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5
Responsible Parties
Omics technologies have ushered in a new era in biomedical research.
Omics data are extremely complex and multidimensional, with a high
risk of inaccuracies being introduced by inappropriate methods, human
error, conflicts of interest, or acts of commission/omission. Omics research
requires a multidisciplinary team with specialized expertise, which adds
to the challenge of conducting scientifically rigorous research and makes
overseeing and reviewing omics studies difficult. This multidimensionality
introduces an inherent risk of overfitting the data, making independent vali-
dation critical. While other fields such as high-energy physics, astrophys-
ics, and cosmology also require specialized expertise and multidisciplinary
collaboration, and deal with data complexity and high dimensionality, the
development of omics-based tests is different in that the tests have potential
commercial value and there is potential for developers to reap financial
gains. In addition, patient safety is paramount for omics-based tests that
are used to aid patient treatment decisions. Although these characteristics
are also true of drug development, that process has more uniform and more
stringent oversight from the U.S. Food and Drug Administration (FDA); all
new drugs must demonstrate clinical utility in well-designed clinical trials
to gain FDA approval. Thus, those responsible for the integrity of omics
research—investigators, institutions, funders, FDA, and journals—should
rethink the processes and protections designed to ensure that omics research
is scientifically rigorous, transparent, and conducted ethically, with proper
institutional and regulatory oversight.
The failures of the omics research at Duke University illustrate that
current practices and safeguards can easily fall short (see Appendix B).
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The Duke events thus provide a watershed illustration—reminiscent of the
Gelsinger gene therapy cases at the University of Pennsylvania, the Santillan
mismatched heart transplant case at Duke University Hospital, the Johns
Hopkins asthma trial death, and the viral link to chronic fatigue syndrome
at the Whittemore Peterson Institute for Neuro-Immune Disease—of how
such research can go awry even though institutions and other responsible
parties have extensive systems in place to ensure research integrity, with
roles and responsibilities delineated (Enserink, 2011; Kolata, 2001; Nelson
and Weiss, 1999; Sloane, 2003; Yarborough and Sharp, 2009). These pro-
cesses need to be rethought in the omics era. In short, the ability of health
care decision makers to rely on the trustworthiness of omics-based tests to
predict disease risk and treatment response will be limited unless renewed
efforts are made by all parties responsible for the integrity of this research.
The committee makes four recommendations related to defining the
roles and responsibilities of the key parties involved in the conduct and eval-
uation of omics research (Recommendations 4-7). These recommendations
are directed toward investigators and institutions (i.e., intrainstitutional
responsibilities), funders, FDA, and biomedical journals. Recommendations
1 through 3, which are discussed in Chapters 2-4, refer to responsibilities
of investigators, and focus on recommended best practices for the develop-
ment, validation, and clinical utility assessment of candidate omics-based
tests. Recommendations 4 to 7 are similarly critical because, without the
participation of institutions, investigators, funders, FDA, and journals,
the committee’s recommended evaluation processes for omics technologies
intended for clinical use (Recommendations 1-3) cannot be implemented.
The committee recognized that the recommendations presented in this
chapter may increase the oversight requirements for omics research in
some cases, but decided that these potential costs were offset by the added
safeguards to the integrity of this research. If an institution does not have
the infrastructure or capability to follow the recommended Test Develop-
ment and Evaluation Process defined in this report, then the committee
believes that the institution should consider not engaging in the translation
of omics-based discoveries into validated tests for use in clinical trials and
potentially clinical practice.
The committee developed the recommendations discussed in this chap-
ter by reviewing the available literature about the design, conduct, analysis,
and reporting of omics research and by identifying lessons learned from
case studies of the development of omics-based tests (see Appendixes A
and B). This chapter emphasizes lessons learned from the Duke University
case study (Appendix B) in particular because the most publicly available
information exists about this case study and because the Duke case was
specifically highlighted in the committee’s statement of task. The commit-
tee also relied heavily on the work of previous National Academies reports
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RESPONSIBLE PARTIES
that have reviewed the roles and responsibilities of the parties involved in
research. It is imperative that all responsible parties prepare for the omics
research era, with its promise as well as its perils. This chapter discusses
the details of how this preparation can be accomplished.
INTRAINSTITUTIONAL PARTIES
The roles and responsibilities of investigators and institutions that are
involved in omics-based research are discussed together because both par-
ties contribute to the scientific research culture in which omics research is
conducted. They are also the two most responsible and the most knowl-
edgeable parties in the entire evaluation process. Investigators control the
culture of individual laboratories embedded within the larger institution.
Individual laboratories can have unique values and cultural norms that are
separate from the broader institutional culture. These variables become
more complex as the research becomes more interdisciplinary, with the
lead investigators setting the culture for the investigational team. Institu-
tions and the institutional leadership, on the other hand, have the primary
responsibility for the policies and procedures, reward systems, and values
that contribute to the overarching institutional culture as well as for the
infrastructure of oversight and support for research. Institutions and their
leaders also have the greatest responsibility for in-depth investigation of
potential lapses in scientific integrity because they employ, promote, and
supervise the investigators who conduct these studies.
The National Academies defined integrity in the research process as
“the adherence by scientists and their institutions to honest and verifiable
methods in proposing, performing, evaluating, and reporting research activ-
ities” (NAS, 1992, p. 27). The challenge is that science is a self-regulating
community, with few comprehensive guidelines for responsible research
practices (Steneck, 2006). The guidelines that do exist often contradict each
other (Emanuel et al., 2000). For example, there are inconsistencies in the
rules governing the deidentification of personal health information, obtain-
ing individual consent for future research, and the recruitment of research
volunteers (IOM, 2009a). The 2011 Report of the Presidential Commission
for the Study of Bioethics Issues recommended that the Common Rule be
revised to include a section on investigators’ responsibilities in order to
bring it into harmony with FDA regulations for clinical research and inter-
national standards (PCSBI, 2011). Moreover, when ethical standards and
best practices are available to guide behavior, some investigators may still
be unaware of these rules, or simply breach them. For example, Martinson
and colleagues (2005) conducted a series of focus groups with investigators
from top-tier research universities to identify the top 10 misbehaviors of
greatest concern in science. They then surveyed more than 7,000 early- and
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mid-career U.S. investigators who have funding from the National Insti-
tutes of Health (NIH) and asked them to report on their own behavior.
Thirty-three percent of the respondents reported engaging in at least 1 of
the 10 misbehaviors during the previous 3 years. The three most common
misbehaviors were: (1) overlooking other researchers’ use of flawed data
or questionable interpretations of data; (2) changing the design, methodol-
ogy, or results of a study in response to pressure from a funding source;
and (3) circumventing certain minor aspects of human-subjects research
requirements (Martinson et al., 2005). This situation is problematic because
the underlying science must be sound if patients are going to participate
in clinical trials and, eventually, in consultation with their physicians, use
research results for medical care decisions.
Investigators
Responsible conduct in any research, including omics research, starts
with the investigators. This includes both junior and senior investigators.
This section of the chapter describes the roles and responsibilities of inves-
tigators who conduct biomedical omics research with the goal to improve
patient care. These responsibilities include the most basic principles of sci-
ence, such as a serious and in-depth consideration in a discussion section
of a journal article of “what might be wrong with the data and conclusions
I have just reported” (Platt, 1964). The specific responsibilities discussed
below include fostering a culture of scientific rigor and welcoming con-
structive criticism, comprehensively reporting the methods and results of a
study, and making data and code publicly available so that a third party can
verify the data and result. Box 5-1 highlights themes extracted from several
representative case studies for investigators to consider.
Culture
All investigators have a responsibility to promote a culture of scientific
rigor and to transmit ethical principles of science to future generations of
investigators. Scientific rigor can be fostered by developing clear standards
of behavior, disseminating those standards through education and mentor-
ing, and reinforcing the standards through exemplary practice at all levels of
the research community (Frankel, 1995). Investigators who do not adhere
to these values are not fulfilling their ethical responsibilities. Although many
cultural issues are not unique to omics research, taking steps to improve
scientific culture is particularly important in omics research because of
the nature of omics discoveries, which depend on large datasets, complex
analyses, and a specialized multidisciplinary team.
A number of influential reports have recommended sets of values,
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BOX 5-1
Themes from the Case Studies for Investigators
The Duke Case Study
Several questions have emerged regarding the degree to which key tenets of
scientific rigor (for both laboratory-based research and clinical trials) were followed
in the Nevins laboratory at Duke University. First, there were numerous errors in
the primary data (Baggerly and Coombes, 2009; Coombes et al., 2007). Predic-
tors derived from the training datasets were not locked down, leading to flaws in
the validation process and the omics-based tests that were developed. Second,
major results in the papers published by the Duke investigators were not repro-
ducible. For example, figures in the Hsu et al. paper could not be reproduced with
the data provided (McShane, 2010b). Third, the Lancet Oncology paper states
that the investigators had access to unblinded data as indicated by the statement
that “MD, PF, AP, CA, SM, JRN, and RDI had full access to the raw data”; it was
subsequently confirmed that the data files had not been blinded by the European
investigators when the data were originally sent (Goldberg, 2009). Fourth, the
Duke investigators did not provide the public with full access to their data and code
(Baggerly and Coombes, 2009; Baron et al., 2010). They also failed to address
the questions and challenges of external investigators who were trying to repro-
duce their work to the mutual satisfaction of all parties involved (Baggerly, 2011;
McShane, 2010c). In response to the National Cancer Institute’s queries, the Duke
investigators acknowledged that their tests were unreproducible and retracted
the original papers (Bonnefoi et al., 2011; Hsu et al., 2010; Potti et al., 2011). Dr.
Joseph Nevins, senior mentor of the investigators whose genomic predictors were
used in the three clinical trials named in the IOM committee’s statement of task,
stated during discussions with the committee that “a critical flaw in the research
effort was one of data corruption” (Nevins, 2011). Throughout this process, the
responsibilities of the coinvestigators on the research team and lines of account-
ability were apparently unclear.
The OvaCheck Case Study
The investigators made their initial datasets publicly available. Independent
investigators found numerous problems with the statistical and experimental meth-
ods and concluded that the results were unreproducible (Baggerly et al., 2004).
Thus, in this case, making the data publicly available may have helped prevent
the routine clinical use of an unvalidated screening test.
Commercially Developed Tests: Data and Code Availability
A review of the six commercially available tests discussed in Appendix A illus-
trates that public availability of all omics-based test data has not been standard
practice. The field of omics is early in its development, and the standards for data
sharing have been unclear and only now slowly evolving toward more transpar-
continued
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BOX 5-1 Continued
ency. Commercial interests and protection of proprietary information also may
have limited the public availability of some data and information.
These six cases highlight several examples in which test developers explicitly
note the availability of data. For example, Paik et al. (2004), Deng et al. (2006), and
Rosenberg et al. (2010) reported the computational model for Oncotype DX, Allo-
Map, and Corus CAD, respectively. Both tests developed as LDTs had published
computational models (Oncotype DX and Corus CAD); only one FDA-cleared test
has a published computational model (AlloMap). Discovery microarray data are
available for MammaPrint, AlloMap, and Corus CAD (Deng et al., 2006; van ‘t Veer
et al., 2002).* Buyse et al. (2006) reports that raw microarray data and clinical data
for the MammaPrint clinical validation study were deposited with the European
Bioinformatics Institute ArrayExpress database. Although there are examples of
developers reporting the availability of a test’s computational model or data used
in discovery or validation, often sufficient information is not publicly available for
external investigators to fully reproduce a test.
NOTE: See Appendixes A and B on the case studies for more information.
*Microarray data from Corus CAD are available, but PCR data used in test devel-
opment are unavailable. Personal communication, Steve Rosenberg, October 21,
2011.
traditions, and standards that investigators should embody to promote a
culture of scientific rigor. The National Academy of Sciences, the National
Academy of Engineering, and the Institute of Medicine (IOM) collabo-
rated in producing the report, Responsible Science, Volume I: Ensuring the
Integrity of the Research Process (NAS, 1992). This report highlighted the
importance of investigators upholding the highest standards of honesty,
integrity, objectivity, and collegiality. The authoring committee directed
individual investigators to accept formal responsibility for ensuring the
integrity of the research process and creating an environment, a reward
system, and a training system that encourage responsible research practices.
A more recent National Academies report, On Being a Scientist: A Guide
to Responsible Conduct in Research (NAS, 2009), identified three sets of
obligations for investigators: (1) an obligation to merit the trust that their
colleagues place in them (i.e., science is cumulative and investigators build
on previous work); (2) an obligation to themselves (i.e., investigators should
adhere to professional standards and develop personal integrity); and (3) an
obligation to act in ways that serve the public (i.e., the public uses science
to make policy decisions). The Office of Research Integrity (ORI) of the
Department of Health and Human Services (HHS) also has outlined several
values that investigators should share in promoting a culture of scientific
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rigor, including (1) honesty: conveying information truthfully and honoring
commitments; (2) accuracy: reporting findings precisely and taking care to
avoid errors; (3) efficiency: using resources wisely and avoiding waste; and
(4) objectivity: letting the facts speak for themselves and avoiding improper
bias (Steneck, 2006). These reports outline general guiding principles for
investigators’ behavior. However, identifying the values and obligations
that investigators should possess does not directly inform investigators on
how they should respond in specific situations and conflicts. Ultimately,
investigators’ actions need to be informed by good judgment and personal
integrity.
Two of the major influences on the development of investigators’ values
and integrity are advisors and mentors (Bird, 2001; NAS, 1992, 2009),
who define, explain, and exemplify scientific norms and ethics. All mem-
bers of the research team, including biostatisticians and bioinformatics
scientists, should have access to mentors with the appropriate expertise
and credentials. Senior investigators’ conduct can reinforce or weaken the
importance of complying with these scientific norms and values. Sprague
and colleagues (2001), for example, conducted a study to identify the
methods by which ethical beliefs are passed on to students. They surveyed
faculty and graduate students and asked respondents to rank methods of
teaching about ethics; 1,451 surveys were distributed to faculty and 627
were returned (45.2 percent return rate). An additional 6,000 surveys were
sent to academic departments to be distributed to graduate students and
1,152 were returned (19.2 percent return rate). A major weakness of this
study is the low response rates. However, both faculty and students ranked
courses dealing with ethical issues as most influential in teaching students
ethical beliefs. Mentors in graduate school also were highly ranked, with
graduate students ranking mentors as more important than faculty did.
Other important influences included discussions in courses, laboratories,
and seminars as well as interactions with other graduate students (Sprague
et al., 2001). In other words, young investigators’ interactions with other
investigators shaped their beliefs and values.
Another important component of promoting a scientifically rigorous
culture, which falls to investigators, is valuing teamwork and mutual respect
and empowering people at lower levels in the hierarchy to speak up if they
observe a problem or have a concern regarding research practices. The avia-
tion and energy industries provide evidence for the pivotal importance of
creating cultures that value these characteristics and consistently expect and
laud persons who speak up to alert the group to problems and concerns.
For example, the aviation industry has recognized for some time that errors
are more likely to happen when there is suboptimal teamwork and com-
munication (Helmreich, 2000). Thus, improvements in aviation safety have
been attributed to training crews on how to address and prevent human
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error, the role of leadership, the need for monitoring and crosschecking
decision-making processes, and the use of checklists. This same approach
has been applied successfully to the patient safety improvement movement
to reduce the effects of human errors (Gawande, 2009; Hudson, 2003;
Longo et al., 2005; Pronovost et al., 2003) and can be applied equally to
the biomedical research enterprise.
Full Reporting
Fully reporting the methods and results of a study is essential for the
reproducibility of research and for reviewers’ and readers’ evaluation of
the validity of a study. Thus, investigators have a fundamental responsibil-
ity to provide a complete and accurate report of their methods and findings
(NAS, 1992, 2009; Steneck, 2006). All publications—and omics publica-
tions in particular—should present a full and detailed description of the
study methodology, the statistical analysis plan that was finalized before
the validation data were analyzed, an accurate report of the results, and an
honest assessment of the findings, including an explanation of limitations
that may affect the conclusions (Platt, 1964; Steneck, 2006). This level of
transparency should allow an independent third party to verify the data
and results.
As discussed in Appendix D, reporting guidelines are tools to help
investigators meet this obligation and report the essential information and
elements of a study. All investigators who are coauthors on a report—
and particularly a senior investigator or mentor—also are responsible for
understanding the specific aims, methods, major findings, and implications
of the interdisciplinary research. They are responsible for reading the com-
plete manuscript, suggesting edits, and for being alert to misinterpretation,
such as misrepresentation of findings and limitations, and discussing such
observations with appropriate members of the team or oversight groups.
Data and Code Availability and Transparency
The scientific community widely agrees that investigators should make
the research data and code supporting a manuscript, as well as the statisti-
cal analysis plan that had been finalized before data were unblinded and
available for analysis, publicly available at the time of publication (NAS,
1992, 2009; NRC, 1985, 2003). Transparency is essential for the interpret-
ability and reproducibility of research and a tenet of any good scientific
method. Indeed, the purpose of methods sections in journal publications is
to provide enough detail so that other investigators can interpret the results
and, if they wish, reproduce the study and obtain the same results. Thus,
providing sufficient detail of methods allows independent investigators to
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verify published findings and conduct alternative analyses of the same data.
It also discourages fraud and helps expedite the exchange of ideas (Peng et
al., 2006). Investigators who refuse to share the evidentiary basis behind
their conclusions, or the materials and analytical methods needed to repli-
cate published experiments, fail to uphold transparency as a basic standard
of science. In an era when much of the Methods section and/or elaborate
data appear only in the Supplementary Materials section, more attention
is needed to guide the reader through well-annotated supplementary mate-
rial. This problem is perpetuated by the brevity of articles published in the
higher impact journals.
The National Academies has issued numerous reports emphasizing
the importance of data sharing. Sharing Research Data recommended that
sharing research data at the time of publication should be a regular practice
in science (NRC, 1985). A later report, Sharing Publication-Related Data
and Materials, developed a uniform principle for sharing integral data and
materials expeditiously (NRC, 2003). It recommended that authors include
the code, algorithms, or other information that are central to verifying
or replicating the claims in a publication. If the data and code cannot be
included in the actual publication (e.g., because the data files are too large),
the report recommended that the data and code be made freely available
through other means in a format that allows an independent investigator to
manipulate, analyze, and combine the data with other scientific data. The
report also stipulated that, if publicly accessible repositories for data have
been developed and are in general use, the relevant data should be deposited
in those repositories. Investigators are responsible for anticipating which
materials are most likely to be requested and should include a statement on
how to access the materials in the published paper.
In On Being a Scientist, the National Academies addressed the chal-
lenge of sharing research data in the current environment, where the quan-
tity and complexity of data are increasing and the cost of sharing data is
high (NAS, 2009). The complications and cost of sharing large datasets also
were recently highlighted in an issue of Science, dedicated entirely to data
collection, curation, and access issues (Science Staff, 2011). The National
Academies concluded that, despite these challenges, investigators have a
responsibility to develop methods to share their data and materials at the
time of publication (NAS, 2009). Investigators may share data through
centralized facilities or undertake collaborative efforts to form large data-
bases, such as the database of Genotypes and Phenotypes (dbGAP), the
European Molecular Biology Laboratory’s European Bioinformatics Insti-
tute (EMBL/EBI), the National Library of Medicine’s Gene Expression
Omnibus (NLM/GEO), Compendia Bioscience, UCSC Gene Browser, and
ProteomeXchange. When data undergo extensive analysis as part of a
scientific study, the requirements to share those data also include a require-
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ment to share the software, code, and sometimes the hardware used in the
analyses (NAS, 2009). Authors can facilitate the use of such information
with graphical user interfaces introduced into the dataset, for example, as
facilitated through nanoHUB (Klimeck, 2011).
Ultimately, many investigators are unwilling to comply with the require-
ment to share their data and code. For example, in an article for the New
York Times, Andrew Vickers, a biostatistician at Memorial Sloan-Kettering
Cancer Center, documented his lack of success in requesting cancer data
from various investigators from numerous institutions (Vickers, 2008).
Vickers also referenced a survey conducted by John Kirwan of the Univer-
sity of Bristol on investigators’ attitudes toward sharing data from clinical
trials. Three-quarters of the investigators surveyed stated that they were
opposed to making original trial data available. They cited several reasons
for refusing, such as the difficulty of putting together a dataset and the risk
of their data being analyzed using invalid methods. Vickers concluded that
investigators are often opposed to the potential use of their data by other
independent investigators who may make influential discoveries, and often
resist challenges to their conclusions that emerge from new analyses. Inves-
tigators may also be resistant to sharing their data and code because of the
time and effort needed to curate and annotate a dataset and support other
investigators’ access to the material.
The obstacles to sharing data and code may seem particularly daunting
in omics research. However, the fields of molecular biology and structural
biology widely use web-based genomic and proteomic databases (e.g.,
GenBank and Protein Data Bank) (Brown, 2003). These databases allow
investigators to share DNA and amino acid sequences, as well as protein
structure data, and many journals mandate deposition of these data as a
condition of publication. Microarray assays do produce an enormous quan-
tity of data (Quackenbush, 2009); as many as 1 million variant positions
on the genome across thousands of samples, and next-generation RNA
sequencing methods raise further challenges.
The scheme for Minimum Information About a Microarray Experi-
ment (MIAME) was created and adopted by investigators in this field to
improve the annotation of microarray data (Brazma et al., 2001). It estab-
lished standard, comprehensive annotation requirements that have been
adopted by most scientific journals. Data from more than 10,000 microar-
ray studies have been deposited into public repositories designed to archive
MIAME-compliant data (Brazma, 2009). MIAME also has stimulated the
proteomics and metabolomics scientific communities to develop reporting
standards and formats. In fact, the Minimum Information for Biological
and Biomedical Investigations (MIBBI) project has cataloged more than 30
different reporting standards for biological and biomedical data (Taylor et
al., 2008). Nevertheless, many investigators still fail to provide fully anno-
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tated data (Brazma, 2009; Quackenbush, 2009). Thus, further steps need to
be taken to ensure investigators share their data and code. The committee’s
recommendations to journals and funders (discussed below) are intended
to create additional incentives for investigators to comply with data- and
code-sharing norms. Issues of proprietary information can be dealt with
by depositing the materials with a responsible third party that can ensure
confidentiality and protection of the material (e.g., FDA). The patent system
also protects private investments in omics research (SACGHS, 2010) (see
Box 2-1 for a more detailed discussion on intellectual property law and
related challenges associated with data sharing).
Institutions and Institutional Leaders
This section describes the roles and responsibilities for institutions that
conduct biomedical omics research aimed at improving patient care, includ-
ing: fostering a culture of scientific integrity, overseeing research, increasing
awareness of reporting systems for lapses in research integrity, investigating
credible concerns about scientific integrity, monitoring and managing finan-
cial and non-financial conflicts of interest, and supporting and protecting
the intellectual independence of biostatisticians, bioinformatics scientists,
pathologists, and other collaborators in omics research. These responsi-
bilities lie ultimately with institutional leadership. Indeed, any institutional
attempt to meet these responsibilities will fail without explicit and visible
support and direction from institutional leadership (Schein, 2004). Some
of these responsibilities are closely related to the responsibilities of the
investigators.
Institutions, such as universities and companies, and the institutional
leaders, in collaboration with their investigators, play an essential role in
promoting a culture that encourages investigators to act ethically and con-
duct scientifically rigorous research. Institutions and their leadership bear
direct responsibility for complying with existing rules and regulations gov-
erning research; overseeing and creating reward systems for investigators;
providing training and education to investigators on relevant topics; and
producing an environment of trust, openness, and honesty. The integrity of
the research enterprise depends on investigators, collaborators, and observ-
ers feeling encouraged and supported when they identify and report either
routine scientific disagreements or potential breaches of scientific integrity,
regardless of their position within the institution. Institutional leaders also
have direct responsibility, when concerns are raised, for establishing and
supervising a “process of evaluation” of specific research results and claims
by their investigators.
In the Duke University case, inadequacies in the institutional oversight
processes and a lack of sufficient checks and balances allowed invalid
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end of funding, and funders should financially support this
requirement.
ii. Provide continuing support for independent repositories to
guarantee ongoing access to relevant omics and clinical data.
iii. Support test validation in a CLIA-certified laboratory and con-
sider the usefulness of an independent confirmation of a candi-
date omics-based test prior to evaluation for clinical use.
iv. Designate an official to alert the institutional leadership when
serious allegations or questions have been raised that may war-
rant an institutional investigation; if the funder (e.g., NIH) has
initiated that question, then the funder and institution should
communicate during the investigation;
v. Establish lines of communication with other funders to be
used when serious problems appear to involve interdependent
research sponsored by another funder along the omics-based
test development process.
5b: Federal funders of omics-based translational research should have
authority to exercise the option of investigating any research being
conducted by a funding recipient after requesting an investigation
by the institution.
RECOMMENDATION 6: FDA
6a: In order to enable investigators and institutions to have a clear
understanding of their regulatory responsibilities, FDA should
develop and finalize a risk-based guidance or a regulation on:
i. Bringing omics-based tests to FDA for review.
ii. Oversight of LDTs.
6b: FDA should communicate the IDE requirements for use of omics-
based tests in clinical trials to the OHRP, IRBs, and other relevant
institutional leadership.
RECOMMENDATION 7: Journals
7: Journal editors should:
7a: Require authors who submit manuscripts describing clinical evalu-
ations of omics-based tests to:
i. Register all clinical trials at www.clinicaltrials.gov or another
trial registry acceptable to the journal.
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ii. Make data, metadata, prespecified analysis plans, computer
code, and fully specified computational procedures publicly
available in an independently managed database (e.g., dbGAP)
in standard format.
iii. Provide the journal with the sections of the research protocol
relevant to their manuscript.
iv. Identify each author’s role in the development, conduct, analy-
sis, writing, and editing of the manuscript. Require the lead
and senior authors to attest to the integrity of the study and the
coauthors to confirm shared responsibility for study integrity,
v. Use appropriate guidelines (e.g., CONSORT, REMARK) and
submitting checklists to certify guideline use.
7b: Develop mechanisms to resolve possible serious errors in published
data, metadata, code, and/or computational models and establish
clear procedures for management of error reports.
7c: Alert the institutional leadership and all authors when a serious
question of accuracy or integrity has been raised.
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