Some thoughts on Coursera’s Personalized Medicine MOOC

I completed Case Studies in Personalized Medicine, a massive open online course (MOOC) offered by Vanderbilt University through Coursera and taught by Dan Roden, MD.

The course

Dr. Roden presented several clinical cases, including both rare, single-gene syndromes (e.g. hypercholesterolemia secondary to PCSK9 deficiency, long-QT syndrome, cystic fibrosis, and some rare forms of diabetes and heart failure) and more common cancers (e.g. BRCA-related tumors and Lynch syndrome), as well as clinically important drug interactions (e.g. the infamous CYP2D61) and side effects (e.g. carbamazepine, statins, venlafaxine and lithium).

Much of the course revolved around genome-wide association studies (GWAS), a powerful technique that can identify potentially abnormal points in the genome where relevant pathophysiological mechanisms may be present. Knowing where to look is a good start, even if figuring out what is happening at the point in question is definitely nontrivial.

The figure below shows an example of a Manhattan plot illustrating an interesting if somewhat trivial finding. Each dot is a single-nucleotide polymorphism (SNP, pronounced “snip”).

File:GWAS of human eye color in the Cape Verdean cohort.pngSingle-nucleotide polymorphisms associated with human eye color in Cape Verde.2

In contrast, chronic diseases such as hypertension, diabetes or degenerative disorders will usually generate more ambiguous findings such as those shown in the plots below, where the SNPs point to a surrogate marker that may be related to cardiovascular health. The underlying mechanisms and their relevance remain unclear, and no specific interventions are available as of yet.

Manhattan plots for (a) retinal venular caliber and (b) retinal arteriolar caliber.Single-nucleotide polymorphisms vs. retinal venular caliber (a) and retinal arteriolar caliber (b) in a sample of Caucasian patients.3

Slow but steady progress in the clinic

One notable trend is that gene sequencing, including whole-genome sequencing, is becoming cheaper and more widely available. This means that clinically relevant genetic defects are being discovered, their pathophysiology is being elucidated, and this knowledge is being put into practice in the clinic. A particularly useful development is that patients susceptible to rare but potentially severe drug side effects can now be identified and this information can be used to guide therapy (e.g. abacavir and HLA B*5701)4.

Unfortunately, progress has been slow and often involves relatively rare diseases where genetic defects are discrete. The chronic, presumably polygenic diseases that cause most morbidity and mortality worldwide will seldom if ever be traced to a localized, treatable defect in a single gene.

Despite these caveats, clinically relevant findings are accumulating and yielding useful results. Perhaps the most important advance are the targeted therapies for cancer. Despite these new drugs, cancer often develops resistance to the drugs, and the disease’s sheer complexity has led to only modest overall results for some indications. These advances have spawned a new paradigm where cancer is considered a disease of the genome.5

Another remarkable result was that obtained with ivacaftor for cystic fibrosis. Ivacaftor can partially restore function of the CFTR gene in patients with mutations. Since low levels of CFTR gene function are enough to avoid the more severe complications of cystic fibrosis, partial restoration of CFTR’s channel activity can have dramatic clinical effects. In the first randomized trials of ivacaftor, the randomization was evident to all because patients assigned to receive the active drug showed obvious clinical improvement.6

Ivacaftor illustrates a paradigm should become more common: treatment will be tailored to individual patients according to genetics. Unfortunately, there will be few diseases in which highly specific treatment will yield spectacular results. Sometimes it will yield no results. In one trials, tailoring warfarin dosages according to pharmacogenomics did not improve clinical outcomes.7

I sometimes wonder whether it would be possible to identify, e.g., “diabetogenic” or “oncogenic” profiles in genomes and use this information to prevent and/or guide treatment for major diseases. It is even possible that in such cases Manhattan plots will reveal no statistically significant SNPs in the usual sense, but instead consist of patterns of genes that do not yield statistically significant peaks. Maybe the whole field will become much more analytical and mathematical in the future. Whatever the case, maybe there is a radically new, devastatingly effective approach out there waiting to be found.

Copious data is available, but is it enough?

Despite all these new discoveries, maybe we actually have too little information given the sheer complexity of genomics (and transcriptomics and metabolomics). How do 20,000 genes encoded in some 6 billion base-pairs interact with the environment—all possible environments—in hundreds of diseases? And what can be done with this information? Which morsels will be clinically useful?

One approach to get more data are electronic medical records (EMRs) and biobanks, which are repositories of biological samples collected from patients in clinical trials. Systematic collection of clinical data and biological samples in large cohorts of patients would be required to provide the massive samples required for relevant genome-wide association studies (GWAS) and uncertain inheritance patterns spanning several generations.

One concern is patient privacy. Complex issues involving tamper-proof anonymization of large datasets, compliance with HIPAA and foreign equivalents, data security, informed consent, and interaction with Institutional Review Boards will have to be addressed. I am optimistic that this issue will be solved and even that security will improve. It should be possible to adopt preemptive controls that prevent leakage of data rather than maintaining multiple files and enforcing access restrictions across study sites.

Conclusions

Genomics in medicine has the potential to transform the entirety of healthcare. In all of history, few developments ever spawned such colossal changes. One could imagine a world of automated trawling of massive datasets for information on how to prevent, diagnose, treat and assess the risks of all diseases known to mankind. This is a sobering prospect, which could change the very nature of medicine and healthcare.

Yet another change is the need to disseminate information. Healthcare professionals must obviously keep abreast of these hugely important developments, and the lay public must learn about the science, what it can and cannot do, how it can benefit them and even how it can be abused by peddlers of false hopes and other disreputable parties.

References

  1. National Center for Biotechnology Information. CYP2D6 cytochrome P450 family 2 subfamily D member 6 [ Homo sapiens (human) ] https://www.ncbi.nlm.nih.gov/gene/1565
  2. Beleza S, Johnson NA, Candille SI, Absher DM, Coram MA, Lopes J, et al. (2013) Genetic Architecture of Skin and Eye Color in an African-European Admixed Population. PLoS Genet9(3): e1003372. https://doi.org/10.1371/journal.pgen.1003372
  3. Ikram MK, Xueling S, Jensen RA, Cotch MF, Hewitt AW, Ikram MA, et al. (2010) Four Novel Loci (19q13, 6q24, 12q24, and 5q14) Influence the Microcirculation In Vivo. PLoS Genet6(10): e1001184. https://doi.org/10.1371/journal.pgen.1001184
  4. Mallal S, Phillips E, Carosi G, Molina JM, Workman C, Tomažič J, et al. PREDICT-1 Study Team. HLA-B*5701 Screening for Hypersensitivity to Abacavir. N Engl J Med 2008; 358:568-579. https://doi.org/10.1056/NEJMoa0706135
  5. MacConaill LE, Garraway LA. Clinical Implications of the Cancer Genome. Journal of Clinical Oncology 2010 28:35, 5219-5228. https://dx.doi.org/10.1200/JCO.2009.27.4944
  6. Ramsey BW, Davies J, McElvaney NG, Tullis E, Bell SC, Dřevínek P, Griese M, McKone EF, Wainwright CE, et al. VX08-770-102 Study Group. A CFTR Potentiator in Patients with Cystic Fibrosis and the G551D Mutation. N Engl J Med 2011; 365:1663-1672 https://dx.doi.org/10.1056/NEJMoa1105185
  7. Chong K. Warfarin Dosing and VKORC1/CYP2C9. Medscape https://emedicine.medscape.com/article/1733331-overview

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