Computational Models of HIV-1 Resistance to Gene Therapy Elucidate Therapy Design Principles

Gene therapy is an emerging alternative to conventional anti-HIV-1 drugs, and can potentially control the virus while alleviating major limitations of current approaches. Yet, HIV-1's ability to rapidly acquire mutations and escape therapy presents a critical challenge to any novel treatment paradigm. Viral escape is thus a key consideration in the design of any gene-based technique. We develop a computational model of HIV's evolutionary dynamics in vivo in the presence of a genetic therapy to explore the impact of therapy parameters and strategies on the development of resistance. Our model is generic and captures the properties of a broad class of gene-based agents that inhibit early stages of the viral life cycle. We highlight the differences in viral resistance dynamics between gene and standard antiretroviral therapies, and identify key factors that impact long-term viral suppression. In particular, we underscore the importance of mutationally-induced viral fitness losses in cells that are not genetically modified, as these can severely constrain the replication of resistant virus. We also propose and investigate a novel treatment strategy that leverages upon gene therapy's unique capacity to deliver different genes to distinct cell populations, and we find that such a strategy can dramatically improve efficacy when used judiciously within a certain parametric regime. Finally, we revisit a previously-suggested idea of improving clinical outcomes by boosting the proliferation of the genetically-modified cells, but we find that such an approach has mixed effects on resistance dynamics. Our results provide insights into the short- and long-term effects of gene therapy and the role of its key properties in the evolution of resistance, which can serve as guidelines for the choice and optimization of effective therapeutic agents

Common genetic variations in the LEP and LEPR genes, obesity and breast cancer incidence and survival

OBJECTIVE: Obesity is a strong risk factor for breast cancer in postmenopausal women and adverse prognostic indicator regardless of menopausal status. Leptin is an important regulator of adipose tissue mass and has been associated with tumor cell growth. Leptin exerts its effects through interaction with the leptin receptor (LEPR). We investigated whether genetic variations in the leptin (LEP) and LEPR genes are associated with risk of breast cancer, or once diagnosed, with survival. METHODS: The polymorphisms LEP G-2548A and LEPR Q223R were characterized in population-based study consisting of mostly European-American women. The study examined 1,065 women diagnosed with first, primary invasive breast cancer between 1996 and 1997. Controls were 1,108 women frequency matched to the cases by 5-year age group. RESULTS: A modest increase in risk of developing breast cancer was associated with the LEP -2548AA genotype when compared to the LEP -2548GG genotype (age-adjusted OR=1.30; 95% CI=1.01–1.66). This association was stronger among postmenopausal women who were obese (OR=1.86; 95% CI=0.95–3.64) although the interaction was of borderline statistical significance (P=0.07). We found no evidence of an association with polymorphisms of either LEP or LEPR in relation to all-cause or breast cancer-specific mortality among women with breast cancer (mean follow-up time=66.7 months). The effects of these genotypes on breast cancer risk and mortality did not vary significantly when stratified by menopausal status. CONCLUSIONS: In summary, our results show that a common variant in LEP may be associated with the risk of developing breast cancer supporting the hypothesis that leptin is involved in breast carcinogenesis