My research focuses on molecular subtyping and biological network analysis of colorectal cancer (CRC) to understand CRC etiology and carcinogenic pathways. I am currently funded by NCI K07 (2014-2019). I have been actively engaged in assembling and organizing molecular pathological data in Harvard cohorts including the Nursesâ€™ Health Study (NHS) and the Health Professionals Follow-up Study (HPFS). I have been playing a pivotal role in expanding and conducting Molecular Pathological Epidemiology (MPE) research, serving as Co-Leader of the MPE Laboratory (PI, Shuji Ogino) since 2014.
As a lead author, I have been contributing to identification of potential biomarkers for CRC prevention and treatment. We have examined interplays among lifestyle, aspirin use, screening colonoscopy, and molecular markers. In the New England Journal of Medicine (Nishihara et al. NEJM, 2013), we published that CRC with a higher level of CpG island methylation (CIMP-high) might evade colonoscopy screening likely due to inherent biological characteristics associated with rapid growth and/or difficulty in detection and complete removal of precursor lesions. Moreover, we found that the status of PIK3CA mutation in CRC can predict a response to aspirin treatment and reported the result in the NEJM [Liao, Lochhead, Nishihara (co-first authors), et al. 2012]. We further found that aspirinâ€™s ability to prevent CRC development depends on mutation status of BRAF. The result was published in JAMA (Nishihara et al. 2013). In the American Journal of Clinical Nutrition (Nishihara et al. 2014), we reported that CRC with the hypomethylation in IGF2 differentially methylated region (DMR0) might be caused by a higher alcohol intake. I am currently applying biological network analysis to understand disease heterogeneity and the complexity of pathogenic mechanisms in CRC. In our ongoing effort of the whole-exome sequencing, we have successfully found RNF43 as a potential new driver gene commonly mutated in microsatellite instable CRC. The result was published in the Nature Genetics (Giannakis et al. 2014).
In summary, I have extensive experience in analyzing large epidemiologic and pathology data. I have worked on MPE research and currently working on biological network analysis in CRC using the approaches of biostatistics, epidemiology, and computational biology. With the interdisciplinary expertise, I hope to promote CRC prevention and treatment in the context of computational biology and precision medicine.