This concept continues to crop up and in a way it is tied to a concept that continually pops-up in science.

How can an observer tell the difference between dangerous and non-dangerous lab results when they both look the same early on?

As our ability to see into the human body improves with each technological step, we often find ourselves at the fork in the road. We can see that there is clearly a physical change, perhaps a small tumor, or a polyp, or perhaps a constellation of genes, or altered genes. That change could lead to a malignant tumor, a parasite, an amalgam of broken cells, that now wage a single-sided war with their host.

Then again; it might be nothing…

Dennis Normile tackles this in his March 4, 2016 article in Science “Epidemic of Fear”. In fact Sarah Fallon has a great perspective piece in Wired (Wired’s science journalism continues to get better and better).

Normile presents a very nice case for care when evaluating new screening data against past norms. In a case study that follows Fukushima related pediatric thyroid screenings, he presents both the initial driver of fear, in the form of screening results that suggested elevated rates of thyroid nodules in children. Further studies of a “normal” or “unexposed” population describe similar elevated rates of nodule detection in the thyroid, a result that suggests better detection, but not higher risk.

Fallon puts this in perspective, and provides the title of this post, indolent vs. malignant tumors. With each technological leap, we are better able to identify smaller and smaller clusters of cells within the human body. But these advances require further study to provide clues (read as markers) as to whether these clusters of cells will go on to become a malignant monster or perhaps lie dormant for the lifetime of the host. This is in no way an insignificant question. One path takes the patient into the world of invasive treatment, while the second suggests that no further treatment is warranted, while the cluster is small and treatable.

There are many ways forward that were not available to clinicians previously. One of the more promising are gene expression profiles that will be used to classify these early clusters of cells as taking an indolent path over a malignant one. This data has been collected for more than a decade now, and is already being used to assist with these decisions.

Hopefully it will continue so.

Let’s say you are interested in researching a gene. Or, maybe, let’s say you are interested in researching a drug.

Or, maybe, just maybe, you want to know about a drug-gene interaction. Where do you find that data?

We have more and more data available to use every minute… which is a good thing. For a researcher, looking through this data the first time (or for the 10th time) the sheer amount of information available can be a little daunting. Initial searches for genes used to yield hundreds of results, and many of those results were incomplete records, or just plan confusing.

Things are improving. Head over to the National Center for Biotechnology Information and search for genes using the keyword “warfarin” and you will get a list of genes back that mention warfarin or have been associated with a record that mentions warfarin. Total hits as of 5 February 2016 was 58, with the top two being VKORC1 and CYP2C9, generally identified as two of the genes encoding products involved in warfarin efficacy.

There are other data sets that to date are even easier to use. Let’s try the same warfarin search on another resource: Online Mendelian Inheritance in Man (OMIM). The search here yields 35 hits, VKORC1 and CYP2C9 are again in the top four, but the first hit is COUMARIN RESISTANCE. A quick click and you are reading a well annotated description, almost a text book chapter, but with more than a hundred links to relevant information on the subject.

But where is all that drug interaction data coming from? PharmGKB provides a more in depth look at all of the gene variants that might influence the function of those genes and the drugs that they are associated with. The same warfain search brings us here, to a wealth of information. I will leave almost all of this data for later, but have a look at the material described in the Pathways segment. Here the role the warfarin plays in the associated gene products functional pathway is graphically displayed. The role that VKORC1 plays is readily apparent, and why warfarins suppression of its function can be seen to play a role in the clotting process.

These are just three sources, covering the same warfarin example. The barriers to this information continue to fall, providing more intuitive access. Perhaps by your second and third searches you will will be more focused on the information you want, rather than wondering how you got there.

In this weeks paper there is a curious statement that comes just towards the end:

The limited availability and cost of pharmacogenetic testing are additional challenges (Tucker, 2008). Most insurance plans will reimburse the cost of pharmacogenetic testing only if it is required by the FDA, medically necessary, or has proven clinical utility (Shin et al., 2009).

• Margaret Mroziewicz, m.Sc. Rachel F. Tyndale, ph.D. “Pharmacogenetics: A Tool for Identifying Genetic Factors in Drug Dependence and Response to Treatment” Addiction Science & Clinical Practice—December 2010

Sometime around 2014 the $1,000 genome race really started to heat up. Nature touched upon the topic here. And then in 2015 Veritas Genetics broke through with their PGP collaboration here.

And with that, the cost barriers seem to be falling, leaving the question of what’s next. Having your genome sequenced comes with allot of challenges, allot of information, both technically and at a deeper level, information with obvious health implications.