Friday, June 14, 2013

Next generation sequencing & TMI

'Next Generation' sequencing, or NGS, was the big theme of the ESHG meeting this year.
We're doing some of that in my lab, though so far in a small way. We chose a machine that most closely duplicated the sorts of thing we did already, so we could make the transition as smoothly as possible. What's working pretty well now is rapid analysis of two genes. Next we'll be moving to a panel of about 35 genes, 25 covering all the different cancer risks we deal with routinely, plus a group of research genes we think will fall into the moderate risk category, or maybe the high risk but rare category.
This is more or less what many of our colleagues are doing, and I was hoping to see results and discussions of the difficulties of this strategy at the ESHG. There was some, but only from the french groups that I communicate with regularly anyway. Nothing new.
The NGS presentations most all took a different tack: that of looking at the entire genome, or entire protein-coding sequence (the « exome »).
It's interesting to do that when the list of genes on your panel changes regularly, or when you're really not sure what to put on it in the first place.
But at least with my panel, I have a reasonable idea of what to expect from each of the genes on it. I've got something to say to the patient about what I find there.
With the whole exome, you're going to find stuff all over the place, and then you've got to decide somehow what's relevant.
I saw a lot of talks on a lot of different disorders. They'd start with 40,000 variants, then filter those this way and that, and filter them again, and come up with the golden nugget that explains their case. Well, at least there's a decent hypothesis about why this variant explains that case, and not any of the other variants they tossed out.
I didn't see any talks where they filtered and filtered and came up with nothing. Guess those studies don't get presented.
One of the major issues with these exome studies is what to do the the off-target findings. We used to say 'incidental', but that word seems inappropriate when you've deliberately looked for stuff. Even my gene panels can come up with off-target findings – if I have a breast cancer case, and find a mutation in the polyposis gene, I wasn't looking at polyposis, it just happens to be on the panel. But my patient wasn't prepared to hear about colon cancer risk; that wasn't the point of the test: what does the clinician say about it? That case is fairly clear, at least in my lab. We give any results that are pertinent for cancer predisposition, and we do now broaden the pre-test consultation to discuss the possible results more generally. There might also be results that indicate one is a carrier of a gene for a serious rare disease, and if it happens that your partner also carries that gene, you could have some very sick kids. It gets complicated.
But it's an even bigger problem when you've gone looking for an explanation for, say, a developmental disorder, and you learn something about cardiac risk or diabetes. On top of the patient's result, most of these studies looking to diagnose childhood syndromes also test both parents (which really helps weed out the irrelevant variants), and now you'll find things in the parents that the kid didn't happen to inherit, but you know they're there and future kids or the parents themselves are at risk.
To face this, people are developing different levels of informed consent. There's already a general consensus not to give results that have no clear consequences. That would be a long list and wouldn't mean much at all. Really pertinent results can get lost in all the noise. Then the patient can consent to be told all results, or just results with significant health risk that you can do something about, or very narrowly just results directly related to the pathology for which he was referred. In Europe there's careful preservation of the right not to know, while in the US, the tendency is toward the obligation to know.
I could ramble on, but the coffee is ready. 

Thursday, June 13, 2013

p = 10 to the -13

I've been knocking myself out at the ESHG for the past few days.
At one of the booths they were handing out free issues of Nature Genetics, so I picked one up. I get Science, so Nature doesn't cross my path often. Just to browse, I mean – any specific paper I can look up on line.
The April issue is dedicated to big papers on big genome-wide studies looking for loci contributing to cancer risk. Worth a read.
Large-scale genotyping identifies 41 new loci associated with breast canceer risk!
3 new loci for ovarian cancer risk!
35 new loci for prostate cancer risk!
wow: just genes all over the place.
And the p-values. P-values of your dreams: 10-6 and better. (A p-value is one measure of how significant, or reliable, a statistical difference is. Usually statistical significance is any p below 0.5. p = 0.5 means there's a one in 200 chance that you're dealing with some random fluke, so 10-6 is one in a million.
But go beyond p, and look at a different measure of importance: the odds ratios. An odds ratio is what are the odds of getting the disease, given that you have the marker. You take an odds ratio and multiply it by the basic risk in the population to estimate a more personalized risk. An OR of 1 is neutral. An OR of 0.5 means the risk is only half; an OR of 2 means twice, and so on.
Odds ratios for the big genes in breast cancer are sometimes too big to multiply, because the risk would be a sure thing, and it's actually not. The moderate risk genes have ORs around 3.
So why did I tell you all that? What odds of cancer did the new genes give?
From 1.05 to 1.26 for the risk alleles, with most not more than 1.1
0.91 to 0.96 for the handful of protective alleles.
A 1.1-fold risk. Gee, I should go out and get that marker tested today.
I mean, really, what is there to get excited about? Why should we care? 1.1 here and 1.1 there; that's nothing.
Nature Frickin Genetics.
What good is it to know this?
Perhaps someday, if you had all the different genes identified, in the right populations because they're not all the same, and if you knew all the interactions between them so you could tell that this one is important only if that one is also present, or is some other marker not otherwise important at all is also present, and if you knew all the interactions with lifestyle and environment (because there too, a gene may only 'count' in women who have had children, or who have a bmi over some threshold, or...). Perhaps that day you could tell somebody she's really at risk of this cancer, much more than other people.
And even then I'm not convinced of the value of this sort of research. We've learned that cancer risk can be due to a mutation in one of a handful of genes, and now that dozens of little tiny effects can add up to something bigger. But for this latter part, is it useful to know that my risk is one in eight, or one in ten?
Let's move on.