Current management of uveal melanoma

Development of new therapeutic options for the treatment of uveal melanoma

Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. Important cytogenetic and genetic risk factors for the development of UM include chromosome 3 monosomy, mutations in the guanine nucleotide-binding proteins GNAQ/GNA11, and loss of the BRACA1-associated protein 1 (BAP 1). Most primary UMs are treated conservatively with radiotherapy, but enucleation is necessary for large tumours. Despite the effectiveness of local control, up to 50% of UM patients develop metastasis for which there are no effective therapies. Attempts to utilise the targeted therapies that have been developed for the treatment of other cancers, including a range of signal transduction pathway inhibitors, have rarely produced significant outcomes in UM. Similarly, the application of immunotherapies that are effective in cutaneous melanoma to treat UM have also been disappointing. Other approaches that have been initiated involve proteasomal inhibitors and histone deacetylase inhibitors which are approved for the treatment of other cancers. Nevertheless, there have been occasional positive outcomes from these treatments in UM. Moreover, combination approaches in UM have also yielded some positive developments. It would be valuable to identify how to apply such therapies efficiently in UM, potentially via individualised tumour profiling. It would also be important to characterise UM tumours to differentiate the potential drivers of progression from those in other types of cancers. The recent identification of novel kinases and metastatic genes in UM tumours makes the development of new UM-specific treatments feasible.

On the use of multi-objective evolutionary algorithms for survival analysis

This paper proposes and evaluates a multi-objective evolutionary algorithm for survival analysis. One aim of survival analysis is the extraction of models from data that approximate lifetime/failure time distributions. These models can be used to estimate the time that an event takes to happen to an object. To use of multi-objective evolutionary algorithms for survival analysis has several advantages. They can cope with feature interactions, noisy data, and are capable of optimising several objectives. This is important, as model extraction is a multi-objective problem. It has at least two objectives, which are the extraction of accurate and simple models. Accurate models are required to achieve good predictions. Simple models are important to prevent overfitting, improve the transparency of the models, and to save computational resources. Although there is a plethora of evolutionary approaches to extract models for classification and regression, the presented approach is one of the first applied to survival analysis. The approach is evaluated on several artificial datasets and one medical dataset. It is shown that the approach is capable of producing accurate models, even for problems that violate some of the assumptions made by classical approaches.