Tumor microenvironment is composed of many different cells, including immune cells, fibroblasts, endothelial cells, among others. The complexity of this microenvironment is the result of the interaction of many factors, such as the presence of the different cell types, the hierarchical structure between cells, and transcriptional activity in response to changes in the microenvironment. In the Swiss Cancer League-funded project, we are profiling the transcriptome of hepatocellular carcinomas on the single-cell level to provide insight into the complexity of the tumor microenvironment of liver cancer, the extent of intra-tumor and inter-tumor cell-to-cell variability and the interaction between cell types, all of which are crucial to a systematic understanding of the tumor ecosystem.
Ion Torrent is the most used sequencing platform for diagnostics in Switzerland, but the proprietary software for data analysis requires extensive manual review of the results and lacks optimized workflows for custom sequencing panels. We developed PipeIT (Garofoli et al, J Mol Diagn, 2019) and demonstrated its superior positive predictive value compared to the proprietary software in identifying somatic mutations from matched tumor-normal sequencing data, substantially reducing the need for manual curation of the results. We are extending PipeIT to accommodate the more clinically relevant scenario in which sequencing data is only available for the tumor but not the matched germline. PipeIT is being integrated into the SPHN driver project SOCIBP (led by Prof Mark Rubin (Bern) and Prof Gunnar Raetsch (SIB/ETH Zurich)) infrastructure and being tested in real-world data.