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Q&A: Genomics and Clinical Trials
A Promising
Project
EBR talks to Jason Mezey and Geoff Low at Medidata, who home in on genomics,
new data sequencing platforms and the development of a scheme that can,
ultimately, combine and analyse several medical records to diagnose diseases
quicker and provide better treatment options
EBR: Can you tell us briefly about
the history of genomics and the
100,000 Genomes Project?
Geoff Low: Genomics focuses on the
analysis of the genome – the order of
a complete set of DNA located within
the individual cells of an organism.
Advances in molecular sequencing and
bioinformatics technology have driven
this approach, which goes well beyond
the gene-based view of genetics towards
one where we can analyse how groups
of genes work together.
Perhaps the most important historical
example of genomic science is the
Human Genome Project (HGP), a large
cooperative undertaking in which the
entire human genome was sequenced
at a cost of $2.7 billion. The aim of the
HGP was to discover the estimated
three billion nucleotides in a human
haploid reference genome, thereby
giving scientists a platform for finding
the genetic basis for disease, as well as
a dataset for a deeper understanding
of the human organism. Given the
success of the HGP, subsequent projects
were carried out to expand the number
of genomes under investigation, as
relatively minor changes in the
genome itself can lead to vastly
different outcomes in development
and behaviour (the phenotype).
Today, the cost of whole genome
sequencing (WGS) has seen a drastic
reduction, approaching the symbolic
$1,000 per individual threshold, which
is ushering in its widespread adoption.
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Jason Mezey is a tenured professor at Cornell
University in the Department of Biological
Statistics and Computational Biology. He has a
joint appointment in the Department of Genetic
Medicine at Weill Cornell Medicine. As a
Senior Consultant at Medidata, he is a lead
architect of the clinical trial genomics platform,
providing data management, standardisation
and analytics utilities for the identification of
genomics-driven insights in clinical trials.
We expect such data will provide
insights into next-generation disease
prevention, enabling us to relate the
genotype and phenotype, and identify
and inhibit the underlying genetic
causes of many ailments.
One such scheme is the 100,000 Genomes
Project. This was initiated in 2012 by
the UK’s Prime Minister, with the goal of
developing a Genomic Medicine Service
within the NHS. As a nationalised health
service, the NHS can access a lifetime of
As Senior Lead Systems Architect for
Medidata, Geoff Low is responsible for
evaluating ways of satisfying business
needs. He has experience with a wide
range of approaches and technologies,
including MapReduce, Python (Django/
Flask), Semantic Technologies, Data
Visualisation (D3JS), XML, Javascript
and R, among many others.
medical records for a patient. The 100,000
Genomes Project is combining medical
records with WGS data from cancer and
rare disease patients – in the latter case,
blood relatives will also be sequenced.
In 2015, the first set of patients was
diagnosed with a rare disease based on
an underlying genetic condition (1). This
was a key milestone for the project, as it
showed the power of linking phenotypic
and genotypic information, and offered
us a vision of where healthcare and
research should go in the future.
January 2017
What were the main drivers
behind today’s advances in
genomics research technology?
Jason Mezey: The major drivers
arose from a need to develop highthroughput sequencing methods.
Sanger Sequencing – the dominant
technology for over a quarter of a
century – was slow and expensive.
More than a decade ago, microarray
technologies were introduced that
led to the analysis of mutations and
genotypes at genome-wide scales. This
proved to be an extremely valuable tool
for research purposes and, as a result,
microarrays are still widely used today.
Within the past few years, highthroughput techniques termed nextgeneration sequencing have been
developed that have made direct
sequencing of genomes possible. At
present, the most common technologies
are so-called ‘short-read’ devices, which
usually require alignment to a reference
genome to determine what variants
an individual possesses. The cost of
such short-read technologies has been
steadily dropping, allowing them to
be applied in many clinical studies.
There are also a number of ‘long-read’
sequencing tools in various phases
of development, which will enable
direct assembly of a genome sequence
without a reference.
Why are personalised data so
important in oncology studies?
Jason: At its heart, cancer is a corruption
of the host’s genomic code, in which
the disease can propagate where it
should not, while hiding from the
host organism’s natural defences.
Tumorigenesis involves a set of complex
genetic and epigenetic transformations;
if we can unwind these changes through
the use of high-throughput sequencing
data, we can gain deeper insights
into better treatment options for the
individual. Ideally, we wish to identify
the original driver mutations: the specific
points responsible for a person’s cells
to start working against their host.
Understanding cancer’s basic biology
could lead to preventative strategies, as
well as improved monitoring, diagnosis
and treatment.
What are the benefits of genomic
methods over cell studies?
Geoff: Cell studies identify treatment
options based on cell characteristics
that can be seen under a microscope.
For example, pathologists use a variety
of staining assays to identify if cancer
cells are hormone-receptive. This is
done to determine whether hormonal
therapy should be prescribed or not.
Genomic methods, which are becoming
more widely available, make it easier
to sequence tumour cells. If important
cancer gene mutations are known,
then microarrays can be used to carry
out quick and relatively inexpensive
identifications, which can propel a more
rapid response to therapy.
Single cell sequencing (SCS) promises
to address key issues in cancer research,
including resolving intratumour
heterogeneity, tracing cell lineages,
understanding rare tumour cell
populations and measuring mutation
rates (2). By isolating and amplifying
the genomic material for a single cell,
SCS gives researchers a more efficient
tool for looking at the genome’s
changes over the cancer’s lifecycle. At
its most beneficial, this will identify
the original mutation that allows the
disease to thrive. Targeting these may
interrupt the continued development
of the cancer and can also highlight
opportunities for prevention.
What attracted you personally
to this sector of the industry?
Geoff: I fell into the industry. I completed
my doctorate and found I was enjoying
the technology more than the science.
I looked for a job that used technology I
was familiar with, and ended up at a small
CRO. Fast forward a few years, and I’m
now working at a large technology firm,
dedicated to making the clinical trials
industry better. Some of the most exciting
work I’ve taken part in has involved the
collection of sensor data, and gaining
insights from a subject’s experience in a
clinical study. I’m currently working on
strategies for pulling in patient electronic
health records into the clinical procedure,
which is a really fascinating area but not
without its challenges.
Jason: I began my career in statistical
genetics before the genomics era. My PhD
and subsequent work as a professor at
Cornell University, US, focused on many of
the same discovery objectives as we have
today, but without the scale of genomics
data that are now available. During the
early parts of my career, I lamented that
a number of our discovery goals – such
as the identification of variants in human
genomes that are important for complex
disease – were going to be out of reach
because we could not obtain WGS data.
Happily, this situation has completely
reversed, and I’m finding it a very exciting
time to be working in this area.
Which part of your job
do you most enjoy?
Geoff: I work in an innovation group,
exploring new technologies and their
application to Medidata products. The
work is very interesting, but keeping up
with developments in big data, artificial
intelligence systems, genomics and
mobile technologies is rife with its own set
of obstacles. To an extent, our role
Within the past few years, high-throughput techniques termed
next-generation sequencing have been developed that have made direct
sequencing of genomes possible
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Image: © dotshock – shutterstock.com
is predictive. While we know that we can
achieve a greater understanding of disease
by linking lifestyle factors such as activity,
sleep patterns, diet and relationships
to medical records and genomic data,
predicting how and when those data
can be brought together, as well as its
implications for individual privacy, is
much tougher. Overcoming the difficulties
and challenges by simplifying complexity
is hugely satisfying and exciting.
Jason: I enjoy collaborating on the
development of genomics analytics tools
that can lead to actionable discoveries for
how we treat diseases. Such developments
can range from the derivation of cuttingedge computational statistics and
machine learning methodologies, to more
simple analysis approaches served up
in a manner to enable researchers and
clinicians to get more out of genomics
data. I find each new biological discovery
exciting, and it is great to be working
at a time when it is possible to make
new and impactful discoveries with
each new genomics dataset.
And which part is the most
challenging?
Geoff: Personally, I find the conservatism
of the industry to be challenging at
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times. We are a very process-heavy
industry and there are a number of
places where we could adopt new
approaches, but typically we do not. We
will maintain our level of due diligence
around patient safety, but anything that
can get good treatments into the hands
of patients should be actively reviewed
and potentially implemented.
Jason: The most challenging aspect is
the problem of understanding under
what conditions we can make precise
inferences from genomics data. I’m often
concerned that, while genomics has
gigantic potential that will be realised
over the coming decades, such data
will often not produce a silver bullet – a
fact that is often downplayed by those
working in academics and industry.
I often caution people to be sceptical of
claims about what has been discovered
or can be predicted from genomics data,
where I think such doubts will often lead
one to the cases where it is possible to
produce accurate answers.
What is the most surprising thing
you have learned?
Geoff: I think it is the C-value paradox.
As self-regarding beings, humans will
consider themselves to be the most
advanced organisms on this planet,
which we conflate with complexity of
design. This often gives people reason to
think that the genome for homo sapiens
must be the largest. However, one of the
findings of the HGP was that the number
of genes in the human genome is
around 20,500 – approximately the same
number found in mice. Surprisingly, the
protozoan responsible for trichomoniasis
has around 60,000.
Do you think genomics will
become more extensively
utilised in clinical trials?
Jason: Absolutely, yes. We have previously
treated subjects in much the same way,
dialling up or down the dose based
on some biometric characteristic like
body mass, gender or age. We should
be dosing using all informative data we
have available to us, including genomics
data. As an example, the rate of drug
metabolism can change based on genome
characteristics – in fact, there is a Warfarin
dosing app that is targeted along both
phenotypic and genotypic dimensions.
More broadly, the reason it will be practical
to incorporate genomics-driven insights
into clinical trials is we will not always need
to know the exact mechanism behind
why certain individuals or cancer subtypes
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respond differently to treatments. That is,
identifying important genomics-defined
clusters of individuals or cancers within
or across clinical trials will, in some cases,
lead to actionable hypotheses. The more
we know about the exact mechanism
behind the differences observed among
individuals the better; as we learn more
about our genomes, more opportunities
will present themselves for targeted
treatments and, ultimately, better cures.
wrong in that complex system, it manifests
as disease. In most cases, we have not
had the ability to look inside the ‘factory’
to analyse the problem and determine its
root cause. Genomics research and gene
therapy can help us understand what
failed in the cell machinery and how to
tailor a specific solution. Of course, this is a
highly simplified view, and one we should
be cautious about when assessing new
research finding – but it is a very real hope.
What would be the long-term
implications of streamlining
genomics research/gene therapy?
In the past, we viewed cancer from a
phenotypic perspective, focusing on
the location and staging of the tumour.
With better tools, we now know that
any given cancer can be further refined
into subtypes based on differences in
the genome constitution of the tumour
cells, which define the mechanism for
cancer growth and survival. Likewise,
in other diseases with a genomic root
cause, we are likely to find that what we
labelled as ‘Disease A’ is really a dozen or
more subtypes, all with different causes
and treatment avenues.
Jason: When we talk about genomics
research, we tend to cite oncology as
this is the most obvious example of
cell machinery going awry, with often
disastrous health consequences. Human
beings are a vast network of cooperating
cell factories, all busy consuming raw
materials and producing proteins,
hormones and other mediating and
messaging signals. When something goes
Discrepancies between the subtypes
make cancer a difficult target for
treatment, as a subject’s response may
vary widely. With increased knowledge
of the basis of the oncogenesis
through sequencing, oncologists can
now target cancer more effectively.
Understanding a subject’s subtype of
cancer allows the use of drugs that
disrupt the specific genes and proteins,
resulting in the growth and survival of
the tumour.
Gaining a better picture of how genetic
differences impact the absorption
and processing of medications can
enable the oncologist to calculate more
precisely how much of a drug is needed
for effective treatment.
References
1. Visit: www.genomicsengland.co.uk/firstpatients-diagnosed-through-the-100000genomes-project
2. Visit: www.genomebiology.biomedcentral.
com/articles/10.1186/s13059-014-0452-9
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