Ischemia Is Related to Tumour Genetics in Uveal Melanoma

Hypoxia-inducible factor 1-alpha (HIF1a) and its regulator von Hippel–Lindau protein (VHL) play an important role in tumour ischemia. Currently, drugs that target HIF1a are being developed to treat malignancies. Although HIF1a is known to be expressed in uveal melanoma (UM), it is as yet unknown which factors, such as tumour size or genetics, determine its expression. Therefore, we aimed to determine which tumour characteristics relate to HIF1a expression in UM. Data from 64 patients who were enucleated for UM were analysed. Messenger RNA (mRNA) expression was determined with the Illumina HT-12 v4 chip. In 54 cases, the status of chromosomes 3 and 8q, and BRCA1-associated protein 1 (BAP1) protein expression (immunohistochemistry) were determined. Findings were corroborated using data of 80 patients from the Cancer Genome Atlas (TCGA) study. A significantly increased expression of HIF1a, and a decreased expression of VHL were associated with monosomy 3/loss of BAP1 expression. The relationship between BAP1 loss and HIF1a expression was independent of chromosome 3. The largest basal diameter and tumour thickness showed no relationship with HIF1a. HIF1a expression related to an increased presence of infiltrating T cells and macrophages. From this study, we conclude that HIF1a is strongly related to tumour genetics in UM, especially to loss of BAP1 expression, and less to tumour size. Tumour ischemia is furthermore related to the presence of an inflammatory phenotype.

Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data

Understanding the evolutionary history and subclonal composition of a tumour represents one of the key challenges in overcoming treatment failure due to resistant cell populations. Most of the current data on tumour genetics stems from short read bulk sequencing data. While this type of data is characterised by low sequencing noise and cost, it consists of aggregate measurements across a large number of cells. It is therefore of limited use for the accurate detection of the distinct cellular populations present in a tumour and the unambiguous inference of their evolutionary relationships. Single-cell DNA sequencing instead provides data of the highest resolution for studying intra-tumour heterogeneity and evolution, but is characterised by higher sequencing costs and elevated noise rates. In this work, we develop the first computational approach that infers trees of tumour evolution from combined single-cell and bulk sequencing data. Using a comprehensive set of simulated data, we show that our approach systematically outperforms existing methods with respect to tree reconstruction accuracy and subclone identification. High fidelity reconstructions are obtained even with a modest number of single cells. We also show that combining single-cell and bulk sequencing data provides more realistic mutation histories for real tumours.