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Bioinformatics for precision medicine

Many molecularly targeted drugs have been developed in recent years. This has brought about advances in personalized genome medicine (precision medicine), which involves prescribing molecularly targeted drugs based on detected gene mutations (Reference: Mamoru Kato, "Cancer Precision Medicine", Anti-Aging Medicine, 2017, Vol. 13, 663-669, in Japanese). In previous testing methods, it was usually only possible to detect one candidate mutation at one test; however, it would be ideal to test for all candidate mutations at one test. Clinical sequencing uses the next-generation sequencer to realize this desirable type of tests. Because the next-generation sequencer produces a massive amount of data, bioinformatics plays an essential role in clinical sequencing (Reference: Mamoru Kato, "Bioinformatics in Cancer Clinical Sequencing", Japanese Journal of Cancer and Chemotherapy, 2016, Vol. 43, 391-397, in Japanese).

At the National Cancer Center, doctors and scientists work together on the project of applying clinical sequencing to clinical practice. Our department is responsible for informatics, where we research on various bioinformatics challenges not encountered in basic research. For example, we are developing algorithms to detect mutations (SNVs/indels/CNAs/fusions) using data obtained from formalin-fixed paraffin-embedded tissue samples. We are also developing medical information systems for use in clinical sequencing.

In the context of clinical sequencing, it is increasingly becoming of importance to idenitfy new molecular biomarkers because it is possible to detect multiple biomarkers at one test (Reference:Mamoru Kato, "The current status and future prospects of biomarker exploration for cancer by bioinformatics analysis", Pharma Medica、2016, 34, 45-51). We seach for new biomarkers that are usable in clinical sequenicng, based on cancer big data such as those of International Cancer Genome Consortium. We analyze the assosications of genome data and other omics data (e.g., epigenome, transcriptome, and proteome data) with clinical data to find predictive and prognosis biomarkers that are detectable in clinical sequencing.