Aims Publications Software Investigators Led by: Danyu Lin, PhD
There is an enormous current interest in identifying genetic determinants of inter-individual differences in the efficacy and toxicity of cancer medications and in tailoring treatment regimens to each patient's genomic profile. The volume and complexity of data from these pharmacogenomic studies and individualized therapy trials pose unique statistical and computational challenges. The broad, long-term objectives of this research are to develop novel and high-impact statistical methods and computational tools for the designs and analysis of such cancer studies. We will focus on four specific aims:
Aim 1: Construction of robust and efficient statistical methods for assessing the effects of SNP genotypes and haplotypes on drug response.
Aim 2: Development of statistical and data-mining techniques for predicting drug response based on high-dimensional and highly correlated genomic data.
Aim 3: Investigation of statistical procedures for providing low-bias estimation of effect sizes with complex and highly multivariate genetic data for follow-up and confirmation studies.
Aim 4: Exploration of machine learning techniques for identifying candidate individualized therapies in both pre-clinical and clinical studies.
The results of this research have the potential to significantly enhance our understanding of the genetic basis of inter-individual variability in drug response and in discovering effective new individualized therapies to improve the quality and longevity of cancer patients.