Scientific Accomplishments

Three graphs related to Drs. Lambert and Ahlquist's human cancer virology program
Pyeon D, Newton MA, Lambert PF, den Boon JA, Sengupta S, Marsit CJ, Woodworth CD, Connor JP, Haugen TH, Smith EM, Kelsey KT, Turek LP, Ahlquist P. Cancer Res 2007;67:4605-4619. Supported by NIIH U01CA082004

Basic Science

Biostatistician: Michael A. Newton, PhD – Cancer Genetics Program

Collaborators: Paul Ahlquist, PhD and Paul F. Lambert, PhD – Human Cancer Virology Program

I/IIa clinical trial in 22 stage D0 prostate cancer patients was conducted to evaluate the safety of a DNA-based vaccine encoding Prostatic Acid Phosphatase (PAP).

Working with Drs. Lambert and Ahlquist, Dr. Newton analyzed whole-genome profiles from human tissue samples. Findings provided novel biomarkers for early detection and emphasized the potential value of targeting E6 and E7 function in the treatment of HPV+ cancers.


Chart identifying patients who benefit most from a targeted therapy, showing which biomarkers led to which profile group using Bayesian adaptive design bia the penalized least sqaure logistic regression
Eickhoff JE, Kim K, Beach J, Kolesar JM, Gee, JR. Clin Trials 2010;7:546. Supported by VA MERIT and NCI P30CA14520 .

Clinical Science 

Biostatisticians: Jens C. Eickhoff, PhD – Experimental Therapeutics Program; KyungMann Kim, PhD – Chemoprevention Program

Collaborators: Jill M. Kolesar, PharmD – Experimental Therapeutics Program; Howard H. Bailey, MD – Chemoprevention Program

Motivated by their collaboration with Drs. Kolesar and Bailey, Drs. Kim and Eickhoff developed optimal clinical trial designs for pharmacogenomics-driven targeted therapies that directly integrated information about biomarkers and clinical outcomes as they become available. The design efficiently identified patients who benefit most from a targeted therapy. There were substantial savings in the sample size requirements when compared to alternative designs.

Map of Wisconsin using generalized additive logistic regression model, estimating geographic risk of local odds of breast cancer.
Gangnon RE, Trentham-Dietz A, Remington P, McElroy JA, Hampton JM, Newcomb P. Am J Epidemiology 2010;171 (Suppl):S23. Supported by NIH U01CA082004.

Population-Based Science

Biostatistician: Ronald E. Gangnon, PhD – Cancer Control Program

Collaborators:  Amy Trentham-Dietz, PhD, Jane McElroy, PhD, Patrick Remington, PhD, and Polly A. Newcomb, PhD – Cancer Control Program

Using geocoded residential locations for case-control study participants, Dr. Gangnon utilized a generalized additive logistic regression model to estimate geographic risk as a local odds ratio using a two-dimensional thin plate spline while adjusting for established risk factors. Results suggest that established breast cancer risk factors do not explain long-standing observations of higher breast cancer mortality in eastern WI counties.