Using bioinformatics and genomics to treat AML

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Published: 20 Jun 2019
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Prof Torsten Haferlach - MLL Münchner Leukämielabor GmbH, Munich, Germany

Prof Torsten Haferlach speaks to ecancer at the 2019 European Hematology Association (EHA) Annual Meeting about the future of AML treatment.

He outlines how complex the many new treatments for AML are, and what we can do to identify which treatment is best for a specific patient.

Prof Haferlach speaks primarily from a genomic perspective, and how important it is to implement bioinformatic and artificial intelligence approaches to handle this data.

ecancer's filming has been kindly supported by Amgen through the ecancer Global Foundation. ecancer is editorially independent and there is no influence over content.


I will take this disease of AML and will ask in this talk where are we right now. I demonstrated to you where I think we are right now and where we have to go. I think it’s quite clear with all these different players, with all these different balls in the air, one doctor, one patient, is not able to understand it and also to weight it and to see what now is the next best step. With so much information about the patient and the AML and so many options to treat and these options will increase. So I will address in my talk the need and the options of next generation sequencing to understand the disease itself better than before. That will also address whole genome sequencing because right now and in the lab where I’m working what we’re working on is that the turnaround time from the patient sample at diagnosis to deliver a complete genome right now in the scientific environment is seven days. So you can really know everything, more or less everything, within a week after diagnosis. This is the first message – we can know a lot of information quite early right now because the techniques are available.

The second thing is even more important. What do we do with the data not to miss any information that the patient may need for best treatment? In that field we need more bioinformatics and that’s not enough. We do not need only algorithms and infrastructure to capture this data, we need to involve artificial intelligence. This where I will spend the second half of my talk because to implement all these things that can be very helpful to get with all this data to the best solution for the patient can only be done by bioinformatics and several tools from artificial intelligence.

We are doctors and we think that’s far away but if we think about our own private life and all our smartphones and tablets and any kind of Alexa and so on, Google Maps, we already live in this world but medicine does not. This step has to be done right now.