Tuesday, 17 December 2013

The Start of a Journey

Last week a new paper, “Policy challenge of clinical genome sequencing,” led by Caroline Wright and Helen Firth and on which I am a co-author, was published in the British Medical Journal. It lays out the challenges of making more widespread use of genetic information in clinical practice, in particular around ‘incidental findings’. Caroline and I have a joint blog on this paper on Genome Unzipped.
This paper also marks an important watershed in my own career, as it is my first paper in an outright clinical journal. Like many other genomicists and bioinformaticians I have started to interweave my work more tightly with clinical research, as the previously mainly basic research world of molecular biology begins to gravitate towards clinical practice.

Worlds apart

Clinical research and basic research are profoundly different, both in terms of scientific approach and culture. Clinical researchers who keep a hand in clinical practice are nearly always time pressured (i.e. with hospital meetings, clinics, inflexible public responsibilities) and their research has to be squeezed to fit around their practice. The language of clinical research is also distinctly different from that of genomics. For example, I used to use the word ‘doctor’ interchangeably with ‘clinician,’ until a generous clinician took me aside and patiently explained that ‘doctor’ is not the word clinicians use, as it does not capture the myriad disciplines in the healthcare system. They use the word… clinician.
But the differences run deeper than terminology and schedules. Clinical practice involves seeing patients, each of whom presents a different constellation of symptoms, tolerance to treatment and set of personal circumstances – it’s a far cry from the nice, uniform draws of statistical distributions that one hopes to see in designed experiments. A clinician has to work out the true underlying problem – often different from the one described by the patient – and find a way to make it better, often under pressure to act quickly and contain costs.
In theory, molecular measurements – from genotypes to metabolomics – should be informative and useful to the clinician. In practice, there is a wide gulf between any given molecular approach (usually from a retrospective study) and the uptake of molecular information into clinical practice.
Hanging out with more clinicians has given me a deeper appreciation about the difficulty of achieving this, and for why clinicians make such a sharp distinction between people who are part of medical practice and those who are or not. I, for one, have never had the responsibility of making a clinical decision (I’m rather glad other people have taken that on, and appreciate the amount of training and mental balance it takes), so I know I haven't  grasped all the crucial details and interactions that make up the whole process.

Different perspectives

Medicine is also quite diverse, and rightly so. A clinical geneticist might be dealing with a family with a suspected genetic disorder, but a number of family members are currently healthy. Meanwhile, a pancreatic cancer specialist might be helping a new patient whose chances of living another five years is around 2% - and who is therefore a lot more willing to look into experimental treatments than the clinical geneticist’s family.
Even within a discipline, it is not so obvious where the new molecular information is best used. I had the pleasure to be the examiner for Dr James Ware, a young clinician and PhD doing research on cardiac arrhythmias (a subset of inherited cardiac diseases) with Dr Stuart Cook. He presented excellent work on geneticially ‘dissecting’ out some new arrhythmia mutations from families. He also revealed a passion not just for using genetics but for finding practical ways to do so. From his perspective, in this particular medical area, the bigger impact for genetics would be after a phenotype-led diagnosis, rather than for diagnosis itself.

Discussions leading to insight

Our recent paper in the BMJ is a good example of how much I have learned in recent years simply by discussing things with clinicians in detail. I have long advocated a more open and collaborative approach to sharing information about variants with ‘known’ pathogenic impact, even considering the daunting complexity of variant reporting and phenotypic definition (progress is steady in this area, e.g. the LRG project), and this seemed to be aligned with the discussion about making definitive list of variants for “incidental findings”  So I was somewhat taken aback to find that many clinicians did not share my enthusiasm about incidental findings.
After a workshop organised with Helen and with strong input from Dr Caroline Wright, both passionate, open-minded clinical researchers, I fundamentally changed my mind about the utility of ‘incidental findings’ (better described as ‘opportunistic genetic screening’). For the vast majority of known variants we either have poor estimates of penetrance or – at best – estimates driven by ‘cascade screening’ in affected families (i.e., an initial case presents to a clinical geneticist, triggering exploration around the family).
While this is a really important aspect to consider, my passion about more open sharing of knowledge around specific variants remains firmly in place. Caroline, Helen and I remain positive about the growing utility of genome information in clinical research and in targeted diagnostic scenarios, but not for incidental findings until more systematic research is performed (see our ‘Genomes Unzipped’blog post).

Bridging the gulf

Working with clinicians has given me deeper insights into my own work, and in this particular instance changed my opinion. I hope that these interactions have also been positive for the clinicians, perhaps changing their minds about the utility of bioinformatics and genomics and giving a new perspective on the possibilities and pitfalls of the technology.

More broadly, the coming decade is expected to be characterised by basic researchers delving deeper into other areas of science, in particular applied science: areas of medicine, public health, epidemiology, agricultural and ecological research. This is a fascinating, if daunting, challenge for us all. New people to meet, new terminology and language to navigate, new science and applications to wrap our heads around… These are all good things, and I’m sure we will get used to it. We have to.