DTD - Digital Tissue Deconvolution

Aims of the project

Tumour tissue is a heterogeneous combination of different cells. Therefore, a tumour consists not only of tumour cells, but also of cells of the immune system, such as B-cells, T-cells and macrophages. The amount of tumor-infiltrating immune cells affect the progression of the disease and the success of treatment. Immune therapies block communication lines between tumor cells and infiltrating immune cells. Whether they are successful or not depends on the presence, quantity, and molecular sub-type of the infiltrating immune cells. Thus, an accurate estimate of a tissue’s cellular composition is essential.

The composition of the tissue can help to predict the progression of a patient's disease. For example, if tumour cells succeed in becoming invisible to the immune system, they will no longer be recruited to the tumour and their proportion in its vicinity will decrease.

Modern high-throughput measurements such as FACS (fluorescence-activated cell sorting) or single-cell sequencing allow us to read out the cellular composition of tumour tissue. These technologies are rarely available for large numbers of patients. A cost-effective alternative is Digital Tissue Deconvolution (DTD), which does not require additional measurements but performs the deconvolution of complex tissue specimens digitally.

Overall, the goals in this project are:

1. Use large single-cell data sets to optimise DTD models.

2. Apply DTD to analyse tumour gene expression profiles.

3. Develop a user-friendly software that allows the user to apply pre-trained models for digital tissue deconvolution

4. Develop a user-friendly software for loss-function learning so that DTD can be adapted to specific tissues.

The project is funded by the DFG (since 2019).

Publications

Görtler, F., Schoen, M., Simeth, J., Solbrig, S., Wettig, T., Oefner, P. J., ... & Altenbuchinger, M. (2020).
Loss-function learning for digital tissue deconvolution. 
Journal of Computational Biology27(3), 342-355.

Schön, M., Simeth, J., Heinrich, P., Görtler, F., Solbrig, S., Wettig, T., ... & Spang, R. (2020).
DTD: an R package for digital tissue Deconvolution. 
Journal of Computational Biology27(3), 386-389.

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