Paper short abstract:

New sequencing technologies change our understanding of cancer and hold the promise of better diagnosis and treatment. However, considerable parts of clinical decisions are delegated to technological means. As a result, drug selection becomes increasingly opaque for the treating physician.

Paper long abstract:

At this time we are observing a fundamental shift in the diagnosis of cancer that holds substantial implications for treatment choices. As swift and comprehensive sequencing technologies become available that change our understanding of cancer. However, extensive research is necessary to produce the body of knowledge that allows classifying DNA-mutations, relating these to phenotypes of cancerous tissue, and ultimately drawing conclusions for treatment.

This research effort has substantial implications. The body of knowledge produced is so vast that the translation into clinical practice cannot be made without computational assistance. Faced with this situation oncologists need the contribution of IT-specialists, among others, who can help to manage and translate the respective data into clinical routine. Bioinformatics is key for identifying evidence of drug-cell interactions that may lead to new treatment options.

Even though this holds promising opportunities for cancer treatment, there are some considerable implications: 1) The search for newly discovered interactions is way too labour intensive for an individual to perform. 2) The required search algorithms can only be performed in a network to which machines belong equally as human beings. 3) As a result, the process of drug selection becomes increasingly opaque for the treating physician. It is the latter who needs to provide a justification for a chosen drug application. Yet, considerable parts of this task become delegated to machinery that requires further specialists trained in bioinformatics and (whole) genome sequencing.