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raremark.com 1
Tackling rare disease with
big and small data
January 2017
2 raremark.com
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
scientif ic approaches, are helping
cl inicians treat patients who previously
had no therapeutic options. At the same
time, deep interrogation of smaller
patient samples can provide information
of great benefit to developers of orphan
drugs. Realizing the full potential of big
data wil l require models that can also
integrate intell igence from datasets that
are small, writes Pete Chan
raremark.com 3
Image by geralt on Pixabay
In 2012, a group of researchers
organized a crowdsourcing competition
to shed new light on amyotrophic lateral
sclerosis (ALS), a rare neurodegenerative
disease. Participants were given three
months of data from ALS patients who
had taken part in cl inical trials and
asked to predict how the disease would
progress in the same individuals over the
fol lowing nine months. More than 1,000
teams from over 60 countries stepped
up to the challenge. Two winning groups
created algorithms that outperformed
the predictions of a panel of leading
ALS cl inicians, and they both picked
up prize money of US$20,000 (Küffner
et al., 2015; Zach et al., 2015). One
algorithm discriminated perfectly
between individuals with slow and fast-
progressing ALS: potential ly useful
insight for the stratif ication of patient
cohorts in cl inical trials. The organizers
of the competition, known as the ALS
Prediction Prize, estimated that by
modeling the progression of disease in
individual ALS patients, the two
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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
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
Table 1: Clinical trials in PRO-ACT
Abbreviation: PRO-ACT = Pooled Resource Open-Access ALS Clinical Trials database
Source: Atassi et al., 2014
1 Clinical trial of arimoclomol in ALS 10 Clinical trial of TCH346 in ALS2 Clinical trial of creatine in ALS 11 Clinical trial of talampanel in ALS3 Clinical trial of celecoxib in ALS 12 Clinical trial of topiramate in ALS4 Clinical trial of gabapentin in ALS 13 French prospective observational
study in ALS5 Clinical trial of lithium in
combination with riluzole in ALS14 Clinical trial of vitamin E in ALS
6 Clinical trial of rHBDNF in ALS 15 Clinical trial of xaliproden in ALS: first Phase III trial
7 Clinical trial of rHCNTF in ALS 16 Clinical trial of xaliproden in ALS: second Phase III trial
8 Clinical trial of riluzole in ALS 17 Unpublished clinical trial of xaliproden in advanced ALS
9 Clinical trial of riluzole in the treatment of advanced ALS
raremark.com 5
algorithms could help reduce the number
of patients required for a hypothetical
cl inical 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.)
The pioneering application of machine-
learning algorithms to ALS research was
made possible by PRO-ACT, an open-
access repository of longitudinal cl inical
trial data (Atassi et al., 2014; PRO-ACT,
2011). At the time, it held data on more
than 8,600 people who had taken part in
Phase II/III ALS studies between 1990
and 2010. Rival teams were given some
sample data to design their algorithms,
before putting these to work on the ALS
Prediction Prize dataset. PRO-ACT was
official ly launched in December 2012
with eight mil l ion data points, growing
since then to more than 10 mil l ion (see
Tables 1&2). More than 400 researchers,
including representatives of around 40
pharma companies, requested access to
PRO-ACT within two years of its launch
(Zach et al., 2015).
Great expectations from big dataPRO-ACT and the research projects it
has enabled i l lustrate how big data
approaches can be applied to biomedical
research in rare disease. They wil l
inspire those who are convinced of the
role of big data in the orphan drug
sector, not just in cl inical trials but also
R&D more broadly. Their excitement is
understandable. On the one hand, they
are faced with the famil iar challenges of
rare disease research, including: small
patient cohorts; poor understanding of
epidemiology; lack of natural history
studies; and the variable quality of
patient registries. On the other, they
are bombarded with a steady stream
of health-related, big data success
stories, with benefits ranging from
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.
6 raremark.com
Delve a l itt le deeper, though, and you’re
just as l ikely to find skeptics who argue
that big data and rare disease research
are two different and incompatible
worlds. Why the divergent views?
The main reason is lack of consensus
about how to define big data: the UC
Berkeley School of Information l ists
no fewer than 43 definit ions (Dutcher,
2014). The definit ion that resonates with
most is the principle that big data should
have three Vs: volume, velocity and
variety. Crit ics say rare disease data –
collected from small patient populations,
but diff icult to source and often of
dubious quality – certainly fai ls on the
volume measure, and possibly the other
two as well.
A more helpful perspective comes from
Viktor Mayer-Schönberger and Kenneth
Cukier, whose 2013 book, Big Data:
A Revolution That Wil l Transform How We
Live, Work, and Think, helped stoke big-
data fervor among the masses. “When we
talk about big data, we mean ‘big’ less in
absolute than in relative terms: relative
to the comprehensive set of data,” they
wrote (Mayer-Schönberger and Cukier,
2013). Their argument is that data
practit ioners shouldn’t get hung up on
the number of data points they gather;
instead they should view big data as
using “as much of the entire dataset
as feasible”. By their logic, sequencing
the entire genome of a person with
a rare disease and using the data to
help that individual qualif ies as a big
data approach. PRO-ACT, now bringing
together 25 years’ worth of longitudinal
data, is the single largest effort to
assemble the entire dataset of cl inical
trials in ALS.
Daphna Laifenfeld, Director, Personalized
Medicine and Pharmacogenomics
at Teva Pharmaceutical Industries,
defines big data as the combination of
genetics, omics [a term used to describe
discipl ines of biology such as proteomics
and transcriptomics], patient-reported
and cl inical data (Laifenfeld, 2016).
Her l ist could be expanded further but,
for researchers, it ’s a helpful guide for
dividing into famil iar categories what
people really mean when they talk about
big data in medicine.
Viewed through this lens, it ’s apparent
that innovative big data methodologies
are being implemented in rare disease.
Key applications include: drug discovery;
Big data should have three Vs: volume, velocity and variety.
raremark.com 7
the discovery of disease-related genes,
genetic mutations and biomarkers;
matchmaking of rare disease cases
to help diagnose patients; and drug
repurposing.
In a stellar example of international
academic collaboration, the Exome
Aggregation Consortium (ExAC) has
aggregated genetic sequencing data
from around 20 separate research
studies, creating an open-access
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
most comprehensive catalogue (to our
knowledge) of human protein-coding
genetic variation to date” (Lek et al.,
2016). Since the launch of the ExAC
database in 2014, researchers the world
over have interrogated the resource,
including the 10 mil l ion identif ied
variants, principally to better understand
the genetic variations seen in rare
disease patients.
In a Broad Institute public lecture,
Daniel MacArthur, the researcher who led
the ExAC consortium, said: “We’ve now
sequenced in our lab more than 1,000
famil ies affected by a rare disease. For
more than 400 of those famil ies, we’ve
been able to give them back a diagnosis
and, for several dozen of those famil ies,
it ’s been possible to convert what had
previously been an untreatable disease
into a disease where it ’s actually possible
It ’s a diverse l ist. But even a cursory
glance at the l iterature reveals that most
efforts are focussed on genomics. There
are scientif ic and economic drivers at
work here: the advent of next-generation
sequencing has made it feasible to
sequence the entire genomes of humans
at a reasonable cost. The other factor
is specif ic to rare disease: the fact that
80% of the known orphan conditions
result from genetic defects, and that the
majority of these are monogenic.
But, to understand which genetic
variants are implicated in rare diseases,
researchers first need to fi lter out those
variants that occur normally. This is no
trivial task, given the tens of thousands
of genetic variants that occur in a typical
exome (the 1-2% of a genome that codes
for proteins). And it is here that big data
analysis has proven invaluable.
Going deep into the genome
8 raremark.com
to give a medication to alleviate at least
some of those symptoms. That small
fraction wil l grow as we begin to develop
more and better drugs to treat rare
diseases.” (Broad Institute, 2016)
One of Dr MacArthur’s case studies
involved two sisters with a rare condition
that led to extreme weakness in the
facial muscles. Before the girls’ DNA
was sent to Broad, the family had
been through nine years of muscle
biopsies, pathology tests and other
procedures; none of which identif ied
the cause of their disease. Thanks to
the availabil ity of the ExAC database,
Dr MacArthur managed to trace it back
to two extremely rare mutations in a
gene known as LMOD3. The sisters were
diagnosed with nemaline myopathy. And
within six months, a dozen other famil ies
with the same mutation were identif ied,
creating a small network of people who
had been medically isolated just a year
before.
Reflecting on the l imitations of their
resource, Dr MacArthur and colleagues
explained that most ExAC samples
are not accompanied by detailed
phenotypic data; that is, information
on the symptoms and other observable
properties in an individual (Lek et al.,
2016). This is an important point. The
abil ity to l ink 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 cl inical settings. Genotype-to-
Sources: Broad Institute, 2016; Lek et al., 2016
Figure 1: ExAC in numbers
No. natural genetic variants identified
No. contributing authors in Nature paper
No. international research studies donating data
No. exomes in raw dataset
No. exomes in final dataset after filtering for quality
raremark.com 9
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 cause.
Spyros Mousses, founder and president
of Systems Imagination, a data analytics
company, says researchers are routinely
“looking at bil l ions of measurements
from an individual’s genome”: activit ies
he calls “deep genotyping” (RARECast,
2016). But in his view, the depth of
analysis being performed in genomics is
absent from phenomes. “We’re measuring
not bil l ions but dozens of traits and
cl inical phenotypes,” said Dr Mousses.
Andrew Morris, a director of the Farr
Institute, a UK-based special ist in health
informatics, wants to see the health-data
debate shift towards “deep phenotyping”
(Morris, 2016).
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 diseaseGoing some way to bridge this gap,
a Canadian-led team has created
PhenomeCentral, an online matchmaking
service for cl inicians and researchers
working in rare disease, often those
whose patients have yet to receive a
diagnosis. PhenomeCentral aggregates
phenotypic and genotypic data from
FORGE Canada, CARE for RARE, the US
NIH Undiagnosed Diseases Project and
other rare disease-focused consortia
(Buske et al., 2015). PhenomeCentral
users query the database by submitting
a patient record that includes cl inical
symptoms and any available information
on patients’ genetic variants.
PhenomeCentral’s algorithms mine the
phenotypic data held in the repository,
identifying patients most l ikely to have
the same condition, and predicting
which genes or genetic variants might
be responsible. Users are then able to
contact others whose patient cases match
theirs, hopefully leading to a positive
diagnosis. In 2015, PhenomeCentral
10 raremark.com
held records on more than 1,000 deeply-
phenotyped rare disease patients. Most
had had their exomes sequenced and
remained undiagnosed.
Achieving scale is an acknowledged
challenge in rare disease, but
PhenomeCentral wil l surely be aided in
this respect by its decision to join The
MatchMaker Exchange (MME), a network
of matchmaking services, each with its
own cohort of users (Phil ippakis et al.,
2015). Under this model, researchers
have the option of querying not just
PhenomeCentral but also other members
of the MME network at the same time.
More shots on goal give them a better
chance of f inding a patient match.
Elsewhere, two high-profi le init iatives
promise to integrate many more diverse
sources of data beyond genotypes and
phenotypes, and have received plenty of
attention for their big data ambitions.
Later this year, the UK’s 100,000
Genomes Project is expected to have
sequenced the genomes of 25,000 cancer
patients and around 17,000 people with
rare diseases, as well as their famil ies
(Genomics England, 2015). Alongside
genomic data, the project wil l also
collect cl inical data, pathology and
histopathology results, imaging results,
information on treatments and risk
factors, hospital records, and other
data gathered during the l ife course of
patients (Hil l, 2016). In other words,
deep phenotypic and longitudinal data
on the sort of scale that the country’s
National Health Service (NHS), among
comparable systems globally, is uniquely
placed to provide.
And working at international level,
RD-Connect is an EU FP7-funded project
that aims to break down historical data
si los in rare disease. A key objective is
to make it easier for the rare disease
research community to share data.
To this end, it is creating a platform to
integrate patient registries, biobanks
and databases of genomic, phenotypic,
natural history and cl inical trial data
(McCormack, 2016; Thompson et al.,
2014).
RD-Connect piloted its model by
pull ing in data from two European
research consortia: NeurOmics, with
a focus on rare neurodegenerative
and neuromuscular disorders, and
EURenOmics in the field of rare kidney
disorders; with each contributing around
1,000 sequenced exomes. The Broad
Institute, Newcastle University and other
international partners have come on
board more recently (see Figure 2).
raremark.com 11
Meanwhile, one of the world’s best-known
artif icial intell igence systems is being
piloted in two rare disease projects, with
the aim of creating what some describe
as a digital doctor ’s assistant.
For the past year, orphan disease
researchers at Boston Children’s Hospital
have been training IBM Watson, the tech
company’s f lagship cognitive computing
platform, to understand steroid-resistant
nephrotic syndrome (SRNS), a rare
kidney disease (IBM, 2015). Watson first
gained notoriety by winning the
gameshow Jeopardy! in 2011. Since then,
it has gone on to capture the imagination
of the data science community with its
abil ity to analyze large quantit ies of
data, to understand complex questions
posed in natural language, and to
propose evidence-based answers.
The Boston team fed medical l iterature
and cl inical data relating to SRNS into
Watson, before adding genomic data
from patients retrospectively. This is
the first t ime Watson has been used
to help doctors diagnose rare disease
and identify treatment options – and
the results wil l be eagerly awaited. If
it proves successful in SRNS, the plan
is to extend the approach to neurologic
disorders and other rare pediatric
diseases studied at Boston Children’s.
And at the end of 2016, researchers in
Germany kicked off their own 12-month
Figure 2: Initiatives contributing exome data to RD-Connect
Source: McCormack, 2016
Cognitive assistant for digital doctors
SeqNMD(US)
1,000 exomes
Key
500 exomes
300 exomes
NCNP Japan
EURenOmics(EU)
CMG Slovenia
CNAG Rare
(Spain)
NeurOmics(EU)
MYO-SEQ(UK)
12 raremark.com
pilot project with Watson, to evaluate its
potential to diagnose any rare disease
(IBM, 2016; Marks, 2016). The Center
for Undiagnosed and Rare Diseases at
the University Hospital Marburg has
been contacted by more than 6,000
patients since it opened in 2013. Most
patients have brought with them years
of unstructured data from their medical
histories, including: lab test results;
cl inical reports; pathology reports; and
drugs they’ve been prescribed. For the
Marburg researchers to review all this
information and combine it with their
own knowledge and the medical l iterature
to reach a diagnosis typically takes
several days for each patient.
The hope is that Watson wil l be able to
automate and accelerate the process,
quickly presenting physicians with
a l ist of possible hypotheses from
which they can make their own data-
driven diagnoses. In a further test of
Watson’s capabil it ies in natural-language
processing, the Marburg pilot wil l require
patients’ medical histories recorded
in German to be matched up with the
body of rare disease-related l iterature
published in English.
Time to downsizeAll well and good. Yet a l imitation that
is common to virtually al l the init iatives
described above is the absence of the
views of patients. This is an important
missed opportunity, given that rare
disease patients and famil ies are in many
cases experts in their own conditions,
capable of interacting with health
providers on a professional level, and
contributing insights that only they
possess.
Addressing this issue requires acceptance
that while great insights can be gleaned
from huge datasets, equally valuable and
complementary intell igence can be
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
A limitation that is common to virtually all current big data-focussed initiatives is the absence of the views of patients.
raremark.com 13
Lindstrom, the Danish author of Small
Data: The Tiny Clues That Uncover Huge
Trends (Lindstrom, 2016). Mr Lindstrom’s
world is that of marketing and branding,
but it doesn’t take a huge leap to apply
his principles of keen observation of
small samples to people l iving with rare
disease.
And recent work in the field of patient-
reported outcomes (PROs) has provided
evidence that patient-generated medical
data can be of comparable quality to
data gathered from traditional sources.
A group of US researchers conducted a
proof-of-concept study using the chronic
lymphocytic leukemia (CLL) community
of PatientsLikeMe, a patient-powered
research network. There are several PRO
instruments specif ic to CLL, meaning the
supporting l iterature contain data the
researchers could use as comparators.
Using a combination of online surveys
and telephone interviews, they found
good alignment between the symptoms
that members of PatientsLikeMe’s CLL
community said were important to them,
and those identif ied through traditional
interviews and patient focus groups
(McCarrier et al., 2016).
Raremark has also been exploring how
to involve patients in the area of data
sharing and donation, the reasons for
doing so, and the implications for the
patient community. In l ine with the
small data model, we posed a series
of well-defined questions to small
groups of patients, both online and
over the phone. The study sample
comprised Raremark users with
an interest in three rare diseases:
adrenoleukodystrophy, myasthenia
gravis and Sanfi l ippo syndrome. Work
conducted from November 2016 to
January 2017 revealed an understanding
of the importance of data sharing for
the benefit of others, and a wil l ingness
to do so: 94% of participants said
they would feel comfortable sharing
selected health-related information about
themselves with the community and the
pharmaceutical industry.
Raremark’s f indings reflect the results
of a larger RD-Connect study that
included similar themes. As long as
the right governance systems are in
place, RD-Connect discovered, the rare
disease patient community generally
has a positive view on the sharing
of data to support medical research.
“All the participants understood the
incentive for [rare disease] in sharing
data and samples; in fact, there were
several pleas for research systems to be
standardised across the EU in order to
make data sharing easier,” the authors
14 raremark.com
wrote in the European Journal of Human
Genetics (McCormack et al., 2016).
Intelligence: from artificial to humanWatch a presentation by Dr Mousses of
Systems Imagination and you’l l be left
with big data-driven visions of the future.
Machines wil l be able to gather medical
data, create their own models and test
hypotheses in vast numbers without the
help of humans. They wil l also be able
to look at medical images and extract
bil l ions of features for interpretation:
a level of resolution that would simply be
impossible for pathologists. Pointing out
that traditional evidence-based medicine
has fai led in rare disease, he uses the
term “intell igence-based medicine”
to describe the mining of deep data
from rare disease patients – genomic,
phenotypic and biometric – before these
are integrated, using machine learning,
and analyzed for the benefit of those
individuals (Global Genes, 2016).
Learning from the ALS Prediction Prize
case study, in which, remarkably, four-
fifths of competitors had virtually no
previous experience in the condition,
injections of fresh thinking from smart
people from non-health discipl ines may
reveal exciting possibil it ies yet to be
imagined.
Machines wil l not be able to model some
truly human things, such as how to
explain to another human what it ’s l ike
to l ive day to day with a rare medical
condition, or whether a drug’s supposed
benefits deliver outcomes that are
meaningful to them. For these insights,
the only true source wil l be patients.
For big data-derived intell igence to
translate into real benefit for the rare
disease community, we need workable
models for combining very large datasets
with the very small.
Pete Chan is Head of Research & Analysis
at Raremark.
Email: pete.chan@raremark.com.
raremark.com 15
Atassi, N. et al. (2014) ‘The PRO-ACT
database: Design, init ial analyses,
and predictive features’, Neurology,
83(19), pp. 1719–1725. doi: 10.1212/
wnl.0000000000000951.
Broad Institute (2016) Midsummer
nights’ science: Using big data to
understand rare diseases. Available
at: https://www.youtube.com/
watch?v=GFNn7z7OWU8&feature=youtu.
be (Accessed: 7 January 2017).
Buske, O.J. et al. (2015)
‘PhenomeCentral: A portal for phenotypic
and genotypic matchmaking of patients
with rare genetic diseases’, Human
Mutation, 36(10), pp. 931–940. doi:
10.1002/humu.22851.
Dutcher, J. (2014) What is big data?
Available at: https://datascience.
berkeley.edu/what-is-big-data (Accessed:
7 January 2017).
Genomics England (2015) Genomics
England and the 100,000 Genomes
Project. Available at: https://www.
genomicsengland.co.uk/wp-content/
uploads/2015/05/Genomics-Englad-
Narrative-May-20152.pdf (Accessed:
7 January 2017).
Global Genes (2016) Big data and
intell igence-based medicine. Available
at: https://www.youtube.com/
watch?v=cTTCDReujdE&feature=youtu.be
(Accessed: 7 January 2017).
Hil l, S. (2016) Beyond 100,000 Genomes:
Transforming the NHS into a personalised
medicine service. BioData World Congress
2016. Cambridge, UK. 27 October 2016.
IBM (2015) Boston Children’s Hospital
to tap IBM Watson to tackle rare
pediatric diseases. Available at:
http://www-03.ibm.com/press/us/en/
pressrelease/48031.wss (Accessed:
7 January 2017).
IBM (2016) Rhön-Klinikum hospitals
to study how IBM Watson can support
doctors in the diagnosis of rare diseases.
Available at: https://www-03.ibm.com/
press/us/en/pressrelease/50803.wss
(Accessed: 7 January 2017).
Küffner, R. et al. (2015) ‘Crowdsourced
analysis of cl inical trial data to
predict amyotrophic lateral sclerosis
progression’, Nature Biotechnology, 33,
pp. 51–57.
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raremark.com 17
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