Let's Get Personal

What are "Omics" Technologies?

2007-03-31T01:39:07Z

I want to get back to considering some ideas to build infrastructure, but I need to take one other detour first. I've used the terms "high-thoughput" and "omics" quite a bit, but what, exactly do they mean? Simply, high-throughput refers to just that, a technology in which a large (or even exhaustive) number of measurements that can be taken in a fairly short time period. "Ome" and "omics" are suffixes that are derived from genome (the whole collection of a person's DNA, as coined by Hans Winkler, as a combinaion of "gene" and "chromosome"¹) and genomics (the study of the genome). Scientists like to append to these to any large-scale system (or really, just about anything complex), such as the collection of proteins in a cell or tissue (the proteome), the collection of metabolites (the metabolome), and the collection of RNA that's been transcribed from genes (the transcriptome). High-throughput analysis is essential considering data at the "omic" level, that is to say considering all DNA sequences, gene expression levels, or proteins at once (or, to be slightly more precise, a significant subset of them). Without the ability to rapidly and accurately measure tens and hundreds of thousands of data points in a short period of time, there is no way to perform analyses at this level.

There are four major types of high-throughput measurements that are commonly performed: genomic SNP analysis (i.e., the large-scale genotyping of single nucleotide polymorphisms), transcriptomic measurements (i.e., the measurement of all gene expression values in a cell or tissue type simultaneously), proteomic measurements (i.e., the identification of all proteins present in a cell or tissue type), and metabolomic measurements (i.e., the identification and quantification of all metabolites present in a cell or tissue type). Each of these four is distinct and offers a different perspective on the processes underlying disease initiation and progression as well as on ways of predicting, preventing, or treating disease.

]]> Genomic SNP genotyping measures a person's genotypes for several hundred thousand single nucleotide polymorphisms spread throughout the genome. Other assays exists to genotype ten thousand or so polymorphic sites that are near known genes (under the assumption that these are more likely to have some effect on these genes). The genotyping technology is quite accurate, but the SNPs themselves offer only limited information. These SNPs tend to be quite common (with typically at least 5% of the population having at least one copy of the less frequent allele), and not strictly causal of the disease. Rather, SNPs can act in unison with other SNPs and with environmental variables to increase or decrease a person's risk of a disease. This makes identifying important SNPs difficult; the variation in a trait that can be accounted for by a single SNP is fairly small relative to the total variation in the trait. Even so, because genotypes remain constant (barring mutations to individual cells) throughout life, SNPs are potentially among the most useful measurements for predicting risk.

Transcriptomic measurements (often referred to as gene expression microarrays or "gene chips" are the oldest and most established of the high-throughput methodologies. The most common are commercially produced "oligonucleotide arrays", which have hundreds of thousands of small (25 bases) probes, between 11 and 20 per gene. RNA that has been extracted from cells is then hybridized to the chip, and the expression level of ~30,000 different mRNAs can be assessed simultaneously. More so than SNP genotypes, there is the potential for a significant amount of noise in transcriptomic measurements. The source of the RNA, the preparation and purification methods, and variations in the hybridization and scanning process can lead to differences in expression levels; statistical methods to normalize, quantify, and analyze these measures has been one of the hottest areas of research in the last five years. Gene expression levels influence traits more directly than than SNPs, and so significant associations are easier to detect. While transcriptomic measures are not as useful for pre-disease prediction (because a person's gene expression levels very far in advance of disease initiation are not likely to be informative because they have the potential to change so significantly), they are very well-suited for either early identification of a disease (i.e., finding people who have gene expression levels characteristic of a disease but who have not yet manifested other symptoms) or classifying patients with a disease into subgroups (by identifying gene expression levels that are associated with either better or worse outcomes or with higher or lower values of some disease phenotype).

Proteomics is similar in character to transcriptomics. The most significant difference is in regards to the measurements. Unlike transcriptomics, where the gene expression levels are assessed simultaneously, protein identification is done in a rapid serial fashion. After a sample has been prepared, the proteins are separated using chromatography, 2 dimensional protein gels (which separate proteins based on charge and then size) or 1 dimensional protein gels (which separate based on size alone), and digested, typically with trypsin (which cuts proteins after each arginine and lysine), and then run through mass spectroscopy. The mass spec identifies the size of each of the peptides, and the proteins can be identified by comparing the size of the peptides created with the theoretical digests of all know proteins in a database. This searching is the key to the technology, and a number of algorithms both commercial and open-source have been created for this. Unlike transcriptomic measures, the overall quantity of a protein cannot be assessed, just its presence or absence. Like transcriptomic measures, though, proteomic measures are excellent for early identification of disease or classifying people into subgroups.

Last up is metabolomics, the high-throughput measure of the metabolites present in a cell or tissue. As with proteomics, the metabolites are measured in a very fast serial process. NMR is typically used to both identify and quantify metabolites. This technology is newer and less frequently used than the other technologies, but similar caveats apply. Measurements of metabolites are dynamic as are gene expression levels and proteins, and so are best suited for either early disease detection or disease subclass identification.

These are obviously fore-shortened descriptions of each of these technologies, but a passing familiarity with the state of technology is really important to understanding what personalized medicine can and can't accomplish and what the best strategies are. By understanding what current technologies can accurately measure and what that in turn can tell us, we can make informed choices about where to focus time, money, and effort developing tools and encouraging infrastructure growth.

Realizing the Promise of Pharmacogeomics

2007-03-27T00:12:18Z

On Friday, the Secretary's Advisory Committee on Genetics, Health, and Society (SACGHS), an advisory body for the Secretary of Health and Human Services (HHS), released its draft report Realizing the Promise of PGx: Challenges and Opportunities for public comment. I want to talk about my impressions of their findings and recommendations. I'm going to constrain myself to the Executive Summary and the Introduction (with the occasional stop into the main text for more context), mainly because I haven't had time to thoroughly read the report's hefty 100 pages.

To begin with, I want to mention one caveat. This report focuses (like the title says) on pharmacogenomics (for brevity I'll use their abbreviation, PGx). This is distinct from personalized medicine, both because personalized medicine is broader (it incorporates a number of facets other than a patient's response to a specific drug) and because PGx is broader (there are some important basic science problems that can be addressed by pharmacogenomic research that, while tangentially related to medicine, are not directly clinically relevant. There is significant overlap, however, and many of the problems and challenges of PGx also apply to personalized medicine more broadly.

The report makes recommendations in fifteen areas. I'm going to focus on just a few of these and talk about their recommendations for

Development and Co-development of PGx Products
Analytic Validity, Clinical Validity, Clinical Utility, and Cost-Effectiveness
Data Sharing and Database Interoperability
Use of PGx Technologies in Clinical Practice
Health Information Technology

]]> The recommendations suggest that the FDA needs to provide guidance for companies intending to develop drugs and associated diagnostic tools to assess the drugs efficacy for a specific person. In particular it says they need to address the review process for the case where the drug is subject to FDA review but the diagnostic test is not. I have a simpler question: should there ever BE a case where a diagnostic test intended to identify people who will respond to a specific drug? This is a case where I'm not sure how far the regulatory oversight of the FDA extends, and what precisely is covered or not, but one important step in both making PGx effective and ensuring public confidence is extensive and exhaustive validation by an objective body. For drugs and diagnostics this means FDA review. The report also recommends providing incentives to the private sector for developing PGx technologies. To a limited extent I think this is an excellent idea, but it has to be executed properly. Being first into a market is potentially expensive, yes, but there are definite benefits to it. I think that providing some financial incentives is a reasonable way to encourage investment, and I think that expedited FDA review (another suggestion) is an excellent idea. The last suggested way of encouraging investment (and I understand that these are simply ideas for discussion and not concrete recommendations) is increasing intellectual property rights of these early investors. This is a very bad idea, and one that is at odds with another goal - equitable and widespread access to PGx technology. Private industry will be an important driver of the field, but the financial rewards they stand to reap should be enough. Strengthening IP protection will only serve to limit access due to cost.

Analytic validity, clinical validity, clinical utility, and cost-effectiveness are the foundations that clinical practice modifications are based on. Unless a new test or technique sufficiently demonstrates these traits, no physician is going to adopt it. The report recommends that HHS work to assess these for PGx applications and develop ways to improve it, such as better datasets and improvements to study methodologies, as well as quantifying the differing levels of evidence required for different uses of PGx technology. More importantly, pharmaceutical manufacturers should publish the results of studies on the clinical validity and utility of PGx, even (I would say especially) non-significant or negative results) or make the data available to be studied by others. I think a better approach may be to require drug makers to report these results to the FDA as part of the approval and surveillance process. They will still want to publish positive results in peer-review journals, and I think that's a fine thing, but the results from all of their studies should be available to other researchers in some other form.

Data sharing is a potential goldmine for researchers. Right now obtaining datasets can quite difficult, both mechanically (because of their size and format) and politically (because they are well-protected even by government-funded researchers). The report recommends that HHS identify the obstacles to data sharing and encourage companies and academic institutions to participate. It is also important for future research to develop ways to share and use patient data, and again, the report suggests that HHS work in coordination with other agencies and programs to ensure the interoperability of the various electronic health records systems in use and in development.

Of course if this work stays in the basic research phase indefinitely, it doesn't do a lot of good. The report recommends that HHS help to catalog and and disseminate applications of PGx technology, work with professional and licensing organizations to improve physician education, publish systematic reviews of PGx and its applications as they become available to help inform usage guidelines, and ensure that package inserts and labels on both drugs and PGx tests contain all available PGx information. This is especially important, because over 70% of current drugs have some PGx information available about them, but almost none contain this on their labels.

Last is health information technology. HHS needs to both encourage the growth of health IT experts as well as the inclusion of PGx in to current and future electronic health records (EHR). The report recommends working with Office of the National Coordinator for Health Information Technology (did you know there was such a thing?) and other agencies to ensure that both EHR and clinical decision support tools take into account currently available PGx information. Also, for the current time (when EHR are not universal), HHS should develop way for physicians to retrieve and utilize PGx information.

Overall, I think the report strikes the right tone - hopeful for the future applications but realistic both of the current state and the challenges that face the field. A number of the recommendations the report makes will also directly benefit personalized medicine broadly as well. My sense is that this report won't change very extensively before becoming finalized, and when it is, it will be an important roadmap within HHS and the NIH specifically as to what PGx projects should have priority. Anyone who is working in the field should read this both to get a sense for how the wind is blowing as well as for the chance to have some impact through your comments on the direction of PGx in the next decade.

The Genetic Information Nondiscrimination Act of 2007

2007-03-24T01:38:05Z

I'm going to deviate a little from the planned topic for today. A bill I've mentioned before, the Genetic Information Nondiscrimination Act, has been in the news recently, and will hopefully pass within the next few weeks. I have a ton of respect for Congresswoman Slaughter (she represents Rochester, NY, where I went to college, and was a big supporter of RIT), the bills primary sponsor in the House, and she has real science bona fides, with a degree in microbiology and masters degree in Public Health, but how good is this bill?

I want to spend a little bit of time dissecting it (not parsing phrase-for-phrase, but rather pulling out important points), and trying to assess its potential impact. Most of the press this bill has received has been positive (if uncritical, but what do I expect from the mainstream media on science?), but I'm always uneasy when I see a very diverse group of people supporting something. If all of these people like it, how can it possibly be doing much of anything? At the same time, at least some health insurers are opposed, and that gives me some visceral, if not intellectual, confirmation that the bill on the right track.

]]> The bill begins with five findings that motivate the bill and inform its purpose:

Genetic tests and research can lead to disease prevention but could be used for discriminatory practices
Government's track record with genetics is spotty, we need to prevent a repeat of the practice of forced sterilization
Genetic markers could lead to stigmatization of groups in which particular dieases are most common
At least some workplace discrimination has already occurred because of genetic tests (Norman-Bloodsaw v. Lawrence Berkeley Laboratory)
Federal law doesn't adequately address the potential for discrimination.

These five findings lay out a pretty compelling argument for some type of legislation - it gives solid examples of the potential consequences of this information, offers a concrete instance of one of the examples happening, and says that the current laws can't fully combat the potential problems. If you don't take issue with any of these facts, then the logical conclusion is a strengthening of current nondiscrimination legislation.

That's precisely what this bill proposes. First, the bill amends Employee Retirement Income Security Act of 1974 (29 U.S.C. 1182) to prevent requirements for genetic tests before enrollment and to prevent modifying the premiums in a group health plan because of information about a plan member or the family of a plan member. At the same time, it also ensures that a person isn't denied access to genetic tests because of this, that a health care provider can discuss or suggest genetic tests with a patient as part of their care, and that a doctor cannot force someone to take a genetic test. The bill proceeds to extend the same protections to both group insurance plans not affiliated with employment, with individual health plans, and to medicare supplemental coverage. Finally the bill places genetic information as protected health information under HIPAA.

The next section deals with employment discrimination. It prevents an employer from refusing to hire or firing an employee because of the results of a genetic test, and it prevents them from segregating employees based on genetic tests in such a way that is harmful to their employment opportunities. Moreover, an employer can't request, require, or purchase genetic information about an employee or a family member of an employee. There are a few exceptions to this, but they typically require informed consent from the employee and no individually identifying data being received by the employer. These regulations are similarly applied to employment agencies, labor unions, and training programs. The bill explicitly excludes discrimination based on genetic information as a cause of action for a federal civil rights case, but establishes a committee to meet in 6 years to reconsider that decision. Lastly, the bill says that medical information about an existing disease, even diseases with a genetic basis, is not considered genetic information.

After giving the bill a fairly thorough reading, I'm satisfied with it, it goes a very long way to preventing health insurance and employment discrimination because of genetic information. The specific penalties for employment discrimination are somewhat opaque to me, but the minimum damages for health insurance discrimination ($2,500 normally or $15,000 in some special cases) seem reasonable to me. I also like the fact that it explicitly defines some medical information as not genetic information even though that medical condition may have a genetic basis. A line is definitely needed to separate genetic and non genetic information, because without that, almost any trait or characteristic can be claimed to have a genetic underpinning. An employee with a bad attitude, who starts fights and is generally a pain in the ass, is someone you'd want to fire. But what if they claim that their attitude is genetically based (and there is certainly evidence that some factors that make up temperament are genetic)? Without that line, an argument could be made that the firing is based on genetic information and therefore illegal. I'm not suggesting that this scenario is likely, but I do think that by creating the distinction the bill prevent some potentially abusive claims.

So I think I've answered my own question. Despite (or maybe because of?) the wide array of people backing this bill, it does have a positive impact and really does accomplish something. The bill is a very good step forward, and hopefully will have the added side effect of recruiting subjects for genetic research a little easier. This next week I should be back to Monday/Thursday blogging; there aren't any deadlines looming to trip me up. On Monday I'll talk about the Secretary’s Advisory Committee on Genetics, Health, and Society draft report Realizing the Promise of Pharmacogenomics: Opportunities and Challenges

What is Pharmacogenomics?

2007-03-19T22:32:38Z

In my post defining "personalized medicine" I mentioned trying to tailor a person's drug treatment to get the best possible effect. Using a person's genetic makeup to choose the optimal drug treatment is called pharmacogenomics. Put another way, this is the study of the way a person's genome influences the effect of drug treatments. Drug response is a very complex phenotype that's influenced by both genetic and environmental factors, but high-throughput technologies such as gene expression microarrays and SNP genotyping arrays allow these genetic factors to be considered on a scale never before possible.

Drug response has two separate components, each of which can be studied in pharmacogenomic terms. The first component of drug response is pharmacokinetics, or the way the body metabolizes a drug. This can be crudely estimated now with some simple genotyping. Polymorphisms in he genes CYP2C19 and CY2D6 are known to affect the rates of metabolisms of many drugs. By modifying the effective concentration of medications, these polymorphisms can either decrease the drug's effectiveness or increase the risk of toxic side effects. The second component is pharmacodynamics, which is how the drug acts to treat the specific condition. Complex diseases from cancer to hypertension are heterogeneous both in their symptoms and in their response to drugs, and some of this variability is due to genetic factors. The underlying molecular cause of the disease, then, can be used to decide which drug is best suited for which patient.

]]> Pharmacogenomic profiling for pharmacokinetic information should lead to a deeper understanding of drug metabolism. As it stands now, a few important polymorphisms are well-known, but have next to no role in clinical practice, because the predictions that can be made with that information are of uncertain accuracy. Knowing more factors that influence metabolism of certain drugs should lead to more nuanced predictions about individual responses. Determining what genetic factors affect the metabolism of a specific drug, however, is a very difficult problem, one that requires a great deal of genetic information and a fairly large sample of people, with blood drawn at regular intervals to measure drug concentrations. This process must be repeated for every drug (and every combination of drugs, too) under investigation.

The application of pharmacogenomics to pharmacodynamic research is similar, and requires a great deal of data and effort. By considering genetic information about a group of patients treated with a specific drug, polymorphisms that mark response can be identified. Finding a number of these can lead to a signature or profile that can be used for prediction. In fact, this has already been done successfully in cancer research^[1,2,3], but it is not routine in cancer treatment, both because the technologies involved are expensive and because the outcomes that are being predicted by these methods are limited. Larger scale investigations will fix the latter problem, technological advances will hopefully fix the former.

Pharmacogenomics is only beginning as an area of research, but it has the potential to improve the way drugs are developed and prescribed. AT the same time, hyperbole about the benefits of pharmacogenomics (and about how close it is to real clinical impact) has led to disappointment⁴. Being realistic about the potential of pharmacogenomics is important, but optimism is definitely warranted. The lessons learned in predicting other complex phenotypes will be of service in pharmacogenomics, and it is realistic to think that in the next 10 or 15 years it will be routine to screen people with common, complex diseases to choose the medicine and dosage most likely to work for them. This profiling may take significantly longer for rarer diseases, particularly uncommon forms of cancer, but the momentum is there, and as the cost of high-throughput measurements drop, the number of samples being collected will climb, pushing this knowledge closer to clinical usage.

Okay, now that we've spent some time defining our terms and delineating some of the problems it's time to start thinking about solutions. I'm going to start with the one I think is most fundamental: infrastructure. I'll talk about some strategies for developing infrastructure and encouraging investment in infrastructure by both private industry and health care providers. I have another presentation this Thursday, so the post may be delayed a day or so.

What are the Ethical Issues in Personalized Medicine?

2007-03-16T16:28:24Z

I've discussed some of the scientific and policy challenges that surround personalized medicine, but I've left for last a much harder task: defining the ethical issues. From my perspective, policy and science problems share at least one important common feature - even if no one can agree on the optimal solution, it's possible to propose a solution, consider it's appropriateness in a reasonably objective fashion (hopefully using some pre-determined metric), and make adjustments based on its performance. Ethical issues are a bit trickier. I would guess that most people are willing to agree on what (most) the issues are, but that's about as far as things go. Trying to decide on appropriate solutions is much harder, because it's essentially a balancing act, and success looks less like an objective criteria and more like alienating the fewest people possible.

Becuase these issues are so much more difficult, I'm going to limit myself to three different classes of issues:

Protecting patient privacy
Protecting patient autonomy
Allowing access to personalized medicine

]]> Protecting patient privacy is one of the most important things that must be done before ordinary people will be willing to take advantage of individualized medical care, and just about everyone agrees that patient's have a right to keep details about their health private from most people (even if not from, say, their insurance company or in some cases state or local governments). But how far does that right extend? Does it cover a person's genetic makeup? That is something that undeniably influences health, and a fair amount of information about what diseases a person has or is at risk for can be extracted from genotype and gene expression information like what would be collected for personalized medicine services. How do you keep that information private and what uses are OK? For example, if a person has this information collected for use in risk profiling or diagnosis, should that then automatically commit them to allowing their data to be used for diagnosing and profiling others? While this can be done without identifiers, this information is, in effect, personally identifying, so it can never be truly anonymous. Additionally, what about the privacy of other family members? Families share genetic information, and by knowing something about their risk, a person also learns about their relatives' risks.

One of the issues of privacy is also directly related to patient autonomy - the right of a patient to choose what happens to them. The question of what uses of a patient's data are permissible is not exclusively a question of privacy but also one of autonomy. Is it OK to require a person to allow their data to be used for risk profiling or diagnosis as a condition of performing the service for them? The reasons for doing this are compelling (because large sample sizes are necessary for high quality risk prediction), but is it undue pressure on person to require it? That is to say, if agreeing to this is a condition, can that agreement really be considered free and consensual? What if the condition was not to allow the data to be used for diagnostic work but for research? Should a company be allowed to offer to collect this data for free in exchange for allowing its use in pharmaceutical development or trials? It's a long established concept in research that the incentives offered for participating in trials and research studies should not be so high as to . High-throughput data collection will likely be fairly expensive even for those with insurance (and probably nearly unattainable for those without insurance), what level of incentive is overly tempting? Another privacy issue is also an autonomy issue: if a relative learning something about their risk of a disease says something about your risk of a disease, should you have to consent before that relative can have those tests performed? How can we deal with this information being effectively forced on people?

Cost, just like with the policy issues last time, is a significant ethical issue as well. Something like 46 million people are without health insurance today, and many more have insurance plans that cover only the most basic things. How can we provide access to personalized medicine to everyone? Is access for everyone a reasonable goal? Is it an attainable one? These are some of the toughest questions that have to be answered, but luckily they're very similar to questions that have already been asked about traditional health care. There will likely be a number of levels of personalized care, and if universal access is a goal, what level is acceptable? Is a tiered system where those who can't afford to pay on their own receive some level of service while those who can afford it get more comprehensive care ethically sound? It's something that looks much like our current health care system, but many people are unhappy with that model.

Although the ethical issues facing personalized medicine are tricky, they're actually not that much different than the ones that face traditional medicine. This means that the research and dialogue about them can be transferred, so the conversation doesn't have to start from scratch. It's back to science next time, with a more in depth look at pharmacogenomics.

What are the Policy Issues in Personalized Medicine?

2007-03-12T18:16:33Z

As with any significant undertaking, the challenges facing personalized medicine are not limited to the science behind it. A large number of public policy challenges exist that must be addressed before personalized medicine can become a reality. Each of these challenges must be dealt with not by a single person or group, but by all of the stakeholders that are affected by it. Who are the stakeholders? That seems like an easier question than it actually is, but in general, the stakeholders are physicians, health care organizations like hospitals and health networks, private insurance providers, public insurance providers such as medicare and medicaid, pharmaceutical companies, state governments, the federal government, and, of course, patients. Not all of these are affected by each issue, but solutions will only be possible when the affected stakeholders work together.

As with the scientific issues, in no way is my listing complete, nor is the discussion about the problems. Rather, I want to give a sense for how broad the policy issues are and who they affect. The main issues I want to describe are

Getting access to data for secondary uses
Preventing healthcare discrimination
Genetics education for healthcare providers
Genetics education for patients
Creating infrastructure
Funding

]]> Risk prediction is an important part of personalized medicine, but it requires a fairly large sample of individuals on which to base the prediction. Moreover, the individuals used should be fairly similar to the patient (or patients) whose disease risk we want to estimate. One of the most promising ways of doing this is by using data collected by hospitals. This is called a "secondary use" of health data since it is not being used for its original purpose - making a diagnosis about the patient from whom it was collected. There are other potential secondary uses also, including using this data for pharmacogenomic drug development - another important goal of personalized medicine. These secondary uses are not currently possible, and policies must be developed to allow this data sharing in such a way that protects a patient's confidentiality and their right to opt-out of these uses.

Genetic information has the potential to reveal a large amount about a person's risk of developing a disease in the future. Insurance companies are likely to want to use this information to set policy rates and limits or even potentially to deny coverage altogether. Even if insurance companies have no immediate plans to do this, fear of it may prevent people from having this information collected. One policy solution has already been proposed, a legislative initiative called the "Genetic Information Nondiscrimination Act". The US Senate passed this act in both 2003 and 2005, but it was never brought out of subcommittee in the House of Representatives. The bill has been re-proposed in both the House and Senate this year, but its future is still unclear. Legislation of this type is extremely important, and must be undertaken at a national level. Hopefully mounting public pressure will force the House to consider and pass a bill to prevent this discrimination.

As of now, even if the scientific issues I've discussed before were solved, many physicians would not understand how to use this information. Doctors who don't specialize in genetic conditions are exposed to genetics mainly during medical school, and often only at a very basic level. Although many understand the impact of genes on simple Mendelian diseases, they are unfamiliar with the role genetics plays in complex disease and the way that genetic information can assist diagnosing and treating patients. Increased education, both during medical school and through continuing education programs, is the answer to this problem, but fitting that into an already overcrowded curriculum is no small task. As insurance companies, hospital systems, and patients realize the benefits of personalized medicine, though, doctors will catch up out of necessity. Being proactive with policy solutions should make that process both quicker and easier.

Physicians aren't alone in not understanding genetics. Patients have an even more limited knowledge of genetics, with much of their information coming from popular media. And let's be honest, if all a person knows about genetics they learned from watching Heroes or CSI, then making informed decisions about how to use genetics in medical care is going to be difficult. They will have unrealistic expectations about what can be done to correct genetic problems ("Isn't there some sort of gene therapy for this?") and exaggerated fears about what might be done with this information ("I don't want my DNA in some database where the government can get it!"). Bringing expectations in line with reality and talking about the reasonable risks of gathering this information then must be accomplished through public education policies. The exact forms of these campaigns could vary, but drawing on the successes of cancer awareness and safer sex awareness programs should inform the process.

Bringing personalized medicine to the public is a large computational challenge. Databases must be created to store the information, computing power to actually perform risk prediction calculations must be purchased, deployed, and maintained, and access to high-throughput technologies (genome-wide genotyping, transcriptomic profiling, proteomic profiling, and metabolomic profiling) must be increased. What is the best way to accomplish this? Are private doctor's offices ever going to have the resources to build and maintain a cluster of servers? And will their patient population be large enough to use for risk prediction? High-throughput screening for now is limited to research hospitals, but just as most doctor's offices use external labs to perform clinical tests, external companies will likely fill this role for them.

Of course, none of these problems will be solved cheaply. Educating physicians and the public require some cost, but are not terribly onerous, and the government and professional groups are likely to step into these tasks, because they have some expertise in providing this education. The infrastructure, on the other hand, is far more expensive. Some of the cost will likely be covered by private industry as companies spring up to do high-thoughput screening or provide application hosting for doctor's offices, but that cost will then be passed to doctors, insurance companies, and inevitably to patients. Policy solutions must be created to ensure that health care consumers (i.e., patients) don't get stuck with a disproportionate amount of the bill, and that these infrastructure concerns aren't used to hike insurance rates unjustifiably.

So there is not shortage of policy issues that have to be addressed to make personalized medicine a reality, but some solutions have already been proposed. In some cases, the problems are similar to other problems that have been extensively studied. By taking advantage of these, we can make sure that these challenges don't prove to be long-term roadblocks. Next up I'll talk about some of the ethical issues in personalized medicine. I have my very first dissertation committee meeting on Thursday, so I may be a day late, but I'll do my best to get the post finished on time.

What are the Scientific Issues Facing Personalized Medicine?

2007-03-08T18:39:01Z

The promise of personalized medicine is one that is fundamentally rooted in science. It's based, at least partly, on the belief that drives all science: knowing more (relevant) information about a process can lead to a deeper understanding of how that process works. Much science, however, (and particularly molecular biology) has followed a fundamentally reductionist paradigm. Each part of a system is studied in isolation, and the information it provides is considered additive to the information provided by a separate piece of the system.

But R.B. Laughlin and David Pines write

So the triumph of the reductionism of the Greeks is a pyrrhic victory: We have succeeded in reducing all of ordinary physical behavior to a simple, correct Theory of Everything only to discover that it has revealed exactly nothing about many things of great importance.¹

Laughlin & Pines are talking about the Theory of Everything in physics, but the principle holds. Human biology is inordinately complex, with variables working not in isolation but in concert. To individualize medical care requires a deeper understanding of that biology, and that is no small order. I'm going to cover three major problems in this post, which is by no means an exhaustive list of the scientific issues facing personalized medicine. Rather, its a subset of issues that are very interesting to me and present significant hurdles to the field. These are

Identifying genetic polymorphisms that have an impact on disease
Predicting what diseases a person is at risk for
Developing drugs that are more effective and have fewer adverse effects

]]> The first problem is how single nucleotide polymorphisms (SNPs) affect a person's risk of disease or how they influence a complex phenotype. Some of these polymorphisms have an obvious impact. They change either an amino acid or an exon splice site, leading to a different protein product. Others might affect a promoter binding sites or have no discernible change whatsoever. Genetic association studies, the typical tool for identifying SNPs with an impact of a disease or quantitative phenotype, have run against a roadblock in recent years; the findings of many promising studies have not been replicated when the studies were repeated in independent samples. The reasons for this lack of reproducibility are numerous, ranging from spurious initial findings to poor choices for replication samples. Although a number of potential solutions, from cross-validation to requiring all association studies to include an independent validation sample, have been proposed, the field still lacks a consistent, realistic, and tractable method for assessing which associations are "real" (i.e., have real predictive power or mechanistic insight and are worth devoting further resources to) and which are either artifacts of the data or are exclusive to the population being studied.

The next problem is predicting a person's risk of developing a disease. Biology and disease etiology are very complex, and attempting to create a mathematical model of who will develop a disease is impractical. At the same time, being able to estimate a person's risk of a disease is an important goal; it allows people who will benefit from early interventions to be identified, and it can both improve people's health and lower the long-term cost of a person's care. Right now, the only way to predict a person's risk of a disease is to have a medium to large study population that measures the outcome of interest and develop a prediction scheme from that. It's laborious, and if there isn't a study that looks at your outcome, there's no way to create one. Worse, it's difficult to know how well a risk prediction scheme will work when applied to a new patient, especially if that patient is very different from the people used to construct the scheme. These systems are built are imperative to personalized medicine (and, not coincidentally, the subject of my dissertation research, but that's a topic for another time - we're laying out problems, not solutions today), and ways to make them more accurate and more broadly applicable must be found.

Last is a group of problems that fall into their own own category: pharmacogenomics. Creating drugs that have fewer adverse side effects and are more effective has long been a goal of researchers and pharmaceutical companies. The genomics revolution means that more information than ever before can be integrated into this process. But to design more effective drugs requires two very difficult things: a deeper understanding of the mechanisms that underlie different disease subtypes and a more thorough understanding of how drugs act. As an exercise, flip through a drug reference book like the Physician's Desk Reference. The precise mechanism of many (if not most) recent blockbuster drugs is unknown. Doctors understand that some drugs work better for some people than for others, but there is no systematic way to tell who will respond. Targeting drugs to specific disease subtypes should help alleviate that problem. But as an extra layer of consternation, there is significant variability not only in how people respond to a drug, but in what dose they need and what side effects they suffer. This is because a number of factors influence drug metabolism. Polymorphisms in the genes CYP2C19 and CY2D6 are known to affect the rates of metabolisms of many drugs. By modifying the effective concentration of medications, these polymorphisms can either decrease the drug's effectiveness or increase the risk of toxic side effects. Despite the fact that the CYP subunits responsible for processing many drugs are known and the effect of polymorphisms in these subunits is fairly well studied, it is still not typical clinical practice to base drug dosage on this information. Hopefully increased accuracy will change this.

This list of problems just begins to scratch the surface, but it should provide a good sense of the scale of the scientific issues that face personalized medicine. None of the problems are easy, and none of them will be solved overnight. It's not even clear right now what the best solution to some of these problems looks like. In fact, the answer to some of these problems depends on the answer to some of the policy and ethical issues facing personalized medicine. On Monday I'll begin to describe what those policy issues are.

R.D. Laughlin & D. Pines The Theory of Everything. PNAS Vol. 97, Issue 1, 28-31, January 4, 2000

What is Personalized Medicine?

2007-03-05T19:27:32Z

If I want to talk about personalized medicine (and I do), I have to begin by saying what I mean by it. (As a side note, I'll use the term individualized medicine interchangeably. Occasionally, people will use them to slightly different effect, but for my purposes, they're the same thing.) And what I mean is pretty simple - the combining of all different types of data (clinical, environmental, and genetic) to predict what diseases a person is at risk for and to identify medical treatments that will work for that specific person.

It's easy to lose sight of how far medicine has come in the past 100 years. We take for granted that most diseases are able to be treated if not cured, and we dedicate significant resources to medical research. Modern chemistry has led to hundreds of drugs that have saved countless lives. For all that, medicine can still be a crude endeavor.

]]> Consider hypertension. It is one of the most prevalent diseases in America, and the single most common reason that people visit their doctors. In spite of that, less than half of people taking drugs to treat they hypertension actually have their blood pressure under control. Why is that? Well, partly because people don't change their lifestyles to combat the disease, but also because there is no way to identify which patient will respond to which drug. Hypertension is extremely heterogeneous, and it stands to reason that different subsets will respond to different medicines. For now, though, there is neither a way to easily assign a person to a subset of hypertensions, nor a mapping for which drug best treats which subtype.

But let's back up for a second. Why is hypertension so common? What leads to a person developing hypertension? For now the best predictors of hypertension are age (the older you are, the higher your risk of high blood pressure) and family history (if your relatives have high blood pressure, you're more likely to, also). But that casts a very wide net, and it's difficult to identify the people who would most benefit from early interventions to prevent them from developing hypertension. One potential application of personalized medicine is being able to combine all of the information available to make better predictions about who is really at risk of developing hypertension

Finding and targeting those at risk, though, will not stop everyone from getting hypertension. And the next potential application of personalized medicine is determining who will respond to which drug. This type of prediction is currently not even considered as part of treating a patient, rather the physician makes an educated guess about what drug may work and then monitors to see if the dosage needs to be increased or if another drug needs to be tried. But by identifying subsets of hypertensives and identifying which drugs work best in a subset, hypertension treatment will not only be more effective, there will likely be fewer adverse reactions and less wasted money.

Now that we know what personalized medicine is, the next three posts will cover the scientific, policy, and ethical issues that face the field. I don't intend to lay out much in the way of answers, and I also doubt that my listing of problems will be exhaustive. Rather, I want to convey a sense of the breadth of the issues that I'll be discussing in more depth over the next few months.

Welcome - Let's Get Personal!

2007-03-01T18:11:39Z

After a long time of thinking about it (and a long time spent procrastinating), I've decided to resurrect the blog. So I've slapped on a new coat of paint, added a few new gew-gaws, and I'm off to the races.

Sort of.

The blog is now actually an experiment of sorts. I'm waist-deep in writing my thesis, which is a risk prediction system that is able to sit at the heart of a personalized medicine system. It's fascinating work and I'm learning incredible amounts both about the mechanism of making a prediction and about what extra steps are necessary to make an algorithm clinically relevant and doctor friendly.

]]> But the prediction side isn't the only part of personalized medicine that's fascinating. It has the potential to drastically change the way medical care is delivered, but there are a number of hurdles to clear first. Some of those are technological, and some of those are at the heart of my thesis project. But other hurdles are policy issues, economic issues, ethical issues, and privacy issues. I have no way of addressing them (other than VERY briefly) in my thesis, both because they're too far afield, and because I have little expertise in those areas.

I like to think broadly about things, however, and I think that this topic is too important to be considered in isolation. This is something that needs to be discussed publicly, both to allay people's fears and also to take advantage of their wisdom. So that's what I'll be trying to do. I'm going to begin by defining some terms and then we'll see where that takes me.

As I said, this is an experiment, so this will be a little different than a typical blog. The posts will tend to be longer and somewhat less frequent. For now the plan is to publish on Mondays and Thursday, but that may change in the future. For me this blog is going to serve two purposes: 1) to keeping up writing while I'm actually doing the work of the thesis (I've just spent a month writing the first two chapters of my thesis, and I know how much constant practice improves writing, so I want to give myself that practice), and 2) to serve as a testing grounds for ideas to put into a publication on the policy of personalized medicine. I'd love to hear input and comments, so please send me an email (my address is in the sidebar).