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What is Cancer Informatics?

Cancer Informatics is a hybrid field which bridges the gap between translational cancer research and bioinformatics. A Cancer Informatics researcher is able to leverage a multitude of technologies such as next generation sequencing, multiomics analysis and radio imagery.

Cancer Informatics in Molecular Tumour Boards.​

Throughout the years next generation sequencing (NGS) has become cheaper and its availability is currently at an all-time high. NGS is an enabling technology for personalized medicine as clinicians and researchers can get a better understanding of tumour biology on an individual level. Simultaneously the literature is flooded with new biomarkers promising better predictive and prognostic predictions. To get a better understanding, both on a macroscopic and an individual level, one requires a specialized researcher, who is able to master genomics, data science and clinical care. Typical clinical examples for the need for cancer informatics are molecular tumour boards, which try to channel the knowledge of multiple domains into one structured process. Cancer informaticians participate in these molecular tumour boards to aid in finding a suitable targeted therapy for every individual patient. The hybrid field enables them to perform and understand NGS analysis with the additional ability to put the information into context and prepare the data for evaluation in a molecular tumour board.

(Big) Data Repositories and How They Change Wet Lab Research​

Some typical in vitro research approaches remain the same over decades. For instance, a researcher might read through literature to develop a novel hypothesis. Later the researcher tests the hypothesis through an in vitro experiment in which the hypothesis can either be proven or refuted. With the advent of big public data repositories such as The Cancer Genome Atlas (TCGA) or the Cancer Cell Line Encyclopedia (CCLE) researchers are now able to interrogate their hypothesis virtually through in silico analysis. For instance, TCGA could be utilized to answer questions such as "How does my gene of interest interact with the expression levels of other genes?" The CCLE, on the other hand, could potentially answer questions such as "How does my cell line of interest behave under treatment with drug X?"

Sources & Further Reading​