Human tumours develop through the accumulation of random mutations in their DNA (genome), leading to individual cells within a tumour having acquired different mutations. The diversity within a tumour results in populations of cells that may respond differently to therapy or acquire different abilities to invade and spread. Genetic diversity within tumours is rarely considered in treatment protocols, yet it contributes to drug resistance, spread, and disease progression. Recent breakthroughs in DNA sequencing technology have enabled researchers to decode genomes in individual tumour cells, however translating this data into clinically actionable insights require software systems that enable researchers to visualize data and allow for interactive data exploration. Existing genome visualization tools, such as genome browsers, were designed to visualize tens or in some cases hundreds of genome sequences, but are overwhelmed by the many thousands of genomes produced by single cell methods.
The project aims to address this analysis bottleneck by introducing highly scalable search engine technology to the field of single cell genomics, specifically Elasticsearch, which is widely deployed in other fields. This technology will have immediate impact in the international cancer research community, where it will accelerate data interpretation and discovery and reveal insights into tumour composition and dynamics. Ultimately this work will be translated in research and clinical settings towards the goal of improved treatments that account for the complexities of diverse tumour cell populations.