Dr Dennis Wang
We focus on translating the complex patterns of genomic data generated in the lab to enable the development of personalized medicines that can benefit patients suffering from complex diseases. Of computational interest, this involves integrating genomics information of various data types in order to build algorithms that predict clinical outcome and identifying genetic biomarkers through feature selection. Of biomedical interest, we survey the genomic landscape of disease subtypes, as well preclinical models, to better understand how we can stratify patients for different treatments. Both of these aims enable us to work towards the ultimate goal of developing data driven approaches for personalizing medicine. Current projects include:
- Prognostic impact of genomic instability and immunogenicity in lung cancer (in collaboration with Princess Margaret Cancer Centre, Toronto)
- Personalizing drug combinations based on genomic features (in collaboration with AstraZeneca and Sage Bionetworks)
- Probabilistic modelling of drug response (in collaboration with Lancaster Univ, AstraZeneca, and Sanger Institute)
- Assessment of variant calling metrics for clinical diagnostics (in collaboration with Personalis inc.)
- Molecular stratification of dementia and Alzheimer’s cases
- Wang, D., Pham, N.-A., Tong, J., Sakashita, S., Allo, G., Kim, L., Yanagawa, N., Raghavan, V., Wei, Y., To, C., et al. Molecular heterogeneity of non-small cell lung carcinoma patient-derived xenografts closely reflect their primary tumors. Int. J. Cancer. 140, 662–673. Feb. 2017
- Silverbush, D., Grosskurth, S., Wang, D., et al. “Cell-Specific Computational Modeling of the PIM pathway in Acute Myeloid Leukemia”. Cancer Research. Jan. 2017.
- Mosely, S.I.S., Prime, J.E., Sainson, R.C.A., Koopmann, J.-O., Wang, D.Y.Q., et al. (2016). Rational selection of syngeneic preclinical tumor models for immunotherapeutic drug discovery. Cancer Immunology Research. Jan. 2017.
- Kim, B.R., Van de Laar, E., Cabanero, M., Tarumi, S., Hasenoeder, S., Wang, D., Virtanen, C., Suzuki, T., Bandarchi, B., Sakashita, S., et al. (2016). SOX2 and PI3K Cooperate to Induce and Stabilize a Squamous-Committed Stem Cell Injury State during Lung Squamous Cell Carcinoma Pathogenesis. PLoS Biol 14. Nov. 2016
- Delpuech, O., Rooney, C., Mooney, L., Baker, D., Shaw, R., Dymond, M., Wang, D., et al. “Identification of Pharmacodynamic Transcript Biomarkers in Response to FGFR Inhibition by AZD4547.” Molecular Cancer Therapeutics. Nov. 2016.
- Di Veroli, G.Y., Fornari, C., Wang, D., et al. “Combenefit: An interactive platform for the analysis and visualisation of drug combinations”. Bioinformatics. April 2016.
- Martin, P., Stewart, E., Pham, N.-A., Mascaux, C., Panchal, D., Li, M., Kim, L., Sakashita, S., Wang, D., et al. Cetuximab inhibits T790M mediated resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKIs) in a lung adenocarcinoma patient-derived xenograft mouse model. Clinical Lung Cancer. 2016
- Stewart, E.L., Mascaux, C., Pham, N.-A., Sakashita, S., Sykes, J., Kim, L., Yanagawa, N., Allo, G., Ishizawa, K., Wang, D., et al. Clinical Utility of Patient-Derived Xenografts to Determine Biomarkers of Prognosis and Map Resistance Pathways in EGFR-Mutant Lung Adenocarcinoma. Journal of Clinical Oncology. Aug. 2015.
- Li, L., Wei, Y., To, C., Zhu, C.-Q., Tong, J., Pham, N.-A., Taylor, P., Ignatchenko, V., Ignatchenko, A., Zhang, W., Wang, D., et. al. Integrated omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact. Nature Communications. 2014
- Wang, D., Rendon, A., Ouwehand, W., Wernisch, L. “Transcription factor and chromatin features predict genes associated with eQTLs.” Nucleic Acids Research. 2013.