Genomics approaches to drug discovery

A structure-based approach for prediction of MHC-binding peptides

Identification of immunodominant peptides is the first step in the rational design of peptide vaccines aimed at T-cell immunity. The advances in sequencing techniques and the accumulation of many protein sequences without the purified protein challenge the development of computer algorithms to identify dominant T-cell epitopes based on sequence data alone. Here, we focus on antigenic peptides recognized by cytotoxic T cells. The selection of T-cell epitopes along a protein sequence is influenced by the specificity of each of the processing stages that precede antigen presentation. The most selective of these processing stages is the binding of the peptides to the major histocompatibility complex molecules, and therefore many of the predictive algorithms focus on this stage. Most of these algorithms are based on known binding peptides whose sequences have been used for the characterization of binding motifs or profiles. Here, we describe a structure-based algorithm that does not rely on previous binding data. It is based on observations from crystal structures that many of the bound peptides adopt similar conformations and placements within the MHC groove. The algorithm uses a structural template of the peptide in the MHC groove upon which peptide candidates are threaded and their fit to the MHC groove is evaluated by statistical pairwise potentials. It can rank all possible peptides along a protein sequence or within a suspected group of peptides, directing the experimental efforts towards the most promising peptides. This approach is especially useful when no previous peptide binding data are available.

[Pharmacogenetics and pharmacogenomics of cancers]

Sequencing the human genome brings new tools for the individualisation of cancer chemotherapy, firstly thanks to the identification of polymorphisms of genes involved in anticancer drug metabolism or activity (Pharmacogenetics), and secondly thanks to the determination of tumour gene expression profiles and their relationship to chemosensitivity and chemoresistance (Pharmacogenomics). A few functional polymorphisms have been known for a long time (thiopurine methyltransferase, glutathion S-transferases), but several new ones have been identified recently, at the level of the genes encoding drug targets (thymidylate synthase), at the level of DNA repair enzymes (XPD) or at the level of transport proteins (MDR1). On the other hand, the research of correlations between gene expression profiles and chemosensitivity has been performed on the in vitro models of the National Cancer Institute and may allow crucial improvements in the identification of patients who would best take advantage of a specific chemotherapy. Clinical trials, first on a retrospective basis, then on a prospective one, are implemented to validate this approach.

Molecular therapeutics: promise and challenges

The ability to analyze the genetic and epigenetic aberrations present in a particular patient's tumor on a global basis is rapidly maturing. The emerging fields of functional genomics and functional proteomics offer the opportunity to translate these advances into a full comprehension of the pathophysiology of cancer. Linking these approaches to chemical genomics and molecular therapeutics should provide an expanding repertoire of targeted therapeutics for clinical evaluation. Novel clinical trial designs that can determine the efficacy of targeted therapeutics in patients selected for aberrations in the target are needed to evaluate the wealth of new drugs becoming available. The promise of these technologies and advances in our understanding of cancer is immense, making it our responsibility to see them to fruition. These technologies should lead to a new era of individualized molecular medicine, wherein we will treat each patient with a "prescription" based on the genetic changes in each patient's tumor and their own genetic make-up, resulting in more effective and less toxic therapy.