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O'Farrelly, C.

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

3 recordsLinked to original sources

Evolutionary double-bind treatment using radiotherapy and NK cell-based immunotherapy in prostate cancer

Evolution-informed therapies exploit ecological and evolutionary consequences of drug resistance to inhibit the expansion of treatment-resistant populations and prolong time to progression. One strategy, termed an evolutionary double-bind, uses an initial therapy to elicit a specific adaptive response by the cancer cells, which is then selectively targeted by a follow-on therapy. Here we examine the combination of radiation therapy and immunotherapy as a quantifiable double-bind strategy. Radiotherapy (RT) induces lethal double-strand DNA breaks, but cancer cells can adapt by upregulating DNA damage response pathways. While this evolutionary strategy increases resistance to DNA damaging agents, it also results in enhanced expression of natural killer (NK) cell ligands potentially increasing vulnerability to an immune response. Using a radiation-resistant human prostate carcinoma cell line (22Rv1), we demonstrate that RT-resistant cells upregulate NK cell ligands, including major histocompatibility complex class I chain-related protein A/B (MICA/B), and poliovirus receptors (PVR1, PVRL2) with a 2-fold increase in sensitivity to NK cell mediated killing. We investigated this potential evolutionary double bind through in vitro studies and evolution-based mathematical models. Radiotherapy alone slowed overall growth but strongly selected for RT-resistant cells. NK cell therapy alone suppressed the RT-resistant population but with a surviving population of radiation-sensitive cells. These dynamics were framed mathematically, and model simulation predicted optimal tumour control would be achieved through initial RT rapidly followed by NK-based immunotherapy. Subsequent experiments confirmed the model prediction. We conclude that radiotherapy and NK cell-based immunotherapy produces an evolutionary double bind that can be exploited in heterogenous tumours to limit RT resistance.

cancer biology↗

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↗