A cancer cell originates from a human cell, in the case of melanoma from melanocytes, and accordingly can hijack and deregulate at least some of the complex mechanisms that shape the human body. These mechanisms can be triggered by a seemingly endless repertoire of changes in the cancer genome, such as mutations, deletions and amplifications, by epigenetic changes, or by interaction with surrounding cells. Frequently, such changes result in increased or reduced activity, i.e., expression level, of a targeted gene. This makes cancer a highly complex disease, to the degree that no two patients have exactly the same tumor.

Despite this tremendous complexity, for melanoma we have shown that tumors can be grouped based on similarities of the gene expression levels of the MITF gene, as well as proliferation, immune, and stroma genes. These groups represent the fundamental biological entities of melanoma. Patient outcome differed between tumor groups, and we hypothesize that therapy response and resistance will also be determined by these groups.

As a bioinformatician at the Melanoma Genomics Group, to uncover the genome-wide processes that govern melanoma, my interests are in cancer genomics and biomedical statistics. With particular focus on gene expression analysis, copy number analysis, DNA methylation arrays, next generation sequencing, mutation calling, batch effects, classification, validation, survival analysis, and statistical methods in general.