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Executive Report
IBM Global Business Services Healthcare and Life Sciences
IBM Institute for Business Value
The evolving promise of
genomic medicine
How advanced technologies are transforming healthcare and life sciences

2 The evolving promise of genomic medicine
IBM Healthcare and Life Sciences
IBM Healthcare & Life Sciences is creating a smarter, more connected healthcare
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addition to the company’s investment in health technology research and innovation,
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IBM Global Business Services 1
ge·no·mic ji-NOH-mik medicine
Genomic medicine is the application of the science of genomes, as
opposed to genes, to the practice of medicine. The National Institutes
of Health describe a genome as “an organism’s complete set of DNA,
including all of its genes. Each genome contains all of the information
needed to build and maintain that organism. In humans, a copy of the
entire genome — more than 3 billion DNA base pairs — is contained in
all cells that have a nucleus.”1
Note: Please see the Glossary on pages 17 and 18 for definition of
important terms and abbreviations
By Aditya Pai, Takahiko Koyama and Leonard Lee
After the Human Genome Project was completed
in 2003, early successes in genomic medicine fell short of the initial high expectations.
But today, a potent mix of influences — including innovation in biology and technology,
market demand and consumerism — is furthering an evolution that crosses industries.
Healthcare providers can now personalize care plans thanks to lower sequencing costs
that allow genomic data to be combined with electronic health data. Life sciences
companies can develop targeted therapies that prevent and alleviate disease symptoms.
To capitalize on new advances in science, cognitive computing, analytics and drug
discovery, senior leaders across the ecosystem should act quickly to: make genomic
medicine a key component of enterprise strategy; address relevant skill gaps; and
determine how partnering can bolster critical capabilities.
In three to five years, an individual
who interacts with the healthcare
system will likely have a genome-
based electronic record or “genomic
health record.”
Sequencing capability will increasingly
happen in a cloud-based environment
enabling the aggregation and analysis
of multiple sources of genomic data.
Transformational opportunity for
industries rests in leveraging cognitive
computing throughout the entire value
chain from pre-clinical discovery to
understanding and engaging consumers,
and improving health outcomes.

Introduction
The Human Genome Project was declared complete in April
of 2003.2
Since then, activity has exploded to make sense of our
human DNA (Deoxyribo Nucleic Acid) map. Genomic
medicine has long had a goal of moving from “one-size-fits-
all” to personalized treatments by applying genomic insights
at the point of care.
While rational drug design has helped to improve overall drug
efficacy levels, many believe that genomic medicine will allow
us to better understand the causes and treatment of disease at a
molecular level. Consider the following scenario:
It is 2013 and a 45-year-old woman has been diagnosed
with adenocarcinoma of the lung. A PET-CT scan
reveals that there is metastasis to the adrenal gland,
brain and bone. She meets with her oncologist, who
does a lung biopsy of the tumor. Immunohistochemistry
confirms that she has Stage IV non-small cell lung cancer.
Her oncologist recommends that she submit her tumor
biopsy for molecular testing for EGFR and ALK mutations,
which confirms that she has an EGFR3 mutation. This
mutation is seen in 30 to 40 percent of women who are non-
smokers and of Asian origin, as she is.
The oncologist offers first-line treatment with a particular
EGFR inhibitor. Eight weeks later, a PET-CT scan shows
shrinkage of her tumors. She does not go through
chemotherapy and continues taking a daily pill as part of
her treatment regimen.
This scenario is a living example of personalized medicine
where genomics research led to the discovery of EGFR
mutations and their key role in adenocarcinoma. While EGFR
inhibitor drugs have existed in the marketplace for some time,
it has been difficult to identify patients for whom a specific
drug would have been most effective.
Since 2001, when the first draft of the Human Genome Project
was announced, hope surged for genomic medicine to be a
panacea for many health conditions from diagnosis to
treatment. Soon after the project was complete in 2003, the
early tangible examples of success did not match the high
expectations of that time. But today, there is renewed
confidence in the healthcare and life sciences industries that
the hype could indeed match the hope.
Consider what is different now:
• In the healthcare industry, the decreased cost of whole-
genome sequencing — coupled with new advances in
cognitive computing and drug discovery — has created a
new paradigm where genomic data will soon be combined
with electronic health data. Care providers are increasingly
relying on genomic data to add a unique level of
personalization to an individual’s care plan. Cognitive
computing and other analytics technologies can provide
for precision care where decision support enables a reliable
diagnosis and care plan, including treatment options.
• In the life sciences industry, the stage is set for a radical
transformation. New medical and technological capabilities
will increasingly lead to innovative clinical trials, the
development of targeted therapies and a focus on health
outcomes to prevent and alleviate disease symptoms.
These major changes are positively impacting the three
“cornerstones” of genomic medicine: sequencing, translational
medicine and personalized healthcare. As genomic medicine
continues to proliferate, the importance of a solid privacy,
ethical and legal framework to support the complexity of
genomic medicine will also become vital.
The field of oncology is already experiencing advances in
genomic medicine that are expected to increase on a larger
scale through sophisticated genome/proteome/RNA analysis.
Coupling such analysis with cognitive computing, oncologists
can provide rapid precision oncology decision support.

It is now 2015 and whole-genome sequencing coupled
with other genomic modalities like RNA-Sequencing
and oncology-specific multiplex panels of genes are
routinely offered to cancer patients.
The woman, now 47 years old, gets her tumor block
analyzed by three such genomic modalities upon
expert advice provided by a cognitive system that has
been trained in medicine, genomics and the oncology
specialty. The cognitive system continues to learn and
provides the oncologist with key confidence intervals
regarding options.
Upon choosing the option with the highest confidence
interval, another mutation is found in the primary tumor
which, fortunately, also has a targeted treatment. The
woman is now taking two targeted medications and
leading a reasonably good quality of life. Her cancer
can be treated as a “complex chronic condition” with
care that is personalized for her own genetic makeup —
targeted care that was not yet available to her in 2013.
Many challenges still exist, but genomic medicine has indeed
moved from a vision to a reality. It exists in small, but ever-
expanding pockets across the healthcare ecosystem and spans
the prediction of drug response to the diagnosis of disease, to
the identification of targeted therapies. Major advances have
occurred in next-generation sequencing, genome-wide
association studies and bioinformatics. In addition, our view
of the human genome has expanded to a broader view of the
human as an ecosystem, including the bacteria that exist within
us, both symbiotic and pathogenic.3
As whole-genome sequencing becomes more affordable,
personalized medicine will benefit from having EGFR-like
regions in the genome analyzed and combined with other
clinical and non-clinical attributes — resulting in a truly
personalized diagnosis. Various similar examples of genes and
corresponding therapies have already been identified for other
cancers, such as breast cancer (HER2/neu)4
and melanoma
(BRAF).5
Similarly, translational medicine is expected to
benefit greatly from having more unique and detailed insights
into different pathways that explain the molecular basis of
disease and provide a sounder rationale for therapies.
The computational challenge in achieving such results on a
whole-genome sequence level and combining them with
various clinical and non-clinical attributes is not trivial.
However, considering the pace at which genomics and its
associated “-omics” technologies — which include
transcriptomics, proteomics and microbiomics — have evolved,
it is only a matter of time before the cancer case scenario has
even more specificity and personalization. That said, a
concerted effort is required to be able to systematically tackle
groups of diseases, each with its own complex mechanisms and
relationships, by using analytics and newer advances in
computational biology.
Forces impacting genomic medicine in
the next five years
Since the completion of the human genome, genomic
medicine has been hailed as a promise. Personalized medical
treatment, gene therapy, pharmacogenomics, novel
biomarkers, and highly specific and sensitive molecular
diagnostics attest to the promise.
In the past few years, increased applications of genomic
medicine have led to improvements in:
• Cancer detection through blood samples and tumor analysis
• Prenatal screening and diagnosis using fetal cells in maternal
blood
• Drug safety labels based on pharmacogenomic data
• Drug dosing, in the case of warfarin,6
and
• Drug efficacy, for example, related to breast cancer.

While some debate regarding its success continues, we
believe that genomic medicine is at a unique inflection point
in medicine’s history due to three major forces: innovation in
biology and technology; market demand; and consumerism.
Innovation in biology and technology
Today, highly specific panels of genes are being used for
cancer testing. Such panels check for the expression of multiple
genes known to be associated with specific cancers.7
The
pharmaceutical industry has an increased understanding of
complex pathways and corresponding development of targeted
drugs. Such drugs include, for example, a list of Tyrokine Kinase
inhibitors, as well as drugs that treat resistance to TKIs.8
Other innovations include the decreasing cost and increasing
capability of high-performance computing platforms that are
keeping pace with that of gene discovery, whole-genome
sequencing cost and speed. As a result, processing large data
sets is becoming easier.
A new facet of computing — cognitive computing — allows
a system to make use of natural language processing and
machine learning to ingest large and ever-expanding data
volumes. It can be trained to provide, with high accuracy and
speed, advice which is otherwise manual, time-consuming
and prone to missing available evidence.
Market demand
The healthcare and life sciences industries are at a
crossroads — characterized by patent expirations,
reimbursement pressures and a thirst for new ways of
impacting health outcomes through innovative therapies.
The increased expectation and market demand for targeted
products (such as drugs and tests) can offer significant
benefits quickly, versus incremental benefits delivered over
time after careful validation and cost benefit analysis.
Genomics is also converging with social media, as evidenced by
the sharing of health experiences and questions online. One
example is patientslikeme, a health data-sharing platform that
includes a blog, where “The future of the personal genome”
has been a featured video topic.9
Increased computing power,
imaging capability, the use of wireless sensors and the plethora
of accessible digital health information are also part of the
technological mix that is helping to push genomics forward.
This convergence has led to near-instant dissemination of new
discoveries, as well as new forms of social and scientific
collaboration.
Consumerism
The growing commercialization of genomics is evident in the
increased uptake of direct-to-consumer genomic testing, and
in more recent regulatory concerns and recommendations
about such technologies.10
These have led to genomics
attracting new media attention and becoming more
“mainstream.”
Regulatory and legislative precedents in the U.S. — including
the 2008 Genetic Non-Discrimination Act (GINA)11
and the
2013 Supreme Court decision regarding gene patenting with a
biotechnology company12
to protect consumers of genomic
data — have led to increased awareness of its sensitive nature.
Exploring major changes ahead from
genomic medicine
Genomic medicine will be a “game-changer” for important
stakeholders that include patients, providers, researchers,
payors, diagnostic companies, policy makers, life sciences and
governments. Three significant impacts are underway: (1) the
growing use of a new genomic health record; (2) greater
benefits for stakeholders in the three genomic medicine
cornerstones of sequencing, translational medicine and
personalized healthcare; and (3) opportunities for radical
industry transformation.

1. Growing use of a new genomic health record
In the next three to five years, it is likely that an individual who
interacts with the healthcare system will have a genome-based
electronic record or “genomic health record.” It will be used in
tandem with other electronic healthcare tools for decision
support, prevention, and customized testing and treatments.13
At first, the record may not contain the whole genome
sequence, but instead perhaps the results from testing specific
genes.
This prediction of such widespread use stems from:
• The decreased costs of sequencing the human genome (see
Figure 1),14
• The proliferation and availability of genome-based tests in
the past five years (see Figure 2),15
• The rising adoption of electronic medical records (EMRs,
see Figure 3),16
• The increased use of genome data to recommend targeted
treatments using companion molecular diagnostics, and
Figure 1: Faster processing speeds have reduced the cost of
sequencing dramatically.
$100M
$10M
$1M
$100K
$10K
$1000
Cost per
genome
2000 2005 2010 2015
$1000
genome
Source: National Human Genome Research Institute” http://www.genome.gov/
images/content/cost_per_genome.jpg; Adapted by IBM Research.
Moore’s Law
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
3000
2500
2000
1500
1000
500
0
Figure 2: The use of genome-based testing has continued to rise sharply in recent years.
Source: Roberta A. Pagon, M.D., Professor of Pediatrics, University of Washington and Editor-in-Chief, GeneReviews www.genereviews.org
Laboratories
Diseases for which testing is available
• A growing willingness of payors to reimburse payments on
some genetic tests today.

Figure 3: EMR adoption in the U.S. and Canada is maturing.
Note: See Glossary for definitions of acronyms used in this figure.
Source: HIMSS Analytics®
EMR Adoption ModelSM
; 2013 N = 5458; 2014 N = 5445.
US EMR Adoption ModelSM
Stage Cumulative Capabilities 2013 Q4 2014 Q1
Stage 7
Complete EMR, CCD transactions to share data; Data
warehousing; Data continuity with ED, ambulatory, OP
2.9% 3.1%
Stage 6
Physician documentation (structured templates), full
CDSS (variance & compliance), full R-PACS
12.5% 13.3%
Stage 5 Closed loop medication administration 22.0% 24.2%
Stage 4 CPOE, Clinical Decision Support (clinical protocols) 15.5% 15.7%
Stage 3
Nursing/clinical documentation (flow sheets), CDSS (error
checking), PACS available outside Radiology
30.3% 27.7%
Stage 2
CDR, Controlled Medical Vocabulary, CDS, may have
Document Imaging, HIE capable
7.6% 7.2%
Stage 1 Ancillary — Lab, Rad, Pharmacy — All Installed 3.3% 3.2%
Stage 0 All Three Ancillaries Not Installed 5.8% 5.6%
A genomic health record would extract and integrate relevant
electronic health data with a person’s genome data. To use such
data effectively, new types of decision support will be required
to personalize risk, prevention and follow-up treatment.
Owing to the complex uses of and needs for such data, the
genomic health record is likely to be distinct from the
traditional EMR often generated from an EMR system.
The healthcare and life sciences industries are poised for
greater use of clinical decision support. This is supported by
recent EMR adoption data.17
In Q1 2014, approximately 40
percent of U.S. hospitals were at Stage 5-7 on the HIMSS
(Healthcare Information and Management Systems Society)
EMR adoption ModelSM
.18
As EMRs adopt more features, the overall integration and
interoperability of health information will increase and with
it, bring a new demand for clinical decision support. Such
integration is only the start of truly leveraging genomic
information in personalizing health data.
With the rising use of EMRs, healthcare integration and
interoperability is finally maturing. Advances in cognitive
computing — such as machine learning and natural language
processing — will build on this trend to accelerate the adoption
of genomic medicine and its integration with electronic health
data into this new genomic health record.
Adoption of genomic health records can provide a completely
different level of decision support to users of genomic data
through the application of cognitive computing. As defined by
IBM Research, “Cognitive computing systems learn and
interact naturally with people to extend what either humans or
machine could do on their own. Cognitive computing systems
are not based on programs that predetermine every answer or
action needed to perform a function or set of tasks; rather, they
are trained using artificial intelligence and machine learning
algorithms to sense, predict, infer and, in some ways, think.
Cognitive computing systems get better over time as they build
knowledge and learn a domain — its language and terminology,
its processes and its preferred methods of interacting.”19
Cognitive computing has the potential to bring unique
advances to translational medicine and to clinical decision
support at the point of care through two capabilities:
understanding natural language text or speech during diagnosis
and treatment; and interrogating vast volumes of medical
information to provide an accurate, probabilistic answer.
Based on the availability of accurate molecular diagnostic tests,
targeted treatments and a greater understanding of pathways
and genes that lead to specific cancers, a select group of
cancers would be excellent candidates to be used as an initial
step. With the massive expansion of knowledge across various
health conditions, a person’s genomic health record could
provide the ultimate source of personalization for a care
provider — creating an entirely new approach to care delivery
that is personalized using molecular information on individuals
and combined with their most current healthcare status.

Figure 4: Three cornerstones of genomic medicine can operate in
the cloud: sequencing, translational medicine and personalized
healthcare.
Sequencing
Process sequencing
raw data into usable
form
Source: Adapted from IBM Global Technology Outlook, 2014.
Translational
medicine
Personalized
healthcare
High-throughput
genomic pipeline
Data integration
and analytics
platform
Intelligent genetic
counseling
support
Drug
Afatinib
Mechanism of action
Afatinib is an irreversible
kinase inhibitor and binds to
the kinase domains of EGFR
(ErbB1), HER2 and HER 4 to
inhibit tyrosine kinase
autophosphorylation.
Find relationship
between genome
and phenotypes
and discover/
develop treatments
Apply useful clinical
insights to patients
Cloud 3.0
Collaboration
Big data
Knowledge
sharing
Distributed
analytics
Computational
elasticity
2. Greater benefits for stakeholders in each genomic
medicine cornerstone
As the genomic health record evolves, each of the three
cornerstones of genomic medicine stands to benefit
substantially:
• Sequencing — Processing raw data into usable form.
• Translational medicine — Finding relationship between
genome and phenotypes and discovering/developing
treatments.
• Personalized healthcare — Applying useful clinical
insights to patients.
All of these cornerstones will work in a cloud-based model
that adds new dimensions of data sharing, collaboration and
efficiency (see Figure 4). A secure cloud offers benefits that
include rapid access of data and greater collaboration of large
data sets that can be exchanged and simultaneously accessed
and worked on.
Cornerstone 1: Sequencing
A sequencing project typically has four phases: experimental
design and sample collection; sequencing; data processing and
downstream analysis.
High-performance computing and scalable storage solutions
are required to process the vast quantities of data from
whole-genome sequencing. Among the most significant
challenges are:
• To process the data fast enough to keep pace with the output
speed of sequencer(s)
• To manage and store the massive amount of data (“really big
data”) generated by sequencers and analysis.
Sequencing has become both more economical and much
faster than in recent years. However, clinical reliability of such
large-scale sequencing efforts remains in question, especially
when whole-genome sequencing information could be used for
translational medicine and clinical decision making. New
advances in whole-genome sequencing technology have led to
greater throughput of sequencing data at a decreasing cost.
And when faster, cheaper machines operate in parallel,
thousands of whole genome sequences could be generated
per year.

In the data processing phase, raw sequencing reads are mapped
to a known reference genome or assembled de novo. The
sequence alignment information is stored and compressed.
The differences (variants) between the reference and sequence
genome are identified. The applications and algorithms are
typically CPU-intensive and input/output-intensive. The de
novo assembly algorithms have a very large memory footprint,
while the reference-mapping applications and variant-calling
algorithms have much smaller memory requirements.
Traditional generation of raw sequence will not only become
faster, cheaper and more accurate, but also infused with
analytics capability that renders more than just raw data
results.20
Many vendors now offer sequence data analysis,
in addition to software-as-a-service analysis platforms for
scientists in research and clinical settings. Such services
aggregate and analyze multiple sources of information
including gene expression, proteomic and sequence data.
It is crucial for stakeholders in the sequencing data collection
cornerstone to have a high-performance computing IT
solution that can scale up appropriately and accelerate the
analysis of the sequencing data. An HPC solution could help
manage the data deluge resulting from the much higher data
generation rate of future sequencers.
Raw sequence data infused with analytics capability can
increase that data’s value to downstream users, including
researchers and the pharmaceutical industry. Sequencing
capability is increasingly going to include analytics capability
offered in a cloud-based environment with the ability to
aggregate and analyze multiple sources of genomic,
proteomic data.
Cornerstone 2: Translational medicine
Translational medicine can broadly encompass the need
for new methodologies and solutions to understand disease
mechanisms. It includes:
• The discovery of biomarkers used for discovery of
drug targets, diagnostics, prognosis and risk factors,
• Genome-specific data models, and
• The integration of genomic and non-genomic analytics
capabilities to discover clinically useful insights.
Translational medicine thus has many diverse data inputs,
such as gene sequence data from the first cornerstone, and
phenotypic and environmental information.
Although the capability to rapidly and cheaply sequence
the human genome is important, translational medicine
analyzes genome data with corresponding phenotype
information — such as those reported in medical records
or environmental information — to provide the real value
to researchers who can then obtain insights into human
biology and health.
Yet, to find such genotype-phenotype relationships, there are
two requirements: large populations with accurate medical
records; and large computational capacity with sophisticated
algorithms (see sidebar, “Rizzoli Orthopedic Institute”).
Phenotypes are subjective in many cases, and vary among
physicians and hospitals. In the translational medicine
cornerstone, data from many sources — such as patients’
medical records, public databases, literature and in-house
experimental data — is heterogeneous, multidimensional and
mostly unstructured. The integration of such large and diverse
healthcare data with genomic data is highly challenging for
researchers who now also face unprecedented data volumes.

Most translational medicine platforms in the last few years
have focused on data processing and data integration. This
step is essential to derive insights from the information
captured in genomic records starting from Single Nucleotide
Polymorphism data and next-generation sequencing of raw
information to fully annotated whole genomes.
It is also critical to link genomic data to additional sources,
such as phenotypic and environmental data in the case of
related patient studies, and to available textual information in
patents and scientific literature. Linking externally available
open databases of genomics, transcriptomics, proteomics,
metabolomics and microbiomics requires another step to bring
all the information together to allow advanced data mining and
deep analysis.
Rizzoli Orthopedic Institute: Analyzing multiple disease fac-
tors to gain insights that allow individualized care for patients
with rare genetic conditions21
Rizzoli Orthopedic Institute aspired to create more evidence-
based treatment protocols for rare bone diseases to guide
more effective treatment and reduce unnecessary testing and
procedures. Integrating clinical, genetic and medical imaging
data from hundreds of patients with a rare inherited bone
disorder is arming doctors at Rizzoli Orthopedic Institute with
new insights into better predicting how disease symptoms will
likely progress in an individual person. Advanced algorithms
identify patterns within the data, guiding research programs to
further understand the underlying disease. With a greater
understanding of the disease, doctors are able to match
treatment strategies to the individual, enhancing patients’ care
and improving their quality of life. The personalized treatment
approach also means that patients with less severe symptoms
are avoiding the pain of unnecessary surgery and the
inconvenience of repeatedly going to the center for imaging
procedures.
To bring together such diverse data, researchers around the
world often build their own integrated data set from internal
data and some external available data. Data subsets are thus
replicated and integrated repeatedly in various ways, each time
to get the desired answer to a different research question of a
specific study.
The result is duplication and inefficiency in advanced data
mining and deep analysis. Once a researcher devises a subset
of integrated data, it is often analyzed separately in a “quick
and dirty” mode. This methodology has limitations including
inefficiencies in the use of big data, lower replicability of
studies, the need for more detailed technical requirements
and a lack of widespread use.
Future translational medicine platforms will have to overcome
these limitations — and include not only the integrated data
layers, but additional layers — to automate and speed up
preprocessing. Such platforms will also need a robust
methodology and pipeline for performing advanced analytics
and enabling repeatable research results. Data analytics is thus
at the core of translational medicine (see sidebar, “A leading
medical and research center”).
It is expected that any big data platform for translational
research will include the required layers for effective analysis,
including cognitive computing capability. Genomic data
and analytics can also be combined with scientific literature
through the use of cognitive computing, the power of
which was demonstrated by Watson in the Jeopardy! Grand
Challenge in 2011.22
It can help scientists and clinical research
doctors obtain insights from vast amounts of data and
publications. In addition, machine learning methods can
be employed to elucidate insights automatically. Such insights
can help construct disease progression models for a variety
of diseases to assess risk factors and treatment planning.

A leading medical and research center: Aiming to grow
translational research capabilities
A cloud-based HPC solution designed and developed with a
business partner will serve as the foundation for a growing
translational research capability at a leading international
medical and research center. The solution will provide
researchers and administrators with the input/output
performance, scalability and flexibility they will need to adjust
to rapidly changing tools and capability demands inherent in
computationally intensive life science research.
The solution is designed to enable two key research
capabilities: 1) the efficient creation and execution of complex
workflows for genomic analysis, and 2) the secure integration
of genomic data with de-identified clinical data in a common,
easily accessible repository for downstream analytics. Through
this effort, the center aims to become an international leader in
translational research — a center of excellence where top
biomedical scientists can work collaboratively to develop
novel clinical therapies that improve both local and global
health outcomes.

Translational medicine is a key innovation lever for the
discovery of targeted treatments that are specific to key
biochemical pathways regulated by genes which — when
awry — can create or predispose a disease state.
Such insights can be achieved with high efficacy and low
side effects, especially toxicity. Treatment insights can
then be shared and validated among translational medicine
communities to improve healthcare and advance personalized
healthcare.
Cornerstone 3: Personalized healthcare
Personalized healthcare focuses on an individual (that is, a
patient) and the ability of a clinician to provide unique
treatment based on the individual’s personal characteristics.
Genomic medicine will add even more complexity for
geneticists, genetic counselors and specialists, such as medical
oncologists who must link a person’s genomic sequence to
appropriate evidence-based personalized treatments. Three
areas in personalized healthcare are expected to stand out: even
more advances in cognitive computing; greater use of disease
progression models; and a growing impact of genomic
medicine on oncology.
Advances in cognitive computing — such as machine learning
and natural language processing — are expected to increase as
clinicians seek machine-based “second opinions” which are
made with high confidence and rigor, and at a pace far greater
than is possible manually. Information sources can include
dbSNP (SNP database), HGMD (Human Genome Mutation
Database), COSMIC (Catalogue Of Somatic Mutations In
Cancer), UMD (UNC Microarray Database), LOVD (Leiden
Open Variation Database), MutDB (database that associates
protein structural information with mutations and
polymorphisms in gene sequences), HVP (Human Variome
Project) and programs like SIFT (Sorting Intolerant From
Tolerant) and Polyphen2.
Such information will comprise the genomic health record at
the point of care. Clinical decision support tools will allow
physicians and genetic counselors to understand and navigate
the massive amount of genomic and drug information in
literature and databases to provide personalized diagnosis,
prevention advice and the most effective treatment plans.
Disease progression models that include historical paths of past
patients, with various information including genomics and
other “-omics information” will guide physicians to evaluate a
patient. Patient similarity will be performed on the models to
define similarities between patients using multi-dimensional
and complex data.

Oncology is the first area in medicine where the impact of
genomic medicine has already been documented from
predisposition testing to diagnosis, to precision treatment
with a targeted drug.23
And this appears to be only the tip of
the iceberg.24
With a reduction in sequencing costs, the
availability of multiple oncology diagnostic panels, the rapid
proliferation of targeted treatments and a new paradigm of
cognitive computing, the oncology field is one that should
quickly experience the benefits from personalized/precision
care and a new genomic health record (see sidebar, “New
York Genome Center”).
With the help of cognitive computing and other computational
techniques, the landscape of risk factor profiling will change
dramatically as we process more human genomes and
translational medicine advances. Certain considerations will be
important in this era of genomic medicine, building upon
strong principles which have been tested with more traditional
genetic disorders (see sidebar, “Privacy, legal, social and ethical
aspects of genomic medicine”).
New York Genome Center: Accelerating personalized, life
saving treatments for cancer patients25
The New York Genome Center (NYGC) and IBM are partnering
in a first-of-a-kind program to accelerate personalized, life-
saving treatment for cancer patients. IBM and NYGC will test
a unique Watson prototype designed specifically for genomic
research as a tool to help oncologists deliver more
personalized care to cancer patients. NYGC and its medical
partner institutions plan to initially evaluate Watson’s ability to
help oncologists develop more personalized care to patients
with glioblastoma, an aggressive and malignant brain cancer
that kills more than 13,000 people in the U.S. each year.
Despite groundbreaking discoveries into the genetic drivers
of cancers like glioblastoma, few patients benefit from
personalized treatment that is tailored to their individual cancer
mutations. Clinicians lack the tools and time required to bring
DNA-based treatment options to their patients and to do so,
they must correlate data from genome sequencing to reams of
medical journals, new studies and clinical records — at a time
when medical information is doubling every five years.
This joint NYGC-Watson initiative aims to speed up this
complex process, identifying patterns in genome sequencing
and medical data to unlock insights that will help clinicians
bring the promise of genomic medicine to their patients. The
combination of NYGC’s genomic and clinical expertise coupled
with the power of IBM’s Watson system will enable further
development and refinement of the Watson tool with the shared
goal of helping medical professionals develop personalized
cancer care.
The new cloud-based Watson system will be designed to
analyze genetic data along with comprehensive biomedical
literature and drug databases. Watson can continually
“learn” as it encounters new patient scenarios, and as more
information becomes available through new medical research,
journal articles and clinical studies. Given the depth and speed
of Watson’s ability to review massive databases, the goal of the
collaboration is to increase the number of patients who have
access to care options tailored to their disease’s DNA.
Watson will complement rapid genome sequencing and is
expected to dramatically reduce the time it takes to correlate
an individual’s genetic mutations with reams of medical
literature, study findings and therapeutic indications that may
be relevant. The intention is to provide comprehensive
information to enable clinicians to consider a variety of
treatment options that the clinician can tailor to a patient’s
genetic mutations. It will also help NYGC scientists understand
the data detailing gene sequence variations between normal
and cancerous biopsies of brain tumors.

3. Opportunities for radical industry transformation
The transformational opportunity for industries rests in
leveraging cognitive computing throughout the entire value
chain; from pre-clinical discovery to understanding and
engaging consumers, and improving health outcomes.
The pharmaceutical industry, in particular, faces numerous
challenges with decreased R&D productivity, high costs of
bringing drugs to market, more stringent regulatory measures
and payors that are not willing to pay for “me-too drugs” or
for drugs that offer incremental benefits.26
By embracing
genomic approaches to drug discovery, new and radical
transformation opportunities will become available.
The finding of new biomarkers, for example, can be used for
a wide range of molecular diagnostics and drug target
identification. Such biomarkers can also provide more
specificity for drug development. Innovative clinical trials can
lead to accelerated approval of targeted drugs that meet criteria
of efficacy, utilization and cost-effectiveness. With recent
advances in genomic medicine, it is not uncommon for patients
with specific cancers to undergo genetic testing.
Privacy, legal, social and ethical aspects of genomic
medicine
The importance of keeping genetic information private is
spurring legal actions to protect patients and citizens. The
most noteworthy legal protection for U.S. consumers came
from the GINA (Genetic Information Nondiscrimination Act) in
May 2008. The GINA legislation protects Americans from
discrimination based on the results derived from a genetic
test. It also forbids insurance companies from reducing
coverage or increasing price, and prevents companies from
making employment decisions based on an individual’s genetic
information. GINA also forbids employers and insurance
companies from requesting or demanding a genetic test.
Genomic information elicits two important privacy
considerations: informed consent and confidentiality.
Informed consent is vital and requires policy measures to be in
place, both at the research and healthcare provider levels. From
a research perspective, citizens or patients from whom DNA is
obtained must be informed about what it is likely to be used for.
In some cases, a genetic test that is ordered by a provider on a
patient may also include the option for additional research on
the DNA sample.
In such cases, separate consent must be obtained for both: the
genetic test being ordered; and the research that would be
conducted on the patient’s DNA sample. For example, the topic
of incidental findings during the course of genome-based
investigations has raised numerous ethical discussions about
genomic testing and its impact on individuals and their families.
Confidentiality is a serious consideration in genomics as it
provides confidence to a given citizen or patient that his or her
genomic information will be treated as confidential and will only
be seen by those in the circle who are providing care. The
security of the information is critical, as genomic information
needs to be protected and safeguarded by appropriate
industry standard security software and encryption.
In the case of more standard genetic tests, these may be part
of a patient’s electronic health/medical record and thus need to
be treated with appropriate security measures. Confidentiality
also requires appropriate measures to “anonymize” data in a
research setting so that a given sample is not readily
associated with a specific individual. In addition, both privacy
and security measures must be in place to protect data from
malicious access.
Social and ethical aspects of genomic medicine will also
require ongoing attention and the future implications have yet
to be defined. For example, managing a diagnosis of Huntington
disease or needing to provide a nurturing environment for a
child with Down syndrome could clearly both have social and
ethical implications that extend beyond the families that are
directly affected.

Improved discovery capabilities can, however, lead to the
detection of rare mutations that only impact a few patients.
Rarity makes it more difficult to study a drug which has a
targeted impact on a specific mutation. In addition, targeting
a mutation with one drug may not be sufficient. Thus the
older method of testing one drug at a time per patient paired
with a genetic test is inefficient and complicates clinical trials
that typically have stringent study criteria and expected end
points. In the case of a stage IV cancer patient, time is of the
essence and so the conventional clinical trial process does not
always work.
To address these challenges, the “Master Protocol” is being
used specifically for squamous cell lung cancer at first.27
The
protocol aims to solve a fundamental problem of cancer
research using genomic medicine by using a standard trial
design for studying many drugs simultaneously. Instead of one
genetic test for each drug, the trial will use high-throughput
DNA sequencing to scan tumors for 16 different genetic
changes (mutations) that could get treated by more than a
dozen drugs.
Based on trial results, patients will be put into separate studies
for each new drug. Those for whom DNA sequencing doesn’t
turn up a potential medicine will get a new type of immune
system drug. The Master Protocol began in March 2014.
It could offer a radically different approach for cancer
diagnosis, treatment and follow-up. It could also provide new
opportunities to partner with healthcare stakeholders such as
life sciences companies, academic medical research centers
and payors.
Advances in cognitive computing will continue to evolve,
allowing pharmaceutical companies to use tools and systems
that can ingest and continuously learn. New awareness,
understanding and clarification will set the stage for the
discovery and generation of new ideas (see Figure 5).
The combination of awareness, understanding and clarification
enables discovery by integrating information related to
problems that previously required large amounts of manual
work and whose solution attempts were potentially prone to
missing some of the evidence. The implications of improving
discovery are highly relevant for the life sciences industry as it
seeks ways to reduce the time and cost of developing drugs that
are efficacious and highly targeted.
The healthcare and life sciences industries now have an
opportunity to radically transform. Genomic medicine can
be a foundation in the development of innovative clinical trial
protocols and the search for a new cadre of precision
treatments that focus on preventing disease or alleviating
symptoms.
Clarify the significance
of new ideas through a
series of deeper inquiry
Gain awareness of all
domain-relevant concepts
and individual relationships
that impact a researcher
Awareness
?Clarification
Understanding
Gain a deeper
understanding of the
meaning of relationships
and their implications
Discovery
Generate and
validate new ideas
Figure 5: Discovery and new idea creation in the healthcare and life
sciences industries.
Source: IBM Global Business Services 2014.

Ask yourself: Considerations for industry
executives
As a first step, answer the following questions to help your
healthcare or life sciences organization develop its genomic
medicine strategies:
• How have you incorporated genomic medicine into your
enterprise vision and strategy at the clinical, research and
IT levels? Genomic data will continue to proliferate into
mainstream healthcare. A data deluge is expected, as well as
serious questions about using genomic data for clinical
decision support. A new genomic health record will become
a reality along with increasing EMR maturity.
• Which types of capabilities, skills and personnel will your
organization need so you can use genomic medicine? Which
do you already possess and which do you currently lack?
How will you develop a plan to fill any identified gaps?
Cognitive computing and analytics leveraging cloud
computing will play a crucial role in genomic medicine, as
advances in these areas will lead to more accurate genome-
based clinical decision support via a system that learns
continuously. Understand the requirements for your
organization to leverage such advances that enable patient
care to be of higher quality, more accurate and safer.
Competencies to comply with evolving regulatory standards,
privacy and security requirements (for example, HIPAA) will
be crucial in an increasingly personalized world where data
breaches can be very damaging.
• What is your approach to deciding who you should partner
with to build complementary capabilities and skills in
genomic medicine? To more fully exploit the revolutionary
technologies and processes for studying and treating
diseases, you will likely need to partner with other
organizations in new ways. For example, as seen in the
Master Protocol, academic research centers are partnering
differently with pharmaceutical companies and primary care
facilities.
Conclusion
A new genomic health record will become a reality as genomic
information gets combined with relevant data extracted from
the traditional EMR. Rapid, precision oncology decision
support is expected to expand on a larger scale by performing
sophisticated genome/proteome/RNA analysis coupled with
cognitive computing. The capabilities and technologies
associated with cognitive computing are critical to the ongoing
genomic medicine evolution by enabling much more
sophisticated decision support, innovative clinical trials, new
targeted therapies, and a focus on health outcomes to prevent
and alleviate disease symptoms.
To benefit from the far-reaching industry transformation that
has begun, forward-thinking executives can: verify that
genomic medicine is part of their enterprise vision and
strategy; assess and plan to fill existing and future skill gaps;
and look closely at how and when partnering will help their
organizations succeed in meeting stakeholder needs.
To learn more about this IBM Institute for Business Value
study, please contact us at iibv@us.ibm.com. For a full catalog
of our research, visit: ibm.com/iibv
Access IBM Institute for Business Value executive reports on
your tablet by downloading the free “IBM IBV” app for iPad
or Android from your app store.

Related publications
Fraser, Heather, Chaturika Jayadewa, Jay Goodwyn, Peter
Mooiweer, Dan Gordon and John Piccone. “Analytics across
the ecosystem: A prescription for optimizing healthcare
outcomes.” IBM Institute for Business Value. September 2013.
http://www-935.ibm.com/services/us/gbs/thoughtleadership/
healthcare-ecosystem/
Mason, Barry, Gary Bacher, Harry Reynolds and Heather
Fraser. “Collaborating beyond traditional boundaries: What
convergence means for our health care systems.” IBM Institute
for Business Value. June 2013. http://www-935.ibm.com/
services/us/gbs/thoughtleadership/healthcare-convergence/
Ferrucci, David, Anthony Levas, Sugato Bagchi, David
Gondek and Erik T. Mueller. “Watson: Beyond Jeopardy!”
Elsevier: Artificial Intelligence. ScienceDirect. May 2012.
http://www.sciencedirect.com/science/article/pii/
S0004370212000872
IBM Institute for Business Value
IBM Global Business Services, through the IBM Institute for
Business Value, develops fact-based strategic insights for senior
executives around critical public and private sector issues. This
executive report is based on an in-depth study by the Institute’s
research team. It is part of an ongoing commitment by IBM
Global Business Services to provide analysis and viewpoints that
help companies realize business value. You may contact the
authors or send an e-mail to iibv@us.ibm.com for more
information.

About the authors
Aditya Pai is a healthcare and life sciences subject matter
expert with the IBM Watson Group. He has 17 years of
experience as a clinician and senior management consultant.
His responsibilities include bringing deep healthcare, genomics
and life sciences insights to clients. Trained in molecular
genetics, genetic counseling and business administration,
Aditya has a co-authored reports such as “Healthcare 2015:
Win-Win or Lose-Lose, Healthcare in India — Caring for over
a billion” and “Genomic Medicine — The tip of the iceberg.”
He also serves as a faculty instructor in the Health Industry
Management Program at the Schulich School of Business,
York University, Toronto teaching a course he co-designed on
Commercialization in Biotechnology and Pharmaceuticals.
Aditya can be reached at aditya.pai@ca.ibm.com.
Takahiko Koyama PhD, is the lead scientist in genomic
medicine in IBM Research. Currently, he collaborates with
New York Genome Center on the glioblastoma project.
Originally, he trained as a physicist but changed his career
through biomedical research at the University of Pennsylvania
School of Medicine and the University of Oxford, Sir William
Dunn School in Pathology. As a bioinformatician and a
computational chemist at Takeda Pharmaceutical Company, he
worked on ten research projects, primarily in oncology.
Takahiko can be contacted at koyama1@jp.ibm.com.
Leonard Lee manages the worldwide IBM portfolio of
software solutions for healthcare and life sciences. His
responsibilities span strategy, mergers and acquisitions,
development, sales execution and partnerships. He has worked
on healthcare portals, predictive medical systems and drug
discovery. In a “past life,” he designed interactive toys, 3D
visualization and other strange things at IBM Research.
Leonard is a graduate of Carnegie Mellon University, where
he researched pervasive computing systems, and virtual and
augmented reality. He can be contacted at thirteen@us.ibm.com.
Contributors
This report was written with contributions made by a global
team at IBM comprising genomics, healthcare and life sciences
experts across Watson Group, Sales & Distribution, Software
Group, Global Business Services and Research: Houtan Aghili,
Ravishankar Balraj, Boaz Carmeli, Shahram Ebadollahi, Yaara
Goldschmidt, Stephen J. Harvey, Richard Hennessy, Joe
Jasinski, Prasad Kodali, Christine M Kretz, Janis Landry Lane,
Frank Lee, Pietro Leo, Eleanor Lewin, Vanessa Michelini ,
Lauren O’Donnell, Arthur Panov, Laxmi Parida, Karen
Parrish, Matthias Reumann, Michal Rosen-Zvi, Ajay K
Royyuru, Amnon Shaabo, Charlie Schick, Raminderpal Singh,
Tzy-hwa K Tzeng, Zeev Waks, Kelly Wyres and Jane Yu.
Acknowledgements
For their help in the development and production of this
paper, thanks go to Heather Fraser and Eric Lesser both of the
IBM Institute for Business Value; and Joni McDonald and
Kristin Biron, both of ITSO Global Content Services, IBM
Sales and Distribution.

Glossary
Adenocarcinoma — A malignant tumor formed from
glandular structures in epithelial tissue.
ALK (anaplastic lymphoma kinase) — an enzyme that in
humans is encoded by the ALK gene
Bioinformatics — The science of collecting and analyzing
complex biological data such as genetic codes
Biomarker — A measurable characteristic that reflects the
severity or presence of some disease state
CCD (Continuity of Care Document) — An electronic
document exchange standard for sharing patient summary
information
CDR (Clinical Data Repository) — A storage interface
between the data repositories of the Department of Defense
and the U.S. Veterans Administration
CDS (Clinical Decision Support) — A process for enhancing
health-related decisions and actions with pertinent, organized
clinical knowledge and patient information to improve health
and healthcare delivery
CDSS (Clinical decision support system) — An application
that analyzes data to help healthcare providers make clinical
decisions
Cognitive computing —Systems that are trained using
artificial intelligence and machine learning algorithms to sense,
predict, infer, and in some ways, think
CPOE (Computerized physician order entry) — A process
of electronic entry of medical practitioner instructions for the
treatment of patients under his or her care
de novo — Without a reference genome
DNA (Deoxyribo Nucleic Acid) — A molecule that encodes
an organism’s genetic blueprint
ED — Emergency department
EGFR (Epidermal growth factor receptor) — The cell-
surface receptor for members of the epidermal growth factor
family of extracellular protein ligands
EMR (Electronic Medical Record) — A systematic collection
of electronic medical information about an individual patient
or population
Environmental Information — Includes information about
air, water, soil, land, flora and fauna, energy, noise, waste and
emissions, as well as information about decisions, policies and
activities that affect the environment
Gene expression — The process by which information from a
gene is used in the synthesis of a functional gene product
Genotype — The genetic constitution of an individual
organism
Genomics — The branch of genetics that studies organisms in
terms of their genomes (their full DNA sequences)
Gene Sequence — The process of determining the precise
order of nucleotides within a DNA molecule
GWAS (Genome Wide Association Studies) — An
examination of many common genetic variants in different
individuals to see if any variant is associated with a trait
Glioblastoma — A malignant tumor of the central nervous
system, usually occurring in the cerebrum of adults.
HIE (Health information exchange) — The mobilization of
healthcare information electronically across organizations
within a region, community or hospital system
HIPAA (Health Insurance Portability and Accountability
Act) — U.S. healthcare law
HPC (High Performance Computing) — Any computational
activity requiring more than a single computer to execute a
task

Immunohistochemistry — The application of immunologic
techniques to the chemical analysis of cells and tissues.
Metabolomics — The scientific study of the set of metabolites
(a substance formed in, or necessary for, metabolism) present
within an organism, cell or tissue
Metastasis — The development of secondary malignant
growths at a distance from a primary site of cancer.
Me-too drugs — Compounds that are structurally very similar
to already-known drugs with minor pharmacological
differences
Microbiomics — The study of all the genomes of the microbes
present in an ecosystem
Nucleotides — Organic molecules that serve as the monomers,
or subunits, of nucleic acids like DNA and RNA
Oncology — The study and treatment of tumors
OP — Out-Patient
Pathogenic — A disease brought about by organisms in
humans
PET-CT techniques (Positron emission tomography–
computed tomography) — medical imaging tools that when
combined allow physicians to pinpoint the location of cancer
within the body before making treatment recommendations
Pharmacogenomics — The branch of genetics concerned with
determining the likely response of an individual to therapeutic
drugs
Phenotype — Trait caused by genes
Polymorphism — Variants of the same gene
Proteomics — The branch of genetics that studies the full set
of proteins encoded by a genome
Rational drug design — The inventive process of finding new
medications based on the knowledge of a biological target
Reference Genome — A digital nucleic acid sequence
database, assembled by scientists as a representative example of
a species’ set of genes
RNA (Ribonucleic Acid) — A single-stranded polymer of
nucleotides that contain the sugar ribose
RNA-Seq (RNA-Sequencing) — A technology that uses the
capabilities of next-generation sequencing to reveal a snapshot
of RNA presence and quantity from a genome at a given
moment in time
R-PACS (Radiology picture archiving and communication
system) — A computer network for digitized radiologic images
and reports
SNP (Single Nucleotide Polymorphisms) — A variation
among individuals at a single position in a DNA sequence
Software-as-a-service — Delivery of software remotely to
customers via the Internet, with applications hosted by one or
more service provider
Symbiotic — Having an interdependent relationship
Translational Medicine — Discipline within biomedical and
public health research that aims to improve the health of
individuals and the community by “translating” findings into
diagnostic tools, medicines, procedures, policies and education
Transcriptomics — The examination of whole transcriptome
(the sum total of all messenger RNA molecules expressed from
an organism’s genes) changes across a variety of biological
conditions
TKIs (Tyrokine Kinase inhibitors) — Substances that block
the action of enzymes called tyrosine kinases
Whole-genome sequencing — Laboratory process that
determines the complete DNA sequence of an organism’s
genome at a single time

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