Unite for powerful research in Zurich and Basel

A golden future awaits Switzerland with the upcoming Swiss Personalized Health Network (SPHN) initiative aiming at harmonizing data formats and IT systems to allow for seamless data exchange between ETH, universities and university hospitals. Starting with funding infrastructure projects, the initiative will subsequently support research projects using the implemented standards. For an executive summary I refer to the «Step#1» newsletter of the SAMW.

One of the major caveats in bringing cutting-edge biomedical research to Swiss clinics is their often outdated IT infrastructure - both sides - software and hardware. It remains to be seen whether the SPHN will have a favorable effect on this predicament - while at the same time not consuming all the national funding for filling these gaps.

Recently, the University Hospital Zurich (USZ) has invested in an integrated data storage to house - eventually - all data or at least connect the gazillion of othe IT systems each clinic seems to have. This is not limited to genomic data or patient anamneses - we are speaking about xrays, histopathological slides, meta data from biobanks (where there are also some several dozens in USZ), test results, etc. A cunning sales person convinced USZ to go for the Oracle Translational Research Center (TRC) platform which is now operational and seeking for projects to be filled with. I am not going to comment on the choice of provider nor the system as such, nevertheless it is important to acknowledge the efforts to have an integrative solution aiming at making patient data holistically available.

As a preparational step towards SPHN ETH has launched together with University Zurich (UZH) and USZ a joint syndicate to concentrate the efforts in Zurich to formulate strong proposals. At a later stage together with Basel, which is already evaluating projects. The Personalized Health Alliance Zurich was initiated September 20 with an info lunch at the ETH main building (not the Sternwarte) and is requesting project ideas by the end of October. The importance of this alliance cannot be too much highlighted; only by uniting researchers and clinicians, it will be possible to get strong proposals with a true chance of translation and ultimately benefit for health care. Furthermore, this top-down approach, initiated by members of executive boards of ETH, UZH and USZ, has the chance to scale projects in an appropriate way - scale which generally needed for genomics and especially for personalized cancer care. However, there will be need for a bottom-up process, initiated by these researchers and clinicians, to get ground-breaking - yet feasible - project ideas. Let's not talk around the bush, many projects/intitiatives have been launched in the last two years, but Zurich as a research location is fragmented and there is risk for reinvention, redundancy and unfruitful lechery. A blend of top-down and bottom-up will enable Zurich and Basel to produce powerful and cutting-edge proposals for personalized health research in the coming years.

In the light of these developments it is basically unconceivable that rumors about a deal between Foundation Medicine and USZ regarding molecular cancer diagnostics are spreading. Amongst other preposterous things it seems that FM has defined a «sample in, report out» process with no access to the data. Not only is this diametrically opposed to SPHN and the Oracle TRC, but also this is hard to link with that first letter in USZ - university.

What do the British say; keep calm and carry on... or...

Tread carefully on the path from tumor mutations to therapy suggestions

Somatic mutation calling on tumor biopsies coming from cancer patients finds an increased foot hold in clinics all over Switzerland. The results of these tests aiming at rationalizing the use of certain cancer drugs, e.g., using Vemurafenib in case of a V600E mutation in melanoma, or in some cases indicating a lack of benefit for a drug, e.g., an NRAS mutation is predictive for the resistance of colon cancer against Cetuximab. While these examples are well studied and thus the causal link between the mutations and the effect of their respective drug is identified, the process of inferring the use of a certain drug with respect to a given mutation inside the genomic landscape of a tumor remains very complicated.

Identifying the presence of a mutation in the genomic landscape of a tumor is also a complicated task, but in contrast to finding the right drug for a given genomic variant, it can be done using algorithms enabling involved technical personnel to focus their time on quality control. When a list of mutations has been put together, an interdiscilinary team of bioinformaticians, pharmacogeneticists, molecular biologists and oncologists has all their work still ahead of them. They will spend many hours on finding useful drug-gene interactions, assessing the scientific literature for their level of evidence, classifying drugs in potentially beneficial or ineffective, and finally also cross-linking study populations with the clinical data presented by the individual patient. Only then it is possible to make an informed suggestion based on molecular evidence.

Or isn't it...

A lot of the time of this team will be spent on manually searching specific mutations in databases for drug-gene interactions, typing names of mutated genes and drugs in PubMed search boxes, looking up clinical trial opportunities and reading irrelevant scientific publications from a huge pool of cancer literature.

However, the information (not data) required to work this process is all out there. Neatly packed in databases and XML dumps, accessible using command line queries in an automated fashion. For instance the Drug Gene Interaction database combines information from 15 other databases on which drugs might target a certain gene and the functional requirement a mutation has to carry, for the drug to work. Repositories of clinical trials can be searched in the same way, e.g. NIH's clinicaltrials.gov. Abstracts and ultimately whole papers can be indexed using text miners quantifying their relevance according mutational and pharmacological information - like Google orders your websites according to relevance.

Do not get me wrong! This will not replace the expertise of the aforementioned team of experts, making a final decision on therapy suggestions. But it will make their work much more efficient; replacing sequential tasks like querying a database with an algorithm, selecting a set of relevant papers using text mining instead of clicking and skimming a text for some buzz words, and so on.

This will ultimately lead to better informed and faster decisions taking into account all available sources.