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4
The Interpretation of Genomic Data
Important Points Highlighted by Individual Speakers
• For genomic testing to be accepted, it should have not only
analytical validity but also clinical and social utility.
• Genomic testing should be used as a tool that is integrated with
traditional tests for making a disease diagnosis and guiding
therapy.
• Human genetic diversity and genetic differences between mater-
nally and paternally derived chromosomes need to be consid-
ered when interpreting genomic data.
The interpretation of genomic data is even more difficult than generating
and curating the data, said Muin Khoury of the Centers for Disease Control
and Prevention (CDC), who moderated the workshop session on interpreta-
tion of genomic data. The genome does not mean the same thing to each per-
son at every point in time. The significance of particular variants can depend
on age, health status, and other contextual factors during different life stages.
The interpretation of genomic data also raises many difficult questions: Will
different vendors use the same data to offer different interpretations? How
will the results be verified? Who will generate second opinions? How will
non-genomic information such as epigenetic data be incorporated into inter-
pretations? And how will interpretation services be regulated?
25
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26 INTEGRATING LARGE-SCALE GENOMIC INFORMATION
DATA INTEPRETATION FROM A CLINICIAN’S PERSPECTIVE
A genetic test can have different types of utility, including clinical util-
ity and social utility, according to Robert Nussbaum of the University of
California, San Francisco. Clinical utility is a measure of how valuable a
test result is to a patient and a clinician in making decisions about whether
to do further diagnostic testing or end the “diagnostic odyssey” as well as
in deciding how treatment is managed or how lifestyle should be altered. A
test that leads to a diagnosis may have tremendous utility for a patient and
clinician, but a third-party payer wants the test to result in an action that
makes a measurable difference in health. Individual patients may have the
option to pay for a test out of pocket if third-party payers refuse to provide
reimbursement, but in that case, Nussbaum observed, many people will be
excluded because they cannot afford to pay for the test.
In addition to clinical utility, a test must have social utility if private
and government insurers are to be willing to pay for it. The insurers need
to be convinced that a new test, when compared with current standards of
care, would lead to improved health by reducing the need for less success-
ful therapy and would decrease costs by preventing more costly outcomes.
“You have to convince them that it is worth their paying for it as opposed
to paying for other things,” Nussbaum said.
Critical Assessments of Genomic Testing for Prevention
Nussbaum outlined six areas in which whole-genome sequencing could
be used in a clinical setting and described the common criticisms that arose
during discussions with his colleagues. First, whole-genome testing could
have clinical use for the identification of carriers of Mendelian disorders
prior to conception. At this point it is far less expensive to test for most
of the common autosomal recessive conditions than to perform complete
genome sequencing. Although the sequencing coverage is not complete for
the tests currently used to detect autosomal recessive genes, current standard
practice probably continues to be cost-effective compared to the use of
whole-genome sequencing for the same indication, Nussbaum said.
A second area, the use of genomics in prenatal and pre-implantation
testing, raises many issues, Nussbaum said. “A very serious decision has to
be made under severe time pressure with unclear genotype and phenotype
correlation.” Pre-implantation testing has advantages over prenatal testing,
but it is often limited by the amount of tissue available. This could change
with the development of epiblast biopsies to take the place of single-cell
testing.
Identification of personal risk for Mendelian disorders is a third area
that also raises unanswered questions, especially with regard to clinical
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27
THE INTERPRETATION OF GENOMIC DATA
validity. This application of genomics raises a variety of questions, includ-
ing which variants are responsible for a particular phenotype, what muta-
tions are pathogenic, and what the penetrance is of a known pathogenic
mutation. “There is a real gap in being able to tie the genotype to the
phenotype,” Nussbaum said, and progress needs to be made in that regard.
A fourth area in which genomics can be used in the clinic is pharmaco-
genetic testing. In some cases it already has clear clinical validity and
proven clinical and social utility. One well-established application, for
example, is human leukocyte antigen typing to prevent idiosyncratic
adverse reactions for drugs such as abacavir, an antiviral for treatment of
human immunodeficiency virus (HIV) infection (Hughes et al., 2008). But
the clinical and social utility of tests for common variants that affect the
pharmacokinetics or dynamics for drugs such as warfarin, clopidogrel,
irinotecan, codeine, and 6-thiopurine is still unclear (EGAPP, 2009; Gong
et al., 2011; Ned, 2010; Teml et al., 2007; Zhou, 2009). Part of the reason
for the lack of clarity, Nussbaum said, is that there may be replacement
drugs used instead, such as dabigatran and rivaroxaban for warfarin
or prasugrel instead of clopidogrel (Brandt et al., 2007). Furthermore,
Nussbaum said, it is too late to perform a complete sequencing when a
drug is about to be prescribed; the information generated by the sequenc-
ing needs to be available in advance.
The fifth area is direct tissue typing for transplantation. This technique
is, at the moment, still less costly to conduct than whole-genome sequenc-
ing, although delays in receiving the results may affect transplantation for
some indications. A possible advantage of whole-genome sequencing in this
case is that it may be possible for tissue typing information to be incor-
porated into networks for organ sharing, which would provide better and
more rapid identification of donor–recipient matches.
The sixth area is the identification of alleles, whether rare or common,
that increase the risk for common disorders. Such identification, at the pres-
ent, has very limited clinical validity and utility, Nussbaum said, suggesting
that this use of genetic testing currently “is more in the realm of entertain-
ment than medicine.” The few people who make the effort to have their
genomes tested do not necessarily consider it an essential component of
their medical care. Furthermore, this information generally cannot clearly
distinguish people who will suffer from a disease from those who will not.
For example, results from a panel of 13 SNPs show that people in the top
quintile of risk for coronary artery disease have a risk that is 1.7-fold higher
than those in the lowest quintile (Ripatti et al., 2010). But the distributions
of people at different levels of risk overlap extensively, Nussbaum said, and
the test offers “very little discrimination between those who have coronary
artery disease and those who do not.”
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28 INTEGRATING LARGE-SCALE GENOMIC INFORMATION
Genomic Testing for Diagnosis
“Could whole-genome sequencing be a cost-effective replacement for
candidate gene panels?” Nussbaum asked. It now costs more than $3,000
to have two genes tested, so in the future, sequencing the entire genome
could be reasonable by comparison, but, he said, “I don’t think we are
anywhere near there yet.” Discovery of the genetic reasons for undiag-
nosed hereditary diseases has yielded some remarkable success stories, but
in other cases extensive searches have not been successful. It remains to be
seen whether the genes that have been uncovered in the recent past, such
as those for Miller syndrome (Ng et al., 2010), are exceptions or the rule,
Nussbaum said. As technology is applied to larger pools of patients and
families, the success rate will go down.
With cancer genomes, sequencing is revealing a tremendous amount of
information about the variants that can be used for classification, prognosis,
and therapeutic management. Thus, it is reasonable to ask whether in the
future the most cost-effective and efficient assay with the most predictive
power will be whole-tumor-genome sequencing or simply sequencing a few
key variants. An important research question will be whether every cancer
is different, which, if true, would make it necessary to scan large amounts
of genomic data to understand each person’s disease.
Identifying a Path Forward
Nussbaum stressed that he was not trying to discourage the discussion
with his criticisms. “The analytical validity of whole-genome sequencing
is improving, the costs are coming down, and the poor state of genotype–
phenotype correlation is a recognized problem.”
Several points need to be emphasized in moving forward, he said. First,
the potential advantages of having complete sequences need to be recog-
nized. “Knowing this element or that element is not the same as knowing
the periodic table.” Because of the huge amounts of data involved, bio-
informatics will be essential in building a genomic basis for clinical work.
Second, a few demonstration projects are needed as part of an overall health
assessment throughout all stages of life that would integrate genomic data
into ongoing health care. A good candidate organization to carry out such
a project would be a large health plan that is also a provider and is willing
to establish a partnership. Third, interpretation should be ongoing, with
genome sequencing becoming more like a subscription service in which
technologies, software, and information constantly improve and knowl-
edge is exchanged. Patients expect their health care providers to be talking
with each other. Furthermore, a subscription implies a continuing relation-
ship among bioinformaticists, providers, and patients and their families
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THE INTERPRETATION OF GENOMIC DATA
in interpreting genomic data. Nussbaum added that efforts to standardize
nomenclature are under way, which should enhance collaboration. “As the
databases grow and become more and more useful,” he said, “there is going
to be constraint on the way people report things, and they will come to a
common reporting. I am actually fairly optimistic about it.”
Finally, it will be important to develop software to interpret vari-
ants and provide decision support. In part, this will require establishing
partnerships among laboratories, clinics, and the institutions that create
and maintain electronic medical records that are functional and allow for
interpretation but not necessarily storage of genomic data. “It is useless
to think about dumping all the sequence data into an electronic medical
record,” said Nussbaum. “It has to live someplace else in a way that makes
sense, and the interpretation and the re-interpretation have to come into the
medical record in a way that is valuable.”
INTEGRATING GENOMIC DATA WITH PATHOLOGY
In 2009 Mark Boguski of Harvard Medical School and two colleagues
published a paper that laid out a futuristic scenario for cancer care in the
year 2020 (Boguski et al., 2009). The process they described begins when
a patient presents with symptoms and needs to rely on the involvement
of a clinical laboratory for care (Figure 4-1). In addition to conventional
analyses of formalin-fixed, paraffin-embedded tissues, such as hematoxylin
and eosin and immunohistochemical staining, genome sequencing is also
performed. In this situation the pathology report is not just a textual
description of what is seen through a microscope and a diagnostic code. It
is a dataset and a collection of therapeutic recommendations that includes
the parameters under which the modeling was conducted. The oncologist
and the rest of the clinical care team then can accept the report’s simula-
tions or develop their own to administer precision targeted therapy. This is
a model in which advances in sequencing, systems biology, and other areas
make it possible to reverse engineer disease pathways, to annotate disease
networks and drug targets, and to simulate therapeutic interventions with
virtual drugs or combinations of virtual drugs.
In 2009 this seemed improbable, Boguski said. A year later, however,
a paper published in Genome Biology demonstrated as a proof of concept
every conceptual step in the scenario (Jones et al., 2010).
Integration of Genomic Data and Cancer Pathology
Boguski described a case study from the paper that demonstrates what
is possible. A 78-year-old man with no prior history of cancer presented
with a sore throat. A biopsy of a lump on the back of his tongue revealed
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30
Primary care
Radiologist
Surgeon
Primary
Oncologist
Surgeon Patient
care
Radiologist Administer develops
performs presents
physician
images precision customized
biopsy or with
orders
tumor therapy care plan
resection symptoms
tests
Annotated
Reverse Simulate
Data Annotated
Molecular Pathology
disease
engineer therapeutic
processing treatment
diagnostics report
networks and
disease interven-
and options
(NGS) drug targets
pathways tions
integration
Pathologist
interprets
results
Manual Automated Data output Interactive
Trigger
Stored data Decision
process process and input display
action
FIGURE 4-1 A model for future cancer care demonstrates how genomic sequencing and network biology will enable personalized
medicine.
NOTE: NGS = next-generation sequencing.
SOURCE: Adapted from Boguski et al., 2009.
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THE INTERPRETATION OF GENOMIC DATA
a papillary adenocarcinoma, which probably originated in a minor salivary
gland. A lymph node dissection revealed that the tumor had spread beyond
his tongue and was present in 3 of 21 lymph nodes in his neck. In response,
he was given adjuvant radiation therapy.
Four months later he returned for a follow-up visit, and a scan revealed
that the tumor had metastasized to both of his lungs. Because this was a
relatively rare tumor, no standard chemotherapy was available. A pathol-
ogy review indicated that the tumor was positive for epidermal growth
factor receptor (EGFR), and the man was started on targeted therapy with
erlotinib. However, the tumor continued to grow.
Both genome and transcriptome sequencing analyses were performed
on the patient’s tissue sample as a part of his clinical care. In this case the
transcriptome analysis was more important than the genome analysis, as
it showed an absence of EGFR expression. This finding indicated that
treatment with a different chemotherapeutic agent, sunitinib, might be
beneficial. After switching the patient to the new treatment, the disease
progression stabilized for 4 to 6 months.
Boguski described a second case in which genomic data was used to
guide therapy. A 60-year-old man with a long history of alcohol and tobacco
abuse presented with difficulty breathing and heart palpitations. Physical
examination of the patient revealed palpable right supraclavicular lymph
nodes, and a biopsy revealed metastatic squamous cell carcinoma originat-
ing in his esophagus. Standard cytotoxic chemotherapy was initiated. The
patient’s tumor genome was then sequenced along with DNA from his
peripheral blood cells. Following genomic analysis, cytotoxic chemotherapy
was discontinued, and the patient was started on imatinib, a targeted ther-
apy that disrupts tyrosine kinase signaling, and the tumor responded.
Cancers can continue to mutate after treatment has begun, Boguski
noted, and this makes them difficult to treat. In the first case above, the
patient eventually became resistant to sunitinib, and a second genomic and
transcriptome analysis was done, and the drug was changed. Again the
patient stabilized, and today the patient is still alive. “Had the genome anal-
ysis not been done, I doubt that would have been the outcome,” Boguski
said, “but this is one of those stories that . . . is quite dramatic when you
see potentially what kind of cost avoidance and precision diagnosis can be
achieved with genome and transcriptome analysis.”
A lesson drawn from the first case study is that whole-genome analysis
will consist of a variable package of genome or exome sequencing with or
without transcriptome analysis, depending on the clinical indication and
diagnostic goals. In the future, such analyses could also include epigenetic
measurements or other data. It is still unclear, Boguski said, “whether the
genome or transcriptome or some combination of both is going to be most
efficacious for certain kinds of cancers.”
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32 INTEGRATING LARGE-SCALE GENOMIC INFORMATION
Another conclusion drawn by Boguski’s paper is that whole-genome
characterization will become a routine part of cancer pathology. Further-
more, it will be done not once but multiple times during the course of the
disease for tumor subtyping, monitoring response to therapy, and diagnos-
ing the reasons for recurrence and therapeutic failures. “2020 is here,”
Boguski said. “This will become [the] standard of care for certain cancers
sooner than we think. I am absolutely convinced of that. . . . If I had that
choice as a patient, I would certainly [have my genome sequenced].”
Increased integration of genomic medicine in routine care also opens
up the potential for increased disparities in health care access and services.
Nicholas Schork from the Scripps Translational Science Institute suggested
that disparities could well be exacerbated in the short term. But when
sequencing becomes routine, he added, disparities will not be as severe.
Boguski made the same point, observing that in the first part of the 20th
century a major source of health care costs was hospitalization because of
infectious diseases, whereas today such diseases are mostly treated with
generic drugs. Similarly, a major therapy for gastric ulcers in the 1960s was
removal of part of the stomach, but the realization that ulcers are caused
by an infectious agent has led to the disease being managed with a 10-day
course of antibiotics. “This is the history of medicine,” Boguski said.
“[Genomic medicine] will eventually become democratized and available
to a larger portion of people.”
Overcoming Obstacles
Genomics is the pathologist’s new microscope, Boguski said. A torrent
of data will emerge from high-technology platforms. To pathologists these
technologies will largely be black boxes. The important factors will be cost,
accuracy, and turnaround time.
Interpretation of whole-genome analysis could be costly, Boguski said,
noting that some have talked of the $1,000 genome and a $1 million
interpretation. But technologies will likely drive down the cost of interpre-
tation. In particular, data annotation will increasingly be outsourced and
automated. What is ultimately needed, Boguski said, is a clinically action-
able knowledge base that any pathologist, genetic counselor, or medical
geneticist can rely on to make a decision.
Workforce issues could be a more severe constraint, Boguski said.
Today the United States has about 1,000 medical geneticists, 3,000 genetic
counselors, and 17,000 pathologists. Many more people in these specialties
will be needed in the relatively near-term future if the potential of genomic
medicine is to be realized.
A paper focused on workforce issues that emerged from an October
2010 meeting (Green and Guyer, 2011) called for several “Blue Dot” pilot
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THE INTERPRETATION OF GENOMIC DATA
projects within 2 to 20 months that would establish a nationwide program
for residency training by July 2012, define the concept of the “primary care
pathologist” in genomic-era medicine, and establish by December 2011 a
prototype “clinical-grade” disease variant database for one disease area.
The other projects are to compile and analyze current genetic, newborn, and
molecular pathology tests and create a whole-genome analysis “replacement
map,” to identify and validate operations models for whole-genome analy-
sis, to formulate the regulatory guidelines to conduct whole-genome analysis
test accreditation, and to address reimbursement issues.
Progress has been made on several of the Blue Dot projects, includ-
ing the development of a program called Training Residents in Genomics,
which is a collaborative project of pathology organizations, the National
Society for Genetic Counselors, and the National Coalition for Health Pro-
fessional Education in Genetics. The group is creating a modular transport-
able curriculum for training pathology residents in personalized genomic
medicine that will be introduced into a third of U.S. pathology residency
programs on a pilot basis by 2012.
Boguski concluded by saying that the current cost of a genome sequence
has decreased significantly from the initial cost of sequencing the first human
genome, which exceeded $2 billion. With the cost of genome sequencing
nearing that of routine clinical tests, the implications of such a capability are
going to be revolutionary, not evolutionary, Boguski said. “Next-generation
sequencing and whole-genome analysis is a disruptive technology.”
USING A BIOINFORMATICS MODEL FOR INTERPRETATION
Individual genomes contain about 4 million variants that are not in
reference genomes, with 50,000 to perhaps 150,000 that have not been seen
before, Schork said. Many of these variants influence phenotypic expres-
sion, but the question is which ones. If a variant is in an exon or an intron,
it can be studied to determine if it is likely to disrupt the functioning of the
gene, but this is not a useful approach for novel variants because they have
not previously been examined by a functional assay. Instead, determining
the likely functional effect of those variants requires bioinformatics.
One bioinformatics approach to determining the functionality of a
variant relies on evolutionary conservation. If a nucleotide or nucleotide
sequence is conserved across species, a variant in that sequence or at that
position in humans is likely to be functional because otherwise it would
have been seen in other species. Analogous strategies for identifying func-
tionally important sites can also be used for determining variants that
disrupt regulatory elements in exons, introns, silencers, and promoters
(Torkamani and Schork, 2008). But there are problems with just using
sequence conservation for determining the functional effects of variants,
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34 INTEGRATING LARGE-SCALE GENOMIC INFORMATION
Schork observed, as a recent analysis showed that structural information
about a protein is more effective than conservation information for deter-
mining which variants are functional (Torkamani et al., 2008).
A recent paper described a program called Variant Annotation, Analysis,
and Search Tool, which uses bioinformatic techniques to identify variants
that are likely to be correlated with idiopathic conditions (Rope et al., 2011).
The program compares a patient’s genome with reference genomes to rule
out variants seen in other individuals who do not have the disease. Any
novel variants that are identified in functional elements are then considered
as potential causative candidates for the idiopathic condition. Annotations
and predicted functional effects then can help prioritize variants.
Schork and his colleagues have applied this approach to every vari-
ant in public domain databases, including those from the 1000 Genomes
Project, dbSNP, and the Online Mendelian Inheritance in Man variants.
Many novel variants in each individual’s genome were predicted to be
functional. Not all of these variants cause disease, however, even where the
coding variants that affect the proteins are predicted to be damaging, which
makes interpretation difficult.
Commenting about using these types of bioinformatics tools, Schork
said that students need to become more computer savvy. Even among stu-
dents at the Scripps Translational Science Institute, he said, many could
improve their knowledge about how to conduct basic BLAST searches or
annotate variants against dbSNP. Exposure to such tools is absolutely criti-
cal, even if they are not used on a day-to-day basis. Furthermore, exposure
to concepts in systems biology is important to build understanding of how
genes work in concert rather than separately.
Genomic Diversity
In a clinical context it will be essential to take into account the genomic
diversity of the human population, Schork said. This will be important,
for example, in comparing individuals with a disease from one part of the
world with individuals without the disease from another part of the world.
Some variants would appear to be more frequent in the diseased individuals
than in the non-diseased individuals, but these would reflect false-positive
results because of population stratification. Similarly, if a reference panel
from one population were to be used to draw inferences about the novelty
of variants from a patient from a different population, the resulting conclu-
sions could be highly misleading (Bustamante et al., 2011).
Human genetic diversity is greater in Africa than elsewhere in the
world because only a subset of the variation present in Africa made its
way through the Middle East into Asia, Europe, and the Americas as
modern humans migrated out of Africa. As a result of this bottleneck,
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THE INTERPRETATION OF GENOMIC DATA
particular genomic positions might have greater homozygosity in Euro-
pean populations than in African populations (Lohmueller et al., 2008).
Using the bioinformatics tools described earlier, Schork’s group found
more functional variation in African genomes than in European, Asian,
or Native American genomes because of the increased diversity in Africa.
Similarly, African genomes have more novel functional variants. However,
the standard human “reference” genome is made up largely of contempo-
rary European DNA, which presents a misleading view of global variation
in the genome. Reference panels need to be larger, Schork said, in order
to determine what is novel and what is not.
Considering the Complexity of the Diploid Genome
The DNA sequencing community often ignores the fact that humans
are diploid. Yet variants that differ between the maternally and paternally
derived chromosomes can have a critical effect on health (Tewhey et al.,
2011). If the maternally derived homolog can compensate for mutations in
a paternally derived chromosome, for example, gene function can be nor-
mal. If it cannot, the result is haploinsufficiency. Similarly, the presence of
different mutations or polymorphisms within the same gene but on separate
alleles, such as in the coding region of the maternal homolog and in the
regulatory region of the paternal homolog, can yield a phenotype unique to
a diploid organism called compound heterozygosity. “Merely knowing that
this individual was heterozygous at these two sites wouldn’t be enough,”
Schork said. “You would have to know that one damaging variant [which
impairs protein function] was on the paternal homolog and [a separate
function-impairing] variant was on the maternal homolog.” Copy number
variations and other insertions and deletions also can have different effects
depending on parental origins.
In order to identify maternally and paternally derived homologs, it is
necessary to sequence families to determine which variant was inherited
from whom, or the assembly of DNA sequences can be used to recover the
two chromosomes that an individual inherited. Collecting phase informa-
tion, however, is not a simple matter, especially with new sequencing tech-
nologies, since the short stretches of DNA that they generate make assembly
of different genomes difficult.
Another way to generate phase information is to use chemical or molec-
ular tweezers to pull apart chromosomes during metaphase and sequence
them separately (Bansal et al., 2011). This approach probably could not
be routinely adopted in clinics, Schork said, but other approaches may be
able to distinguish between maternally and paternally derived homologs
and deal with issues like compound heterozygosity.
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36 INTEGRATING LARGE-SCALE GENOMIC INFORMATION
Risk Predictions and Perceptions
Schork concluded his remarks by talking about the utility of genomic
information compared with other measures that clinicians use. For exam-
ple, to predict diabetes, an individual could be typed at all known diabetes
susceptibility loci, or else a family history, body mass index, glucose and
insulin responses, and other indicators could be collected. Comparisons of
these two approaches reveal that genetic information does not provide a
benefit over clinical information, Schork said (Lyssenko et al., 2008). How-
ever, genotyping did prove to be better at making long-term predictions, as
compared with the clinical predictors, which were better at predicting who
would become diabetic within 1 or 2 years. Thus, if long-term predictions
were of interest, “there might be utility in using genetic information over
and above clinical information,” Schork said.
Schork has been involved in an investigation to study the behavior
of 3,000 individuals who received genetic information after undergoing
consumer-oriented genome-wide testing (Bloss et al., 2011). The study
found that such testing did not result in any measurable short-term changes
in physiological health (e.g., anxiety), diet or exercise behavior, or the use of
screening tests. Furthermore, other studies have shown that people respond
best when they are involved in social networks or have some other kind of
support to change their behaviors.
ENVISIONING CLINICAL GENOMICS IN 2020
Each of the three speakers commented on how genomic data will be
interpreted in the year 2020 if research and development continue to prog-
ress on their current trajectory. Interpretation will focus on patterns in a
genome that point to biological pathways subject to perturbation rather
than on single mutations that might explain a particular disease, Schork
predicted. It will be necessary to integrate information from many parts of
the genome using a greater understanding of systems biology in order to
derive actionable conclusions, he said.
Boguski emphasized the role of empowered patients and participatory
medicine in making actionable conclusions. When a patient comes into an
emergency room with a stroke, the emergency room personnel will prob-
ably not ask the patient for the password to a commercial genome testing
website to check on warfarin sensitivity, but they may check a patient’s
status on an online site where such information is routinely posted. “In
some cases it is going to be easier to get medical information out of a
person’s Facebook profile than legacy EMR systems,” Boguski said. For
example, the Association of Cancer Online Resources has about 18,000
cancer patients who share information on diagnoses and therapeutic inter-
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THE INTERPRETATION OF GENOMIC DATA
ventions. “If the medical profession is not going to be capable of doing this,
it will be crowd sourced. That is my prediction for 2020.”
Nussbaum predicted that instead of a single solution there will be
multiple solutions based on partnerships. Patient empowerment is impor-
tant, he said, “but at the same time I firmly believe that this has to be
embedded in traditional medical care in some way.” Research into genomic
interpretation is now progressing on a broad front, but clinical and family
information still will be needed to understand the effects of genetic vari-
ants. Furthermore, this information needs to be in the public domain so
that people can use it.
How Clinical Trials Will Be Affected by Genomics
Each participant in this section also commented on how the realization
of genomic medicine will change the role of clinical trials. Nussbaum sug-
gested that the era of randomized controlled trials is fading. Cohort sizes
are too small for such trials, because every cancer patient has a unique set of
genetic variants, making large-scale trials impractical. Instead, clinical trial
designs will need to be adaptive and engage patients during Phase IV1 of the
clinical trial. “Once a drug is out there, we have to be collecting information
from [patients] about efficacy and side effects.” Furthermore, genomic infor-
mation will need to be re-interpreted throughout a patient’s life. Physicians
will gather clinical information and write an order for updated interpreta-
tion of the genome based on the validated information that has accrued since
the patient’s last encounter with the health care system.
Boguski added that clinical trials may still exist, but they will not be
organized by drug companies. Instead, they will be organized by other
organizations once a new drug becomes available. Patients will be much
more active in contributing observations to organizations that collect and
act on their data. “We have to expand our notion of what a clinical trial is,
and I think it is going to involve post-marketing surveillance and so-called
Phase IV use of those drugs.”
Schork emphasized that trials for individual patients can be done as a
part of the standard of care. “If you can show that treating each patient
based on the genomic profile is the way to go rather than standard care, then
despite the fact that you are treating each patient individually, you vetted the
whole concept.” This becomes more complicated with rare diseases, how-
ever, as a surrogate endpoint is needed to measure the efficacy of treatments.
1 During Phase I clinical trials, researchers evaluate the safety of a drug or treatment,
establish a dosage range, and identify side effects. In Phase II, efficacy of the drug or treat-
ment is evaluated. In Phase III, drug or treatment effectiveness is confirmed, side effects are
further observed, comparisons to current treatments are performed, and safe-use information
is obtained. In Phase IV, post-marketing studies are performed to gather additional evidence
of benefits and risks of a drug or treatment.
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