DrSCS

Tumours evolve as heterogeneous populations of cells, which may be distinguished by different genomic aberrations and transcriptomic differences. The resulting intra-tumour heterogeneity plays an important role in cancer patient relapse and treatment failure, so that obtaining a clear understanding of each patient's tumour composition and evolutionary history is key for personalised therapies. Single-cell DNA sequencing now provides the possibility to resolve tumour heterogeneity at the highest resolution of individual tumour cells, while single-cell RNA sequencing offers expression profiling of tumour subpopulations as well as characterisation of the immune compartment of the tumourmicroenvironment. In parallel, ex-vivo drug screening gives a direct measurement of the aggregate response of tumour cells to various compounds.

With 136 samples profiled in a recent collaboration, we aim to develop and incorporate new prediction models for the drug response of the individual subclones in each patient's tumour. This approach will account for the tumour heterogeneity and for potentially different responses of constituent tumour parts to treatment. To integrate the different data modalities, we aim to first integrate the single-cell DNA and RNA information with joint modelling of the tumour evolution to more accurately understand its composition in terms of the existing subpopulations and their expression and copy number profiles. By additionally integrating drug response information, we can, for the first time, build and test drug prediction models at the level of tumour subclones. The potential of understanding the effect of different treatments on the heterogeneous parts of a tumour and thereby designing treatments for the full composite tumour could have great impact in aiding precision oncology.

This project is co-funded by PHRT.