These are excerpts from http://www.ostp.gov/galleries/PCAST/pcast_report_v2.pdf.

• “The Federal government should make critical investments in the enabling tools and resources essential to moving beyond genomic discoveries to personalized medicineproducts and services of patient and public benefit.”
• “FDA should implement a more transparent, systematic, and iterative approach to the regulation of genomics-based molecular diagnostics.”

“Molecular diagnostics can be used in a variety of ways to inform personalized medicine

• Assess the likely efficacy of specific therapeutic agents in specific patients.
• Identify patients who may suffer disproportionately severe adverse effects from a given treatment
  or dosage.
• Determine optimal dosages for drugs whose therapeutic effect is known to vary widely.
• Assess the extent or progression of disease – Molecular diagnostics have the potential to provide more accurate and timely information on disease prognosis or treatment effectiveness than the imaging and pathology methods currently used for this purpose, though future diagnostic approaches may integrate all of these methods.
• Examine surrogate measures for clinical outcomes – Researchers are investigating whether  biomarker-based molecular diagnostics can provide reliable proxies for longterm outcomes such as relapse or survival. Such tests could be used to shorten the length and expense of clinical trials.
• Identify patients who can benefit from specific preventive measures

Clinical Decision Support

• To date, few genomics-based diagnostic tests have reached the market, and these few products have been targeted primarily at clinical specialists and subspecialists who have been able to assimilate them into practice without special measures. However, if the number of innovative personalized medicine diagnostics and linked diagnostic therapeutic combinations reaching the market increases substantially, widespread adoption of these products

Molecular Diagnostics

• The ability to generate genetic profiles using microarrays and sequence-based approaches promises to expand greatly the utility of genetic tests in clinical medicine. This is because human diseases resulting from a single genetic alteration are rare. Most common diseases including cancer, cardiovascular disease, and diabetes result from a variety of genetic changes acting in concert. Moreover, the exact combination of genetic factors resulting in a specific disease often varies among individuals. To address this complexity, many companies and academic groups are developing complex molecular diagnostics (including IVDMIA tests based on microarrays) with the goal of establishing correlations between a specific pattern of genetic modification and/or gene expression and disease outcomes such as progression, response to therapy, or adverse reactions. In some cases, these correlations and their predictive value are strong enough that the tests can have clinical utility even in the absence of a full understanding of the effects of and interactions among the component genes. As with genome-wide association studies, there are many pitfalls in establishing robust and reliable disease correlations for genomic profiling tests.46, 47 Reproducible sample collection and processing is essential to avoid artifacts in gene expression patterns due to cell population subtypes or effects of processing on the apparent levels of expression. Standards for the measurement, analysis, and reporting of biomarker data are essential to allow data to be compared across different studies and different laboratories and reduce duplication in defining assay methods and data requirements. Complicated statistical methodologies are required because probing the expression of 10,000 or more genes can lead to spurious correlations simply by chance. Moreover, these studies require not only large sample sizes but also validation using independent sample sets.

The following statement issued by the office of the President of the United States answers this question in the following summary (see http://www.ostp.gov/galleries/PCAST/pcast_report_v2.pdf):

“‘Personalized medicine’ refers to the tailoring of medical treatment to the individual characteristics of each
patient. It does not literally mean the creation of drugs or medical devices that are unique to a patient, but rather the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or their response to a specific treatment. Preventive or therapeutic interventions can then be concentrated on those who will benefit, sparing expense and side effects for those who will not.

“The principle of adjusting treatment to specific patient characteristics has, of course, always been the goal of physicians. However, recent rapid advances in genomics and molecular biology are beginning to reveal a large number of possible new, genome-related, molecular markers for the presence of disease, susceptibility to disease, or differential response to treatment. Such markers can serve as the basis of new genomics-based diagnostic tests for identifying and/or confirming disease, assessing an individual’s risk of disease, identifying patients who will benefit from particular interventions, or tailoring dosing regimens to individual variations in metabolic response. These new diagnostics can also pave the way for development of new therapeutics specifically targeted at the physiological consequences of the genetic defect(s) associated with a patient’s disease.

“The current high level of interest in personalized medicine from a policy perspective is attributable not only to the promise of improved patient care and disease prevention, but also to the potential for personalized medicine to positively impact two other important trends – the increasing cost of health care and the decreasing rate of new medical product development. The ability to distinguish in advance those patients who will benefit from a given treatment and those who are likely to suffer important adverse effects could result in meaningful cost savings for the overall health care system. Moreover, the ability to stratify patients by disease susceptibility or likely response to treatment could also reduce the size, duration, and cost of clinical trials, thus facilitating the development of new treatments, diagnostics, and prevention strategies.”

A group of researchers from the University of Sydney in Australia presented findings they obtained from a publicly available astronomy data set at the 211th meeting of the American Astronomical Society in Austin, Texas. What made it super interesting is that they concluded the Milky Way galaxy is twice as thick as originally thought!!

While I don’t understand the physics behind this, the point is that making data sets available publicly has great value because it expands the pool of researchers who can investigate a particular data set. This helps in validating findings but also in allowing researchers to test new ways of looking at data. Had the data been locked in someone’s closet, the original, presumably incorrect results would have been assumed for a longer period of time. In this case, the implications of the subsequent analysis were huge…a 6,000 light-year difference from the original results.

And it’s fortunate for the PhD student on the project who had the opportunity to work with a data set for her dissertation without having to collect it first. This allowed her to focus on her analysis methods rather than data collection.

See http://www.usyd.edu.au/news/84.html?newsstoryid=2163.

Andy Grove, former CEO of Intel and fabled leader, trashed the biomedical research community in the US, stating that the current system discourages innovation. He has some interesting points.

It seems he’s simplifying quite a bit. I’m sure there are problems in the system and that innovation is reduced by near-sighted focusing on smaller ideas that can get funded rather than bigger questions that can win Nobel Prizes. However, I think those smaller pieces will continue coming together to address big problems over the coming years, despite some inefficiencies in the system (which should be addressed).

In a way, this reminds me of the Bill and Melinda Gates Foundation that aims to run their non-profit like a business to maintain high efficiency and wise use of resources. Maybe Grove can use his assets and leadership to show that something similar can be accomplished in biomedical research.

http://www.newsweek.com/id/68221/page/2

I came across a PBS show that talks about an emerging area of genetics research called epigenetics. Two people (for example, identical twins) can have the same genetic makeup yet be very different. Scientists are discovering that these differences are partially attributable to “epigenetic” changes that don’t physically alter a person’s DNA but change how the DNA is activated (or not). As these changes occur in sex cells, they are also propagated to a person’s posterity.

As people go through life, they acquire more and more of these changes, and it varies from person to person depending on lifestyle factors such as diet, smoking, environmental exposures, etc. This may explain why one identical twin gets a heritable form of cancer while the other does not, even though they have exactly the same DNA (and no mutations have occurred).

One key realization is that the way we choose to live can not only impact us negatively (or positively), but it can have a real impact on our posterity. Another realization is that if scientists can better understand epigenetics, they can devise treatments that address it. The PBS video explains it in an understandable way.

Nice overview article about microarray data and how it can be analyzed.

http://www.fmv.ulg.ac.be/genmol/Essential_genomics/References/Butte_2004.pdf

Craig Venter’s latest pursuit is to artificially construct a chromosome of a simple bacterium and then insert it into an existing bacterium of another species with the goal of converting that organism to that specified in the artificial DNA. Lots of interesting ethical questions if you think about the long-term implications of this kind of capability.

http://www.guardian.co.uk/science/2007/oct/06/genetics.climatechange 

Another article that talks about issues related to genome-wide association studies. Also emphasizes the value of sharing data publicly.

http://content.nejm.org/cgi/content/full/357/5/436

Interesting resource I had not previously encountered. It’s great to see data sharing.

https://caintegrator.nci.nih.gov/cgems/aboutSetup.do

In the process of learning to use Linux, I have come across a few Web sites that have been helpful. Below are a few that helped me get started.

• Linux Command Quick Reference
• File Permission
• Writing Robust Bash Shell Scripts
• How to Use the vi Editor
• Configuring and Administering
• vim Quick Reference Card

This is by no means a comprehensive list. Please let me know if you have any other suggestions for Linux tutorial sites.