Imagine a physician sitting down with his laptop and a morning cup of coffee. On a website that he uses to help manage his practice, an alert pops up. It tells him that a series of studies have demonstrated a connection between multiple rare mutations found in 10 percent of people and the likelihood that they might convert to type 2 diabetes. Nearly all of his patients have had their entire genome sequenced and entered into their electronic medical record – a process that takes only a week, costs a few hundred dollars, and is reimbursed by insurance companies because of the many benefits it provides to lifelong health management. He conducts a quick search of his 2,000 patient database and finds about 80 who are at risk. To half of those patients, he sends a strong reminder and advice on diet and lifestyle choices they can take to avoid the disease. To the other half, whose medical records reveal pre‐diabetic symptoms, he sets up appointments to consider more proactive treatment with drugs that can prevent the onset of disease.

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[15]
GENOMICS AND
PERSONALIZED
MEDICINE
ABSTRACT
Imagine a physician sitting down with his laptop and
a morning cup of coffee. On a website that he uses to
help manage his practice, an alert pops up. It tells
him that a series of studies have demonstrated a
connection between multiple rare mutations found
in 10 percent of people and the likelihood that they
might convert to type 2 diabetes. Nearly all of his
patients have had their entire genome sequenced
and entered into their electronic medical record – a
process that takes only a week, costs a few hundred
dollars, and is reimbursed by insurance companies
because of the many benefits it provides to lifelong
health management. He conducts a quick search of
his 2,000 patient database and finds about 80 who
are at risk. To half of those patients, he sends a strong
reminder and advice on diet and lifestyle choices
they can take to avoid the disease. To the other half,
whose medical records reveal pre‐diabetic
symptoms, he sets up appointments to consider
more proactive treatment with drugs that can
prevent the onset of disease. [1]
By
Sumeet Deshpande
WMBA 621 – New Age Of Innovation

2.
Introduction
Medicine today already contemplates the use of molecular markers to predict health risks.
Glucose and cholesterol measurements, for example, are standard of care. However, whereas
these are good screening tests for occult disease, they are not necessarily 'predictive' of future
events. Genomic information provides the opportunity to develop and refine the prediction for
risk and increase the precision of that prediction for the individual. Sir William Osler recognized
that "variability is the law of life, and no two individuals react alike and behave alike under the
abnormal conditions which we know as disease." [1]
We now have a new set of tools that can be used to understand biological and disease
variability. However, health and disease management today are further complicated by a
pharmaceutical industry that has developed medications using a one‐size‐fits‐all paradigm rather
than medications tailored to human variability. Thus, in medical practice today, many drugs work
in fewer than 50% of the patients to whom they are prescribed. Furthermore, more than 100,000
people die annually from drug‐related adverse events [1] A more personalized or customized
approach to medical care might be part of the solution to these healthcare woes.
What is personalized Medicine
Personalized medicine is a rapidly advancing field of healthcare that is informed by each
person's unique clinical, genetic (DNA‐based), genomic (whole genome or its products), and
environmental information. [2] The goals of personalized medicine are to take advantage of a
molecular understanding of disease to optimize preventive healthcare strategies and drug
therapies while people are still well or at the earliest stages of disease. Because these factors are
different for every person, the nature of disease, its onset, its course, and how it might respond
to drug or other interventions are as individual as the people who have them. For personalized
medicine to be used by healthcare providers and their patients, these findings must be translated
into precision diagnostic tests and targeted therapies. Because the overarching goal is to optimize
medical care and outcomes for each individual, treatments, medication types and dosages,
and/or prevention strategies may differ from person to person ‐‐ resulting in unprecedented
customization of patient care. [3]
What is Genomic Medicine?
Genomic medicine is an essential component of the broader personalized medicine
concept. Simply defined, it is the use of genomic information to guide medical decision making.
[4]The prospect of examining a person's entire genome (or at least a large fraction of it) to make
individualized risk predictions and treatment decisions is tantalizingly within reach.
Scientists have made extensive progress over the last two decades in understanding
human genetics and the role of proteins and chemicals in gene behavior. In 1989, the National
Institutes of Health launched the Human Genome Project (HGP) in an effort to identify the basic
building blocks for human beings. By 2003, investigators had sequenced the genome and
identified three billion discrete “chemical units.” [5]

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Advances in DNA sequencing have made it possible to develop greater understanding
regarding the role of genetic structures in disease susceptibility and treatment efficacy. Scientists
have identified genes that raise the odds of getting illnesses such as breast cancer, or increase
the likelihood of adverse reactions or bleeding. [6] Since the completion of the sequence of the
human genome, genomic information has been used, albeit on a small scale, to tailor care to
individual patients. The opportunity, however, is enormous: for the first time, we are in a position
to characterize health and disease states by their molecular fingerprints, develop meaningful
stratifiers for patient populations, elucidate mechanistic pathways based on genome‐wide data,
and develop new preventive, diagnostic, and therapeutic strategies that will shift the focus of
care from intervention to prevention. [7]
Convergence of Technology and Genomics for Personalized Medicine
This year, over 1,600 people per day are expected to die of cancer or similar
disease. Similarly grim numbers can be found with newborn medical care: One in 20 babies born
in the U.S. are admitted to the neonatal intensive care unit (NICU), and 20 percent of infant
deaths are a result of congenital or chromosomal defects. Both of these trends can be fixed with
the rise of personalized medicine, which is a classic technology‐based transformation. [8]
Here are some technological and medical advancements happening genomics that will
make the personalized medicine a reality.
Ultrafast, accurate, and low cost DNA sequencing (Primary Analysis)
Cost and complexity have been the biggest bottlenecks in the process so far, but we are
closer now than we have ever been to harnessing the power of DNA‐based therapies. In last 10
years since the completion of the Human Genome Project (HGP), advances in the genome
technology have led to an exponential decrease in DNA sequencing cost (more than 16000 fold)
and it made it easier to discover links between DNA sequence variations and human disease. The
cost to sequence first gnome was $3 billion and it took 15 years.
Recent innovation in sequencing technology have also dramatically cut the time it takes
to sequence entire gnome from years to less than 24 hours. Processing power is doubling every
six months, and decreasing costs and increasing speeds have made it far easier to discover links

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between DNA sequence variations and human disease. Please refer to Figure 1. [3] They key
break through here would to reduce the cost to below $1,000 and time to few hours.
Rapid, low cost, and accurate secondary analysis
Once a genome is sequenced, it must be assembled into a complete genome. It is typically
done using supercomputers and takes several days to complete. During secondary analysis, raw
reads are assembled to form a complete genome, allowing scientists to from a complete picture
of an individual’s genetic variations. The process is similar to putting together jigsaw puzzle with
billions parts. The cost of this step could reach up to $10,000 per genome factoring in
infrastructure, supercomputers, trained bioinformaticians. [8]
In addition, there is the data storage question. Recently, the new technologies are able to
assemble a complete genome in a few hours with accuracy and low cost. While creating a single
genome creates terabytes of raw data, data from assembled genome that has gone through
secondary analysis cuts that data a thousand fold to merely gigabytes. As a result, secondary
analysis is also the area in which big data, high‐performance computing, and genomics really start
to overlap. Recent innovations in the former two categories are what make secondary analysis
possible in four hours or less.
Connecting the dots between genetic mutations and disease (Tertiary Analysis)
Completed genomes and their identified genotype then need to be interpreted for
biological relevance. This is where a certain mutation (or set of mutations) is matched with a
certain disease or physical trait (phenotype). This is a big data problem, whether establishing
standard processes and putting some real horsepower behind it will make it possible to perform
effective tertiary analysis. Essentially, we have to create software tools to enable scientists and
clinicians to extract maximum biological meaning from complex genomic data. The key
breakthrough here is the ability to identify specific disease phenotypes that are linked to specific
genotypes — connecting specific mutations to specific markers — in a repeatable way.
Injecting genetic information into medical care
Now that the patient’s genome have sequenced, and identified the list of candidate
mutations, the next step is to combine this knowledge with other medical factors — the patient’s
history, environment, family background, microbiome, diet, etc. Improving patient care and
developing personalized therapies depends on intelligently leveraging complex molecular and
clinical data from a variety of internal, external, and public sources. The key innovation at this
juncture is mainly cultural. Genomic data needs to be integrated with patient health records so
that it can be interpreted by trained physicians who can put genomic insights in wider context.
The goal is nothing less than redefining disease at the molecular level and integrating this data
with patient histories.
The challenges here are several. Again, there are no standard protocols for pulling this
information together in a common environment. Today, too many databases exist in isolation

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and are not cross‐mineable. Even basic availability is an issue, not to mention structure,
formatting, and method of data delivery. This summer Google announced plans to create a
database of human genetic information to streamline processes. Beyond alleviating a storage
burden though, by collecting so much medical information in one open space, such as cloud,
doctors have much more research to refer to when trying to help patients. One example would
be to figuring out what drugs might work best against what cancers by comparing a patient with
similar stored genomes. [9]
Here’s a simple chart spelling out the phases of escalation as the genomics landscape matures
and draws closer to realizing our long‐held vision of personalized medicine. [8]
Talent and investment dollars have already started to flow downstream in an effort to address
the challenges that cheap and fast sequencing has itself created — namely, taking piles upon
piles of data and deriving insights from that pipeline raw material. There’s a huge opportunity
right now, again, at the intersection of high‐performance computing, big data, and genomics.
The Challenges of Enabling Personalized Medicine
There are a number of challenges that can hinder the pace of advancement and ability to
gain the benefits of personalized medicine. Some of the issues are discussed here:
Regulatory Policy
Regulatory processes will be strained by genomic discoveries. There will be no way to conduct
conventional clinical studies for every genetic signature as a unique diagnostic test. FDA policies
pertaining to personalized medicine tests, pharmaceuticals, and companion diagnostics are
important and will determine the pace. One solution would be a statistical strength standard that
must be demonstrated before genetics can be applied to medical decision‐making. Statistical
strength could be determined through mining of federated data pools. This mechanism could
alleviate capacity constraints and costs associated with clinical studies and speed innovation to
the marketplace. [6]
Interoperability
Interoperability represents a major challenge because of the difficulty of integrating data from
different sources. If researchers and healthcare providers are not able to exchange information,

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it raises the cost of health care and makes it difficult to learn in real‐time. A considerable amount
of medical information is collected, but too little of it is integrated or put into databases that are
usable for research or public health purposes. [3]
Medical Education
As personalized medicine becomes a reality in mainstream medical practice, physicians and other
health care providers will have to advise on the application of growing numbers of molecular and
genetic tests and pharmacogenomically‐guided drugs, make treatment decisions based on more
predictive evidence, use information systems for managing patient care, and deal with new
ethical and legal issues. The adoption will depend heavily on the degree to which the provider
community is educated in the field and is prepared to engage in medical practice focused on risk
assessment and predictive/prognostic modeling. [10]
Health Information Technology
As our understanding of diseases becomes ever more stratified by their genomic signatures, even
larger data sets will be needed to establish treatment protocols. Patient data across geography
and health care plans will need to be queried simultaneously. This can only be achieved through
large, federated pools of information that includes patient genomic data and their health
histories.
Privacy Concerns
Legitimate concerns over privacy and confidentiality complicate secondary use of health care
information. Even when data are aggregated and depersonalized, it is hard for researchers to
gain access to information that helps spot trends or gain insights into public health trends.
Coverage and Payment Policy
There also are problems in terms of reimbursement policies. Many programs are not well aligned
with laudatory goals such as preventive medicine or positive health outcomes. This mismatch
makes it difficult to judge quality or build incentives for healthy outcomes. We need to reward
providers for good behavior and reduce incentives for wasteful or unnecessary treatment. [6]
Looking ahead
We are moving from the inefficient and experimental medicine of today towards the data‐
driven medicine of tomorrow. Soon, diagnosis, prognosis, treatment, and most importantly,
prevention will be tailored to individuals’ genetic and phenotypic information. [8] As we enter
the second decade of the 21st century, investments in molecular biology, bioinformatics, disease
management and the unraveling of the human genome are all finally bearing fruit. Additionally,
the collection, processing, and storing of molecular information will become a new and significant
driver of demand for information technology. Personalized medicine promises to revolutionize
the practice of medicine, transform the global healthcare industry, make healthcare affordable
and ultimately lead to longer and healthier lives. [11]
Bibliography

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[3] PMC, "THE CASE FOR PERSONALIZED MEDICINE 4th Edition," PMC, 2014.
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[5] H. Genet, "Personalized medicine: new genomics, old lessons," US National Library of Medicine
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[6] D. M. West, "Enabling Personalized Medicine through Health Information Technology: Advancing
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