BERLIN, Germany, June 20, 2016 – A European research team headed by Professor Peter Coveney, Centre for Computational Science at the University College London (UCL), achieved a major success in the field of personalised medicine: Peter Coveney and his team were able to prove that by using genomic data of individual patients they are able to predict whether a specific standard drug for the treatment of breast cancer will help or not. The findings were achieved during an “Extreme Scaling Workshop” in which the researchers had the entire supercomputer SuperMUC of GCS member Leibniz Supercomputing Centre (LRZ) in Garching near Munich with all its resources at their disposal to generate and plough through a vast amount of data looking for further clues into how drugs work.
For a patient suffering from breast cancer, several standard drugs are at the doctor's disposal today. The effect of the drug is, however, depending on the individual patient's genomic fingerprint. Which means: While a certain drug treatment would work for one person it may not for the other. Clearly, it would increase the efficiency of the treatment dramatically if a physician had the chance to match - within a reasonable time frame - the analysed predispositions written into the genome of the individual patient with the behaviour of certain drugs. It would put the physician in the position to choose a product tailored to the patient. At this point in time, calculations of these dimensions can only be performed on the largest supercomputers available. Considerable progress has been made in the field of personalised medicine in recent years, and supercomputers will play a major role in its further advancement.
Peter Coveney holds a chair for Physical Chemistry. He investigates why a range of drugs exhibit varying degrees of binding to proteins within patients with specific mutations. Coveney knows about the details of the proteins interacting with the drugs and potential drugs. With the help of high performance computing (HPC) systems, this interaction can be computed and understood using state-of-the-art simulation technologies. Depending on the strength of this interaction, one can judge whether the drug will be effective or not. Mutations change the sequence of amino acids, and as a consequence the structure of the protein is modified and with it its interaction with the drug. It is precisely this interaction, that Peter Coveney is able to compute very rapidly and accurately leveraging the computing power of HPC system SuperMUC which delivers a peak performance of 6.8 Petaflops.
The existing very detailed knowledge of the molecules, a knowledge collected over decades of experimental and theoretical research, allows for very accurate scientific computations -- if the necessary gigantic compute resources, required for number-crunching jobs of this type and magnitude, are available. Which is what happened to the researchers by having access to all of the 250,000 compute cores of the LRZ HPC system in the framework of an Extreme Scaling Workshop. These workshops are held regularly at the GCS member centres with the aim to improve the computational efficiency of HPC applications by expanding their parallel scalability across the hundreds of thousands of compute cores of the GCS supercomputers.
“We were given the whole machine for an uninterrupted period of 37 hours--that is equivalent to something like a quarter of a million people beavering away on everyday personal computers for 37 hours--and we generated about 5 terabytes of data. We achieved all our objectives and more because the jobs in the workflow ran much faster than anticipated. Preliminary analysis indicates that the simulations will provide insight into how the two most common mutations responsible for acquired resistance to major anti-breast cancer drugs interfere with drug binding,” stated Prof Coveney.
The purpose of this project was to demonstrate that, within just a few hours, scientists can work out the way that candidate as well as existing drugs will act on a target in the body - a protein. “Fifty drugs and candidate drugs were studied to determine how they bind with protein targets in a range of disease cases, in order to rank their potency for drug development and for drug selection in clinical decision making,” Coveney explained.
If this approach evolved into a sustained procedure, it would mark an important advance for personalised medicine, that is medicine designed with one specific patient in mind so that it works efficiently, and without side effects. “It is taking far too long and costing far too much to discover new drugs by conventional experimental means; computer-based methods are the way forward,” said Prof Coveney.
“Now that we can read the genetic makeup of a person relatively cheaply we should be able to develop and target many more drugs to specific individuals,” he explained. “Most existing drugs are not effective against large swathes of the general population. In such clinical contexts, decisions regarding matching a drug to a patient will need to be taken within hours to effectively treat individuals and that is what we are trying to achieve.”
The research activities were carried out in the framework of COMPAT, an EU funded research project in which eleven European partners are involved including LRZ and University College London. COMPAT defines itself “as a science driven project. The urgent need to push the science forward, and stay at the forefront in simulation driven science and engineering is our major motivation.” Because of the very challenging requirements of the numerical computations, the application software was selected as a candidate for the Extreme Scaling activities at the GCS centre LRZ.
“We are very proud that our SuperMUC system proved to be extremely useful in the research activities on personalised medicine,” says Prof. Arndt Bode, Chairman of the Board of Directors of LRZ. And Prof. Dieter Kranzlmüller, Director of LRZ and project partner in COMPAT, adds: “Supercomputing technologies play a key role in almost all fields of science, and looking at the exciting results of this project, life science can't go without it either. As these new findings substantiate, supercomputing is an indispensable tool to boost the advancement of personalised medicine research, too, and thus will help to cure disease and improve human lives.”
About GCS: The Gauss Centre for Supercomputing (GCS) combines the three national supercomputing centres HLRS (High Performance Computing Center Stuttgart), JSC (Jülich Supercomputing Centre), and LRZ (Leibniz Supercomputing Centre, Garching near Munich) into Germany’s Tier-0 supercomputing institution. Concertedly, the three centres provide the largest and most powerful supercomputing infrastructure in all of Europe to serve a wide range of industrial and research activities in various disciplines. They also provide top-class training and education for the national as well as the European High Performance Computing (HPC) community. GCS is the German member of PRACE (Partnership for Advance Computing in Europe), an international non-profit association consisting of 25 member countries, whose representative organizations create a pan-European supercomputing infrastructure, providing access to computing and data management resources and services for large-scale scientific and engineering applications at the highest performance level.
GCS is jointly funded by the German Federal Ministry of Education and Research and the federal states of Baden-Württemberg, Bavaria, and North Rhine-Westphalia.
GCS has its headquarters in Berlin/Germany.
Regina Weigand, GCS Public Relations
+49 711 685-87261
This press release as pdf file: SuperMUC Enables Major Finding in Personalised Medicine (PDF, 715 kB)
Pressemeldung der Bayerischen Akademie der Wissenschaften: Duchbruch für personalisierte Medizin dank SuperMUC