Professor Innocenti serves as the Chair of the Gastrointestinal Solid Tumor Correlative Science Group in the Alliance for Clinical Trials in Oncology (previously, Cancer and Leukemia Group B). He is also the Translational Science Representative of the NCI Colon Task Force of the Gastrointestinal Steering Committee.
Professor Innocenti has published more than 150 peer-reviewed publications and book chapters in clinical pharmacology, pharmacogenomics, and oncology. Major findings from landmark studies and seminal discoveries have been reported in the Journal of Clinical Oncology, JAMA, Cell, Clinical Pharmacology and Therapeutics, Clinical Cancer Research, Nature Genetics and other noteworthy journals. He is the editor of five books in pharmacogenomics and oncology. Professor Innocenti sits on the editorial board of Clinical Pharmacology and Therapeutics, and Pharmacogenetics and Genomics, among other journals. He is the Associate Editor for Pharmacogenomics.
Professor Innocenti received the Leon I Goldberg Young Investigator Award from the American Society of Clinical Pharmacology and Therapeutics in 2012, as well as a Young Investigator Award from the Cancer Research Foundation in 2006. He has received the National Scientific Qualification as Full Professor of Pharmacology, Clinical Pharmacology, and Pharmacognosy from Italy in 2014. Professor Innocenti is frequently invited to speak internationally on the topics of precision medicine and genomics in oncology. He has organized several international symposia and meetings on genomic and translational medicine and has chaired the Oncology Section of the American Society of Clinical Pharmacology and Therapeutics.
Among Professor Innocenti’s exemplary achievements is the elucidation of the genetic basis of severe neutropenia in cancer patients treated with irinotecan, a poster child for pharmacogenetics. Dr. Innocenti is the co-inventor of the FDA-approved UGT1A1 genetic test for patients treated with irinotecan. As a result of this pioneering work, the labeling of irinotecan has since been revised.
Professor Innocenti’s NIH-funded program applies genomic technologies to discover novel determinants of efficacy and safety of cancer therapy. This research aims to achieve the goal of precision therapy in oncology through the selection of the most effective treatment regimen for any given patient.
Featured project: Exploring Statin Pleiotropic Effects within a Very Large EHR Cohort
Statins (HMG-CoA reductase inhibitors), among the most widely prescribed agents worldwide for prevention of cardiovascular diseases, produce substantial pleiotropic effects. Pleiotropic effects are unanticipated outcomes other than those for which the drug was originally developed, either therapeutic (beneficial) or detrimental (adverse drug reactions). Statin pleiotropic effects are unanticipatedly broad, including increasing the risk of developing type 2 diabetes mellitus (T2DM), decreasing cancer-related mortality, and reducing dementia. Many effects are still not determined. In addition, individual responses to statins are highly variable. In this project, we investigate statin pleiotropic effects using whole de-identified electronic health records (EHRs) of >2.5 million patients at Vanderbilt, including >100,000 statin recipients. We will evaluate genetic predictors of statin pleiotropic effects as well.
Collen Masimirembwa is a pioneer of pharmacogenetics research in Africa and continues to champion its development to clinical applications. He conducted the first molecular genetic studies in African populations in the early 90s leading to the discovery of the African specific CYP2D6 genetic variant, CYP2D6*17. Over the past 25 years he has endeavored to integrated pharmacogenetics in the drug discovery and development value chain with a particular interest in understanding the genomic diversity of African populations and its implications for drug safety and efficacy. He has published over 90 original papers on the metabolism & pharmacokinetics of antiparasitic drugs and on the population genetic polymorphism of ADME genes in Africa. This has allowed him to combine pharmacokinetics modeling & simulation and molecular structural & functional studies of genetic variation of ADME to bridge basic genetic polymorphism research to clinical applications. This research has led to the award-winning pharmacogenetics test and dosing algorithm for the safe use of the antiretroviral drug, efavirenz (EFV). The product, GeneDoseÒ-EFV is now under commercial development.
To build capacity for pharmacogenomics research in Africa Collen has used his institute and extensive international network of collaborators to train Masters and PhD students in Africa, who are, in their various settings conducting basic and translational pharmacogenetics research. This has culminated in an EDCTP (European and Developing Countries Clinical Trial Partnership) training grant to initiate a 2-year Master’s degree in Genomic Medicine and Molecular Diagnostics, whose first intake will be in January 2019. Towards this, Collen is appealing for support from the international pharmacogenetics community to assist with mentorship and opportunities for research project attachment for students of this MSc program through AiBST’s Virtual Global Faculty program (contact him at email@example.com).
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.
PGRN Hub & the Featured Investigator