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O'Farrelly, C.

Publications and source records attributed to O'Farrelly, C..

2 recordsLinked to original sources

Enhanced cytokine responsiveness in natural killer cells from a pilot cohort of uninfected seronegative women exposed to hepatitis C virus contaminated anti-D immunoglobulin

BackgroundSome people exposed to hepatitis C virus (HCV) appear to be capable of preventing infection in the absence of detectable antibody responses. These exposed seronegative (ESN) people appear naturally resistant to HCV infection. Here, we aimed to examine innate immune mechanisms in ESN individuals amongst rhesus negative Irish women exposed to HCV via contaminated anti-D immunoglobulin between 1977-79 and 1991-94.\n\nMethodsA total of 16 ESN individuals were recruited, along with 9 age- and gender-matched healthy controls. All tested negative for HCV-specific antibodies using conventional diagnostic assays. Peripheral blood cells were analysed for presence of adaptive immune response markers, innate immune responsiveness and natural killer cell phenotype and function.\n\nResultsThe innate immune cell profile of ESN women in the present study was characterised by a significant decrease in monocyte frequency and elevated levels of interleukin-8 and -18 compared to age- and gender-matched healthy controls. NK cells from ESN women had normal expression of NK cell receptors but increased IFN{gamma}-production upon cytokine and target cell stimulation as well as enhanced natural killer (NK) cell STAT3 phosphorylation in response to Type I IFN.\n\nConclusionsWe describe for the first time ESN individuals amongst Irish women with past exposure to HCV via contaminated anti-D immunoglobulin. NK cells from these ESN individuals are more responsive to cytokine signalling compared with age- and gender-matched controls. Human ESN cohorts can provide unique insights into the biological mechanisms associated with antigen-independent natural resistance to viral infection.

immunology

Harnessing patient-specific response dynamics to optimize evolutionary therapies for metastatic clear cell renal cell carcinoma - Learning to adapt

Renal cell carcinoma (RCC) is one of the ten most common and lethal cancers in the United States. Tumor heterogeneity and development of resistance to treatment suggest that patient-specific evolutionary therapies may hold the key to better patients prognosis. Mathematical models are a powerful tool to help develop such strategies; however, they depend on reliable biomarker information. In this paper, we present a dynamic model of tumor-immune interactions, as well as the treatment effect on tumor cells and the tumor-immune environment. We hypothesize that the neutrophil-to-lymphocyte ratio (NLR) is a powerful biomarker that can be used to predict an individual patients response to treatment. Using randomly sampled virtual patients, we show that the model recapitulates patient outcomes from clinical trials in RCC. Finally, we use in silico patient data to recreate realistic tumor behaviors and simulate various treatment strategies to find optimal treatments for each virtual patient.

cancer biology