SUMMARY: A new research group at the University of Arizona Cancer Center is recruiting individuals with computational expertise in the analysis of large data sets and the application of mathematical and statistical methods to complex problems. Individuals with interest in the analysis of molecular data from cancer clinical cases and preclinical models, correlations with clinical outcome, and identifying new approaches to data analysis are encourged to apply.
Projects include direct analysis of molecular data developed within the laboratory (DNA or RNA sequencing), statistical analysis of prognostic and predictive markers, development of algorithms for the integration of independent forms of data. Representative publications are provided below:
Metabolic Reprogramming of Pancreatic Cancer Mediated by CDK4/6 Inhibition Elicits Unique Vulnerabilities. Franco J, Balaji U, Freinkman E, Witkiewicz AK, Knudsen ES. Cell Reports. 2016
Immunologic and Metabolic Features of Pancreatic Ductal Adenocarcinoma Define Prognostic Subtypes of Disease. Hutcheson J, Balaji U, Porembka MR, Wachsmann MB, McCue PA, Knudsen ES, Witkiewicz AK. Clinical Cancer Research. 2016
Whole-exome sequencing of pancreatic cancer defines genetic diversity and therapeutic targets. Witkiewicz AK, McMillan EA, Balaji U, Baek G, Lin WC, Mansour J, Mollaee M, Wagner KU, Koduru P, Yopp A, Choti MA, Yeo CJ, McCue P, White MA, Knudsen ES. Nat Communications. 2015.
Systematically defining single-gene determinants of response to neoadjuvant chemotherapy reveals specific biomarkers.Witkiewicz AK, Balaji U, Knudsen ES. Clinical Cancer Research. 2014