical test set of sera from 10 individuals 

with a diagnosis of colorectal cancer, 10 with 

adenomas and 10 healthy subjects suggests 

at least 6 proteins that may be altered in 

abundance as a function of cancer. 

The Drexel University EDRN BDL is 

collaborating with the GLNE CEVC to 

determine whether urine can be used as 

a source of DNA for early colon cancer 

detection. Their data indicate that mutant 

K-ras DNA derived from colorectal cancer 

cells is present in human urine. While they 

can consistently detect mutant K-ras in urine, 

this assay does not have sufficient sensitivity 

and specificity. This issue is being addressed by 

adding multiple proto-oncogene markers to 

the assay in collaboration with a commercial 

partner, Ambergen, Inc. 

Colorectal and Other Gastrointestinal Cancers 37
38 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

An EDRN Associate member at the Fred 

Hutchison Cancer Research Center showed 

that methylated CDKN2A and MGMT genes 

can be detected in fecal DNA from patients 

with colon adenomas (Methylation Marker 

Panel). Overall, methylation of at least one of 

the candidate genes was detected in the fecal 

DNA from 57% of patients with adenomas. 

Although less sensitive than colonoscopy, 

the current gold standard for colon cancer 

screening, these methylated genes have the 

potential to be cost-effective screening markers 

considering the current price for the molecular 

marker assay commercially available for 

colorectal cancer screening.

Figure 3-1. Optical Probe for Colon Cancer Screening

Through collaboration between clinicians/biologists at Evanston-Northwestern Healthcare 

and Biomedical Engineers at Northwestern University, the EDRN has been testing the ability of 

powerful new optical technologies such as four-dimensional elastic light-scattering fingerprinting 

(4D-ELF) to cancer screening. The light scattering information, harnessed by spectral biomarkers, is 

exquisitely sensitive to the nanoscale architectural changes of cells. This provides a highly accurate 

and practical means of detecting the genetic/epigenetic changes in field carcinogenesis that occur 

in microscopically normal epithelium. In a study of 250 patients, spectral markers obtained from the 

endoscopically normal rectal mucosa had a 100% sensitivity and 89% specificity for predicting the 

presence of advanced neoplasia anywhere in the colon. Ongoing studies employing a fiber optic 

probe will aim to validate this minimally intrusive rectal test as a pre-screen, thus enabling rational 

tailoring of colon cancer screening regimens. Moreover, through the Network collaborative process, 

spectral markers are being tested for risk stratification of a number of other malignancies including 

gastric, biliary and lung.

Source: EDRN investigators at Evanston-Northwestern Healthcare and Northwestern University

These arrows represent different wavelengths of incident light. The scattering angle and 

intensity for a particular wavelength of light are determined by the size and structure of the 

scattering particle. These three objects give rise to different light scattering diagrams.
Candidate Colorectal Cancer Biomarkers 

Candidate Biomarker Discovery Pre-validation Validation

CCSA-2, 3 and 4 (Serum)

Spectral Markers (Tissue)

ColoUp 1 and 2 (Serum)

2D Mass Map (Serum)

K-Ras (Urine)

Methylation Marker Panel (Stool) 

TIMP-3 Methylation (Tissue)

SELDI Profile (Serum)

MALDI Profile (Serum)

K-Ras (Stool Guiac)

Flat adenoma (Tissue)

GOS (Stool)

Galectin-3 Lig (Serum)

TIMP-1 (Serum)

Discovery

Predictive Analysis

Assay Refinement

Blinded Limited 

Cross-Sectional

Large Cross-Sectional

Colorectal and Other Gastrointestinal Cancers 39
40 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Approximately 50% of patients above the 

age of 60 have polyps discovered on colonoscopy. Current practice is to have repeat 

colonoscopies at 3-year intervals following the 

initial polypectomy. Many of these follow-up 

procedures do not show recurrent adenomas. 

A means of identifying those patients who are 

most likely to have recurrent polyps would be 

useful to reduce the number of negative follow-up colonoscopies and to identify patients 

in whom polyp recurrence is most likely. 

EDRN investigators at Johns Hopkins University are exploring the use of DNA methylation markers to predict polyp recurrence. 

These investigators analyzed the methylation 

status of 15 candidate genes in polyps that 

were removed during colonoscopy from 53 

patients. Each of these patients had follow-up 

colonoscopies performed. The EDRN investigators found that the methylation status of 

several of these genes in the polyps removed 

during the first colonoscopy could be used to 

predict those patients who would have polyps 

during their follow-up colonoscopies. Results with TIMP-3 methylation in predicting 

polyp recurrence at 24 months are shown in 

Figure 3-2. A panel of methylation markers is 

currently being created from studies with an 

expanded cohort of patients. These prediction 

biomarkers may stratify patients into followup groups to determine the necessity and appropriate interval for follow-up colonoscopies.

Figure 3-2: Ability of TIMP-3 Test to Identify Likelihood of Polyp Recurrence at 24 Months

The dotted line would indicate the test had no ability to predict recurrence, while a “perfect” 

test would reach 1 in sensitivity and go straight across.

1 - Specificity (false positives)

Sensitivity (true positives)

1

0.8

0.6

0.4

0.2

0 0.5 1

No discrimination TIMP3

Source: EDRN investigators at Johns Hopkins University
Hepatocellular Carcinoma/Primary 

Liver Cancer

Primary hepatocellular carcinoma (HCC) 

is fifth in cancer incidence worldwide and 

the third leading cause of cancer death. It is 

also the fastest growing, in incidence, in the 

United States with a 5-year survival rate of 

less than 5%. The high mortality associated 

with HCC is primarily due to the advanced 

stage of disease at initial diagnosis when 

therapy is not successful. Infection with 

hepatitis B or hepatitis C virus (HBV and 

HCV) is responsible for at least 80% of all 

HCC. Since patients with cirrhosis (with 

or without HBV or HCV infection) are at 

significantly increased risk of developing 

HCC, surveillance of a clinically identifiable 

population is realistic and logical. Thus, 

screening for HCC focuses on patients with 

cirrhosis. By analyzing existing biomarker 

candidates as well as through its own 

proteomic program, EDRN identified three 

new leading biomarker candidates, two of 

which are the focus of a large multi-site 

validation trial.

Definition of liver cancer:

Primary liver cancer is one that forms in the 

tissues of the liver. 

Estimated new cases and deaths from 

liver and intrahepatic bile duct cancer 

in the United States in 2007: 

New cases: 19,160 

Deaths: 16,780

Primary liver cancer incidence rates have 

increased 2.2% per year from 1995 to 2004, 

the most recent year statistics are available. 

The level of alpha-fetoprotein (AFP) in blood 

is currently used in the diagnosis of HCC, 

with a sensitivity ranging from 39% to 62% 

and a specificity from 56% to 80%. This 

results in a positive predictive value ranging 

from 16% to 35%, missing many early stage 

HCC and giving many false-positive results. 

At the University of Michigan, an EDRN 

Associate Member, conducted a case-control 

study to compare the accuracy of circulating 

AFP levels versus levels of des-gamma-carboxy 

prothrombin (DCP) as a biomarker of HCC. 

DCP was found to have excellent sensitivity in 

HCC detection (89%) and specificity (95%), 

significantly better than the performance of 

AFP in the same patients. These results have 

been validated in a multi-center trial, using 

blood collected from patients with early stage 

HCC. (See Chapter 6, Validation Studies, 

Case 1.) The ability of AFP-L3% to detect 

early stage HCC was also evaluated. AFP-L3% 

is a form of AFP recently approved by the 

FDA to help doctors determine the chances 

that a patient will advance to liver cancer 

in the next two years so the patient can be 

appropriately managed and treated. 

Current Tests to Diagnose Liver Cancer

There are no early detection tests for liver cancer. 

However, the following tests are FDA-approved 

for use in the diagnostic process:

alpha-fetoprotein (AFP)

A protein normally produced by a fetus. AFP 

levels are usually undetectable in the blood 

of healthy adult men or women (who are not 

pregnant). An elevated level of AFP suggests the 

presence of either a primary liver cancer or germ 

cell tumor.

AFP-L3%

A subgroup of AFP with a particular sugar 

structure alteration that is known to be highly 

produced by liver cancer cells. When this form 

exceeds 10% of the total AFP, the test is considered positive.

At Drexel University an EDRN team, working 

with The Hepatitis B Foundation, used new 

glycomics techniques to discover several novel 

glycoprotein biomarkers of liver disease. 

Their work focused on the subset of polypeptides that contain the sugar fucose. This group 

identified more than 50 fucosylated 

Colorectal and Other Gastrointestinal Cancers 41
42 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

serum proteins whose levels are elevated in 

the serum of people with HCC. One protein, 

GP73, normally resides inside the cell on the 

Golgi apparatus. At present, GP73 levels have 

been measured in samples from more than 

700 individuals and were shown to be more 

sensitive than AFP in detecting both early and 

late stage HCC. In distinguishing early stage 

HCC from cirrhosis, GP73 had a sensitivity 

of 62% compared with 25% for AFP. The assay was adapted to specifically detect fucosylated GP73, which significantly improved its 

performance: fucosylated GP73 was elevated 

in 90% of those with HCC and none of those 

with cirrhosis. HCC is more closely associated 

with fucosylation than protein biomarker 

levels, as the fucosylated forms of other serum 

proteins, such as hemopexin and kinnogen, 

also had sensitivities of more than 80% with 

specificities more than 90%. Once the assays 

for these fucosylated proteins are fully developed, the results will be validated using 

the specimens collected for the EDRN DCP 

Validation Trial.

Glycomics for Biomarker Discovery

Research is showing that the glycosylation of 

proteins may vary with disease state. That is, the 

same protein from malignant and non-malignant 

tissues may occur in different and characteristic 

glycoforms. EDRN scientists at Drexel University 

have developed methods by which N-glycans 

derived from certain serum glycoproteins as a 

function of liver disease can be analyzed and, the 

glycans that correlate with hepatocelluar carcinoma (HCC) can be identified. The approach can be 

used for any disease, but beginning with an animal 

model, these scientists identified glycans and 

the glycoproteins to which they are attached that 

were associated with HCC. An assay to detect the 

most prominently associated protein, called GP73, 

was developed and now more than 1,000 samples 

from people have been tested in blinded studies. 

GP73 and its fucosylated glycoform, are proving to 

outperform the standard of care, alpha-fetoprotein, 

in detection of early stage cancer. This represents 

an important proof of concept as well as a delivery 

of superior early detection markers of liver cancer. 

Treatment decisions for people with cirrhosis 

are often based upon the degree of fibrosis or 

scarring in their liver. A blood test to determine the degree of liver fibrosis would permit 

treatment decisions to be made without 

further invasive procedures. Investigators 

at Drexel University found that the major 

fucosylated polypeptide in the circulation of 

those with a diagnosis of cirrhosis is a distinct 

immunoglobulin. Based on these findings, a 

simple enzyme linked immunosorbent assay 

(ELISA) to detect this form of immunoglobin 

was developed that can distinguish fibrosis 

stage F3 and greater from stage F2 and less, 

with a positive predictive value of 97%. If 

validated, this test could be used to determine 

treatments.

An EDRN Associate Member at Georgetown 

University is analyzing the low molecular 

weight proteins found in the serum of liver 

cancer patients. A computational method 

was used to select six marker candidates for 

classification of HCC. The performance of 

these candidates was assessed by examining 

sera of 78 HCC cases and 72 age- and gender- matched cancer-free controls recruited 

from an Egyptian population. A combination 

of the six markers using a matrix-assisted 

laser desorption (MALDI) profile achieved 

100% sensitivity and 91% specificity. These 

results await verification using specimens from 

American patients. 

Barrett’s Esophagus and Esophageal 

Adenocarcinoma

Despite advances in surgical techniques 

and multimodal therapy, the 5-year survival 

rate for esophageal cancer remains dismal 

at 5% to 15%. Advanced stage of disease at 

initial diagnosis and high rates of recurrence 

contribute to this low survival. Developing 

and refining methods for early cancer 

detection is a key to improving survival in this 

deadly disease.
Candidate Hepatocellular Cancer Biomarkers 

Candidate Biomarker Discovery Pre-validation Validation

Des-gamma Carboxy Prothrombin 

(DCP) (Serum) 

AFP-L3% (Alpha-fetoprotein) 

(Serum)

GP73 (Serum)

f-GP73 (Fucosylated-GP73) 

(Serum)

Fucosylated (f)-kinnogen (Serum)

Fucosylated (f)-hemopexin 

(Serum) 

Fucosylated polypeptide for 

fibrosis (Serum)

MALDI Profile (6 protein peaks) 

(Serum)

Discovery

Predictive Analysis

Assay Refinement

Blinded Limited 

Cross-Sectional

Large Cross-Sectional

Colorectal and Other Gastrointestinal Cancers 43
44 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Definition of esophageal cancer: 

Cancer that forms in tissues lining the esophagus 

(the muscular tube through which food passes 

from the throat to the stomach). Two types of 

esophageal cancer are squamous cell carcinoma 

(cancer that begins in flat cells lining the esophagus) and adenocarcinoma (cancer that begins 

in cells that make and release mucus and other 

fluids). 

Estimated new cases and deaths from esophageal cancer in the United States in 2007:

New cases: 15,560 

Deaths: 13,940

Definition of Barrett’s esophagus:

A condition in which the cells lining the lower part 

of the esophagus have changed or been replaced 

with abnormal cells that could lead to cancer of 

the esophagus. The backing up of stomach contents (reflux) may irritate the esophagus and, over 

time, cause Barrett’s esophagus.

There are no early detection tests for esophageal 

cancer.

Chronic reflux of acidic gastric contents 

can cause gastroesophageal reflux disease or 

GERD. Long-term GERD, in turn, can cause 

Barrett’s esophagus, a premalignant condition 

that increases a patient’s risk of developing 

esophageal adenocarcinoma. Because of this 

increase in cancer risk, patients with a known 

diagnosis of Barrett’s esophagus undergo 

endoscopic surveillance at regular intervals, 

usually every two to three years. Patients may 

undergo these surveillance endoscopies for 

the rest of their lives, sometimes submitting 

to as many as ten in a lifetime. However, 

most patients with Barrett’s esophagus do not 

progress to cancer and a biomarker test to 

predict those likely to progress could reduce 

the number of endoscopies and improve 

surveillance.

An EDRN investigator at Johns Hopkins 

University developed a three-tiered risk 

model that incorporates both epigenetic

(methylation of tumor suppressor genes) and 

clinical parameters to improve the efficiency 

of Barrett’s esophagus surveillance. As 

progression-free survival differed significantly 

among the three risk groups, clinicians may 

be able to base the frequency of endoscopies 

on an individual patient’s risk calculated using 

these epigenetic and clinical parameters. A 

related project also involves analyzing levels 

of methylated DNA in plasma from patients 

with Barrett’s esophagus or esophageal 

adenocarcinoma. Among 24 patients with 

esophageal adenocarcinoma studied to date, 

70% had hypermethylated HPP1 in their 

blood, compared with only 13% of control 

subjects. Thus, DNA methylation in sera may 

be useful for early detection of esophageal 

adenocarcinoma and as prognostic or 

recurrence biomarkers for this deadly disease.

EDRN investigators at the University 

of Michigan are working with a number 

of collaborators to develop and validate 

both proteomic and genomic biomarkers 

for esophageal adenocarcinoma. Their 

collaborators at the Mayo Clinic have 

performed a pre-validation study of a panel 

of fluorescence in situ hybridization 

(FISH)-based biomarkers to detect highgrade dysplasia and adenocarcinoma using 

esophageal cytology. These FISH probes 

examine alterations (loss or gains) in specific 

genetic loci associated with the progression 

of Barrett’s esophagus to esophageal 

adenocarcinoma. Abbott Molecular, Inc., the 

sponsor of this study, is ready to collaborate 

with EDRN to perform a multi-site study. 

In another collaboration, EDRN investigators 

at UCLA will evaluate the use of ploidy 

(numbers of chromosomes in a cell) as a 

predictor for progression of esophageal cancer.
Candidate Esophageal Cancer Biomarkers 

Candidate Biomarker Discovery Pre-validation Validation

2D Mass Map (Serum)

Methylation Panel (Tissue)

FISH Panel (Cytology)

Ploidy (Tissue)

Discovery

Predictive Analysis

Assay Refinement

Blinded Limited 

Cross-Sectional

Large Cross-Sectional

Pancreatic Cancer

Pancreatic cancer is the fourth most common 

cause of cancer death in the United States, 

although it comprises only approximately 2% 

of new cancer diagnoses. The median survival 

for all patients diagnosed with pancreatic 

cancer is less than six months while the 5-year 

survival is less than 5%. This dismal survival 

rate is largely due to being unable to diagnose 

this cancer at a stage when the option of 

curative surgery is still possible. 

Definition of pancreatic cancer:

A disease in which malignant (cancer) cells are 

found in the tissues of the pancreas. 

Estimated new cases and deaths from pancreatic cancer in the United States in 2007:

New cases: 37,170 

Deaths: 33,370

Commonly used imaging methods, such as 

endoscopic ultrasound, abdominal CT scan, 

or MRI, are inadequate for the detection 

of early stage pancreatic cancer. CA 19-9 

is presently the most widely used serum 

marker for pancreatic cancer, but as a 

screening test in an asymptomatic population, 

its positive predictive value is below 1%. 

EDRN investigators are actively exploring 

both genomic and proteomic markers to 

improve the ability to detect early stage 

pancreatic cancers.

At the University of Nebraska EDRN 

investigators are working to improve the 

utility of CA 19-9 by adding a test to 

determine the mucin protein to which the CA 

19-9 carbohydrate antigen is attached. This 

proposal is based on recent discoveries about 

the different mucin core proteins expressed by 

different types of cancers. This group found 

that levels of expression of specific mucin 

genes are increased in pancreatic cancer tissues 

and that an antibody against one of these 

mucin proteins can detect 91% of pancreatic 

cancer in endoscopic ultrasound-guided 

fine needle aspirate samples. This group is 

currently working to develop serum assays for 

these mucin proteins. 

Scientists at the University of Texas 

M. D. Anderson Cancer Center are taking 

a targeted approach to identify biomarkers 

for early detection of pancreatic cancer by 

focusing on abnormal genetic pathways 

in pancreatic cancer. They have identified 

a number of genes that are consistently 

differentially expressed in pancreatic cancer 

Colorectal and Other Gastrointestinal Cancers 45
46 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

and are examining these genes as candidate 

biomarkers. Among them, Sel-1L is of 

particular interest as at least one form encodes 

a secreted protein. Ductal epithelial associated 

ring chromosome 1 (DEAR1), a gene that 

maps into a region of high frequency loss 

of heterozygosity in sporadic breast and 

pancreatic cancer, is another candidate 

gene discovered by this laboratory. DEAR1

methylation assays as well as DEAR1 mutation 

assays are being developed.

There are no FDA-approved early detection tests for pancreatic cancer.

The CA 19-9 assay is used as a tumor marker 

when pancreatic cancer is diagnosed, but is not a 

good diagnostic test. The test measures the level 

of CA 19-9 in the blood from both cancer cells and 

normal cells. Higher than normal amounts of CA 

19-9 in the blood can be a sign of gallbladder or 

pancreatic cancer or other conditions.

A protein array system to analyze blood 

samples from patients with pancreatic 

cancer is being exploited at the University 

of Pittsburgh Cancer Institute. These 

investigators found marked differences in the 

expression of many proteins in patient samples 

compared with those from controls. This led 

to the development of a 10-biomarker panel 

that distinguishes pancreatic cancer patients 

from healthy controls with a sensitivity of 

87% and a specificity of 98%. This panel 

specifically recognized patients with pancreatic 

cancer and excluded patients with other 

cancers, including lung, esophageal, head 

and neck, ovarian, breast, endometrial and 

melanoma. 

Another EDRN team at the Fred Hutchinson 

Cancer Research Center developed a panel 

of protein biomarkers in serum that can 

distinguish patients with pancreatic cancer 

from those with pancreatitis (inflammation 

of the pancreas) with nearly 95% sensitivity 

and specificity. Biomarker panels developed 

by both EDRN teams are very promising and 

plans are in progress to validate those using 

larger numbers of specimens, especially from 

early stage disease, collected from multiple 

sites. Clues to discovery of these biomarkers 

came from the investigators’ study on mouse 

proteomics.

Candidate Pancreatic Cancer Biomarkers 

Candidate Biomarker Discovery Pre-validation Validation

Mucin Proteins (Serum)

Sel-1L (Serum)

DEAR mutation (Serum)

DEAR methylation (Serum 

and pancreatic juice)

10-Protein Biomarker panel (Serum)

Protein Biomarker panel (Serum)

Discovery

Predictive Analysis

Assay Refinement

Blinded Limited 

Cross-Sectional

Large Cross-Sectional
Lung cancer continues to be 

the most lethal cancer in the United 

States. The reasons for this high mortality are 

advanced stage at diagnosis, the biological 

aggressiveness of the tumor and its resistance 

to standard radiation and chemotherapy. The 

5-year survival rate after diagnosis remains 

discouragingly low at only 15%. If lung cancer 

is detected early, survival is much improved, 

up to 90% 5-year survival. Computerized 

tomography (CT) can detect a large number 

of non-specific lung nodules that require 

follow-up scans to determine growth rate or 

invasive procedures to establish a diagnosis. 

This process causes anxiety, significant 

financial burdens to the patient and the health 

care system and exposes patients to potentially 

harmful amounts of radiation. As the majority 

of these nodules are benign, a biomarker

or panel of biomarkers that can distinguish 

benign nodules from cancer are needed. 

EDRN set goals to identify and validate 

non-invasive biomarkers in blood or sputum 

for the early detection of lung cancer, which 

could be combined with CT scanning or other 

imaging methods. Smoking is the leading 

risk factor for lung cancer; however a second 

significant, but less common, risk factor is 

asbestos exposure. EDRN biomarker discovery 

efforts for lung cancer take these causative 

agents into account. In addition, EDRN 

supports diagnostic studies for early detection 

of mesothelioma, a malignancy that is almost 

always associated with exposure to asbestos.

“O 

ur research provides solid evidence on universal involvement of forerunner genes 

in human carcinogenesis. In fact, they are used in methylation panels for other 

organs such as lung .... In general the forerunner gene may represent novel 

therapeutic preventive targets and early detection markers for other cancer types. 

EDRN has provided critical funding and collaborating platforms for this PI not 

available via other funding mechanisms. The goals of EDRN are ideal for our 

research on early phases of human carcinogenesis and markers development for 

early cancer diagnosis.” 

 Bogdan Czerniak, M.D., Ph.D. 

 Principal Investigator

EDRN Biomarker Development Laboratory 

University of Texas M. D. Anderson Cancer Center

Lung and Upper Aerodigestive Cancers 47

Lung and Upper

Aerodigestive Cancers

C hapter FOUR
48 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

EDRN Lung Cancer Collaborative Group Members 

William Rom, M.D., M.P.H., Chair

New York University School of Medicine

Adi Gazdar, M.D., Co-Chair

University of Texas Southwestern Medical 

Center

David Beer, Ph.D.

University of Michigan

William Bigbee, Ph.D.

University of Pittsburgh Cancer Center

Steven Belinsky, Ph.D.

Lovelace Respiratory Research Institute

Wilbur Franklin, M.D.

University of Colorado Health Science Center

Lee Goodglick, Ph.D.

University of California, Los Angeles

Samir Hanash, M.D., Ph.D.

Fred Hutchinson Cancer Research Center

Karl Krueger, Ph.D.

National Cancer Institute

Carolyn Muller, M.D.

University of New Mexico 

Zvi Livneh, Ph.D.

Weizmann Institute of Science

Harvey Pass, M.D.

New York University School of Medicine

David Sidransky, M.D.

Johns Hopkins University

Lynn Sorbara, Ph.D.

National Cancer Institute

Mark D. Thornquist, Ph.D.

Fred Hutchinson Cancer Research Center

Elizabeth Unger, Ph.D.

Centers for Disease Control and Prevention

Lung Cancer

Gene promoters play a central role in gene 

regulation. Promoter methylation is a process 

by which cancer cells add methyl groups 

to cytosine bases in promoters thereby 

turning off the gene. These methylated genes 

become inactivated and their vital cellular 

pathways are altered, often increasing the 

malignant potential of the cells. In addition, 

promoter methylation is readily detectable 

by quantitative molecular techniques based 

on polymerase chain reaction (PCR)

amplification. The identification of multiple 

methylated sites on promoters is emerging as 

a powerful marker of a cell’s transformation 

from normal to cancer.

EDRN investigators have extensive 

experience in the identification of new 

methylated markers in lung cancer. These 

discovery efforts have led to the testing of 

various methylated promoters as markers 

of lung cancer by testing sputum and cellfree DNA in blood. One interesting study 

involving collaboration between Johns 

Hopkins University and New York University 

investigators allowed comparisons of current 

high-profile imaging techniques (spiral CT 

scanning) with emerging methylated markers 

in the corresponding clinical samples. The 

Clinical Epidemiological and Validation 

Center (CEVC) at New York University 

is using spiral CT scanning to screen over 

1,000 people at high risk for developing lung 
cancer (see Figure 4-1). These people are 

heavy smokers or were occupationally exposed 

to carcinogenic agents. Studies show that 

CT-scan screening can increase detection of 

early lung cancer seven-fold compared to the 

chest x-ray, with the greatest improvement in 

determining Stage I and II adenocarcinoma. 

In these early stages of lung cancer, surgical 

therapy can allow up to 90% survival for four 

or more years. The objective of the National 

Lung Screening Trial (NLST) is to determine 

if mortality can be affected by spiral CT 

screening.

Definition of lung cancer: 

Cancer that forms in tissues of the lung, usually in 

the cells lining air passages. The two main types 

are small cell lung carcinoma and non-small cell 

lung carcinoma. These types are diagnosed based 

on the cellular morphology under a microscope. 

Estimated new cases and deaths from 

lung cancer (non-small cell and small cell 

combined) in the United States in 2007: 

New cases: 213,380 

Deaths: 160,390

Figure 4-1. CT Scan Screening for Lung Cancer

CT-screening detects several kinds of tissue changes in lungs (see figure below). In over half of 

high-risk (>20 pack-years) smokers, noncalcified nodules 4-8 mm in size are found and approximately 

another 10% of subjects have ground-glass opacities (GGOs). These nodules and GGOs require 

follow-up to determine if they are cancer. EDRN investigators are exploring different avenues to 

complement CT-screening by identifying biomarkers in blood or sputum applicable to the diagnosis of 

these suspicious abnormalities. The importance of such biomarker tests is that clinical decisions can 

be made by non-invasive means to establish early stage cancer or prevent unnecessary surgeries, 

minimize stress and avoid prolonged follow-up to patients with benign nodules. 

Profile of subjects entering spiral CT screening at New York University CEVC

60

40

0

Solid Nodules

GGOs

Pleural Plaques

Asbestosis

Normal 

Cancer

Percent of Individuals

Lung and Upper Aerodigestive Cancers 49

Source: CEVC at New York University
50 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

DNA in plasma and sputum samples from 

patients with lung abnormalities detected 

upon CT scan was examined for aberrant 

methylation of four gene promoters (CDH1, 

RASSF1A, p16 and MGMT). The patients 

were divided into three groups based on 

characteristics of the abnormalities detected: 

nodules, ground-glass opacities (GGOs) and 

cancerous tumors. Plasma and sputum DNA 

from age-matched nodule-free individuals 

were used as controls. In plasma, 30% of 

patients with nodules, 32% of patients with 

GGOs and 48% of patients with cancerous 

tumors showed methylation of at least one 

gene while only 4% of control patients 

showed methylation. In sputum, 18% of 

patients with nodules and 41% of patients 

with GGOs showed methylation whereas no 

control subjects exhibited methylation.

Methylation of at least one marker in 

either the patient’s plasma or sputum was 

observed in 6% of controls and 40% of 

patients with abnormal findings. Promoter 

hypermethylation of a panel of genes increases 

the likelihood of detecting such changes 

in plasma and sputum from patients with 

abnormal spiral CT findings in the lung. An 

extended set of genes is being investigated 

including APC, Cyclin D2, b-Catenin, 

FHIT, DNA Methyltransferase (DNA MTase), 

Calcitonin-Related Peptide and Deleted in 

Colorectal Cancer (DCC). Three laboratories 

will independently evaluate these methylation 

markers for reproducibility and performance. 

Longitudinal follow-up of the present 

cohort will determine the risk of developing 

neoplastic lung disease for patients with 

abnormal CT findings who do not exhibit 

methylation of these genes.

Similar studies are being conducted by EDRN 

investigators at the Lovelace Respiratory 

Research Institute to determine whether 

a panel of genes can be identified whose 

methylation in sputum will predict early lung 

cancer. This hypothesis received considerable 

support through recent findings from a nested, 

case-control study within the Colorado HighRisk Smokers cohort. The findings showed 

that the concomitant methylation of three 

or more of the six genes in the panel was 

associated with a six-fold increased risk for 

lung cancer. A sensitivity and specificity of 

64% were seen for identifying incident lung 

cancer cases three to eight months prior to 

clinical diagnosis. Additional genes are under 

evaluation to increase the sensitivity and 

specificity of the methylation test in sputum. 

Other studies of methylation in sputum are 

being conducted in two high-risk cohorts 

with longitudinal follow-up: a veterans male 

cohort and the Lovelace Smokers cohort that 

is predominantly female. Cross-sectional and 

nested, case-control studies are in progress. 

A third study based on diagnosis of current 

lung cancer involves collaboration between 

EDRN investigators at the University of 

Texas-Southwestern with a group from the 

Netherlands. They have developed novel 

methods for sputum collection and processing 

and have amassed over 600 specimens from 

cancer patients and heavy smokers without 

cancer, often with extensive follow-up 

data on these patients. In two preliminary 

blinded experiments, a panel of only two 

marker genes readily identified lung cancers 

at specificity and sensitivity values exceeding 

those of conventional cytology by two to 

three times. These promising data are now 

being pursued by testing a larger panel of 

methylation markers in 40 specimens from 

Lovelace. Pending the success of this study, 

a large validation study will be proposed 

using specimens collected from multiple 

EDRN sites and by the Dutch group with 

methylation analysis to be performed 

independently at laboratories in the United 

States and the Netherlands.

In a related study a collaboration between 

New York University investigators with 

researchers at the Fox Chase Cancer 

Center have shown that plasma levels of Sadenosylmethionine, the methyl donor for 

DNA methylation, are markedly increased 

in patients with lung cancer compared with 

smokers with normal CTs. These findings 

suggest that plasma S-adenosylmethionine 

may serve as an additional biomarker 

for malignancy; the increase in Sadenosylmethionine may be related to 

increased gene methylation.
One of the most consistent properties of lung 

cancers is their high level of chromosome 

instability that is reflected in imbalance of 

chromosome copy number and widespread 

structural abnormalities. Researchers at the 

University of Colorado have found clonal

changes that precede the development of 

carcinoma in the central airway. The most 

frequent are abnormalities in chromosome 

number (usually chromosomes 5, 7, 8 and 

18) that result from mis-segregation during 

cell division. Chromosomal instability 

associated with the development of spreading 

mutant clones in the airways was found in 

approximately 40% of high-risk smokers who 

do not have lung carcinoma. In addition, 

clonal changes were identified in benign 

epithelium from approximately half of the 

patients whose tissues have been removed 

for lung carcinoma. No abnormalities 

were observed in never-smoking controls. 

In collaboration with Abbott Laboratories, 

fluorescence in situ hybridization (FISH) 

probes are being developed that will recognize 

all of these lesions.

In search of other biologic markers, 

EDRN investigators at Johns Hopkins and 

the National Institute of Standards and 

Technology (NIST) have turned to mutations 

in mitochondria, the energy factories of cells. 

Reports from several laboratories have found 

somatic mutations of mitochondrial DNA 

in most human tumors. Efficient efforts 

of mutation detection were hampered by 

the 16,000 mitochondrial DNA bases that 

must be tested. This barrier was recently 

overcome by the development of a sensitive 

mitochondrial DNA sequencing chip called 

the Mito Chip. This chip was exploited to 

investigate whether mitochondrial mutations 

can be detected from non-invasively collected 

bodily fluids (sputum or bronchoalveolar 

lavage) in lieu of primary tumor tissue from 

lung cancer patients. Mitochondrial mutations 

were identified in tumor samples and subsets 

of the identical mutations were also detected 

in the corresponding bodily fluid. Overall, 

eight out of nine matched serum DNA 

samples from cancer cases and nine out of 

nine sputum DNA samples contained an 

identical mutation to that detected in the 

primary tumor. It was found that mutations 

throughout the coding regions are frequently 

found in bodily fluids of cancer patients. 

These findings support the expectation that a 

relatively simple diagnostic test using the Mito 

Chip could provide early detection of lung 

cancer.

Smoking is the Prevailing Risk Factor 

for Lung Cancer 

Over 80% of all lung cancers cases are attributed to smoking. People who have smoked for 

more than 10 years are at 12 to 50 times greater 

risk of developing lung cancer by age 75 than 

nonsmokers. Greater risk correlates with greater 

smoking history. EDRN investigators at the Johns 

Hopkins University and Lovelace have conducted 

numerous studies in heavy smokers as a high-risk 

group to discover lung cancer early detection 

biomarkers. Extensive gene methylation studies 

have revealed that current smokers with over 

30 years of smoking history exhibit methylation 

profiles that overlap significantly with those from 

lung cancer patients with similar smoking history. 

These profiles are very different from nonsmokers. Similar observations were made by EDRN 

scientists at the University of Colorado examining 

chromosomal aberrations from bronchial airway 

epithelium. Persistent smoking induces considerable molecular changes in the lungs and airways 

that set the stage for disease progression to 

occur. Many of the expected biomarkers for early 

stage lung cancer progression are already present in current smokers, limiting their usefulness 

in determining lung cancer risk. These studies 

are now being extended to former smokers to 

determine if methylation profiles can distinguish 

them from former smoker lung cancer patients. It 

may then be possible to use methylation markers 

for early detection in former smokers.

Lung and Upper Aerodigestive Cancers 51
52 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

DNA repair plays a major role in all cancer 

pathogenesis and lung cancer in particular, 

primarily because of its importance in removing DNA damage and preventing mutations. 

An EDRN group at the Weizmann Institute 

of Science is exploiting functional DNA repair 

enzyme assays as biomarkers for risk assessment and early detection of lung cancer. They 

have previously shown that reduced activity of 

a specific DNA repair enzyme termed OGG 

(8-oxoguanine DNA glycosylase), which 

removes the oxidative defect 8-oxoguanine 

from DNA, is a risk factor for non-small cell 

lung cancer. Moreover, they found that the 

combination of low OGG activity and smoking causes a much greater estimated relative 

risk for lung cancer (see Figure 4-2) suggesting 

that OGG activity may be a useful biomarker 

of risk. The group is now expanding the repertoire of enzymatic DNA repair biomarkers 

by developing enzymatic assays for two additional DNA repair enzymes, AP (apurinic/

apyrimidinic) endonuclease and methylpurine 

DNA glycosylase, that likewise repair oxidative DNA damage. In addition, researchers at 

Lovelace are investigating whether the identification of specific haplotypes of genes affecting DNA repair capacity can provide a more 

comprehensive profile of risk prediction and 

identify individuals that could benefit from 

chemoprevention.

Figure 4-2. Reduced DNA Repair as a Risk Biomarker for Lung and Head and Neck Cancers 

EDRN investigators at the Weizmann Institute of Science have shown that low DNA repair activity 

(OGG) in combination with smoking is associated with a much higher risk of lung and head and neck 

cancers. DNA repair is a housekeeping process responsible for DNA maintenance and prevention 

of mutations. DNA damage is caused by byproducts of intracellular metabolism, as well as external 

chemicals and radiation, such as tobacco smoke and sunlight. DNA repair enzymes scan the DNA, 

identify lesions and repair them using several mechanistic strategies thereby minimizing genetic 

damage that can lead to cancer. Based on these results, screening of smokers for low DNA repair 

activity may be used to identify individuals at extra-high risk for lung cancer. These individuals may be 

more motivated to enter smoking cessation programs, thereby reducing their risk for developing the 

disease. In addition, smokers with low activities may be a high-risk and cost-effective cohort for lung 

cancer early detection methods, which are too expensive for general population screening.

Smoking and Low DNA Repair Activity Greatly Increases Lung Cancer Risk Estimated Relative Risk

– +

0

– +

1

– +

2

– +

3

– +

4

Smoking

Units OGG Decreased

250

200

150

100

50

0

Source: EDRN investigators at the Weizmann Institute of Science
Lung and Upper Aerodigestive Cancers 53

Interesting corollary studies are being conducted at New York University looking into 

the nature of mutations induced by smoking 

that occur in critical tumor suppressor genes. 

One of these projects examined acrolein, a byproduct of incomplete combustion of fossil oil 

and organic substances that is one of the most 

reactive and environmentally abundant aldehydes. Although acrolein content in cigarette 

smoke is 1,000-fold higher than polycyclic 

aromatic hydrocarbons, its role in cigarette 

smoke-related lung cancer remains unclear. 

Through its formation of DNA adducts, 

acrolein can cause mutations. The tumor suppressor gene p53 is one of the most frequently 

mutated genes in human cancer and over 50% 

of smoking-related lung cancers have p53

mutations. New York University researchers 

have found that the DNA adducts induced by 

cigarette smoke carcinogens such as acrolein 

and polycyclic aromatic hydrocarbons preferentially occur at p53 mutational hotspots that 

are poorly repaired. Their results suggest that 

acrolein is a major etiological agent for smoking-related lung cancer and that it contributes 

to lung carcinogenesis through two detrimental mechanisms: DNA damage and inhibition 

of DNA repair.

Researchers at the Fred Hutchinson Cancer 

Research Center (FHCRC) are working to 

develop blood tests for early detection of lung 

cancer. There is substantial evidence that 

the immune system reacts to the presence of 

tumors by inducing an antibody response to 

tumor antigens in a manner similar to the 

response that occurs against certain viruses 

such as HIV. This response may not be effective against the tumor but could be taken 

advantage of to detect the presence of the 

tumor or to develop more effective therapies 

directed against the tumor antigens. The 

identification of panels of such tumor antigens 

has utility in cancer screening and diagnosis 

in the same way seropositivity to HIV virus is 

used to identify individuals with HIV. Several approaches are currently available for the 

identification of tumor antigens. The FHCRC 

laboratory has pioneered a proteomic-based 

approach to identify those proteins, among 

thousands of proteins produced by tumors, 

that are most informative for cancer diagnosis 

based on their immunogenicity and specificity. 

Ongoing work in the FHCRC laboratory is 

leading to discovery of more tumor antigens 

in addition to validation of previously identified ones, annexins and PGP 9.5. Results from 

ongoing validation studies are quite promising 

based on the blinded analysis of 60 sera collected as part of a previous study, the BetaCarotene and Retinol Efficacy Trial (CARET). 

Sera were drawn from 30 subjects approximately a year before diagnoses of lung cancer 

and 30 sera were from matched controls. A 

significant difference in reactivity is observed 

for the designated antigens between those 

subjects that later developed lung cancer and 

the control subjects that did not.

Mesothelioma

Individuals exposed to asbestos have an 

increased risk of developing lung cancer 

and mesothelioma, a malignancy of the lung 

lining. There is an increasing realization by 

the scientific community that this population represents an ideal group to undertake 

early cancer detection because: (1) the probability that a disease marker will be significant 

is higher in this cohort than in the general, 

unexposed population; (2) asbestos-exposed 

cohorts can be closely followed in validation 

studies; (3) at-risk individuals are motivated 

to participate in follow-up studies; and 

(4) they present an opportunity to test early 

therapeutic intervention in patients with 

mesothelioma. 
54 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Candidate Lung Cancer Biomarkers 

Candidate Biomarker Discovery Pre-validation Validation

Diagnostic Methylation Panel 

(Plasma)

Diagnostic Methylation Panel 

(Sputum)

Risk Methylation Panel (Sputum)

Chromosomal Instability

FISH (Sputum)

Mitochondrial Mutations (Sputum)

DNA Repair Activity (Risk) (Cells)

Autoantibodies (Annexins, 

PGP9.5, others) (Serum)

Discovery

Predictive Analysis

Assay Refinement

Blinded Limited 

Cross-Sectional

Large Cross-Sectional

Two markers, serum mesothelin-related peptide (SMRP) and osteopontin, are the subject 

of EDRN investigations. Mesothelin is a protein attached to the cell surface of mesotheliomas, ovarian cancers and pancreatic cancers, 

that is thought to have a role in cell adhesion 

and cell-cell communication. One member 

of the mesothelin family is SMRP. Recently, 

an EDRN investigator in Australia reported 

determination of SMRP in serum with a 

sensitivity of 83% and specificity of 95% in 

the first 48 malignant mesothelioma patients 

tested. Changes in serum SMRP levels parallel clinical course/tumor size and SMRP was 

elevated in 75% of a larger cohort of patients 

at diagnosis. 

The EDRN laboratory at New York University BDL found that another protein, 

osteopontin, could be an early marker for 

mesothelioma. Osteopontin is overexpressed 

in many cancers, including lung, where it mediates cell-matrix interactions and is regulated 

by proteins in cell-signaling pathways that 

have been associated with asbestos-associated 

cancer. In a study that compared serum osteopontin levels of patients with mesothelioma 

to individuals exposed to asbestos but without 

cancer, serum osteopontin levels rose with 

duration of asbestos exposure (0-9 years versus 

10+ years) and degree of changes on an x-ray 

or CT scan (plaques and fibrosis versus other 

lesser findings). The mean serum osteopontin 
level in individuals with mesothelioma was 

significantly higher than in the group exposed 

to asbestos, 77.6% sensitivity and 85.5% specificity when comparing the group exposed to 

asbestos to the group with mesothelioma. This 

study was the first to recognize that serum 

osteopontin levels could possibly distinguish 

persons with asbestos exposure without cancer 

from those with exposure who have developed 

pleural mesothelioma. 

Definition of malignant mesothelioma: 

A rare type of cancer in which malignant cells 

are found in the sac lining the chest or abdomen. 

Exposure to airborne asbestos particles increases 

one’s risk of developing malignant mesothelioma.

U.S. and Australian investigators are now 

designing trials to validate whether these two 

markers and others could be used to screen for 

mesothelioma. A validation study of SMRP 

and osteopontin is under way. (See Chapter 

6, Validation Studies, Case 4.) This study 

involves many noted cohorts including: 

the Selikoff Foundation at Mt. Sinai in New 

York; the Prostate Lung Colon and Ovarian 

Cancer Screening Trial; the CARET chemoprevention trial; the Center for Asbestos 

Related Diseases in Libby Montana where 

vermiculite was contaminated with tremolite asbestos; and Cappadocia Turkey where 

mesothelioma is epidemic due the presence of 

mineral fibers in building materials extracted 

from the neighboring mountains. Specimens 

are actively being collected from these sites 

where, hopefully, a group of markers will 

predict whether mesothelioma is developing 

in high-risk individuals exposed to asbestos.

Candidate Mesothelioma Biomarkers 

Candidate Biomarker Discovery Pre-validation Validation

SMRP and Osteopontin 

(Serum/Plasma)

MMP9 (Serum/Plasma)

Discovery

Predictive Analysis

Assay Refinement

Blinded Limited 

Cross-Sectional

Large Cross-Sectional

Lung and Upper Aerodigestive Cancers 55
Prostate cancer is the most 

frequently diagnosed non-skin cancer 

in men in the United States. The prevalence 

of the diagnosis makes the disease a major 

health burden. While many men will die 

from prostate cancer, a majority of them 

will survive the disease as it is not uniformly 

fatal. Identification of aggressive forms of the 

disease is needed to spare men who might not 

need extensive treatments.

Bladder cancer is less prevalent than prostate 

cancer, yet four times more common in men 

than women and also twice as common in 

white men than black men. A priority for 

early detection of this disease is to identify 

cancers when they are superficial (early stage); 

however, even superficial bladder cancers are 

varied in their genetic makeup. 

Kidney cancers can be successfully treated 

when diagnosed at an early stage. Renal cell 

carcinoma (RCC) is the most lethal of the 

common urologic cancers, with approximately 

40% of patients eventually dying of disease.

The major focus of the EDRN Prostate 

and Urologic Cancer Collaboration Group 

is towards discovery and validation of 

biomarkers for early detection and risk 

assessment of urological cancers, including 

prostate, bladder and kidney. 

In the last 2 years, the group developed 

standard reference materials, primarily plasma 

and serum (cases and matched controls) 

for detection and evaluation of prostate 

cancer biomarkers. Urine reference sets are 

being developed for bladder and prostate 

cancers. These reference sets are being used 

to commence blinded prevalidation and 

validation studies of candidate biomarkers. 

Two multi-institutional validation studies and 

five prevalidation studies have also begun in 

these disease areas.

Prostate and Other 

Urological Cancers

“T 

he EDRN has had an immense impact on discovery by providing samples, 

infrastructure and a multi-disciplinary team to make these discoveries happen. 

The EDRN will be very integral in the validation of these 

gene-fusion based biomarkers by providing a framework for systematic 

biomarker validation. It was really the only avenue for focused systematic 

biomarker discovery and validation.”

 Arul Chinaiyan, M.D., Ph.D.

 Principal Investigator 

 EDRN Biomarker Development Laboratory 

University of Michigan

56 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

C hapter FIVE
EDRN Prostate and Urologic Cancers Collaborative Group Members 

Daniel W. Chan, Ph.D., Chair

Johns Hopkins Medical Institute

O. John Semmes, Ph.D., Co-Chair

Eastern Virginia Medical School

Joseph Bigley, M.Sc.

OncoMethylome Sciences, Inc.

Paul Cairns, Ph.D.

Fox Chase Cancer Center

David Chia, Ph.D.

University of California, Los Angeles

Arul M. Chinnaiyan, M.D., Ph.D.

University of Michigan

Bogdan Czerniak, M.D., Ph.D.

University of Texas M. D. Anderson Cancer 

Center

Elefterios Diamandis, Ph.D.

Mount Sinai Hospital, Toronto

Sandra M. Gaston, Ph.D.

Beth Israel Deaconess Medical Center

William E. Grizzle, M.D., Ph.D.

University of Alabama at Birmingham

Jacob Kagan, M.S., Ph.D.

National Cancer Institute

Robin J. Leach, Ph.D.

University of Texas at San Antonio

Daniel W. Lin, M.D.

University of Washington

Alvin Y. Liu, Ph.D.

University of Washington

Alan W. Partin, M.D., Ph.D.

Johns Hopkins University 

Martin Sanda, M.D.

Beth Israel Deaconess Medical Center

Mark P. Schoenberg, M.D.

Johns Hopkins University

Lori Sokoll, Ph.D.

Johns Hopkins Medical Institutions

Sanford Stass, M.D.

University of Maryland School of Medicine

Ian M. Thompson, M.D.

University of Texas at San Antonio

Dean Troyer, M.D.

University of Texas at San Antonio

Robert W. Veltri, Ph.D.

Johns Hopkins University

Prostate Cancer

Prostate cancer accounts for about one-third 

of all cancers found in men in the United 

States and is the second most common cause 

of cancer-related deaths. Recent advances 

indicate that prostate cancer is caused by both 

genetic and epigenetic alterations. 

Definition of prostate cancer:

Cancer that forms in tissues of the prostate (a 

gland in the male reproductive system found 

below the bladder and in front of the rectum). 

Prostate cancer usually occurs in older men. 

Estimated new cases and deaths from prostate cancer in the United States in 2007:

New cases: 218,890 

Deaths: 27,050

Prostate and Other Urological Cancer 57
58 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Risk Factors

The search for the causes and ultimate 

prevention of prostate cancer entered a new 

era with recent developments allowing the 

correlation of environmental exposures with 

genetic/epigenetic variation and patient 

outcomes. Investigators at the University 

of Texas San Antonio have developed a 

prostate cancer risk-calculator that is based 

on factors such as PSA levels, digital rectal 

exam results (DRE), race, age and family 

history. Confirmation of the utility of this 

tool was demonstrated in a recently published 

study by the EDRN CEVC San Antonio 

Center for Biomarkers of Risk of Prostate 

Cancer (SABOR). This tool synchronizes 

currently established risk factors into a single 

composite score that can be used to identify 

those at high-risk for inclusion in prospective 

biomarker studies. Future refinements may 

include adding elements such as detrimental 

and protective polymorphisms in genes 

associated with critical pathways (for example, 

enzymes involved with androgen metabolism, 

DNA repair and hereditary susceptibility 

genes). The calculator can be accessed at 

http://www.compass.fhcrc.org/edrnnci/bin/

calculator/main.asp 

Genetic and Epigenetic Alterations 

as Cancer Biomarkers

The University of Michigan Biomarker 

Developmental Laboratory discovered 

frequent chromosomal rearrangements that 

juxtapose the TMPRSS2 promoter and 

first non-coding exon to genes of the ETS

family of oncogenes (ETV1, ETV4 and 

ERG). Fluorescence in situ hybridization 

(FISH) analysis revealed that the majority 

of prostate cancers (~60%) harbor these 

rearrangements. This is the first demonstration 

of chromosomal rearrangements in epithelial 

cancer. These chromosomal rearrangements 

offer distinct advantages over the current 

biomarkers for prostate cancer, such as PSA, 

because they occur only in the cancerous 

cells and are partly responsible for the 

transformation mechanism. Current research 

is examing whether these rearrangements can 

be detected in urine sediment.

PCA3 is a non-coding prostate-specific mRNA 

that was reported to be frequently overexpressed in prostate tumor cells. Investigators 

at Johns Hopkins University are collaborating 

with Gen-Pobe, Inc. and Diagnocure, 

Inc. in the analysis of PCA3. A prototype 

quantitative PCA3 urine test demonstrated 

potential as an adjunct to current methods 

for prostate cancer diagnosis and additional 

studies are in progress. 

Epigenetic modification of DNA (particularly 

hypermethylation of CpG islands within the 

5’ promoter region and the first exon) is a 

common alteration in cancer-related genes and 

is often associated with complete or partial 

repression of transcription. This mechanism 

is an alternative pathway for inactivation of 

tumor suppressor genes such as p16 and APC

in a variety of cancers. An EDRN laboratory 

at Johns Hopkins University reported that 

promoter methylation of several genes is 

a common feature of prostate cancer and 

high-grade prostatic intraepithelial neoplasia 

(HGPIN), the noncancerous growth of cells 

lining the internal and external surfaces 

of the prostate gland which may increase 

the risk of developing prostate cancer. The 

team assembled a panel of methylated 

genes as a new molecular marker for early 

cancer detection. The assay is based on the 

percentage of methylated alleles (PMA). 

PMA values of APC and RARß2 are higher 

in HGPIN, carcinoma and normal prostate 

tissue; however, the median PMA values for all 

three genes is higher in prostate cancer. 

Clonal expansion in cells carrying methylated 

alleles (APC, GSTP1 and RARß2) is observed 

in HGPIN and prostate carcinoma and is 

consistent with cancer progression. GSTP1 

promoter methylation is mainly observed 

in prostate carcinoma and some HGPIN 

lesions, representing an important marker 

for the transition to invasive neoplasia. The 

laboratory also developed a non-invasive test 

for prostate cancer based on a quantitative 

methylation-specific polymerase chain 

reaction (QMSP) of multiple genes in urine 

sediment DNA. A combination of four genes 

(p16, ARF, MGMT and GSTP1) theoretically 
permits the detection of 87% of prostate 

cancers with 100% specificity. A separate 

multi-center study led by other investigators 

at Johns Hopkins focuses on the GSTP1

methylation assay.

Proteomic Alterations as Biomarkers 

CD90 (Thy-1) is a cell surface protein 

frequently over-expressed in prostate cancer 

and T-cells. Investigators at the University of 

Washington recently discovered that increased 

levels of the protein CD90 was present in all 

of the 30 tumors examined. The increased 

levels were detected only immediately around 

the tumor, in the stromal cells. In addition, 

increased expression of CD90 peptide 

fragments have been detected in all urine 

samples of prostate cancer patients tested so 

far. Currently, the laboratory is developing 

an enzyme-linked immunosorbent assay 

(ELISA) for quantitative detection of CD90

in urine. 

CD10, an enkephalinase, is a 100kDa 

transmembrane glycoprotein involved in the 

cleavage and inactivation of peptide hormones 

important for signal transduction including 

the enkephalins, bombesin and substance P. 

The biological function of these potential 

CD10 substrates in the prostate is unknown. 

However, CD10 is strongly expressed by 

normal prostatic luminal epithelial cells. A 

high percentage of prostate tumors show an 

early loss of CD10 expression. Research is 

planned to examine CD10 expression in urine 

sediment. 

JM27 is an androgen-regulated gene 

expressed in the prostate, testis and the 

uterus. This potential serum marker was 

originally identified by Matritech, Inc. 

EDRN investigators at Johns Hopkins 

University recognized that JM27 protein is 

highly expressed in serum of patients with 

severe forms of benign prostatic hyperplasia 

(BPH). A serum-based ELISA was developed, 

tested and found to distinguish between 

symptomatic and asymptomatic men with 

BPH. The sensitivity and specificity of 

the assay are 90% and 77%, respectively. 

Interestingly, the presence of prostate cancer in 

these men does not appear to alter the marker 

levels. Doxazosin has been used to treat BPH 

down-regulated JM27 protein expression. This 

is the first reported serum-based marker for 

severe BPH. 

A Johns Hopkins EDRN laboratory in a 

collaboration between Beckman Coulter, 

Inc. performed a blinded test to evaluate the 

utility of a new isoform of PSA, %proPSA. 

The %proPSA alone and a model of PSA 

derivatives that included proPSA, had better 

overall clinical utility for prostate cancer 

detection in this blinded standard reference 

set than did free PSA, BPSA and testosterone. 

These findings provide the rationale for 

broader validation studies to determine 

whether %proPSA can supplant other 

multiple molecular PSA assays for improving 

accuracy of prostate cancer screening. 

EDRN investigators completed validation 

studies of proteomic patterns as potential 

diagnostic markers for prostate cancer 

detection. They were able to confirm the 

portability and reproducibility of the test, but 

found that it did not perform well enough 

to advance to further testing. (See Chapter 

6, Validation Studies, Case 5, for more 

information.)

Autoantibodies against peptides derived 

from prostate cancer tissue could be 

used as the basis for a screening test for 

prostate cancer. This suggestion is based 

on recent observations from laboratories 

at the University of Michigan and Beth 

Israel-Deaconess Hospital where patients 

with prostate cancer produced antibodies 

against N-methylacyl-coenzyme A racemase 

(AMACR), which is frequently overexpressed 

in prostate cancer. This autoantibody had 

72% specificity and 62% sensitivity in 

detecting prostate cancer. Following on this 

observation, the laboratory built a phage 

display prostate cancer peptide library to 

screen for additional autoantibodies in 

prostate cancer patients. The laboratory 

developed a panel of 22 autoantibodies 

that displayed an 88% specificity and 82% 

sensitivity in discriminating between prostate 

cancer patients and the control group. This 

panel of peptides outperformed PSA in 

distinguishing between the two.

Prostate and Other Urological Cancer 59
60 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

The ability to localize and follow changes at 

the molecular level by imaging the protein 

distributions in specific tissues is a promising 

improvement in pathological examination of 

specimens and as a discovery tool of markers 

for early detection. Investigators at the Eastern 

Virginia Medical School are applying MatrixAssisted Laser Desorption/Ionization Mass 

Spectrometric Imaging (MALDI-MSI) for 

spatial visualization of peptides and proteins 

specific to different cell types to delineate 

differentially expressed proteins in various 

pathological lesions of prostate cancer (e.g., 

evaluate peptide expression pattern differences 

between HGPIN and prostate cancer). If 

successful, this technology could be applied to 

better classification, grading and staging.

As shown in Figure 5-1, a prostate tissue 

section MALDI-MSI was used to analyze 

the contained regions of HGPIN and prostate 

cancer. Peaks found to be differentially 

expressed using MALDI-MSI can be seen 

in Figure 5-2. 

Figure 5-1. Prostate tissue used for MALDI-MSI analysis

 

 Figure 5-2. MALDI-MSI analysis of two peaks in frozen prostate tissue found upregulated in different 

microdissected cell types. 

 

Source: EDRN investigators at Eastern Virginia Medical School

Source: EDRN investigators at Eastern Virginia Medical School
Candidate Prostate Biomarkers 

Candidate Biomarker Discovery Pre-validation Validation

Fused transcripts (Tissue and Urine 

Sediment) TMPRSS2-EST gene 

family by RT-PCR

Fused transcript: TMPRSS2-EST 

gene family by FISH

PCA3 (Urine)

Panel of methylated gene (p16, 

ARF, MGMT,GSTP1) (Tissue, Urine 

and Serum)

Panel of methylated gene to 

distinguish HGPIN and cancer 

from normal ( APC, RARβ, 

GSTP1) (Serum)

CD90 (Thy-1) (Tissueand Urine)

CD10 (NEP) (Serum)

Percent of proPSA (proPSA/

freePSA) (Serum)

SELDI/MALDI Profile (Serum)

N-Methylacyl-CoA Racemase 

(AMACR) (Tissue)

Panel of autoantibodies 

(Serum/Plasma)

Discovery

Predictive Analysis

Assay Refinement

Blinded Limited 

Cross-Sectional

Large Cross-Sectional

Bladder Cancer

Over 90% of bladder cancer cases are transitional cell (urothelial) carcinoma (TCC), approximately 5% are squamous cell carcinoma 

and less than 2% are adenocarcinoma. Death 

due to bladder cancer is most often the result 

of muscle-invasive disease that accounts for approximately one-third of diagnoses. Superficial 

bladder tumors (non-muscle-invasive cancers) 

are a heterogeneous group of malignancies, 

including: papillary cancers that are limited 

to the mucosa; high-grade, flat and restricted 

to the epithelium; and invasive cancers that 

invade the sub-mucosa (lamina propria). 

Prostate and Other Urological Cancer 61
62 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Definition of bladder cancer: 

Cancer that forms in tissues of the bladder (the 

organ that stores urine). Most bladder cancers are 

transitional cell carcinomas (cancer that begins 

in cells that normally make up the inner lining of 

the bladder). Other types include squamous cell 

carcinoma (cancer that begins in thin, flat cells) 

and adenocarcinoma (cancer that begins in cells 

that make and release mucus and other fluids). 

The cells that form squamous cell carcinoma and 

adenocarcinoma develop in the inner lining of the 

bladder as a result of chronic irritation and inflammation.

Estimated new cases and deaths from bladder cancer in the United States in 2007:

New cases: 67,160 

Deaths: 13,750 

Aurora A (also known as STK15 and BTAK) 

is a gene encoding a centrosome-associated 

serine/threonine kinase, which is frequently 

amplified and overexpressed in multiple 

human tumor cell types including bladder 

cancer. Overexpression of this gene is involved 

in tumorigenic transformation, induction of 

centrosome duplication-distribution abnormalities and aneuploidy. Recently, EDRN 

investigators at the University of Texas M. D. 

Anderson Cancer Center developed a FISHbased assay for the analysis of cells from urine 

to detect increased copy numbers of Aurora 

A. Three to four copies were detected in all 

bladder cancer specimens when the normal 

number should be two. Interestingly, patients 

with low-grade TCC had three to four copies 

of Aurora A, while patients with high-grade 

TCC had more than four copies of Aurora 

A. Normal copy numbers of Aurora A were 

detected in urine sediments of 17 unaffected 

controls. These studies suggest that amplification and overexpression of Aurora A is ubiquitous in bladder cancer and can be detected in 

bladder cells in urine.

At Johns Hopkins University, EDRN investigators identified somatic mitochondrial DNA 

(mtDNA) mutations in a variety of cancers. 

The frequency of mitochondrial mutations in 

these studies is high, with one-half to twothirds of cancers harboring at least one mutation. These observations were independently 

verified, using the same samples by the team 

from the National Institute of Standards and 

Technology (NIST). Identical mtDNA mutations were detected in primary cancers and in 

urine sediments from the same of bladder cancer patients. There are several advantages in 

using mtDNA as a potential cancer biomarker 

including: (1) detection can be performed 

in noninvasive clinical samples such as from 

exfoliated cells in urine; (2) there are multiple 

copies of mtDNA in each mitochondrion; (3) 

there is an abundance of mitochondria (each 

cell contains hundreds to thousands of these 

organelles); and (4) most of the mutations and 

deletions are detected in limited regions.

EDRN investigators at Johns Hopkins also 

have established a panel of four methylated 

genes in bladder cancer (CDKN2A, p14ARF, 

MGMT and GSTP1) that displayed 100% 

specificity when evaluating paired DNA 

from samples, from primary tumor and from 

urine sediment. The paired samples displayed 

identical promoter methylation patterns. Of 

the 175 bladder cancer patients, 121 displayed promoter methylation in at least one 

of these genes, whereas all control subjects 

were negative for such methylation. Testing a 

small panel of genes by methylation-specific 

qPCR (qMSP) in urine sediment DNA is a 

powerful noninvasive approach for the detection of bladder cancer. Another panel (APC, 

RASSF1A and p14ARF) was independently 

established by EDRN investigators at the Fox 

Chase Cancer Center. A third panel (CDH1, 

RASSF1A, APC and CDH13) was established 

by investigators at the University of Texas 

Southwest Medical Center and the University of Texas M. D. Anderson Cancer Center. 

Larger independent confirmatory cohorts 

with longitudinal follow-up will be required 

in future studies to define the impact of these 

biomarkers on early detection, prognosis and 

disease monitoring before clinical application. 

A validation study of microsatellite analysis on 

urine is also under way. (See Chapter 6, Valiation Studies, Case 2.)
Candidate Bladder Cancer Biomarkers 

Candidate Biomarker Discovery Pre-validation Validation

Aurora A (Tissue and Urine)

Aurora B and C (Tissue and Urine)

Alterations in Mitochondrial DNA 

(Urine)

Panel of Methylated DNA 

sequences (CDKN2A, ARF, MGMT, 

GSTP1) (Tissue and Urine)

Panel of Methylated DNA 

sequences (APC, RASF1A, p14) 

(Urine and Tissue)

Panel of Methylated DNA 

sequences (CDH1, RASF1A, APC, 

CDH13) (Urine and Tissue)

Microsatellite Analysis (MSA) 

(Urine)

Discovery

Predictive Analysis

Assay Refinement

Blinded Limited 

Cross-Sectional

Large Cross-Sectional

Prostate and Other Urological Cancer 63

“T he EDRN has truly reinvented scientific investigation in the United States. Traditionally, we have been 

a ruggedly individualistic scientific culture in which the individual’s achievements are paramount. 

Unfortunately, when we face the challenge of early detection of cancer and the discovery and validation 

of biomarkers for this lethal group of diseases, one individual or one institution alone simply cannot 

achieve that goal. It has been a very satisfying experience to watch this incredibly dedicated group of 

individuals who have subordinated their personal rewards to those of the group and, in so doing, created 

the foundation for the early diagnosis and cure of cancer.”

 Ian M. Thompson, M.D. 

 Principal Investigator, EDRN Clinical Epidemiology and Validation Center

University of Texas Health Science Center
64 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Renal/Kidney Cancer

In 2007, a projected 51,190 patients will be 

newly diagnosed with kidney and renal pelvis 

cancers and an estimated 12,890 will die from 

the disease. Although surgical treatment is 

efficient for localized cancer, 20% to 30% of 

patients with localized disease at presentation 

and 25% of patients with locally advanced 

or metastatic disease will develop systemic 

recurrence. Early detection of kidney cancer is 

essential for successful treatment.

Recently, EDRN investigators at Fox Chase 

Cancer Center developed a panel of six 

tumor suppressor genes VHL, p16, p14, 

APC, RASSF1A and TIMP-3 that are 

frequently methylated in kidney cancer but 

not in normal kidney. An identical pattern 

of hypermethylation to that found in the 

tumor could be detected in the corresponding 

pre-operative urine DNA with high sensitivity 

and specificity (normal controls were 

methylation negative). As was discussed 

for bladder cancer, development of qMSP 

assay of this specific panel of genes offers 

promise for early detection of renal cancer 

from urine samples.

Definition of kidney cancer: 

Cancer that forms in tissues of the kidneys. Kidney 

cancer includes renal cell carcinoma (cancer that 

forms in the lining of very small tubes in the kidney 

that filter the blood and remove waste products) 

and renal pelvis carcinoma (cancer that forms in 

the center of the kidney where urine collects). It 

also includes Wilms’ tumor, which is a type of 

kidney cancer that usually develops in children 

under the age of 5. 

Estimated new cases and deaths from kidney 

(renal cell and renal pelvis) cancer in the 

United States in 2007: 

New cases: 51,190 

Deaths: 12,890

Candidate Kidney Cancer Biomarkers 

Candidate Biomarker Discovery Pre-validation Validation

Panel of Methylated DNA 

sequences (VHL, p16, p14, APC, 

RASSF1A and TIMP-3) (Urine)

Discovery

Predictive Analysis

Assay Refinement

Blinded Limited 

Cross-Sectional

Large Cross-Sectional
Title of chapter goes here 65

Part Two

Process and 

Collaboration 
The Early Detection Research 

Network (EDRN) takes a systematic 

approach to biomarker validation. A fivephase methodology was established as both 

a standard and a roadmap for successfully 

translating research on biomarker applications 

from the laboratory to the bedside. Designed 

to expedite procedures to evaluate and validate 

biomarkers for clinical application during the 

early stages of investigation, these five phases, 

shown in Figure 6-1, are:

• Phase 1: Discovery through exploratory 

studies to identify potentially useful 

biomarkers. 

• Phase 2: Validation via studies that 

determine the capacity of biomarkers to 

distinguish between people with cancer 

and those without cancer (sensitivity and 

specificity).

• Phase 3: Studies to assess the capacity of a 

biomarker to detect preclinical disease by 

testing the marker against tissues collected 

longitudinally from research cohorts. 

• Phase 4: Prospective screening studies.

• Phase 5: Large-scale population studies to 

determine overall impact of screening on 

health outcomes in the target population.

Validation Stages 

and Processes

“W hile discovering and validating a biomarker that is associated with cancer is 

relatively easy, discovering and validating a biomarker that has clinical utility 

is very challenging. The reason is that to show clinical utility, a new biomarker 

needs to show advantages over the current clinical diagnostic practice which 

has been evolved and optimized during decades of medical research. However, 

the potential payoff is huge. A single discovery and validation of a marker for 

a major cancer would justify all the resources invested in EDRN because of the 

direct clinical relevance.”

 Ziding Feng, Ph.D. 

 Principal Investigator

EDRN Data Management and Coordinating Center 

Fred Hutchinson Cancer Research Center 

66 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

C hapter SIX
Within the Network structure, the Biomarker 

Developmental Laboratories (BDLs) develop 

and characterize new biomarkers, or refine 

existing biomarkers (Phase 1 and Phase 

2). The Biomarker Reference Laboratories 

(BRLs) serve as the resource for clinical and 

analytical validation of biomarkers, including 

development of technology, standardization 

of assay methods and refinement of existing 

methods. The Clinical Epidemiology and 

Validation Centers (CEVCs) conduct or 

participate in early stages (Phase 2 and Phase 

3) of clinical validation and epidemiological 

research for the application of biomarkers.

EDRN’s ability to effectively organize the 

resources to conduct validation studies is 

highlighted below. In the clinical research 

community, such studies are generally rare 

since few BDLs ever achieve the necessary 

requirements or suitable resources.

Moving Discovery in Phases: 

Standard Specimen Reference Set

A biomarker that looks very promising in its 

initial laboratory work may not hold up when 

it undergoes the rigorous validation process 

that EDRN performs. The Network is working to develop discovery methods to increase 

the likelihood of identifying those markers 

that perform well in both the discovery phase 

and in validation.

One challenge facing investigators in this 

process is gathering properly stored biological 

specimens needed to test biomarkers. Often, 

an investigator obtains specimens from colleagues. The sample source from individuals 

with cancer is different from the source of 

specimens from individuals without cancer. 

Testing markers on such convenience specimens runs the risk of finding markers that are 

not associated with the disease of interest, but 

which show the differences in how specimens 

were collected (e.g., study population, method 

of collection, storage conditions). 

Figure 6-1. Five Phase Approach to Biomarker Translational Research

 

Preclinical Exploratory

Exploratory studies to identify useful biomarkers

Clinical Assay and Validation

Studies to determine the capacity of biomarkers to distinguish between people with cancer and those without

Retrospective Longitudinal Determine how well biomarkers detect preclinical disease 

by testing the markers against tissues collected longitudinally from research cohorts

Prospective Screening Identify the extent and characteristics of 

disease detected by the test and determine the false referral rate

Cancer Control Evaluate both the role of the 

biomarkers for detection of cancer and the 

overall impact of screening on the population 

through large-scale popluation studies

Source: Journal of the National Cancer Institute 93, 1054-1061, 2001

Validation Stages and Processes 67
68 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Reference Sets Enable 

Accelerated Validation

A common problem encountered in assessing 

biomarkers worthy of clinical validation is that 

biomarker developmental work typically has been 

performed on samples from cases and controls 

collected in a variety of ways. This makes comparisons of biomarkers from different laboratories 

difficult and subject to significant bias. With the 

creation of shared reference sets of specimens 

from well-characterized cancer cases and matched 

controls, EDRN will overcome many of the logistic 

and design issues in preliminary and advanced 

biomarker validation. Already these reference 

sets enable direct performance comparisons of 

biomarker panels from different laboratories. This 

resource is accessible to any investigator within or 

outside of EDRN based on a common and transparent set of criteria used to evaluate applications. 

Interested scientists can obtain further details and 

request forms on existing reference sets at the 

EDRN web site (http://edrn.nci.nih.gov/resources/

sample-reference-sets). 

EDRN’s solution to these difficulties is to 

create standard specimen reference sets 

(SSRSs)—collections of high-quality, wellcharacterized specimens that can be used for 

discovery and early validation of potential 

markers. By taking advantage of its large, diverse group of CEVCs, EDRN is able to create SSRSs with controls well-matched to cases 

on risk factors, as well as specimen collection, 

processing and storage conditions. Careful design by DMCC statisticians, in collaboration 

with EDRN investigators, ensures that the 

SSRSs are sufficiently powered to detect clinically important markers. The SSRSs include 

many different types of control individuals—ones with no disease, ones with benign 

diseases, ones with other types of cancer—so 

that the specificity of markers against potentially confounding conditions can be evaluated. Different SSRSs are created for different 

screening scenarios. For example, EDRN, in 

collaboration with Specialized Programs of Research Excellence (SPORE) investigators, created one SSRS to test markers that would be 

applied as a screen to a population at high risk 

for lung cancer and is creating another SSRS 

to test markers in individuals with abnormalities found on computerized tomography (CT) 

screening of the lung. EDRN is creating both 

retrospective SSRSs from previous cohorts as 

well as prospectively collecting specimens for 

other SSRSs.

Request for Biomarkers

EDRN publicly solicits potential cancer biomarkers 

from the greater scientific community to facilitate 

translational validation. The Network has a history 

of allowing outside investigators to join with 

EDRN scientists and thus gain access to clinical 

samples, reference sets and laboratory resources 

necessary for validation studies. Financial support 

for such studies is also available pending approval 

via a review process involving the appropriate 

organ site Collaborative Group. A number of 

prevalidation studies listed in this chapter were 

initiated via this solicitation.

A typical patient specimen may be divided 

into 20 or more samples or aliquots. Thus, as 

different investigators evaluate their markers 

in the SSRS and the resulting data are centrally deposited, DMCC statisticians have the 

ability to examine panels of markers combining all of these data together. By combining 

information from markers of different types 

(e.g., proteins and DNA methylation), the 

centralized data allow the creation of panels of 

markers that cannot be done by the individual 

laboratories and that may provide a multi-factor combination of markers that is more sensitive and specific than any single marker.
By June 2007, EDRN had created SSRSs 

for prostate, ovarian and lung cancers. The 

prostate cancer SSRS was so popular that it 

is already exhausted and a second prostate 

cancer SSRS is being developed. Other SSRSs 

in development include those for breast, 

pancreatic, colon and bladder cancers. As 

reference sets are created, they are deposited at 

the Frederick, Maryland facility of NCI and 

advertised in appropriate journals. Application 

methods and the review process for each SSRS 

are clearly indicated. Details on all SSRSs are 

available on the EDRN Public Portal 

(http://www.cancer.gov/edrn).

Private Sector Licenses Encourage 

Partnerships with Public Sector

EDRN-supported research is attracting the private 

and public sectors into partnerships. More than 90 

patents have been issued to individual investigators and more than 40 licenses granted to private 

sectors. Among recently arranged licenses are: Assays for GSTP-1 (prostate) to OncoMethylome Science; EPCA-1 and EPCA-2 (prostate) to Onconome; 

Gene Fusion Assay (TMPRSS2-ETS) to Gene-Probe; 

GP-73 (liver) to Beckman-Coulter and SMRP (serum 

mesothelin-related protein) and osteopontin to 

Fujirebio Diagnostics. These markers are being 

brought to EDRN for further validation studies.

Validation Studies 

Case 1: Validation of Serum Markers 

for Early Detection of Hepatocellular 

Carcinoma 

Hepatocellular carcinoma (HCC), primary 

liver cancer, is the fifth most common tumor 

and the third cause of cancer-related deaths 

worldwide with a 5-year survival rate of less 

than 5%. The high mortality associated with 

HCC is primarily due to diagnosis at a late 

stage when the tumor is unresponsive to treatment. However, when diagnosed early, 5-year 

survival rates can be as high as 70%.

Alpha-fetoprotein (AFP) is the only serum 

marker currently available to detect HCC, but 

its specificity and sensitivity are low and early 

stage HCC often goes undetected. In a pilot 

study, an EDRN Associate Member showed 

that the level of des-gamma carboxyprothrombin (DCP) in sera was significantly better than 

AFP in differentiating patients with HCC 

from those with cirrhosis. Cirrhosis is the 

major risk factor, appearing in approximately 

90% of patients with HCC.

EDRN sponsored a large multi-site trial to 

validate these observations and to determine if 

DCP can accurately detect early stage HCC in 

cirrhotic patients. Soon after commencement 

of the trial, AFP-L3%, a form of AFP that 

was recently approved by the Food and Drug 

Administration for risk assessment was added. 

DCP is measured using an enzyme-linked 

immunosorbent assay (ELISA) kit provided 

by Eisai Company; AFP and AFP-L3% are 

measured by a commercial test from Wako 

Diagnostics. This EDRN validation study 

has determined the sensitivity and specificity of DCP for the diagnosis of early HCC; 

performance characteristics of DCP, AFP and 

AFP-L3% singly and in combination; and 

whether demographic and etiology of underlying liver disease alter the expression of DCP, 

AFP or AFP-L3%. A publication of this data 

is pending.

Validation Stages and Processes 69
70 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Case 1: Serum Markers for Hepatocellular Carcinoma 

Design:

• Multi-center case-control study

• 450 cases: modified TNM stage I and II HCC (eligible for liver transplant)

• 450 controls: cirrhosis without tumor

• All study data entered into the EDRN Validation Study Information Management System, 

a secure, web-based system at the Fred Hutchinson Cancer Research Center

• Sera, plasma and DNA from peripheral blood cells are collected for this trial and stored 

to validate new biomarkers as they are discovered

Data Quality Management Committee Chair:

Richard K. Sterling, M.D., Medical College of Virginia, Richmond, VA

Participating Institutions:

University of Michigan Medical Center, Ann Arbor, MI

(PI: Jorge Marrero, M.D.)

Fred Hutchinson Cancer Research Center, Seattle WA

Mayo Clinic, Rochester, MN

Mayo Clinic, Jacksonville, FL

Milestones:

November 2004 Protocol completed

February 2005 Begin enrollment

July 2007 End enrollment

September 2007 Finish assays and begin data analysis

November 2007 Finish analysis

Status:

12/1/2007 Publication pending

Case 2: Bladder Cancer Detection by 

Microsatellite Analysis of Urinary Sediment, 

Multi-Institution Study

Bladder cancer is the fourth most common 

malignancy among American men and the 

seventh most common malignancy among 

American women. Seventy-five percent of 

these patients have superficial bladder cancers 

and 70% of patients with superficial disease 

relapse after initial treatment. Consequently, 

individuals with superficial bladder cancer 

require frequent surveillance for recurrence.

There is a need to improve the current 

practice of bladder cancer surveillance. 

Although urine cytology and cystoscopy are 

considered standard of care, they are less than 

optimal in detecting bladder cancer. The 

sensitivity and specificity of urinary cytology 

is less than 50%. Cystoscopy, an invasive 

procedure, has a sensitivity of 90-100%. 

In a preclinical study, a panel of 15 

microsatellite markers was shown to detect 

greater than 90% of bladder cancers, using 

DNA from cells in urine sediment. EDRN is 

Mount Sinai Hospital, New York, NY

Stanford University, Palo Alto, CA

St. Louis University, St. Louis, MO

University of California, Los Angeles, CA

University of Pennsylvania, Philadelphia, PA
sponsoring a large multi-site trial to determine 

the usefulness of microsatellite analysis 

(MSA) in early detection and monitoring for 

recurrence of superficial bladder cancer. MSA 

is based on detection of genetic instability that 

results in gain or loss of heterozygosity.

This EDRN validation trial aims to 

determine:

(1) the sensitivity and specificity of MSA 

(a panel of 15 microsatellite markers) 

using urinary sediment to detect bladder 

cancer compared to cystoscopy and urine 

cytology;

(2) the temporal performance characteristics 

of MSA in urinary sediment; and 

(3) which of the 15 individual markers 

or combinations of markers are most 

predictive of the presence of bladder cancer. 

Case 2: MSA of Urinary Sediment for Bladder Cancer 

Design:

• Case-control study

• 300 individuals with a superficial bladder urothelial malignancy, either incident or recurrent

• 100 healthy individuals with no known urologic disease

• 100 individuals with potentially confounding conditions (BPH, foreign bodies, hematuria, or GU infection)

Data Quality Management Committee Chair:

H. Barton Grossman, M.D., Department of Surgery, University of Texas M. D. Anderson Cancer Center

Participating Institutions:

School of Medicine, Baltimore, MD 

(PI: Mark Schoenberg, M.D.)

Baylor College of Medicine, Houston, TX

Brigham and Women’s Hospital, Boston, MA

CURC Carolina Urologic Research Center, 

 Myrtle Beach, SC

Harborview Medical Center, Seattle, WA

LURN, Daytona Beach, FL

LURN, Orange City, FL

LURN, West Orange, NJ

University of Texas M. D. Anderson Cancer Center, 

 Houston, TX

Milestones:

December 2003 Protocol approved

January 2004 First meeting of investigators

May 2006 Eleven urological clinics added to the study

September 2008 Final results expected

Status:

12/30/2007 282 cases and 210 controls enrolled

Validation Stages and Processes 71

Memorial Sloan Kettering, New York, NY

Stanford University, Palo Alto, CA

University of Alabama, Birmingham, AL

University of Chicago, Chicago, IL 

University of Michigan, Ann Arbor, MI

University of Rochester Medical Center, 

 Rochester, NY

University of Texas, San Antonio TX

University of Toronto, Ontario, Canada

Washington University, St. Louis, MO
72 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Case 3: Validation of Biomarker Consensus 

Panel for Early Detection of Ovarian Cancer, 

EDRN-SPORE-PLCO Phase II Study

Most cases of ovarian cancer are diagnosed 

at advanced stages, which is associated with 

poor survival. Yet when clinicians are able to 

make an early diagnosis, survival can reach up 

to 90%. While approaches to ovarian cancer 

screening might include pelvic examination and sonography, tests that can measure 

biomarkers in blood are likely to be the most 

cost-effective first-line screen.

CA-125, a serum marker currently used for 

ovarian cancer, gives a true positive result for 

about 50% of Stage I ovarian cancer patients, 

but is not an adequate early detection tool 

when used alone. Consequently, EDRN and 

NCI SPORE investigators have joined forces 

to develop a two-phase study to investigate 

a panel of biomarkers that could be used to 

screen for ovarian cancer. 

In the first phase, which is nearing completion, investigators at Brigham and Women’s 

Hospital, Fred Hutchinson Cancer Research 

Center, University of Texas M. D. Anderson 

Cancer Center and University of Pittsburgh 

Cancer Institute are validating biomarkers that 

performed well in preliminary studies in their 

respective laboratories. This is being done on 

a blinded test set of sera from 80 early-staged 

and 80 late-staged ovarian cancer cases, 160 

controls with benign disease and 480 healthy 

controls. Results from these individual laboratories will be combined to form a biomarker 

consensus panel that most accurately detects 

early stage ovarian cancer.

In phase 2 of this study, this biomarker 

consensus panel will be used to test sera collected one or more years prior to diagnosis of 

ovarian cancer. These specimens and matched 

controls will come from NCI’s Prostate, Lung, 

Colorectal and Ovarian Cancer Screening 

Trial (PLCO). The investigators’ hypothesis 

is that a panel of biomarkers will have a 

sufficient lead time to identify ovarian cancer 

cases two or more years earlier than current 

tests allow.

Case 4: Biomarker Validation for Early 

Detection of Mesothelioma, American/

Australian Mesothelioma Consortium

Individuals exposed to asbestos are at high risk 

of developing mesothelioma, a malignancy 

of the lung lining. Mesothelioma is currently diagnosed by assessment of clinical and 

radiological findings and confirmed by tissue 

biopsy. Treatment options for mesothelioma 

have not proved successful and patients have a 

median survival time of 6-12 months. 

Patients with early stage disease can survive 

five or more years if their tumor is promptly 

resected. Currently, less than 5% of mesothelioma patients are diagnosed at an early stage of 

disease. Thus, there is a need for a biomarker, 

or panel of biomarkers, that can predict the 

development of mesothelioma, or detect it at 

an early stage. Research by EDRN and other 

investigators strongly suggests that two serum 

proteins, serum mesothelin-related protein 

(SMRP) and osteopontin are biomarkers of 

early detection of mesothelioma and that these 

biomarkers might be useful to screen asbestosexposed individuals.

EDRN initiated a validation trial to determine 

the performance of these two blood-based 

protein biomarkers in the diagnosis of early 

stage mesothelioma. The trial aims to determine:

(1) the performance of SMRP and osteopontin in case-control studies in retrospective 

specimens collected from asbestos-exposed 

individuals at a number of centers in the 

United States and Australia;

(2) the performance in a prospective study on 

longitudinal samples collected from several 

high-risk mesothelioma cohorts at several 

sites around the world; and

(3) the effectiveness of these biomarkers in 

detecting the onset of mesothelioma prior 

to diagnosis. 
Case 3: Biomarker Consensus Panel for Ovarian Cancer 

Design:

• Case-control study

• 80 early stage and 80 late-stage ovarian cancer cases

• 160 controls with benign disease

• 480 healthy controls

Participating Institutions:

Brigham and Women’s Hospital, 

 Boston, MA (Daniel Cramer, M.D., EDRN PI)

Fred Hutchinson Cancer Research Center, 

 Seattle, WA (Nicole Urban, ScD., SPORE PI)

Timeline for Completion of the Project:

March 2007 Phase I specimens sets were received by the assay sites (Boston, 

Houston, Pittsburgh, Seattle)

May 2007 Completion of assays on the Phase I specimens

Summer 2007 Completion of data analysis by the DMCC 

October 2007 Final decision on the structure of the samples for the PLCO specimens 

and panel of ovarian cancer biomarkers 

November 2007 PLCO specimens shipped to assay sites

December 2007 Assays on PLCO specimens completed and results to PLCO

January-March 2008 Discussion with PLCO regarding findings, forums for presentation and 

write-up

Milestones:

• Identification of a consensus panel comprising the biomarkers that is most informative 

when used singularly as well as in combination

• Assay preclinical sera from ovarian cancer patients enrolled in the PLCO trial

The New York University investigators and 

their Australian collaborators have involved 

many noted cohorts including those from: 

the Selikoff Foundation at Mt. Sinai in 

New York; the PLCO; the Beta-Carotene 

and Retinol Efficacy Trial (CARET) chemoprevention trial; the Center for Asbestos 

Related Diseases in Libby Montana; and 

from Cappadocia, Turkey. Specimens are 

actively being collected from these sites.

Case 5: SELDI-TOF-MS Serum Proteomic 

Profiling Does Not Reliably Detect Prostate 

Cancer 

Initiated in 2003, the EDRN investigators 

started a multi-institutional collaborative 

project to validate proteomics patterns as 

potential diagnostic markers for cancer 

detection. This was triggered by the publication of a number of research articles in 2002-

2003 on the use of protein expression patterns 

as potential biomarkers for ovarian, prostate, 

lung and other cancers. 

Validation Stages and Processes 73

University of Texas M. D. Anderson Cancer Center, 

 Houston, TX

Fox Chase Cancer Center, Philadelphia, PA

University of Pittsburgh, Pittsburgh, PA

University of Alabama at Birmingham, Birmingham, AL
74 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Case 4: Biomarker Validation for Early Detection of Mesothelioma 

Design:

• Case-control study

• 200 mesotheliomas

• 500 asbestos exposed individuals

• Most controls are asbestos-exposed individuals

Participating Institutions:

New York University, New York, NY 

 (PI, Harvey I. Pass, M.D.)

University of Western Australia, Perth, Australia

Peter MacCallum Cancer Institute, 

 Melbourne, Australia

Milestones:

Summer 2007 Protocol completed

Fall-Winter 2007-2008 Receipt of all samples

Status:

12/10/2007 All samples for validation have been blinded 

and are waiting analysis

EDRN investigators meticulously designed a 

three-stage protocol to validate the reproducibility of the platform (Stage 1), to validate 

diagnostic use of protein patterns (Stage 2) 

and to conduct clinical validation (Stage 

3), using well-annotated, prospective specimens from stratified risk groups and prostate 

cancer cases and controls. Results from Stage 

1 of the study confirmed the portability and 

reproducibility of the surface-enhanced laser 

desorption-time of flight mass spectrometry 

(SELDI-TOF-MS) platform. Encouraged 

by this finding, investigators conducted the 

second stage of the validation process. 

The results from the Stage 2 study concluded 

that the results from previously published 

studies in which discrimination between 

prostate cancer and non-cancer was demonstrated is not generalizable. Earlier study 

samples likely had biases in sample selection 

that upon removal, as in the present study, 

resulted in inability of the technique to discriminate cancer from non-cancer cases. Thus, 

in the second phase of the planned validation 

process, the SELDI-TOF-MS-based protein 

expression profiling approach did not perform 

well enough to advance to the third (prospective study) stage. Results of the Stage 2 study, 

which are being submitted for publication, 

will discuss the impact these findings have on 

the biomarker discovery field and propose that 

the EDRN validation design be the standard 

protocol for analysis of biomarkers for disease 

detection.

Other Validation Studies

Early Detection of Prostate Cancer Based 

on Detection of PCA3 Transcript in Urine 

Supernatant

• PI: John Wei, University of Michigan, 

Ann Arbor, MI. Collaborators, Harry 

Rittenhouse, Alan Partin, Martin Sanda, 

Arul Chinnaiyan, Ziding Feng.

• Industrial Collaborator: Gen-Probe; PI: 

Harry Rittenhouse

• Status: protocol is being developed.

University of Hawaii, Honolulu, HI 

Fujirebio Diagnostics, Inc., Malvern, PA
Case 5: Novel Protein Profiling Techniques for Prostate Cancer 

Design:

• Case-control study

Participating Institutions:

Eastern Virginia Medical School, Norfolk, VA 

(PI, John O. Semmes, Ph.D.)

University of Alabama at Birmingham, 

 Birmingham, AL

University of Texas Health Science Center, 

 San Antonio, TX

Milestones:

February 2004 Stage I completed

September 2006 Stage II completed

Status:

January 2008 Reports in press (Clinical Chemistry)

Validation of Percent proPSA in Combination with PSA Isoforms for Early Detection 

of Prostate Cancer

• Industrial collaborator: Beckman-Coulter

• PI: Lori Sokoll, JHMI. Collaborators: Martin Sanda, Alan Partin, Ian Thompson, Dan 

Chan and Beckman-Coulter

Prevalidation Studies in the 

EDRN Pipeline

The feasibility and performance in relatively 

small case-control design studies for certain 

promising biomarkers in cancer diagnosis or 

risk prediction, termed prevalidation studies, are underway. The results will determine 

whether a larger cohort study should be considered. EDRN continues to receive proposals 

from investigators outside the Network who 

seek assistance in bringing their biomarkers 

forward for clinical validation. The following 

list describes a number of such ongoing studies that are likely to lead to full-scale validation studies.

Serum-Based Protein Markers for the 

Detection of Colorectal Cancer 

• PIs: Robert Getzenberg, Johns Hopkins 

University and Robert Schoen, University 

of Pittsburgh

• Aim: To validate the sensitivity and 

specificity of two serum proteins (CCSA-3 

and CCSA-4) in their ability to distinguish 

colon cancer from benign colon.

• Projected clinical use: Screening for 

colorectal cancer.

• Biomarkers: Colon Cancer Specific 

Antigen-3 and -4, nuclear matrix proteins 

specific to colorectal cancer that can be 

measured in sera. 

Early Detection of Bladder Cancer Using DNA 

Methylation Markers in Urine Sediment

• PI: Paul Cairns, FCCC

• Industrial Collaborator: OncoMethylome

• Status: The protocol for the study was 

completed and shared with the collaborators 

for comments (David Sidransky, Bogdan 

Czerniak, Adi Gazdar and Martin Sanda).

Validation Stages and Processes 75

University of Pittsburgh Cancer Institute Hillman 

 Cancer Center, Pittsburgh, PA 

Johns Hopkins University, Baltimore, MD

Center for Prostate Disease Research Walter 

 Reed Army Medical Center, Washington, DC

Fred Hutchinson Cancer Research Center, Seattle, WA 
76 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Early Detection of Prostate Cancer Based 

On Prostate Cancer Translocations in Urine 

Exfoliated Cells

• PI: Arul Chinnaiyan, University of Michigan

• Industrial Collaborator: Gen-Probe

• Status: Protocol is being developed.

Protein Markers of Lung Cancer

• PI: Samir Hanash, Fred Hutchinson Cancer 

Research Center

• Aim: To determine the efficacy of a panel 

of at least five biomarkers in blood for 

diagnosis of early stage lung cancer.

• Projected clinical use: Identify people in 

high-risk groups (smokers) with early stage 

lung cancer.

• Biomarkers: Autoantibodies to Annexins I 

and II and PGP9.5; C-reactive protein and 

serum amyloid A will also be analyzed to see 

if their inclusion in this panel can enhance 

the performance of the autoantibodies.

Barrett’s Esophagus Progression Biomarkers

• EDRN PI: Stephen Meltzer, Johns Hopkins 

University 

 SPORE PI: Richard Sampliner, University 

of Arizona

• Aim: To test a three-class stratification model 

for risk of progression from Barrett’s esophagus to esophageal adenocarcinoma.

• Projected clinical use: Patients classified as 

high risk for progression would undergo endoscopy more frequently than the currently 

recommended surveillance interval; those at 

intermediate risk, at the customary interval; and the low risk group would undergo 

endoscopy less frequently.

• Biomarkers: Methylation status of three tumor suppressor genes (p16, HPP1, RUNX3) 

used in combination with four clinical 

parameters.

Ovarian Cancer Biomarker Validation Study

• PI (EDRN Associate Member): Gil Mor, 

Yale University (in partnership with 

LabCorp)

• Aim: To validate a panel of serum protein 

biomarkers for the detection of early ovarian 

cancers using multicenter patient specimen 

collections.

• Projected clinical use: Identify patients with 

early ovarian cancers that will enable further 

diagnostic evaluation and early clinical and 

therapeutic intervention. 

• Biomarkers: Prolactin, osteopontin, leptin, 

insulin-like growth factor II (IGF-II), macrophage migration inhibitory Factor (MIF2) and CA-125 will be analyzed by Luminex 

technology.

GSTP1 Methylation Marker for Prostate 

Cancer

• PI: Alan Partin, Johns Hopkins University

• Aims: To validate methylated GSTP1 and 

three additional markers as a panel for detection of prostate cancer in biopsy specimens.

• Projected clinical use: Diagnosis of patients 

with both positive digital rectal examinations 

(DRE) and “normal” PSA and in patients 

with rising PSA levels but negative biopsies.

• Biomarkers: GSTP1, p16, ARAF, MGMT.

Validation Study of Cervical Cancer Progression Biomarkers

• PI: Thomas Ried, NCI Center for Cancer 

Research 

 EDRN PI: Elizabeth Unger, CDCP

• Aim: To validate biomarkers for progression 

to cervical cancer. 

• Projected clinical use: Reduction in the need 

for repeated colposcopies and in the costs for 

screening cervical cancer.

• Biomarkers: Chromosomal gain of 3q and 

methylated genes used together in Pap smear 

samples. 

Additional Studies Under 

Consideration

Mutations and Deletions in mtDNA as 

Markers for Bladder and Other Cancers

• PI: David Sidransky and Mark Schoenberg, 

Johns Hopkins Medical Institutes

• Industrial collaborator: None at this time

• Status: discussions.

Meythlated DNA Markers for 

Prostate Cancer

• PI: David Sidransky, Johns Hopkins 

Medical Institutes

• Industrial Collaborator: Oncomethylome

• Status: discussions.
The Early Detection Research 

Network (EDRN) is a leader in incorporating new technologies into research 

devoted to the discovery and development of 

biomarkers. Although EDRN investigators 

pioneer cutting-edge technologies, the Network invites collaboration from other scientists and companies to adapt their platforms to 

early detection diagnostic tests. This chapter 

highlights some of the novel technologies and 

supporting infrastructure implemented by 

EDRN to maximize progress through the use 

of well-designed interactive tools.

Nanotechnology Supports Sensitive 

Detection of Blood-Based Biomarkers

The detection of specific proteins in blood or 

other biological fluids is important for both 

clinical and research applications. Techniques 

for detection typically involve capturing 

the protein of interest from the blood using 

specific antibodies, then detecting this 

captured protein with an antibody linked to 

an amplification tag, such as a fluorochrome, 

enzyme or radioisotope marker. EDRN 

investigators are utilizing novel electronic 

device architecture for the detection of 

proteins in blood by taking advantage of a 

room-temperature carbon nanotube (CNT) 

network fabrication technology. The approach 

will form a charged circuit that is sensitive 

to changes in the amount of charge near the 

CNT network. Antibodies immobilized on 

the CNT surface serve to specifically bind 

proteins, thus altering the surface capacitance. 

As a model test system, investigators have 

demonstrated the quantitative detection and 

measurement of human prostate-specific 

antigen (PSA) added to calf serum.

Enabling Technologies

“Our investigations have shown that finding a suitable cancer biomarker (in blood) 

depends in part on the detection methodology used. In this regard, the EDRN 

network has funded different technologies, perhaps more powerful, to identify 

these markers. More importantly, the EDRN group has created a standardized 

panel of test material for validation. This cannot be done by individual research 

groups. Above all, the coordination among Marker Discovery, Marker Validation 

and Data Analysis made possible by EDRN is unique and crucial to the success of 

this program. The free exchange of data at regular intervals ensures that various 

expertise and pertinent experimental results are communicated to the community.” 

 Alvin Liu, Ph.D. 

 Principal Investigator 

EDRN Biomarker Development Laboratory

University of Washington 

Enabling Technologies 77

C hapter SEVEN
78 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

xMAP Assay

Recently, LUMINEX Corporation introduced a novel protein array system (xMAP for 

Multianalyte Profiling) that allows for simultaneous quantitation of up to 100 soluble 

analytes in one sample. xMAP technology 

uses polystyrene microspheres internally dyed 

with differing ratios of two spectrally distinct 

fluorophores to create a family of 100 differentially spectrally addressed bead sets. Each of 

the 100 spectrally addressed bead sets can be 

conjugated with a capture antibody specific 

for a unique target protein. In a multiplexed 

assay, antibody-conjugated beads are allowed 

to react with sample (plasma, serum or cell 

culture supernatant). After washing, detection 

antibodies are added to a microtiter plate well 

to form a capture sandwich immunoassay. 

Using the xMAP assay (see Figure 7-1), thousands of beads can be analyzed in seconds, 

allowing up to 100 analytes to be measured in 

a 96-well microplate in one hour.

Investigators at the University of Pittsburgh 

are using the xMAP to test a number of 

biomarker panels based on cytokines, chemokines, angiogenic and growth factors for 

pancreatic, ovarian and lung cancers. Because 

xMAP permits simultaneous evaluation of 

many analytes, a large panel of analytes can 

be tested initially with this technology and 

then focused to an optimal panel for distinct 

cancers.

Multiple Platforms Accelerate 

Biomarker Discovery

Investigators at the University of Michigan 

integrated bioinformatics tools to mine gene 

expression data derived from prostate cancer 

cell lines, prostate tumors and other model 

systems. The team is a leader in the cataloging 

and bioinformatics analyses of DNA microarray data through the development of Oncomine (http://www.oncomine.org). They also 

developed a method termed Cancer Outlier 

Profile Analysis (COPA), which analyzes 

DNA microarray data for genes markedly 

over-expressed in a subset of cases. 

The COPA transformation effectively 

compresses typical biomarker profiles 

characterized by general overexpression 

in cancer relative to normal tissue. At the 

same time, it accentuates the outlier profiles 

characterized by general low expression 

with marked overexpression in a fraction of 

cancer samples ultimately providing a list 

of outlier genes. This analysis successfully 

identified several well-known cancer genes in 

specific cancer types with well-documented 

recurrent chromosomal rearrangements or 

amplifications. The striking observation was 

made that two ETS transcription factors 

known to be involved in gene fusions in 

Ewing’s sarcoma and myeloid leukemia, ERG 

(21q22.3) and ETV1 (7p21.2), were highly 

ranked outliers in multiple independent 

prostate cancer profiling studies and 

Figure 7-1. xMAP Assay, a new protein array system, allows for simultaneous quantitation of up to 100 

soluble analytes in one sample. 

A. Analytes B. Antibody-Analyte Complex C. Detection System

Source: EDRN investigators at the University of Pittsburgh
furthermore, the outlier profiles of ERG and 

ETV1 were mutually exclusive. The discovery 

of ETS fusions with the prostate-specific gene 

TMPRSS2 represents a paradigm shift for 

major epithelial tumors. The presence and 

high frequency of TMPRSS2:ETS fusions in 

prostate cancers suggest that this may be a 

causal event, similar to the role of recurrent 

rearrangements in hematological and 

mesenchymal malignancies.

Metabolomics in Breast Cancer

In collaboration with EDRN investigators at 

the University of California at San Francisco, 

scientists at Lawrence Livermore Laboratories 

developed a unique application of imaging 

mass spectrometry based on the time-offlight secondary ion mass spectrometer 

(ToF-SIMS). This instrument is being used 

to achieve chemical mapping of breast cells 

and tissues. Investigators are using ToF-SIMS 

to cluster individual breast cancer cells from 

established cell lines into their respective 

groups, including discrimination of cell lines 

with distinct phenotypes. They also perform 

ToF-SIMS on paraffin-embedded formalinfixed primary tumors as well as representative 

cell lines from which expression profiles 

have been previously obtained. Cluster 

analysis by ToF-SIMS is being correlated 

with transcriptomic/genomic profile analysis. 

Results are integrated into the genomic 

analysis that include total ion images, images 

based on masses of interest (ones identified 

by loading plots), images in red/green 

pseudocolor defining the differences and 

zoomed mass images of regions of interest, 

thus differentiating cancer cell lines from each 

other.

High-Throughput Sequencing to 

Detect Mitochondrial Mutations

Somatic mutations in the mitochondrial 

genome have recently been discovered to be 

characteristic of many cancers. To pursue 

mitochondrial DNA (mtDNA) mutations 

for cancer diagnostics, EDRN investigators 

at Johns Hopkins in collaboration with the 

Biomarker Reference Laboratory (BRL) 

at the National Institute of Standards 

and Technology (NIST) developed an 

oligonucleotide-based sequencing microarray 

called the MitoChip. This chip, now in its 

second version, enables resequencing of 

the entire mitochondrial genome of about 

16,500 base pairs via a simplified and 

high-throughput process. Accuracy and 

reproducibility of sequences determined using 

automated software is very high and sufficient 

to sensitively identify mtDNA mutations. 

This technology is being applied to multiple 

cancers and preneoplastic lesions (lung, 

head and neck, bladder, Barretts esophagus, 

colorectal adenomas) to explore applications 

for early detection and diagnosis of cancer.

Sensitive High-Throughput ELISA 

Technology

The Breast and Gynecological Cancers 

collaborative group of EDRN is partnering 

with Meso Scale Diagnostics (MSD) to use 

their sensitive electrochemiluminescencebased technology to screen a large number 

of biomarkers for their potential to detect 

breast, endometrial and ovarian cancers. MSD 

will analyze EDRN serum reference sets for 

ovarian, endometrial and breast cancers. MSD 

enzyme-linked immunosorbent assays 

(ELISA) for serum-based measurements have 

sensitivities as low as 0.1 pg/ml, a dynamic 

range of 3-5 orders of magnitude, with rapid 

throughput. The sensitivity and mulitplexed 

formats of MSD assays enable testing of large 

numbers of biomarkers with small amounts of 

serum. These studies would form the basis for 

future studies using individual case specimens 

obtained through EDRN collaborators to 

identify early cancer detection markers, 

particularly for ovarian cancer for which 

early detection is currently very inefficient. 

This new technology assessment will be 

an important step towards development of 

suitable early cancer detection screening tests. 

MSD technology would be made accessible to 

EDRN investigators to incorporate into their 

study programs.

Enabling Technologies 79
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Nucleic Acid Programmable Protein Array 

Most currently available methods for producing protein microarrays require purification of 

proteins for printing on the array. The laboratory at Harvard Medical School developed a 

novel DNA-based protein array technology 

called nucleic acid programmable protein array (NAPPA) where proteins are transcribed in 

situ from an immobilized DNA template using a cell-free expression system (see Figure 7-

2). The freshly expressed proteins are captured 

via their epitope tag at the site of synthesis. 

This approach overcomes the need to separately express and purify proteins for printing. 

These microarrays are then used to identify 

autoantibodies directed to tumor antigens in 

cancer patient sera, with equivalent sensitivity 

and specificity to standard ELISA. Current 

EDRN developmental projects are adapting 

the NAPPA protein microarray technology for 

use in the rapid and efficient screening of sera 

from breast cancer patients for antibodies to 

2,000 known and potential tumor antigens. 

A number of promising activities complementing EDRN efforts in genomics, proteomics and epigenomics are being discussed 

with industrial partners who possess the 

resources and financial backing to initiate “incubator projects” with the Network. Technology developers will provide platforms, reagents 

and assays. In return, EDRN will provide 

specimens and expertise in conducting small 

projects on technology validation, refinement 

of assays and reagents and statistical interpretations for performance metrics. Some of the 

incubator programs under discussion concern 

transcriptomic analyses, gene methylation

analyses and microRNA, comparative genomic hybridization and protein array analysis.

Figure 7-2. Nucleic Acid Programmable Protein Array Technology

This figure illustrates the DNA-based protein array NAPPA technology. Proteins are synthesized 

in situ from an immobilized DNA template using the cell-free expression system. The freshly 

expressed proteins are captured via their epitope tag at the site of synthesis. The approach 

overcomes the need to separately express and purify proteins for printing. The efficient expansion 

of over 2,000 full-length human proteins is confirmed using an anti-epitope tag (known as 

anti-glutathione-S-transferase) antibody.

Source: Current Opinion in Chemical Biology, Joshua LaBaer and Niroshan Ramachandran, Vol 9, Author(s), “Protein microarrays as tools for functional 

proteomics,” pp. 14–19, Copyright Elsevier (2005).

Immunoprofiling of cancer patients using self assembling protein arrays
EDRN Knowledge Environment

EDRN’s investment in informatics made it 

a leader in applying new technology for the 

NCI. Informatics plays a key role in supporting the scientific discovery process by building 

the infrastructure and tools that connect the 

EDRN research institutions together into a 

virtual knowledge system. 

Coordinated discovery of biomarkers 

across cancer research centers provides an 

opportunity for the Network to increase 

the accuracy of study results. However, the 

distributed nature of EDRN represents 

a challenge for building a bioinformatics 

infrastructure to capture and distribute the 

science and ancillary data acquired during 

biomarker studies within the enterprise. 

EDRN requires a knowledge system that links 

highly diverse systems together into a virtual 

data grid to support new analysis mechanisms 

ultimately identifying and validating new 

biomarkers. This data grid allows for linking 

loosely related data items across a highly 

heterogeneous, distributed environment.

The EDRN Knowledge System promises 

to dramatically improve the capability of 

scientific research by enabling real-time 

access to a variety of information that crosses 

institutional research center boundaries. While 

there are clear scenarios for how such a system 

can improve the discovery process, flexibility 

and agility are crucial to support new 

approaches to discovering cancer biomarkers. 

By decomposing the knowledge system into 

a set of communicating information services 

based on a domain information model, the 

Network is able to support the evolutionary 

needs of the program. Clearly, virtualized 

data grids are in their infancy, but the needs 

of programs like EDRN are demonstrating 

the benefits and the criticality for bringing 

scientific research endeavors together into a 

secure, integrated enterprise.

The National Aeronautics and Space 

Administration’s (NASA) Jet Propulsion 

Laboratory (JPL), the Data Management 

and Coordinating Center (DMCC) and 

NCI have all played key roles in developing 

the informatics systems for EDRN. The 

first application developed for the Network 

focused on providing a common informatics 

framework for accessing heterogeneous 

biospecimen repositories located at participating sites across the United States. 

As the infrastructure evolved, the core 

principles of building services that integrate 

general client applications with heterogeneous, 

distributed data resources have not changed. 

Recognizing the need to build an effective 

knowledge system where biospecimens, 

scientific data, study specific data and 

biomarker data can be captured, accessed and 

shared at a national level via a transparent, 

grid-type architecture, the Network focused 

on addressing five critical informatics goals:

(1) defining an information model for 

describing the EDRN problem space; 

(2) enabling all components of the knowledge 

system to be distributed;

(3) providing software interfaces for capture, 

discovery and access of data resources 

across the knowledge system;

(4) providing a secure transfer and distribution 

infrastructure to meet United States federal 

regulations for data sharing; and

(5) providing an integrated portal 

environment across the distributed 

EDRN. 

Recently, EDRN made significant advances 

in extending and deploying to Network 

centers the informatics framework to support 

the management of biomarker information, 

including specific annotations of markers, 

capture of science data and management of 

the study-specific information along with a 

scientific portal for accessing this information. 

A major new release of this capability 

integrated with a science portal was deployed 

in 2007. Figure 7-3 shows the architecture 

and components of the EDRN knowledge 

system.

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Figure 7-3. Architecture and Components of the EDRN Knowledge System

Bioinformatics

Tools

Biomarker DB VSIMS,

other tools

Public Portal

Protocol DB

CDE Repository

Participant DB

Science data results

Biomarker research

and phasing

EDRN Knowledge Environment

Study management

and other tools

Distributed Specimen

Databases

Characteristics of 

participants

Descriptions of 

protocols

Data elements

Core Data

eCAS science

warehouse

ERNE, virtual

specimen bank 

The basis for constructing the EDRN knowledge system is the use of common data 

elements (CDEs). These provide a common 

language that future studies can use and enable consistency across institutions collecting 

data. The Network continues to curate CDEs 

for managing the EDRN’s data assets in a 

consistent way across informatics systems and 

Network studies.

Informatics experts have also been careful to 

ensure that any data shared is compliant with 

federal privacy and security regulations including the Health Insurance Portability and Accountability Act (HIPPA). This requires that 

certain identifiers be removed to protect the 

confidentiality of the patients described by the 

research data. In addition, careful attention 

was applied to the national informatics infrastructure to ensure secure data transmission.

Informatics Infrastructure Connects 

and Builds Databases

The informatics infrastructure for the EDRN 

knowledge system is based on a distributed software framework developed at JPL 

called the Object-Oriented Data Technology 

(OODT) framework. OODT was selected in 

2003 as NASA’s Runner-up for “Software of 

the Year” within the agency. The framework, 

used to support NASA’s planetary and earth 

science missions, provides a set of software 

tools capable of both connecting heterogeneous databases together and building new 

databases capable of archiving data (see Figure 

7-4).

The EDRN informatics infrastructure combines the Network CDEs with the OODT 

software to enable common mechanisms for 

searching databases located at EDRN-funded 

research institutions. Because the software 

is intelligent enough to handle mappings 

between different database implementations, 

scientists and other researchers can make discoveries using different data sets produced by 

different organizations with different meanings, as if they are a single, large repository of 

knowledge. This means that the software can 

be configured to fit several different domains 

that are critical to scientific research, such as 

biomedicine and space science. A great advantage is that it connects disparate databases and 

systems together over the Internet without 

requiring those systems to be re-implemented 

or modified.

Source: EDRN Informatics Team
Figure 7-4. EDRN’s Informatics Infrastructure is Based on the NASA/JPL OODT Data Grid Architecture.

CDEs Produce Interoperability Among 

Groups

Data architecture is critical to effectively 

search heterogeneous distributed data systems 

and to enable correlative science. This structure defines the CDEs and their relationships 

within the EDRN knowledge environment 

and enables the interoperability between distributed institutions by providing a common 

semantic language for communication. 

Several standards have been adopted that 

support the definition of data architecture. 

ISO/IEC 11179 provides a standard definition for describing data elements. This enables 

consistency when developing data dictionaries. 

The ISO/IEC standard recommends that a 

data element consists of attributes for four key 

categories: identification, definitional, representational and administrative. EDRN uses 

ISO/IEC 11179 in conjunction with Dublin 

Core as a mechanism for developing a minimal set of data elements that must be provided 

in any data architecture.

EDRN developed data architecture for its 

knowledge system that provides an over-arching ontology model for describing critical 

cancer data objects. An ontology model is a 

concept used to represent knowledge in a domain (e.g., management of biomarkers). This 

model was captured as a set of CDEs, standard data terms and associated values that are 

critical for enabling data sharing and capture 

systems.

Each participating institution within the 

EDRN knowledge system was using the 

Network CDEs to map their local data models 

to the knowledge system model in order to 

provide semantic consistency across the entire 

system. Specific mapping tools were developed 

to allow informatics experts to then capture 

the mapping of local site data models to the 

EDRN knowledge system model. Attributes 

of the data element, including permissible values, units, format and data type, were in turn 

captured and mapped to one another. This 

enabled the informatics infrastructure software 

to run a translation function as part of the 

process of querying and retrieving data from 

the distributed EDRN institutions. 

Enabling Technologies 83

Source: NASA Jet Propulsion Laboratory
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Biospecimen Data Unified Across 

Institutions

EDRN already deployed the knowledge 

system to over 10 institutions providing a 

common web-based client interface called 

“ERNE” or the EDRN Network Exchange 

system. ERNE unifies search and retrieval of 

biospecimen data from all institutions regardless of its location, storage, or differences in 

the underlying data models. This helps scientists, for example, to locate tissue specimens 

for breast cancer by searching data catalogs at 

participating institutions across the country 

(see Figure 7-5).

As the knowledge system evolves, the governing cancer CDE model and the use-cases 

derived in the working groups will be used 

to drive the relationships between the data 

sets enabling discovery through data mining. 

Scientists will be able to query an assay result 

from a validation study and then find the associated specimens that were collected as part 

of that assay.

Figure 7-5. Distributed ERNE Specimen System

Source: DMCC at Fred Hutchinson Cancer Research Center
Figure 7-6. Web Site Screen Capture of EDRN Protocol Management Systems 

Enabling Technologies 85

Source: DMCC at Fred Hutchinson Cancer Research Center
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Information System for Study Management

The Validation Studies Information Management System (VSIMS) is a major component 

of the EDRN knowledge system. Critical to 

any knowledge system is its ability to capture 

data as part of the science data processing and 

analysis infrastructure. Within the Network, 

this occurs as part of the process to identify 

and validate cancer biomarkers.

EDRN designed a secure, web-based system 

that includes the main components needed for 

capturing and preserving the necessary metadata and data objects that integrate into the 

overall knowledge system architecture. These 

components include protocol management 

tools (see Figure 7-6), communication tools, 

a data collection and processing system and 

a specimen tracking system. All are based on 

having a robust data architecture. Information 

maintained in the system is secure and stored 

separately for each multisite study, allowing 

multiple protocols to be coordinated centrally 

through the same data management system.

Biomarker Data Management

Biomarker data management involves managing a database for tracking biomarker research, 

including collection of such data as phase of 

development, studies and related trials and 

specific science data captured during the study 

of the biomarker.

Each phase of a biomarker is tracked throughout the development process from preclinical 

exploratory studies to cancer control studies. 

Figure 7-7 shows an example biomarker study. 

Figure 7-7. Web Site Screen Capture of Biomarker Study Tracking Example

Source: EDRN Informatics Team
Data is captured consistently using the same 

set of CDEs and therefore, applications can 

interoperate and automatically correlate 

information. This forms the logical EDRN 

Knowledge System. For example, a biomarker 

tracked in the biomarker database can link to 

a cell count in a specimen record in ERNE; 

the result of an analysis can reference science 

data captured as well. Access to this information through a shared mechanism provides an 

integrated view of the information within the 

EDRN enterprise.

EDRN is also establishing a science data warehouse called the EDRN Catalog and Archive 

System (eCAS). eCAS is a distributed metadata-driven system for the capture, tracking, 

processing and retrieval of scientific data from 

biomarker validation studies. eCAS promises 

to be an invaluable tool that will make it possible to share results, correlate data, discover 

new biomarkers and much more.

eCAS also will allow for capture and release 

of public data sets housed at NCI, as well as 

sharing specific science data from institutions. 

eCAS is being used to establish a Biomarker 

Atlas as a means for discovering other related 

data such as images that have been catalogued 

and stored according to organ-specific groups 

across EDRN institutions. Figure 7-8 below 

shows a prototype under development within 

EDRN that allows for the search and discovery of lung images based on regional queries 

of the bronchial map.

Figure 7-8. Web Site Screen Capture of Prototype Biomarker Atlas for the Lung

Source: EDRN Informatics Team

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Public Information Disseminated 

through Portals

The EDRN public portal (http://www.cancer.

gov/edrn) serves as a dynamic information 

dissemination service for the Network and 

the greater research community. This includes 

facts concerning investigators, on-going studies, meetings, funding opportunities, working 

groups, scientific discoveries and release of 

public data sets, publicly available informatics 

tools and news as shown in Figure 7-9. The 

DMCC, NCI and NASA’s JPL each play a 

critical role in developing and operating the 

informatics systems. Each partner, along with 

other EDRN institutions, requires the capability to share data, tools and information with 

both the Network and the broader scientific 

community. 

EDRN is preparing to release an upgrade to 

the public portal that will transform it into 

a knowledge portal for accessing the science 

information produced during EDRN studies. The public portal will play a pivotal role 

by permitting Google-like searching of the 

EDRN information space, allowing users to 

navigate the complex set of information available within the EDRN enterprise. Figure 7-10 

demonstrates a search of information related 

to prostate cancer within the EDRN.

Figure 7-9. Search for Science Data Using the EDRN Public Portal

Source: NASA Jet Propulsion Laboratory
The cancer Biomedical Informatics Grid, 

or caBIGTM, is helping to lead NCI 

in developing a research informatics 

infrastructure for scientists. At the same time, 

EDRN’s scalable infrastructure will advance 

the Network’s ability to expand its data and 

tools and provide a long-term platform for 

cancer research. New methods that make it 

possible for scientists to mine and correlate 

information across multiple data sets and 

studies will be created to aide the discovery 

process. This includes introducing dataunderstanding software and algorithms 

capable of developing the existing knowledge 

system infrastructure and constructing 

knowledge bases of metadata using automatic 

feature detection. This additional metadata 

will augment existing metadata used to 

describe EDRN data products, enhancing 

the informatics infrastructure overall and 

enabling more sophisticated search and 

correlation capabilities.

NCI supports programs for emerging 

technologies, such as the NCI Alliance for 

Nanotechnology in Cancer (http://nano.

cancer.gov/) and developing standards for 

evaluating the performance of multiple 

platforms, such as the Clinical Proteomic 

Technologies Initiative for Cancer (http://

www.proteomics.cancer.gov). EDRN works 

closely with these programs and stays abreast 

of maturing standards and technologies that 

are likely to accelerate biomarker analysis.

It is expected that standardization of technologies such as high-throughput genotyping, 

genomics, proteomics, molecular imaging 

and nanotechnology will be necessary to generate data that are consistent and comparable. 

By leveraging resources and collaborations, 

EDRN will be able to develop interventions 

to identify individuals at risk for cancer, 

detect early stage disease and improve patient 

management.

Enabling Technologies 89

Figure 7-10. Results from an Online Search for EDRN Prostate Cancer Protocols

Source: EDRN Informatics Team
90 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk Part Three

Investing in

Biomarker Research
Why do “big science” proposals 

that have such great promise, so often 

require decades from concept to fruition (if 

they make it to practice at all)? The health 

care industry, while accounting for more than 

13% of the U.S. gross domestic product and 

growing at triple the rate of inflation, remains 

a fragmented industry. Perhaps health care can 

learn from computer electronics, where “big 

science” is always part of the equation, but so 

is a business model that drives the translation 

from “art to part.” Part of the problem is the 

difference in the business models that drive 

the respective systems. 

Health care organizations have generally 

grown organically, which typically results in 

structures that are organized along functional 

“silos,” i.e., in areas of expertise where depth 

of knowledge in one particular area is critical. 

Such a “horizontal” structure fosters excellent 

solutions for primary scientific problems. 

However, it often generates barriers if 

knowledge must be shared between silos. In 

contrast, computer chip manufacturers are 

organized in a more “vertical” structure. In 

this structure, formal “hand-off” procedures 

have been designed to ensure that discoveries 

in one aspect of chip design and construction 

are rapidly and efficiently conveyed to others 

who require the information. This allows for 

rapid vetting of ideas, quickly culling out 

the poor concepts and fostering the rapid 

acceptance of good concepts. 

In a vertical design there are a number of 

focused experts in a single organizational 

unit, generating rapid discovery of ideas 

with a primary focus on: coordination of 

multiple entities; using shared resources; and 

emphasizing the “hand-offs” between entities.

Business Model

“W e are victims of our own success. The sheer number of candidate biomarkers 

creates an impediment to their further development as it is not easy to recognize those markers that have the greatest potential.... (T)he development of 

biomarkers would benefit from an organized community effort that allows 

progression from discovery to validation. A case in point is the National Cancer 

Institute’s Early Detection Research Network, which fosters a collaborative effort 

and provides access to critically needed standardized reference specimens.... It 

is time to recognize that developing biomarkers is just as complex as developing 

drugs and cannot just be done on an ad-hoc basis but through concerted efforts 

that bring together academia, industry, government and foundations.” 

 Sam Hanash, M.D., Ph.D. 

 Principal Investigator, EDRN Biomarker Development Laboratory

 Fred Hutchinson Cancer Research Center 

 (from the Journal of Proteomics Research) 

C hapter EIGHT

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Novel Mechanism Enhances 

Collaboration

EDRN promotes a vertical approach for 

conducting biomarker research, whereby 

biomarkers are developed in Biomarker 

Developmental Laboratories (BDLs), refined 

and cross-validated by Biomarker Reference 

Laboratories (BRLs) and validated in 

collaboration with Clinical Epidemiology and 

Validation Centers (CEVCs), all within one 

organization (see Figure 8-1). The focus is in 

coordinating multiple resources with a goal 

of minimizing the barriers to the rapid and 

efficient “hand-off” between entities. One 

method used for achieving this is a structured 

set of criteria for assessing the roles and 

clinical significance of each newly discovered 

biomarker, along with criteria and strategies 

for judging biomarkers in relationship to one 

another (see Figure 8-2). 

This is in contrast to a horizontal approach, 

which may result in rapid discoveries of many 

biomarkers by participating laboratories, 

but limits advancement of the biomarkers to 

validation while increasing duplication and 

reducing potential synergies across disciplines. 

In either model, particularly in health care, 

one must consider the influence and interests 

of constituents or forces that can either drive 

or hinder the process of forward movement. 

EDRN developed methods, policies and 

procedures for relating with each of these 

major constituent groups in an approach 

adapted from the Harvard Business Review. 

EDRN Business Model

Stakeholders Activities Outputs Outcomes Impact

• Investigators

• Technology 

Developers

• Policy 

Makers

• Sponsors & 

Investors

• Regulatory 

Authority

• Customer 

Base

Advances in 

Biomarker 

Research & 

Development

• Discovery

• Reference

• Formulation

 - Standards

 - Reagents

 - Assay etc.

• Validation

Biomarkers 

for Cancer 

Diagnostics

• Improved 

Survival

• Reduced 

Mortality & 

Morbidity

Decision Criteria

Proof of Principle

Laboratory Validation

Prioritization

Validation for Clinical Use

(For details, see Figure 8-2)

key: BDL = Biomarker Developmental Laboratory

 BRL = Biomarker Reference Laboratory

CEVC = Clinical Epidemiology & Validation Center

EDRN Steering Committee

BDL

BRL CEVC

Organization of 

Activities

Figure 8-1. The Vertical-Approach Business Model followed by EDRN

Any business model is defined by the organization’s clients, core values, inputs, expected outcomes and what impact is projected in business.

Adapted from the Harvard Business Review
Figure 8-2. Decision Criteria for Judging Relationships and Strategies in Biomarker Development and Fruition

Decision Criteria Workflow

Proof of Principle

Reagents/Biomarkers

Laboratory 

Validation

Prioritization

Validation for 

Clinical Use

Establish Association of Biomarkers with Risk, 

Disease and Prognosis 

Multi-laboratory Validation;

Q/A Programs, etc.

Retrospective Studies

Genotypic-Phenotypic Correlative Studies

Generalization, Accuracy, Frequency of Test;

Integration of Information from Multiple Sites

Use of Analytical/Statistical Model

Near-term vs. Long-term Opportunities

Potential Value of the Discovery

Establish When to Initiate, Continue or Stop

Prospective/Retrospective Studies

Define the Target Population; Establish Protocols

Solicit Proposals for Studies; Monitor and 

Review Successes Against the Set Criteria

Investigators

Players or stakeholders are groups or 

individuals with a stake in the results of the 

EDRN’s efforts. The biomarker research 

enterprise has many stakeholders with 

substantial resources and power to influence 

the outcome and adaptability of biomarkerbased clinical applications. These include 

basic science researchers, clinicians, health care 

professionals, policy makers, regulators and 

the groups and agencies that fund research. 

This diverse assemblage can have competing 

priorities and agendas; any successful business 

model must identify a common theme 

around which all can work synergistically. 

EDRN joins stakeholders around the hub 

of translational research. The Network 

structure is defined by a mission based on 

inclusiveness, openness, coordination and 

cooperation among normally disparate groups.

Sponsors and Investors

Due to the long investment time needed 

to discover, develop and use biomarkers 

in clinical application, investors in 

biotechnologies and pharmaceutical 

companies are less inclined to support 

biomarker diagnostics research. Progress in the 

field has been slow due to a lack of sustained 

funding. EDRN is one of the few mechanisms 

in use to jump-start the process. Funding 

is a major force shaping the Network, 

Source: NCI EDRN

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without which this model is not viable. With 

sustained funding in place, EDRN established 

relationships with industry, foundations and 

international consortia, to generate and share 

precompetitive and prevalidation data on 

biomarkers and to sponsor validation projects 

that develop diagnostic biomarkers. 

Sponsors and investors need to take a longterm view of the development cycle of EDRN. 

The Internet, for example, was an Advanced 

Research Projects Agency project of the U.S. 

Defense Department under construction for 

years before it generated today’s World Wide 

Web. Without sustained funding by sponsors, 

the necessary infrastructure of the Internet 

would never have been developed. 

The funding mechanism to support this 

business model is derived from the principles 

and concepts found in other sectors of 

the national economy. In these sectors, 

“production,” be it the creation of a restaurant 

meal, the building of a computer chip or the 

flying of passengers from one destination to 

another, is accomplished through a series of 

independent, but synergistic units that work 

together to create the end products or service. 

For example, a restaurant may have a wine 

steward, dessert chef and head chef, all of who 

must be coordinated to “produce” the best 

meal for the consumer. Similarly, the various 

components of EDRN are coordinated to 

assure that good science rapidly and efficiently 

conveys from one aspect of development to 

the next. 

Policy Makers and Regulators

Existing policies and regulations on 

biomarkers in health care are not clearly 

delineated and continue to emerge. EDRN 

is in an excellent position to work with 

regulators, such as the Food and Drug 

Administration (FDA), to clearly define 

approval requirements for biomarker tests and 

molecular diagnostics. Also, NCI has ongoing 

discussions with the Centers for Medicare and 

Medicaid Services for covering nine NCIsponsored clinical trials of colorectal and other 

cancers. 

Technology Developers

Stiff competition exists among technology 

developers. Many are not receptive to 

participating in technology comparisons and, 

not surprisingly, frequently resist working with 

their rivals. EDRN overcame this obstacle 

by bringing in developers at the outset of 

studies and building confidence in favor of 

the proposed study that provides a “win-win” 

situation for all stakeholders. 

Customer Base

The cancer community has an abundance 

of engaged and empowered individuals. 

Organ-specific advocacy groups increase 

awareness of cancer and vigorously support 

sustained funding for the various national and 

international programs. Many professional 

societies, such as the American Cancer Society, 

American Association for Cancer Research, 

American Society of Clinical Oncologists 

and others, lobby for research funds. EDRN 

investigators link with these societies on a 

regular basis and invite their leaders to the 

EDRN-sponsored meetings and conferences. 

The Network recognizes and leverages the 

influence of the societies, consumers and 

interest groups in promoting biomarker 

research.

Increasingly, informed stakeholders are 

demanding accountability from networks 

or consortia perceived to be favored over 

individual-based science. EDRN is at the 

forefront of adopting critical benchmarks for 

measuring productivity and accountability 

in its business model. The model promotes 

collaboration, discourages “Zombie projects” 

and rewards team-science. “Zombie Project” 

is a term used in project management for 

projects that continue endlessly without a 

closure and delivering on their promises.

EDRN accomplishes this task through 

various laboratories and centers under 

the administrative guidance of a Steering 

Committee and NCI program staff. Together 

they monitor performance and coordinate 

incentives and rewards for collaborating 

investigators through novel funding 

approaches. Proficient administrative tools 

track projects and ensure completion. 

Another element of accountability ensures 
that investigators meet their stated goals and 

develop collaborations with other investigators 

before a specific set of funds is released. 

Monies released from set-aside or special core 

funds are provided only if the investigators’ 

performance is at an acceptable level as 

judged by the site visit team, EDRN Steering 

Committee and NCI program staff. These 

set-aside funds (20% of individual grants) 

and Core Funds are utilized to promote team 

science and some “Big Hairy Audacious 

Goals,” as well as to accelerate the discovery 

and validation process when additional 

resources and expertise are needed. “Big Hairy 

Audacious Goals” (BHAG) refers to using 

ambitious, even outrageous goals to motivate 

people and focus them toward concrete 

accomplishments.

In the EDRN funding process, individual 

grantees apply for the release of set-aside funds 

from their grants for collaborative projects. 

The request is reviewed and approved by the 

EDRN Executive Committee and then read 

by other EDRN investigators with appropriate 

expertise. A request for core funds undergoes 

a rigorous two-step review that involves 

submission of a three-page pre-application, 

followed by a full application that is evaluated 

by two external reviewers and the EDRN 

Executive Committee. Associate Membership 

is used to seek additional expertise not 

currently available within the Network. 

Applications for Associate Membership are 

reviewed online and by a Standing Review 

Committee.

This adaptive funding approach ensures that 

elegant, novel ideas win and are supported in a 

timely manner. Additionally, the management 

oversight provides necessary “checks and 

balances” to utilize scarce resources wisely 

and enforce benchmarks for timely project 

conclusion.

EDRN Business Model Promotes 

Collaboration

While NCI leads the nation’s investment 

in cancer research, it is not the nation’s 

sole contributor in the fight against cancer. 

Critical contributions are made by other 

government agencies, academic and 

charitable organizations and private industry. 

Coordinated efforts of the parties can result 

in productive partnerships where the various 

groups, collectively and individually, each 

contribute to forwarding progress.

EDRN established numerous platforms 

to facilitate and enable cancer research. 

Such platforms create an infrastructure for 

translational and clinical research, providing 

unique resources, reagents, information 

exchange and the critical mass of researchers, 

facilities, technologies and disciplines. The 

goal and demonstrated talent of NCI is 

to make investments that maximize the 

opportunities for progress.

Industry plays a distinctive role in bringing 

the products of the nation’s investment in 

research from development to clinical use, 

as well as serving as a valuable contributor 

in all other stages of the research and 

discovery process. Industry requires scientific 

infrastructure that may overlap the needs of 

ongoing research efforts supported by NCI.

Costs and management of investments to 

support developing infrastructure frequently

Associate Members and Their 

Contributions

Category A

Members submit basic or translational 

research consistent with EDRN priorities. 

They bring in new ideas and proposals for 

biomarker discovery.

Category B

Members contribute to Network priorities 

by sharing available technologies, 

contributing specimens, providing high-risk 

registries, cohorts and other resources. 

Funding can be applied for annually.

Category C

Members include scientists, clinicians, 

patient advocates and ethicists, who 

participate in EDRN workshops and 

conferences and Collaborative Group 

meetings, but do not receive EDRN 

funding or support for travel expenses.

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96 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

exceed the capabilities of any one company 

and do not require proprietary access. Again, 

consider the Internet, where the development 

cost was well beyond the reach of any single 

firm but not beyond the collective effort of 

public-private partnerships.

Both EDRN and industry benefit from a 

mechanism that allows formal partnering 

in development and operation of necessary 

infrastructure. Partnerships strengthen 

collaborations by creating the vehicle 

for industry to interact with and, where 

appropriate, co-fund development of 

infrastructure with NCI. Industry benefits 

by having an opportunity to access new 

resources, expertise, databases and reagents 

resulting from NCI-coordinated infrastructure 

investments. NCI benefits by having access 

to the strengthened capabilities of industrial 

partners, which are often essential for bringing 

the products of research investments to the 

American public: products such as industry 

expertise and technology; an expanded 

investment base to build critical infrastructure 

for research; and expanded scientific scope for 

the scientific priority-setting process. 

Significant ongoing activities within EDRN 

are poised to accelerate the discovery process 

and regulatory approval. Not only does 

it take many years to bring biomarkers to 

clinical use but development requires a 

sizeable dollar investment and infrastructurerelated resources. Both private sectors and 

government institutions face regulatory 

hurdles, but their investigators together 

can help regulators learn from researchers’ 

concerns and suggestions to improve the 

review process. 

Historical Collaboration with Industries

Diagnostic firms work with EDRN to help 

accelerate the discovery, evaluation and 

validation processes. Some organizations are 

listed in Table 8-1.

EDRN-Gordon Research Conference 

EDRN sponsors the Gordon Research 

Conference (http://www.grc.org) on “New 

Frontiers in Cancer Detection and Diagnosis” 

every other year to emphasize that the accurate 

detection of early stage cancer is critical 

to improve patient care. Progress must be 

made in the development of cancer-specific 

interventions to avert invasion, metastatic 

dissemination and subsequent, advanced 

disease. The conference brings together junior 

and senior physicians and scientists with 

expertise in basic, translational and clinical 

oncology and experts in computational 

biology and informatics. 

The conference setting provides an 

opportunity and venue for intense discussion 

and evaluation of cancer research; for 

establishing the merit of research priorities; 

and for advancing the field through newly 

forged collaborations. In an atmosphere 

focused on uniting biology, oncology and 

technology, physicians and scientists work 

cooperatively to cultivate new avenues 

of research and reveal potential clinical 

applications to improve patient care.

Dialogue with the Food and Drug 

Administration

EDRN is in a unique situation to liaise 

between industry and government and 

regularly consults FDA. Two such meetings 

with FDA scientists, EDRN investigators, 

NCI scientists and diagnostic firms were the 

EDRN-FDA Education Workshop at the 

National Institutes of Health, February 15, 

2007; and the Joint NCI-FDA Workshop 

on Research Strategies, Study Designs and 

Statistical Approaches to Biomarker Validation 

for Cancer Detection and Diagnosis, July 23-

25, 2004.

Collaboration with Foundations

EDRN collaborates with the Canary 

Foundation on ovarian cancer and the 

Lustgarten Foundation on pancreatic cancer. 

The founder and CEO of the Canary 

Foundation agreed that investigations 

supported through Canary will consult on 

validation needs and propose validation of a 

biomarkers panel through EDRN. Lustgarten 

is consulting EDRN on developing highquality monoclonal antibodies through a 

Request for Application to be issued by the 

Foundation and reviewed using the EDRN 

online review system. The antibodies will 

likely be stored at NCI-Frederick and 

managed and distributed by EDRN.
Table 8-1. Some EDRN Collaborations with Diagnostic Firms

ENTITY CONTRIBUTION VALIDATION STUDY 

Reagents Assay

Eisai, Japan Yes No DCP for Hepatocellular Carcinoma

Wako Yes Yes AFP, AFL-L3 for Hepatocellular Carcinoma

Gen-Probe Yes Yes Assay for PCA3; Fused transcripts 

(TMPRSS2-ETV1 and TMPRSS2-ERG) 

for Prostate Cancer

Abbott ELISA Yes TIMP-1 for Colon Cancer and LOH

FISH Probe for Esophageal Cancer; Chromosomal 

aberrations for Lung Cancer

Fujrebio Yes Yes SMRP in Mesothelioma; HE-4, 

CA-125 and CA72.4 antibodies for 

Ovarian panel

Diadexus Yes Yes B7-H4, Spondin and DCR3 for 

Ovarian marker panel

Milagen Yes Yes Antibody panel for several cancers

LabCorp Yes Yes Ovarian panel

OncoMethylome Yes Yes GSTP-1 for Prostate Cancer

Beckman-Coulter Yes Yes % proPSA for Prostate Cancer

Meso Scale Diagnostics Yes Yes Validating platform for antibody array 

for Ovarian Cancer

Business Model Stimulates Innovation 

and High-Risk Projects

The phrase BHAG (“Big Hairy Audacious 

Goal”) was proposed by James Collins and 

Jerry Porras in their article “Building Your 

Company’s Vision,” Harvard Business Review, 

(1996) Vol. 74, Iss. 5, pp 65-77. 

 “A true BHAG is clear and compelling, serves 

as unifying focal point of effort and acts as 

a clear catalyst for team spirit,” according to 

their research. “It has a clear finish line, so the 

organization can know when it has achieved 

the goal; people like to shoot for finish. Such a 

concept has been used by many big industrial 

houses, such as Boeing, IBM, Motorola, etc.”

EDRN uses a similar concept for promoting 

multi-institutional, multidisciplinary projects, 

such as the EDRN-Human Proteomics 

Organization (HUPO) collaboration on the 

Plasma Proteome Project and the EDRNNIH Women’s Health Initiative Project 

on discovery of colon markers. In these 

collaborations, a team is formed for a specific 

project and is dissolved once the project is 

completed. The team has clearly defined goals, 

timelines, milestones and closure clauses. 

Source: NCI EDRN

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This approach rapidly identifies the outcome 

of the mission and lends its support either 

for continuation or dissolution in a timely 

manner.

Over 7,000 articles are published per year on 

potential biomarkers, yet the FDA approves 

only approximately one marker every other 

year. The problem is clear: there is an 

enormous gap between the development of 

potential biomarkers and the conversion of the 

beneficial ones into approval. An additional 

gap exists between FDA approval and use 

in general practice. This raises the “BHAG” 

of EDRN: to convert this heterogeneous 

mixture into a seamless, fine-tuned network 

of systems that can significantly, measurably 

and dramatically reduce the time between 

discovery and widespread use of critically 

important biomarkers in the oncology 

community.

As the Network strives to sail across the 

knowledge stream—navigating from gathering 

data, to sorting information, to accumulating 

knowledge and, finally to translating it all into 

usable products—a business model should be 

prepared to mitigate the many potential obstacles and dangers inherently associated with 

the continuum from discovery to translation 

to biomarker-based diagnostic assays. 

As discussed earlier, all stakeholders must 

work together on today’s tremendous opportunities, particularly on those technologies 

that will accelerate biomarker discovery and 

validation and lead to greater diagnostics for 

patients. 

The vertical approach business model 

presented here allows EDRN to operate 

within a “forecast, prevent and manage” 

paradigm. The paradigm includes, but is 

not limited to, the following aspects:

• Cancer will be forecast on the basis of 

clinical and biological profiling.

• Institutional investment in cancer 

prevention will increase. 

• Early cancer detection will be monitored 

through regular, inexpensive biomarker tests.

• Genetically defined subtypes of disease 

will be identified and personalized care 

will be offered.

Business models for industry often perform 

poorly and the companies fail. Manufacturing sectors have evolved their business models 

to meet the challenges of the time. Large 

biomedical science enterprises, however, 

never had well defined models and, therefore, 

precedents are lacking. EDRN’s model has 

the flexibility to evolve and adapt to needs as 

Network collaborators gain more experience, 

learn about new challenges and obstacles and 

experience both setbacks and successes.

EDRN Investigator Team 

Receives Team Science Award 

From the American Association 

for Cancer Research

EDRN principal investigator Arul Chinnaiyan, M.D., 

Ph.D. and his team from the University of Michigan 

SPORE were awarded the Inaugural Team Science 

Award at the Centennial American Association for 

Cancer Research (AACR) Annual Meeting for their 

work on gene fusion in prostate cancer. This award 

has been established by AACR and Eli Lilly and 

Company to recognize the growing importance of 

interdisciplinary teams in the translation of scientific 

research discoveries into clinical and diagnostic 

cancer applications. Team members also included 

Ken Pienta, M.D. (SPORE grantee), James Montie, 

M.D., John Wei, M.D., and Mark Rubin, M.D. 

Their major accomplishments are:

• The discovery of gene fusion (TMPRSS2-ERG, 

TMPRSS2-ETS; Science 310: 644, 2005) in 

prostate cancer; 

• The use of DNA microarrays to develop a molecular 

signature for prostate cancer (Nature 412:822, 

2001) and linking of the Polycomb Group Protein and 

histone methylatransferase EZH2 to solid tumors 

(Nature 419: 624, 2002/ PNAS 100:11606, 2003);

• Autoantibody signatures of prostate cancer (NEJM

353:1224) and integrative molecular approaches to 

study molecular alterations in cancer (Cancer Cell

8:393, 2005 Nature Genetics 37:579, 2005); 

• The team, along with others, was among the first 

to discover AMACR as a tissue biomarker 

of prostate cancer (JAMA, 287:1662, 2002); 

• The Chinnaiyan Lab, through EDRN, is attempting 

to validate the non-invasive detection of prostate 

cancer gene fusions in urine.
Reductions in cancer mortality 

are primarily due to early detection and 

risk reduction behaviors. NCI established the 

Early Detection Research Network (EDRN) 

as a vertically integrated environment to 

discover and validate biomarkers for both 

the early detection of cancer and for cancer 

risk assessment. Throughout EDRN’s existence, NCI advisory groups, such as the Board 

of Scientific Advisors (BSA), the National 

Cancer Advisory Board (NCAB), the Translational Research Working Group (TRWG) 

and other key working groups, supported the 

Network’s model for translational research.

Management through 

Quantifiable Metrics

EDRN recognizes a need to develop 

metrics that might be tracked and captured 

more easily by methods other than those 

originally developed for the program 

(e.g., annual progress reports and site 

visits). The informatics system developed 

with the National Aeronautics and Space 

Administration’s (NASA) Jet Propulsion 

Laboratory (JPL) became fully operational 

in summer 2007. It provides the types of 

metrics suggested by the TRWG, such as 

charting milestones and goals, with a system 

of incentives for moving quickly from Phase 

1 to Phase 2 of biomarker development (see 

Chapter 1). Such progress is being tracked, 

but the new informatics system allows a 

greater capacity to monitor progress within 

individual grants and across the EDRN 

network and is expected to vastly improve 

the quantity and quality of metrics. To date, 

the primary process for evaluating metrics 

involved the annual written progress report 

by EDRN members, teleconferences and site 

visits. 

Biomarkers

The most important overall metric for EDRN 

is the number of biomarkers that have moved 

forward into validation (see Chapter 6). 

Evaluating Biomarker Progress 

in Translational Research

“Translational research requires cooperative expertise at the clinical,

epidemiological and basic scientific levels. If you lack any of these, 

as all programs excepting the EDRN currently lack, then the research 

will not be successfully transferred to the clinic. My group is interested 

in having success at the clinic via biomarker discovery.”

 William Grizzle, M.D., Ph.D. 

Principal Investigator 

 EDRN Biomarker Development Laboratory

University of Alabama at Birmingham 

Evaluating Biomarker Progress in Translational Research 99

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The Network created strong momentum in 

biomarker development in the past 7 years. In 

this type of research, it is expected that there 

will necessarily be many more markers in the 

discovery phase (Phase 1) of development 

than in prevalidation and validation phases 

(Phases 2 and 3). EDRN has over 120 

biomarkers in Phase 1, 27 in Phase 2 and two 

in Phase 3.

Publications 

The number of peer-reviewed publications by 

EDRN-funded investigators is an important 

metric to illustrate progress. More than 460 

manuscripts have been produced by EDRN 

investigators and NCI program staff. A list 

of key collaborative publications appears in 

Appendix I. Publications produced by two 

or more EDRN laboratories or with industry 

show EDRN meeting the key goal of fostering 

collaborative work. 

Conferences and Workshops

As an organization based on a nationwide 

network of collaboration, EDRN emphasizes 

participation in Network-sponsored activities, 

including workshops and conferences. Absent 

members are considered at risk for exclusion 

from the program.

Program Evaluation

Metrics for programmatic evaluation, applied 

to individual laboratories and centers, 

include quality assurance. These members 

are also questioned about innovations and 

future plans, which are vital to ensuring 

the Network’s ability to adapt to changing 

technologies and scientific progress. 

Each component of EDRN—Biomarker 

Developmental Laboratories (BDLs), 

Biomarker Reference Laboratories (BRLs), 

Clinical Epidemiology and Validation Centers 

(CEVCs) and the Data Management and 

Coordinating Center (DMCC)—established 

a list of programmatic evaluation metrics. 

Metrics used by each component are listed 

below. 

• BDL—Biomarkers identified; biomarkers 

forwarded to CEVCs or BRLs; biomarkers 

added to early detection or riskassessment panels; and biomarkers used in 

chemoprevention clinical trials.

• BRL—Assays performed; number of 

samples; types of samples; quality control of 

samples; number and type of developmental 

projects approved; and use of common data 

elements (CDEs).

• CEVC—Numbers of samples collected; 

types of samples; sources of samples; 

numbers of samples provided to BDLs and 

BRLs; amount of set-aside funds released; 

and number of requests for release of 

developmental funds.

• DMCC—Standards of informatics support; 

types of informatics; quality-control 

procedures; development of network-wide 

communication systems; network-wide 

communications for data and specimen 

sharing; and statistical methodology 

development.

Collaborative Metrics

Collaborative metrics measure how well 

the EDRN is developed and maintained. 

Metrics for this area are evidenced by data 

on the number of EDRN sites collaborating 

with other EDRN sites; the number of 

collaboration projects; the number of joint 

peer-reviewed publications; the number 

of BDLs that have requested the release of 

restricted funds; and the number of sites 

that have collaborated with CEVCs, BRLs, 

or BDLs. Participation in EDRN can be 

measured by the number of committees, 

working groups and task forces attended by 

members; special projects completed; use of 

EDRN CDEs for data capture and sharing; 

data and specimens shared; and willingness to 

streamline technology transfer.

Outreach Metrics

EDRN outreach, vital to the continuation 

of the program, speaks to the health of 

the existing Network. Measures include 

the number of new Associate Members, 

the amount of Chair funds allocated to 

new Associate Members and the number 

of applications for Chair funding. Actions 

to broaden outreach to professional 

organizations provide an opportunity to 

recruit new EDRN Associate Members and 

to strengthen EDRN collaborations within 

the professional community. Participation 

includes, for example, involvement in the 

American Association for Cancer Research 

(AACR), the American Society of Clinical 

Oncology (ASCO) and the American Society 

of Molecular Pathologists (ASMP). 
External Committee Evaluation 

NCI’s Division of Cancer Prevention 

instituted an external committee composed 

of Bernard Levin, M.D., of the University 

of Texas M. D. Anderson Cancer Center, 

Kenneth Cowan, M.D., of the University 

of Nebraska Medical Center, Barry Kramer, 

M.D., of the National Institutes of Health, 

Brian Reid, M.D., of Fred Hutchinson 

Cancer Research Center and Arnold Kaluzny, 

Ph.D., of the University of North Carolina at 

Chapel Hill, to evaluate the EDRN program. 

They found that the “overall outcomes and 

accomplishments of EDRN to date have 

been well worth the cost.” According to the 

committee:

Given that biomarker development must 

begin at the earliest stage of discovery, 

EDRN’s single accomplishment is having 

produced a developmental pipeline that 

provides standardized procedures and 

measurable milestones...There have been a 

large number of biomarkers (101 to date) 

that have entered the pipeline; progress is 

moving rapidly and is facilitated by an 

organized management and informatics system 

that is both adaptable and efficient. This 

comprehensive list illustrates the breadth and 

depth of the program across cancer sites and 

cancer types. 

Metrics developed in the past 5 years are 

maturing with the new database being 

developed by EDRN and JPL... This database 

will facilitate methods of improving the 

program’s cost-effectiveness and will provide 

intensive monitoring of EDRN activities. 

EDRN challenged the very culture of academic 

research by its emphasis both on team science 

as well as close attention to milestones of 

biomarker development, a rigorous process 

not familiar to many academic environments. 

This challenge, though more difficult in the 

early years of the program, is well on the way 

to being overcome.

Progress in Translational Research

EDRN made significant progress in: 

• Developing an organized effort for 

biomarker discovery and validation; 

• Building resources to support this effort;

• Demonstrating the capabilities of several 

genomic and proteomic platforms; 

• Identifying candidate biomarkers; and 

• Undertaking multi-center validation studies. 

The way EDRN is organized provides 

flexibility to respond in a timely fashion to 

new opportunities; there are no barriers that 

prevent EDRN from responding to changes 

in research priorities. EDRN made a major 

educational contribution to the research 

community at large by providing criteria and 

new standards for validating biomarkers via 

published validation study protocols designed 

by EDRN investigators. The Network 

continues to address a multiplicity of needs 

in discovery, validation, tissue collection, 

informatics, public sector collaboration and 

engaging academia and the private sector. 

Fulfilling the expectations for rapid discovery 

and validation of cancer biomarkers requires 

continued and sustained investment in 

biomarker research (see Chapter 10). The 

process of bringing new biomarkers to the 

clinic faces challenges similar to the process 

of bringing new pharmaceuticals to the clinic. 

But with the current infrastructure in place, 

these expectations can be realized in the near 

future.

EDRN adheres to principles and practices of 

effective management that meet the standards 

and ideals recommended by the TRWG, some 

of which are highlighted below: 

• Integrated Organizational Approach

EDRN stays abreast of similarly funded 

programs and rather than duplicate efforts, 

proposes collaboration and coordination 

with other NCI biomarker research efforts. 

The Network operates a secure website 

for member-investigators (for example, 

to obtain access to the catalogues of 

specimens across EDRN sites). There is 

significant interest in this data from outside 

investigators and the specimen information 

on the secure site is being moved to 

the public site pending IRB review and 

approval. EDRN embarked on creating 

standards for methods, tools, specimens and 

technologies and sought consensus on best 

practices.

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EDRN Biomarker Development Laboratory Achieves Model

For Integrative Analysis Through Public-Private Partnership and Metabolomic Profiling

The University of Michigan EDRN Biomarker Development 

Laboratory (BDL) received grant money from NCI to both 

extend a collaboration with Harvard University and to 

establish a new collaboration with Metabolon, Inc., 

of Raleigh, N.C. This unique public-private partnership 

focuses research on metabolomic profiling in the study 

of human disease.

The Michigan BDL was focused on a number of prostate 

cancer biomarkers, including TMPRSS2-ETS gene fusions 

and cancer autoantibody signatures. Metabolon is a leading 

company in the area of unbiased metabolomic profiling of 

biological specimens.

The overall goal of the project is to integrate genomic, 

proteomic and metabolomic data to better understand 

prostate cancer progression and to nominate new 

biomarkers. Multiple complex molecular events characterize 

prostate cancer initiation, unregulated growth, invasion and 

metastasis. Distinct sets of genes, proteins and metabolites 

dictate prostate cancer progression. Deciphering the 

molecular networks that distinguish organ-confined prostate 

cancer from metastatic disease may lead to the identification 

of biomarkers of invasion and disease aggressiveness. 

Although gene and protein expression have been extensively 

monitored to understand prostate cancer biology, not much 

is known about the metabolomic profile that represents 

the distal read-out of this disease pathophysiology. 

Using a combination of high-throughput liquid and gas 

chromatography-based mass spectrometry, more than 1,265 

metabolites across 262 clinical samples related to prostate 

cancer (tissue, urine and plasma) were profiled. 

The metabolomic profiles derived from tissues were able 

to segregate benign prostate disease, clinically localized 

prostate cancer and metastatic disease. Interestingly, 

these metabolomic profiles revealed increased methylation 

potential that could drive late-stage epigenetic silencing. 

Matched transcriptomic data validated the existence of 

the observed metabolomic alterations. As a reflection of an 

increase in both the amino acid pool and the methylation 

potential of the tumor, metabolomic profiling revealed 

sarcosine as being significantly elevated in metastatic 

prostate cancer. Increased sarcosine levels were validated 

in an independent set of metastatic prostate cancer tissues 

and invasive prostate cancer cell lines. Thus, metabolomic 

profiling may serve as an important complement to other 

multi-dimensional molecular approaches to study human 

disease.

The team included: Principal Investigator Arul Chinnaiyan, 

M.D., Ph.D. University of Michigan Medical School; 

collaborators from the University of Michigan, Arun 

Sreekumar, Ph.D, John Wei, M.D., Debashis Ghosh, Ph.D., 

Rajal Shah, M.D., Subramaniam Pennathur, M.B., B.S., Gil 

Omenn, M.D., Ph.D., Laila Poisson, B.S., T. Rajendiran, Ph.D., 

Xuhong Cao, M.S., Ph.D., K. Shanker, M.S., Bo Han, M.D., 

Ph.D., Anuradha Giri; collaborators from Metabolon, Inc., 

Chris Beecher, Ph.D., Alvin Berger, Ph.D., Bruce McCreedy; 

and collaborators from Harvard University, Marty Sanda M.D. 

and Mark Rubin, M.D.
• Designated Funds to Facilitate Promising 

Translational Research EDRN was 

designed from the beginning to support 

promising translational early detection 

biomarker projects. Money is set aside 

from core funds specifically and separately 

for validation and for discovery. Teams 

who conduct validation are separate from, 

but collaborate with, the discovery teams. 

The opportunity for validation is built 

into the grant mechanism, with organ site 

groups responsible and accountable for 

moving promising markers to validation. 

Resources for validation are not an 

obstacle and specimen banks are available 

through Network collaborations and 

EDRN reference sets. Decisions on which 

biomarkers should be validated are guided 

by the EDRN Executive Committee 

according to priorities set out for translation 

of biomarkers. 

• Prioritization Process for Early 

Translational Research The EDRN 

principal investigators comprise the steering 

committee. Every investigator must also 

be a member of an organ site group that 

decides priorities for validation. Each 

investigator serves on subcommittees or 

working groups, such as one on cross-cutting 

technology or one to set EDRN priorities. 

The decision process described in the 

manual of operations is transparent. The 

manual reflects processes voted on by the 

steering committee. Any investigator from 

inside or outside the Network can propose 

a candidate biomarker for validation. 

Investigators collaborate to create common 

specimen resources to test biomarkers from 

the larger scientific community. Decisions 

to proceed are based on scientific merit, peer 

approval and clinical feasibility.

• Incentives for Participation and Tailored 

Funding The funding mechanism requires 

investigators to apply annually for peerreviewed set-aside funds (set-aside funds are 

restricted funds within each investigator’s 

grant). For set-aside funds to be released, the 

investigator must demonstrate collaboration 

with other EDRN investigators. 

Participation in steering committee meetings 

are an explicit requirement of the grant and 

the group is small enough so that teams 

can work effectively together. Communal 

investment is critical and promoted. One 

of the Network’s strengths, the structure of 

the grant mechanism created culture change 

whereby the group succeeds together.

• Core Services for Early Translational 

Research EDRN published metrics on 

the five phases of biomarker development. 

Dialogue about how to successfully move 

biomarker discovery projects forward into 

validation is a constant. The Enterprise 

Research Network Exchange (ERNE) 

monitors all tissue and specimen resources in 

EDRN. The Network addressed barriers in 

core services. Sometimes it is difficult, using 

the consensus process, to have finite position 

statements on the best ways to collect 

specimens. EDRN advocates establishing 

consensus standards and developed standard 

operating procedures (SOP) for providing 

a ready source of serum or plasma for 

proteomic, methylation and other studies. 

Cooperative groups, clinically focused 

investigators and basic scientists benefit. 

• Management Structure for Early 

Translational Research EDRN has an 

explicit structure to identify, facilitate 

and coordinate access to resources and 

collaborators. EDRN requires investigators 

to attend and participate at meetings (three 

per year) as a condition of funding. Set-aside 

monies are not available to investigators 

unless they demonstrate collaborative efforts. 

Integration with other NCI-funded activities 

is actively supported and encouraged. 

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As part of an initiative to evaluate 

NCI’s effort in translational research, 

the NCI Translational Research Working 

Group (TRWG) analyzed the fiscal year 

2004 research portfolio. For the first time, 

NCI projects funded through a variety of 

mechanisms that fit the definition of research 

that “transforms scientific discoveries arising 

from laboratory, clinical, or population studies 

into clinical applications to reduce cancer 

incidence, morbidity and mortality” were 

assessed. 

TRWG constructed six “developmental 

pathways” that characterize the transformation 

of scientific discoveries into new clinical 

modalities for oncology. These modalities 

are categorized as:

1. Risk assessment modalities, intended 

to characterize the cancer-related health 

status of an individual, which includes 

biospecimen-based risk assessment 

devices (protocols, reagents, instruments), 

image-based risk assessment (agents or 

techniques); and 

2. Interventive modalities, intended to 

change the cancer-related health status of 

an individual via prevention or treatment, 

which includes agents (drugs or biologics), 

immune response modifiers (vaccines, 

cytokines, etc.), interventive devices and 

lifestyle alterations.

Investing in Biomarker 

Research for Early Detection

“To achieve our vision of modern medicine, we also need research scientists with broad 

expertise, from widely varied disciplines, coming together in highly cooperative and 

efficient teams to answer ever more complex questions. To this end, NIH recently 

changed a long-held policy of having only a single principal investigator on any 

NIH grant to a new policy that allows, when appropriate to the science, multiple 

principal researchers to apply for a grant together. This new policy is encouraging 

collaboration across disciplines and enabling academic scientists to exercise creative 

leadership in a project while bringing more of the best and brightest from physical, 

biological and behavioral sciences to the task of solving the multifaceted and 

complex health-related problems.”

 Elias A. Zerhouni, M.D., Director 

National Institutes of Health, March 6, 2007 

C hapter TEN
The developmental pathway diagrams, such 

as the one shown in Figure 10-1, specify 

key activities and decision points, clarify 

dependencies among corresponding steps and 

events and show important feedback loops 

and iterative processes that are embedded 

within the development process. 

In many ways, the initiatives set forth by 

TRWG parallel the existing management 

structure of EDRN (see Chapter 9). Steps 

similar to those in the pathway schema have 

already been implemented by the Network, 

allowing EDRN to effectively achieve its goals 

despite some limitations related to scope, 

funding and staffing constraints. 

TRWG found that approximately $1.33 

billion of the total $4.4 billion NCI fiscal year 

2004 funding was awarded for translational 

research projects (see Figure 10-2). About 

41% ($547 million) of that was dedicated to 

program and cooperative awards. Of those 

awards, 4% ($21.8 million) was awarded to 

EDRN. 

NCI Investment in Translational 

Research

EDRN was envisioned as an approach to 

provide a seamless translational research 

pathway that would connect a diverse group 

of outstanding investigators into a productive 

unit capable of sharing ideas, technologies, 

skills and products. Ample financial support 

and incentives for collaboration were an 

indispensable aspect of the program. NCI 

sought to create the critical mass necessary 

to invent and validate new tools and to 

exploit novel technologies to enhance cancer 

screening and early detection and ultimately 

reduce cancer mortality.

Other NCI programs have been established to 

realize clinical use of diagnostic biomarkers

(see Figure 10-3). Future infrastructure 

improvements rely on continuing 

improvements in the Network’s collaborative 

culture, which the EDRN established 

despite an already entrenched culture that 

rewarded individual achievement rather than 

collaborative work, through the funding 

process.

As discussed in Chapter 9, EDRN produced 

hundreds of publications and well over 100 

biomarkers in the discovery phase. The costeffectiveness of the biomarkers effort could 

only be realized after there are a number of 

fully validated and clinically useful biomarkers 

in healthcare settings. This, however, will 

require a sustained, long-term investment.

EDRN’s portfolio of biomarkers is expanding 

and its collection of sample sets, critically 

important for both discovery and validation,

is growing. Over the next 2 years, at least one 

and likely more biomarkers for early detection 

of common, high-mortality cancers (colon, 

breast, lung or prostate) will enter Phase 2 

or 3 validation trials. Continued investment 

in strong analytical technology, informatics, 

statistics, epidemiology and biosample 

management will pay dividends through 

high-quality data that will meet regulatory 

requirements.

An analysis was performed to evaluate 

NCI biomarker grants in the area of early 

detection and diagnosis and to analyze trends 

in publications, patents and collaborations 

that resulted from these grants. Specifically, 

biomarker-related grants initially funded in 

FY1999 or FY2000 by the EDRN (a U01 

mechanism) or by other grant mechanisms 

(R01 and other U01 programs) were 

tracked through FY2005 and the metrics 

of their success (patents, publications and 

collaborations) were evaluated.

The average number of cumulative 

publications through 6 years post-grant 

initiation per award type is shown in Figure 

10-4. EDRN projects (U01) yielded an 

average of 14 publications per grant, nonEDRN U01s yielded an average of 11 

publications per grant and R01s yielded an 

average of five publications per grant. 

The average number of publications per 

million dollars invested by NCI is shown 

in Figure 10-5 for each award type. R01 

biomarker grants yielded an average of 2.8 

publications per million dollars invested, 

non-EDRN U01s yielded an average of 2.9 

publications per million dollars and EDRN 

grants yielded an average of 3.2 publications 

per million dollars.

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106 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Figure 10-1. Translational Research Developmental Pathway

Redirect research 

effort elsewhere

Discovery of molecular biomarker with clinical potential

Sensitivity and 

specificity expected to be sufficient for 

clinical utility (“credentialed biomarker”)?

Does envisioned clinical need justify 

expenditure of resources?

Is it feasible to develop the 

protocol/reagent/device?

Developmental Pathway

Biospecimen-Based

Risk Assessment

Devices

(Protocols/Reagents/

Instruments)

Biomarkers for screening,

Diagnosis, Staging, Response 

Assesement, Prognosis, 

Prediction

NCI TRWG

version 020607

Box = Action

Diamond = Decision

Fundamental research

Define patient subset with 

biomarker using assay on a 

limited number of specimens in a 

single lab

Identify or develop biospecimen

repositories linked with 

outcomes data

Develop reproductive assay 

and standard reagent(s) for 

biomarker or profile

Is correlation statistically robust?

Develop /refine

clinical grade

biomarker assay

protocol / reagent / device

Is correlation statistically robust?

Validate correlation of

biomarker with outcome as

a primary or secondary endpoint

in a prospective human study

Is correlation statistically robust? Can it be fixed?

Human study of utility of biomarker to direct 

therapy or chemoprevention or predict 

outcome/risk

Outcome =

Disease occurrence

Disease progression

Response to therapy

Biomarker =

Single gene / protein

Molecular profile

Validate assay and correlation of biomarker 

with outcome retrospectively using a large 

number of specimens in different labs

Source: http://www.cancer.gov/images/trwg/Biospecimen-RiskAssessmentPathway050807.pdf
Figure 10-2. NCI Investment in Translational Research

Treatment

34%

Cancer Control, 

Outcomes and 

Survivorship

5%

Model Systems

1%

Biology

8%

Etiology

20%

Prevention

13%

Early Detection, 

Diagnosis and Prognosis

19%

 Source: http://www.cancer.gov/trwg/portfolio-analysis.pdf

As shown in Figure 10-6, the articles resulting 

from these NCI-funded biomarker grants 

(R01, non-EDRN U01 and EDRN U01) had 

an average impact factor of six. The majority 

(75%) of these articles were published in 

journals with an impact factor of 3 to 20, 

while a few articles were published in journals 

with an impact factor greater than 20 (see 

Figure 10-7). Impact factor measures the 

number of citations to science journals.

EDRN investigators generated more than 

26 patents and 14 licenses with diagnostic 

companies willing to invest in EDRNdeveloped biomarkers for further development 

and eventual submission to FDA. These 

outcomes are likely to improve over the next 

5 years, since a significant portion of EDRN 

efforts during the first 5 years was directed 

toward the organization, coordination and 

management.

Challenges and Solutions 

The EDRN collaborative group infrastructure 

has matured, with the greatest achievements 

occurring during the last 3 years. Individual 

groups have built differing approaches to 

promote and support collaborative research, 

but each developed a portfolio of products 

and technologies that can exploit the 

translational research resources of the group. 

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Figure 10-3. Major NCI-Funded Translational Research Programs, FY 2006

13

Millions ($)

EDRN – Early Detection Research Network

SPORE – Specialized Programs of Research Excellence

NANOTECH – Nanotechnology Program 

ICBP – Interactive Cancer Biology Program

NTROI – Network for Translational Research in Optical Imaging 

CPTAC – Clinical Proteomic Technology Assessment for Cancer

MMHCC – Mouse Models of Human Cancer Consortium

12.5

12

4

3.5

3

2.5

2

1.5

1

0.5

0

EDRN

SPORE

NANOTECH

ICBP

NTROI

CPTAC

MMHCC

Total amount (x10)

Average cost/grant

For example, the Prostate and Urologic 

Cancer Collaborative Group developed 

microsatellite markers for bladder cancer and 

SELDI/MALDI-TOF for prostate cancer. 

The Colorectal and Other Gastrointestinal 

Cancers Collaborative Group developed 

and implemented a validation trial of 

des-carboxyprothrombin (DCP) for the 

detection of hepatocellular carcinoma. The 

sample set collected for this validation trial 

is being leveraged for use as prevalidation 

samples (Phases 1 and 2). EDRN Biomarker 

Developmental Laboratories (BDLs) have 

studied new markers (e.g., GP73, a novel 

glycoprotein marker of liver disease) and 

technologies (such as proteomics profiles) 

using a subset of this sample set. The derived 

data will determine whether these additional 

biomarkers for the early detection of 

hepatocellular carcinoma should be validated 

in a Phase 2 cross-sectional trial. Similarly, 

the Lung Collaborative Group developed a 

panel of epigenetic biomarkers that are being 

studied with a reference set that was jointly 

collected by members of the group. 

Through the funding of new members 

and investment in Associate Memberships, 

EDRN’s technological infrastructure 

markedly improved. Improvements have 

raised the quality and reproducibility of 

complex technologies such as proteomics, 

antibody arrays and genomics tools. For 

instance, Ambergen, Inc. brought a new, 

automated artificial gene expression system to 

EDRN that solves many of the problems of 

heterogeneity in detecting multiple mutations 

at a given genetic locus. An EDRN Clinical 

Epidemiology and Validation Center (CEVC) 

and an EDRN BDL are collaborating with 

Ambergen to detect common colorectal 

adenocarcinoma associated mutations in the 

APC gene in DNA extracted from human 

urine. The new technology captures 85% of 

Source: Adapted from the NCI Translational Research Working Group
Figure 10-4. Average Number of Publications 

per NCI-funded Biomarker Grant, 

FYs 2000-2005

Average Number of

Publications per Grant

8

6

4

2

0

RO1

10

12

14

16

U01 (non-EDRN) U01 (EDRN)

Award Mechanism

Figure 10-5. Average Number of Publications 

per Million Dollars Invested for 

NCI-related Biomarker Grants that 

Began in FY1999 or FY2000 to 2005 

Average Number 

of Publications

3.00

2.75

2.50

RO1

3.25

3.50

U01 (non-EDRN) U01 (EDRN)

Award Mechanism

Figure 10-6. Average Impact Factors for Publications 

Resulting from NCI Biomarker Grants 

that Began in FY1999 or FY2000 to 2005

Average Impact Factor

RO1

4

3

2

1

0

5

6

7

8

U01 (non-EDRN) U01 (EDRN)

Award Mechanism

 Figure 10-7. Impact Factors for Publications Resulting from NCI Biomarker Grants that 

Began in FY1999 or FY2000 to 2005

Percent of Publications

40%

30%

20%

10%

0

<3

50%

60%

70%

80%

3 to 20 >20

Impact Factor

90%

RO1 U01 (non-EDRN) U01 (EDRN)

Source: Analysis done by SAIC-NCI

Figure 10-4 to 10-7. Trends in Early Detection and Diagnosis Grants

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all known APC mutations in a single, highthroughput, automated assay and creates 

a new, potentially more efficient and cost 

effective early detection and cancer screening 

tool. 

The progression of biomarkers from the 

discovery phase to the validation phase is 

slow to date, reflecting initial challenges with 

cultural and infrastructural issues. Perhaps 

the most important challenge facing EDRN 

has been to create and nourish a culture of 

collaboration that attracts top-level scientists, 

most of whom have been accustomed to 

working in and being rewarded for less 

collaborative work. 

At the EDRN’s inception, the diversity of 

scientific backgrounds caused immediate 

clashes due to problems in communication, 

misplaced incentives and individualistic 

tendencies to work separately. Numerous 

conflicts have since been resolved through a 

high-quality, respected internal peer-review 

process that sets the tone for the future. At 

the same time, NCI program staff worked 

fairly, systematically and strategically to defuse 

discord that could shut down group-to-group 

teamwork. 

There is a growing consensus of collaboration 

and credit-sharing within and between the 

EDRN organ-based collaborative groups. 

Substantial attention is paid to grappling with 

problems that previously were considered 

simplistic; an example is the quality control of 

sample collection and management. EDRN 

investigators invest considerable effort and 

time into building excellence in these critical 

resources.

Some investigators fail to recognize the 

importance of sharing resources and expertise 

and continue to see EDRN mainly as a source 

of funding for their laboratory-based research, 

not as a place where the products of this 

research can be brought for validation and 

generalization. Most EDRN investigators, 

however, choose to work through the Network 

precisely because of its translational research 

vision, which allows clinical, epidemiology 

and statistical research groups’ access to 

investigators and technologies that would 

otherwise not be available. Many investigators 

have established strong collaborative ties 

with first-rate laboratories across the country 

and internationally that have enabled 

implementation of the translational research 

paradigm. 

When EDRN was initiated, the translational 

infrastructure necessary to meet the premise of 

the Network was non-existent. For example, 

there were no quality control mechanisms 

to ensure reproducible laboratory analytical 

performance, no common data elements 

(CDEs) required to work with human 

biosamples, no quality human biosample 

collections with appropriate good clinical 

practices and good computing practices. 

Informatics needed development to manage 

large amounts of clinical and biological 

data. Problems with the increasing burden 

of regulation—human subjects protections, 

materials transfer agreements and intellectual 

property protections—caused delays and 

problems in information exchange and in the 

willingness of investigators to collaborate.

By leveraging other government resources, 

such as the National Aeronautics and Space 

Administration’s (NASA) Jet Propulsion 

Laboratory, EDRN developed a novel 

informatics infrastructure (see Chapter 7) that 

permits interrogation of diverse databases at 

long distances. This infrastructure enhances 

group cohesion and provides investigators 

with the critical information about biosample 

quality and quantity that permits development 

of collaborative translational projects. 

EDRN built a set of CDEs for translational 

research that included demographic, clinical, 

biosample, research and clinical data. 

Development and utilization of these data 

elements have been critical factors in creating 

group cohesion and in linking resources from 

diverse units throughout EDRN. 

EDRN learned from these problems, grown 

scientifically and culturally and transmitted 

lessons learned to other parts of the NCI and 

the wider scientific community. Obstacles, 

should not be interpreted as weakness of the 

concept or the model rather, they should be 

interpreted as a process of evaluation and 

change as the commitment of the stakeholders and the scientific community alike 

continues to grow collectively.
The first grant solicitations to establish 

EDRN specified multiple units to create 

a cohesive translational paradigm for the 

discovery and validation of biomarkers for 

cancer early detection and risk assessment. 

As noted, early performance was uneven. 

CEVCs, in collaboration with others 

components of EDRN, have introduced 

new products and tools for development in 

EDRN for prostate, lung, bladder, esophageal, 

hepatocellular, pancreatic and colorectal 

adenocarcinomas. Examples in the GI and 

Lung Collaborative environment include the 

detection of epigenetic changes in promoter

regions of tumor suppressor genes; the ability 

to rapidly screen exons of tumor suppressor 

genes in human stool, urine and plasma; 

new-generation proteomics tools that rapidly 

detect and identify differentially over- and 

underexpressed proteins in prostate, lung, 

colon and liver diseases; and, automated 

FISH-based technology for the detection of 

neoplasia associated mutations. 

EDRN created a rigorous peer-review 

system that ensures that preliminary data—

analytical, clinical and quantitative—are of 

excellent quality. The process begins with an 

internal review with clinical, biostatistical 

and analytical expertise. The project then 

receives external peer-review and, finally, 

NCI program staff review resulting in 

an exceptionally robust and high-quality 

validation trial. The data collection and 

sample collection processes and analytic 

procedures are continuously reviewed and 

audited by the Data Management and 

Coordination Center (DMCC). The data 

developed and disseminated by EDRN is 

expected to be of high quality and the multisite EDRN validation trials are likely to 

substantiate the prevalidation data. Hence, 

the high quality of the data, the produced 

documentation and multi-site infrastructure 

will permit such data to be used for regulatory 

review and approval. 

The Associate Membership Program 

was highly productive in bringing new 

technologies and products into the Network. 

More than 120 Associate Members from 

academia and industry have joined EDRN. 

Practical challenges have also been faced, 

including Intellectual Property (IP) issues 

posed by a number of patented and licensed 

tools and technologies. Frequent questions 

are: Who owns the IP for collaborative 

products resulting from individually licensed 

reagents and assays; and how would the IP 

of an individual investigator be protected? 

EDRN developed IP guidelines and requires 

investigators to share their IP plans. 

Collaborative projects often involve transfer 

of specimens from one institution to 

another, requiring separate Material Transfer 

Agreements (MTA) between the providers 

and the receiving institutions. Despite the 

guidance provided by NIH, each institution 

has its own MTA requirements. Such 

constraints continue to hamper completion 

of collaborative projects in a timely manner, 

sometimes resulting in the loss of interest and 

motivation among participating members.

EDRN’s Impact on Cancer Prevention 

Research 

Without EDRN, research into new 

biomarkers of early cancer detection and 

risk would have remained on the periphery 

of research with a strong, but fragmented 

laboratory presence and little translational 

interest in the academic scientific community. 

But with the Network, a new translational 

paradigm is defining the organization, 

approaches and standards by which 

biomarkers are developed and assessed. The 

Network created major focus, energy and 

new research in the field of early detection. 

The Network’s publications, meetings, 

funding opportunities and infrastructure 

have fashioned a new environment for cancer 

prevention research.

EDRN’s work represents a paradigm shift 

that brought international attention, new 

investigators and increasing involvement 

by academic and industry communities. 

For example, two companies with major 

markets in diagnostics, Abbott Molecular, 

Inc. and Roche, Inc.; by collaborating 

with Network scientists promise a strong 

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marketing pathway for EDRN-discovered 

products. Another impressive indication is 

the increase of solicitations for NCI-industryfocused meetings on biomarker technology, 

development, validation or regulation.

An estimated total cumulative NCI 

investment per American over the past 30 

years is about $258, or about $9 per American 

per year over the entire period, NIH Director 

Elias Zerhouni, M.D., told Congress in 2007. 

He cited EDRN as one of the major programs 

with significant outcomes for the investment. 

Because of a hundredfold reduction in the 

unit cost of genomic technology, researchers 

can now study at affordable costs, he noted. 

EDRN’s approach fits with the NIH’s 

research paradigm for the future, which seeks 

to transform medicine from curative and 

reactionary to preemptive and anticipatory. As 

Dr. Zerhouni testified, “A more predictive, 

personalized and preemptive form of medicine 

is no longer just a dream but a vision to strive 

for, because it can reduce disease burden and 

its costs while improving individual quality 

of life.” Other NIH institutes have emulated 

the EDRN model for their respective clinical 

programs, such as Rare Disease Research 

Network (RDRN), Office of Director, 

NIH; Network for Translational Research in 

Optical Imaging (NTROI), NCI; and the 

Osteoarthritis Initiative, National Institute of 

Arthritis, Musculoskeletal and Skin Diseases, 

NIH. 

The pipeline of biomarkers to be studied in 

a prevalidation environment appears to be 

growing. The funnel analogy suggests that 

the large bulk of biomarkers under study in 

EDRN will be in discovery and prevalidation 

stages. Within the next two years, it is 

expected that at least three validation studies 

will be completed on bladder, hepatic, lung 

and prostate cancers. Biomarkers or biomarker 

panels from the GI area—a serum-based 

test using nuclear-matrix proteins for the 

detection of colorectal adenocarcinoma, a 

stool- and urine-based gene panel of four 

genes for the early detection of colorectal 

cancer and a 4-gene FISH-based panel for 

detection of adenomatous neoplasia of the 

lower esophagus—derived from EDRN 

collaborative research will be entering Phase 2 

(cross-sectional validation). 

By the end of the current grant period (2010), 

it is expected that at least one and probably 

two to three, biomarker products will have 

been submitted to the FDA for regulatory 

approval. The des-carboxyprothrombin 

validation trial for the early detection of 

hepatocellular carcinoma and microsatellite 

markers trial for bladder cancer may generate 

a sufficient quality and quantity of data to 

justify FDA review for approval as early 

diagnostic products.

Thus, the Network’s structure provides a 

solid approach to early translational research. 

Discovery leads to work that confirms and 

improves the accuracy of the biomarker, 

which then moves to early clinical validation 

of the test. Through this approach to early 

translational research EDRN built and 

implemented a vertically integrated pipeline 

of biomarkers for cancer early detection 

and risk assessment. The Network attracts 

excellent academic and industry scientists by 

providing access to diverse top-quality assays, 

clinical specimens, methodological expertise, 

industrial resources and financial resources 

that are not organized or readily available 

through other governmental or industry 

funding mechanisms.

This Network structure within the NCI 

vision of early translational research is 

expected to lead in a few short years to 

the molecular diagnostics that will allow 

physicians and health care professionals 

to prevent or eliminate many cancers and 

ultimately transform cancer into manageable, 

rather than fatal, diseases. 
Title of chapter goes here 113
114 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk
Acharyya S, Butchbach ME, Sahenk Z, Wang H, Saji M, 

Carathers M, et al. Dystrophin glycoprotein complex dysfunction: a regulatory link between muscular dystrophy and 

cancer cachexia. Cancer Cell 2005;8(5):421-32.

Allison M, Garland C, Chlebowski R, Criqui M, Langer R, 

Wu L, et al. The association between aspirin use and the 

incidence of colorectal cancer in women. Am J Epidemiol 

2006;164(6):567-75.

Ankerst DP, Thompson IM. Understanding mixed messages 

about prostate specific antigen: biases in the evaluation of 

cancer biomarkers. J Urol 2007;177(2):426-7.

Arakawa H, Wu F, Costa M, Rom W, Tang MS. Sequence 

specificity of Cr(III)-DNA adduct formation in the p53 gene: 

NGG sequences are preferential adduct-forming sites. 

Carcinogenesis 2006;27(3):639-45.

Baillargeon J, Platz EA, Rose DP, Pollock BH, Ankerst DP, 

Haffner S, et al. Obesity, adipokines and prostate cancer 

in a prospective population-based study. Cancer Epidemiol 

Biomarkers Prev 2006;15(7):1331-5.

Belinsky SA, Liechty KC, Gentry FD, Wolf HJ, Rogers J, Vu 

K, et al. Promoter hypermethylation of multiple genes in 

sputum precedes lung cancer incidence in a high-risk cohort. 

Cancer Res 2006;66(6):3338-44.

Bensmail H, Aruna B, Semmes OJ, Haoudi A. Functional 

clustering algorithm for high-dimensional proteomics data. J 

Biomed Biotechnol 2005;2005(2):80-6.

Berchuck A, Iversen ES, Lancaster JM, Pittman J, Luo J, Lee 

P, et al. Patterns of gene expression that characterize longterm survival in advanced stage serous ovarian cancers. Clin 

Cancer Res 2005;11(10):3686-96.

Bierl C, Karem K, Poon AC, Swan D, Tortolero-Luna G, Follen 

M, et al. Correlates of cervical mucosal antibodies to human 

papillomavirus 16: results from a case control study. Gynecol 

Oncol 2005;99(3 Suppl 1):S262-8.

Blehm KN, Spiess PE, Bondaruk JE, Dujka ME, Villares GJ, 

Zhao YJ, et al. Mutations within the kinase domain and 

truncations of the epidermal growth factor receptor are 

rare events in bladder cancer: implications for therapy. Clin 

Cancer Res 2006;12(15):4671-7.

Block TM, Comunale MA, Lowman M, Steel LF, Romano 

PR, Fimmel C, et al. Use of targeted glycoproteomics to 

identify serum glycoproteins that correlate with liver cancer 

in woodchucks and humans. Proc Natl Acad Sci U S A 

2005;102(3):779-84.

Block TM, Mehta AS, Blumberg BS, Dwek RA. Does rapid 

oligomerization of hepatitis B envelope proteins play a 

role in resistance to proteasome degradation and enhance 

chronicity? DNA Cell Biol 2006;25(3):165-70.

Brabender J, Marjoram P, Lord RV, Metzger R, Salonga D, 

Vallbohmer D, et al. The molecular signature of normal 

squamous esophageal epithelium identifies the presence of 

a field effect and can discriminate between patients with 

Barrett’s esophagus and patients with Barrett’s-associated adenocarcinoma. Cancer Epidemiol Biomarkers Prev 

2005;14(9):2113-7.

Bradley SV, Oravecz-Wilson KI, Bougeard G, Mizukami I, Li L, 

Munaco AJ, et al. Serum antibodies to huntingtin interacting 

protein-1: a new blood test for prostate cancer. Cancer Res 

2005;65(10):4126-33.

Brand RM, Jones DD, Lynch HT, Brand RE, Watson P, 

Ashwathnayaran R, et al. Risk of colon cancer in hereditary 

non-polyposis colorectal cancer patients as predicted by 

fuzzy modeling: influence of smoking. World J Gastroenterol 

2006;12(28):4485-91.

Brand TC, Hernandez J, Canby-Hagino ED, Basler JW, 

Thompson IM. Prostate cancer detection strategies. Curr 

Urol Rep 2006;7(3):181-5.

Brenner DE, Rennert G. Fecal DNA biomarkers for the detection of colorectal neoplasia: attractive, but is it feasible? J 

Natl Cancer Inst 2005;97(15):1107-9.

Brown NM, Stenzel TT, Friedman PN, Henslee J, Huper G, 

Marks JR. Evaluation of expression based markers for the 

detection of breast cancer cells. Breast Cancer Res Treat 

2006;97(1):41-7.

Brumback LC, Pepe MS, Alonzo TA. Using the ROC curve 

for gauging treatment effect in clinical trials. Stat Med 

2006;25(4):575-90.

Key Publications by Investigators in 

the Early Detection Research Network

Appendix I 115

appendix i
116 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Bunn PA, Jr., Dziadziuszko R, Varella-Garcia M, Franklin WA, 

Witta SE, Kelly K, et al. Biological markers for non-small cell 

lung cancer patient selection for epidermal growth factor 

receptor tyrosine kinase inhibitor therapy. Clin Cancer Res 

2006;12(12):3652-6.

Cai T, Pepe MS, Zheng Y, Lumley T, Jenny NS. The sensitivity 

and specificity of markers for event times. Biostatistics 

2006;7(2):182-97.

Cappuzzo F, Hirsch FR, Rossi E, Bartolini S, Ceresoli GL, 

Bemis L, et al. Epidermal growth factor receptor gene and 

protein and gefitinib sensitivity in non-small-cell lung cancer. 

J Natl Cancer Inst 2005;97(9):643-55.

Cappuzzo F, Toschi L, Domenichini I, Bartolini S, Ceresoli GL, 

Rossi E, et al. HER3 genomic gain and sensitivity to gefitinib 

in advanced non-small-cell lung cancer patients. Br J Cancer 

2005;93(12):1334-40.

Cappuzzo F, Varella-Garcia M, Shigematsu H, Domenichini 

I, Bartolini S, Ceresoli GL, et al. Increased HER2 gene copy 

number is associated with response to gefitinib therapy in 

epidermal growth factor receptor-positive non-small-cell 

lung cancer patients. J Clin Oncol 2005;23(22):5007-18.

Chatterjee M, Draghici S, Tainsky MA. Immunotheranostics: 

breaking tolerance in immunotherapy using tumor autoantigens identified on protein microarrays. Curr Opin Drug 

Discov Devel 2006;9(3):380-5.

Chatterjee M, Ionan A, Draghici S, Tainsky MA. Epitomics: 

global profiling of immune response to disease using protein 

microarrays. Omics 2006;10(4):499-506.

Chen G, Wang X, Yu J, Varambally S, Yu J, Thomas DG, et 

al. Autoantibody profiles reveal ubiquilin 1 as a humoral immune response target in lung adenocarcinoma. Cancer Res 

2007;67(7):3461-7.

Clarke W, Chan DW. ProteinChips: the essential tools for 

proteomic biomarker discovery and future clinical diagnostics. Clin Chem Lab Med 2005;43(12):1279-80.

Comunale MA, Lowman M, Long RE, Krakover J, Philip R, 

Seeholzer S, et al. Proteomic analysis of serum associated 

fucosylated glycoproteins in the development of primary 

hepatocellular carcinoma. J Proteome Res 2006;5(2):308-15.

Cramer DW, Titus-Ernstoff L, McKolanis JR, Welch WR, 

Vitonis AF, Berkowitz RS, et al. Conditions associated with 

antibodies against the tumor-associated antigen MUC1 and 

their relationship to risk for ovarian cancer. Cancer Epidemiol 

Biomarkers Prev 2005;14(5):1125-31.

Dinney CP, McConkey DJ, Millikan RE, Wu X, Bar-Eli 

M, Adam L, et al. Focus on bladder cancer. Cancer Cell 

2004;6(2):111-6.

Douglas JA, Gruber SB, Meister KA, Bonner J, Watson P, 

Krush AJ, et al. History and molecular genetics of Lynch syndrome in family G: a century later. Jama 2005;294(17):2195-

202.

Drake RR, Cazare LH, Semmes OJ, Wadsworth JT. Serum, 

salivary and tissue proteomics for discovery of biomarkers for head and neck cancers. Expert Rev Mol Diagn 

2005;5(1):93-100.

Drake RR, Schwegler EE, Malik G, Diaz J, Block T, Mehta A, 

et al. Lectin capture strategies combined with mass spectrometry for the discovery of serum glycoprotein biomarkers. 

Mol Cell Proteomics 2006;5(10):1957-67.

Drukier AK, Ossetrova N, Schors E, Krasik G, Grigoriev I, 

Koenig C, et al. High-sensitivity blood-based detection of 

breast cancer by multi photon detection diagnostic proteomics. J Proteome Res 2006;5(8):1906-15.

Dziadziuszko R, Witta SE, Cappuzzo F, Park S, Tanaka K, 

Danenberg PV, et al. Epidermal growth factor receptor 

messenger RNA expression, gene dosage and gefitinib 

sensitivity in non-small cell lung cancer. Clin Cancer Res 

2006;12(10):3078-84.

Faca V, Coram M, Phanstiel D, Glukhova V, Zhang Q, 

Fitzgibbon M, et al. Quantitative analysis of acrylamide 

labeled serum proteins by LC-MS/MS. J Proteome Res 

2006;5(8):2009-18.

Franklin WA. RAGE in lung tumors. Am J Respir Crit Care 

Med 2007;175(2):106-7.

Freedland SJ, Partin AW. Prostate-specific antigen: update 

2006. Urology 2006;67(3):458-60.

Gao WM, Kuick R, Orchekowski RP, Misek DE, Qiu J, Greenberg AK, et al. Distinctive serum protein profiles involving 

abundant proteins in lung cancer patients based upon 

antibody microarray analysis. BMC Cancer 2005;5:110.

Gorelik E, Landsittel DP, Marrangoni AM, Modugno F, Velikokhatnaya L, Winans MT, et al. Multiplexed immunobeadbased cytokine profiling for early detection of ovarian cancer. 

Cancer Epidemiol Biomarkers Prev 2005;14(4):981-7.

Gretzer MB, Chan DW, van Rootselaar CL, Rosenzweig 

JM, Dalrymple S, Mangold LA, et al. Proteomic analysis of 

dunning prostate cancer cell lines with variable metastatic 

potential using SELDI-TOF. Prostate 2004;60(4):325-31.

Hamilton JP, Sato F, Greenwald BD, Suntharalingam M, 

Krasna MJ, Edelman MJ, et al. Promoter methylation and response to chemotherapy and radiation in esophageal cancer. 

Clin Gastroenterol Hepatol 2006;4(6):701-8.
Appendix I 117

Hamilton JP, Sato F, Jin Z, Greenwald BD, Ito T, Mori Y, et al. 

Reprimo methylation is a potential biomarker of Barrett’s-Associated esophageal neoplastic progression. Clin Cancer Res 

2006;12(22):6637-42.

Hathaway B, Landsittel DP, Gooding W, Whiteside TL, Grandis JR, Siegfried JM, et al. Multiplexed analysis of serum 

cytokines as biomarkers in squamous cell carcinoma of the 

head and neck patients. Laryngoscope 2005;115(3):522-7.

Hauskrecht M, Pelikan R, Malehorn DE, Bigbee WL, Lotze 

MT, Zeh HJ, et al. Feature selection for classification of 

SELDI-TOF-MS Proteomic Profiles. Appl Bioinformatics 

2005;4(4):227-46.

Hernandez J, Balic I, Johnson-Pais TL, Higgins BA, Torkko 

KC, Thompson IM, et al. Association between an estrogen 

receptor alpha gene polymorphism and the risk of prostate 

cancer in black men. J Urol 2006;175(2):523-7.

Huang X, Raskovalova T, Lokshin A, Krasinskas A, Devlin 

J, Watkins S, et al. Combined antiangiogenic and immune 

therapy of prostate cancer. Angiogenesis 2005;8(1):13-23.

Ibanez de Caceres I, Dulaimi E, Hoffman AM, Al-Saleem T, 

Uzzo RG, Cairns P. Identification of novel target genes by an 

epigenetic reactivation screen of renal cancer. Cancer Res 

2006;66(10):5021-8.

Janes H, Pepe M, Kooperberg C, Newcomb P. Identifying 

target populations for screening or not screening using logic 

regression. Stat Med 2005;24(9):1321-38.

Jhala N, Jhala D, Vickers SM, Eltoum I, Batra SK, Manne 

U, et al. Biomarkers in diagnosis of pancreatic carcinoma in 

fine-needle aspirates. Am J Clin Pathol 2006;126(4):572-9.

Kan T, Yamasaki S, Kondo K, Teratani N, Kawabe A, Kaganoi 

J, et al. A new specific gene expression in squamous cell 

carcinoma of the esophagus detected using representational difference analysis and cDNA microarray. Oncology 

2006;70(1):25-33.

Katayama H, Sasai K, Kawai H, Yuan ZM, Bondaruk J, Suzuki 

F, et al. Phosphorylation by aurora kinase A induces Mdm2-

mediated destabilization and inhibition of p53. Nat Genet 

2004;36(1):55-62.

Kennedy TC, Franklin WA, Prindiville SA, Cook R, Dempsey 

EC, Keith RL, et al. High prevalence of occult endobronchial 

malignancy in high risk patients with moderate sputum 

atypia. Lung Cancer 2005;49(2):187-91.

Kim JH, Tuziak T, Hu L, Wang Z, Bondaruk J, Kim M, et al. 

Alterations in transcription clusters underlie development of 

bladder cancer along papillary and nonpapillary pathways. 

Lab Invest 2005;85(4):532-49.

Kim MS, Jeong J, Majewski T, Kram A, Yoon DS, Zhang RD, 

et al. Evidence for alternative candidate genes near RB1 

involved in clonal expansion of in situ urothelial neoplasia. 

Lab Invest 2006;86(2):175-90.

Kim YL, Liu Y, Turzhitsky VM, Wali RK, Roy HK, Backman V. 

Depth-resolved low-coherence enhanced backscattering. Opt 

Lett 2005;30(7):741-3.

Kim YL, Liu Y, Wali RK, Roy HK, Backman V. Low-coherent 

backscattering spectroscopy for tissue characterization. Appl 

Opt 2005;44(3):366-77.

Kusy S, Potiron V, Zeng C, Franklin W, Brambilla E, 

Minna J, et al. Promoter characterization of Semaphorin 

SEMA3F, a tumor suppressor gene. Biochim Biophys Acta 

2005;1730(1):66-76.

Lawson T, Ouellette M, Kolar C, Hollingsworth M. Culture 

and immortalization of pancreatic ductal epithelial cells. 

Methods Mol Med 2005;103:113-22.

Lee KM, Yasuda H, Hollingsworth MA, Ouellette MM. Notch 

2-positive progenitors with the intrinsic ability to give rise to 

pancreatic ductal cells. Lab Invest 2005;85(8):1003-12.

Li J, White N, Zhang Z, Rosenzweig J, Mangold LA, Partin 

AW, et al. Detection of prostate cancer using serum proteomics pattern in a histologically confirmed population. J 

Urol 2004;171(5):1782-7.

Li J, Zhao J, Yu X, Lange J, Kuerer H, Krishnamurthy S, 

et al. Identification of biomarkers for breast cancer in 

nipple aspiration and ductal lavage fluid. Clin Cancer Res 

2005;11(23):8312-20.

Liu AY, Zhang H, Sorensen CM, Diamond DL. Analysis of 

prostate cancer by proteomics using tissue specimens. J 

Urol 2005;173(1):73-8.

Lynch HT, Boland CR, Gong G, Shaw TG, Lynch PM, Fodde 

R, et al. Phenotypic and genotypic heterogeneity in the 

Lynch syndrome: diagnostic, surveillance and management 

implications. Eur J Hum Genet 2006;14(4):390-402.

Lynch HT, de la Chapelle A, Hampel H, Wagner A, Fodde 

R, Lynch JF, et al. American founder mutation for Lynch 

syndrome. Prevalence estimates and implications. Cancer 

2006;106(2):448-52.

Lynch HT, Grady W, Suriano G, Huntsman D. Gastric cancer: 

new genetic developments. J Surg Oncol 2005;90(3):114-33; 

discussion 133.

Lynch HT, Snyder C, Lynch JF, Karatoprakli P, Trowonou A, 

Metcalfe K, et al. Patient responses to the disclosure of 

BRCA mutation tests in hereditary breast-ovarian cancer 

families. Cancer Genet Cytogenet 2006;165(2):91-7.
118 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Lynch HT, Watson P, Tarantolo S, Wiernik PH, Quinn-Laquer 

B, Isgur Bergsagel K, et al. Phenotypic heterogeneity in 

multiple myeloma families. J Clin Oncol 2005;23(4):685-93.

Maitra A, Arking DE, Shivapurkar N, Ikeda M, Stastny V, 

Kassauei K, et al. Genomic alterations in cultured human 

embryonic stem cells. Nat Genet 2005;37(10):1099-103.

Makarov DV, Humphreys EB, Mangold LA, Walsh PC, Partin 

AW, Epstein JI, et al. Pathological outcomes and biochemical progression in men with T1c prostate cancer undergoing 

radical prostatectomy with prostate specific antigen 2.6 to 

4.0 vs 4.1 to 6.0 ng/ml. J Urol 2006;176(2):554-8.

Malik G, Ward MD, Gupta SK, Trosset MW, Grizzle WE, 

Adam BL, et al. Serum levels of an isoform of apolipoprotein 

A-II as a potential marker for prostate cancer. Clin Cancer 

Res 2005;11(3):1073-85.

Malyarenko DI, Cooke WE, Adam BL, Malik G, Chen H, 

Tracy ER, et al. Enhancement of sensitivity and resolution of 

surface-enhanced laser desorption/ionization time-of-flight 

mass spectrometric records for serum peptides using timeseries analysis techniques. Clin Chem 2005;51(1):65-74.

Malyarenko DI, Cooke WE, Tracy ER, Drake RR, Shin S, 

Semmes OJ, et al. Resampling and deconvolution of linear 

time-of-flight records for enhanced protein profiling. Rapid 

Commun Mass Spectrom 2006;20(11):1670-8.

Malyarenko DI, Cooke WE, Tracy ER, Trosset MW, Semmes 

OJ, Sasinowski M, et al. Deconvolution filters to enhance 

resolution of dense time-of-flight survey spectra in the 

time-lag optimization range. Rapid Commun Mass Spectrom 

2006;20(11):1661-9.

Marks LS, Kojima M, Demarzo A, Heber D, Bostwick DG, 

Qian J, et al. Prostate cancer in native Japanese and 

Japanese-American men: effects of dietary differences on 

prostatic tissue. Urology 2004;64(4):765-71.

Marrero JA, Romano PR, Nikolaeva O, Steel L, Mehta A, 

Fimmel CJ, et al. GP73, a resident Golgi glycoprotein, is a 

novel serum marker for hepatocellular carcinoma. J Hepatol 

2005;43(6):1007-12.

Merhige PM, Both-Kim D, Robida MD, Hollingsworth MJ. 

RNA-protein complexes that form in the spinach chloroplast atpI 5’ untranslated region can be divided into two 

subcomplexes, each comprised of unique cis-elements and 

trans-factors. Curr Genet 2005;48(4):256-64.

Merrick DT, Haney J, Petrunich S, Sugita M, Miller YE, Keith 

RL, et al. Overexpression of vascular endothelial growth factor and its receptors in bronchial dypslasia demonstrated by 

quantitative RT-PCR analysis. Lung Cancer 2005;48(1):31-45.

Merrick DT, Kittelson J, Winterhalder R, Kotantoulas G, Ingeberg S, Keith RL, et al. Analysis of c-ErbB1/epidermal growth 

factor receptor and c-ErbB2/HER-2 expression in bronchial 

dysplasia: evaluation of potential targets for chemoprevention of lung cancer. Clin Cancer Res 2006;12(7 Pt 1):2281-8.

Misek DE, Kuick R, Wang H, Galchev V, Deng B, Zhao R, et 

al. A wide range of protein isoforms in serum and plasma 

uncovered by a quantitative intact protein analysis system. 

Proteomics 2005;5(13):3343-52.

Mori Y, Cai K, Cheng Y, Wang S, Paun B, Hamilton JP, et al. 

A genome-wide search identifies epigenetic silencing of somatostatin, tachykinin-1 and 5 other genes in colon cancer. 

Gastroenterology 2006;131(3):797-808.

Moskowitz CS, Pepe MS. Comparing the predictive values of 

diagnostic tests: sample size and analysis for paired study 

designs. Clin Trials 2006;3(3):272-9.

Munkarah A, Chatterjee M, Tainsky MA. Update on ovarian 

cancer screening. Curr Opin Obstet Gynecol 2007;19(1):22-6.

Omenn GS, States DJ, Adamski M, Blackwell TW, Menon R, 

Hermjakob H, et al. Overview of the HUPO Plasma Proteome 

Project: results from the pilot phase with 35 collaborating 

laboratories and multiple analytical groups, generating 

a core dataset of 3020 proteins and a publicly-available 

database. Proteomics 2005;5(13):3226-45.

O’Neill JP, Velalar CN, Lee DI, Zhang B, Nakanishi T, Tang 

Y, et al. Thapsigargin resistance in human prostate cancer 

cells. Cancer 2006;107(3):649-59.

Osada M, Nagakawa Y, Park HL, Yamashita K, Wu G, Kim 

MS, et al. p63-specific activation of the BPAG-1e promoter. J 

Invest Dermatol 2005;125(1):52-60.

Osada M, Park HL, Nagakawa Y, Begum S, Yamashita K, Wu 

G, et al. A novel response element confers p63- and p73-

specific activation of the WNT4 promoter. Biochem Biophys 

Res Commun 2006;339(4):1120-8.

Osada M, Park HL, Nagakawa Y, Yamashita K, Fomenkov A, 

Kim MS, et al. Differential recognition of response elements 

determines target gene specificity for p53 and p63. Mol Cell 

Biol 2005;25(14):6077-89.

Palmai-Pallag T, Khodabukus N, Kinarsky L, Leir SH, Sherman 

S, Hollingsworth MA, et al. The role of the SEA (sea urchin 

sperm protein, enterokinase and agrin) module in cleavage 

of membrane-tethered mucins. Febs J 2005;272(11):2901-11.

Parekh DJ, Ankerst DP, Higgins BA, Hernandez J, CanbyHagino E, Brand T, et al. External validation of the Prostate 

Cancer Prevention Trial risk calculator in a screened population. Urology 2006;68(6):1152-5.
Appendix I 119

Parry S, Sutton-Smith M, Heal P, Leir SH, Palmai-Pallag T, 

Morris HR, et al. Evaluation of MUC6 mucin tandem repeats. 

Biochim Biophys Acta 2005;1722(1):77-83.

Paz-Elizur T, Brenner DE, Livneh Z. Interrogating DNA repair 

in cancer risk assessment. Cancer Epidemiol Biomarkers 

Prev 2005;14(7):1585-7.

Pepe MS. Evaluating technologies for classification and 

prediction in medicine. Stat Med 2005;24(24):3687-96.

Pepe MS, Longton G. Standardizing diagnostic markers to 

evaluate and compare their performance. Epidemiology 

2005;16(5):598-603.

Perner S, Demichelis F, Beroukhim R, Schmidt FH, Mosquera JM, Setlur S, et al. TMPRSS2:ERG fusion-associated 

deletions provide insight into the heterogeneity of prostate 

cancer. Cancer Res 2006;66(17):8337-41.

Ping P, Vondriska TM, Creighton CJ, Gandhi TK, Yang Z, 

Menon R, et al. A functional annotation of subproteomes in 

human plasma. Proteomics 2005;5(13):3506-19.

Pisarev VM, Kinarsky L, Caffrey T, Hanisch FG, Sanderson S, 

Hollingsworth MA, et al. T cells recognize PD(N/T)R motif 

common in a variable number of tandem repeat and degenerate repeat sequences of MUC1. Int Immunopharmacol 

2005;5(2):315-30.

Rahman SM, Shyr Y, Yildiz PB, Gonzalez AL, Li H, Zhang X, et 

al. Proteomic patterns of preinvasive bronchial lesions. Am J 

Respir Crit Care Med 2005;172(12):1556-62.

Rajeevan MS, Swan DC, Duncan K, Lee DR, Limor JR, Unger 

ER. Quantitation of site-specific HPV 16 DNA methylation by 

pyrosequencing. J Virol Methods 2006;138(1-2):170-6.

Rajeevan MS, Swan DC, Nisenbaum R, Lee DR, Vernon SD, 

Ruffin MT, et al. Epidemiologic and viral factors associated 

with cervical neoplasia in HPV-16-positive women. Int J 

Cancer 2005;115(1):114-20.

Ranamukhaarachchi DG, Unger ER, Vernon SD, Lee D, 

Rajeevan MS. Gene expression profiling of dysplastic 

differentiation in cervical epithelial cells harboring human 

papillomavirus 16. Genomics 2005;85(6):727-38.

Rauch A, Bellew M, Eng J, Fitzgibbon M, Holzman T, Hussey 

P, et al. Computational Proteomics Analysis System (CPAS): 

an extensible, open-source analytic system for evaluating 

and publishing proteomic data and high throughput biological experiments. J Proteome Res 2006;5(1):112-21.

Reddy J, Shivapurkar N, Takahashi T, Parikh G, Stastny V, 

Echebiri C, et al. Differential methylation of genes that 

regulate cytokine signaling in lymphoid and hematopoietic 

tumors. Oncogene 2005;24(4):732-6.

Roy HK, Karolski WJ, Wali RK, Ratashak A, Hart J, Smyrk 

TC. The nonsteroidal anti-inflammatory drug, nabumetone, 

differentially inhibits beta-catenin signaling in the MIN 

mouse and azoxymethane-treated rat models of colon carcinogenesis. Cancer Lett 2005;217(2):161-9.

Roy HK, Kim YL, Wali RK, Liu Y, Koetsier J, Kunte DP, et al. 

Spectral markers in preneoplastic intestinal mucosa: an 

accurate predictor of tumor risk in the MIN mouse. Cancer 

Epidemiol Biomarkers Prev 2005;14(7):1639-45.

Roy HK, Kunte DP, Koetsier JL, Hart J, Kim YL, Liu Y, et al. 

Chemoprevention of colon carcinogenesis by polyethylene 

glycol: suppression of epithelial proliferation via modulation of SNAIL/beta-catenin signaling. Mol Cancer Ther 

2006;5(8):2060-9.

Rubinstein WS, Roy HK. Practicing medicine at the 

front lines of the genomic revolution. Arch Intern Med 

2005;165(16):1815-7.

Santos-Silva F, Fonseca A, Caffrey T, Carvalho F, Mesquita 

P, Reis C, et al. Thomsen-Friedenreich antigen expression in 

gastric carcinomas is associated with MUC1 mucin VNTR 

polymorphism. Glycobiology 2005;15(5):511-7.

Sathyanarayana UG, Maruyama R, Padar A, Suzuki M, 

Bondaruk J, Sagalowsky A, et al. Molecular detection of 

noninvasive and invasive bladder tumor tissues and exfoliated cells by aberrant promoter methylation of laminin-5 

encoding genes. Cancer Res 2004;64(4):1425-30.

Sato F, Abraham JM, Yin J, Kan T, Ito T, Mori Y, et al. Pololike kinase and survivin are esophageal tumor-specific promoters. Biochem Biophys Res Commun 2006;342(2):465-71.

Sato F, Shimada Y, Selaru FM, Shibata D, Maeda M, 

Watanabe G, et al. Prediction of survival in patients with 

esophageal carcinoma using artificial neural networks. 

Cancer 2005;103(8):1596-605.

Schulmann K, Brasch FE, Kunstmann E, Engel C, Pagenstecher C, Vogelsang H, et al. HNPCC-associated small bowel 

cancer: clinical and molecular characteristics. Gastroenterology 2005;128(3):590-9.

Schulmann K, Mori Y, Croog V, Yin J, Olaru A, Sterian A, et 

al. Molecular phenotype of inflammatory bowel disease-associated neoplasms with microsatellite instability. Gastroenterology 2005;129(1):74-85.

Schulmann K, Sterian A, Berki A, Yin J, Sato F, Xu Y, et 

al. Inactivation of p16, RUNX3 and HPP1 occurs early in 

Barrett’s-associated neoplastic progression and predicts 

progression risk. Oncogene 2005;24(25):4138-48.
120 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Schwegler EE, Cazares L, Steel LF, Adam BL, Johnson DA, 

Semmes OJ, et al. SELDI-TOF MS profiling of serum for 

detection of the progression of chronic hepatitis C to hepatocellular carcinoma. Hepatology 2005;41(3):634-42.

Semmes OJ, Feng Z, Adam BL, Banez LL, Bigbee WL, 

Campos D, et al. Evaluation of serum protein profiling 

by surface-enhanced laser desorption/ionization timeof-flight mass spectrometry for the detection of prostate 

cancer: assessment of platform reproducibility. Clin Chem 

2005;51(1):102-12.

Shibata D, Mori Y, Cai K, Zhang L, Yin J, Elahi A, et al. 

RAB32 hypermethylation and microsatellite instability in 

gastric and endometrial adenocarcinomas. Int J Cancer 

2006;119(4):801-6.

Shigematsu H, Lin L, Takahashi T, Nomura M, Suzuki M, 

Wistuba, II, et al. Clinical and biological features associated 

with epidermal growth factor receptor gene mutations in 

lung cancers. J Natl Cancer Inst 2005;97(5):339-46.

Shigematsu H, Suzuki M, Takahashi T, Miyajima K, Toyooka 

S, Shivapurkar N, et al. Aberrant methylation of HIN-1 (high 

in normal-1) is a frequent event in many human malignancies. Int J Cancer 2005;113(4):600-4.

Shigematsu H, Takahashi T, Nomura M, Majmudar K, Suzuki 

M, Lee H, et al. Somatic mutations of the HER2 kinase 

domain in lung adenocarcinomas. Cancer Res 2005;65(5):

1642-6.

Shiraga T, Winpenny JP, Carter EJ, McCarthy VA, Hollingsworth MA, Harris A. MZF-1 and DbpA interact with DNase 

I hypersensitive sites that correlate with expression of the 

human MUC1 mucin gene. Exp Cell Res 2005;308(1):41-52.

Shivapurkar N, Stastny V, Suzuki M, Wistuba, II, Li L, Zheng 

Y, et al. Application of a methylation gene panel by quantitative PCR for lung cancers. Cancer Lett 2007;247(1):56-71.

Shivapurkar N, Stastny V, Takahashi T, Suzuki M, Echebiri C, 

Reddy J, et al. Novel real-time PCR assay using a universal 

molecular marker for diagnosis of hematologic cancers. Int J 

Cancer 2005;116(4):656-60.

Singh AP, Chauhan SC andrianifahanana M, Moniaux N, 

Meza JL, Copin MC, et al. MUC4 expression is regulated 

by cystic fibrosis transmembrane conductance regulator 

in pancreatic adenocarcinoma cells via transcriptional and 

post-translational mechanisms. Oncogene 2007;26(1):30-41.

Skates SJ, Horick NK, Moy JM, Minihan AM, Seiden MV, 

Marks JR, et al. Pooling of case specimens to create standard serum sets for screening cancer biomarkers. Cancer 

Epidemiol Biomarkers Prev 2007;16(2):334-41.

Song J, Patel M, Rosenzweig CN, Chan-Li Y, Sokoll LJ, Fung 

ET, et al. Quantification of fragments of human serum interalpha-trypsin inhibitor heavy chain 4 by a surface-enhanced 

laser desorption/ionization-based immunoassay. Clin Chem 

2006;52(6):1045-53.

Sorensen AL, Reis CA, Tarp MA, Mandel U, Ramachandran K, Sankaranarayanan V, et al. Chemoenzymatically 

synthesized multimeric Tn/STn MUC1 glycopeptides elicit 

cancer-specific anti-MUC1 antibody responses and override 

tolerance. Glycobiology 2006;16(2):96-107.

Spiess PE, Czerniak B. Dual-track pathway of bladder 

carcinogenesis: practical implications. Arch Pathol Lab Med 

2006;130(6):844-52.

Spiess PE, Tuziak T, Kassouf W, Grossman HB, Czerniak B. 

Malignant mesothelioma of the tunica vaginalis. Urology 

2005;66(2):397-401.

States DJ, Omenn GS, Blackwell TW, Fermin D, Eng J, 

Speicher DW, et al. Challenges in deriving high-confidence 

protein identifications from data gathered by a HUPO 

plasma proteome collaborative study. Nat Biotechnol 

2006;24(3):333-8.

Stearman RS, Dwyer-Nield L, Zerbe L, Blaine SA, Chan Z, 

Bunn PA, Jr., et al. Analysis of orthologous gene expression 

between human pulmonary adenocarcinoma and a carcinogen-induced murine model. Am J Pathol 2005;167(6):

1763-75.

Steg A, Wang W, Blanquicett C, Grunda JM, Eltoum IA, 

Wang K, et al. Multiple gene expression analyses in paraffin-embedded tissues by TaqMan low-density array: Application to hedgehog and Wnt pathway analysis in ovarian 

endometrioid adenocarcinoma. J Mol Diagn 2006;8(1):76-83.

Steinau M, Lee DR, Rajeevan MS, Vernon SD, Ruffin MT, Unger ER. Gene expression profile of cervical tissue compared 

to exfoliated cells: impact on biomarker discovery. BMC 

Genomics 2005;6(1):64.

Steinau M, Rajeevan MS, Lee DR, Ruffin MT, Horowitz IR, 

Flowers LC, et al. Evaluation of RNA markers for early detection of cervical neoplasia in exfoliated cervical cells. Cancer 

Epidemiol Biomarkers Prev 2007;16(2):295-301.

Steinau M, Rajeevan MS, Unger ER. DNA and RNA references for qRT-PCR assays in exfoliated cervical cells. J Mol 

Diagn 2006;8(1):113-8.

Sterian A, Kan T, Berki AT, Mori Y, Olaru A, Schulmann 

K, et al. Mutational and LOH analyses of the chromosome 4q region in esophageal adenocarcinoma. Oncology 

2006;70(3):168-72.
Appendix I 121

Suzuki M, Hao C, Takahashi T, Shigematsu H, Shivapurkar N, 

Sathyanarayana UG, et al. Aberrant methylation of SPARC in 

human lung cancers. Br J Cancer 2005;92(5):942-8.

Suzuki M, Shigematsu H, Shames DS, Sunaga N, Takahashi 

T, Shivapurkar N, et al. DNA methylation-associated inactivation of TGFbeta-related genes DRM/Gremlin, RUNX3 and 

HPP1 in human cancers. Br J Cancer 2005;93(9):1029-37.

Suzuki M, Shigematsu H, Shivapurkar N, Reddy J, Miyajima 

K, Takahashi T, et al. Methylation of apoptosis related 

genes in the pathogenesis and prognosis of prostate cancer. 

Cancer Lett 2006;242(2):222-30.

Suzuki M, Toyooka S, Shivapurkar N, Shigematsu H, 

Miyajima K, Takahashi T, et al. Aberrant methylation profile 

of human malignant mesotheliomas and its relationship to 

SV40 infection. Oncogene 2005;24(7):1302-8.

Takahashi T, Shigematsu H, Shivapurkar N, Reddy J, Zheng 

Y, Feng Z, et al. Aberrant promoter methylation of multiple 

genes during multistep pathogenesis of colorectal cancers. 

Int J Cancer 2006;118(4):924-31.

Takahashi T, Suzuki M, Shigematsu H, Shivapurkar N, Echebiri C, Nomura M, et al. Aberrant methylation of Reprimo in 

human malignancies. Int J Cancer 2005;115(4):503-10.

Terry KL, De Vivo I, Titus-Ernstoff L, Shih MC, Cramer DW. 

Androgen receptor cytosine, adenine, guanine repeats and 

haplotypes in relation to ovarian cancer risk. Cancer Res 

2005;65(13):5974-81.

Terry KL, De Vivo I, Titus-Ernstoff L, Sluss PM, Cramer DW. 

Genetic variation in the progesterone receptor gene and 

ovarian cancer risk. Am J Epidemiol 2005;161(5):442-51.

Thompson IM. PSA: a biomarker for disease. A biomarker for 

clinical trials. How useful is it? J Nutr 2006;136(10):2704S.

Thompson IM, Ankerst DP, Chi C, Goodman PJ, Tangen CM, 

Lucia MS, et al. Assessing prostate cancer risk: results from 

the Prostate Cancer Prevention Trial. J Natl Cancer Inst 

2006;98(8):529-34.

Thompson IM, Chi C, Ankerst DP, Goodman PJ, Tangen CM, 

Lippman SM, et al. Effect of finasteride on the sensitivity 

of PSA for detecting prostate cancer. J Natl Cancer Inst 

2006;98(16):1128-33.

Thompson IM, Lucia MS. Diagnosing prostate cancer: 

through a glass, darkly. J Urol 2006;175(5):1598-9.

Tomlins SA, Mehra R, Rhodes DR, Smith LR, Roulston D, 

Helgeson BE, et al. TMPRSS2:ETV4 gene fusions define 

a third molecular subtype of prostate cancer. Cancer Res 

2006;66(7):3396-400.

Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, 

Mehra R, Sun XW, et al. Recurrent fusion of TMPRSS2 and 

ETS transcription factor genes in prostate cancer. Science 

2005;310(5748):644-8.

Tsutsumida H, Swanson BJ, Singh PK, Caffrey TC, Kitajima 

S, Goto M, et al. RNA interference suppression of MUC1 

reduces the growth rate and metastatic phenotype of 

human pancreatic cancer cells. Clin Cancer Res 

2006;12(10):2976-87.

Tuziak T, Jeong J, Majewski T, Kim MS, Steinberg J, Wang 

Z, et al. High-resolution whole-organ mapping with SNPs 

and its significance to early events of carcinogenesis. Lab 

Invest 2005;85(5):689-701.

Veltri RW, Khan MA, Marlow C, Miller MC, Mikolajczyk 

SD, Kojima M, et al. Alterations in nuclear structure and 

expression of proPSA predict differences between native 

Japanese and Japanese-American prostate cancer. Urology 

2006;68(4):898-904.

Veltri RW, Park J, Miller MC, Marks L, Kojima M, van Rootselaar C, et al. Stromal-epithelial measurements of prostate 

cancer in native Japanese and Japanese-American men. 

Prostate Cancer Prostatic Dis 2004;7(3):232-7.

von Mensdorff-Pouilly S, Kinarsky L, Engelmann K, Baldus 

SE, Verheijen RH, Hollingsworth MA, et al. Sequence-variant repeats of MUC1 show higher conformational flexibility, 

are less densely O-glycosylated and induce differential B 

lymphocyte responses. Glycobiology 2005;15(8):735-46.

Wang S, Zhan M, Yin J, Abraham JM, Mori Y, Sato F, et al. 

Transcriptional profiling suggests that Barrett’s metaplasia is 

an early intermediate stage in esophageal adenocarcinogenesis. Oncogene 2006;25(23):3346-56.

Wang W, Huper G, Guo Y, Murphy SK, Olson JA, Jr., Marks 

JR. Analysis of methylation-sensitive transcriptome identifies GADD45a as a frequently methylated gene in breast 

cancer. Oncogene 2005;24(16):2705-14.

Wang X, Yu J, Sreekumar A, Varambally S, Shen R, Giacherio D, et al. Autoantibody signatures in prostate cancer. N 

Engl J Med 2005;353(12):1224-35.

Wang X, Zhu W, Pradhan K, Ji C, Ma Y, Semmes OJ, et al. 

Feature extraction in the analysis of proteomic mass spectra. 

Proteomics 2006;6(7):2095-100.

West HL, Franklin WA, McCoy J, Gumerlock PH, Vance R, 

Lau DH, et al. Gefitinib therapy in advanced bronchioloalveolar carcinoma: Southwest Oncology Group Study S0126. J 

Clin Oncol 2006;24(12):1807-13.
122 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

White AM, Daly DS, Varnum SM anderson KK, Bollinger 

N, Zangar RC. ProMAT: protein microarray analysis tool. 

Bioinformatics 2006;22(10):1278-9.

Witta SE, Gemmill RM, Hirsch FR, Coldren CD, Hedman K, 

Ravdel L, et al. Restoring E-cadherin expression increases 

sensitivity to epidermal growth factor receptor inhibitors in 

lung cancer cell lines. Cancer Res 2006;66(2):944-50.

Zangar RC, Daly DS, White AM. ELISA microarray technology as a high-throughput system for cancer biomarker 

validation. Expert Rev Proteomics 2006;3(1):37-44.

Zangen R, Ratovitski E, Sidransky D. DeltaNp63alpha levels 

correlate with clinical tumor response to cisplatin. Cell Cycle 

2005;4(10):1313-5.

Zeh HJ, Winikoff S, Landsittel DP, Gorelik E, Marrangoni 

AM, Velikokhatnaya L, et al. Multianalyte profiling of serum 

cytokines for detection of pancreatic cancer. Cancer Biomark 

2005;1(6):259-69.

Zhang Z, Chan DW. Cancer proteomics: in pursuit of “true” 

biomarker discovery. Cancer Epidemiol Biomarkers Prev 

2005;14(10):2283-6.

Zhou S, Kassauei K, Cutler DJ, Kennedy GC, Sidransky D, 

Maitra A, et al. An oligonucleotide microarray for highthroughput sequencing of the mitochondrial genome. J Mol 

Diagn. 2006;8(4):476-82.
Barker PE, Wagner PD, Stein SE, Bunk DM, Srivastava S, 

Omenn GS. Standards for plasma and serum proteomics in 

early cancer detection: a needs assessment report from the 

NIST-NCI SMART Workshop, August 18-19, 2005. Clinical 

Chem 2006;52:1669-72. 

Dixit R, Srivastava S. Cancer biomakers. Preface. Cancer 

Biomarkers 2005;1:3-4.

Harvey CB, Bassett JH, Maruvada P, Yen PM, Williams 

GR. The rat thyroid hormone receptor (TR) {Delta}{beta}3 

displays cell-, TR isoform- and thyroid hormone response 

element specific actions. Endocrinology 2007;148:1764-73.

Jakupciak JP, Barker PE, Wang W, Srivastava S, Atha DH. 

Preparation and characterization of candidate reference materials for telomerase assays. Clin Chem 2005;51:1443-50. 

Jakupciak JP, Wang W, Barker PE, Srivastava S, Atha DH. 

Analytical validation of telomerase activity for cancer early 

detection: TRAP/PCR-CE and hTERT mRNA quantification 

assay for high-throughput screening of tumor cells. J Mol 

Diagn 2004;6:157-65. 

Jakupciak JP, Wang W, Markowitz ME, Ally D, Coble M, 

Srivastava S, et al. Mitochondrial DNA as a cancer biomarker. J Mol Diagn 2005;7:258-67. 

Kagan J, Srivastava S, Barker PE, Belinsky SA, Cairns P. 

Towards clinical application of methylated DNA sequences 

as cancer biomarkers: a joint National Cancer Institute’s 

Early Detection Research Network and National Institute of 

Standards and Technology workshop on standards, methods, 

assays, reagents and tools. Cancer Res 2007; 67: (in press).

Krueger KE. The potential of serum proteomics for detection 

of cancer: promise or only hope? Onkologie 2006;29:498-9. 

Krueger KE, Srivastava S. Posttranslational protein modifications: current implications for cancer detection, prevention 

and therapeutics. Mol Cell Proteomics 2006;5:1799-810. 

Krueger KE, Srivastava S. Biomarkers. In: Rom WN editor. 

Environmental and Occupational Medicine, 4th edition. 

Philadelphia (PA): Lippincott Williams & Wilkins; 2006. p. 

109-20. 

Manne U, Srivastava RG, Srivastava S. Recent advances in 

biomarkers for cancer diagnosis and treatment. Drug Discov 

Today 2005;10:965-76.

Maruvada P, Srivastava S. Joint National Cancer InstituteFood and Drug Administration workshop on research strategies, study designs and statistical approaches to biomarker 

validation for cancer diagnosis and detection. Cancer 

Epidemiol Biomarkers Prev 2006;Jun; 15 (6):1078-82.

Maruvada P, Wang W, Wagner PD, Srivastava S. Biomarkers in molecular medicine: cancer detection and diagnosis. 

Biotechniques 2005;April Suppl:9-15. 

McGruder BM, Atha DH, Wang W, Huppi K, Wei WQ, Abnet 

CC, et al. Real-time telomerase assay of less-invasively 

collected esophageal cell samples. Cancer Lett 2006;244:91-

100. 

Srivastava S. Cancer biomarkers: an emerging means of 

detecting, diagnosing and treating cancer. Dis Markers 

2005;21:1-2.

Srivastava S. Molecular screening of cancer: the future is 

here. Mol Diagn Ther 2006;10:221-30.

Srivastava S, Srivastava RG. Proteomics in the forefront of 

cancer biomarker discovery. J Proteome Res 2005;4:1098-

103.

Srivastava S, Verma M, Gopal-Srivastava R. Proteomic maps 

of the cancer-associated infectious agents. J Proteome Res 

2005;4:1171-80.

Srivastava S, Wagner PD: Risk-based and diagnostics-linked 

personalized medicine for cancer. Person Med 2007;4:33-43. 

Verma M, Maruvada P, Srivastava S. Epigenetics and cancer. 

Crit Rev Clin Lab Sci 2004;41:585-607. 

Wagner PD, Maruvada P, Srivastava S. Molecular diagnostics: a new frontier in cancer prevention. Expert Rev Mol 

Diagn 2004;4:503-11. 

Wagner PD, Srivastava S. The promise of proteomics: 

biology, applications and challenge, in: Informatics and 

Proteomics. CRC Press. 2005; p. 1-17. 

Yen PM ando S, Feng X, Liu Y, Maruvada P, Xia X. Thyroid 

hormone action at the cellular, genomic and target gene 

levels. Mol Cell Endocrinol 2006;246:121-7.

Publications Co-Authored 

by NCI Program Staff 

appendix I i

Appendix II 123
124 T H E E A R LY D E T E C T I O N R E S E A R C H N E T W O R K : Investing in Translational Research on Biomarkers of Early Cancer and Cancer Risk

Biomarker – A characteristic that is objectively measured 

and evaluated as an indicator of normal biologic processes, 

pathogenic processes, or pharmacologic response to a 

therapeutic intervention.

Bioinformatics – Computational analysis and management of 

biomedical information.

Clinical endpoint – A characteristic or variable that reflects 

how a patient feels, functions or survives.

Clonal changes – Changes observed in a subset of a larger 

group of cells originating from a single parent cell.

Enzyme-linked immunosorbent assay (ELISA) – A method 

where antibodies are used to quantify levels of a biological 

marker.

Epigenetics, epigenomics – The study of events affecting 

the functional state of DNA and gene expression without 

changing its sequence or linear arrangement.

Fluorescence in situ hybridization (FISH) – A technique 

using fluorescent probes to visualize locations of specific gene 

sequences on chromosomes. Often used for gene mapping and 

identifying chromosomal abnormalities.

Fucosylation – The process of attaching a fucose sugar unit to 

a molecule.

Genomics – Characterization of the entire DNA and gene 

expression within a cell, tissue, or organism.

Glycomics – The study of the structure and function of all 

complex carbohydrate structures from a biological source.

Glycoprotein – A protein with attached sugar structures.

Haplotype – An assortment of DNA sequence or gene 

variations that are typically coinherited as a unit on a single 

chromosome.

Horizontal approach – Characterized by a number of 

independent players or entities in an organization, generating 

economies of scales embedded with duplicated efforts and 

coordination challenge 

Matrix-Assisted Laser Desorption Ionization-Time of 

Flight (MALDI-TOF) – A platform for profiling a population 

of proteins by mass spectrometery according to the size and 

net charge of individual proteins. The peaks identified by this 

method require further analysis to determine the identity of the 

corresponding proteins.

Methylation – The addition of a methyl group to specific sites 

on DNA. The methylation of a gene can change its expression.

Methylation specific PCR (MSP) – An adaptation of PCR to 

identify and quantitate relative levels of methylated genes in 

DNA.

Microarray – A system of printing large numbers of DNA 

sequences, proteins, antibodies or tissue lysates on a slide 

which can then be analyzed in a high-throughput fashion.

Microsatellite (Instability) Analysis (MSA) – 

Microsatellites are short sequences of DNA, usually 1 to 4 

base pairs in length, repeated any number of times in various 

locations of DNA. Microsatellite instability analysis is a test to 

determine if the number of repeating units has changed at any 

specific location(s).

Ontology model – A way to describe critical cancer data 

objects. An ontology model is a conceptual model used to 

represent knowledge in a domain (e.g., management of 

biomarkers).

Polymerase chain reaction (PCR) – A technique to amplify 

or produce multiple copies of a defined DNA span. 

Predictive value [positive/negative] – The fraction of 

people who test [positive/negative] and [have/do not have] the 

disease.

Promoter – The segment of a gene where expression is 

regulated by binding specific proteins to initiate mRNA 

transcription.

Proteomics – Characterization of all proteins from a biological 

source.

Quantitative PCR (qPCR) / quantitative Methylation 

Specific PCR (qMSP) – An adaptation of PCR to quantify 

levels of defined mRNA transcripts or methylated genes.

Risk stratification (prediction) – Quantifying the relative 

level of risk for a disease based on defined criteria.

Sensitivity – The proportion of individuals with a disease who 

test positive.

Specificity – The proportion of individuals without a disease 

who test negative.

Standard specimen reference sets (SSRSs) – collections of 

high quality, well-characterized specimens that can be used for 

discovery and early validation of potential markers.

Surface-enhanced Laser Desorption-Time of Flight 

(SELDI-TOF) – A modification of MALDI-TOF where some 

selectivity of proteins can be achieved prior to analysis.

Throughput – The number of samples that can be processed in 

a defined time period.

Translational research – Studies intended to bring 

developments from laboratory investigation to clinical 

application.

Validation – Confirmation of the accuracy, precision, or 

effectiveness of experimental results.

Vertical approach – Characterized by distinct job 

classifications or responsibilities among entities and verticallydefined flow of decision-making free from the burden of 

coordination with other entities.

The entries defined here are highlighted in bold type 

at the first occurrence in each section of this report.

GLOSSARY