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5
The Delivery of Genomic Data
Important Points Highlighted by Individual Speakers
• Pharmacogenetic results can be important for patient care, but
data need to be carefully integrated into patient records and
care processes.
• If patients are empowered to make their privacy preferences
available to caregivers and researchers, the delivery of care and
the use of patient data for research could both be enhanced.
Genomic data are of no value unless they can be communicated in an
effective way to people who can act on that information. Understanding
what the message needs to be is only part of the challenge, said Greg Feero
of the National Human Genome Research Institute, who moderated the
workshop session on communicating genomic data. The actual delivery of
that information to both the health care professional and the consumer is
essential to improving outcomes. Will such information be delivered at the
point of care or in some other setting? What infrastructure is needed to
deliver genomic information? Who has the responsibility for delivering the
information and ensuring that it is understood?
39
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40 INTEGRATING LARGE-SCALE GENOMIC INFORMATION
IMPLEMENTING PHARMACOGENETICS IN CLINICAL PRACTICE
St. Jude Children’s Research Hospital, in association with the
Pharmacogenomics of Anticancer Agents Research 4Kids program under
the Pharmacogenomics Research Network (PGRN) at the NIH, has been
using well-known genetic polymorphisms to adjust drug therapy in patients
in real time. St. Jude has the advantage of being able to overcome (or
ignore) many obstacles to preemptive genotyping, said Mary Relling of
St. Jude. The hospital covers all patient care costs and provides all medi-
cations for 5,000 unique, high-risk patients per year, 80 percent of whom
have cancer and 20 percent of whom have sickle cell disease, HIV infection,
or other life-threatening diseases. St. Jude has a collaborative approach to
patient care, in which pharmacists are integrated into the team that delivers
care and are responsible for signing off on every consult associated with a
pharmacogenetic test result. St. Jude also maintains comprehensive EMRs
that fully integrate every aspect of outpatient and inpatient care, which
Relling said was a large motivating factor for St. Jude to incorporate genetic
testing into the patient record.
For pharmacogenomic testing, St. Jude is now using the Affymetrix
DMET-plus array, which tests for more than 1,900 polymorphisms in 225
genes that are likely to be important for pharmacogenetics. Previously it
had been screening for only two of the genes included on that panel and
doing so at a higher cost. The medical staff would rather not have the
medical record populated with genomic information of uncertain clinical
utility, Relling said. As a result, the hospital obtains consent from patients
to withhold results that are not clinically interpretable, though it also dis-
cusses with patients the possibility that these findings may in the future have
implications for disease risk.
A new program known as PG4KDS has the goal of migrating a larger
number of pharmacogenetic tests from the laboratory into routine patient
care so that results are available for preemptive use. The primary program
objective is to estimate the proportion of patients who have high-risk or
actionable pharmacogenetic results entered in their EMR with decision sup-
port (automated information alerts generated to assist health care providers
in making decisions about a patient’s care). The secondary objectives are to
use systematic procedures for prioritizing and migrating pharmacogenomic
test results to the EMR, to incorporate clinical decision-support tools link-
ing test results to medication use, and to assess the attitudes and concerns
of research participants and clinicians.
An educational video made with the hospital’s Family Advisory Council
is available to provide information for families. Information is also avail-
able on the program’s website (www.stjude.org/pg4kds) which lists which
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41
THE DELIVERY OF GENOMIC DATA
genes are tested for and which genes are reportable, along with links to
more detailed information for clinicians.
After patients are enrolled, their DNA is genotyped, and the results
that pass quality control thresholds are posted in the research database.
Only the small portion that meets the clinical threshold is migrated to
the EMR. A limited number of the clinical results that are based on high-
risk genotypes and high-risk drugs have decision-support rules that send
automatic alerts to clinicians. The study investigators, with the input of an
oversight committee, decide how to update the information that is entered
into the chart, including the addition of new clinically actionable variants
and which results are designated to receive decision support, starting with
widely accepted results for high-risk genotypes. “We won’t tackle the most
controversial ones until later, or maybe never. We do a big disservice to
implementing clinical genomics by trying to implement stuff too fast. We
should concentrate on the home runs first,” Relling said.
When it is decided to put a new gene into the EMR, the results for that
gene are inserted into the medical record for all past and future patients,
with participants having the choice of getting a letter for each new gene
that is posted. “So far every single patient has asked for that information,”
Relling said, “and of course that could be converted to something electronic
in the future.”
To facilitate access to information, the EMR at St. Jude Children’s
Research Hospital has been customized with a pharmacogenetics tab, where
genotypes are entered along with a detailed laboratory report of how
the test was performed and clinically relevant gene-specific information
explained. These are lifetime results, Relling said, and physicians should not
have to search for them by date or order a test on a gene that has already
been interrogated.
When the decision to prescribe a high-risk drug conflicts with the pres-
ence of a high-risk or high-priority genotype, a decision-support alert is
sent to the clinician. For example, the EMR will automatically generate a
warning if codeine is prescribed for the 10 percent of patients who are poor
metabolizers based on their CYP2D6 profile. There are two types of warn-
ings. The first is a post-pharmacogenetic test result: If the patient already
has a high-risk genotype in the EMR, the clinician will get an alert. The
second is a pre-genetic test warning: If thiopurine is ordered for a patient
and the patient has not had thiopurine S-methyltransferase (TPMT) tested,
an alert is issued. Alerts are also being linked to an explanation of why a
high-risk drug–gene pair exists for use by clinicians who are interested in
learning more.
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42 INTEGRATING LARGE-SCALE GENOMIC INFORMATION
Challenges in Implementation
Implementing this system has revealed several challenges that future
genomic medicine initiatives will likely face, Relling said. First, she said,
there is a lack of consensus or guidelines on which drug–gene diplotypes
are most important, although she acknowledged that “it is better to have
experts review the evidence and come up with some recommendations”
even if they are not in agreement than to make each individual clinician
have to synthesize his or her entire knowledge and medical practice experi-
ence. The PG4KDS program is addressing this issue through the PGRN.
Specifically, a subgroup called the Clinical Pharmacogenetics Implementa-
tion Consortium has been formed to evaluate drug–gene pairs using stan-
dard grading systems, the peer-reviewed literature, and other information.
Severity of disease, therapeutic alternatives, consequences of giving the
wrong drug, and consequence of giving the right dose all have to be taken
into account when making these decisions.
Another complication is that once a gene test is in the EMR, clinicians
are obligated to use those results for all drugs affected by the test. Even
rarely prescribed drugs need decision support. “It is going to take a lot
of work to go through and make rules that we are all comfortable with,”
Relling said.
Diplotypes are sometimes ambiguous because of the nature of the test-
ing. For example, the DMET array often produces ambiguous calls, Relling
said. “We have to look very carefully at the reasons for those ambiguous
diplotype assignments. Is it the fact that you can’t phase haplotypes, so
we can’t always distinguish between a heterozygous- and a homozygous-
deficient patient? Or is it because of a simple no call of a probe for a rarely
involved SNP? Someone has to decide whether those [ambiguous assign-
ments are actually important] or not, and again that takes a high level of
knowledge of the genes and the drugs.”
Using a software program called PHASE, a much higher percentage of
diplotype assignments can be made non-ambiguous, but it is a judgment
call as to whether to deliver that information to the clinician. Interpreta-
tion is complex and time consuming, and it changes over time, Relling
observed. For example, even with the relatively simple TPMT diplotypes,
about 8 percent of patients have an ambiguous diplotype. Some patients
are homozygous deficient, and if they get a normal dose of thiopurine, there
is a high probability they will die of toxicity, whereas other patients can
tolerate doses for a much longer time period. “Basically, we have to write
very specific [reports] to say what the caveat is in interpreting these kinds
of results.”
Multiple testing of the same gene over the lifetime of the patient
requires that someone check to see whether the results contain discrep-
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THE DELIVERY OF GENOMIC DATA
ancies. One must also check other details such as whether the race of a
patient in the EMR agrees with the patient’s self-declared race or whether
the sex in the EMR agrees with the self-declared sex. “Flagging some of
those genotypes and eventually manually approving them to move from
the research laboratory into the EMR takes a lot of steps,” Relling said.
Another critical issue is who will pay for preemptive genotyping. Array-
based genotyping can be cheaper, easier, and more effective than testing one
gene at a time even when no drug is being contemplated for treatment, but
it may not be easy to get reimbursement for such tests. Finally, Relling said,
until patients have universal lifetime EMRs, the fragmentation that affects
all of health care is going to affect genomic medicine.
GRANTING ACCESS TO PERSONAL HEALTH INFORMATION
“Remember when using an ATM was a mysterious and improbable
experience?” asked Robert Shelton of Private Access, Inc. “That’s about
where health care data sharing is today.”
The first automated teller machines (ATMs) were introduced in 1967,
but their use was limited until networks of ATMs enabled people to with-
draw money from almost any machine. Over time ATMs also developed a
compelling business justification because they saved money spent on human
tellers and generated revenue from ATM fees. Today there are 2.2 million
ATMs around the world, with a new one being added every 4 minutes.
Using a search engine also used to be a mysterious and improbable
experience, Shelton observed. In 1990 a search engine called Archie offered
access to a directory of directories. In July 2008 Google announced that it
had indexed 1 trillion pages and was adding several billion new pages per
day. As with ATMs, several intermediate technologies and changes made
this massive growth possible, including natural language search of con-
tents, analytics-assisted search that helped a user find the desired Web page
despite the terms entered, and compelling business models.
“What if people could search for health information as easily as we do
public documents?” Shelton asked. In that case physicians or patients could
call up any information contained in an EMR as easily as they access infor-
mation on the Web. But privacy needs to be protected. Thus, access provi-
sions need to be interposed between a search query and a result to determine
whether the searcher has the right to see the results of the search.
The tipping point for integrating privacy protections is occurring
now, Shelton said. In December 2010, the President’s Council of Advisors
on Science and Technology released a report that called for a “univer-
sal exchange language for health care information that enables health
IT [information technology] data to be shared across institutions, along
with network infrastructure that enables a patient’s data to be located and
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44 INTEGRATING LARGE-SCALE GENOMIC INFORMATION
accessed across institutional boundaries subject to strong, persistent, pri-
vacy preferences” (PCAST, 2010). Even before the release of the report, the
company Shelton directs had built and tested such a system. The centerpiece
of the system is that patients log onto a secure website and set their own
privacy preferences, after which researchers can search for information. For
example, Shelton said, if a researcher is looking for subjects for a clinical
trial, patients “need to have their hand raised for interest in clinical trials
already.”
Private Access does not actually hold patient data. Instead it holds the
patient index and search capability, with the privacy directives acting as
the switch for searches. For example, if a researcher logs in and searches
for potentially relevant subjects through the search index, the system filters
the results based on patients’ preferences. If a patient has indicated inter-
est in being part of a trial, the researcher gets contact information and can
make an offer.
The system has been tested in patient populations, and the lessons
learned have been applied in successive generations of the system. First,
patients need to be educated and empowered, not coerced. Consumers are
more likely to engage in an environment of trust if they are referred by a
trusted intermediary, a friend, a health care provider, or peers, Shelton said.
Patients should control the use of their personal information for expressed
purposes, they need guide-based assistance to help make informed choices,
and the consent tools should be dynamic and granular. To make sure that
patients have the information needed to set preferences, links should be
available to more detailed educational sites.
On the searcher’s side, the system needs to be fast, easy to use, and
powerful. It should use familiar Web-based search conventions such as
bookmarks and automated alerts. And, Shelton said, researchers should be
able to receive pre-authorized access to personal health data.
Implementation
When using such a system, a patient who is undergoing the registration
process at a hospital would also convey privacy preferences to the hospital,
perhaps through a smartphone application. The patient would be asked if
information could be deposited in a personal health record (PHR), whether
or not the patient has established one previously. If the patient agrees, infor-
mation from the hospital would be moved beyond the hospital’s firewall to
a PHR where it could be searched through a search index.
Other data, such as pharmacy histories, laboratory results, or the
results of genetic tests, can also be moved into the PHR. Data can also be
moved into other repositories, such as EMRs or searchable databases in
each institution visited.
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THE DELIVERY OF GENOMIC DATA
Patients have heterogeneous preferences, Shelton said. Attitudes range
from “It is okay for researchers to use my data without my consent at
all” to “I am willing to give general consent in advance for the use of my
data without being consented” to “Consent is not needed if my identity
will never be revealed and the study is supervised by an IRB [institutional
review board]” to “I want each study seeking to use my data to contact me
in advance and get my specific consent each time” to “I would not want
researchers to contact me or use my data under any circumstances.” Most
patients fall in the middle of this distribution of preferences, but it used to
be logistically impossible to satisfy their wishes. New technologies have
now made it possible, Shelton said. Furthermore, he added, “It is empower-
ing, and it creates the data liquidity that we all are looking for.”
In one example cited by Shelton, a researcher at the University of
California, Los Angeles, looking for lupus patients found six within 25 miles
of campus in under a minute. “Why? Because he was using a search engine
to find them.”
Many resources are currently flowing to the basic science of genomic
medicine, Shelton said, but resources also need to be expended on sociologi-
cal research directed at how the science will be applied. This research in
turn could inform the business models that will drive adoption.
The patient is the key, Shelton concluded. “We have to give them the
tools, and we have to give them the chance.”
ADVANCING THE UTILIZATION OF GENOMIC INFORMATION
Increasing the use of genomic information by providers and consumers
will be a slow process. Many people may be able to understand genetic data
if they are provided with information, Relling said, but people who are old
or poor or who can barely read will have much more difficulty. “There are
a lot of people in this country who need us to be a little paternalistic,” she
said. If highly trained physicians sometimes cannot understand the infor-
mation linking genetic variation with clinical recommendations, creating
such understanding among others may not be feasible in the short term.
“We have to crawl before we walk,” she said. “We have to get this to work
in health care institutions where we have highly trained people and get it
understood and adopted, and then we can maybe start pushing it out more
to consumers.”
Shelton added that he did not expect consumers to have the ability to
understand the data that they are receiving. “What I am advocating is that
a consumer knows that I don’t want Aunt Betty to see my data. ‘If you tell
me that Aunt Betty can’t see it, then I am fine with anybody else who is
a researcher seeing it.’ Consumers understand their privacy wishes. That
is one thing they do understand and the law doesn’t.” Patients should be
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46 INTEGRATING LARGE-SCALE GENOMIC INFORMATION
empowered to sign up for a personal genome project if they wish, just as
they should be empowered to forbid the use of any of their genetic data.
“We don’t use sushi-grade data in the medical establishment. We use chum.
It is deidentified and it is all ground up because we have to in order to come
under the IRB requirements and in order to avoid a lot of the challenges
of asking for permission. If we ask for permission, empower it, and make
it easy, it will happen.”
BUILDING A BUSINESS CASE FOR GENETIC TESTING
Given the limited associations between genetic test results and most
diseases, Shelton suggested that the business case for using health IT sys-
tems that incorporate genetic results rests on two motivations. The first is
recruiting subjects for clinical trials. The second is the reduction of dupli-
cate tests. In the future an advertising model may evolve as well, he added,
as patients become willing to have their medical information used to make
them aware of offers that are custom tailored to them.
More generally, he said, the most important piece of health care in the
future will be data, which will make the IT department the profit center of
health organizations. “The IT department in health care gets a very small
fraction of what IT departments get in other industries,” he said. “The
reason is because it is a cost center. It is not a profit center. Make it into a
profit center, and budgets will go up.”
