Advances in diagnosis and therapy based on genetics in oncology

The use of genetic variants as risk indicators as well as diagnostic and prognostic markers has become generalized in the field of oncology during the last years. High-throughput technologies and growing DNA banks have prompted a shift of the focus from high-risk to moderate- and low-risk mutations. The two approaches, however, differ not just quantitatively but also qualitatively. While the presence of a single high-risk marker may be enough to recommend tailored prophylactic programmes, low-risk markers per se mostly do not justify more than just the option of a specific intervention. In contrast, recent studies have shown that particular combinations of several low-risk markers (and among them, epigenetic factors) may interact to increase the risk in a non-linear way. The relevance of these combinations is different for distinct clinical subgroups, where “low-risk” markers may easily turn into high-risk genetic combinations. Similarly, such combinations seem to influence the outcome of a specific cancer therapy in the same way as high-risk mutations do. The challenge is, therefore, not just an upgrading of databases with more detailed genetic profiles, but mainly their integration with expression profiles and clinical data in bioinformatic models capable of implementing the interdependencies and generating cancer risk assessments, reliable diagnosis, and predictions on responsiveness to potential cancer therapies.


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