Biology subjects

Thomas, D. M.

Publications and source records attributed to Thomas, D. M..

3 recordsLinked to original sources

bioRxiv2018

Network-aware mutation clustering of cancer

The grouping of cancers across tissue boundaries is central to precision oncology, but remains a difficult problem. Here we present EPICC (Experimental Protein Interaction Clustering of Cancer), a novel technique to cluster cancer patients based on DNA mutation profile, that leverages knowledge of protein-protein interactions to reduce noise and amplify biological signal. We applied EPICC to data from The Cancer Genome Atlas (TCGA), and both recapitulated known cancer clusterings, and identified new cross-tissue cancer groups that may indicate novel cancer molecular subtypes. Investigation of EPICC clusters revealed new protein modules which were recurrently mutated across cancers, and indicate new avenues for research into cancer biology. EPICC leveraged the Vodafone DreamLab citizen science platform, and we provide our results as a resource for researchers to investigate the role of protein modules in cancer.

bioinformatics↗

bioRxiv2018

The Medical Genome Reference Bank: a whole-genome data resource of 4,000 healthy elderly individuals. Rationale and cohort design

Allele frequency data from human reference populations is of increasing value for filtering and assignment of pathogenicity to genetic variants. Aged and healthy populations are more likely to be selectively depleted of pathogenic alleles, and therefore particularly suitable as a reference populations for the major diseases of clinical and public health importance. However, reference studies of the healthy elderly have remained under-represented in human genetics. We have developed the Medical Genome Reference Bank (MGRB), a large-scale comprehensive whole-genome dataset of confirmed healthy elderly individuals, to provide a publicly accessible resource for health-related research, and for clinical genetics. It also represents a useful resource for studying the genetics of healthy aging. The MGRB comprises 4,000 healthy, older individuals with no reported history of cancer, cardiovascular disease or dementia, recruited from two Australian community-based cohorts. DNA derived from blood samples will be subject to whole genome sequencing. The MGRB will measure genome-wide genetic variation in 4,000 individuals, mostly of European decent, aged 60-95 years (mean age [≥] 75 years). The MGRB has committed to a policy of data sharing, employing a hierarchical data management system to maintain participant privacy and confidentiality, whilst maximizing research and clinical usage of the database. The MGRB will represent a dataset of international significance, broadly accessible to the clinical and genetic research community.

genomics↗

bioRxiv2017

Whole genome optical mapping reveals multiple fusion events chained by large novel sequences in cancer

Genomic rearrangements are common in cancer, with demonstrated links to disease progression and treatment response. These rearrangements can be complex, resulting in fusions of multiple chromosomal fragments and generation of derivative chromosomes. While methods exist for detecting individual fusions, they are generally unable to reconstruct complex chained events. To overcome these limitations, we adopted a new optical mapping approach, allowing for megabase length DNA to be captured, and in turn rearranged genomes to be visualized without loss of integrity. Whole genome mapping (Bionano Genomics) of a well-studied highly rearranged liposarcoma cell line, resulted in 3,338 assembled haploid genome maps, including 101 fusion maps. These fusion maps represent 175 Mb of highly rearranged genomic regions, illuminating the complex architecture of chained fusions, including content, order, orientation, and size. Spanning the junction of 151 chromosomal translocations, we found a total of 32 Mb of novel interspersed sequences that were not detected from short-read sequencing. We demonstrate that optical mapping provides a powerful new approach for capturing a higher level of complex genomic architecture, creating a scaffold for renewed interpretation of sequencing data of particular relevance to human cancer.

genomics↗