Tumour heterogeneity in primary prostate cancer is a well-established phenomenon. However, how the subclonal diversity of tumours changes during metastasis and progression to lethality is poorly understood. Here we reveal the precise direction of metastatic spread across four lethal prostate cancer patients using whole-genome and ultra-deep targeted sequencing of longitudinally collected primary and metastatic tumours. We find one case of metastatic spread to the surgical bed causing local recurrence, and another case of cross-metastatic site seeding combining with dynamic remoulding of subclonal mixtures in response to therapy. By ultra-deep sequencing end-stage blood, we detect both metastatic and primary tumour clones, even years after removal of the prostate. Analysis of mutations associated with metastasis reveals an enrichment of TP53 mutations, and additional sequencing of metastases from 19 patients demonstrates that acquisition of TP53 mutations is linked with the expansion of subclones with metastatic potential which we can detect in the blood.

Human colorectal cancer cell lines are used widely to investigate tumor biology, experimental therapy, and biomarkers. However, to what extent these established cell lines represent and maintain the genetic diversity of primary cancers is uncertain. In this study, we profiled 70 colorectal cancer cell lines for mutations and DNA copy number by whole-exome sequencing and SNP microarray analyses, respectively. Gene expression was defined using RNA-Seq. Cell line data were compared with those published for primary colorectal cancers in The Cancer Genome Atlas. Notably, we found that exome mutation and DNA copy-number spectra in colorectal cancer cell lines closely resembled those seen in primary colorectal tumors. Similarities included the presence of two hypermutation phenotypes, as defined by signatures for defective DNA mismatch repair and DNA polymerase ϵ proofreading deficiency, along with concordant mutation profiles in the broadly altered WNT, MAPK, PI3K, TGFβ, and p53 pathways. Furthermore, we documented mutations enriched in genes involved in chromatin remodeling (ARID1A, CHD6, and SRCAP) and histone methylation or acetylation (ASH1L, EP300, EP400, MLL2, MLL3, PRDM2, and TRRAP). Chromosomal instability was prevalent in nonhypermutated cases, with similar patterns of chromosomal gains and losses. Although paired cell lines derived from the same tumor exhibited considerable mutation and DNA copy-number differences, in silico simulations suggest that these differences mainly reflected a preexisting heterogeneity in the tumor cells. In conclusion, our results establish that human colorectal cancer lines are representative of the main subtypes of primary tumors at the genomic level, further validating their utility as tools to investigate colorectal cancer biology and drug responses. Cancer Res; 74(12); 3238–47. ©2014 AACR.

BackgroundAnalyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces; highly available, scalable computational resources; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise.ResultsWe designed and implemented the Genomics Virtual Laboratory (GVL) as a middleware layer of machine images, cloud management tools, and online services that enable researchers to build arbitrarily sized compute clusters on demand, pre-populated with fully configured bioinformatics tools, reference datasets and workflow and visualisation options. The platform is flexible in that users can conduct analyses through web-based (Galaxy, RStudio, IPython Notebook) or command-line interfaces, and add/remove compute nodes and data resources as required. Best-practice tutorials and protocols provide a path from introductory training to practice. The GVL is available on the OpenStack-based Australian Research Cloud (http://nectar.org.au) and the Amazon Web Services cloud. The principles, implementation and build process are designed to be cloud-agnostic.ConclusionsThis paper provides a blueprint for the design and implementation of a cloud-based Genomics Virtual Laboratory. We discuss scope, design considerations and technical and logistical constraints, and explore the value added to the research community through the suite of services and resources provided by our implementation.