Incorporating Genetic Biomarkers into Predictive Models of Normal Tissue Toxicity.

There is considerable variation in the level of toxicity patients experience for a given dose of radiotherapy, which is associated with differences in underlying individual normal tissue radiosensitivity. A number of syndromes have a large effect on clinical radiosensitivity, but these are rare. Among non-syndromic patients, variation is less extreme, but equivalent to a ±20% variation in dose. Thus, if individual normal tissue radiosensitivity could be measured, it should be possible to optimise schedules for individual patients. Early investigations of in vitro cellular radiosensitivity supported a link with tissue response, but individual studies were equivocal. A lymphocyte apoptosis assay has potential, and is currently under prospective validation. The investigation of underlying genetic variation also has potential. Although early candidate gene studies were inconclusive, more recent genome-wide association studies are revealing definite associations between genotype and toxicity and highlighting the potential for future genetic testing. Genetic testing and individualised dose prescriptions could reduce toxicity in radiosensitive patients, and permit isotoxic dose escalation to increase local control in radioresistant individuals. The approach could improve outcomes for half the patients requiring radical radiotherapy. As a number of patient- and treatment-related factors also affect the risk of toxicity for a given dose, genetic testing data will need to be incorporated into models that combine patient, treatment and genetic data.NGB is supported by the NIHR Cambridge Biomedical Research Centre.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.clon.2015.06.01

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The power sector’s meteorological information needs are diverse and cover many different distinct applications and users. Recognising this diversity, it is important to understand the general nature of how weather and climate influence the energy sector and the implications they have for quantitative impact modelling. Using conceptual examples and illustrations from recent research, this chapter argues that the traditional ‘transfer function’ approach that is common to many industrial applications of weather and climate science—whereby weather can be directly mapped to an energy impact—is inadequate for many important power system applications (such as price forecasting and system operations and planning). The chapter concludes by arguing that a deeper understanding of how meteorological impacts in the energy sector are modelled is required

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