TRANSLATIONAL GENOMICS: THE IMPACT OF GENETIC RISK …...to analysis. In the lab, Lilly Zheng,...
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TRANSLATIONAL GENOMICS: THE IMPACT OF GENETIC RISK SCORE ON PATIENTS AND PHYSICIANS BY AUBREY R. TURNER A Dissertation Submitted to the Graduate Faculty of WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY In Molecular Genetics and Genomics May 2016 Winston-Salem, North Carolina Approved By: Jianfeng Xu, M.D., Dr.P.H., Advisor Gregory A. Hawkins, Ph.D., Chair Timothy D. Howard, Ph.D. Deborah A. Meyers, Ph.D. Kathryn E. Weaver, Ph.D., M.P.H.
Transcript of TRANSLATIONAL GENOMICS: THE IMPACT OF GENETIC RISK …...to analysis. In the lab, Lilly Zheng,...
TRANSLATIONAL GENOMICS: THE IMPACT OF GENETIC RISK SCORE
ON PATIENTS AND PHYSICIANS
BY
AUBREY R. TURNER
A Dissertation Submitted to the Graduate Faculty of
WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES
in Partial Fulfillment of the Requirements
for the Degree of
DOCTOR OF PHILOSOPHY
In Molecular Genetics and Genomics
May 2016
Winston-Salem, North Carolina
Approved By:
Jianfeng Xu, M.D., Dr.P.H., Advisor
Gregory A. Hawkins, Ph.D., Chair
Timothy D. Howard, Ph.D.
Deborah A. Meyers, Ph.D.
Kathryn E. Weaver, Ph.D., M.P.H.
ii
DEDICATION AND ACKNOWLEDGMENTS
It really takes a team to do just about anything meaningful, and I have been
blessed with a great team. It is an impossible task, to thank everyone that has supported
me along the way to this goal. The following “thank-you’s” are too brief, and are bound
to leave someone out. Whether named or not, for anyone that has made an investment in
me, I pray that I am able to return on that investment, paid forward to others.
At Wake Forest, I had the pleasure of working with a great team of folks in the
lab and office. Jianfeng Xu, you have been an inspirational leader and advisor, and it was
truly fun building the group from just you, me and Bao-Li Chang. Speaking of Bao-Li, I
admire you as a smart and hard-working scientist, that can do everything well, from lab
to analysis. In the lab, Lilly Zheng, I’ll always admire your hard working and personal
approach to running the genotyping lab and supporting multiple projects across the entire
team. Then there was the “dream team”, which included Wennuan Liu, Jielin Sun, Lucho
Dimitrov (Mr. ping-pong), Jishan Sun, Seong-Tae Kim, Yi Zhu, Tamara Adams (wonder-
twin), Linda Itterly, and Jenny Morris; I’ll never be able to thank all of you enough, for
the good memories created while we attempted to save the world from prostate cancer.
When is the next group retreat, because I’m ready for a hike!? To the top of the stairs or
the top of a mountain, let’s go!
In addition to the core group at Wake Forest, I was fortunate to meet and work
with several close collaborators from other institutions across the globe. These people,
including Bill Baer, Brian Lane, Dan Rogers, Karim Kader, Henrik Gronberg, and Bill
Isaacs, all serve as proof that it is possible to be super smart while also very nice.
iii
To my graduate committee, thank you. Greg Hawkins, committee chair, I really
appreciate the encouragement to finish up, as well as all the talks about music, family,
and sports. Tim Howard, you might be the funniest person I know, and I appreciate your
efforts to lighten the mood while also remaining the consummate genetics pro. Debbie
Meyers, I’ve enjoyed getting to know you from the earliest days of the Genomics Center
at WFUSM, and hearing about adventures with agility courses, travels, and good wine.
Kate Weaver, you made a huge positive impact on the design and interpretation of our
provider study, you are the best editor, and you may have helped more than anyone to
spur me to complete the final steps of this degree with your thoughtful check-in emails.
I’ve had the pleasure to be influenced by many good folks at UNC-Greensboro.
Most recently, Valera Francis you gave me nice and funny reminders that I need to finish
up, and always made it clear that I had your full support and encouragement. Vince
Henrich, you’ve been my mentor since my first days as a scientist, and you also made
sure all of us learned to have a little fun while working hard. Vince, I also thank you for
pairing me up with your graduate student at the time, Peter Bonnette, AKA Pedro and/or
PCB. Peter, you were the best lab-bench teacher I ever had, and between you and Vince,
I was ready to enter the workforce.
Among many great folks at the University of South Carolina, I’ll especially thank
Janice Edwards, Bob Best, and Tara Stamper. Tara, you helped get me out of the lab and
into the genetic counseling clinic. Janice and Bob, you two continue to be role models of
professionalism, hard work, and kindness. My time in Columbia was a time of
professional growth as well as a special time for my new and growing family. Thanks for
all you did to make it happen.
iv
Gerry O’Sullivan was another important person along the way. Gerry, you gave
me my first job at Wake Forest. Looking back, that was the coolest job ever, with
molecular biology, virology, OB-GYN, and primate research. What a wonderful learning
opportunity you provided me. Plus, you helped teach me to make homebrew.
Outside of work, I have been blessed with so many personal mentors that guided
me in various ways. Shorty Waddell, you taught me how to fish, and I thank you for
demonstrating how tough times do not give anyone permission to whine or stop living.
Mrs. Jones, my 5th grade teacher at Farmington Woods Elementary, you were the first to
teach me about genetics, and your amazing flipped classroom was revolutionary, plus you
modeled classiness and kindness. Mr. Strange, you were an amazing high school physics
teacher, and your high-expectations approach to teaching challenging material helped
ignite my passion to pursue science as a career. Don Lee, as a scoutmaster, you were
able to help me learn basic lessons of duty to God, duty to others, and duty to self, plus a
love of nature, that stays with me today; I constantly strive to pay it forward to others.
Brett Oslon you are like an extra brother, and I am amazed at how the world brought us
and our families together. Phil Ly, thank you for being a really good friend in college and
beyond; I appreciate your wisdom.
Bob Anderson, we became friends when I was running a study on prostate cancer
and you were newly diagnosed with prostate cancer. We worked together to lobby for
prostate cancer awareness and to get the NC Governor to issue prostate cancer awareness
proclamations. You would engage in mind-blowing scientific conversations with some
of the brightest doctors at Wake Forest. I still can’t comprehend that you are gone, but
your passing changed the way I thought about my job, because you put a face on prostate
v
cancer for me. Thank you, Bob. I hope my research pays some tribute to you and your
amazing efforts on prostate cancer advocacy.
How can I ever thank my family enough? I can’t. My inspirational Grandparents,
Tex, Phyllis, Bill, and Dorothy, you all helped in major ways with my education and
well-being. My Parents, Terry, Irene, plus the best Step-dad ever, Robert Mann. The
three of you provided a foundation for life, and taught me about sacrifice, hard work, and
love. My mom in particular has been a constant teacher and source of encouragement.
My siblings, Asheli, Adam, Liz, you were so much fun to grow up with, I continue to
appreciate the encouragement that you provide, and I love our growing families that we
all rejoice in together.
My best friend and wife, Jenna, what can I even say? We’ve come a long way
since Ms. Allgood’s class and my old Camaro. We now are blessed with these beautiful
children, Seth, Simon, and Stella. I thank you for giving me support and love. No matter
how crazy things have been with both of us in grad school, both working, raising these
children, and giving back to the community…you are always the glue holding everything
together. I picked the right one. Love you.
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TABLE OF CONTENTS
PAGE
LIST OF ILLUSTRATIONS AND TABLES vii
LIST OF ABBREVIATIONS ix
ABSTRACT xi
CHAPTER I INTRODUCTION 1
CHAPTER II RANDOMIZED TRIAL FINDS PROSTATE 33
CANCER GENETIC RISK SCORE FEEDBACK
TARGETS PSA SCREENING AMONG
AT-RISK MEN
CHAPTER III BARRIERS TO PRIMARY CARE PROVIDER 79
ADOPTION OF CANCER GENOMIC TESTS
MAY BE ADDRESSED THROUGH SHORT
CONTINUING EDUCATION SESSIONS.
CHAPTER IV SUMMARY AND CONCLUSIONS 101
CURRICULUM VITAE 108
vii
LIST OF ILLUSTRATIONS AND TABLES
Page
Chapter I
Figure I-1. Diagram of 76 SNPs associated with Prostate Cancer
at Genome-Wide Significance (p < 1.0 x 10-8), mapped to each
chromosome, as of March 20, 2016.
7
Figure I-2. Trend of increasing absolute risk of PCa for carriers
of increasing number of risk alleles of GWAS SNPs.
10
Table I-1.
Summary of SNPs reproducibly associated with PCa as of 2011,
assayed in the REDUCE study described above and in the new
study described in chapter II.
15
Chapter II
Figure II-1. Study design, enrollment, and outcomes. 40
Figure II-2. Distribution of Genetic Risk Scores Given to Study
Participants
a. Family History Group
b. Genetic Risk Score Group
42-43
Table II-1. Subject demographics by randomization group. 48
Figure II-3. Participant health behaviors, in FH and GRS arms,
stratified by given risk.
a. Discussed PSA screening with physician per self-report
during 3 month follow-up
b. Engaged in PSA screening per self-report during 3
month follow-up
c. Engaged in PSA screening, per medical record review,
during 3 years of follow-up
51
Supplemental Figure II-1a. Risk report template for participants
randomized to receive risk results based on genetic risk score in
numeric format.
67
Supplemental Figure II-1b. Risk report template for participants
randomized to receive risk results based on genetic risk score in
pictograph format.
68
Supplemental Figure II-1c. Risk report template for participants
randomized to receive risk results based on family history in
numeric format.
69
Supplemental Figure II-1d. Risk report template for participants
randomized to receive risk results based on family history in
pictograph format.
70
viii
Supplemental Figure II-2. Risk Given to Participants is
positively related to Risk Recall.
a. Linear relationship of Given Risk with Risk Recall
Immediately Following Results Disclosure
b. Linear relationship of Given Risk with Risk Recall 3
Months Following Results Disclosure
71-72
Supplemental Figure II-3. Risk given to participants is positive
and linearly related to post-result anxiety among participants
who received risk feedback based on genetic risk score
information
73
Supplemental Figure II-4. Text of Study Resource Card, as
given to all study participants during study visit 2.
74
Supplemental Figure II-5. Prostate Screening Brochure from
Centers for Disease Control, as given to all study participants
during study visit 2.
75
Supplemental Table II-1. Average anxiety pre- and post-results 76
Supplemental Table II-2. Comparison of behavioral outcomes
between groups.
77
Supplemental Table II-3. Relationship between given risk and
behavioral outcomes within each randomization group.
78
Chapter III
Table III-1. Demographic and background characteristics 87
Table III-2. Genetics in clinical practice 87
Table III-3. Pre-Post Changes
3a. Confidence in understanding and utilizing genetics
3b. Effectiveness and predicted use of genetic testing
3c. Inclusion of genetic testing in clinical practice and impact on
patients
3d. Continuing education in genetics
3e. Provision and sufficiency of standard genetic services
88
89
90
91
91
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LIST OF ABBREVIATIONS
ACS American Cancer Society
AMA American Medical Association
ANOVA Analysis of Variance
AUA American Urological Association
AUC Area Under the Curve
CAPS Cancer of the Prostate in Sweden
CER Comparative Effectiveness Research
ChIP-on-chip Chromatin Immunoprecipitation on DNA microarray
CLIA Clinical Laboratory Improvement Amendments
CME Continuing Medical Education
DNA Deoxyribonucleic Acid
EBI European Bioinformatics Institute
EAU European Association of Urology
ERSPC European Randomized Study of Screening for Prostate Cancer
FDA Food and Drug Administration
FH Family History
GINA Genetic Information Non-discrimination Act of 2008
GRS Genetic Risk Score
GWAS Genome-Wide Association Study
HNPCC Hereditary Nonpolyposis Colorectal Cancer
HPC Hereditary Prostate Cancer
LD Linkage Disequilibrium
NHGRI National Human Genome Research Institute
MAF Minor Allele Frequency
NSPCP National Survey of Primary Care Physicians' Recommendations &
Practice for Breast, Cervical, Colorectal, & Lung Cancer Screening
ORs Odds Ratios
x
PCa Prostate Cancer
PCP Primary Care Provider
PLCO Prostate, Lung, Colorectal, and Ovarian Screening Study
PPV Positive Predictive Value
PSA Prostate-Specific Antigen
PSCS Physician Survey on Cancer Susceptibility Testing
RCT Randomized Controlled Trial
ROC Receiver Operating Characteristic
RR Relative Risk
SEER Surveillance, Epidemiology, and End Results Program
SNPs Single Nucleotide Polymorphisms
STAI State-Trait Anxiety Inventory
TR Translational Research
USPSTF United States Preventive Services Task Force
xi
ABSTRACT
Genomewide Association Studies have identified thousands of associations between
genetic markers and complex disease risk. When several of these markers are used in
combination, the predictive performance is strong; however, the clinical utility of multi-
marker tests is not known. This thesis addresses a unifying question: Can genomic test
results safely and effectively target healthcare decisions for patients and physicians?
For patients, we conducted a randomized trial of multi-marker genomic test risk
assessment versus family history risk assessment, giving risk results to 700 study
participants. During three years of follow-up, we found no evidence that multi-marker
genomic testing increases anxiety or subsequent uptake of prostate cancer screening.
Level of risk was associated with subsequent cancer screening in the group that received
multi-marker risk results, but not the group receiving standard family history risk
assessment. This is likely the first randomized trial to find that multi-marker genomic
risk results can motivate targeted uptake of cancer screenings by those at highest risk.
This is important because the ability to target cancer screening to those at highest risk has
been suggested as an important approach for reducing side effects and financial costs.
For physicians, we evaluated the readiness of Primary Care Providers to utilize multi-
marker genetic tests in the clinic. We tested whether short educational sessions address
known barriers to clinical use of genetics, including limited knowledge, costs, time, and
discrimination. The session significantly increased confidence explaining genetic test
results to patients, as well as many educational, logistical, and ethical aspects of genetic
xii
testing. This is one of the first studies to show that short educational sessions on
emerging technologies effectively build upon existing genetics knowledge, efficiently
preparing them to utilize new genomic testing technologies.
Together, these projects suggest a path forward for translation of genomic technologies
from the laboratory to the clinic.
1
CHAPTER I
INTRODUCTION
This introductory chapter is partially based on two publications:
1. A published manuscript: Turner AR, Kader AK, Xu J. Utility of genome-wide
association study findings: prostate cancer as a translational research paradigm. J
Intern Med. 2012 Apr;271(4):344-52.
2. A published book chapter: Turner A, Feng J, Liu W, Kim JW, and Xu J (2013).
Prostate Cancer. In: Genomics and Personalized Medicine, 2nd Edition.
Academic Press.
When the first successful Genome-Wide Association Study (GWAS) was published in
2005, it not only revealed genetic variants associated with age-related macular
degeneration, but it also showed that the GWAS approach could be successful [1]. This
ushered in a rush of additional GWAS studies. GWAS provided researchers with a new
tool to identify genetic associations with a variety of phenotypes, and was especially
powerful for evaluating common phenotypes.
Almost as rapidly as GWAS studies were providing new discoveries, a debate emerged
regarding clinical applications of the findings. Several companies pushed forward with
commercial offerings of direct-to-consumer testing for multiple GWAS variants. Some
clinicians began to consider how to implement new multi-marker GWAS testing, and the
2
term “personalized medicine” took hold. Others, often those speaking from a public
health perspective or a medical genetics perspective, pointed out the unknown impact of
this new genetic information on patients and unknown readiness of healthcare providers.
The current thesis considers the opportunities and concerns of personalized medicine.
Specifically, we focused on the clinical application of multi-marker genomic tests for
prostate cancer that are based on the findings from GWAS. Accordingly, the
translational research described herein was based on two Specific Aims within a larger
project:
Aim 1. To compare the provision Family History(FH) versus Genetic Risk Score
(GRS) for Prostate Cancer (PCa) risk from the perspective of at-risk patients.
Aim 2. To evaluate whether a short educational intervention can address key
barriers to clinical implementation of GRS among primary care providers.
The remainder of this introductory chapter provides the framework through which we
addressed these Aims, by reviewing key topics; Prostate Cancer and Screening, Germline
genetics of PCa, GWAS for Prostate Cancer, Potential multi-marker application of
GWAS findings, Personalized Medicine for PCa, Debate on clinical applications,
Questions of health benefit, Translational research, and Hypothesis and approach.
Chapter II addresses Aim 1, and Chapter III addresses Aim 2.
3
Prostate Cancer and Screening
PCa is the most common cancer in US men and the second leading cause of cancer-
related death, with about 181 thousand new diagnoses and 26 thousand deaths estimated
to occur in 2016 [2]. These statistics translate to average lifetime risks of 1 in 7 men
being diagnosed with PCa and 1 in 39 men dying from PCa [2]. PCa mortality
significantly decreased following widespread introduction of Prostate-Specific Antigen
(PSA) screening in the 1990s; however, it was not clear whether PCa screening versus
advances in treatment were responsible for the observed reduction in mortality [3,4].
PSA screening brings downstream risks, leading about 1 million U.S. men to have a
prostate biopsy each year; about 70% to 75% of these men are found to not have PCa [5].
Of the approximately 250 thousand men found to have pathology-defined PCa, many are
subjected to treatments that include surgery, radiation, hormone blockers, and
chemotherapy, all of which impact quality of life. As outlined in the next paragraph, to
save one life, thousands of men would need to be screened and dozens of the resulting
cases would need to be subjected to unnecessary treatments and the resulting side effects.
This is the crux of the arguments against PSA screening for PCa.
Two large prospective studies attempted to resolve the PSA screening controversy [3-8].
Starting in 1993, the Prostate, Lung, Colorectal, and Ovarian (PLCO) Screening Study
[6,7] followed nearly 77 thousand men for 10 years and found no reduction in mortality
between the “PSA screening arm” versus the “non-PSA screening arm”; however, critics
noted high levels of contamination (i.e. PSA screening) among participants in the control
4
(i.e. non-screening) group, thus indicating the study did not actually compare the impact
of PSA screening versus non-screening on mortality, and thus might not have had
sufficient power to detect significant differences [3,4]. In contrast, a significant 20%
reduction in PCa mortality was reported by the European Randomized Study of
Screening for Prostate Cancer (ERSPC) [3,8], which has randomized and followed more
than 162 thousand participants to PSA screening or non-PSA screening arms. Additional
analysis for 12 years of follow-up found a 31% reduction of metastatic PCa. While the
reduction in mortality observed in ERSPC justified the ability of PSA to save lives, the
question of “what cost?” remained; overdiagnosis of indolent PCa remained a key
concern because to prevent one death from prostate cancer, 48 cases of prostate cancer
would need to be treated, and 1410 men would need to be screened [8]. That is, most
PCa identified by PCa is indolent and will not lead to death, while treatment may have a
significant impact on quality of life. In 2010, as a result of those findings from large
randomized trials, the U.S. Preventive Services Task Force (USPSTF) recommended
against routine screening, citing over-diagnosis and over-treatment leading to
unnecessary risks [9]. In contrast, the American Cancer Society (ACS), American
Urological Association (AUA), and European Association of Urology (EAU) maintain
PSA screening should be considered, but only after physician-patient discussion of the
risks and benefits [10-12]. Commentaries on those prospective trials and the resulting
recommendations have made the case that a favorable balance of risks and benefits can
be achieved by targeting PSA screening to patients that can gain the most, while reducing
screening among those who will gain little [3,4,13].
5
Germline genetics of PCa
A variety of risk factors have been proposed as underlying PCa risk and mortality;
however, the only confirmed risk factors to date are age, race, and family history. It is
arguable that genetic inheritance plays a significant role in the pathogenesis of PCa, as
demonstrated by the observation that the risk of PCa increases with additional affected
first degree relatives [14], and by the findings from a large twin study determining that
the heritability of PCa is 42%, being notable as the greatest among all common cancers
[15]. These findings highlighted the need to identify, and study the role of, inherited
(germline) genetic variants in association with altered risks for PCa onset or progression.
Initially, linkage studies and candidate gene association studies were the primary tools
used in this search.
From about 1995 through 2005 genome-wide linkage studies and candidate gene-based
association studies were the primary tools used in the search for germline genetic variants
for cancer, including prostate cancer [16-24]. Genome wide linkage studies were used to
identify chromosomal regions harboring susceptibility genes in hereditary prostate cancer
(HPC) families, typically defined as three or more first degree affected relatives. Linkage
results have led to the identification of several loci potentially involved in HPC, including
HPC1 [22] RNASEL [23], and MSR1 [24]. However, these are not major risk genes on
par with BRCA1 and BRCA2 for hereditary breast cancer or MSH2 and MLH1 for
hereditary nonpolyposis colorectal cancer (HNPCC). Candidate genetic association
studies were conducted using case-control designs to examine variants in candidate genes
and later in candidate pathways. For the most part, candidate genes-based association
6
studies failed to consistently demonstrate that specific genetic variants were associated
with PCa risk or prognosis; results that were initially promising were largely not
confirmed by subsequent independent studies, and those that were confirmed appear to
confer very modest alteration of PCa risk [25,26]. Overall, findings from candidate gene
association studies and linkage studies remain unlikely to lead to clinical applications.
Genome-wide association studies for PCa.
GWAS seeking to identify risk factors for a particular disease phenotype will typically
begin with a case-control study, although case-case designs are used when the aim is to
identify associations with disease severity. A carefully defined phenotype is critical to
GWAS. Cells, typically blood or buccal in origin, are collected from thousands of study
subjects for subsequent Deoxyribonucleic acid (DNA) extraction. This DNA can then be
used for microarray-based genotyping of millions of Single Nucleotide Polymorphisms
(SNPs). GWAS data must undergo a systematic quality control process, with adjustment
for population stratification. Then tests of association are performed between SNPs and
disease phenotypes, allowing for comprehensive and unbiased assessment across the
genome. This basic study design has been adapted to many specific applications, and
allowed for the identification of novel associations of SNPs with many complex diseases
including cancer.
As evidence of the diverse utilization of GWAS, the online reference, “The NHGRI-EBI
Catalog of published genome-wide association studies” lists approximately 1772 SNPs
associated with 174 cancer phenotypes as of March 7, 2016 [27,28]. For PCa, 294 SNPs
7
met the minimum threshold for inclusion in the GWAS catalogue (p < 1.0 x 10-5), and 76
SNPs met a more significant threshold (p < 1.0 x 10-8), as of March 20, 2016 (Figure I-1).
The catalogue also includes two GWAS SNPs that are associated with aggressive PCa
[28]. Beyond cancer, the catalog includes associations with many additional common
diseases such as diabetes, asthma, cardiovascular disease. Associations listed in the
catalog meet stringent criteria for genome-wide statistical significance and have been
validated in independent study populations, all but eliminating the likelihood of chance
associations.
Figure I-1.
Diagram of 76 SNPs associated with Prostate Cancer at Genome-Wide
Significance (p < 1.0 x 10-8), mapped to each chromosome, as of March 20, 2016.
The question is, what did GWAS ultimately yield? Unlike most single-gene association
studies that preceded the new GWAS approach, association results from GWAS were
confirmed in multiple independent study populations, strongly suggesting the
8
associations are not false positives, but rather are true markers of the associated
phenotype. Unlike prior linkage studies that had located wide genetic regions that were
probably most relevant to rare hereditary phenotypes, GWAS implicated relatively small
regions (haplotype blocks) containing genetic variants associated with common
phenotypes that affect the majority of most any population. Importantly, most of the risk
alleles of SNPs identified by GWAS are common (>5%) in the general population,
meaning they might have potential utility in the general population, another key contrast
to the rare variants known from prior Mendelian genetic studies. From a functional
standpoint, GWAS identified many phenotype associations with the risk alleles of SNPs
that are located in introns or intergenic regions (i.e. “gene-deserts”) of the genome,
suggesting traditional molecular mechanisms that were known from Mendelian genetics
might not apply. Most of the independent SNP associations have a small effect on
disease risk, with 1.2 Odds Ratios (ORs) on average, raising significant doubt for their
clinical utility. [29,30]
Potential multi-marker application of GWAS findings
Despite the small individual effect of each SNP on disease risk, an important paradigm
shift occurred when it was discovered that multiple SNPs could be combined into a panel,
and the risk alleles assayed, allowing for significantly larger ORs to be observed for
prostate cancer (PCa) and breast cancer [31-34], as well as many other common diseases.
For example, when the first five PCa risk-associated SNPs identified from GWAS and
family history were examined, men who have five or more of these risk factors have OR
of 9.46 for PCa, as compared with men without any of the risk factors [31]. Absolute risk
9
can be calculated for individuals based on the SNP-specific Relative Risk (RR),
calibrated incidence rate of PCa, and mortality rate for all causes excluding PCa in the
U.S [32,35]. In a population based study of 2,893 PCa cases and 1,781 controls, Xu et.al
found that, among individuals with a positive family history, the lifetime risk of PCa
jumps from 23% to 52% among those who carry the population average number of risk
alleles (n=11 risk alleles, average) versus individuals carrying 14 or more risk alleles
[32]. A similar increase was also observed among men with a negative family history;
from an 11% lifetime risk (n=11 risk alleles, average) to a 24% lifetime risk (n≥14 or
more risk alleles) [32]. These trends are shown in Figure I-2.
10
Figure I-2.
Trend of increasing absolute risk of PCa for carriers of
increasing number of risk alleles of GWAS SNPs.
To put this into perspective, the cumulative levels of PCa risk as predicted by associated
risk alleles of SNPs are comparable to current population risk screening methods for
various other types of cancers, such as for lung cancer based on smoking status [36], or
breast cancer based on mammography [37]. The germ-line genetic markers discovered
by GWAS are unique in that they can be objectively and accurately measured, do not
change with age, and always precede associated phenotypes. Discovery of these
11
increased risks provided the first hint that SNPs discovered by GWAS had real potential
for clinical application in risk prediction, that is, personalized medicine.
Personalized Medicine for PCa
As more GWAS results were published, and as we came to fully appreciate the ability to
combine multiple risk SNPs into a testing panel, the idea of Personalized Medicine
crystallized. The aim of Personalized Medicine is to provide individual risk assessment
for medical conditions or to predict the efficacy of measures intended to monitor,
prevent, or treat these conditions [38]. Personalized Medicine could be important in
addressing the clinical and public health issues involved in a variety of diseases,
including cancers that are detected via population-level screening. This could be
particularly relevant to PCa, where concerns have been raised regarding prostate-specific
antigen (PSA) screening, subsequent over-diagnosis of low-grade diseases, and
ultimately over-treatment of many indolent cancers that for the most part are not life-
threatening. Improved risk estimation may help to address this major public health
problem, as the prostate is the most common site of cancer diagnosis, accounting for
approximately 30% of all new cancer diagnoses, and 11% of cancer deaths, in US men.
This translates to an estimated 220,900 prostate cancer diagnoses and 28,900 deaths, in
U.S. men each year [2].
Debate on clinical applications
Despite the strong statistical evidence and the promise of personalized medicine, there
are ethical and technical arguments for and against clinical applications of these SNPs
12
discovered by GWAS. Many of the arguments deal with the clinical validity and clinical
utility of the associated SNPs [39,40].
Ethical concerns center on the balance of benefit versus risk (beneficence/non-
maleficence). Thus if high-risk results create excessive anxiety, or if low-risk results
create a false sense of security, either of these may lead to inaction or over-reaction in
subsequent medical decisions, such as whether or not to have PSA screening in men or
mammography in women. On the other side of the argument, autonomy could be
emphasized while condemning paternalism, giving individuals the right to make
informed decisions to access their own personal genetic information. The argument is,
patient or physician knowledge of this genetic information is not inherently dangerous,
and perhaps patients could obtain some benefit from this new technology if we begin to
examine how to appropriately implement these new genetic markers in clinical settings.
Some may remain concerned about increased potential for genetic discrimination as
genetic testing goes more mainstream, while others counter that legal protections such as
the Genetic Information Non-discrimination Act of 2008 (GINA) are already in place.
Various technical concerns have been raised as well. First, many of these associated
SNPs are located in non-coding and intronic areas of the genome, and the molecular
mechanisms by which they act is poorly understood, thus leaving their causal role in
question. While most GWAS associations cannot be explained based on our existing
knowledge of causal mechanisms, GWAS findings have provided many novel biological
insights that serve as leads for additional studies. For example, a Chromatin
Immunoprecipitation on DNA microarray (ChIP-on-chip) study has suggested an
13
interaction between androgen receptor binding sites and many of the prostate cancer
associated SNPs, suggesting an androgen dependent pathway by which many of these
SNPs act [41]. Study designs such as these together with a more comprehensive
assessment of the genome and a better understanding of the role of non-coding regions
will result in an appreciation of the functional significance of these SNPs. Next-
generation sequencing, epigenetic studies, and proteomics could help reveal the
functional impact of these sets of variants, which will be important to understand etiology
and to eventually develop targeted therapies. However, considering the huge number of
associated variants, the process of functional characterization and therapeutic
development may require years, or even decades, to complete. It is difficult to imagine
that laboratories will ever complete the job of characterizing the functional impact of
every genetic variant. Therefore, functional characterization should probably focus on
the variants that have the greatest impact on risk or the greatest potential for therapeutic
intervention. Complete functional characterization of the genome should not be an
impediment to other lines of applied research, and there is nothing wrong with doing the
best we can with what we have available at a given point in time. Given the massive
public health impact of common diseases such as cancer, it could be argued that we
should move forward with utilizing the best currently available information; indeed, that
is exactly the model that was used for BRCA1/2 testing, where clinical testing was
offered in medical genetics clinics well before we had any clarity regarding the
mechanistic underpinnings. Although all of the biological mechanisms are not yet
understood, we already know that GWAS findings represent true associations in
populations, based on consistent observations across independent study populations. This
14
supports the need for additional research to evaluate the validity and utility of these SNPs
for risk prediction. Risk-assessment testing does not preclude additional mechanistic
research into the causal role of current SNPs or the discovery of additional variants;
rather results from GWAS should continue to stimulate additional research in closely
related fields.
Another persistent technical concern is based on the fact that the predictive performance
of GWAS markers is generally modest as estimated by the area under the curve (AUC)
statistic of the receiver operating characteristic (ROC) [42-44]. In the case of PCa, an
AUC of 62% can be obtained when using the very best baseline clinical parameters in
combination (age, family history, free/total PSA ratio, number of cores at pre-study entry
biopsy, and prostate volume) to predict PCa among repeat biopsies in the REDUCE
study, which is 12% higher than chance (50%) [45]. When 33 PCa risk-associated SNPs
(Table I-1.) are added to these clinical parameters, an AUC of 66% is observed, and this
increase in AUC is statistically significant [45]. Although this AUC only represents a 4%
absolute increase, it represents a 33% (4%/12%) relative improvement over the best
clinical risk prediction model [45]. As additional risk variants are identified and
validated from GWAS and other methods, the AUC of the prediction model should
continue to increase.
15
Table I-1.
Summary of SNPs reproducibly associated with PCa as of 2011, assayed in the REDUCE
study described above and in the new study described in chapter II.
While AUC has found widespread use in assessing predictive performance, a more
fundamental question is whether the findings have clinical meaning. Unfortunately,
AUC is an abstract value that has no inherent clinical meaning. While AUC assesses the
ability of an assay to distinguish risk across all risk strata, is that really the goal? Rather,
CHR SNPs Region Position Known genes
m/M
allele
Risk
allele
2 rs1465618 2p21 43,407,453 THADA A/G A
2 rs721048 2p15 62,985,235 EHBP1 A/G A
2 rs12621278 2q31.1 173,019,799 ITGA6 G/A A
3 rs2660753 3p12 87,193,364 -- T/C T
3 rs10934853 3q21.3 129,521,063 EEFSEC A/C A
4 rs17021918 4q22.3 95,781,900 PDLIM5 T/C C
4 rs7679673 4q24 106,280,983 TET2 A/C C
6 rs9364554 6q25 160,753,654 SLC22A3 T/C T
7 rs10486567 7p15 27,943,088 JAZF1 A/G G
7 rs6465657 7q21 97,654,263 LMTK2 T/C C
8 rs2928679 8p21.2 23,494,920 SLC25A37 A/G A
8 rs1512268 8p21.2 23,582,408 NKX3.1 T/C T
8 rs10086908 8q24 (5) 128,081,119 -- C/T T
8 rs16901979 8q24 (2) 128,194,098 -- A/C A
8 rs16902094 8q24.21 128,389,528 -- N/A G
8 rs620861 8q24 (4) 128,404,855 -- A/G G
8 rs6983267 8q24 (3) 128,482,487 -- G/T G
8 rs1447295 8q24 (1) 128,554,220 -- A/C A
9 rs1571801 9q33 123,467,194 DAB2IC G/A A
10 rs10993994 10q11 51,219,502 MSMB T/C T
10 rs4962416 10q26 126,686,862 CTBP2 C/T C
11 rs7127900 11p15.5 2,190,150 IGF2, IGF2AS, INS, TH G/A A
11 rs12418451 11q13 (2) 68,691,995 -- A/G A
11 rs10896449 11q13 (1) 68,751,243 MYEOV A/G G
17 rs11649743 17q12 (2) 33,149,092 HNF1B A/G G
17 rs4430796 17q12 (1) 33,172,153 HNF1B A/G A
17 rs1859962 17q24.3 66,620,348 -- G/T G
19 rs8102476 19q13.2 43,427,453 PPP1R14A T/C C
19 rs887391 19q13 46,677,464 -- C/T T
19 rs2735839 19q13 56,056,435 KLK3 A/G G
22 rs9623117 New 22q13 38,782,065 TNRC6B C/T C
22 rs5759167 New 22q13.2 41,830,156 TTLL1, BIK, MCAT, PACSIN2 T/G G
23 rs5945619 Xp11 51,258,412 NUDT10, NUDT11, LOC340602 C/T C
16
in a clinical setting, the goal of such testing is typically to identify men at considerably
elevated risk. Methods based on a risk cutoff, such as positive predictive value (PPV),
offer more clinical meaning than AUC. A study that evaluated 28 PCa risk-associated
SNPs within a Swedish population-based PCa case-control study (CAPS, Cancer of the
Prostate in Sweden) found the PPV of this test was 36% when 3-fold increased risk over
population median risk was used as a cutoff to define high risk [46]. This is comparable
to PSA screening based on a 4 ng/mL cutoff [46]. This result has direct clinical meaning,
as PPV is the disease detection rate among subjects predicted to be at risk based on this
specific set of genetic risk markers. While AUC has merits as an objective measure of
test performance that is well known in the field of public health and population screening,
our results reinforce the view that AUC should not be viewed in isolation, but rather, in
the context of other available measures, including PPV, that have more direct clinical
relevance for the needs of patients and physicians.
Questions of health benefit
Even if GWAS SNPs allow accurate prediction of overall risk, and if the ethical concerns
are addressed to some extent, questions of health benefit remain [47-50]. This is
particularly important in a disease such as PCa, where most PCa tumors are not
aggressive or life threatening, and thus treatment can cause more harm than good.
Unfortunately, most GWAS SNPs identified to date are not associated with
aggressiveness or survival, and are unable to predict these clinical features, leading to
concerns for unnecessary biopsies, overdiagnosis, and overtreatment of indolent disease.
This is not surprising, given the original studies primarily used early stage cases of PCa
17
for association discovery and validation. However, risk stratification might be sufficient
to make a significant clinical impact. Prospective trials suggest that PSA-based over-
detection of indolent PCa can be reduced by targeting PSA screening to higher risk
patients.[3,4,13] This is illustrated by a recent analysis of the PLCO Cancer Screening
Trial, showing PCa specific mortality can be reduced ~50% by targeting PSA screening
to men with a positive family history (FH), a sharp contrast to the observation that PSA
screening in the entire PLCO cohort increased PCa mortality by ~9% [51]. Multi-marker
testing of GWAS SNPs has the potential to build on the risk estimation from FH, offering
additional potential for risk stratification that can be used to target PSA screening. A
recently published analysis of 9,404 PCa cases and 7,608 controls combined from three
ongoing studies in the United Kingdom suggested that targeting screening to men at
higher multi-marker genic risk could reduce overdiagnosis, with a 56% decrease in
overdiagnosis between men in the lowest and the highest polygenic risk quartiles [52]. A
similar study in Finnish men reached a similar conclusion, with a 21% decrease in
overdiagnosis when comparing between men above versus below the mean risk as
determined by multi-marker genetic testing for PCa [53]. In a retrospective analysis of a
Swedish cohort of men who underwent a prostate biopsy between 2005 and 2007, use of
a genetic prediction model that included PCa risk-associated SNPs and existing clinical
variables (age, PSA, free-to-total PSA, and family history) would have led to
significantly fewer biopsies (22.7%) than the non-genetic clinical model, at a cost of
missing a PCa diagnosis in 3% of patients characterized as having an aggressive disease
[54].
18
Translational research framework: to the individual, clinic, and public health
As described above, many of the technical and ethical arguments for and against the
clinical application of multi-marker genetic testing have been addressed to some extent.
For potential translation from the basic science laboratory bench to clinical care for
patients, additional research is needed. In the following, we use prostate cancer to
highlight a translational research (TR) framework that will allow us to capitalize upon the
exciting results from multi-SNP tests and make a positive health impact. Research
findings do not automatically jump from the lab to the clinic, and rather TR can be
envisioned as a series of intermediate phases that comprise TR [55-60]. Applied to
Genomics, TR stages will often include; T1) confirm association and establish clinical
validity; T2) evaluate clinical utility; T3) conduct practice-based implementation
research; and finally T4) population/community wide outcomes assessment.
To date, most of the studies following up on GWAS finings have emphasized T1. After
initial discovery of candidate associations, the goal of T1 is to minimize the possibility of
spurious associations. Statistical concerns are primarily addressed by utilizing
independent populations with large numbers of samples for confirmation analyses,
reducing the possibility of false positives due to chance. Prospective studies may be
needed to assess clinical sources of spurious association that are difficult to address in
case-control studies, such as for PSA detection bias. T1 research aims to answer
questions such as, “Are these SNPs truly associated with PCa, and not with PSA levels
that lead to the detection of most PCa cases?” By answering this type of question, we can
establish the validity of associations.
19
To examine whether the valid associations from T1 have clinical utility, T2 research
includes prospective studies, either observational or interventional (clinical trials), and
comparative effectiveness research (CER). Unfortunately, very few of the initially
promising associations are tested in prospective studies that can pave the way through the
T2 phase [60-61], in part because prospective studies are costly and require many years.
One efficient approach is to utilize previously completed prospective studies by
examining predictors at baseline (e.g. clinical parameters and genotypes) in relation to
outcome data. This approach is particularly appropriate for genetic studies in which
genetic markers are effectively blinded to patients and observers, reducing potential bias.
Another approach to T2 is CER, defined by the Institute of Medicine as, “The generation
and synthesis of evidence that compares the benefits and harms of alternative methods to
prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of
care” [62]. By comparing multi-marker genomics tests to existing clinical markers such
as family history and PSA screening, CER gives clinical meaning to statistical
associations. T2 research hopes to answer questions such as “How does the PPV of a
combined SNP test for PCa risk compare to the PPV of family history or PSA?” Clear
answers to these types of questions can inform the subsequent development of
professional guidelines, policy, and clinical use.
T3 research examines the practical issues impacting clinical usage, thus seeking to
maximize the utility that is established by T2 research. T3 studies might evaluate
physician motivation to offer tests, patient uptake of tests, patient interpretation of results,
20
physician recommendations based on test results, and any downstream decisions of those
receiving test results. T3 research may also explore the differential impact when testing
is applied in different clinical settings from private practice to specialty clinics or
academic centers. Initial cost effectiveness analysis, utilizing data gathered in specific
clinical settings, may occur in conjunction with T3, in an effort to predict the costs of
more widespread clinical implementation. Another area for T3 research is the evaluation
of various implementation scenarios, such as population screening versus just targeting
the testing to high risk families. Questions addressed by T3 research might include, “Do
genomic test results for PCa risk alter perception and accordingly patterns of PSA
screening?”
Following the introduction of a new intervention, T4 research focuses on health outcomes
amongst communities. Rather than the well-defined groups of patients studied in T3
research, T4 monitors the real world impact. For example, when new genomic tests are
introduced, population based registries may be used to observe disease incidence; if a
decrease in incidence or mortality is observed, then this may be attributable to the test,
particularly if evidence from T1 through T3 would predict the observed effect in absence
of other significant factors. Formal cost effectiveness analysis may be another important
component of T4, utilizing real world data on cost, test usage, and outcomes. Questions
addressed in T4 could include “Following the widespread introduction of a new genomic
risk assessment test, how many cases of PCa are prevented in a population, and at what
financial cost?” By answering these questions, it is possible to monitor whether the test
is having the effects that were expected based on results of T1-T3 research. Projects
21
funded by the National Cancer Institute have been heavily skewed toward the discovery
phase, or T1 of TR [60-61]. If we are to reap the full benefit of the heavy investment in
discovery approaches such as GWAS, then it is imperative that scientists and clinicians
commit to carrying out T2, T3, and T4 research.
Hypothesis and approach
This thesis is based on the hypothesis that multi-marker genetic testing for PCa risk is
safe and effective for use by patients and clinicians. Again, the aims of this work were:
Aim 1. To compare the provision Family History(FH) versus Genetic Risk Score
(GRS) for Prostate Cancer (PCa) risk from the perspective of at-risk patients.
Aim 2. To evaluate whether a short educational intervention can address key
barriers to clinical implementation of GRS among primary care providers.
Chapter II contains a manuscript that addresses Aim 1, which is practice based clinical
research to evaluate practical issues at stages T2 and T3 of a translational research
framework. This was accomplished by a new prospective randomized clinical trial to
assess the impact of the SNP panel on risk perception and behavioral outcomes. Subject
recruitment in the trial consisted of men age 40 to 49 years, Caucasian, and never had
prior PSA screening or PCa diagnosis. Baseline surveys collected data on their
perception of PCa risk, numeracy, and health attitudes. Subjects were randomized, with
half to receive a standard risk assessment (family history and age), and the other half to
receive a risk assessment based on SNPs plus standard risk assessment. Immediately
22
following the disclosure of the risk assessment based on these two methods, we assessed
recall and the perception of risk in each group. After three months we evaluated behavior
outcomes such as discussion of results with family members, engaging in medical
appointments, discussion of PCa screening options with a medical provider, engaging in
PCa screening such as PSA, and uptake of preventative measures such as
chemoprevention. Three years later, we assessed medical records for uptake of PSA
screening. By comparing the two randomization groups, we were able to measure the
impact of the SNP panel on risk perception and behavioral outcomes. We also evaluated
the effect of different levels of risk information that were provided to participants within
each group.
Chapter III contains a manuscript that addresses Aim 2. Primary care providers (PCP)
have been surveyed extensively regarding genetics knowledge and confidence, showing
that they are competent and confident regarding many basic genetics concepts; however,
significant barriers remain with regard to implementation of multi-marker testing. We
conducted a T3 study that assessed a 15-minute continuing medical education session to
improve PCP understanding and confidence regarding genetic testing, using multi-marker
genomic testing for prostate cancer as an example. We collected pre- and post- surveys
from 45 PCPs to evaluate changes in knowledge and confidence.
23
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54. Aly M, Wiklund F, Xu J, Isaacs WB, Eklund M, D’Amato M, Adolfsson J,
Grönberg H. Polygenic Risk Score Improves Prostate Cancer Risk Prediction:
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continuum of translation research in genomic medicine: how can we accelerate
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to sustainable solutions for public health problems” BMJ 2008;337:a863.
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report.pdf Accessed March 20, 2016.
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two phases of a continuum. In: From clinical trials to community: the science of
translating diabetes and obesity research. Natcher Conference Center, National
Institutes of Health, Bethesda, Maryland, USA, 2004:11-4.
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60. Ioannidis JP: Materializing research promises: opportunities, priorities and
conflicts in translational medicine. J Transl Med 2004, 2:5.
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Research in Cancer Genetics: The Road Less Traveled. Public Health Genomics.
2009 Dec 29.
32
62. Committee on Comparative Effectiveness Research Prioritization, Institute of
Medicine (IOM). Initial National Priorities for Comparative Effectiveness
Research. Washington , DC : The National Academies Press, 2009.
33
CHAPTER II
RANDOMIZED TRIAL FINDS PROSTATE CANCER GENETIC RISK SCORE
FEEDBACK TARGETS PSA SCREENING AMONG AT-RISK MEN
Manuscript accepted by: Cancer
Title: Randomized trial finds Prostate Cancer Genetic Risk Score Feedback Targets PSA
Screening Among At-Risk Men
Short running title: Genetic Risk Score Targets PSA Screening
Authors: Aubrey R. Turner, M.S.1*, Brian R. Lane, M.D., Ph.D.2,3*, Dan Rogers, B.S.4,
Isaac Lipkus, Ph.D.5, Kathryn Weaver, Ph.D.6, Suzanne C. Danhauer, Ph.D.6, Zheng
Zhang, M.S.1, Fang-Chi Hsu, Ph.D.7, Sabrina L. Noyes, B.S.2, Tamara Adams, M.S.1,
Helga Toriello, Ph.D.2, Thomas Monroe, Ph.D.2, Trudy McKanna, M.S.2, Tracey Young,
B.S.1, Ryan Rodarmer, M.S.2, Richard J. Kahnoski, M.D.2, Mouafak Tourojman, M.D.2,
A. Karim Kader, M.D., Ph.D.8, S. Lilly Zheng, M.D.1, William Baer, M.D.4, Jianfeng Xu,
Dr.P.H., M.D.1^
1Center for Cancer Genomics, Wake Forest School of Medicine, Winston-Salem, NC
27157, USA
2 Spectrum Health Hospital System, Grand Rapids, MI, 49546
3Michigan State University College of Human Medicine, Grand Rapids, MI 49546
4Grand Valley Medical Specialists, Grand Rapids, MI
5School of Nursing, Duke University, Durham, NC
34
6Department of Social Sciences & Health Policy, Division of Public Health Sciences,
Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
7Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest
School of Medicine, Winston-Salem, NC 27157
8Department of Surgery, University of California San Diego, San Diego, CA
*Co-First Author
^Corresponding Author: Dr. Jianfeng Xu, 1 Medical Center Blvd, Center for Cancer
Genomics, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA, 336-918-
6329, [email protected]
Trial Registration: clinicaltrials.gov, NCT02381015.
Funding provided by The National Cancer Institute: 1RC2CA148463-01. We also thank
the Betz Family Endowment for Cancer Research for their support.
The authors do not have any potential conflicts of interest or financial disclosures to
report.
Precis: This prospective trial found that provision of individual genetic risk scores for
prostate cancer did not lead to significant increases in anxiety or use of PSA screening.
Rather, genetic risk scores led to targeted use of PSA screening among higher risk men,
which may improve PSA screening performance.
Author contributions: The study was design was led by J.X., with significant input and
revision from R.J.K., A.K.K., S.L.Z., W.B., A.R.T., and B.R.L. The study design was
implemented as an approved functional protocol by D.R., A.R.T., B.R.L., H.T., T.A.,
35
Z.Z., I.L., T.Mck., S.Z., and A.K.K. Recruitment and data work (collection, entry, and
checks) were performed by D.R., S.N., T.A., H.T. T.M., T.Mck., T.Y., R.R., W.B., Z.Z.,
A.R.T., and B.R.L. Laboratory assays and associated quality control were designed and
conducted by T.Y., S.Z, and T.M. Risk reports were generated and checked by A.R.T.,
T.Y., T.Y., T.M., and S.Z. Data analysis was conducted by A.R.T. under supervision of
Z.Z. and F.H. All authors assisted with interpretation of the results. Individual sections
of the manuscript were drafted by A.R.T., B.R.L., D.R., S.N., and I.L. First complete
draft of the manuscript was assembled by A.R.T. and B.R.L. Significant conceptual and
technical revision and additions to the manuscript was provided by all co-authors. Final
review and approval of the manuscript was provided by all co-authors. A.R.T. and B.R.L.
had full access to all the data in the study and take responsibility for the integrity of the
data and the accuracy of the data analysis.
36
Abstract
Background
Prostate-specific antigen (PSA) screening may reduce death from prostate cancer (PC),
but leads to overdiagnosis of many cases of indolent cancer. Targeted use of PSA
screening may reduce overdiagnosis. Multi-marker genomic testing shows promise for
risk assessment, and could be used to target PSA screening.
Methods
To test whether counseling based on family history (FH) versus counseling based on
Genetic Risk Score (GRS)+FH differentially affects subsequent Prostate Specific Antigen
(PSA) screening at 3 months (primary outcome), we conducted a randomized trial of FH
vs. GRS+FH was conducted in 700 Caucasians aged 40-49 years without prior PSA
screening. Secondary outcomes included anxiety, recall, physician discussion at three
months, and PSA screening at 3 years. We also evaluated pictographs versus numeric
presentations of genetic risk.
Results
At three months, no significant differences were observed in rates of PSA screening
between FH (2.1%) and GRS+FH (4.5%) arms (x2=3.13, p=0.077); however, PSA
screening rates at three months significantly increased with given risk in the GRS+FH
arm (p=0.013). Similar results were observed for discussion with physician at three
months and PSA screening at three years. Average anxiety levels decreased after
providing individual cancer risk (p=0.0007), with no differences between groups. Visual
presentation by pictographs did not significantly alter comprehension or anxiety.
37
Conclusion
This is likely the first randomized trial of multi-marker genomic testing to report
genomic-targeting of cancer screening. We found little evidence of concerns for excess
anxiety or over/under use of PSA screening when providing multi-marker genetic risks to
patients.
Key Words: Randomized Controlled Trial, Prostate Cancer, Genetic Risk Score, Genetic
Testing, PSA Screening, Genetic Counseling
38
Introduction
The United States Preventive Services Task Force recommends against routine Prostate-
Specific Antigen (PSA) screening, citing risks of over-diagnosis and over-treatment1.
The American Cancer Society (ACS), American Urological Association (AUA), and
European Association of Urology (EAU) maintain that PSA screening should be
considered after physician-patient discussion, and earlier age screening (<age 55 years)
should be offered to men at increased risk (e.g. positive family history)2, 3, 4. Prospective
trials suggest that a favorable balance of risks and benefits can be achieved by targeting
PSA screening to higher risk patients.5, 6, 7
Family history (FH) risk assessment is an established approach that enables targeted PCa
screening in clinical settings, and is actively promoted by The Centers for Disease
Control and The US Surgeon General.8, 9 Genetic Risk Score (GRS) is a new approach to
determine individual risk that builds on family history by assessing single nucleotide
polymorphisms (SNPs) associated with PCa risk.10-14 The clinical validity and utility of
risk prediction using GRS has been demonstrated in large prospective studies.15-16
However, there are concerns for clinical application of GRS, including increased anxiety
and avoidance or overuse of medical care.17-20 In consideration of these concerns, we
studied a specific application of GRS plus FH, for personalized risk assessment of PCa,
and evaluated the potential for targeted Prostate Specific Antigen (PSA) screening.
39
Methods
Design
We conducted a prospective, randomized-controlled study primarily comparing the
impact of GRS+FH versus FH risk feedback information on PSA screening rates as the
primary outcome. Secondary outcomes included discussion of PSA screening with a
physician and subject anxiety. We also compared anxiety and PCa risk recall for numeric
versus pictograph presentations of this risk feedback for a 2x2 matrix of 4 randomization
groups. Prior studies suggested pictographs increase comprehension and reduce worry.21
The design and data collection timepoints are shown in Figure II-1.
Randomization
Computerized randomization of 700 study IDs into 175 blocks of four each was
completed prior to study enrollment, ensuring subjects were continuously randomized
into the four groups during the recruitment timeframe. The randomization list was kept at
Wake Forest, and not available to the enrollment team in Michigan. This process masked
randomization status until the risk report was provided to each participant.
Randomization groups were (Group 1) GRS+FH as a number; (Group 2) GRS+FH as a
number and pictograph; (Group 3) FH as a number; and (Group 4) FH as a number and
pictograph.
40
Figure II-1. Study design, enrollment, and outcomes.
Enrollment
Recruiters worked with primary care offices in West Michigan (Spectrum Health Medical
Group and Grand Valley Medical Specialists) to query patient databases for qualified
patients from June 2011 through February 2012. Potential participants were screened
Lost 3 Lost 0 Lost 2
3-month phone survey: N=682 (97.4%) completed (Avg. 3.28 months, SD=1.03)
(Measures: recall, actions: PSA discussion PSA Screening)
Group 1 (n=172) Group 4 (n=169) Group 3 (n=173) Group 2 (n=168)
Visit 1: N=700 participants enrolled
Baseline survey (demographics, anxiety)
Saliva sample collection
Blinded Randomization
Visit 2: N=695 (99.3%) participants completed (Avg. 5.57 weeks, SD=1.99)
Pre-Result Survey (anxiety)
Post-Result Survey (recall, anxiety)
Group 1
Genetic Risk
Score Number
only (n=175)
Group 4
Standard FH
Number+pictograph
(n=172)
Group 3
Standard FH
Number only
(n=175)
Group 2
Genetic Risk Score
Number+pictograph
(n=173)
3-year record review: 700 (100%) medical records evaluated (Avg. 3.02 years)
492 (70.3%) had at least one observed medical care visit
Review of Medical Records (Measures: actions: PSA Screening)
Genetic Risk
Score Number
only (n=125)
Genetic Risk Score Number+pictograph
(n=134)
Standard FH Number only
(n=115)
Standard FH Number+pictograph
(n=118)
Group 1 (n=175) Group 2 (n=175) Group 3 (n=175) Group 4 (n=175)
Lost 0
Lost 5 Lost 2 Lost 3 Lost 3
No Obs.=51 No Obs.=58 No Obs.=47 No Obs.=34
41
over the phone or in person and qualified individuals received detailed information about
undergoing genetic testing for PCa and randomization details as part of the study
informed consent process. Each subject was offered personalized risk feedback and up to
$80 compensation if they completed the trial.
Eligibility criteria were age 40 to 49 years, self-defined Caucasian background, and no
prior PSA screening nor PCa diagnosis. These criteria helped ensure inclusion of high-
risk men that were PSA-naïve, and were consistent with ACS and AUA guidelines for
earlier offer of screening for men at increased risk. Participation was limited to
Caucasians due to lacking information regarding risk prediction with these genetic
markers in other races/ethnicities at the time of study startup. Informed consent was
documented in writing. Prior to enrollment, this study was approved by institutional
review boards at each participating institution.
Data collection and study participation
At baseline, participants completed the State-Trait Anxiety Inventory and donated saliva
samples (Oragene, Inc). A CLIA-certified lab at Spectrum Health Hospital in Grand
Rapids Michigan assayed a validated panel of 46 SNPs. A report for lifetime risk of PCa
was generated for each subject (Supplemental Figure II-1). The report content and format
was based on the 4 randomization groups. Participants in the FH arm were given lifetime
PCa risks of either 17% or 23% based on negative or positive family history (Figure II-
2a), respectively, based on the estimated OR for FH, calibrated incidence rates, and
mortality rates excluding PCa as derived from SEERS data.12, 22 In the GRS+FH arm,
risk calculations included the SEERS-derived FH+/- risk and a sum of the estimated OR
for each SNP, resulting in risks that were distributed more broadly (Figure II-2b) based
42
on the number of risk alleles.12, 22 Four to six weeks after baseline, participants met with
a certified genetic counselor to receive their personal risk report and complete survey
measures. At the three-month phone survey, participants repeated survey measures, with
new questions about behaviors following the intervention, including further discussion of
PCa screening with their PCP and PSA screening. Electronic medical records of all
subjects were queried for PSA screening (median: 3.02 years).
Figure II-2. Distribution of Genetic Risk Scores Given to Study Participants
Figure II-2a. Family History Group
0
10
20
30
40
50
60
70
80
90
100
2 4 6 8
10
12
14
16
18
20
22
24
26
28
30
33
35
38
40
44
46
49
51
53
56
62
67
77
80
Sub
ject
s (%
)
Risk Given to Subject (%)
Mean = 18
Std.Dev. = 1
Median = 17
43
Figure II-2b. Genetic Risk Score Group
Genetic Testing & Risk Reports
All participants were asked to donate saliva samples (Oragene, Inc) to maintain the blind
status of participants until the results were disclosed. All saliva samples and data were
labeled with unique study identifiers to protect confidentiality. Samples were transported
to a CLIA-certified laboratory at Spectrum Health for DNA isolation and SNP
genotyping. The lab assayed a validated panel of 46 SNPs, consisting of 33 analytical
SNPs and 13 built-in quality controls and duplicated SNPs. The design of this panel was
based on prior published research by our study team, with SNPs drawn from the results of
prior GWAS and confirmation studies in Caucasian populations (p-value <1.0×10−6) and
limited to one SNP from each independent LD block.12,22 The Illumina BeadXpress
Reader was selected for genotyping based on several factors, namely the 96 well format
that allowed a total of 48 samples and controls to be processed in duplicate, ability to
0
1
2
3
4
5
6
2 4 6 8
10
12
14
16
18
20
22
24
26
28
30
33
35
38
40
44
46
49
51
53
56
62
67
77
80
Sub
ject
s (%
)
Risk Given to Subject (%)
Mean = 20
Std.Dev. = 14
Median = 16
44
multiplex (simultaneous genotype) the 46 selected SNPs per sample, and FDA clearance
for in vitro diagnostics. De-identified SNP genotyping results were then sent from the lab
at Spectrum Health to the Wake Forest team for risk report generation.
The Wake Forest team subsequently generated a de-identified risk report for each subject,
containing the estimated lifetime risk for PCa and a description of factors comprising this
risk (Supplemental Figure II-1). The content and format of these reports were based on
the 4 randomization groups. Pictographs were included in half of the risk reports and
were based on a pictograph generator from the University of Michigan.23 Participants
who responded “no” or “do not know” for FH of prostate cancer were classified as
having a negative family history. Absolute risk was calculated using the method we have
described previously,12,22 which is based on the SNP-specific RR, calibrated incidence
rate of PCa, and mortality rate for all causes excluding PCa in the U.S. Risks greater than
80% were reported as 80% due to concerns about stability of estimates above that point.
The risk reports were then transmitted via a secure study website to the study team in
Michigan in preparation for visit two. The study coordinator in Michigan linked the study
ID back to personal identifiers, added these identifiers, and then finalized the re-identified
risk report.
Genetic counseling.
Genetic counselors followed prepared scripts at study intake and when disclosing results.
All deviations/unexpected questions were recorded. Each subject was given a copy of a
Centers for Disease Control brochure on PCa screening, and provided with a resource
card for more information (Supplemental Figures II-4 and II-5).
45
Measures
The State-Trait Anxiety Inventory (STAI) is a validated assessment tool that has been
used extensively in both clinical and research settings to measure anxiety.24 It comprises
separate self-report scales for measuring state and trait anxiety. In order to measure
changes in anxiety at three study timepoints, we utilized the S-Anxiety scale (State
Anxiety). The S-Anxiety scale assesses current feelings “at this moment”: 1) not at all, 2)
somewhat, 3) moderately so, and 4) very much so. While the full S-Anxiety scale
includes 20 items, we utilized a shortened version, consisting of ten items (Questions 1,
3, 5, 9, 11, 12, 13, 15, 17, and 19) from the STAI Form X1. This subset of items has been
used in prior studies by members of the study team. Each item within the STAI is scored
on a scale of 1 to 4, so with ten items the possible range of total scores would be from 10
(lowest anxiety) to 40 (highest anxiety) for each participant. To identify anxiety levels
of potential clinical importance, we utilized a pro-rated S-Anxiety score threshold equal
to 41 or greater (i.e. threshold=20.5 on scale of 10-40); this threshold was selected based
on gender and age-specific normative data in STAI manual, and then setting a cutoff 0.5
S.D. above the respective mean24,25. The STAI was assessed at baseline, immediately
pre-result, immediately post-result, and at 3 month follow-up.
46
Risk recall was assessed immediately post result, by the question:
“Based on the information given to you, what were you told is your chance of
developing prostate cancer in your lifetime from 0-100%?” [fill in the blank]
At the 3 month follow-up, behavioral outcomes were assessed by the following
questions:
“Since we last spoke to you, have you talked to a doctor about having a PSA
test to further determine your chance of having prostate cancer?” [yes or no]
“Since we last spoke to you, did you have a PSA test performed?” [yes or no]
Statistical analyses
The primary outcome was self-reported PSA screening by 3 months. Secondary
outcomes included: 1) State-Trait Anxiety Inventory (immediate pre/post), 2) risk recall
(immediate post, 3 months), 3) discussion with a physician regarding PSA screening by 3
months, and 4) medical record confirmed PSA screening by 3 years. We assessed main
effects for risk type [GRS+FH (Groups 1 and 2) versus FH (Groups 3 and 4)]. Non-
parametric one-way ANOVA was used to compare continuous data across groups.
Fisher’s exact test or chi-square test were used to evaluate associations between
categorical measures and groups. Associations between risk estimates provided to
subjects and continuous or binary outcomes were tested using linear (recall) or logistic
(physician discussion, and had PSA screening) regression, respectively. Repeated
measures (anxiety) were evaluated using Wilcoxon signed-rank tests. Statistical analyses
were performed using SAS 9.2.
47
Results
Sample Characteristics
700 patients were enrolled and 97.4% completed the 3-month follow-up. During 3 years
of follow-up by medical records, 70.3% were positively observed to receive any health
care subsequent to study participation, such as clinic visits or labwork (Figure II-1). The
demographic distributions suggest randomization resulted in four groups with similar
characteristics (Table II-1). The average participant was age 45 years, had annual income
of $50,000 to $100,000 (46%), a college graduate (45%), married (76%), and a negative
first degree FH (91.2%, including 14% in whom FH was unknown).
48
Table II-1. Subject demographics by randomization group.
RANDOMIZATION GROUP p-value* GRS-
number GRS-pict
FH-number
FH-pict
Participants (N)
Enrolled 175 175 175 175
Completed visit 2 175 173 175 172 0.62
Completed 3 mo. followup 172 168 173 169 0.24
Medical records observation during 3 yrs. follow-up
125 134 115 118 0.12
Age in years (mean, SD) 44.9 (2.85)
44.8 (3.06)
44.7 (2.84)
45.0 (2.76)
0.76
Annual Income in U.S.D (%) 0.2
<20K 2 4 4 3
20-50K 15 22 22 19
50-100K 47 38 45 47
>100K 33 29 28 29
declined 3 7 2 2
Education (%) 0.54
<12th grade 0 0.6 0 0.6
High School Grad. 8 9 11 10
Some College/Trade 21 29 26 29
College Grad. 49 45 45 39
Post-grad. 21 17 19 21
% declined 0.6 0.6 0 0
Marital Status (%) 0.77
married 77 77 73 75
single 9 10 13 11
divorced 13 10 13 12
widowed 0 0 0 0
Positive Family history PCa (%)
7 14 6 10 0.062
* t-tests for continuous data, and Fisher’s exact tests for categorical data.
49
PSA Discussions and Screening by Group
Three months after risk counseling, 16.3% (n=111 of 681) of participants reported
discussion of PSA screening with a physician, and 3.25% (n=22 of 677) reported having
PSA screening (Supplemental Table II-1). For the primary outcome, no significant
differences were observed in the rate of PSA screening at three months across by type of
feedback (GRS+FH versus FH, x2=3.13, p=0.077); similar non-significant results were
seen when comparing across all four randomization groups (x2=3.25, p=0.35), and by
format of risk feedback (NM versus PT, x2=0.13, p=0.72) (Supplemental Table II-1).
Similarly, physician discussion did not vary significantly between all four randomization
groups (x2=3.78, p=0.29), by type of feedback (GRS+FH versus FH, x2=3.41, p=0.065)
or by format of feedback (NM versus PT, x2=0.37, p=0.54) (Supplemental Table II-1).
Three years after results disclosure, medical records showed 33% (n=160 of 492) of
participants had undergone PSA screening. This rate of PSA screening at three years did
not significantly vary between all four randomization groups (x2=3.78, p=0.29), by type
of feedback (GRS+FH versus FH, x2=1.7, p=0.19) or based on format of the risk
feedback (NM versus PT, x2=0.61, p=0.44) (Supplemental Table II-1).
Effects of Lifetime PCa Risk on Anxiety, PSA Discussions, & Screening
In the GRS+FH arm, as participants were given increased lifetime PCa risks, they were
significantly more likely to report PSA discussion with a physician by three months
(Wald x2=9.11, p=0.0025) (Figure II-3a), engage in PSA screening by three months
(Wald x2=6.13, p=0.013) (Figure II-3b), and engage in PSA screening by three years
(Wald x2=9.7, p=0.0018) (Figure II-3c) (Supplemental Table II-2). For example, among
50
men whose given PCa risk were <1, 1-2, 2-3, and >3-fold higher than population average,
we observed 2.7%, 4.8%, 9.4%, and 16.7% of these men elected PSA screening by three
months, respectively. Among subjects with a negative family history in the GRS+FH
arm (n=229), given risk was significantly associated with PSA screening (n=77, 33.6%)
at 3 years (Wald x2=8.9, p=0.0027). In contrast, none of these outcome trends for PSA
screening and physician discussion were observed in the FH arm (Figure II-3a-c and
Supplemental Table II-2).
51
Figure II-3. Participant health behaviors, in FH and GRS arms, stratified by given risk.
a. Discussed PSA screening with physician per self-report during 3 month follow-up
b. Engaged in PSA screening per self-report during 3 month follow-up
c. Engaged in PSA screening, per medical record review, during 3 years of follow-up
4.52.7
4.8
9.4
16.7
0
2
4
6
8
10
12
14
16
18
All <1 1 to 2 2 to 3 >3
Pe
rce
nta
ge o
f Su
bje
cts
Category of Given Risk
PSA Screening in GRS Arm
18.9
13.2
24.8 25.0
41.7
0
5
10
15
20
25
30
35
40
45
All <1 1 to 2 2 to 3 >3
Pe
rce
nta
ge o
f Su
bje
cts
Category of Given Risk
Physician Discussion in GRS Arm
13.7 13.6 14.8
0
5
10
15
20
25
30
35
40
45
All <1 1 to 2
Pe
rce
nta
ge o
f Su
bje
cts
Category of Given Risk
Physican Discussion in FH arm
p trend=0.86 p trend=0.0025
2.1 1.6
7.4
0
2
4
6
8
10
12
14
16
18
All <1 1 to 2
Pe
rce
nta
ge o
f Su
bje
cts
Category of Given Risk
PSA Screening in FH Arm
p trend=0.064 p trend=0.013
35.128.6
39.0
51.9 50.0
0
10
20
30
40
50
60
70
All <1 1-2 2-3 >3
Pe
rce
nta
ge o
f Su
bje
cts
Category of Given Risk
PSA Screening in GRS Arm by 3yr
p trend=0.012
29.6 26.5
66.7
0
10
20
30
40
50
60
70
All <1 1-2
Pe
rce
nta
ge o
f Su
bje
cts
Category of Given Risk
PSA Screening FH Arm 3yr
p trend=0.0034
52
Post-result anxiety was positively related to given risk, with a significant linear
relationship in the GRS+FH feedback arm (β=10.6, s.e.=1.77, t=5.97, p=<0.0001) but not
the FH feedback arm (β=19.1, s.e.=13.7, t=1.39, p=0.16) (Supplemental Figure II-2).
Given risk was significantly associated with post-result anxiety levels indicative of
clinical importance (STAI S-Score pro-rated cutoff of 41 or greater24,25) in the GRS+FH
arm (Wald x2=24.9, p<0.0001), but not in the FH arm (Wald x2=2.77, p=0.09).
Post-Result Anxiety and Pre-Post Change in Anxiety by Group
Immediate post-result anxiety did not significantly differ by randomization group (x2=
2.39, p=0.49), by feedback type (GRS+FH versus FH, x2=0.93, p=0.34) or format (NM
versus PT, x2=1.46, p=0.23) of risk feedback (Supplemental Table II-3). From immediate
pre-result (mean= 15.66) to immediate post-result (mean= 15.31), the average anxiety
score for the complete study sample decreased significantly (S=-11660, p=0.0007)
(Supplemental Table II-3). The change in anxiety scores did not vary significantly across
all four randomization groups (x2=2.18, p=0.54), by feedback type (GRS+FH versus FH,
x2=0.93, p=0.33) or risk feedback format (NM versus PT, x2=0.081, p=0.78)
(Supplemental Table II-3). Although no group differences were observed in baseline
anxiety level (Supplemental Table II-3), we ran linear regression adjusting for baseline
and still observed no group differences in pre-post anxiety.
When anxiety levels indicative of clinical concern were evaluated (STAI S-Score pro-
rated cutoff of 41 or greater24,25), group differences were observed in the number of
subjects with clinically important anxiety level at baseline; linear regression adjusting for
53
baseline anxiety showed no group differences in post result clinically important anxiety.
Comparing the 104 subjects that exceeded this threshold post-results versus the
remaining 590 subjects, there was no significant difference in rate of PSA discussion with
a physician by three months (x2=0.018, p=0.89) or engaging in PSA screening by three
months (x2=0.018, p=0.89) (Figure II-3b); however, this subset of subjects was
significantly more likely to engage in PSA screening by three years (x2=3.99, p=0.046).
Regression analysis adjusting for baseline showed no significant relationship between
post result clinically important anxiety and post-result PSA screening by three years.
Recall of Risk
As a measure of comprehension, we evaluated recall of the risk given to subjects (i.e.
given risk). We observed a significant linear relationship between given risk and recalled
risk at the immediate post-results assessment (β=93.5, s.e.-1.55, t=60.2, p<0.0001)
(Supplemental Figure II-3a), and at the 3-month follow-up assessment (β=94.7, s.e.=2.93,
t=32.4, p<0.0001) (Supplemental Figure II-3b). At both assessments, the relationship
between given risk and recall was significant in each of the four randomization groups
(p<0.001).
Discussion
The prospective, randomized study design allowed for comparison of counseling
regarding individual lifetime risk of PCa based on GRS+FH vs. FH. We found no
significant differences in the overall rate of PSA screening (3% at 3 months, 23% by 3
years) between study groups after risk assessment, suggesting neither the source of risk
feedback (genetic or family history) nor the presentation format (numerical only or
54
numerical plus pictograph) were important determinants of subsequent screening
behavior. The level of given risk in the GRS+FH arm was a significant determinant of
screening behavior. At 3 months, participants in the GRS+FH arm that were given >3-
fold greater risk were 2.2 times more likely to discuss PSA screening with a physician,
and 3.8 times more likely to engage in PSA screening compared with those given average
risk, providing evidence of increased screening utilization among individuals that were
given higher-risks. Similar trends were observed upon review of 3-year medical record
data. Our observation that given risk was significantly associated with PSA screening at 3
years among subjects with a negative family history in the GRS+FH arm further
underscores the potential for GRS+FH to target PSA screening amongst a group of men
typically considered at uniformly low risk. These observations of genomic-targeted PSA
screening are important in light of the PSA screening debate. Several current screening
guidelines recommend targeted application of PSA screening; we now provide new
evidence of effective genomic targeting of PSA tests. Our results also address patient-
centered concerns for clinical application of GRS+FH, including the potential for
increased anxiety and avoidance or overuse of medical care.17-20
A prior multi-marker genetic risk assessment study reported no differences in diet,
exercise, or use of screening tests, from baseline to 5.6 months average follow-up.20
Another recent randomized trial of genetic and environmental risk assessment for
colorectal cancer did not observe screening differences within six months by group or
given risk level.26 That study utilized subjects that were already non-compliant with
screening, whereas we focused on screening-naïve subjects. The present study is likely
the first prospective randomized trial to observe screening behavior changes, confirmed
55
by medical records, in response to multi-marker genetic risk feedback in an average-risk
population. Our findings of targeted screening are similar to prior studies of high-
penetrance genes, with changes in mammography rate following disclosure of BRCA1
results.27,28 and in colonoscopy associated with HNPCC genes.29 The finding of
behavioral change associated with increasing risk in the GRS+FH arm may reflect a
combination of a prospective randomized design, large study population, retention of
97% participants until 3 month follow-up, confirmed medical record observations on
70% by three years, focus on PCa, prediction of cancer risk, characteristics of the study
population, and the broad spectrum of risk stratification.
Studies of hereditary PCa have shown that risk increases with additional affected family
members30-32. Despite this, we are not aware of clinical guidelines for PCa risk
estimation based on family history. Current clinical guidelines for PSA screening (AUA,
ACS, EAU) and prior large prospective studies have considered FH as either positive or
negative. Accordingly, FH risk estimates were binary in the present study, 17% negative
versus 23% positive. This binary approach allowed for accurate and stable risk estimates
to all subjects, while reflecting the binary FH risk assessment used in the primary care
settings where PSA screening occurs. Unfortunately, binary FH risk assessment limits
risk stratification, and as seen in our study, limits the potential to motivate behavior
change. GRS+FH adds information from genetic markers to FH, distributing risk from
0% to 80% in this study. The importance of the larger range of risk feedback from
GRS+FH is highlighted by our observation that the level of given risk was critical in
explaining screening behaviors in the GRS+FH arm. In light of our findings, clinicians
and future studies should consider incremental rather than binary assessment of family
56
history risk for PCa. In this regard, development of clinical guidelines for incremental
FH risk assessment for PCa may be helpful in terms of establishing a standard.
Regardless, FH and GRS+FH are complimentary, neither is diagnostic, and both can
stratify risk to enable targeted screening. GRS+FH could reduce misclassification of risk
for patients in which FH is negative (77% in the present study) or not available (14% in
the present study).33-36
Based on our results, a subset of patients will experience clinically important levels of
anxiety in connection with the disclosure of risk feedback; clinicians and laboratories
should therefore ensure individuals who undergo risk assessment and testing receive
adequate pre- and post-results counseling. Overall, the provision of risk feedback led to
statistically significant decreases in average anxiety, consistent with genetic testing
studies of colon cancer and Alzheimer’s disease.26,29,37 The provided risk feedback was
accurately recalled, and anxiety modestly increased (although not clinically relevant) in
direct proportion to given risk, similar to previous multi-marker genetic studies.20, 38
These findings add to the evidence suggesting concerns of providing multi-marker
genetic feedback to patients may be over-estimated.
The lack of differences in recall associated with format of risk feedback conflicts with
findings from several prior studies that indicated pictographs are a superior method to
convey risk feedback to patients across all numeracy levels.21 However, much of the prior
work in risk communication has utilized hypothetical scenarios in controlled laboratory
studies rather than personal risk results delivered to patients in clinical settings.39 A
recent empiric study on communication of breast cancer risks is consistent with our
findings of no observable benefit in recall when using pictographs to communicate risk.39
57
Given that clinical genetic results are conveyed in a variety of formats, our findings may
help inform clinical practices in risk communication, as well as future research. Perhaps
there is no single approach for the communication of genetic risks that is beneficial
across all numeracy levels and in all settings; in this case, it will be crucial to identify
subsets of patients and clinical settings in which different methods have measurable
benefit.
Limitations of this study include the inability to evaluate PCa detection rates and
outcomes of PCa treatment based on low (expected) event rate during the follow-up
period. Demographic features of the study population may limit generalization. Some
subjects were lost to follow-up, although intent-to-treat analysis utilizing all 700 subjects
yielded similar results for major outcomes. Current validated SNPs and/or FH are
believed predictive of any PCa rather than high-grade PCa; coupled with PSA screening,
this may lead to increased diagnosis of early stage PCa that would never require
treatment, and is worth long-term study. Despite the limitations, these results indicate
how genetic testing results are perceived and acted upon.
In summary, in this prospective RCT of 700 men, provision of individual GRS+FH did
not lead to significant increases in anxiety or use of PSA screening. Rather, GRS+FH led
to targeted use of PSA screening among higher risk men, which may improve PSA
screening performance.
58
Acknowledgements
We thank the collaborating institutions and their supportive staff at Wake Forest School
of Medicine, ClinXus, Van Andel Research Institute, Spectrum Health, and Grand Valley
Medical Specialists. Also, thanks to Kevin McCormick, MD and George Bruins, MD and
their staff at their respective Spectrum Health Medical Group offices for assistance in
recruitment efforts.
Funding provided by The National Cancer Institute: 1RC2CA148463-01. We also thank
the Betz Family Endowment for Cancer Research for their support.
The authors do not have any potential conflicts of interest or financial disclosures to
report.
59
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65
Figure Legends.
Figure II-1. Study design, enrollment, and outcomes.
Figure II-2. Distribution of Genetic Risk Scores Given to Study Participants in FH
(A) and GRS+FH (B) arms. Red bars represent the percentage of participants given a
specific risk value. Black dashed line at 18.3% represents the average risk that was also
provided on each participant risk report.
Figure II-3. Participant health behaviors, in FH and GRS+FH arms, stratified by
given risk. Black bars represent the percentage of participants that reported engaging in
health behaviors during three month follow-up. Blue bars represent these same
percentages, but stratified by category of given risk.
a. Discussed PSA screening with physician per self-report during 3 month
follow-up.
b. Engaged in PSA screening per self-report during 3 month follow-up.
c. Engaged in PSA screening, per medical record review, during 3 years of
follow-up.
66
Supplemental Materials.
1. Supplemental Figure II-1a. Risk report template for participants randomized to receive
risk results based on genetic risk score in numeric format.
2. Supplemental Figure II-1b. Risk report template for participants randomized to receive
risk results based on genetic risk score in pictograph format.
3. Supplemental Figure II-1c. Risk report template for participants randomized to receive
risk results based on family history in numeric format.
4. Supplemental Figure II-1d. Risk report template for participants randomized to receive
risk results based on family history in pictograph format.
5. Supplemental Figure II-2. Risk Given to Participants is positively related to Risk Recall.
6. Supplemental Figure II-2a. Linear relationship of Given Risk with Risk Recall
Immediately Following Results Disclosure
7. Supplemental Figure II-2b. Linear relationship of Given Risk with Risk Recall 3 Months
Following Results Disclosure
8. Supplemental Figure II-3. Risk given to participants is positive and linearly related to
post-result anxiety among participants who received risk feedback based on genetic risk
score information
9. Supplemental Figure II-4. Text of Study Resource Card, as given to all study participants
during study visit 2.
10. Supplemental Figure II-5. Prostate Screening Brochure from Centers for Disease Control,
as given to all study participants during study visit 2.
11. Supplemental Table II-1. Average anxiety pre- and post-results
12. Supplemental Table II-2. Comparison of behavioral outcomes between groups.
13. Supplemental Table II-3. Relationship between given risk and behavioral outcomes
within each randomization group.
67
Supplemental Figure II-1a. Risk report template for participants randomized to receive risk
results based on genetic risk score in numeric format.
68
Supplemental Figure II-1b. Risk report template for participants randomized to receive
risk results based on genetic risk score in pictograph format.
69
Supplemental Figure II-1c. Risk report template for participants randomized to receive risk
results based on family history in numeric format.
70
Supplemental Figure II-1d. Risk report template for participants randomized to receive
risk results based on family history in pictograph format.
71
Supplemental Figure II-2. Risk Given to Participants is positively related to Risk Recall.
Supplemental Figure II-2a. Linear relationship of Given Risk with Risk Recall Immediately
Following Results Disclosure (β=93.5, s.e.-1.55, t=60.2, p<0.0001).
Each plotted circle represents the risk recall for one subject. The trend line, 95% confidence
limits, and 95% prediction limits were generated by SAS software.
72
Supplemental Figure II-2b. Linear relationship of Given Risk with Risk Recall 3 Months
Following Results Disclosure (β=94.7, s.e.=2.93, t=32.4, p<0.0001).
Each plotted circle represents the risk recall for one subject. The trend line, 95% confidence
limits, and 95% prediction limits were generated by SAS software.
73
Supplemental Figure II-3. Risk given to participants is positive and linearly related to post-
result anxiety among participants who received risk feedback based on genetic risk score
information (β=10.6, s.e.=1.77, t=5.97, p=<0.0001).
Each plotted circle represents the post-result anxiety score for one subject. The trend line, 95%
confidence limits, and 95% prediction limits were generated by SAS software.
p
ost
-res
ult
an
xiet
y
74
Supplemental Figure II-4. Text of Study Resource Card, as given to all study participants
during study visit 2.
PROSTATE CANCER STUDY RESOURCE CARD
If you would like more information, talk to your primary care physician or urologist
about:
1. Your risk assessment
2. The benefits and limitations of prostate cancer screening
3. The pros and cons of potentially risk-reducing medications
If you have a strong family history of prostate and/or other cancers you may wish to
discuss this further with a genetic counselor.
To find an urologist, visit: http://www.urologyhealth.org/find_urologist/html/index.asp
To find a genetic counselor, visit:
http://www.nsgc.org/FindaGeneticCounselor/tabid/64/Default.aspx
Or: http://www.magcinc.org/
75
Supplemental Figure II-5. Prostate Screening Brochure from Centers for Disease Control,
as given to all study participants during study visit 2.
76
Supplemental Table II-1. Comparison of behavioral outcomes between groups.
Talked to doctor about PSA by 3
months§ Had PSA by 3 months§
Had PSA by 3 years||
Analytic
group
Participants* N† Percent
(95%CI)
p
(between
groups) ‡
N† Percent
(95%CI)
p
(between
groups)‡
N† Percent
(95%CI)
p
(between
groups)‡
All 111/681
16.3
(13.5-19.1) 22/677
3.2
(19.1-45.9)
160/492 32.5
(28.4-36.7)
Ran
dom
izat
ion G
roup GRS-
number 34/171
19.9
(13.8-25.9)
0.29
8/172
4.7
(1.5-7.8)
0.35
45/125
36.0
(27.5-44.5)
0.29
GRS-
pictograph
30/167 18.0
(12.1-23.8)
7/164
4.2
(1.1-7.4)
46/134
34.3
(26.2-42.5)
FH-number
25/173 14.5
(9.2-19.7)
4/172
2.3
(0-4.6)
29/115
25.2
(17.1-33.3)
FH-
pictograph
22/170 12.9
(7.8-18)
3/169
1.8
(0-3.8)
40/118
33.9
(25.2-42.5)
Fee
db
ack
typ
e GRS 64/338
18.9
(14.7-23.1) 0.065
15/336
4.5
(2.2-6.7) 0.077
91/259
35.1
(29.3-41) 0.19
FH 47/343 13.7
(10-17.4)
7/341
2.1
(0.5-3.6)
69/233
29.6
(23.7-35.5)
Fee
db
ack
form
at
NM
59/344 17.2
(13.1-21.2) 0.54
12/344
3.5
(1.5-5.4) 0.72
74/240
30.8
(24.9-36.7) 0.44
PC 52/337 15.4
(11.6-19.3)
10/333
3.0
(1.2-4.8)
86/252 34.1
(28.2-40)
* GRS=genetic risk score; FH=family history risk; NM=number format; PC=pictograph format † Differences of N amongst groups are due to missing data ‡ chi-square tests § Based on phone survey || Based on medical records review
77
Supplemental Table II-2. Relationship between given risk and behavioral outcomes within each randomization group. R
andom
izat
ion
Gro
up
*
Talked to doctor about PSA by 3 months‡ Had PSA by 3 months‡ Had PSA by 3 years§
did
(N)
Average
Given
risk (%)
did
not
(N)
Avg.
Given
risk (%)
p
(within
groups)†
did
(N)
Avg.
Given
risk
(%)
did
not
(N)
Avg.
Given
risk
(%)
p
(within
groups)†
did
(N)
Avg.
Given
risk
(%)
did
not
(N)
Avg.
Given
risk
(%)
p
(within
groups)†
GR
S-
num
ber
34
24.26
137
19.22 0.059
8 33.25 164 19.50
0.011
45 21.2 80 20.7 0.84
GR
S-
pic
togra
ph
30
26.07
137
18.80 0.018
7 25.00 157 19.90
0.37
46 25.5 88 18.5 0.016
FH
-
nu
mber
25
17.48
148
17.36 0.72
4 18.50 168 17.36
0.17
29 17.8 86 17.1 0.033
FH
-
pic
tog
rap
h
22
17.55
148
17.57 0.96
3 19.00 166 17.54
0.2
40 18.2 78 17.3 0.018
* GRS=genetic risk score; FH=family history risk † Logistic regression ‡ Based on phone survey § Based on medical records review
78
Supplemental Table II-3. Average anxiety pre- and post-results
Average Anxiety‡ Pre-post Difference
Analytic
grouping Participants* N†
Baseline
(S.D.) p§ Pre (S.D.) p§ Post (S.D.) p§
p (within
group)||
p (between
groups) §
All 692 15.01 (4.22) 15.66 (4.20) 15.31 (4.52) 0.0007
Randomization
Group
GRS-number 175 14.91 (4.91)
0.10
15.34 (3.93)
0.53
15.25 (4.43)
0.49
0.4744
0.54
GRS-
pictograph
172
15.83 (4.75) 16.09 (4.49) 15.95 (5.19)
0.1642
FH-number
174
14.77 (4.16) 15.70 (4.16) 14.92 (4.40)
0.0010
FH-pictograph
171
14.52 (3.69) 15.50 (4.20) 15.12 (3.93)
0.1478
Feedback type GRS
347
15.37 (4.47) 0.06
15.71 (4.23) 0.75
15.60 (4.83) 0.34
0.1401 0.33
FH
345
14.64 (3.93) 15.60 (4.18) 15.02 (4.17)
0.0008
Feedback
format
NM
349
14.84 (4.14) 0.3
15.52 (4.04) 0.6
15.09 (4.41) 0.23
0.0063 0.78
PC
343
15.18 (4.30) 15.80 (4.36) 15.53 (4.62)
0.0393 * GRS=genetic risk score; FH=family history risk; NM=number format; PC=pictograph format †. Differences of N amongst groups are due to missing data ‡. Anxiety was measured by State-Trait Anxiety Inventory § Non-parametric ANOVA (Kruskal Wallis test) was used for between group tests || Wilcoxon signed rank tests were used for pre-post comparisons
79
CHAPTER III
BARRIERS TO PRIMARY CARE PROVIDER ADOPTION OF CANCER GENOMIC
TESTS MAY BE ADDRESSED THROUGH SHORT CONTINUING EDUCATION
SESSIONS.
Manuscript under review.
Title. Barriers to primary care provider adoption of cancer genomic tests may be
addressed through short continuing education sessions.
Authors: Aubrey R. Turner, M.S.1, Kathryn E. Weaver, Ph.D.2, Elizabeth Crowder,
B.A.1, Alison Witkowski, B.S.1, Jianfeng Xu, M.D. Dr.P.H.1^
1Center for Cancer Genomics, Wake Forest School of Medicine, Winston-Salem, NC
27157, USA
2Department of Social Sciences & Health Policy, Division of Public Health Sciences,
Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
^Corresponding Author: Jianfeng Xu, 1 Medical Center Blvd, Center for Cancer
Genomics, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA, 336-918-
6329, [email protected]
ACKNOWLEDGMENTS.
This work was partially supported by a grant from the National Cancer Institute:
1RC2CA148463-01.
80
ABSTRACT.
Purpose: Primary care provider (PCP)-targeted continuing medical education (CME)
regarding genomic testing is effective at increasing knowledge and confidence. It is not
known if short format CME on genetics can address barriers related to limited
knowledge, costs, time, and discrimination concerns.
Methods: We evaluated a 15-minute session delivered during a CME conference to
improve PCP knowledge and confidence regarding genetics and new genomic testing.
Multi-marker genomic testing for prostate cancer was used as an example. We collected
pre- and post- surveys from PCPs.
Results: PCPs (N=45) commonly encountered genetics in clinical practice, although
baseline confidence in understanding and utilizing genetics was low. PCP confidence
increased significantly from pre- to post-CME in many aspects of genetic testing.
Confidence explaining genetic test results to patients significantly increased (31% to 62%
confident/very confident; p=<0.0001), but there was no change in the likelihood of
ordering genetic tests.
Conclusions: Our results suggest several previously identified barriers to PCP adoption
of new genetic technologies may be addressed through short CME sessions. Time and
patient cost appear to be key barriers to future adoption of new genetic approaches by
PCPs.
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Key Words: Primary Care Provider; Genomic Testing; Family History; Genetics;
Education
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INTRODUCTION.
Genetics is already a common aspect of patient care in primary settings, as 65% of
surveyed primary care providers (PCP) had counseled patients on genetic issues in the
last 6 months [1-3]. In conjunction with the rapid expansion in PCP use of genetics, a
number of studies have evaluated PCP knowledge and attitudes toward clinical genetic
testing. These studies have revealed concerns regarding limited knowledge of genetics
[4], clinical utility of tests [5], a lack of training and experience [1,6], patient anxiety [2],
costs [7], time [8], and potential for discrimination [5,7]. A strong majority of PCP’s
(73.7%) rate their knowledge as very/somewhat poor concerning genetics [8].
Fortunately, most PCPs want to learn more about this emerging field, with up to 82%
expressing the need for more training on when to order these tests, how to counsel
patients, interpret results, and maintain privacy [1,8].
The increasing integration of genetics across medical specialties has been supported by
continuing medical education (CME) [9]. Randomized trials and pre-post studies have
shown that PCP-targeted CME regarding genetics, particularly for Mendelian forms of
breast and colon cancer, is highly effective for increasing genetics knowledge, confidence
in management, accuracy of referral decisions, and intent to change practice [10-14].
Genetics education is a moving target, with constant rapid changes. The emergence of
new multi-marker genomic tests, and now whole genome sequencing, for common
diseases increases the potential for use of genetics in a variety of primary clinical
settings, but may also pose new challenges for providers [15]. Effective CME in this area
could have broad clinical impact.
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Most prior studies of genetics CME for PCPs have reported on long-format trainings
consisting of several hours to multi-day events [10-15]. The emphasis on long-format
CME for genetics is consistent with many publications on the general effectiveness of
CME to improve a variety of educational and practice outcomes [16-20]. Very little
research has been reported on the impact of short format CME that would assume the
audience has a baseline understanding that can be built upon [21-22]. A recent study
found that family practitioners answered more questions correctly following short format
(15 minute) CME sessions compared to longer-format (1hr) CME sessions (91% vs 85%
correct rate, respectively) [21]. This study evaluates the impact of a short educational
session within the standard setting of a regional CME conference, on understanding and
confidence of PCPs regarding multi-marker genomic testing.
MATERIALS AND METHODS
Overview
Pre- and post-survey data collection and the 15 min educational intervention were
designed to fit into a 40-minute conference timeslot. We conducted this study at two
continuing education conferences that offered CME credits through the American
Medical Association and American Association of Family Practitioners, during 2013 in
the southeast U.S. Both conferences are marketed for PCP’s, and PCP’s have been the
primary audience each year.
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Content
A master's level genetic counselor led the educational content development based on a
review of literature on barriers to PCP use of genetics and prior CME efforts on genetics.
The educational session covered content areas identified as barriers by previous PCP
studies and new information regarding multi-marker genomic testing. A case study
integrating a multi-marker genetic risk report for prostate cancer, illustrated key concepts
relevant to common diseases. Topics included: chromosomes, genes, genome-wide
association studies, Single Nucleotide Polymorphisms (SNPs) and variation, genetic risk
score based on cumulative effect of multiple variant SNPs, a case example of prostate
cancer risk assessment using genetic risk score, risk assessment by family history versus
genetic risk score, assessment of tests using area under the curve (AUC), comparison of
AUC when using family history and genetic risk score to assess prostate cancer risk,
clinical utility of genetic risk scores, logistics of genomic test ordering, example genetic
risk score results from a research study, example genetic risk score result from a
commercial source, genetic discrimination and legal protections, impact of genetic results
on patient anxiety, and impact of genetic results on patient medical decision making. At
several points, the presentation included several clear distinctions between single gene
(Mendelian) genetics, versus new multi-marker genomic tests for common disease.
Surveys
Pre- and post-session surveys assessed six domains with 32 questions; demographics and
background characteristics, confidence in understanding and utilizing genetics,
effectiveness and predicted use of genetic testing, inclusion of genetic testing in clinical
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practice and perceived impact on patients, continuing education in genetics, and
perceived provision and sufficiency of standard genetic services. Questions and responses
are shown in tables 1 and 2. Paper survey packets were numbered to link pre- and post-
surveys. The content and structure of survey questions was modeled after two surveys
from the National Cancer Institute, the Physician Survey on Cancer Susceptibility Testing
(PSCS) [23] and the National Survey of Primary Care Physicians' Recommendations &
Practice for Breast, Cervical, Colorectal, & Lung Cancer Screening (NSPCP) [24].
Changes were made to questions based on content needs and feedback from pilot testing.
We conducted extensive pilot testing of the surveys and the educational session. PCPs
and other medical staff provided feedback and confirmed an average completion time of
less than 15 minutes for both surveys.
Approval
This study was reviewed and approved by institutional review board at Wake Forest
School of medicine.
Statistics
Statistical analyses were performed using SAS 9.2. Comparisons of repeated measures,
evaluated using Wilcoxon signed-rank tests (S statistic), assessed the pre-post impact of
the short-format CME session. In preparation for the primary analyses, descriptive
statistics were calculated to identify variables with pre- to post-session changes.
Secondary analyses were calculated using Fisher’s Exact Test to evaluate whether pre-
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post session changes were associated with group differences (age, gender, year of
graduation, provider type, affiliation with a medical school, or use of family history).
RESULTS.
Description of Study Sample
Approximately 150 physicians registered for the two CME conferences, 48 of whom
attended the study educational session. We collected completed surveys from 45 PCPs
attending our sessions; 93% of whom reported providing primary care for male patients
age >40 years (Table I-1). Genetics has been a common feature of their clinical practice
during the past two years, as almost all of these PCPs often ascertain cancer family
history information, about a third ordered a genetic test, nearly three quarters had a
patient ask about genetic testing, and many had a patient bring genetic results to an
appointment (Table II-2).
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Table III-1. Demographic and background characteristics
Question Response Options N
Enrolled 45
Gender
Male 23 (51%)
Female 20 (44%)
Not Reported 2 (4%)
Age
31-40 6 (13%)
41-50 10 (22%)
51-60 13 (29%)
>60 16 (36%)
Graduation Date
Before 1964 1 (2.2%)
1964-1973 6 (13%)
1974-1983 13 (29%)
1984-1993 12 (27%)
1994-2003 11 (24%)
2004-2013 2 (4%)
Provider type
Physician 39 (87%)
Physician Assistant 5 (11%)
Nurse Practitioner 1 (2%)
Specialty
Family Medicine 23 (51%)
Internal Medicine 21 (47%)
Neurology 1 (2%)
Affiliation with Medical School
Yes 9 (20%)
No 36 (80%)
Provide primary care for male patients >40 38 (93%)
Table III-2. Genetics in clinical practice
How frequently do you ask your patients about their family
history of cancer
Always 23 (53%)
Often 17 (40%)
Sometimes 3 (7%)
Rarely, never, n/a 0
In past two years, have you:
Ordered a genetic test 14 (31%)
Ordered a genetic test for cancer susceptibility 13 (29%)
Referred a patient for genetic testing 25 (56%)
Had a patient ask you about genetic testing 31 (71%)
Had a patient bring genetic test results to an appointment 9 (20%)
Seen advertising materials for genetic tests 23 (52%)
Positive response to any of the above exposure questions 37 (82%)
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Confidence
At baseline, the majority of PCP’s had low confidence in understanding genetic
principles, answering patient’s questions about genetic testing, explaining the results of a
genetic test to a patient, and recommending genetic testing; post-session, all of these
concerns shifted to the minority, for statistically significant decreases (ps<=0.002) (Table
III-3a). Understanding the science behind genetic tests was unchanged, with nearly half
expressing low confidence (Table III-3a).
Table III-3. Pre-Post Changes
3a. Confidence in understanding and utilizing genetics
A little/not at all
confident (%) P
pre post
Understanding genetic principles 58 38 0.002
Answering patient’s questions about genetic testing 69 40 <0.0001
Understanding the science behind a genetic test 47 47 n.s
Explaining the results of a genetic test to a patient 69 38 <0.0001
Recommending genetic testing 67 33 0.0003
Effectiveness and Utilization
Almost all initially believed genetic testing to assess susceptibility for inherited cancers is
very to somewhat effective, and this number declined significantly (p<0.05) following
the session (Table III-3b). There were no significant changes in likelihood to utilize
genetic services in the next two years from pre- to post- session with regard to ordering
tests and using genetic test results in medical management (Table III-3b). When asked
about changing aspects of patient care based on results of genetic testing for cancer
susceptibility, a majority endorsed all aspects both before and after the educational
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session. The only significant pre-post change was an increase in the % reporting they
would change the frequency of screening tests (p<0.05,Table III-3c).
Table III-3. Pre-Post Changes
3b. Effectiveness and predicted use of genetic testing
Very/Somewhat
(%) P
pre post
How effective do you think genetic testing is in assessing
susceptibility for inherited cancers 93 84 0.046
How likely to order a genetic test in next 2 years 45 45 n.s.
How likely to order a genetic test for cancer susceptibility next
2 years 45 47 n.s.
How likely to order a genetic test for assessing a patient's risk
for prostate cancer next 2 years 27 36 n.s.
How likely to take a patient's genetic test results into
consideration when formulating your medical management
plan (e.g. when to refer for screening tests, when to refer to a
specialist, etc.)
73 70 n.s.
Do you believe that you have time in your practice to explain
a genetic test and results to patients 34 43 n.s.
Do you believe that a majority of your patients will be willing
to pay for a genetic test 14 16 n.s.
Logistics
Questions about test logistics had large pre- to post-session changes, as less than a third
knew where to order a test, refer patients, and identify candidates for testing at baseline;
following the education session, all of these percentages shifted significantly toward the
positive side (ps<0.005) (Table III-3c). There was a non-significant increase after the
intervention in endorsement of having time to explain genetic testing results to a patient.
A majority (60%) of PCP’s initially believed patient distress would increase from the
results of a genetic test, but this decreased significantly to 27% following the session
(p=0.0007, Table III-3c).
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Table III-3. Pre-Post Changes
3c. Inclusion of genetic testing in clinical practice and
impact on patients
Yes (%) P
pre post
Which aspects of patient care would you consider changing
based on results of genetic testing for cancer susceptibility
a. Frequency of screening tests offered 69 81 0.031
b. Age of first screening test 76 84 n.s.
c. Referral to a genetic counselor or specialist 76 84 n.s.
d. Frequency of follow up appointments
scheduled 63 74 n.s.
Do you know where you can order a genetic test for prostate
cancer risk 14 67 <0.0001
Do you know where you can refer patients for genetic
counseling regarding a genetic test for prostate cancer risk 32 60 0.0034
Can you identify patients who are candidates for prostate
cancer susceptibility genetic testing 14 51 0.0007
Would you advise a patient against genetic testing based on
concern about genetic discrimination 13 4 0.125
Do you believe a majority of your patients would decline
genetic testing based on concerns about genetic discrimination 16 16 n.s.
Do you believe that results of a genetic test would increase
patient distress 60 27 0.0007
Do you believe that you have time in your practice to explain
a genetic test and results to patients 34 43 n.s.
Do you believe that a majority of your patients will be willing
to pay for a genetic test 14 16 n.s.
Opinions on Education and Implementation
A strong majority of PCPs felt they should continue to learn about new advances in
genetics and would be interested in receiving CME credit on this topic when applied to
cancer, and this did not change with CME (Table III-3d). Similarly, only about a third of
PCPs thought genetic testing for cancer susceptibility should be provided by specialists
rather than PCPs, with no changes from pre- to post-session (Table III-3e). The percent
endorsing that family history is sufficient to inform a PCP about inherited risk for cancer
fell significantly for prostate cancer (27% to 17%, p<0.05), but not for general cancer risk
(Table III-3e).
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Table III-3. Pre-Post Changes
3d. Continuing education in genetics
Very/Somewhat
(%) P
pre post
How interested would you be in receiving CME for training in
genetic risk assessment and testing for cancer susceptibility 62 65 n.s.
How important is it for you to learn about new advances in
genetics 84 84 n.s.
3e. Provision and sufficiency of standard genetic services
Strongly
agree/agree (%) P
pre post
Genetic test for cancer susceptibility should be provided by a
specialist, rather than by the primary care provider. 31 33 n.s.
A patient's family history is sufficient to inform me about
inherited cancer risk 34 20 n.s.
A patient's family history is sufficient to inform me about his
inherited risk for developing prostate cancer. 27 17 0.026
Group differences
Secondary analyses found almost no evidence that group differences (age, gender, year of
graduation, provider type, affiliation with a medical school, or use of family history) were
associated with pre-post session changes. Interestingly, PCP’s that reported less
exposure to genetics during the past two years (i.e. had not ordered a test, made referral,
had patient ask about, had patient bring results, seen advertising materials on genetics),
had greater increases in confidence answering patient questions about genetic testing
(p<0.05), compared to those with less exposure. Further inspection of the data showed
that these “low-exposure” PCPs also had lower baseline confidence levels answering
patient questions about genetic testing, and this difference was significant (p<0.005)
compared to “high exposure” PCPs; however, post-session confidence was not
significantly associated with the reported baseline exposure to genetics during the past
two years.
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DISCUSSION.
This study found that a short (15 min) in-person CME intervention delivered in the
conference setting produced significant increases in PCPs knowledge and confidence in
delivering genetic services to patients. Following the educational session, we observed
changes in three of the four most common barriers to genetics in primary care identified
by a recent systematic review, including 1) a lack of knowledge about genetics and
genetic risk assessment; 2) concern for patient anxiety; and 3) lack of access to genetics
[5]. The subset of “low-exposure” PCPs started at a lower baseline confidence and
therefore had greater potential for significant increases in their post-session confidence,
reaching the same post-session confidence as “high exposure” PCPs. The fourth
common barrier, perception of lack of time, was not altered by the session. Although
there was a 9% increase in the number of PCP’s that felt they would have time to explain
the results to a patient following the education session, this increase was not statistically
significant, and a majority still felt they would not have enough time to explain such
results. The session included an example risk report from a research study, and the
presentation script stated those results were conveyed to patients within a 15-minute
appointment; apparently this time was perceived as being too long for PCP’s, and
additional studies might wish to explore acceptable appointment time limits for PCP’s to
spend explaining genetics results to patients.
We observed mixed findings for a variety of additional barriers that relate to the four
common barriers described above. These additional barriers include test cost, concerns
regarding genetic discrimination, and confidence in the utility of risk assessment using
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either family history or genetic risk score. On test cost, most PCP’s did not believe their
patients would be willing to pay for a genetic test, and there was very little change
following the education session. Our educational session indicated that the current cost
for some services can be as low as $99, and will include risk estimation for many
conditions in a single assay. Since these PCP’s felt this price point is too high for a
majority of their patients, it would be interesting to further explore the topic of test cost in
future studies, particularly if there is downward pressure on test costs. On genetic
discrimination, significantly fewer PCP’s would advise patients against testing based on
concerns for genetic discrimination, which suggests the educational session alleviated
this concern by describing protections offered by the Genetic Information Non-
Discrimination Act (2008) and the Affordable Care Act (2010). On PCP confidence for
risk assessment by family history and genetic risk score, the results were quite mixed.
While the overall utility of family history barely changed, we observed significantly
decreased confidence in family history estimation of hereditary prostate cancer risk. This
result is consistent with the content of the session, which included discussion of the Area
Under the Curve (AUC) for PCa risk assessment by family history, and did not describe
such metrics for family history in general. Similarly, application of AUC information
may also explain the significant decrease in confidence for genetic testing to estimate
hereditary cancer risk. Together, these results provide further evidence that our study
participants were able to quickly parse and evaluate details of the presentation.
A key finding was the lack of significant change in PCP expected future use of genetics.
We observed little pre- to post-session movement in future intent to utilize genetic
information to alter patient care, and no change in predicted ordering of genetic tests in
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the next two years. Although few significant changes were observed amongst this subset
of measures, we did observe positive increases following the CME session; it is possible
that a larger sample size would provide more power to evaluate the moderate differences
observed. On the other hand, the general lack of significant differences may provide an
overall estimation of the realities facing PCP’s. Although the educational session was
able to significantly reduce most of the concerns that have been previously expressed by
PCP’s, the inability to strongly address their concerns of time and cost may pose
insurmountable barriers for change in practice among this group. We believe these
barriers, time and cost, should be central to future efforts to integrate emerging genetic
technologies in primary care.
We did not observe changes in PCPs reported understanding of the science behind
genetic tests, and this was the only survey item that was not directly addressed in the
presentation. This content exclusion was by design, as the 15-minute educational format
required that we focus on only the most essential content, and omitted some background
content on the science behind genetic tests. We embraced the concept put forth by Feero
and Green, that PCPs don’t need to become geneticists to utilize advances in genetics
[15]. By aligning educational efforts with the needs and priorities of primary providers,
we will likely have a greater impact on their continuing genetics education and clinical
practice [15].
While our study did not directly compare short format versus long format CME, our
study adds to the emerging evidence in support of the positive impact of short format
CME [21-22]. We set out to activate and build upon basic genetics knowledge that is
taught at all levels of education leading up to and through medical school. Other types of
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emerging technologies may or may not be well suited to short format CME, dependent on
whether there is already some integration of basic information in the medical education
pipeline that can be used as a foundation. Although we observed significant pre-post
changes across a group of PCPs, we also observed variation within the study population
that suggested the session had variable impact on individuals. Accordingly, the
educational approach that is optimal for most PCPs might not be optimal for every PCP.
Future studies may evaluate whether there are differences in the type of content or
specific audiences most suitable for short versus long format sessions, and even
alternative methods such as online and in-clinic.
We recognize that our study has several strengths and weaknesses. The sample size of
only 45 PCPs may be considered small, but was balanced by use of a pre-post design. It
is also possible that conference attendees might not accurately represent the general
population of PCP’s. While this is a potential issue in terms of generalizing our
observational findings, our results showing the positive impact of a short educational
intervention should be transferrable to many other CME settings. Third, we were not able
to evaluate the extent to which the intervention may change PCPs clinical practices over
time; different study designs might be needed for such an evaluation. As a strength, the
demographics of our study sample compare favorably with those of U.S. physicians. A
report based on the 2013 American Medical Association (AMA) Physician Masterfile
(n=1,096,347, all U.S. Physicians excluding medical students), indicates an age
distribution that is similar to the age distribution of participating physicians in the current
report [25]; however, our study population contained modestly increased percentages of
subjects in the 50+ age categories (22% vs 29%). Our study included a higher percentage
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of female participants when compared with the AMA Masterfile (51 vs 69% male). A
second strength is the alignment between previously identified potential primary care
barriers and objectives for the educational session [1-8]. We also believe this
intervention has strong dissemination potential; many genetic professionals should be
able to convey similar information to primary care providers using a similar format.
Overall, PCPs reported greater confidence in educational, logistical, and ethical aspects
of genetic testing for prostate cancer risk after completing the education session,
suggesting many of the previously identified barriers can be addressed through short
educational sessions. However, careful examination of cost and time barriers will be
critical to future studies of genomic practice changes. Our findings also suggest most
PCPs already have a working background of genetics, and that a short educational session
can activate and build upon existing knowledge and alter opinions by providing
supplemental information on new developments in the field. Future studies should
evaluate longer term outcomes of these initial changes, including changes in clinical
practice. Progress in this area of educational research is critical to ensuring continued
implementation of genetics in primary care practice, thus helping ensure maximum health
benefit is derived from significant investments made in basic and applied research.
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CHAPTER IV
SUMMARY AND CONCLUSIONS
The projects that make up this thesis were built around a unifying question: Can genomic
test results safely and effectively target healthcare decisions for patients and physicians?
For patients, our findings were remarkable and somewhat surprising. We placed genomic
test results for PCa risk in the hands of study participants (i.e. at-risk men), and
empowered them to make decisions regarding later PSA screening. We observed that
participants used those results to guide (i.e. target) their subsequent cancer screening. We
believe this is the first such report showing targeted screening based on multi-marker
genomic testing results. Follow-up studies in other populations and with other diseases
will be needed to evaluate the universality of these results. However, our results are
consistent with studies of high-penetrance genes, BRCA1 and HNPCC, that reported
targeting of mammography and colonoscopy, respectively; as patients received higher-
risk results, they were significantly more likely to engage in related cancer screenings
[1,2,3]. This suggests that risk level serves as a common motivation that drives uptake of
various cancer screenings. Based on the strong effects we observed, it will be important
for clinical applications to provide precise and accurate genomic risk assessment, because
risk levels matter.
On the other hand, we are aware that our findings contrast those from prior multi-marker
genetic risk assessment studies, one of which included a variety of potential disease risks
while the other focused on colorectal cancer [4,5]. Those prior studies did have
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significant differences from ours. The randomized trial of genetic and environmental risk
assessment for colorectal cancer utilized subjects that were already non-compliant with
screening, and they reported risk results as “average” or “elevated” [5]. Given our major
finding, that level of risk given to the patient made a significant impact on subsequent
screening uptake, the different results between the two studies is not surprising. The
other prior multi-marker study focused on primary outcomes of dietary and lifestyle
change in their evaluation of a direct-to-consumer genomic testing situation [4]. That
study had several major differences versus our study design; risk results were delivered
online, there was no assessment for whether subjects had engaged in disease screenings
prior to study enrollment, and the design led to the enrollment of a unique population that
had sought out direct-to-consumer testing prior to being invited to participate in the
study. Again, it is not surprising that our key findings differed from those multi-marker
studies. Yet, there was one consistent finding in each of these studies of multi-marker
testing, and in most studies of single gene testing to date; the receipt of testing results
tends to result in a reduction of overall anxiety, and there is little evidence of major
concerns for patient well-being [4,5].
For physicians, we took an indirect approach to the question of whether genomic test
results can safely and effectively target healthcare decisions. Our literature review found
many surveys in which PCPs expressed significant doubts about their ability to utilize
genetic testing information. Therefore, we sought to test an approach to improve PCP
preparedness to use this new information in the clinical setting. Following a 15-minute
educational session, confidence explaining genetic test results to patients significantly
103
increased, and a majority would change aspects of patient care. This project is important
because it shows that several specific barriers to use of genetics in primary care can be
addressed rather quickly. While time and cost remain a concern, with the availability of
direct-to-consumer testing paired with downward pressure on prices, patients may have
greater opportunity to order the test on their own, thus reducing these time and cost
barriers; however, PCP’s would need to be adequately prepared for patients that bring
such results to clinic visits. Similar to prior studies, we found that genetics is already
common in PCP clinics, further underscoring the importance of CME for PCPs on
emerging technologies such as genomics.
Just as important as “what” we did, is “why” we undertook the projects described herein.
Overall, this thesis focused on T3 stage translational research, which examines the
practical issues impacting clinical usage, thus seeking to maximize the utility that is
established by T2 research [6]. This thesis did not aim to assess the effectiveness of the
genomic markers for risk prediction in patients, which would be T2 stage research. As
described in the introduction, by the time our RCT began, a number of publications had
already established a relatively solid case for the accuracy, precision, and discriminative
power of the risk variants included in our multi-marker SNP panel; that evidence
continues to mount. In an effort to push forward to the next step of translational research,
T3, we chose to apply this new method of risk assessment in a pre-clinical study of
patients. Likewise, for PCP’s, we focused on T3 stage research. Rather than run yet
another survey of PCPs knowledge and attitudes, and rather than rush into a primary care
clinic-based intervention, we took the next step of establishing which PCP barriers could
104
or could not be addressed through a short CME session. Altogether, the rationale and
design for both of these projects was built on prior studies that constituted T1, T2 and
early T3 stage research.
Consistent with the translational nature of this thesis, the results reported herein have
clear relevance for an issue of direct clinical importance, PSA screening for PCa.
Hundreds of thousands of unnecessary biopsies and tens of thousands of treatments occur
as a result of PSA screening, and these procedures represent serious concerns. The
results of this thesis, that multi-marker testing of GWAS SNPs can be used to stratify risk
and motivate targeted PSA screening, compliment recent publications that have
suggested that genomic targeting of PSA screening to men at highest risk may reduce
overdetection and mortality.
The findings reported in this thesis show that men will utilize the risk information to
guide their own decision-making process for PSA screening, with men at highest risk
opting to pursue PSA screening much more frequently than those given lower risks.
While in some ways our finding may seem like “common sense,” as noted above, prior
genomic interventions have failed to show an impact on behavior. Furthermore, cancer
prevention studies on topics as diverse as smoking cessation and dietary changes have
often failed to observe resulting changes in behavior. While there could be significant
differences for each disease (e.g. cancer versus heart disease versus diabetes may all have
different motivational thresholds) and for each type of behavioral objective (e.g. dietary
and smoking behaviors are notoriously difficult to modify) the results reported in this
105
thesis suggest that provision of personalized genomic risk information directly to at-risk
individuals can motivate behavior changes. The potential to do so, is quite encouraging
for future efforts.
A “big picture” question is whether there is anything unique and enduring about the
results for the specific application described herein, using risk alleles of GWAS SNPs to
assess risk? The GWAS approach truly provided a surge forward in the ability to find
genetic associations with complex phenotypes. It should be mentioned that the GWAS
targets mostly common genetic variants, with minor allele frequency (MAF) ≥ 0.05. As
the ongoing 1000 Genome project (http://www.1000genomes.org/) has revealed, there are
many rare genetic variants (MAF < 0.05) that have been or are being discovered. These
rare variants are usually not included in conventional GWAS but nonetheless bear the
potential to influence complex diseases such as PCa in a non-trivial way. With the
decreasing costs associated with next-generation sequencing technologies and Exome
SNP arrays, it is expected that additional rare genetic polymorphisms will be identified
and studied for their association with PCa. Beyond next generation sequencing of
exomes and genomes, there are innovations that allow for the study of epigenetics,
proteomics, cell-free circulating DNA, and single cell molecular methods. There are
germline and somatic applications for most of those methods. Findings from all such
studies have great potential for clinical impact on PCa and other common diseases. As
the findings with greatest potential for clinical impact emerge from those studies,
translational research similar to what is reported in this thesis should be considered as a
tool to develop clinical applications from those discoveries. Therefore, regardless of the
106
specific application we evaluated, the findings of this thesis have a certain degree of
permanence for future clinical translation of basic discoveries.
107
REFERENCES
1. Lerman C, Hughes C, Croyle RT, Main D, Durham C, Snyder C, et al.
Prophylactic surgery decisions and surveillance practices one year following
BRCA1/2 testing. Prev Med 2000;31:75-80.
2. Botkin JR, Smith KR, Croyle RT, Baty BJ, Wylie JE, Dutson D, et al. Genetic
testing for a BRCA1 mutation: prophylactic surgery and screening behavior in
women 2 years post testing. Am J Med Genet A 2003;118A:201-9.
3. Collins VR, Meiser B, Ukoumunne OC, Gaff C, St John DJ, Halliday JL. The
impact of predictive genetic testing for hereditary nonpolyposis colorectal cancer:
three years after testing. Genet Med 2007; 9:290-7.
4. Bloss CS, Schork NJ, and Topol EJ. Effect of Direct-to-Consumer Genomewide
Profiling to Assess Disease Risk. N Engl J Med 2011; 364:524-534. February 10,
2011.
5. Weinberg DS, Myers RE, Keenan E, Ruth K, Sifri R, Ziring B, et al. Genetic and
environmental risk assessment and colorectal cancer screening in an average-risk
population: a randomized trial. Ann Intern Med. 2014 Oct 21;161(8):537-45. doi:
10.7326/M14-0765.
6. Turner AR, Kader AK, Xu J. Utility of genome-wide association study findings:
prostate cancer as a translational research paradigm. J Intern Med. 2012
Apr;271(4):344-52.
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CURRICULUM VITAE
Aubrey R. Turner, M.S.
Proposal Development Officer
Office of Sponsored Programs
University of North Carolina at Greensboro
Greensboro, North Carolina
Office: (336) 334-4920
E-mail: [email protected]
HOME: 506 Bent Creek Trail
Kernersville, NC 27284
(336) 992-4661
BIRTH: August, 1973
Fort Worth, TX
EDUCATION:
08/09 – Present PhD Candidate, Molecular Genetics and Genomics Program, Wake
Forest University School of Medicine, NC.
08/98 - 05/00 MS Genetic Counseling Program, University of South Carolina School
of Medicine, Columbia, SC.
08/92 - 05/96 BS Biology, University of North Carolina at Greensboro, Greensboro,
NC.
09/95 – 12/95 Foreign Travel Student, Semester at Sea Program, University of
Pittsburg.
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STAFF POSITIONS:
09/13 - present Proposal Development Officer
Office of Sponsored Programs
University of North Carolina at Greensboro
Greensboro, North Carolina
I am primarily responsible for developing and submitting large multi-
disciplinary and multi-center collaborative grant proposals to a wide range
of funding agencies and organizations. This includes tracking ongoing
funding opportunities, providing writing and editing support, and overall
coordination of complex proposals for submission. I also develop and
implement new Office of Sponsored Programs (OSP) workshops. I serve
all departments, centers, schools and community organizations at UNCG.
06/00 - 08/13 Genetic Counselor / Research Assistant
Center for Human Genomics
Wake Forest University School of Medicine
Winston-Salem, North Carolina
I managed the complete life cycle of genetic research projects. This
included concept generation, identification of funding opportunities, grant
proposal preparation, budget development, grant submission, post award
documentation, study start-up, regulatory approvals, hiring study staff,
training study staff, project management, data management, sample
management, laboratory coordination, clinic coordination, data analysis,
results interpretation, figure preparation, manuscript writing, manuscript
submission, and submission of final reports to funding agencies.
Additional responsibilities included regulatory oversight (IRB and
HIPAA) for a large center, teaching, project mentorship, community
workshops, and media relations.
07/96 - 08/98 Laboratory Technician II
Department of Comparative Medicine
Wake Forest University School of Medicine
Winston-Salem, North Carolina
In this prior position, I started up a new molecular biology laboratory as an
addition to an existing research team. I was responsible for developing and
implementing molecular assays to detect and quantify parvovirus B19
within a variety of sample types. This position involved researching the
110
existing literature, development and extensive testing of new lab
protocols, ordering supplies and equipment, documentation of labwork,
production of publication quality images, and written summaries of
experimental methods. Techniques primarily consisted of phlebotomy,
preparation of samples, DNA extraction, DNA quality control, PCR,
nested PCR, gel electophoresis, radio-labeling, fluorescent-labeling,
southern blotting, dot blotting, film processing, and phosphorimaging. I
also participated in the daily care of non-human primates, performed
phlebotomy, and assisted with procedures such as amniocentesis.
11/93 - 07/96 Undergraduate Research Assistant
Biology Department
University of North Carolina at Greensboro
Greensboro NC
Assisted with a project to localize a gene. Learned and utilized techniques
including DNA extraction, gel electrophoresis, PCR, Southern Blotting,
Radiolabeling, and Plasmid cloning.
HONORS/AWARDS:
1992 Eagle Scout, Boy Scouts of America
1993 Resident Assistant of the Semester, University of North Carolina at Greensboro
1995 President, BBB biology honor society, University of North Carolina at
Greensboro
2010 Woodbadge Training, Boy Scouts of America
2012 Coaching Hall of Fame, YMCA of Kernersville.
PUBLIC ADVOCACY AND AWARENESS:
2000 North Carolina Prostate Cancer Awareness Proclamation from Governor
Hunt
2001 - 2008 North Carolina Prostate Cancer Awareness Proclamation from Governor
Easley
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2003 Participant, Prostate Cancer Coalition of North Carolina planning
conference
2006 Participant, Prostate Cancer Coalition of North Carolina planning
conference
COMMUNITY RELATIONS:
2006 - 2012 Organizer, Community and Student Lab Tours, Center for Human
Genomics, Wake Forest University School of Medicine
EDITORIAL TASKS:
2001 Associate Editor, National Society of Genetic Counselors website
2002- 2004 Editor, National Society of Genetic Counselors website (www.nsgc.org)
PROFESSIONAL COMMITTEE ACTIVITIES:
2002 Co-Chair, Meeting of the North Carolina Medical Genetics Association,
Wake Forest University School of Medicine, Winston-Salem, NC, April
26.
2003 Co-Chair, National Society of Genetic Counselors Region III Annual
Educational Conference, Asheville, NC.March 22.
2002- 2004 Liaison to the Genetic Resources on the Web (GROW), on behalf of the
National Society of Genetic Counselors
2004- 2006 Liaison to the National Coalition for Healthcare Professional Education in
Genetics (NCHPEG), on behalf of the National Society of Genetic
Counselors
112
2005- 2006 Board Member and Communications Committee Chair, National Society
of Genetic Counselors (NSGC)
THESIS COMMITTEE/GRADUATE STUDENTS:
2002 Tamara Adams, BS
Practicum Supervisor
The University of North Carolina at Greensboro, Gerontology Program
2002 - 2003 Angela Schwab, MS
Chair, MS Thesis Committee
“The Hereditary Nature of Prostate Cancer: What A Patient Needs to
Know”
The University of North Carolina at Greensboro, Genetic Counseling
Program
2007 - 2008 Linda Smith, MS
Chair, MS Thesis Committee
“How African American Men Share Prostate Cancer Risk with Family
Members: A Pilot Study”
The University of North Carolina at Greensboro, Genetic Counseling
Program
2012 - 2013 Elizabeth Watson, RN
Advisor, MS Thesis Project
“Caucasian Men’s Awareness of PSA Screening”
The University of North Carolina at Charlotte, Nurse Practitioner Program
2013 Alison Witkowski, MD
Advisor, Medical Student Summer Research Project
“Attitudes of Primary Care Providers Toward Genomic Testing”
Wake Forest University School of Medicine, MD Program
2013 Elizabeth Crowder, MD
Advisor, Medical Student Summer Research Project
“Attitudes of Primary Care Providers Toward Genomic Testing”
Wake Forest University School of Medicine, MD Program
INVITED TALKS
2000 “Genetic Counseling for Prostate Cancer”. Forsyth County USTOO prostate
cancer support group, Forsyth Medical Center, Winston-Salem, NC. October 9.
113
2001 “Genetic Counseling for Prostate Cancer”. National Society of Genetic
Counselors Region III Annual Educational Conference, Columbia, SC, March 31.
2002 “Hereditary Prostate Cancer”. Cancer Support Group, Comprehensive Cancer
Center, Wake Forest University Baptist Medical Center, Winston-Salem, NC.
January 24.
2002 “The Role of a Genetic Counselor in a Research Setting”
The University of North Carolina at Greensboro Genetic Counseling Program
Greensboro, NC
2002 “Hereditary Prostate Cancer”. High Point USTOO prostate cancer support group,
High Point Regional Hospital. April 25.
2002 “Hereditary Prostate Cancer”. Hickory USTOO prostate cancer support group,
Catawba Valley Memorial Hosptial, Hickory, NC. September 12.
2003 “Hereditary Prostate Cancer”. Greenville USTOO prostate cancer support group,
Pitt County Memorial Hospital, Greenville, NC. February 11.
2004 “The Role of a Genetic Counselor in a Research Setting” Genetic Counseling
Program, University of South Carolina School of Medicine, Columbia, SC.
2005 “Molecular Epidemiology of Prostate Cancer”. Quarterly Meeting of the North
Carolina Medical Genetics Association, Wake Forest University School of
Medicine, Winston-Salem, NC, April 22.
2006 “The Role of a Genetic Counselor in a Research Setting” Genetic Counseling
Program, University of South Carolina School of Medicine, Columbia, SC.
114
2007 “Genetic Counseling for Prostate Cancer”. Forsyth County USTOO prostate
cancer support group, Forsyth Medical Center, Winston-Salem, NC.
2014 “Careers in Genetics”. UNCG Biology Freshman Introductory Course. (M.
Schug)
2015 “Careers in Genetics”. UNCG Biology Freshman Introductory Course. (S. Faeth)
2015 “Careers in Genetics”. UNCG Biology Senior Course. (R. Cannon)
PUBLICATIONS
Journal Articles:
1. Chang B, Zheng SL, Hawkins GA, Isaacs SD, Wiley KE, Turner A, Carpten JD,
Bleecker ER, Walsh PC, Trent JM, Meyers DA, Isaacs WB, Xu J. Polymorphic
GGC repeats in the androgen receptor gene are associated with hereditary and
sporadic prostate cancer risk. Hum Genet 2002; 110:122-129.
2. Chang B, Zheng SL, Hawkins GA, Isaacs SD, Wiley KE, Turner A, Carpten JD,
Bleecker ER, Walsh PC, Trent JM, Meyers DA, Isaacs WB, Xu J. Joint effect of
HSD3B1 and HSD3B2 genes is associated with hereditary and sporadic prostate
cancer susceptibility. Cancer Res 2002; 62:1784-9.
3. Xu J, Zheng SL, Turner A, Isaacs SD, Wiley K, Hawkins GA, Chang B,
Bleecker ER, Walsh PC, Meyers DA, Isaacs WI. Associations between hOGG1
sequence variants and prostate cancer susceptibility. Cancer Res 2002; 62:2253-7
4. Xu J, Zheng SL, Komiya A, Mychaleckyj J, Isaacs SD, Hu JJ, Sterling D, Lange
E, Hawkins GA, Turner A, Ewing CM, Johnson JR, Faith DA, Suzuki H,
Bujnovszky P, Wiley KE, DeMarzo A, Bova GS, Chang B, Hall MC,
McCullough DL, Partin AW, Isaacs WB, Meyers DA. Germline mutations and
sequence variants of the macrophage scavenger receptor 1 gene are associated
with prostate cancer risk. Nature Genet 2002; 32:321-5.
5. Zheng SL, Chang B, Isaacs SD, Turner A, Wiley KE, Bleecker ER, Walsh PC,
Meyers DA, Isaacs WB, Xu J. Sequence variants of α-Methylacyl-CoA
Racemase are associated with prostate cancer risk. Cancer Res 2002; 62:6485-
6488.
115
6. Xu J, Turner A, Little J, Bleecker ER, Meyers DA. Positive results in
association studies are associated with departure from Hardy-Weinberg
Equilibrium: hint for genotyping error? Human Genet 2002; 111:573-574.
7. Xu J, Zheng SL, Komiya A, Mychaleckyj JC, Isaacs DS, Chang B, Turner AR,
Ewing CM, Wiley KE, Hawkins GA, Bleecker ER. Walsh PC, Meyers DA, Isaacs
WB. Common sequence variants of the macrophage scavenger receptor 1 gene
are associated with prostate cancer risk. Am J Hum Genet 2003 72:208-12.
8. Zheng SL, Mychaleckyj JC, Hawkins GA, Isaacs SD, Wiley KE, Turner A,
Chang B, von Kap-Herr C, Carpten JD, Pettenati M, Bleecker ER, Walsh PC,
Trent JM, Meyers DA, Isaacs WB, Xu J. Evaluation of DLC1 as a prostate
cancer susceptibility gene: mutation screen and association study. Mut Res 2002.
Jul 25;528(1-2):45-53.
9. Chang B, Zheng SL, Isaacs DS, Hawkins GA, Turner A, Wiley KE, Bleecker
ER, Walsh PC, Meyers DA, Isaacs WB, Xu J. Polymorphisms in the CYP1A1
gene are associated with prostate cancer risk. Int J Can 2003 Sep 1;106(3):375-8.
10. Chang B, Zheng SL, Isaacs SD, Turner AR, Bleecker ER, Walsh PC, Meyers
DA, Isaacs WB, Xu J. Evaluation of SRD5A2 sequence variants insusceptibility
to hereditary and sporadic prostate cancer. The Prostate 2003. Jun 15;56(1):37-
44.
11. Chang B, Zheng SL, Isaacs SD, Turner A, Hawkins GA, Wiley KE, Bleecker
ER, Walsh PC, Meyers DA, Isaacs WB, Xu J. Polymorphisms in the CYP1B1
gene are associated with increased risk of prostate cancer. British J of Cancer
2003 Oct 20;89(8):1524-9.
12. Zheng SL, Augustsson-Balter K, Chang B, Hedelin M, Li L, Adami HO, Bensen
J, Li G, Johnasson JE, Turner AR, Adams TS, Meyers DA, Isaacs WB, Xu J,
Gronberg H. Sequence variants of toll-like receptor 4 are associated with prostate
cancer risk: results from the CAncer Prostate in Sweden Study. Cancer Res. 2004
Apr 15;64(8):2918-22.
13. Chang BL, Zheng SL, Isaacs SD, Wiley KE, Turner A, Li G, Walsh PC, Meyers
DA, Isaacs WB, Xu J. A polymorphism in the CDKN1B gene is associated with
increased risk of hereditary prostate cancer. Cancer Res. 2004 Mar 15;64(6):1997-
9.
14. Sun J, Hedelin M, Zheng SL, Adami HO, Bensen J, Augustsson-Balter K, Chang
B, Adolfsson J, Adams T, Turner A, Meyers DA, Isaacs WB, Xu J, Gronberg H.
116
Interleukin-6 sequence variants are not associated with prostate cancer risk.
Cancer Epidemiol Biomarkers Prev. 2004 Oct;13(10):1677-9.
15. Xu J, Thornburg T, Turner AR, Vitolins M, Case D, Shadle J, Hinson L, Sun J,
Liu W, Chang B, Adams TS, Zheng SL, Torti FM. Serum levels of phytanic acid
are associated with prostate cancer risk. Prostate. 2005 May 15;63(3):209-14.
16. Sun J, Wiklund F, Zheng SL, Chang B, Balter K, Li L, Johansson JE, Li G,
Adami HO, Liu W, Tolin A, Turner AR, Meyers DA, Isaacs WB, Xu J,
Gronberg H. Sequence variants in Toll-like receptor gene cluster (TLR6-TLR1-
TLR10) and prostate cancer risk. J Natl Cancer Inst. 2005 Apr 6;97(7):525-32.
17. Chang BL, Gillanders EM, Isaacs SD, Wiley KE, Adams T, Turner AR, Zheng
SL, Meyers DA, Carpten JD, Walsh PC, Trent JM, Xu J, Isaacs WB. Evidence for
a general cancer susceptibility locus at 3p24 in families with hereditary prostate
cancer. Cancer Lett. 2005 Mar 10;219(2):177-82.
18. Sun J, Wiklund F, Zheng SL, Chang B, Bälter K, Li L, Johansson JE, Li G,
Adami HO, Liu W, Tolin A, Turner AR, Meyers DA, Isaacs WB, Xu J,
Grönberg H. Sequence variants in Toll-like receptor gene cluster (TLR6-TLR1-
TLR10) and prostate cancer risk. J Natl Cancer Inst. 2005 Apr 6;97(7):525-32.
19. Xu J, Dimitrov L, Chang BL, Adams TS, Turner AR, Meyers DA, Eeles RA,
Easton DF, Foulkes WD, Simard J, Giles GG, Hopper JL, Mahle L, Moller P,
Bishop T, Evans C, Edwards S, Meitz J, Bullock S, Hope Q, Hsieh CL, Halpern J,
Balise RN, Oakley-Girvan I, Whittemore AS, Ewing CM, Gielzak M, Isaacs SD,
Walsh PC, Wiley KE, Isaacs WB, Thibodeau SN, McDonnell SK, Cunningham
JM, Zarfas KE, Hebbring S, Schaid DJ, Friedrichsen DM, Deutsch K, Kolb S,
Badzioch M, Jarvik GP, Janer M, Hood L, Ostrander EA, Stanford JL, Lange EM,
Beebe-Dimmer JL, Mohai CE, Cooney KA, Ikonen T, Baffoe-Bonnie A,
Fredriksson H, Matikainen MP, Tammela TLj, Bailey-Wilson J, Schleutker J,
Maier C, Herkommer K, Hoegel JJ, Vogel W, Paiss T, Wiklund F, Emanuelsson
M, Stenman E, Jonsson BA, Gronberg H, Camp NJ, Farnham J, Cannon-Albright
LA, Seminara D; The ACTANE Consortium. A combined genomewide linkage
scan of 1,233 families for prostate cancer-susceptibility genes conducted by the
international consortium for prostate cancer genetics. Am J Hum Genet. 2005
Aug;77(2):219-29. Epub 2005 Jun 29.
20. Xu J, Lowey J, Wiklund F, Sun J, Lindmark F, Hsu FC, Dimitrov L, Chang B,
Turner AR, Liu W, Adami HO, Suh E, Moore JH, Zheng SL, Isaacs WB, Trent
JM, Grönberg H. The interaction of four genes in the inflammation pathway
significantly predicts prostate cancer risk. Cancer Epidemiol Biomarkers Prev.
2005 Nov;14(11 Pt 1):2563-8.
117
21. Sun J, Hsu FC, Turner AR, Zheng SL, Chang BL, Liu W, Isaacs WB, Xu J.
Meta-analysis of association of rare mutations and common sequence variants in
the MSR1 gene and prostate cancer risk. Prostate. 2006 May 15;66(7):728-37.
22. Sun J, Wiklund F, Hsu FC, Bälter K, Zheng SL, Johansson JE, Chang B, Liu W,
Li T, Turner AR, Li L, Li G, Adami HO, Isaacs WB, Xu J, Grönberg H.
Interactions of sequence variants in interleukin-1 receptor-associated kinase4 and
the toll-like receptor 6-1-10 gene cluster increase prostate cancer risk. Cancer
Epidemiol Biomarkers Prev. 2006 Mar;15(3):480-5.
23. Thornburg T, Turner AR, Chen YQ, Vitolins M, Chang B, Xu J. Phytanic acid,
AMACR and prostate cancer risk. Future Oncol. 2006 Apr;2(2):213-23. Review.
24. Liu W, Chang B, Sauvageot J, Dimitrov L, Gielzak M, Li T, Yan G, Sun J, Sun J,
Adams TS, Turner AR, Kim JW, Meyers DA, Zheng SL, Isaacs WB, Xu J.
Comprehensive assessment of DNA copy number alterations in human prostate
cancers using Affymetrix 100K SNP mapping array. Genes Chromosomes
Cancer. 2006 Nov;45(11):1018-32.
25. Zheng SL, Liu W, Wiklund F, Dimitrov L, Bälter K, Sun J, Adami HO,
Johansson JE, Sun J, Chang B, Loza M, Turner AR, Bleecker ER, Meyers DA,
Carpten JD, Duggan D, Isaacs WB, Xu J, Grönberg H. A comprehensive
association study for genes in inflammation pathway provides support for their
roles in prostate cancer risk in the CAPS study. Prostate. 2006 Oct 1;66(14):1556-
64.
26. Schaid DJ, McDonnell SK, Zarfas KE, Cunningham JM, Hebbring S, Thibodeau
SN, Eeles RA, Easton DF, Foulkes WD, Simard J, Giles GG, Hopper JL, Mahle
L, Moller P, Badzioch M, Bishop DT, Evans C, Edwards S, Meitz J, Bullock S,
Hope Q, Guy M, Hsieh CL, Halpern J, Balise RR, Oakley-Girvan I, Whittemore
AS, Xu J, Dimitrov L, Chang BL, Adams TS, Turner AR, Meyers DA,
Friedrichsen DM, Deutsch K, Kolb S, Janer M, Hood L, Ostrander EA, Stanford
JL, Ewing CM, Gielzak M, Isaacs SD, Walsh PC, Wiley KE, Isaacs WB, Lange
EM, Ho LA, Beebe-Dimmer JL, Wood DP, Cooney KA, Seminara D, Ikonen T,
Baffoe-Bonnie A, Fredriksson H, Matikainen MP, Tammela TL, Bailey-Wilson J,
Schleutker J, Maier C, Herkommer K, Hoegel JJ, Vogel W, Paiss T, Wiklund F,
Emanuelsson M, Stenman E, Jonsson BA, Grönberg H, Camp NJ, Farnham J,
Cannon-Albright LA, Catalona WJ, Suarez BK, Roehl KA. Pooled genome
linkage scan of aggressive prostate cancer: results from the International
Consortium for Prostate Cancer Genetics. Hum Genet. 2006 Nov;120(4):471-85.
118
27. Liu W, Chang B, Li T, Dimitrov L, Kim S, Kim JW, Turner AR, Meyers DA,
Trent JM, Zheng SL, Isaacs WB, Xu J. Germline copy number polymorphisms
involving larger than 100 kb are uncommon in normal subjects. Prostate. 2007
Feb 15;67(3):227-33.
28. Sun J, Liu W, Adams TS, Sun J, Li X, Turner AR, Chang B, Kim JW, Zheng SL,
Isaacs WB, Xu J. DNA copy number alterations in prostate cancers: a combined
analysis of published CGH studies. Prostate. 2007 May 15;67(7):692-700.
29. Chang BL, Liu W, Sun J, Dimitrov L, Li T, Turner AR, Zheng SL, Isaacs WB,
Xu J. Integration of somatic deletion analysis of prostate cancers and germline
linkage analysis of prostate cancer families reveals two small consensus regions
for prostate cancer genes at 8p. Cancer Res. 2007 May 1;67(9):4098-103.
30. Sun J, Turner A, Xu J, Gronberg H, Isaacs W. Genetic variability in inflammation
pathways and prostate cancer risk. Urol Oncol 2007; 25:250-9.
31. Liu W, Chang BL, Cramer S, Koty PP, Li T, Sun J, Turner AR, Von Kap-Herr
C, Bobby P, Rao J, Zheng SL, Isaacs WB, Xu J. Deletion of a small consensus
region at 6q15, including the MAP3K7 gene, is significantly associated with high-
grade prostate cancers. Clin Cancer Res. 2007 Sep 1;13(17):5028-33.
32. Liu W, Ewing CM, Chang BL, Li T, Sun J, Turner AR, Dimitrov L, Zhu Y, Sun
J, Kim JW, Zheng SL, Isaacs WB, Xu J. Multiple genomic alterations on 21q22
predict various TMPRSS2/ERG fusion transcripts in human prostate cancers.
Genes Chromosomes Cancer. 2007 Nov;46(11):972-80.
33. Zheng SL, Sun J, Cheng Y, Li G, Hsu FC, Zhu Y, Chang BL, Liu W, Kim JW,
Turner AR, Gielzak M, Yan G, Isaacs SD, Wiley KE, Sauvageot J, Chen HS,
Gurganus R, Mangold LA, Trock BJ, Gronberg H, Duggan D, Carpten JD, Partin
AW, Walsh PC, Xu J, Isaacs WB. Association between two unlinked loci at 8q24
and prostate cancer risk among European Americans. J Natl Cancer Inst. 2007 Oct
17;99(20):1525-33. Epub 2007 Oct 9.
34. Chen SH, Sun J, Dimitrov L, Turner AR, Adams TS, Meyers DA, Chang BL,
Zheng SL, Grönberg H, Xu J, Hsu FC. A support vector machine approach for
detecting gene-gene interaction. Genet Epidemiol. 2008 Feb;32(2):152-67.
35. Zheng SL, Sun J, Wiklund F, Smith S, Stattin P, Li G, Adami HO, Hsu FC, Zhu
Y, Bälter K, Kader AK, Turner AR, Liu W, Bleecker ER, Meyers DA, Duggan
119
D, Carpten JD, Chang BL, Isaacs WB, Xu J, Grönberg H. New England Journal
of Med. 2008 Feb 28;358(9):910-9.
36. Hsu FC, Lindström S, Sun J, Wiklund F, Chen SH, Adami HO, Turner AR, Liu
W, Bälter K, Kim JW, Stattin P, Chang BL, Isaacs WB, Xu J, Grönberg H, Zheng
SL. A multigenic approach to evaluating prostate cancer risk in a systematic
replication study. Cancer Genet Cytogenet. 2008 Jun;183(2):94-8.
37. Liu W, Xie CC, Zhu Y, Li T, Sun J, Cheng Y, Ewing CM, Dalrymple S, Turner
AR, Sun J, Isascs JT, Chang BL, Zheng SL, Isaacs WB, Xu J. Homozygous
deletions and recurrent amplifications implicate new genes involved in prostate
cancer. Neoplasia. 2008 Aug;10(8):897-907.
38. Sun J, Zheng SL, Wiklund F, Isaacs SD, Purcell LD, Gao Z, Hsu FC, Kim ST,
Liu W, Zhu Y, Stattin P, Adami HO, Wiley KE, Dimitrov L, Sun J, Li T,
Turner AR, Adams TS, Adolfsson J, Johansson JE, Lowey J, Trock BJ, Partin
AW, Walsh PC, Trent JM, Duggan D, Carpten J, Chang BL, Grönberg H, Isaacs
WB, Xu J. Evidence for two independent prostate cancer risk-associated loci in
the HNF1B gene at 17q12. Nat Genet. 2008 Aug 31.
39. Xu J, Isaacs SD, Sun J, Li G, Wiley KE, Zhu Y, Hsu FC, Wiklund F, Turner
AR, Adams TS, Liu W, Trock BJ, Partin AW, Chang B, Walsh PC, Grönberg H,
Isaacs W, Zheng S. Association of prostate cancer risk variants with
clinicopathologic characteristics of the disease. Clin Cancer Res. 2008 Sep
15;14(18):5819-24.
40. Sun J, Zheng SL, Wiklund F, Isaacs SD, Li G, Wiley KE, Kim ST, Zhu Y, Zhang
Z, Hsu FC, Turner AR, Stattin P, Liu W, Kim JW, Duggan D, Carpten J, Isaacs
W, Grönberg H, Xu J, Chang BL. Sequence variants at 22q13 are associated with
prostate cancer risk. Cancer Res. 2009 Jan 1;69(1):10-5.
41. Chang BL, Cramer SD, Wiklund F, Isaacs SD, Stevens VL, Sun J, Smith S, Pruett
K, Romero LM, Wiley KE, Kim ST, Zhu Y, Zhang Z, Hsu FC, Turner AR,
Adolfsson J, Liu W, Kim JW, Duggan D, Carpten J, Zheng SL, Rodriguez C,
Isaacs WB, Grönberg H, Xu J. Fine mapping association study and functional
analysis implicate a SNP in MSMB at 10q11 as a causal variant for prostate
cancer risk. Hum Mol Genet. 2009 Apr 1;18(7):1368-75.
42. Zheng SL, Sun J, Wiklund F, Gao Z, Stattin P, Purcell LD, Adami HO, Hsu FC,
Zhu Y, Adolfsson J, Johansson JE, Turner AR, Adams TS, Liu W, Duggan D,
Carpten JD, Chang BL, Isaacs WB, Xu J, Grönberg H. Genetic variants and
family history predict prostate cancer similar to prostate-specific antigen. Clin
Cancer Res. 2009 Feb 1;15(3):1105-11.
120
43. Zheng SL, Stevens VL, Wiklund F, Isaacs SD, Sun J, Smith S, Pruett K, Wiley
KE, Kim ST, Zhu Y, Zhang Z, Hsu FC, Turner AR, Johansson JE, Liu W, Kim
JW, Chang BL, Duggan D, Carpten J, Rodriguez C, Isaacs W, Grönberg H, Xu J.
Two independent prostate cancer risk-associated Loci at 11q13. Cancer Epidemiol
Biomarkers Prev. 2009 Jun;18(6):1815-20.
44. Xu J, Kibel AS, Hu JJ, Turner AR, Pruett K, Zheng SL, Sun J, Isaacs SD, Wiley
KE, Kim ST, Hsu FC, Wu W, Torti FM, Walsh PC, Chang BL, Isaacs WB.
Prostate cancer risk associated loci in African Americans. Cancer Epidemiol
Biomarkers Prev. 2009 Jul;18(7):2145-9.
45. Sun J, Kader AK, Hsu FC, Kim ST, Zhu Y, Turner AR, Jin T, Zhang Z,
Adolfsson J, Wiklund F, Zheng SL, Isaacs WB, Grönberg H, Xu J. Inherited
genetic markers discovered to date are able to identify a significant number of
men at considerably elevated risk for prostate cancer. Prostate. 2011 Mar
1;71(4):421-30.
46. Turner AR, Kader AK, Xu J. Utility of genome-wide association study findings:
prostate cancer as a translational research paradigm. J Intern Med. 2012
Apr;271(4):344-52.
47. Liu W, Lindberg J, Sui G, Luo J, Egevad L, Li T, Xie C, Wan M, Kim ST, Wang
Z, Turner AR, Zhang Z, Feng J, Yan Y, Sun J, Bova GS, Ewing CM, Yan G,
Gielzak M, Cramer SD, Vessella RL, Zheng SL, Grönberg H, Isaacs WB, Xu J.
Identification of novel CHD1-associated collaborative alterations of genomic
structure and functional assessment of CHD1 in prostate cancer. Oncogene. 2012
Aug 30;31(35):3939-48.
48. Kim JW, Kim ST, Turner AR, Young T, Smith S, Liu W, Lindberg J, Egevad L,
Gronberg H, Isaacs WB, Xu J. Identification of new differentially methylated
genes that have potential functional consequences in prostate cancer. PLoS One.
2012;7(10)
49. Kader AK, Sun J, Reck BH, Newcombe PJ, Kim ST, Hsu FC, D'Agostino RB Jr,
Tao S, Zhang Z, Turner AR, Platek GT, Spraggs CF, Whittaker JC, Lane BR,
Isaacs WB, Meyers DA, Bleecker ER, Torti FM, Trent JM, McConnell JD, Zheng
SL, Condreay LD, Rittmaster RS, Xu J. Potential impact of adding genetic
markers to clinical parameters in predicting prostate biopsy outcomes in men
following an initial negative biopsy: findings from the REDUCE trial. Eur Urol.
2012 Dec;62(6):953-61.
50. Lin X, Qu L, Chen Z, Xu C, Ye D, Shao Q, Wang X, Qi J, Chen Z, Zhou F, Wang
M, Wang Z, He D, Wu D, Gao X, Yuan J, Wang G, Xu Y, Wang G, Dong P, Jiao
Y, Yang J, Ou-Yang J, Jiang H, Zhu Y, Ren S, Zhang Z, Yin C, Wu Q, Zheng Y,
Turner AR, Tao S, Na R, Ding Q, Lu D, Shi R, Sun J, Liu F, Zheng SL, Mo Z,
121
Sun Y, Xu J. A novel germline mutation in HOXB13 is associated with prostate
cancer risk in Chinese men. Prostate. 2013 Jan;73(2):169-75.
51. Palmer NR, Tooze JA, Turner AR, Xu J, Avis NE. African American prostate
cancer survivors' treatment decision-making and quality of life. Patient Educ
Couns. 2013 Jan;90(1):61-8.
Book Chapters
1. Chang B, Turner A, Xu J, and Isaacs WB (2003). Hereditary prostate cancer: the
search for major genes and the role of genes involved in androgen action. In: Ross
RK, (Ed.) Hormones, genes and cancer.
2. Turner A, Xu J, and Isaacs WB (2003). Hereditary prostate cancer. In:
Handbook of prostate cancer. Academic Press.
3. Turner A, Feng J, Liu W, Kim JW, and Xu J (2013). Prostate Cancer. In:
Genomics and Personalized Medicine, 2nd Edition. Academic Press.
