We aim to understand how changes to the genome lead to transcriptomic changes to finally cause cancer.
Deep understanding of the cancer transcriptome is confounded by expression signals originating from admixed normal cells. Gene expression analysis by massively parallel sequencing (RNAseq) allows allele-specific expression measurements.
It can be shown that, given the fraction of tumour cells, the allele-specific copy number profiles of the tumour cells, and under a few reasonable assumptions, the expression in tumour cells can be separated from that in normal cells (Fig. 1). We aim to develop such bioinformatics approaches to deconvolute the tumour cell transcriptomes from transcriptomes of admixed normal cells.
We will apply these methods to large pan-cancer RNAseq datasets, allowing a transcriptome-wide view of cancer across cancer types. We expect these tumour-cell-specific expression profiles will result in a better taxonomy of cancer than mixed-cell-population expression profiles. Finally, expression profiles of admixed normal cells will allow insight into the cellular composition and transcriptional state of the tumour stroma.
In the longer term, we aim to develop integrative genomics-transcriptomics approaches that study the influence of point mutations, copy number changes and structural variants on transcription at the gene or transcript level and at the transcriptome level, and to apply these approaches in a large-scale pan-cancer setting, to understand the basic principles of cancer development and cancer evolution within and across tumour types.