Perspectives for cancer immunotherapy mediated by p19Arf plus interferon-beta gene transfer

While cancer immunotherapy has gained much deserved attention in recent years, many areas regarding the optimization of such modalities remain unexplored, including the development of novel approaches and the strategic combination of therapies that target multiple aspects of the cancer-immunity cycle. Our own work involves the use of gene transfer technology to promote cell death and immune stimulation. Such immunogenic cell death, mediated by the combined transfer of the alternate reading frame (p14ARF in humans and p19Arf in mice) and the interferon-β cDNA in our case, was shown to promote an antitumor immune response in mouse models of melanoma and lung carcinoma. With these encouraging results, we are now setting out on the road toward translational and preclinical development of our novel immunotherapeutic approach. Here, we outline the perspectives and challenges that we face, including the use of human tumor and immune cells to verify the response seen in mouse models and the incorporation of clinically relevant models, such as patient-derived xenografts and spontaneous tumors in animals. In addition, we seek to combine our immunotherapeutic approach with other treatments, such as chemotherapy or checkpoint blockade, with the goal of reducing dosage and increasing efficacy. The success of any translational research requires the cooperation of a multidisciplinary team of professionals involved in laboratory and clinical research, a relationship that is fostered at the Cancer Institute of Sao Paulo.

In silico comparison of bacterial strains using mutual information.

Fast-sequencing throughput methods have increased the number of completely sequenced bacterial genomes to about 400 by December 2006, with the number increasing rapidly. These include several strains. In silico methods of comparative genomics are of use in categorizing and phylogenetically sorting these bacteria. Various word-based tools have been used for quantifying the similarities and differences between entire genomes. The simple di-nucleotide frequency comparison, codon specificity and k-mer repeat detection are among some of the well-known methods. In this paper, we show that the Mutual Information function, which is a measure of correlations and a concept from Information Theory, is very effective in determining the similarities and differences among genome sequences of various strains of bacteria such as the plant pathogen Xylella fastidiosa, marine Cyanobacteria Prochlorococcus marinus or animal and human pathogens such as species of Ehrlichia and Legionella. The short-range three-base periodicity, small sequence repeats and long-range correlations taken together constitute a genome signature that can be used as a technique for identifying new bacterial strains with the help of strains already catalogued in the database. There have been several applications of using the Mutual Information function as a measure of correlations in genomics but this is the first whole genome analysis done to detect strain similarities and differences.