Dr. Lussier, Professor of Medicine, serves as Associate Director for Informatics of BIO5 and the Cancer Center, as well as Associate Vice-President for Health Sciences of the University of Arizona. He holds medical and engineering degrees and completed a post-doctoral training in the Dept. of Biomedical Informatics at Columbia University and the New York Presbyterian Hospital. He previously worked as Professor of Medicine, Bioengineering and Pharmaceutical Sciences and Assistant Vice-President for Health Affairs of the University of Illinois Hospital and Health Sciences System; as Associate Professor of Medicine at the University of Chicago (2006-11); as an Assistant Professor of Medicine at Columbia University (2001-5); and as the Vice-President for R&D at Purkinje.com, the electronic medical record company that he cofounded. He has authored 130 publications and delivered more than 100 invited presentations in precision medicine, systems medicine, and translational bioinformatics, including 14 opening conference keynotes. His research focuses on computationally personalizing the understanding and treatments of diseases and has been featured in the New York Times (2008), and the Wall Street Journal (2010). Dr. Lussier’s honors include three IBM Faculty Awards (2003-4), inducted Fellow of the American College of Medical Informatics (2006), 3 IBM Faculty awards, 1st recipient of the Columbia University Faculty Mentoring Award, and 16 outstanding publication awards from the American Medical Informatics Association (2010, 2011, 2012, 2013), the International Society for Computational Biology (2007, 2011, 2013), and the Translational Bioinformatics Conference (2012, 2013), among others. To date, his teams have developed many solutions powered with ontologies: (i) the 1st tablet-based electronic medical records anchored on the 1st commercial ontology (Purkinje.com, 1991-; 65,000 terms, 7 semantic types; SCAMC 1992), (ii) Vigilens: the 1st ontology-anchored clinical event monitor of the Columbia University New York Presbyterian Hospital (IBM awards, 6x106 patients, in operations since 2002; Decision Support Syst 2007), (iii) bridging the gap between NLP and ontologies (Bioinform 2006), (iv) single patient sample interpretation of RNA-seq transcriptomes for precision health using genetic mechanisms anchored ontologies (Best Paper Award Translat Bioinfrom Conf 2013, JAMIA in press. Further, his research group has contributed significantly to the field of translational biomolecular informatics by integrating ontologies to the discovery pipeline. They validated the following computational modeling predictions in vitro and in vivo: (i) predicted ab initio and in silico from first principles and validated a novel tumor suppressor microRNA (miR-204) that we have shown overtargeting oncogenes using sequence alignment and protein interaction network modeling (ISCB award, PLoS Comput Biol. 2010 Apr 1;6(4):e1000730), and (iii) rescued a drug and repositioned another by predicting ab initio from SPAN network modeling and biologically-validated a network-targeting therapy to sensitize head and neck cancers resistant to anti-EGFR therapy currently evaluated in a clinical trial (3UL1RR024999-03S3 and NCI/CTEP, Multicenter randomized phase II study of Temsirolimus versus Cetuximab +Temsirolimus in patients with recurrent/metastatic head and neck cancer). In BIG DATA SCIENCE, they have been modeling ontology-anchored empirical models using 5% of the “Beagle” Cray XE6 Supercomputer of the Computation Institute in collaboration with Ian Foster the pioneer of GRID computing [1S10RR029030-01), 18000 processors], (iv) they first predicted and biologically confirmed the biological underpinning of oligo- vs poly- metastatic progression of human cancers, (~30% of the 90,000 new cases in US could be potentially be cured or controlled with local therapy (PLoS ONE 2011: 6(12): e28650).
As 'omics' biotechnologies accelerate the capability to contrast a myriad of molecular measurements from a single cell, they also exacerbate current analytical limitations for detecting meaningful single-cell dysregulations. Moreover, mRNA expression alone lacks functional interpretation, limiting opportunities for translation of single-cell transcriptomic insights to precision medicine. Lastly, most single-cell RNA-sequencing analytic approaches are not designed to investigate small populations of cells such as circulating tumor cells shed from solid tumors and isolated from patient blood samples.