Abstract the computational tools used to analyze large

Abstract

The health
science field is becoming more advanced and sophisticated every year. With
these medical advances, it is important that medicine become suited to each
individual in order to better diagnose and treat each person. Precision
medicine, which takes individual variability into account, has become a larger
part of this field in recent years. Bioinformaticians have played a role in
this growth, as these scientists have developed methods for characterizing
patients through the use of genomics, proteomics, and metabolomics, and through
the advancement of the computational tools used to analyze large sets of data.  As bioinformatics tools continue to advance
and biologic databases continue to grow, precision medicine will become a
greater part of clinical practice around the world.

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Bioinformatics and Precision
Medicine

Precision medicine is a growing approach
to preventative health and treatment of disease. This technique allows medical
professionals to study individuals and create a preventive and/or treatment
plan based on that study. This approach uses environmental and lifestyle
factors along with genetic factors that are gathered thanks to the work of
bioinformatics scientists. In January 2015, Precision Medicine Initiative (PMI)
was announced to extend precision medicine to all diseases and enlisted the
help of more than a million volunteers (2016). This groups long-term goal is to
create an electronic health record of genetic date, biological samples, and
lifestyle information to help advance pharmacogenomics and lay a foundation for
precision medicine for many diseases (PMI, 2016).

Precision medicine
has two main components: in the near-term a focus on cancers and long-term is
to generate knowledge applicable to the whole range of health and disease
(Collins & Varmus, 2015). The basis for this initiative is rooted deeply in
bioinformatics. The ability to study biologic date at omics levels has led to
significant advances in personalized medicine (Chen & Snyder, 2012). With
the advanced study of individual omes such as the genome, epigenome, transcriptome,
metabolome, and other omics, patients can be treated according to their genetic
makeup. This changes the health field from a symptom-oriented approach to focus
on preventative health and early diagnosis. In a personalized setting patients
can also be treated based on their genetic makeup.

Figure 1. This shows the progression
of precision medicine and the areas of focus for researchers. (Akdis &
Ballas, 2016)

Precision in clinic
requires five main components according to Cezmi Akdis, MD and Zuhair K.

Ballas, MD. These five components are improved disease taxonomy; including
subgroups, full patient monitoring with digital technology, disease phenotypes
and endotypes, biomarkers, and the ultimate goal of tailored individual
treatment (2016).

A major aspect of personalized
medicine to be considered is the economic impact. The global precision medicine
market is estimated to be worth around $56 billion by the end of 2016 and is
expected to expand at an approximate compound annual growth rate (CAGR) of 14%
from 2016-2024 (PMI, 2016). The market is growing due in part to the increasing
approval of drugs, the increased cost-effectiveness of DNA profiling, and the
rise of data-driven healthcare. As healthcare prices continue to increase
people are looking for a personalized solution.

Current State of the Field

            The healthcare fields with the most promise currently are oncology and
immunology. These fields have been implementing measures of precision medicine
for years.

            Oncology is
an obvious starting point in precision medicine. Given that each person’s
cancer is different, an individualized plan is already needed to pursue an
effective treatment plan. Each patient presents differently in clinic,
prognosis, response, and tolerance to treatment. If we are better able to
understand the biological background of cancer and how it affects the genome,
we can personalize treatment to each genome.

            One issue
currently facing researchers in cancer research is the variability seen in
cancer. Cancer is a highly heterogeneous disease in each patient and within
patient populations. Because of this the ability to handle changes in the
clinical trial setting is challenging (Shin, Bode, & Dong, 2017). Many
ideas have not moved from bench to clinic because of the difficulty in clinical
trials. One promising route is the microbiota-the microorganism of a particular
site-as it has been recognized as a key player in health. The microbiota
influences everything from disease status to drug response and resistance.

Sequencing the human microbiome and discovering the interactions in individuals
may be one effective approach to increasing therapeutic outcomes (Shin, Bode,
& Dong, 2017).

            One notable
example of target-based therapy is the Bcr-Abl
gene in chronic myeloid leukemia (CML). Using bioinformatics tools researchers
were able to develop a selective inhibitor of the Bcr-Abl. This provided broader treatment coverage because this gene
fusion occurs in almost all CML patients. Survival rates improved to 90% over 5
years and 88% over 8 years (Shin, Bode, & Dong, 2017). This is a major
success in the path towards precision medicine but because the gene affects CML
patients almost unilaterally it is difficult to gauge the effectiveness on a
broader scale. But using the pathways and discoveries made in this arena, the
research can be broadened to more genes and therapies.

Figure 2. A timeline of TRACERx study. The study
followed 842 patients with stage l-lllA non-small cell lung cancer (NSCLC)
over a three to four year period. (Jamal-Hanjani
et al., 2014).

            A potential breakthrough in oncology
precision medicine comes in the form of Lung TRACERx (TRAcking non-small
cell lung Cancer Evolution through therapy Rx). This
approach incorporates “multiregional and longitudinal tumor sampling and
sequencing and aims to define the genomic landscape of NSCLC and to understand the
impact of tumor clonal heterogeneity upon therapeutic and survival outcome” (Jamal-Hanjani
et al., 2014). In this study samples were collected from diagnosis to relapse
to gain insights into how each cancer responds to treatment, mutational processes,
and the mechanisms involved in drug resistance. By taking samples from multiple
regions were able to obtain a more accurate view of the genomic landscape.

Also, the intratumor approach allows the researchers to gain more than a single
set of data that is set in a specific date and time. Understanding how the subclones
of the tumor impacts clinical outcome and how these subclones interact with
each other and the host give a greater insight into the future of precision
medicine. In conclusion, this study integrated genomic data with phenotypic
response in order discover the heterogeneity of the cancer genome and the
mutational pathways involved. As with most scientific studies, it had its
limitations. Because the study relied on surplus tissue, the entire tumor was
not sequenced. While significant coverage of the tumor was obtained a workable
genomic landscape was able to be achieved (Jamal-Hanjani et al., 2014). This
study shows the plausibility of scientific studies into precision medicine, but
also how far the field has yet to come.

            Immunology
is another major starting point for those looking toward precision medicine. This
is one of the best candidates because we currently know the main immunological
and molecular events underlying allergic symptoms, which are the more specific
and sensitive standard diagnostic tests to identify reactions, the relevant
molecules in involved in allergic reactions, and purified and standardized
documented products for effective and safe allergen immunotherapy (AIT) (Passalacqua & Canonica, 2015). Because AIT can modify the
immune response against the allergen it can modify the history of the disease
itself. This is a unique factor of fighting allergies and gives it an advantage
in precision medicine. There are still limitations in this field. Biomarkers
are still needed to predict the effectiveness of treatment. This biomarker identification
would need to be researched further by bioinformaticians. A larger spread on
molecular allergy is needed in order to stick to wholly stick to precision
medicine.

            Precision
medicine in all aspects of healthcare requires interdisciplinary collaboration
between experts in medical, clinical, biological, technical, translational, and
biotechnological practices (Servant et al., 2014). All of these disciplines
must work collaboratively to build the databases and infrastructure necessary
to take on the momentous task that lies ahead of the field.

Discussion

            Precision
medicine is a rapidly growing field, yet is still in its infancy. Clearly the
use of bioinformatics strategies and methods are important to this growing
field. The basis of precision medicine is understanding the human genome and
other omes. By understanding the human genome and the interaction of the
processes within the human body the limit of precision medicine is almost nonexistent.

This is a monumental task and something that bioinformatics scientists have
been researching since the field began. The research of bioinformatics could be
quite possibly the key to the breakthrough and advancement of this field.

            Currently
the field relies upon specific biological markers and instances that affect most
of the population such as with the CML gene. While this is a useful starting
point and gives key insights into the interactions of the human body, this
continues to be a specific instance. By broadening the findings and techniques
used to treat diseases such as this, precision medicine can be put on a whole
new path.

            Another major
shortcoming of precision medicine thus far is the public health aspect. In
order to fully implement this on a large scale a public health database needs
to be formed with data from people of differing ethnic, environmental, and
lifestyle backgrounds. With the continuing advancement of computer technologies
this is not an impossible task in terms of computationally. The struggle in this
aspect will be the cooperation of large organizations, governments, and
international entities. As this is always difficult to implement, I don’t see
this kind of cooperation happening for many years, and when it does it will
take the advocacy and hard work of a major figure.

            Precision
medicine will be the healthcare of the future. With all the advancements in
medical technology and the rapid growth of the bioinformatics field, the path towards
this type of healthcare is imminent. As bioinformatics continues to grow and
advance along with computational technology being integrated into other fields
of science, the information to implement precision medicine will be accessible
in a fairly short period of time.

            Even with
all the shortcomings to this point, precision medicine has a very bright
future. As a young child, I was diagnosed with allergy-induced-asthma. With immunology
being on the forefront of precision medicine since the beginning, I have been
able to see first-hand the growth and advancement of this field. To be hopefully,
this field has grown immensely over a fairly short period of time. This field
should give hope to all that the world could soon be rid of many devastating
disease that continually plague our society.

Conclusion

Even though the scientific aspects of
precision medicine are advancing quickly, faster than expected, the goal of
completely personalized medicine is far from bearing fruit. Precision medicine
cannot fully be realized without public health records and databases. Populations
of varying ethnic backgrounds will have differing genetic pools and different
environments will have differing effects on their specific populations. Other
factors such as access to healthcare and disease prevalence vary according to
geographic location. In order to truly realize the end goal, scientific
implementation must be coupled with public health measures.

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