The Huang laboratory’s main research focus is translational pharmacogenomics with particular interest in the pharmacogenomics of anti-cancer agents. By systematically evaluating human genome and its relationships to drug response and toxicity, our goal is to develop clinically useful models that predict risks for adverse drug reactions and non-response prior to administration of chemotherapy. With her broad training background, Dr. Huang assembles and leads a multi-disciplinary team that consists of computational biologist, geneticist, physician, molecular biologist and biostatistician to tackle a series of serious problems in cancer research. These include the lack of mechanistic understanding of genomic regulation of cancer phenotypes; the lack of reproducible predictive biomarkers for cancer therapeutic agents; and the lack of effective treatment for many hard to treat cancers.
Bridging pre-clinical drug screening with patient genomic profiles in order to accurately predict patient response to therapy.
By leveraging new data and improved methods, the Huang lab has shown that gene expression based models derived from very large panels of cancer cell lines could directly inform clinical response to drugs. Furthermore, using an HDAC inhibitor as an example, they elucidated the polygenic architecture of drug response by comparing and contrasting the predicted power derived from a single gene, a pathway, or all genes in the genome. In this case, they clearly demonstrated the superior power of prediction when applying a machine learning method in deriving polygenic predictors of drug response. More recently, they described a novel statistical approach that improves the success rate of biomarker discovery by conditioning on shared drug sensitivity features among many drugs. Their work showed that despite a controversial history, drug screening in large panels of cell lines is more useful than was previously appreciated. Recently, they integrated data from large clinical cancer studies (e.g. The Cancer Genome Atlas (TCGA)) with data from pre-clinical disease models to impute drug sensitivity in patient data sets. These newly imputed drug sensitivity phenotypes enable large sequencing studies such as TCGA to be effectively used for pharmacogenomics discovery, which has previously been a severe limitation of these data.
He has made significant contributions to the field of pharmacogenomics including the identification of a gene that is involved in cardiomyopathy and congestive heart failure after cancer treatment (Aminkeng F et al, Nature Genetics. 2015 Sep; 47(9):1079-84.). He is the recipient of a number of fellowships including the Canadian Institutes of Health Research, Michael Smith Foundation for Health Research and the British Columbia Children Hospital Research Institute Bertram Hoffmeister Postdoctoral Fellowships. He has also been recognized for a distinguish academic and scientific career through a number of career awards including the Canadian Society of Pharmacology and Therapeutics Boehringer Ingelheim Postdoctoral Award in Pharmacology, the British Columbia Children Hospital Research Institute award for outstanding achievement by a postdoctoral fellow, the Golden Helix Top Prize and the Canadian Society of Pharmacology and Therapeutics Publication Award. He currently serves on the Editorial Board of the Journal of Population Therapeutics and Clinical Pharmacology and is a member of the Canadian Society of Pharmacology and Therapeutics Scientific Program Committee.
Instructor in Medicine, Harvard Medical School
Associate Physician, Brigham and Women's Hospital
The primary focus of Dr Tantisira's research efforts, have been in the pharmacogenetics of asthma therapeutics. With a focus on the pharmacogenetics of the beta-agonist and corticosteroid pathways and have made significant progress, they have identified genes that influence bronchodilator response in asthmatics, corticosteroid response over time as manifested by changes in FEV1, differences in airways hyperresponsiveness in patients on corticosteroids, and prediction of who will be hospitalized while on inhaled steroids
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