Johannes Textor has received a KWF young investigator grant of 500,000 euro.

Advances in biotechnology have led to an explosion in the amount of data that can be generated

for diagnostic purposes; examples are DNA and RNA sequencing, digital pathology, proteomics, and

multi-color flow cytometry. Many researchers feel that this "big data" era will finally bring

us closer to personalized medicine, in which each patient is given a tailored treatment rather

than an off-the-shelf generic product. But the challenges in truly harnessing big, heterogeneous

data for patient benefit are enourmous. Since just adding more data does not automatically lead

to deeper insight or better treatment decisions, robust methodologies are required to analyze and

utilize patient data in the best possible way. 

Johannes Textor's project will employ a method called Causal Bayesian networks that can integrate

very diverse types of data, while clearly distinguishing factors that have a causal role from 

others that are merely predictive. Once built, such networks are capable of deriving personalized

therapy outcome predictions from all available information, and can inform clinicians which 

additional tests would be of greatest value to reduce the uncertainty in this prediction. 
 

Specifically, we will build networks to attempt predicting the result of dendritic cell vaccination

for melanoma patients, a novel treatment which our lab is currently testing in a clinical trial.

The data from this trial will be directly used to inform the network building. Among the types

of data we will use are flow cytometric data and multi-color digital immunohistochemistry images

of the tumour microenvironment.