Nova Acta Leopoldina NF Nr. 419, 137–138 (2017) 137
Discussion of Session 5
Helbing: Was your result on the critical vaccination thresholds bad news for pharma com-panies?
Brockmann: The system predicts that we do not have to vaccinate as much. But, despite this, there still was a big measles outbreak in Berlin last year. The reason cannot be a self-sustained measles epidemic. Instead, there were local outbreaks. So even if we are actually above this critical vaccination threshold, we may still have local outbreaks due to the ‘injection’ of the disease at certain places.
Friedrich: If I understand correctly, HIV treatment is a fantastic example for a successful individualised approach or individualised medicine. But you still ask for a theory, since the data are not sufficient. Do you have a strategy? For instance, you showed us the studies of HIV protease – would it help to develop a theory?
Lengauer: It wouldn’t help our patients in the short term, I think. But, for the first time, we would be able to speculate about mutations that we have not seen before. We could deve-lop new drugs with the added knowledge of what resistance mutations they might evoke.
This knowledge could lead to better drugs, which would help patients in the long run.
What would help patients more immediately is a reliable mechanistic model of virus-host interaction. We are happy that, with blind data analysis in this system, we already achieve high predictiveness of drug resistance. But this is not true for all systems. For instance, it is unlikely to be the case in cancer. And it is difficult to recognise in advance whether a system submits to pure data analysis without mechanistic knowledge or not.
Guest: You both commented on the importance of the quality of data. More data may not be providing all the solutions, as Dr. Lengauer said. Dr. Brockmann just added that the data can be biased and that there are mathematical methods to de-bias them. Could you comment on the importance of the quality of the data? In particular, what are the ways to de-bias data?
Lengauer: One approach in personalised medicine is to train models only on data from controlled studies. That is the approach we are using. Controlled studies are naturally limited in scope with, say, a few hundred highly controlled patients. Still, we have col-lected 150,000 therapy exchanges based on such data. Our geno2pheno server is receiving queries involving new data daily. But we do not store this data, and we do not use it for training because it does not come from a controlled setting and because the users would not appreciate that.
The other approach is the one Google Flu uses, namely to take data from everywhere and basically monitor every sneeze in the world. They have to take into account the risk of people unintentionally corrupting or even intentionally faking that data. I don’t believe that just increasing the volume of data without worrying about its quality is going to be the solution, so we are sticking to the first route.
Discussion of Session 5
De-biasing always means removing biases. But biases can only be defined in terms of re-ference. And this reference is subjective. In HIV therapy, for instance, the Italian reference is different from the German reference. Italians rule out different drug combinations com-pared to Germans. So, the de-biasing issue is a very difficult one. The only thing I can say at this point is that absolute objectivity does not exist. When you de-bias something, you de-bias it with reference to your own view of the world. We can only offer the methods for doing this. The view of the world is inherently subjective and should be supplied by the respective medical community.
Brockmann: The word ‘data’ is so very broad that it may be helpful to differentiate what we mean by it. Some data comes out of controlled experiments. That data can be huge, too, like the data that came out of the machinery that measured gravitational waves. Then there is genetic information like meta-genomic data. This is clean data.
In contrast, Google Flu Trend data or the data that is scraped off the internet is not clean.
It has biases that you do not even know about. You just look at something and then you scrape it off and then you see signals in it. But you cannot repeat this. You cannot test it in any way by generating a new data set. I find that very problematic, although I do it myself.
But you should be very careful, and there are many people who are not.
Guest: Is data about individual genome profiles included in your models? Or, what happens if it is included?
Lengauer: We are only working with the viral genome. The viral genome has 10,000 ba-ses. It is not large at all. We have also been looking at the relevant region in the patient genome, the HLA genes, which is also comparatively limited. We have conducted initial studies, but it looks like it is not worth the trouble of including this region in the analysis.
Otherwise, we would have to change clinical procedures and patients would have to be genotyped. All of this is difficult. People in clinical routines usually resist it unless it is necessary. And, so far, it has not seemed to be pressing enough.
Guest: There is also the problem of some treatments that are very complicated and depend a lot on the way in which they are taken. And that, of course, would depend on the popula-tion of patients and whether or not they are actually going to respect those rules. So is there a sort of second qualification?
Lengauer: Yes. That the computer suggests the ideal treatment is definitely an overstate-ment. What we do is provide an interpretation of the viral genome. The doctor takes this interpretation and crafts his or her own therapy. They do not automatically take up what the report says. And that is due to all the things we do not consider, for instance, whether the patient can tolerate a drug or how committed the patient is to taking the drugs. Of course, sometimes patients do not take the drugs and they claim to have taken them. But this is a quite difficult area. Sometimes, the genomic fingerprint of the virus is indicative of the patient’s compliance. But the patients cannot be expected to tell the truth in this respect.
Session 6
Chair: Thomas Lengauer ML (Saarbrücken)
Nova Acta Leopoldina NF Nr. 419, 141–148 (2017)
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