Adenoviral Gene Therapy for Advanced Head and Neck Cancer

Advanced stage head and neck cancers (HNC) with distant metastasis, as well as prostate cancers (PC), are devastating diseases currently lacking efficient treatment options. One promising developmental approach in cancer treatment is the use of oncolytic adenoviruses, especially in combination therapy with conventional cancer therapies. The safety of the approach has been tested in many clinical trials. However, antitumor efficacy needs to be improved in order to establish oncolytic viruses as a viable treatment alternative. To be able to test in vivo the effects on anti-tumor efficiency of a multimodal combination therapy of oncolytic adenoviruses with the standard therapeutic combination of radiotherapy, chemotherapy and Cetuximab monoclonal antibody (mAb), a xenograft HNC tumor model was developed. This model mimics the typical clinical situation as it is initially sensitive to cetuximab, but resistance develops eventually. Surprisingly, but in agreement with recent findings for chemotherapy and radiotherapy, a higher proportion of cells positive for HNC cancer stem cell markers were found in the tumors refractory to cetuximab. In vitro as well as in vivo results found in this study support the multimodal combination therapy of oncolytic adenoviruses with chemotherapy, radiotherapy and monoclonal antibody therapy to achieve increased anti-tumor efficiency and even complete tumor eradication with lower treatment doses required. In this study, it was found that capsid modified oncolytic viruses have increased gene transfer to cancer cells as well as an increased antitumor effect. In order to elucidate the mechanism of how oncolytic viruses promote radiosensitization of tumor cells in vivo, replicative deficient viruses expressing several promising radiosensitizing viral proteins were tested. The results of this study indicated that oncolytic adenoviruses promote radiosensitization by delaying the repair of DNA double strand breaks in tumor cells. Based on the promising data of the first study, two tumor double-targeted oncolytic adenoviruses armed with the fusion suicide gene FCU1 or with a fully human mAb specific for human Cytotoxic T Lymphocyte-Associated Antigen 4 (CTLA-4) were produced. FCU1 encodes a bifunctional fusion protein that efficiently catalyzes the direct conversion of 5-FC, a relatively nontoxic antifungal agent, into the toxic metabolites 5-fluorouracil and 5-fluorouridine monophosphate, bypassing the natural resistance of certain human tumor cells to 5-fluorouracil. Anti-CTLA4 mAb promotes direct killing of tumor cells via apoptosis and most importantly immune system activation against the tumors. These armed oncolytic viruses present increased anti-tumor efficacy both in vitro and in vivo. Furthermore, by taking advantage of the unique tumor targeted gene transfer of oncolytic adenoviruses, functional high tumor titers but low systemic concentrations of the armed proteins were generated. In addition, supernatants of tumor cells infected with Ad5/3-24aCTLA4, which contain anti-CTLA4 mAb, were able to effectively immunomodulate peripheral blood mononuclear cells (PBMC) of cancer patients with advanced tumors. -- In conclusion, the results presented in this thesis suggest that genetically engineered oncolytic adenoviruses have great potential in the treatment of advanced and metastatic HNC and PC.

Expected Performance of the ATLAS Experiment - Detector, Trigger and Physics

A detailed study is presented of the expected performance of the ATLAS detector. The reconstruction of tracks, leptons, photons, missing energy and jets is investigated, together with the performance of b-tagging and the trigger. The physics potential for a variety of interesting physics processes, within the Standard Model and beyond, is examined. The study comprises a series of notes based on simulations of the detector and physics processes, with particular emphasis given to the data expected from the first years of operation of the LHC at CERN.

Discriminative learning of Bayesian networks via factorized conditional log-likelihood

We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (ˆfCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient estimation of the optimal parameters, achieving the same time and space complexity as the traditional log-likelihood scoring criterion. The resulting criterion has an information-theoretic interpretation based on interaction information, which exhibits its discriminative nature. To evaluate the performance of the proposed criterion, we present an empirical comparison with state-of-the-art classifiers. Results on a large suite of benchmark data sets from the UCI repository show that ˆfCLL-trained classifiers achieve at least as good accuracy as the best compared classifiers, using significantly less computational resources.