Recent Advances in Application of Pharmacogenomics for Biotherapeutics

Reassessing pharmacogenomic cell sensitivity with multilevel statistical models

Pharmacogenomic experiments allow for the systematic testing of drugs, at varying dosage concentrations, to study how genomic markers correlate with cell sensitivity to treatment. The first step in the analysis is to quantify the response of cell lines to variable dosage concentrations of the drugs being tested. The signal to noise in these measurements can be low due to biological and experimental variability. However, the increasing availability of pharmacogenomic studies provides replicated data sets that can be leveraged to gain power. To do this, we formulate a hierarchical mixture model to estimate the drug-specific mixture distributions for estimating cell sensitivity and for assessing drug effect type as either broad or targeted effect. We use this formulation to propose a unified approach that can yield posterior probability of a cell being susceptible to a drug conditional on being a targeted effect or relative effect sizes conditioned on the cell being broad. We demonstrate the usefulness of our approach via case studies. First, we assess pairwise agreements for cell lines/drugs within the intersection of two data sets and confirm the moderate pairwise agreement between many publicly available pharmacogenomic data sets. We then present an analysis that identifies sensitivity to the drug crizotinib for cells harboring EML4-ALK or NPM1-ALK gene fusions, as well as significantly down-regulated cell-matrix pathways associated with crizotinib sensitivity.

Antibodyomics: bioinformatics technologies for understanding B-cell immunity to HIV-1

Numerous antibodies have been identified from HIV-1-infected donors that neutralize diverse strains of HIV-1. These antibodies may provide the basis for a B cell-mediated HIV-1 vaccine. However, it has been unclear how to elicit similar antibodies by vaccination. To address this issue, we have undertaken an informatics-based approach to understand the genetic and immunologic processes controlling the development of HIV-1-neutralizing antibodies. As DNA sequencing comprises the fastest growing database of biological information, we focused on incorporating next-generation sequencing of B-cell transcripts to determine the origin, maturation pathway, and prevalence of broadly neutralizing antibody lineages (Antibodyomics1, 2, 4, and 6). We also incorporated large-scale robotic analyses of serum neutralization to identify and quantify neutralizing antibodies in donor cohorts (Antibodyomics3). Statistical analyses furnish another layer of insight (Antibodyomics5), with physical characteristics of antibodies and their targets through molecular dynamics simulations (Antibodyomics7) and free energy perturbation analyses (Antibodyomics8) providing information-rich output. Functional interrogation of individual antibodies (Antibodyomics9) and synthetic antibody libraries (Antibodyomics10) also yields multi-dimensional data by which to understand and improve antibodies. Antibodyomics, described here, thus comprise resolution-enhancing tools, which collectively embody an information-driven discovery engine aimed toward the development of effective B cell-based vaccines.