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Our work has continued its focus on three main areas: Statistical methods for the analysis of next‑generation sequencing data, evolutionary approaches to cancer and methods for the analysis of genomics data.
We are continuing our collaboration with the International Cancer Genome Consortium projects on oesophageal adenocarcinoma, led by Professor Rebecca Fitzgerald from the MRC Cancer Unit, and prostate cancer, co-led by Professor David Neal (CRUK CI). These projects, funded by Cancer Research UK, are sequencing many tumour-normal samples, and should provide interesting and medically relevant information about the aberrations that occur in the genomes of these cancers. In both projects we are investigating the integration of multiple data types, the heterogeneity of tumours and their evolutionary history, with analysis methods being developed to facilitate these aims. A dedicated analysis group is now in place focusing on the International Cancer Genome Consortium (ICGC). The Bioinformatics core facility led by Dr Matt Eldridge in the CRUK CI collaborate with us in this work.
We continued a collaboration with the Griffiths lab in the area of metabolomics. Our goal was to identify and measure the concentrations of different metabolites in a biological system. The metabolites form an important layer in the complex metabolic network, and the interactions between different metabolites are often of interest. It is crucial to perform proper normalization of metabolomics data, but current methods may not be applicable when estimating interactions in the form of correlations between metabolites. We proposed a normalization approach based on a mixed model, with simultaneous estimation of a correlation matrix (Figure 1).
We showed with both real and simulated data that our proposed normalization method is robust and has good performance when discovering true correlations between metabolites.
Illumina technologies (both sequencing and BeadArray) are essential tools in cancer studies, and we, in collaboration with Mark Dunning (Bioinformatics core) and Matt Ritchie (WEHI, Australia), continue to update and support the beadarray Bioconductor package in order to facilitate transparent and flexible statistical analyses of full bead-level array data. We have developed over 20 software packages, and our group is committed to providing open source computational tools for the analysis of sequencing data. We ran the European Bioconductor Developers’ Meeting in December. We have a number of other ongoing collaborations within the CRUK CI, in particular with the Narita and Rosenfeld labs. We continue to study intra‑tumour heterogeneity in glioblastoma with Dr Colin Watts’ lab in Clinical Neurosciences. This has led, inter alia, to the development of novel statistical methods for the analysis of transposable data, particularly in the “p >> n” setting typified by expression data sampled from multiple sites in the tumour.
We have continued our research in the area of evolutionary methods in cancer biology, focussing in particular on:
- Spatial stochastic models for the evolution of tumours. Such models allow us to study cancer stem cells by comparing the dynamics of particular molecular markers;
- Approximate Bayesian computation (ABC) for inference, particularly in the setting in which observations from the underlying model cannot be simulated sufficiently quickly;
- Methods for estimating the complexity of sequencing libraries.
This year the lab has several new recruits with PhD students Sam Abujudeh, Ed Williams and Meltem Gurel joining us. We welcomed postdoctoral scientists Daniele Biasci, Valeria Bo and Juliane Perner and said farewell to Achilleas Achilleos, Alexey Larionov, Shamith Samarajiwa, Michael Smith. Alex Tunnicliffe also left the group after completing his PhD study.