Over many years, Roger S. Day, ScD has studied how computational and modeling tools could help people create better biological understanding, then apply it to better individual treatment decisions. The technical developments that he works on to support this goal constitute a collection of topics which all related to understanding cancer treatment better. These topics are knowledge representation, software architecture for comprehensive modeling and validation, multi-scale modeling in cancer, strategies for overcoming drug resistance in cancer, and how pharmaceutical and biological interactions should be statistically modeled.

A major thrust of this effort is combining biomathematical models with research results and other knowledge, to better understand the natural history of cancer and develop individualized treatment strategies for cancer. The OncoTCap provides a platform for identifying and solving problems along this path. Developed by the Educational Resource for Tumor Heterogeneity, OncoTCap integrates cancer knowledge engineering with a biomathematical modeling environment, used in education of cancer professionals. A sub-theme of great current interest is the connection with cancer stem cells. A hypothetic treatment strategy, the “worst drug rule”, was discovered long ago with an ancestor program of OncoTCap. It fits neatly with new discoveries about cancer stem cells (A. and V. Donnenberg), with which it could be combined to yield new treatment plans of great effect.

A related effort is the study of biological, statistical and drug interaction, and the relationships among these related but distinct concepts. A generalized additive effects model has been developed for drug interactions, and computational methods are being developed and applied to data of the Combinatorial Chemistry for Cancer Drug Development project (J. Lazo, A. Vogt). A unique “weakest link” methodology for combining predictors has been developed and applied to microarray data for lung cancer (T. Richards, N. Kaminski). This methodology is currently being applied to laser scanning cytometry data on lung cancer primaries in relation to recurrence (T. Luong, S. Shackney).

A distinct highly active research area is the development of statistical and computational methodologies and support software for ethically-oriented clinical trial design based on Bayesian decision theory. Methods have been developed with M. Wang for Phase I clinical trials, to consider both adverse events and response endpoints, and to incorporate pharmacogenetic information. Currently he works with a doctoral student, Y. Wang, on development of a Bayesian evaluation platform for early clinical trial designs.

The most extensive collaboration recently has focused on the prevention, detection, and treatment of endometrial cancer, in collaboration with consortium of scientists from several institutions led by  L. Maxwell of Walter Reed Army Medical Center. This work focuses primarily on the analysis of microarray data and proteomics data, including the development of methods for combining the two types of data, process quality analysis, and ranking proteins or genes for validation. Related projects include a proteomics data quality experiment, and evaluation of methods for linking genomic and proteomic data. Collaborations include analysis of tissue microarrays, shotgun proteomics on tissue, shotgun proteomics on serum, and microarrays.