1. CSE4095/5810: Personalized Medicine in era of cloud computing
Aya Saleh
Software Engineer
Student Affairs Information Technology Department
at UCONN

2. Motivation
Empirical medicine has been around for decades and still medical services are using it.
Personalized medicine is a new trend that has evolved through past few decades since human genomic project had started.
There’s a need to explore the advancement in area of personalized medicine and integrate it in the context of cloud computing era.

3. Empirical Medicine Treat patients as one population.
Use symptoms and conventional tests like blood work to define disease and judge severity.
Hardly no predictions for future diseases or consequences of current status.
Trial and error is scheme for medical treatment
One size fits all scheme of treatment
Most doctors used a “one-size-fits-all” approach to prescribing medicines. They usually started with standard doses, and then observed how patients responded. If necessary, doctors changed the doses or drugs by a “trial and error” process.No one understood the biochemical reasons why certain medicines did not work for a small percentage of people or why some patients experienced serious adverse side effects.Although scientists and doctors suspected that a person’s genes could play a role in the response to medicines, genetic technology was not advanced enough to reveal which genes — and which variations of those genes — were relevant.

4. What is Genome?

5. Genomic sequencing
It’s part of human genomic project and it’s a world wide effort that aimed at defining pairs of sequence for genes

6. Genomic Sequencing

7. Clinical Data EHR : records for patients..etc. Administrative data.
Claims : which includes all insurance companies claims, pharmacy and enrollment.
Clinical trials.
Surveys.

8. Clinical Data Clinical research is mainly: 1) Clinical studies
Ex: Researchers may collect data about medical exams, tests, surveys for a specific group of adults to learn about life style effect on cognitive health.
2) Clinical trials
Ex: startups like “Trial spark” is working on lessening time and money with advanced technology and analysis by simulating trials and therefore less time and money are spent to get a drug, diet or medical device out.
1)clinical studies where researchers gather information , group volunteers according to broad charecteristics and compare changes over time.
ex: researchers may collect data about medical exams, tests, surveys for a specific group of adults to learn about life style effect on cognitive health
What is cognitive health?
A healthy brain is one that can perform all the
mental processes that are collectively known
as cognition, including the ability to learn new
things, intuition, judgment, language, and
remembering.
Clinical studies may help identify new possibilities for clinical trials.
2) clinical trials where research studies are performed in people that are aimed in evaluating medical, surgical or behavioral interventions.
usually clinical trials are used to learn if a new treatment is more effective than others or if it has less side effects.
Intersting research point: using clinical trials to find disease early , even before symptoms. Also, it looks for how to make life better for people with life-threatening disease of chronic diseases
Sometimes clinical trials study the role of support groups too. Example, for cancer researches proved high-spirit helps fighting the disease.
Before release of new drug: 3 phases are passed in order to have FDA approval.Each phase is trial to make sure the new treatment is safe & effective on certain number of people.
startups like trial spark is working on lessening time and money with advanced technology and analysis by simulating trials and therefore less time and money are spent to get a drug, diet or medical device out.

9. Clinical Data

10. Personalized Medicine
Personalized medicine is patient-centric.
It’s based on a specific population reaction to certain drugs not individually.
This could be done through identifying relations between diseases and genetics
The goal of personalized medicine is
to achieve patient-centric care
through providing tailored healthcare.

11. Personalized Medicine
The idea was to translate omics profiles into subject-specific care based on their disease networks However, our ability to decipher molecular mechanisms that regulate complex relationships remains limited despite growing access to omics profiles. Biological processes are very complex, and this coupled with the noisy nature of experimental data (e.g. cellular heterogeneity) and the limitations of statistical analyses (e.g. false positive associations) poses many challenges to detecting interactions between “networks” and “networks of networks”. As an illustration, only a minority of the genetic variants predisposing to type 2 diabetes have been identified so far, despite large-scale studies involving up to 150,000 subjects [1, 35]. It becomes more and more obvious that the bottleneck in laboratories has shifted from data generation to data management and interpretation [36].

12. Personalized Medicine
Advances in understanding the genetic basis of individual drug responses come from the NIH Pharmacogenomics Research Network (PGRN) ( Since its founding in 2000, this nationwide alliance of research groups has studied genes and medications relevant to a wide range of diseases like Alzheimer's , cancer , heart diseases and diabetes.
Ex: If it is found that a DNA mutation increases a person’s risk of developing Type 2 Diabetes, this individual can begin lifestyle changes that will lessen their chances of developing Type 2 Diabetes later in life.

13. Personalized Medicine
Personalized medicine is called p4 too i.e ( predictive, preventive, personalized, and participatory medicine)
There is an urgent need to bridge the gap between advances in high-throughput technologies and our ability to manage, integrate, analyze, and interpret omics data
An increasing lag has been observed in our ability to generate versus integrate and interpret omics data these last ten years
Personalized medicine is patient-centric.
It’s based on a specific population reaction to certain drugs not individually.
Although researchers are now beginning
to examine the combined impact of multiple genetic
risk factors, we still know very little about how
these different genetic risks influence each other
and how they interact with environmental factors:
some might have additive or synergistic effects,
some might cancel each other, some might have an
effect only when other risk factors are present, and
some might have independent effects. When
researchers estimate the likely effect of testing for
many different genetic risk factors, they predict
that most people will be found to have middle
range risks, from a little below to a little above
average. So for most people, genetic testing may
have limited value in individualizing health care.
Therefore a need to process all these relationships between genetic data and other risk factors with clinical data that is fed every visit in the system.

14. Personalized Medicine
Clinical Sequencing Evidence-Generating Research (CSER2) program, there is an initiative to integrate genomic sequencing into practice of medicine.
1) Define, generate and analyze evidence regarding the clinical utility of genome sequencing;
2) Research the critical interactions among patients, family members, health practitioners, and clinical laboratories that influence implementation of clinical genome sequencing;

15. Personalized Medicine
3) Identify and address real-world barriers to integrating genomic, clinical, and healthcare utilization data within a healthcare system to build a shared evidence base for clinical decision-making.
(Add more slides about world’s initiatives table for achieving this in long presentation)

16. Personalized Medicine
It's not only genetic information that could help in identifying or predicting diseases, there are other factors too like environmental factors.
Precision Medicine:
- Identify other factors for treatment than genomics scan and professionally include it.
- Genetics study is by population not by Individual.
Precision Health:
- Formal naming to precision medicine with information about people’s lifestyles, environments, and communities.
Personalized medicine to precision medicine, is kind of making sure that study of genetics and its affection by drugs is not per person , however it's per population and sometimes backgrounds has different reactions to medicine. So for better naming it's called precision , to make sure the clinical use of genomic sequencing related to opportunistic clinical identification and disclosure of genetic risk markers without specific patient consent.

17. Personalized Medicine

18. Legality and ethical issues
Who will really receive the benefits of PM?
Will patients with private insurance and greater disposable income have unfettered access and better health care?
Genetic Information and Nondiscrimination Act
- ensuring privacy of information between health care providers, insurers and employers discrimination based on risk factors of genetic information.
HIPAA rules filled some gaps in protections against discrimination by shielding workers from unauthorized disclosure of their medical information to employers. However, insurers may still request genetic information or may require genetic testing of those applying for a health insurance policy. Enforcement of HIPAA rules is also fairly weak, relying solely on administrative action.
The Genetic Information Nondiscrimination Act of 2005 (GINA) explicitly prohibited employers and health insurers from discriminating against individuals on the basis of their genetic risk factors, thus filling certain gaps in HIPAA privacy protections.8

19. Cloud Computing

20. Cloud Computing
Cancer research in the era of big data presents major challenges: computing on large datasets, combining expertise from various disciplines, and developing the infrastructure needed to enhance research efficiency. 
The National Cancer Institute (NCI) has initiated multiple projects to characterize tumor samples using multi-omic approaches

21. Cloud Computing

22. Conclusion Personalized Medicine
- Advancements in the field and challenges in integrating these advancements.
- Need to consider other variables like environment, lifestyle , even support groups work in clinical decision support systems.
- Ethical and social issues like human-breeding using genomics or ability to have genetic screening under insurance.
Cloud Computing role in fastening integration processes and how to make the world work together on the same topic.

23. Thank you