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January 2017
Tackling rare disease with
big and small data
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Big data promises to create valuable
insights in rare disease. Technologies
such as next-generation sequencing and
natural-language processing, alongside
whole-exome analyses and other novel
scientific approaches, are helping
clinicians treat patients who previously
had no therapeutic options. At the same
time, deep interrogation of smaller
patient samples can provide information
of great benefi t to developers of orphan
drugs. Realizin g the full potential of big
data will require models that can also
integrate intelligence from datasets that
are small, writes Pete Chan
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In 2012, a group of researchers
the predictions of a panel of leading
organized a crowdsourcing competition
ALS clinicians, and they both picked
to shed new light on amyotrophic lateral
up prize money of US$20,000 (Küffner
sclerosis (ALS), a rare neurodegenerative
et al., 2015; Zach et al., 2015). One
disease. Participants were given three
algorithm discriminated perfectly
mon ths of data from ALS patients who
between individuals with slow and fast-
had taken part in clinical trials and
progressing ALS: potentially useful
asked to predict how the disease would
insight for the stratification of patient
progress in the same individuals over the
cohorts in clinical trials. The organizers
following nine months. More than 1,000
of the competition, known as the ALS
teams from over 60 countries stepped
Prediction Prize, estimated that by
up to the challenge. Two winning groups
modeling the progression of disease in
created algorithms that outperformed
individual ALS patients, the two
Image by geralt on Pixabay
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Table 1: Clinical trials in PRO-ACT
1
2
3
4
Clinical
Clinical
Clinical
Clinical
trial
trial
trial
trial
of
of
of
of
arimoclomol in ALS
creatine in ALS
celecoxib in ALS
gabapentin in ALS
10
11
12
13
14
6
Clinical trial of lithium in
combination with riluzole in ALS
Clinical trial of rHBDNF in ALS
7
Clinical trial of rHCNTF in ALS
16
8
Clinical trial of riluzole in ALS
17
9
Clinical trial of riluzole in the
treatment of advanced ALS
5
15
Clinical trial of TCH346 in ALS
Clinical trial of talampanel in ALS
Clinical trial of topiramate in ALS
French prospective observational
study in ALS
Clinical trial of vitamin E in ALS
Clinical trial of xaliproden in ALS: first
Phase III trial
Clinical trial of xaliproden in ALS:
second Phase III trial
Unpublished clinical trial of xaliproden
in advanced ALS
Abbreviation: PRO-ACT = Pooled Resource Open-Access ALS Clinical Trials database
Source: Atassi et al., 2014
Table 2: PRO-ACT in numbers
Data category
No. subjects
No. records
No. values
Adverse events
8,628
74,545
748,566
ALSFRS(R)
6,844
60,775
791,473
Concomitant
medications
7,656
111,848
376,098
Death report
4,633
4,634
8,033
Demographics
10,723
10,723
39,107
Family history
1,007
1,071
2,452
Forced vital
capacity
8,848
48,856
200,200
Laboratory data
8,342
2,445,059
9,659,191
Riluzole use
8,817
8,817
17,633
Slow vital capacity
2,717
9,525
25,532
Subject ALS history
9,394
12,058
35,967
Treatment group
9,640
9,640
16,830
Vital signs
9,973
72,422
717,715
Abbreviations: ALS = amyotrophic lateral sclerosis; ALSFRS(R); = revised ALS Functional
Rating Scale; PRO-ACT = Pooled Resource Open-Access ALS Clinical Trials database
Source: PRO-ACT, 2011
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algorithms cou ld help reduce the number
of patients required for a hypothetical
clinical study by up to 20%. (In diseases
with a wide range in natural rates of
progression, cl inical trials need larger
numbers of patients to help discern the
effects of the investigational drug.)
Great
expectations
from big data
The pioneering application of machinelearning algorithms to ALS research was
PRO-ACT and the research projects it
made possible by PRO-ACT, an open-
has enabled illustrate how big data
access repository of longitudinal clinical
approaches can be applied to biomedical
trial data (Atassi et al., 2014; PRO-ACT,
research in rare disease. They will
2011). At the time, it held data on more
inspire those who are convinced of the
than 8,600 people who had taken part in
role of big data in the orphan drug
Phase II/III ALS studies between 1990
sector, not just in clinical trials but also
and 2010. Rival teams were given some
R&D more broadly. Their excitement is
sample data to design their algorithms,
understandable. On the one hand, they
before putting these to work on the ALS
are faced with the familiar challenges of
Prediction Prize dataset. PRO-ACT was
rare disease research, including: s mall
officially launched in December 2012
patient cohorts; poor understanding of
with eight million data points, growing
epidemiology; lack of natural history
since then to more than 10 million (see
studies; and the variable quality of
Tables 1&2). More than 400 researchers,
patient registries. On the other, they
including representatives of around 40
are bombarded with a steady stream
pharma companies, requested access to
of health-related, big data success
PRO-ACT within two years of its launch
stories, with benefits ranging from
(Zach et al., 2015).
the prediction of patient responses to
drugs and side effects through to better
patient segmentation and the delivery
of personalized medicine. They hope
big data might do for rare disease what
it has delivered in common medical
conditions.
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Delve a little deeper, though, and you’re
wrote (Mayer-Schönberger and Cukier,
just as likely to find skeptics who argue
2013). Their argument is that data
that big data and rare disease research
practitioners shouldn’ t get hung up on
are two different and incompatible
the number of data points they gather;
worlds. Why the divergent views?
instead they should view big data as
using “as much of the entire datas et
The main reason is lack of consensus
as feasible”. By their logic, sequencing
about how to define big data: the UC
the entire genome of a person with
Berkeley School of Information lists
a rare disease and using the data to
no fewer than 43 definitions (Dutcher,
help that individual qualifies as a big
2014). The definition that resonates with
data approach. PRO-ACT, now bringing
most is the principle that big data should
together 25 years’ worth of longitudinal
have three Vs: volume, velocity and
data, is the single largest effort to
variety. Critics say rare disease data –
assemble the entire dataset of clinical
collected from small patient populat ions,
trials in ALS.
but difficult to source and often of
dubious quality – certainly fails on the
Daphna Laifenfeld, Director, Personalized
volume measure, and possibly the other
Medicine and Pharmacogenomics
two as well.
at Teva Pharmaceutical Industries,
defines big data as the combinatio n of
A more helpful perspective comes from
genetics, omics [a term used to describe
Viktor Mayer-Schönberger and Kenneth
disciplines of biology such as proteomics
Cukier, whose 2013 book, Big Data:
and transcriptomics], patient-reported
A Revolution That Will Transform How We
and clinical data (Laifenfeld, 2016).
Live, Work, and Think, helped stoke big-
Her list could be expanded further but,
for researchers, it ’s a helpful guide for
dividing into familiar categories what
Big data should have three Vs:
volume, velocity and variety.
people really mean when they talk about
data fervor among the masses. “When we
Viewed through this lens, it ’s apparent
talk about big data, we mean ‘big’ less in
that innovative big data methodologies
absolute than in relative terms: relative
are being implemented in rare disease.
to the comprehensive set of data,” they
Key applications include: drug discovery;
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big data in medicine.
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the discovery of disease-related genes,
In a stellar example of international
genetic mutations and biomarkers;
academic collaboration, the Exome
matchmaking of rare disease cases
Aggregation Consortium (ExAC) has
to help diagnose patients; and drug
aggregated genetic sequencing data
repurposing.
from around 20 separate research
studies, creating an open-access
Going deep into
the genome
database of genetic variants in more
than 60,000 people; in other words, the
genetic variation we might expect to find
in a normal population (see
Figure 1). Writing in Nature, the ExAC
team described their undertaking as “the
It ’s a diverse list. But even a cursory
most comprehensive catalogue (to our
glance at the literature reveals that most
knowledge) of human protein-coding
efforts are focussed on genomics. There
genetic variation to date” (Lek et al.,
are scientific and economic drivers at
2016). Since the launch of the ExAC
work here: the advent of next-generation
database in 2014, researchers the world
sequencing has made it feasible to
over have interrogated the resource,
sequence the entire genomes of humans
including the 10 million identified
at a reasonable cost. The other factor
variants, principally to better understand
is specific to rare disease: the fact that
the genetic variations seen in rare
80% of the known orphan conditions
disease patients.
result from genetic defects, and tha t the
majority of these are monogenic.
In a Broad Institute public lecture,
Daniel MacArthur, the researcher who led
But, to understand which genetic
the ExAC consortium, said: “We’ve now
variants are implicated in rare diseases,
sequenced in our lab more than 1,000
researchers first need to filter out those
families affected by a rare disease . For
variants that o ccur normally. This is no
more than 400 of those families, we’ve
trivial task, given the tens of thousands
been able to give them back a diagnosis
of genetic variants that occur in a typical
and, for several dozen of those families,
exome (the 1-2% of a genome that codes
it ’s been possible to convert what had
for proteins). And it is here that big data
previously been an untreatable disease
analysis has proven invaluable.
into a disease where it ’s actually possible
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Figure 1: ExAC in numbers
No. exomes in final
dataset after filtering
for quality
No. natural
genetic
variants
identified
No. international
research studies
donating
data
No. contributing
authors in
Nature paper
No. exomes in raw
dataset
Sources: Broad Institute, 2016; Lek et al., 2016
to give a medication to alleviate at least
within six months, a dozen other families
some of those symptoms. That small
with the same mutation were identified,
fraction will grow as we begin to develop
creating a small network of people who
more and better drugs to treat rare
had been medically isolated just a year
diseases.” (Broad Institute, 2016)
before.
One of Dr MacArthur ’s case studies
Reflecting on the limitations of their
involved two sisters with a rare condition
resource, Dr MacArthur and colleagues
that led to extreme weakness in the
explained that most ExAC samples
facial muscles. Before the girls’ DNA
are not accompanied by detailed
was sent to Broad, the family had
phenotypic data; that is, information
been through nine years of muscle
on the symptoms and other observable
biopsies, pathology tests and other
properties in an individual (Lek et al.,
procedures; none of which identified
2016). This is an important point. The
the cause of their disease. Thanks to
ability to link genotypic and phenotypic
the availability of the ExAC database,
data is precisely what ’s needed if
Dr MacArthur managed to trace it back
the troves of big data generated by
to two extremely rare mutations in a
DNA sequencing are to be interpreted
gene known as LMOD3. The sisters were
correctly, and translated into patient
diag nosed with nemaline myopathy. And
benefit in clinical settings. Genotype-to-
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phenotype connections need to be made
not only in individual cases, but also in
unrelated people if scientists are to be
confident in a condition’s genetic ca use.
The ability to link genotypic and
phenotypic data is precisely
what’s needed if the troves
of big data generated by DNA
sequencing are to be interpreted
correctly, and translated into
patient benefit in clinical settings.
Matchmaking for
clinicians in rare
disease
Going some way to bridge this gap,
a Canadian-led team has created
PhenomeCentral, an online matchmaking
service for clinicians and researchers
working in rare disease, often those
Spyros Mousses, founder and president
whose patients have yet to receive a
of Systems Imagination, a data analytics
diagnosis. PhenomeCentral aggregates
company, says researchers are routinely
phenotypic and genotypic data from
“looking at billions of measurements
FORGE Canada, CARE for RARE, the US
from an individual’s genome”: activities
NIH Undiagnosed Diseases Project and
he calls “deep genotyping” (RARECast,
other rare disease-focused consortia
2016). But in his view, the depth of
(Buske et al., 2015). PhenomeCentral
analysis being performed in genomics is
users query the database by submitting
absent from phenomes. “We’re measuring
a patient record that includes clinical
not billions but dozens of traits and
symptoms and any available information
clinical phenotypes,” said Dr Mousses.
on patients’ genetic variants.
Andrew Morris, a director of the Farr
PhenomeCentral’s algorithms mine the
Institute, a UK-based specialist in health
phenotypic data held in the repository,
informatics, wants to see the health-data
identifying patients most likely to have
debate shift towards “deep phenotyping”
the same condition, and predicting
(Morris, 2016).
which genes or genetic variants might
be responsible. Users are then able to
contact others whose patient cases match
theirs, hopefully leading to a posit ive
diagnosis. In 2015, PhenomeCentral
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held records on more than 1,000 deeply-
factors, hospital records, and other
phenotyped rare disease patients. Most
data gathered during the life course of
had had their exomes sequenced and
patients (Hill, 2016). In other words,
remained undiagnosed.
deep phenotypic and longitudinal data
on the sort of scale that the country’s
Achieving scale is an acknowledged
National Health Service (NHS), among
challenge in rare disease, but
comparable systems globally, is uniquely
PhenomeCentral will surely be aided in
placed to provide.
this respect by its decision to join The
MatchMaker Exchange (MME), a network
And working at international level,
of matchmaking services, each with its
RD-Connect is an EU FP7-funded project
own cohort of users (Philippakis et al.,
that aims to break down historical data
2015). Under t his model, researchers
silos in rare disease. A key objective is
have the option of querying not just
to make it easier for the rare disease
PhenomeCentral but also other members
research community to share data.
of the MME network at the same time.
To this end, it is creating a platform to
More shots on goal give them a better
integrate patient registries, biobanks
chance of finding a patient match.
and databases of genomic, phenotypic,
natural history and clinical trial data
Elsewhere, two high-profile initiatives
(McCormack, 2016; Thompson et al.,
promise to integrate many more diverse
2014).
sources of data beyond genotypes and
phenotypes, and have received plenty of
RD-Connect piloted its model by
attention for their big data ambitions.
pulling in data from two European
Later this year, the UK’s 100,000
research consortia: NeurOmics, with
Genomes Proje ct is expected to have
a focus on rare neurodegenerative
sequenced the genomes of 25,000 cancer
and neuromuscular disorders, and
patients and around 17,000 people with
EURenOmics in the field of rare kidney
rare diseases, as well as their families
disorders; with each contributing around
(Genomics England, 2015). Alongside
1,000 sequenced exomes. The Broad
genomic data, the project will also
Institute, Newcastle University and other
collect clinical data, pathology and
international partners have come on
histopathology results, imaging results,
board more recently (see Figure 2).
information on treatments and risk
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Figure 2: Initiatives contributing exome data to RD-Connect
E URenOmic s
(EU)
SeqNMD
(US)
NeurOmic s
(EU)
NCNP
Japan
M YO-S EQ
(UK)
Key
1,000 exomes
500 exomes
300 exomes
CNAG
Rare
(Spain)
CMG
Slovenia
Source: McCormack, 2016
gameshow Jeopardy! in 2011. Since then,
Cognitive
assistant for
digital doctors
it has gone on to capture the imagination
of the data science community with its
ability to analyze large quantities of
data, to understand complex questions
posed in natural language, and to
propose evidence-based answers.
The Boston team fed medical literature
Meanwhile, one of the world’s best-known
and clinical data relating to SRNS into
artificial intelli gence systems is being
Watson, before adding genomic data
piloted in two rare disease projects, with
from patients retrospectively. This is
the aim of creating what some describe
the first time Watson has been used
as a digital doctor ’s assistant.
to help doctors diagnose rare disease
and identify treatment options – and
For the past year, orphan disease
the results will be eagerly awaited. If
researchers at Boston Children’s Hospital
it proves successful in SRNS, the plan
have been training IBM Watson, the tech
is to extend the approach to neurologic
company’s flagship cognitive computing
disorders and other rare pediatric
platform, to understand steroid-resistant
diseases studied at Boston Children’s.
nephrotic syndrome (SRNS), a rare
kidney disease (IBM, 2015). Watson first
And at the end of 2016, researchers in
gain ed notoriety by winning the
Germany kicked off their own 12-month
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pilot project with Watson, to evaluate its
potential to diagnose any rare disea se
(IBM, 2016; Marks, 2016). The Center
for Undiagnosed and Rare Diseases at
the University Hospital Marburg has
been contacted by more than 6,000
Time to
downsize
patients since it opened in 2013. Most
patients have brought with them years
All well and good. Yet a limitation that
of unstructured data from their medical
is common to virtually all the initiatives
histories, inclu ding: lab test results;
described above is the absence of the
clinical reports; pathology reports; and
views of patients. This is an impor tant
drugs they’ve been prescribed. For the
missed opportunity, given that rare
Marburg researchers to review all this
disease patients and families are in many
information and combine it with their
cases experts in their own conditions,
own knowledge and the medical literature
capable of interacting with health
to reach a diagnosis typically takes
providers on a professional level, and
several days for each patient.
contributing insights that only they
possess.
The hope is that Watson will be able to
automate and accelerate the process,
Addressing this issue requires acceptance
quickly presenting physicians with
that while great insights can be gleaned
a list of possible hypotheses from
from huge datasets, equally valuable and
which they can make their own data -
complementary intelligence can be
driven diagnoses. In a further test of
Watson’s capabilities in natural-language
processing, the Marburg pilot will require
patients’ medical histories recorded
in German to be matched up with the
body of rare disease-related literature
published in English.
A limitation that is common to
virtually all current big datafocussed initiatives is the absence
of the views of patients.
derived from rigorous interrogation of
datasets that are relatively small. As it
happens, a small data movement has
also emerged in the past few years; its
loudest cheerleader being Martin
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Lindstrom, the Danish author of Sm all
doing so, and the implications for the
Data: The Tiny Clues That Uncover Huge
patient community. In line with the
Trends (Lindstrom, 2016). Mr Lindstrom’s
small data model, we posed a series
world is that of marketing and branding,
of well-defined questions to small
but it doesn’ t take a huge leap to apply
groups of patients, both online and
his principles of keen observation of
over the phone. The study sample
small samples to people living with rare
comprised Raremark users with
disease.
an interest in three rare diseases:
adrenoleukodystrophy, myasthenia
And recent work in the field of patient-
gravis and Sanfilippo syndrome. Work
reported outcomes (PROs) has provided
conducted from November 2016 to
evidence that patient-generated medical
January 2017 revealed an understanding
data can be of comparable quality to
of the importance of data sharing for
data gathered from traditional sources.
the benefit of others, and a willingness
A group of US researchers conducted a
to do so: 94% of participants said
proof-of-conce pt study using the chronic
they would feel comfortable sharing
lymphocytic leukemia (CLL) community
selected health-related information about
of PatientsLikeMe, a patient-powered
themselves with the community and the
research network. There are several PRO
pharmaceutical industry.
instruments specific to CLL, meaning the
supporting lite rature contain data the
Raremark’s findings reflect the results
researchers could use as comparators.
of a larger RD-Connect study that
Using a combination of online surveys
included similar themes. As long as
and telephone interviews, they found
the right governance systems are in
good alignment between the symptoms
place, RD-Connect discovered, the rare
that members of PatientsLikeMe’s CLL
disease patient community generally
community said were important to them,
has a positive view on the sharing
and those identified through traditional
of data to support medical research.
interviews and patient focus groups
“All the participants understood the
(McCarrier et al., 2016).
incentive for [rare disease] in sharing
data and samples; in fact, there were
Raremark has also been exploring how
several pleas for research systems to be
to involve patients in the area of data
standardised across the EU in order to
sharing and donation, the reasons for
make data sharing easier,” the authors
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wrote in the European Journal of Human
Learning from the ALS Prediction Prize
Genetics (McCormack et al., 2016).
case study, in which, remarkably, fourfifths of competitors had virtually no
previous experience in the condition,
Intelligence:
from artificial to
human
injections of fresh thinking from smart
people from non-health disciplines may
reveal exciting possibilities yet to be
imagined.
Machines will not be able to model some
truly human things, such as how to
explain to another human what it ’s like
Watch a presentation by Dr Mousses of
to live day to day with a rare medical
Systems Imagination and you’ll be left
condition, or whether a drug’s supposed
with big data-driven visions of the future.
benefits deliver outcomes that are
Machines will be able to gather medical
meaningful to them. For these insights,
data, create their own models and test
the only true source will be patients.
hypotheses in vast numbers without the
help of humans. They will also be able
For big data-derived intelligence to
to look at medical images and extract
translate into real benefit for the rare
billions of features for interpretation:
disease community, we need workable
a level of resolution that would simply be
models for combining very large datasets
impossible for pathologists. Pointing out
with the very small.
that traditional evidence-based med icine
has failed in rare disease, he uses the
term “intelligence-based medicine”
Pete Chan is Head of Research & Analysis
to describe the mining of deep data
at Raremark.
from rare disease patients – genomic,
phenotypic and biometric – before these
Email: [email protected].
are integrated, using machine learning,
and analyzed for the benefit of those
individuals (Global Genes, 2016).
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