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Effective Project Management of a Pan-African Cancer Research Network: Men of African Descent and Carcinoma of the Prostate (MADCaP)

Authors:
  • Hopital General Grand Yoff, Dakar, Senegal

Abstract and Figures

Purpose: Health research in low- and middle-income countries can generate novel scientific knowledge and improve clinical care, fostering population health improvements to prevent premature death. Project management is a critical part of the success of this research, applying knowledge, skills, tools, and techniques to accomplish required goals. Here, we describe the development and implementation of tools to support a multifaceted study of prostate cancer in Africa, focusing on building strategic and operational capacity. Methods: Applying a learning organizational framework, we developed and implemented a project management toolkit (PMT) that includes a management process flowchart, a cyclical center-specific schedule of activities, periodic reporting and communication, and center-specific monitoring and evaluation metrics. Results: The PMT was successfully deployed during year one of the project with effective component implementation occurring through periodic cycles of dissemination and feedback to local center project managers. A specific evaluation was conducted 1 year after study initiation to obtain enrollment data, evaluate individual quality control management plans, and undertake risk log assessments and follow-up. Pilot data obtained identified areas in which centers required mentoring, strengthening, and capacity development. Strategies were implemented to improve project goals and operational capacity through local problem solving, conducting quality control checks and following compliancy with study aims. Moving forward, centers will perform quarterly evaluations and initiate strengthening measures as required. Conclusion: The PMT has fostered the development of both strategic and operational capacity across project centers. Investment in project management resources is essential to ensuring high-quality, impactful health research in low- and middle-income countries.
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1 jgo.org JGO – Journal of Global Oncology
© 2018 by American Society of Clinical Oncology Licensed under the Creative Commons Attribution 4.0 License
Development, Evaluation, and
Implementation of a Pan-African Cancer
Research Network: Men of African
Descent and Carcinoma of the Prostate
INTRODUCTION
In 2015, approximately 460,000 individuals
died of cancer in sub-Saharan Africa (SSA).1 By
2035, that figure is expected to more than dou-
ble, partly as a result of increasing life spans (as
a result of decreasing infectious disease mortal-
ity), lifestyle changes associated with increasing
cancer risk (eg, smoking, obesity), and limited
capacities for cancer prevention and treatment.
The United Nations Sustainable Development
Goals, adopted in 2015, set specific targets for
reducing global premature mortality associated
with noncommunicable diseases, including can-
cer.2
Cancer of the prostate (CaP) is the leading non-
cutaneous cancer in men worldwide,3-5 and
worldwide, men of African descent have higher
CaP incidence and mortality than men of other
races or ethnicities.6 In SSA, the number of CaP
deaths per year is predicted to increase from
39,000 in 2015 to 76,000 by 2035.4 CaP is more
frequently diagnosed at a late (usually incurable)
stage in SSA than in other parts of the world,7-10
and little is known about the roles of exposure
or genetic susceptibility loci in its etiology. CaP
has the highest heritability of the common can-
cers.11,12 Many genetic susceptibility loci have
been identified in men of European and Asian
descent; however, these loci have not gener-
ally been validated in men of African descent,
underscoring the need for these studies in
African-descent populations.
Purpose Cancer of the prostate (CaP) is the leading cancer among men in sub-Saharan Africa
(SSA). A substantial proportion of these men with CaP are diagnosed at late (usually incurable)
stages, yet little is known about the etiology of CaP in SSA.
Methods We established the Men of African Descent and Carcinoma of the Prostate Network,
which includes seven SSA centers partnering with five US centers to study the genetics and
epidemiology of CaP in SSA. We developed common data elements and instruments, regulatory
infrastructure, and biosample collection, processing, and shipping protocols. We tested this in-
frastructure by collecting epidemiologic, medical record, and genomic data from a total of 311
patients with CaP and 218 matched controls recruited at the seven SSA centers. We extracted
genomic DNA from whole blood, buffy coat, or buccal swabs from 265 participants and shipped it
to the Center for Inherited Disease Research (Baltimore, MD) and the Centre for Proteomics and
Genomics Research (Cape Town, South Africa), where genotypes were generated using the UK
Biobank Axiom Array.
Results We used common instruments for data collection and entered data into the shared data-
base. Double-entered data from pilot participants showed a 95% to 98% concordance rate, sug-
gesting that data can be collected, entered, and stored with a high degree of accuracy. Genotypes
were obtained from 95% of tested DNA samples (100% from blood-derived DNA samples) with
high concordance across laboratories.
Conclusion We provide approaches that can produce high-quality epidemiologic and genomic
data in multicenter studies of cancer in SSA.
© 2018 by American Society of Clinical Oncology Licensed under the Creative Commons Attribution 4.0 License
abstract
original report
Caroline Andrews
Brian Fortier
Amy Hayward
Ruth Lederman
Lindsay Petersen
Jo McBride
Desiree C. Petersen
Olabode Ajayi
Paidamoyo Kachambwa
Moleboheng Seutloali
Aubrey Shoko
Mamokhosana Mokhosi
Reinhard Hiller
Marcia Adams
Chrissie Ongaco
Elizabeth Pugh
Jane Romm
Tameka Shelford
Frank Chinegwundoh
Ben Adusei
Sunny Mante
Nana Yaa Snyper
Ilir Agalliu
David W. Lounsbury
Thomas Rohan
Alex Orfanos
Yuri Quintana
Judith S. Jacobson
Alfred I. Neugut
Edward Gelmann
Joseph Lachance
Cherif Dial
Thierno Amadou Diallo
(continued)
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Copyright © 2018 American Society of Clinical Oncology. All rights reserved.
To address those gaps in our knowledge, we
developed a multicenter Men of African Descent
and Carcinoma of the Prostate (MADCaP) Net-
work. This article describes the scientific prin-
ciples, research methods, and standardized
procedures and protocols developed by MAD-
CaP investigators for our CaP research in SSA.
These approaches may serve as a model for
other sorely needed cancer research studies in
SSA.
METHODS
Network Organization
Figure 1 depicts the MADCaP Network’s organi-
zational structure, which has seven participant
accrual centers in Ghana, Nigeria, Senegal, and
South Africa with several SSA and US centers
partnered to enhance capacity building and
offer mentorship. Additional research partners
include the National Cancer Institute (Bethesda,
MD), the Center for Inherited Disease Research
(CIDR; Baltimore, MD), the Centre for Proteom-
ics and Genomics Research (CPGR; Cape Town,
South Africa), and the Georgia Institute of Tech-
nology (Atlanta, GA).
Study Design
In this clinic-based case-control study, eligible
participants were men older than age 30 years
residing in each MADCaP center’s catchment
area and self-identifying as black African with
no known or self-reported European, Middle
Eastern, or Asian ancestry. All participants were
required to reside within a prespecified catch-
ment area surrounding the clinical ascertain-
ment sites. Information about town and country
of birth and residential location for the past
10 years was queried. As with patients, the lower
age limit for controls was 30 years. The lower
bound for inclusion was set at age 30 years on
the basis of the earliest age of a CaP diagnosis
recorded in any of our centers, which occurred
before age 40 years. Eligible patients with CaP,
ascertained in urology and oncology clinics or
through primary referral, must have had a his-
tologically confirmed first primary CaP of any
stage, grade, or pathologic classification. Patient
status was confirmed by pathologic diagnosis
and medical record review using a standardized
abstraction form. Only incident patients, whose
first diagnosis of CaP occurred no more than
6 months before study contact, were eligible.
Men previously diagnosed with cancer at any
other center were excluded.
Controls were frequency matched to patient
cases by age, ascertained through nonurology
and nononcology clinics (including orthopedics,
internal medicine, family medicine, general sur-
gery, GI, geriatrics, neurosurgery, dermatology,
cardiology, and ophthalmology clinics) at partic-
ipating SSA institutions (Fig 1) residing within
the catchment area from which the patients
with CaP were drawn, and had no history of
cancer. The controls represent a wide range of
other diagnoses or no diagnosis and may have
received examinations including cancer screen-
ing, particularly digital rectal exams or pros-
tate-specific antigen tests. They may have been
diagnosed with cancer subsequently but did not
arrive at the clinics specifically for cancer diag-
nosis, treatment, or screening. Ascertainment of
all participants was undertaken without regard to
family history of cancer or any other traits.
Regulatory Requirements, Ethical Considerations,
and Oversight
To comply with local, national, and international
regulations governing human subjects research
and data sharing, we implemented a series of
regulatory protocols. Center and study staff cer-
tifications were obtained including US-required
federal-wide assurances,13 System for Award
Management registration (including Data Uni-
versal Numbering System number and North
Atlantic Treaty Organization Commercial and
Government Entity code),14 Electronic Research
Administration Commons registration, federal
conflict of interest statements, and Collabo-
rative Institutional Training Initiative human
subjects research training.15 Local (sometimes
national) and centralized institutional review
boards approve study protocols annually, follow-
ing local laws, regulations, and guidelines, and
study compliance is monitored frequently follow-
ing the Office for Human Research Protections
international compilation of human research
standards.16 US Department of State compli-
ance documents were obtained and distributed
to ensure proper implementation of current poli-
cies and embargoes.17
All participating institutions signed data use
agreements and material transfer agreements.
Import and export permits were generated
2 jgo.org JGO – Journal of Global Oncology
Mohamed Jalloh
Serigne Magueye Gueye
Papa Moussa Sène Kane
Halimatou Diop
Anna Julienne Ndiaye
Amina Sow Sall
Ndeye Coumba Toure-
Kane
Ezenwa Onyemata
Alash’le Abimiku
Andrew A. Adjei
Richard Biritwum
Richard Gyasi
Mathew Kyei
James E. Mensah
Julian Okine
Vicky Okyne
Isabella Rockson
Evelyn Tay
Yao Tettey
Edward Yeboah
Wenlong C. Chen
Elvira Singh
Michael B. Cook
Christine N. Duffy
Ann Hsing
Cassandra Claire Soo
Pedro Fernandez
Hayley Irusen
Oseremen Aisuodionoe-
Shadrach
Abubakar Mustapha
Jamda
Peter Oluwole Olabode
Maxwell Madueke
Nwegbu
Olalekan Hafees Ajibola
Olushola Jeremiah Ajamu
Yakubu Garba Ambuwa
Akindele Olupelumi
Adebiyi
Michael Asuzu
Olufemi Ogunbiyi
Olufemi Popoola
Olayiwola Shittu
Olukemi Amodu
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Copyright © 2018 American Society of Clinical Oncology. All rights reserved.
enabling biosamples to be shipped to the CPGR
in Cape Town, South Africa, where all central
processing, biobanking, and genotype screening
are conducted. In addition, MADCaP investiga-
tors formed a series of oversight working groups.
Each of our centers and the overall study
obtained approval for the ethical conduct of the
research. These approvals are in accordance
with both local and international principles. We
took a series of steps to ensure the welfare of our
research participants. First, the potential harms
to the research participant were minimal. These
included minor bruising at the phlebotomy site
during peripheral-blood collection. The remain-
ing data collection issues were also minimal risk
because they involved questionnaires and med-
ical record abstraction. Second, patient infor-
mation was protected using standards similar to
those outlined in the Health Insurance Portabil-
ity and Accountability Act. Although the Health
Insurance Portability and Accountability Act is a
US regulation not in effect in African countries,
we have based our patient confidentiality and
privacy standards on those regulations, account-
ing for local laws and regulations that may differ
by country. Finally, although the risks to the par-
ticipants were minimal, the individual benefits to
the participants were also small; we did not pro-
vide the results of our research to the individual
participant. All data are presented in aggregate
only. However, the potential benefits to African
men may be large as we understand and trans-
late our research results to the clinical and pub-
lic health of the populations being studied.
Biosample, Biobanking, and Laboratory
Resources
We standardized biosample collection processes
to facilitate consumable and reagent sourcing
and obtained high-quality DNA selecting Qia-
gen’s (Hilden, Germany) QIAamp DNA Blood
Midi or Mini Kits on the basis of availability, cost
effectiveness, DNA quality, and yield. We col-
lected at least 7 mL of blood in EDTA vacutainer
tubes from each participant, maintaining them
at 4°C while in transit to the local laboratory and
subsequently storing them at −30°C before DNA
extraction. When a participant did not provide
a blood sample, centers could collect a saliva
sample using the Oragene OG-500 kit (DNA
Genotek, Kanata, Ontario, Canada).
CPGR staff visited each SSA center providing
training videos supporting biosample protocol
adoption and local capacity building.18 Common
quality control (QC) processes have been imple-
mented to ensure that defined QC metrics are
obtained. The QC parameters for DNA include
A260/A280 ratios between 1.7 and 2.1; A260/A230
ratios ≥ 1.5; and visualization of high-molecular-
weight DNA after gel electrophoresis.
DNA was shipped from each SSA center to
CPGR at ambient temperature in DNAstable 2D
Barcode 96-well Tube Plates (Matrix; Biomatrica,
San Diego, CA) and stored upon receipt at the
CPGR at ambient temperature until processed.
After sample resuspension and removal of ali-
quots for QC purposes, DNA was stored at
−80°C for long-term storage before downstream
processing.
None of our centers had serious power supply
issues. However, each of our study centers had
generator backups and power surge protec-
tors for the freezers in which our samples were
stored. Backup generators kicked in within sec-
onds of a power failure. In addition, we stored
our DNA samples in DNA-stable plates, which
maximizes DNA quality during shipping as well
as potentially variable storage conditions.
Data Elements, Collection, and Management
After multiple teleconferences and an in-person
meeting (January 2017), we finalized common
data elements and study protocols. We created
two Web-accessible, password-protected data-
bases, residing on secure servers—a Research
Electronic Data Capture database19 to provide
identifiable tracking of participant recruitment,
and a DatStat Illume database (DatStat, Seat-
tle, WA) for deidentified storage of study data
that include no personal identifying information
other than the patient’s date of birth. Each study
participant was assigned a unique study identi-
fication number, concatenated with center iden-
tification, year of accrual, and patient case or
control status. Center staff received remote data-
base training20 via training guides and tutorials.
Videos were available for ongoing training and
support.18 The Data Coordinating Center (DCC)
monitored data collection, ensuring there were
no duplicate identifiers, and generated missing
data reports.
3 jgo.org JGO – Journal of Global Oncology
Emeka Odiaka
Ifeoluwa Makinde
Maureen Joffe
Audrey Pentz
Timothy R. Rebbeck
Author affiliations and
support information (if
applicable) appear at the
end of this article.
Corresponding author:
Timothy R. Rebbeck, PhD,
1101 Dana, 450 Brookline
Ave, Dana-Farber Cancer In-
stitute, Boston, MA 02215;
e-mail: Timothy_Rebbeck@
dfci.harvard.edu.
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Copyright © 2018 American Society of Clinical Oncology. All rights reserved.
A synopsis of the data collection processes is
presented in Figure 2. Staff at each SSA center
collected data via paper surveys, entering the
data using the unique participant identifier. Sur-
vey modules relied on authentication, by means
of login credentials and encryption, to maintain
security. Secure https connections were used
with 128-bit encryption and signed Secure
Sockets Layer certificates to enable the highest
level of security. Data could be entered live or,
because some centers have difficulty accessing
the Internet as a result of low bandwidth, stati-
cally using DatStat’s Remote Data Collection with
bulk uploading. Center investigators kept origi-
nal data at their local centers. Web-based survey
data were stored on secure, password-protected
servers at DatStat’s headquarters in Seattle,
Washington. For QC, 10% of all data were re-
entered by the DCC.
Pilot Studies
We conducted two pilot studies to evaluate pro-
cesses and protocols.
Data collection. All SSA study staff were trained
in person. Data collection was monitored to
evaluate protocol adherence and the capacity
of each center to collect and enter high-quality
data.
Biosampling and genotyping. We evaluated each
center’s ability to collect, process, and ship
DNA across Africa and to use the DNAstable
plate for stabilization, storage, and shipment
of DNA at room temperature to the CIDR and
CPGR. Center staff processed both prospectively
and retrospectively collected samples (biosam-
ples previously obtained after unstandardized
protocols). By studying these retrospectively
collected samples, we could evaluate samples
4 jgo.org JGO – Journal of Global Oncology
DFCI
SU
AEMC
CU
GT
CIDR
NCI
IFRU
37MIL
KBTH
UCH UATH
CPT
IFRU
JNB
UATH
37MIL
KBTH
UCH
DFCI
CPGR
DFCI
DFCI
AEMC
SU
Central Data
Repository
Central
Biobank
and
Genomics
CU
DNA
Raw Data
Data for Analysis
G
e
n
o
t
y
p
e
s
CIDR
CPGR CPT
JNB
Fig 1. Men of African Descent and Carcinoma of the Prostate (MADCaP) organizational and collaborative structure. African centers (blue circles)
include 37 Military Hospital (37MIL), Accra, Ghana; Stellenbosch University and Tygerberg Hospital (CPT), Cape Town, South Africa; Institut de For-
mation et de la Recherche en Urologie (IFRU), Dakar, Senegal; Wits Health Consortium and National Health Laboratory Service (JNB), Johannesburg,
South Africa; Korle-Bu Teaching Hospital (KBTH), Accra, Ghana; University of Abuja Teaching Hospital (UATH), Abuja, Nigeria; and University College
Hospital (UCH), Ibadan, Nigeria. US partner institutions (orange circles) include Albert Einstein College of Medicine (AECM), Bronx, New York; Colum-
bia University (CU), New York, New York; Dana-Farber Cancer Institute (DFCI), Boston, Massachusetts; Georgia Institute of Technology (GT), Atlanta,
Georgia; and Stanford University (SU), Palo Alto, California. Central resource facilities (green circles) include the Centre for Proteomics and Genomics
Research (CPGR), Cape Town, South Africa, and DFCI. Other collaborating MADCaP partners include the Center for Inherited Disease Research (CIDR)
and the Intramural Program of the National Cancer Institute (NCI).
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Copyright © 2018 American Society of Clinical Oncology. All rights reserved.
collected under a wide variety of conditions in
SSA. Centers were encouraged to provide a
range of samples with optimal and suboptimal
QC metrics, including low A260/A230 ratios and
partially degraded samples, submitting 1 to 3
µg of DNA in DNAstable plates after the drying
down of DNA samples in a laminar flow hood.
The 265 biosamples collected in this pilot were
evaluated in parallel experiments using the UK
Biobank Axiom Array (Affymetrix, Santa Clara,
CA) at CPGR. CIDR tested 234 DNA samples,
assessing QC by running e-gels, Picogreen, and/
or Nanodrop and the Illumina QC Array (Illu-
mina, San Diego, CA). CPGR tested 223 samples
using TaqMan OpenArray Genotyping Barcodes
(Thermo Fisher Scientific, Waltham, MA). By
design, some samples were sent to both CIDR
and CPGR to evaluate reproducibility of results.
Duplicate samples were included to help deter-
mine technical reproducibility within and across
genotyping centers and assess the overall mini-
mal sample quality threshold required.
RESULTS
Standardized Procedures and Protocols
We developed a study binder consisting of 11
protocols and 15 data collection forms to provide
comprehensive guidance to the SSA centers
relating to regulatory, ethical, and data collection
procedures. The data collected for this study
include basic demographic and clinical charac-
teristics, epidemiologic risk factors, and pathol-
ogy features.
Communications and Training
We created a communication platform (www.
madcapnetwork.org)21 built on the Alicanto
social learning platform (www.alicantocloud.
com) supporting creation of public and private
groups, with document sharing, threaded dis-
cussion, and videoconferencing using Zoom
software (https://zoom.us). We used these com-
munication tools initially to ensure standardiza-
tion across study centers and continue to use
them to facilitate and monitor study progress,
share information, and nurture collaboration. In
its first year, the Web site had 1,075 unique visi-
tors, 1,875 visitors’ sessions, 29,030 page views,
112 registered users, and 11 groups available to
registered users. The MADCaP study enrollment
video22 guides the project manager through all
enrollment steps. The Web site was recently
awarded a Gold Davey award in the Education
category by the Academy of Interactive and
Visual Arts.
5 jgo.org JGO – Journal of Global Oncology
POST ENROLLMENT PROCESS
Enrollment checklist
completed
Database entry
(all forms)
Data access
request
Data analysis
Article
preparation
DNA shipment
to genotyping
center
DNA extraction,
QC, storage
Medical record
abstraction
After patient/
control departs
Refused
consent
Eligible and
interested
Ineligible or not
interested
Consented Enrollment
complete
ENROLLMENT PROCESS
Review of
inclusion/exclusion
criteria
Cognitive assessment
Eligibility status
Patient/control
approached
Complete
cover sheet
questionnaire
Obtain body
mass index
Consent
process
Biospecimen
obtained
Patient/control
departs
Stop
Fig 2. Men of African
Descent and Carcinoma of
the Prostate study schema
separated into the enroll-
ment and postenrollment
processes. QC, quality
control.
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Copyright © 2018 American Society of Clinical Oncology. All rights reserved.
Data Collection Pilot
Compliance documentation including institu-
tional review board protocol approvals, document
translation (French, IsiXhosa, and Afrikaans),
and material transfer and data use agreements
was obtained. Across seven centers, a total of
186 patients and 122 controls were enrolled onto
the pilot study (Table 1). To monitor data integ-
rity, two-pass verification (double data entry) was
undertaken on 275 (10%) different pilot study
survey sets to verify center entry accuracy. A
high concordance of 95% to 98% was achieved
between participating centers and verification
data entry (Table 2). Double data entry will con-
tinue using a 10% subset of the total sample
as in the pilot throughout the study’s duration.
Additional quality assurance includes ongoing
data cleaning by the DCC to identify incomplete,
incorrect, or inaccurate data; the DCC circu-
lates monthly reports describing erroneous data
points and specific errors to participating centers
to ensure error correction.
Biosampling and Genotyping Pilot
For the laboratory pilot, documentation was gen-
erated, and the SSA centers successfully trans-
ported both retrospectively and prospectively
collected biosamples to the CPGR and CIDR.
After DNA extraction, centers performed QC
analysis and shipped an aliquot of each sam-
ple to the CPGR for corroborative QC analysis.
Both CIDR and CPGR performed QC analysis
including Nanodrop, gel electrophoresis, and/or
Picogreen assays to measure the quality, quan-
tity, and integrity of the biosamples. Appendix
Table A1 shows the high correlation in QC anal-
ysis between SSA centers and the CPGR, with
the correlation in median DNA yield being par-
ticularly high (Spearman correlation coefficient =
0.964; P < .001).
Average center call rate from the Illumina
Infinium QC array was > 99% for blood and
buffy coat samples, whereas samples derived
from buccal swab had lower call rates (≤ 92%).
Similar performance of samples was observed
for the OpenArray Genotyping Barcode Panel
for 60 single nucleotide polymorphisms. The
6 jgo.org JGO – Journal of Global Oncology
Table 1. Accrual of Patients and Controls During the Pilot Phase (June 2016 to December 2016) and After the Pilot
Phase (January 2017 to August 2017) Demonstrating Enhanced Accrual Since the Full Phase Was Initiated
Center
No. of Patients and Controls
Pilot Phase
Pilot Accrual Per
Month
After the Pilot
Phase
Accrual Per Month After
the Pilot Phase
IFRU 48 8.0 112 14.0
KBTH 87 14.5 176 22.0
37 Military 40 6.7 105 13.1
UATH 17 2.8 63 7.9
UCH 53 8.8 73 9.1
Tygerberg 34 5.7 33 4.1
JNB 29 4.8 402 50.3
Total 308 51.3 964 120.5
Abbreviations: IFRU, Institut de Formation et de la Recherche en Urologie; JNB, Wits Health Consortium and National Health Labora-
tory Service; KBTH, Korle-Bu Teaching Hospital; UATH, University of Abuja Teaching Hospital; UCH, University College Hospital.
Table 2. Average Percent Concordance in Pilot Survey Double Database Entry
Survey Name
No. (%) of Surveys Reviewed
(N = 275)
Average Concordance Across
Centers (%)
Eligibility 61 (22) 95.8
Body mass index 51 (19) 97.7
Participant questionnaire 62 (23) 98.3
Medical record abstraction 56 (20) 97.7
Biospecimen 39 (14) 95.0
DNA 6 (2) 95.1
Overall concordance 96.6
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Copyright © 2018 American Society of Clinical Oncology. All rights reserved.
samples with the lowest success in both pre-
testing assays were derived from buccal swabs,
which showed significant DNA degradation and
displayed the poorest QC metrics (Table 3) and
lowest call rates. For all centers, intact high-mo-
lecular-weight DNA was obtained in sufficient
quantities, but DNA degradation was more com-
mon in the buccal swab samples compared with
blood or buffy coat.
Of the samples assayed on the UK Biobank Axiom
Array at both CIDR and CPGR, approximately
47% were derived from blood, 23% from buffy
coats, and 30% from buccal swabs representing
various DNA samples. Samples were included
from three different African countries (Senegal,
Ghana, and South Africa). Fig 3A shows the
representative success rate for these sample
types, with strong concordance between the two
genotyping centers. Like the pretesting assays,
samples derived from blood and buccal swabs
performed optimally, displaying high call rates
(ie, > 97%), whereas only buccal swab–derived
samples with intact high-molecular-weight DNA
passed QC. In addition, genomic ancestry of the
genotyped individuals was evaluated (Fig 3B),
with the majority of participants being genomi-
cally of African ancestry, whereas a few South
African individuals (n = 6) were confirmed to
have European or admixed ancestry. This anal-
ysis allows for comparison with self-identified
population groups and will be used to confirm
the ancestry of all study participants in future
genome-wide association studies.
DISCUSSION
SSA has been greatly under-represented in
studies of cancer.23 However, we have now
developed a multicenter research network to
study CaP in SSA men. The results presented
provide strong evidence that each center can
ascertain controls and patients and obtain the
required data to achieve the study aims, high-
lighting the importance of the pilot phase in
establishing appropriate and adequate study
implementation. The MADCaP Network serves
as a paradigm for multicenter cancer research
in SSA and among researchers and clinicians
in SSA centers with US partners. This collabo-
ration is building capacity and sustainability for
cancer research in SSA. SSA centers maintain
control of their own studies and may publish
independently. Contributing SSA principal inves-
tigators (PIs) may also use consortium resources
and data to address questions of interest. Data
sharing with center PI permission and through
confidentiality and data use agreements protects
investigators and allows free exchange of data
and ideas among consortium members.
This report describes how SSA centers can
become equipped to perform participant ascer-
tainment, data and biosample collection, and
DNA processing. QC analysis of prospectively
collected data and biospecimens indicated that
each center could adequately follow the project’s
protocols and procedures to generate data and
to extract, store, and ship high-quality biosam-
ples. The biosampling pilot study confirmed the
suitability of Biomatrica plates for shipping DNA
samples from each African center to the CPGR
and CIDR at ambient temperature, validating
its continued use for shipping samples for the
main study and thus eliminating the need for
dry ice shipments, an important shipping option
in Africa. Moreover, DNAstable, the DNA stabi-
lizing reagent used to coat the plate wells, did
not negatively affect the DNA integrity or sample
performance on the Axiom assays using the UK
Biobank Array.
A high percentage of retrospectively collected
buccal samples failed the assay as a result of
7 jgo.org JGO – Journal of Global Oncology
Table 3. Sample Source of Retrospective Samples and Long-Term Stability of DNA
Center No. of Samples Sample Origin
% of Samples Passing Quality Control Metric
A260/A280 A260/A230
Picogreen Yield
> 500 ng
A 37 Buffy coat 97 49 35
C 19 Blood 58 11 0
D 65 Buccal swab 67 34 38
E 28 Buffy coat/blood 96 77 93
F 42 Blood 57 50 97
NOTE. Center B is omitted from this list because this center did not contribute retrospective samples to the pilot study.
Downloaded from ascopubs.org by Stellenbosch University on October 5, 2018 from 146.232.195.084
Copyright © 2018 American Society of Clinical Oncology. All rights reserved.
sample degradation. Blood and buffy coat sam-
ples, irrespective of sample age, performed well,
with DNA of high molecular weight and little or
no degradation. Older samples that had gone
through repeat freeze-thaw cycles provided a
lower DNA yield, although they still contained
sufficient DNA to perform required assays. The
genotyping assays used can also tolerate lower
than expected QC metrics, in particular with
respect to the A260/A230 ratio. These results indi-
cate that prospective and retrospective samples
will produce a high call rate for genotypes using
genotyping assays on a variety of platforms. Mov-
ing forward, a standard operating procedure for
buccal cell collection, preprocessing, storage,
and transport will be optimized and universally
used for DNA for MADCaP.
We have identified principles critical to devel-
opment of a sustainable research network that
has clinical and public health impact. Elements
required for successful network development
include the commitment of a dedicated, well-
connected, and influential local PI willing to pro-
mote and mentor junior researchers. An academic
environment enabling research is also required,
particularly to provide dedicated research time,
given the clinic loads of most clinician investiga-
tors. Appropriate training and academic career
ladders may need to be developed or tailored to
8 jgo.org JGO – Journal of Global Oncology
95
96
96
97
97
98
98
99
99
100
100
A
95 96 96 97 97 98 98 99 99 10
01
00
CPGR QC Call Rate (%)
CIDR QC Call Rate (%)
Genotyping center QC call rate concordance
Blood
Buccal
Buffy
West African
Southern African
0.05
0.05
–0.05
–0.15
0.15
Eigenvector 1
Eigenvector 2
0.25
–0.1
0.1 0.2 0.3
0
B
0
African
Admixed
European
Fig 3. Results of geno-
type analyses of study
samples. (A) Success rate
of samples assayed on the
Axiom Array. (B) Results
of principal components
analysis to evaluate genomic
ancestral population groups
where different colors rep-
resent different population
groups; orange represents
African, blue represents
admixed, and green
represents European. Eigen-
values indicates the amount
of variation in the sample
data. Eigenvectors are the
linear combinations showing
how variables contribute to
each axis. CIDR, Center for
Inherited Disease Research;
CPGR, Centre for Proteom-
ics and Genomics Research;
QC, quality control.
Downloaded from ascopubs.org by Stellenbosch University on October 5, 2018 from 146.232.195.084
Copyright © 2018 American Society of Clinical Oncology. All rights reserved.
permit committed individuals to dedicate effort
to a research career. Some SSA institutions do
not yet provide these pathways.24,25 Success-
ful networks may need to identify incentives,
rewards, and recognition for research faculty
and staff, modeling a locally appropriate change
management approach, and develop easy-to-
use platforms that can scale to larger size stud-
ies as well as studies of other noncommunicable
diseases. The research itself must be developed
in terms of achievable goals with realistic time-
lines. Finally, the network must provide clear
expectations and metrics for collaborators’ con-
tributions and communication, including regular
calls and bidirectional center visits. We have also
used these lessons to identify key overarching
goals for the network, including the development
of improved research infrastructure considering
the needs and setting in SSA.
We also identified several challenges in develop-
ing our network. We initially had trouble identify-
ing well-trained, experienced technical staff, but
we found we could identify and train center staff
to meet our research needs. We observed the
value of regular, consistent, targeted communi-
cation to address the needs of individual centers
and keep the project on track. Early in network
development, we learned that we needed to
identify realistic study goals and timelines to
avoid unmet expectations. We had to set com-
mon standards and make sure that protocols
were consistent with the centers’ previous expe-
riences and expectations. The development of
tools for protocol and data harmonization by a
consensus helped avoid unnecessary conflict
among groups with different past research expe-
riences. Our adoption of Web-based tools and a
communications platform, through which each
investigator and staff member could readily find
documentation on the study protocols and prog-
ress, helped avoid confusion or miscommunica-
tion. A major challenge for data collection was
being able to obtain consistent Internet access.
Once this limitation was identified, we switched
from a system of online data entry to a protocol
allowing local data entry and bulk data upload
at centers having difficulty accessing the Inter-
net. On the basis of our experience to date, we
have realistic expectations of returns on the sub-
stantial investments of money, resources, and
time that MADCaP entails, including improved
research infrastructure, a trained local work-
force, improved research capacity, and poten-
tially important contributions to science.
DOI: https://doi.org/10.1200/JGO.18.00063
Published online on jgo.org on September 27, 2018.
AUTHOR CONTRIBUTIONS
Conception and design: Caroline Andrews, Brian Fortier,
Amy Hayward, Lindsay Petersen, Olabode Ajayi, Reinhard
Hiller, Ilir Agalliu, David W. Lounsbury, Thomas Rohan,
Judith S. Jacobson, Thierno Amadou Diallo, Mohamed
Jalloh, Serigne Magueye Gueye, Papa Moussa Sène Kane,
Richard Biritwum, Richard Gyasi, Mathew Kyei, James E.
Mensah, Isabella Rockson, Yao Tettey, Christine N. Duffy,
Ann Hsing, Pedro Fernandez, Oseremen Aisuodionoe-
Shadrach, Peter Oluwole Olabode, Maxwell Madueke
Nwegbu, Yakubu Garba Ambuwa, Olufemi Ogunbiyi,
Olufemi Popoola, Emeka Odiaka, Timothy R. Rebbeck
Administrative support: Brian Fortier, Reinhard Hiller, Jane
Romm, Vicky Okyne, Isabella Rockson, Evelyn Tay, Yao
Tettey, Michael Asuzu
Provision of study material or patients: Aubrey Shoko, Chrissie
Ongaco, Ben Adusei, Sunny Mante, Ilir Agalliu, Thomas
Rohan, Alfred I. Neugut, Thierno Amadou Diallo, Serigne
Magueye Gueye, Ndeye Coumba Toure-Kane, Ezenwa
Onye mata, Andrew A. Adjei, Richard Biritwum, Richard Gyasi,
Mathew Kyei, Vicky Okyne, Isabella Rockson, Yao Tettey,
Edward Yeboah, Elvira Singh, Michael B. Cook, Christine N.
Duffy, Abubakar Mustapha Jamda, Peter Oluwole Olabode,
Maxwell Madueke Nwegbu, Olalekan Hafees Ajibola, Olushola
Jeremiah Ajamu, Yakubu Garba Ambuwa, Akindele Olupelumi
Adebiyi, Olufemi Popoola, Olayiwola Shittu, Olukemi Amodu,
Emeka Odiaka, Ifeoluwa Makinde, Maureen Joffe, Audrey Pentz
Collection and assembly of data: Caroline Andrews, Brian
Fortier, Ruth Lederman, Lindsay Petersen, Jo McBride,
Olabode Ajayi, Moleboheng Seutloali, Aubrey Shoko,
Marcia Adams, Chrissie Ongaco, Jane Romm, Tameka
Shelford, Ben Adusei, Sunny Mante, Nana Yaa Snyper,
Alex Orfanos, Yuri Quintana, Alfred I. Neugut, Cherif Dial,
Thierno Amadou Diallo, Serigne Magueye Gueye, Halimatou
Diop, Anna Julienne Ndiaye, Amina Sow Sall, Ndeye
Coumba Toure-Kane, Ezenwa Onyemata, Alash'le Abimiku,
Andrew A. Adjei, Richard Biritwum, Mathew Kyei, James
E. Mensah, Julian Okine, Vicky Okyne, Isabella Rockson,
Evelyn Tay, Yao Tettey, Edward Yeboah, Wenlong C. Chen,
Elvira Singh, Michael B. Cook, Christine N. Duffy, Ann
Hsing, Cassandra Claire Soo, Pedro Fernandez, Hayley
Irusen, Oseremen Aisuodionoe-Shadrach, Abubakar
Mustapha Jamda, Maxwell Madueke Nwegbu, Olalekan
Hafees Ajibola, Olushola Jeremiah Ajamu, Yakubu Garba
Ambuwa, Akindele Olupelumi Adebiyi, Michael Asuzu,
Olufemi Ogunbiyi, Olufemi Popoola, Olayiwola Shittu,
Olukemi Amodu, Emeka Odiaka, Ifeoluwa Makinde,
Maureen Joffe, Audrey Pentz
Data analysis and interpretation: Caroline Andrews, Brian
Fortier, Lindsay Petersen, Desiree C. Petersen, Olabode
Ajayi, Paidamoyo Kachambwa, Mamokhosana Mokhosi,
Elizabeth Pugh, Frank Chinegwundoh, Ilir Agalliu, Thomas
Rohan, Alex Orfanos, Yuri Quintana, Alfred I. Neugut,
Edward Gelmann, Joseph Lachance, Thierno Amadou
9 jgo.org JGO – Journal of Global Oncology
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Copyright © 2018 American Society of Clinical Oncology. All rights reserved.
Diallo, Serigne Magueye Gueye, Ezenwa Onyemata, James
E. Mensah, Edward Yeboah, Christine N. Duffy, Ann
Hsing, Pedro Fernandez, Oseremen Aisuodionoe-Shadrach,
Yakubu Garba Ambuwa, Michael Asuzu, Olufemi Ogunbiyi,
Olufemi Popoola
Manuscript writing: All authors
Final approval of manuscript: All authors
Accountable for all aspects of the work: All authors
AUTHORS' DISCLOSURES OF
POTENTIAL CONFLICTS OF INTEREST
The following represents disclosure information provided
by authors of this manuscript. All relationships are
considered compensated. Relationships are self-held
unless noted. I = Immediate Family Member, Inst = My
Institution. Relationships may not relate to the subject
matter of this manuscript. For more information about
ASCO's conflict of interest policy, please refer to www.
asco.org/rwc or ascopubs.org/jco/site/ifc.
Caroline Andrews
No relationship to disclose
Brian Fortier
No relationship to disclose
Amy Hayward
No relationship to disclose
Ruth Lederman
No relationship to disclose
Lindsay Petersen
No relationship to disclose
Jo McBride
No relationship to disclose
Desiree C. Petersen
No relationship to disclose
Olabode Ajayi
Employment: CPGR
Research Funding: CPGR (Inst)
Travel, Accommodations, Expenses: CPGR
Other Relationship: H3 Bionnet
Paidamoyo Kachambwa
No relationship to disclose
Moleboheng Seutloali
No relationship to disclose
Aubrey Shoko
No relationship to disclose
Mamokhosana Mokhosi
No relationship to disclose
Reinhard Hiller
Employment: Artisan Biomed
Leadership: Artisan Biomed
Marcia Adams
No relationship to disclose
Chrissie Ongaco
No relationship to disclose
Elizabeth Pugh
No relationship to disclose
Jane Romm
No relationship to disclose
Tameka Shelford
No relationship to disclose
Frank Chinegwundoh
No relationship to disclose
Ben Adusei
No relationship to disclose
Sunny Mante
No relationship to disclose
Nana Yaa Snyper
No relationship to disclose
Ilir Agalliu
No relationship to disclose
David W. Lounsbury
No relationship to disclose
Thomas Rohan
Stock and Other Ownership Interests: Metastat
Patents, Royalties, Other Intellectual Property: MetaSite
Breast Score
Alex Orfanos
No relationship to disclose
Yuri Quintana
No relationship to disclose
Judith S. Jacobson
No relationship to disclose
Alfred I. Neugut
Stock and Other Ownership Interests: Stemline Therapeutics
Consulting or Advisory Role: Pfizer, Otsuka, United
Biosource Corporation, EHE International
Expert Testimony: Hospira
Edward Gelmann
Stock and Other Ownership Interests: Exelixis, Johnson &
Johnson (I), Eli Lilly (I), Bristol-Myers Squibb (I), Seattle
Genetics
Research Funding: Syndax
Joseph Lachance
No relationship to disclose
Cherif Dial
No relationship to disclose
Thierno Amadou Diallo
No relationship to disclose
Mohamed Jalloh
No relationship to disclose
Serigne Magueye Gueye
No relationship to disclose
Pape Moussa Sène Kane
No relationship to disclose
Halimatou Diop
No relationship to disclose
Anna Julienne Ndiaye
No relationship to disclose
10 jgo.org JGO – Journal of Global Oncology
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Copyright © 2018 American Society of Clinical Oncology. All rights reserved.
Amina Sow Sall
No relationship to disclose
Ndeye Coumba Toure-Kane
No relationship to disclose
Ezenwa Onyemata
No relationship to disclose
Alash'le Abimiku
No relationship to disclose
Andrew A. Adjei
No relationship to disclose
Richard Biritwum
No relationship to disclose
Richard Gyasi
No relationship to disclose
Mathew Kyei
No relationship to disclose
James E. Mensah
No relationship to disclose
Julian Okine
No relationship to disclose
Vicky Okyne
No relationship to disclose
Isabella Rockson
No relationship to disclose
Evelyn Tay
No relationship to disclose
Yao Tettey
No relationship to disclose
Edward Yeboah
No relationship to disclose
Wenlong C. Chen
No relationship to disclose
Elvira Singh
Research Funding: MSD (Inst), Amgen (Inst)
Michael B. Cook
No relationship to disclose
Christine N. Duffy
No relationship to disclose
Ann Hsing
No relationship to disclose
Cassandra Claire Soo
No relationship to disclose
Pedro Fernandez
No relationship to disclose
Hayley Irusen
Employment: GlaxoSmithKline (I)
Honoraria: GlaxoSmithKline (I)
Travel, Accommodations, Expenses: GlaxoSmithKline (I)
Oseremen Aisuodionoe-Shadrach
No relationship to disclose
Abubakar Mustapha Jamda
No relationship to disclose
Peter Oluwole Olabode
No relationship to disclose
Maxwell Madueke Nwegbu
No relationship to disclose
Olalekan Hafees Ajibola
No relationship to disclose
Olushola Jeremiah Ajamu
No relationship to disclose
Yakubu Garba Ambuwa
No relationship to disclose
Akindele Olupelumi Adebiyi
No relationship to disclose
Michael Asuzu
No relationship to disclose
Olufemi Ogunbiyi
No relationship to disclose
Olufemi Popoola
No relationship to disclose
Olayiwola Shittu
No relationship to disclose
Olukemi Amodu
No relationship to disclose
Emeka Odiaka
No relationship to disclose
Ifeoluwa Makinde
No relationship to disclose
Maureen Joffe
No relationship to disclose
Audrey Pentz
Honoraria: NETCARE
Timothy R. Rebbeck
No relationship to disclose
Affiliations
Caroline Andrews, Brian Fortier, Amy Hayward, Ruth Lederman, and Timothy R. Rebbeck; Dana-Farber Cancer Institute;
Alex Orfanos and Yuri Quintana, Beth Israel Deaconess Medical Center; Timothy R. Rebbeck, Harvard T.H. Chan School
of Public Health, Boston, MA; Lindsay Petersen, Jo McBride, Desiree C. Petersen, Olabode Ajayi, Paidamoyo Kachambwa,
Moleboheng Seutloali, Aubrey Shoko, Mamokhosana Mokhosi, and Reinhard Hiller, Centre for Proteomic and Genomic
Research; Pedro Fernandez and Hayley Irusen, Stellenbosch University and Tygerberg Hospital, Cape Town; Wenlong
C. Chen and Elvira Singh, National Cancer Registry, National Health Laboratory Service; Wenlong C. Chen, Elvira Singh,
Maureen Joffe, Audrey Pentz, and Cassandra Claire Soo, University of Witwatersrand, Johannesburg, South Africa; Marcia
Adams, Chrissie Ongaco, Elizabeth Pugh, Jane Romm, and Tameka Shelford, Center for Inherited Disease Research,
Baltimore; Michael B. Cook, National Cancer Institute, National Institutes of Health, Bethesda, MD; Frank Chinegwundoh,
11 jgo.org JGO – Journal of Global Oncology
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Copyright © 2018 American Society of Clinical Oncology. All rights reserved.
Bart’s Health National Health Services Trust, London, United Kingdom; Ben Adusei, Sunny Mante, and Nana Yaa Snyper,
37 Military Hospital; Andrew A. Adjei, Richard Biritwum, Richard Gyasi, Mathew Kyei, James E. Mensah, Julian Okine, Vicky
Okyne, Isabella Rockson, Evelyn Tay, Yao Tettey, and Edward Yeboah, Korle-Bu Teaching Hospital, Accra, Ghana; Ilir
Agalliu, David W. Lounsbury, and Thomas Rohan, Albert Einstein College of Medicine, Bronx; Judith S. Jacobson, Alfred
I. Neugut, and Edward Gelmann, Columbia University, New York, NY; Joseph Lachance, Georgia Institute of Technology,
Atlanta, GA; Cristine N. Duffy and Ann Hsing, Stanford University, Stanford Cancer Institute, Stanford, CA; Cherif Dial,
Thierno Amadou Diallo, Mohamed Jalloh, Serigne Magueye Gueye, and Papa Moussa Sène Kane, Hôpital Général de Grand
Yoff, Institute de Formation et de la Recherche en Urologie et de la Santé de la Famillie; Halimatou Diop, Anna Julienne
Ndiaye, Amina Sow Sall, and Ndeye Coumba Toure-Kane, Hôpital Aristide Le Dantec, Dakar, Senegal; Ezenwa Onyemata
and Alash’le Abimiku, Institute of Human Virology, H3 African Biorepository Initiative; Oseremen Aisuodionoe-Shadrach,
Abubakar Mustapha Jamda, Peter Oluwole Olabode, Maxwell Madueke Nwegbu, and Olalekan Hafees Ajibola, University of
Abuja; Oseremen Aisuodionoe-Shadrach, Abubakar Mustapha Jamda, Peter Oluwole Olabode, and Maxwell Madueke Nwegbu,
University of Abuja Teaching Hospital, Abuja; Olushola Jeremiah Ajamu and Yakubu Garba Ambuwa, Federal Medical
Center, Keffi; Akindele Olupelumi Adebiyi, Michael Asuzu, Olufemi Ogunbiyi, Olufemi Popoola, Olayiwola Shittu, Olukemi
Amodu, Emeka Odiaka, and Ifeoluwa Makinde, University College Hospital, Ibadan, Nigeria.
Support
Supported by National Institutes of Health Grant No. U01-CA184374 and the Dana-Farber Cancer Institute.
Prior Presentation
Presented in part at the 11th International African Organization for Research and Training in Cancer Conference, Kigali,
Rwanda, November 7-10, 2017.
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14 jgo.org JGO – Journal of Global Oncology
Appendix
Table A1. CPGR Prospective Sample QC Results Correlate With SSA Center Sample QC Results
Center
CPGR Average
Concentration
(ng/µL)
Center Average
Concentration
(ng/µL)
CPGR
Median
(ng/µL)
Center
Median
(ng/µL)
CPGR
Average
A260/A280
CPGR
Average
A260/A230
CPGR
Median
A260/A280
CPGR
Median
A260/A230
Center A 136.83 114.97 110.62 96.057 1.73 0.97 1.74 0.95
Center B 284.20 245.76 285.7 225.4 1.82 1.28 1.86 1.46
Center C 220.49 126.10 169.04 32.90 1.73 0.54 1.79 0.41
Center D 97.82 97.93 58.56 76.95 1.85 0.58 1.87 0.81
Center E 110.55 101.80 91.24 92.1 1.89 2.58 1.89 2.6
Center F 98.90 103.40 107.26 111.00 1.89 2.19 1.87 4.50
Center G 141.87 123.51 119.64 112.5 1.71 1.10 1.75 1.12
Abbreviations: CIDR, Center for Inherited Disease Research; CPGR, Centre for Proteomics and Genomics Research; QC, quality control.
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Copyright © 2018 American Society of Clinical Oncology. All rights reserved.
... Ideally, these resources will follow from the definition and prioritisation of logistical research needs and the ability to support specific types of research. 242,243 These resource and infrastructure elements might therefore include research laboratory space, equipment, and reagents. Many of these resources require consideration of supply chains (eg, for reagents), service contracts (eg, for equipment), as well as training and certification of operators and technicians. ...
... For example, a project management toolkit for study staff has been developed that begins to fill a gap in support of research in SSA. 243 Other supportive training, such as histotechnologist and laboratory technician training, should be advanced to support the needs of the research principal investigator. Institutions in SSA need to do more in establishing and supporting academic tracks, expectations for promotion, and structures that support and reward research activities. ...
... Cancer research in SSA cannot be undertaken without adequate research administrative structure and appropriate ethical and regulatory oversight. 243 Most SSA institutions have Human Subjects Protection and Institutional Review Boards on the local and national levels that can oversee the informed consent process and compliance with local and national ethical guidelines. However, some of these bodies have not had broad experience with all aspects of cancer research or have not yet set standards for particular aspects of cancer research. ...
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... We excluded men who had any prior cancer diagnoses, except for non-melanoma skin cancer. All centers used common standardized protocols for subjects' recruitment, interviews, and data collection and processing [25,26]. ...
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Purpose African men are disproportionately affected by prostate cancer (PCa). Given the increasing prevalence of obesity in Africa, and its association with aggressive PCa in other populations, we examined the relationship of overall and central obesity with risks of total and aggressive PCa among African men. Methods Between 2016 and 2020, we recruited 2,200 PCa cases and 1,985 age-matched controls into a multi-center, hospital-based case–control study in Senegal, Ghana, Nigeria, and South Africa. Participants completed an epidemiologic questionnaire, and anthropometric factors were measured at clinic visit. Multivariable logistic regression was used to examine associations of overall and central obesity with PCa risk, measured by body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR), respectively. Results Among controls 16.4% were obese (BMI ≥ 30 kg/m²), 26% and 90% had WC > 97 cm and WHR > 0.9, respectively. Cases with aggressive PCa had lower BMI/obesity in comparison to both controls and cases with less aggressive PCa, suggesting weight loss related to cancer. Overall obesity (odds ratio: OR = 1.38, 95% CI 0.99–1.93), and central obesity (WC > 97 cm: OR = 1.60, 95% CI 1.10–2.33; and WHtR > 0.59: OR = 1.68, 95% CI 1.24–2.29) were positively associated with D’Amico intermediate-risk PCa, but not with risks of total or high-risk PCa. Associations were more pronounced in West versus South Africa, but these differences were not statistically significant. Discussion The high prevalence of overall and central obesity in African men and their association with intermediate-risk PCa represent an emerging public health concern in Africa. Large cohort studies are needed to better clarify the role of obesity and PCa in various African populations.
... The narrative review by Kleckner et al. noted where oncologists may integrate mobile health (mHealth) interventions to support improvements in clinical decision support systems. 10 While these interventions have received critical attention in previous reviews, their implications for preventing premature death by prostate cancer reflect how strongly Oncologists may consider using technology to assist with the delivery of interventions for improving population outcomes. 7,10 For example, the systematic review and metaanalysis of RCTs by Ivlev et al. presented results suggesting that oncologists may work toward reducing health disparities and preventing premature death by integrating screening decision aids in clinical practice. ...
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... 14 Local collaborations included a study on hepatocellular carcinoma 11 , knowledge of HIV status at cancer diagnosis 31 and versions of the questionnaire were used by colleagues in two other local settings. 32,33 Current collaborations include an extensive assessment of genomic effects on prostate 34 , cervical, breast and oesophageal cancer, and the role of key lifestyles and 18 infectious agents ('onco-agents', 10 known and eight suspected to increase cancer risk) in association with several cancer types. 35 ...
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Background Genome-wide association studies do not always replicate well across populations, limiting the generalizability of polygenic risk scores (PRS). Despite higher incidence and mortality rates of prostate cancer in men of African descent, much of what is known about cancer genetics comes from populations of European descent. To understand how well genetic predictions perform in different populations, we evaluated test characteristics of PRS from three previous studies using data from the UK Biobank and a novel dataset of 1298 prostate cancer cases and 1333 controls from Ghana, Nigeria, Senegal, and South Africa. Results Allele frequency differences cause predicted risks of prostate cancer to vary across populations. However, natural selection is not the primary driver of these differences. Comparing continental datasets, we find that polygenic predictions of case vs. control status are more effective for European individuals (AUC 0.608–0.707, OR 2.37–5.71) than for African individuals (AUC 0.502–0.585, OR 0.95–2.01). Furthermore, PRS that leverage information from African Americans yield modest AUC and odds ratio improvements for sub-Saharan African individuals. These improvements were larger for West Africans than for South Africans. Finally, we find that existing PRS are largely unable to predict whether African individuals develop aggressive forms of prostate cancer, as specified by higher tumor stages or Gleason scores. Conclusions Genetic predictions of prostate cancer perform poorly if the study sample does not match the ancestry of the original GWAS. PRS built from European GWAS may be inadequate for application in non-European populations and perpetuate existing health disparities.
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