The tumor microenvironment (TME) is a complex and dynamic landscape that plays a pivotal role in the progression and behavior of cancer. Comprising various non-cancerous cells such as immune cells, fibroblasts, and endothelial cells, the TME interacts intimately with tumor cells, influencing growth, metastasis, and response to therapy. Understanding these interactions is crucial for the development of targeted therapies that can more effectively combat cancer by disrupting the supportive network tumors rely on.
However, this requires knowledge of the cell types which in its turn requires single cell data thus, not available for data such as bulk sequencing. Cell deconvolution is a computational technique that plays a crucial role in unraveling the complexities of the TME. By analyzing bulk tumor data, cell deconvolution separates the mixed signals into individual proportions from various cell types, offering a clearer view of the cellular composition and interactions within the TME.
A recent methodology called “EcoTyper”, a machine-learning framework integrating transcriptome deconvolution and single-cell RNA sequencing that extends beyond quantification of each cell type to also characterize distinct cell states within those types, offering a more detailed view of the cellular dynamics within the TME.
The aim of this student project, part of a larger PhD project, is to combine “EcoTyper” with spatially resolved RNA data from the “GeoMx DSP” platform to characterize the TME of large cohorts of hundreds of patients, from two different types of B-cell lymphomas.
To read more about EcoTyper.
https://www.sciencedirect.com/science/article/pii/S1535610821004517?via%3Dihub#sec3
To read more about the spatial omic platform/ GeoMX DSP data. See the latest publication from our group – based on such analysis.
https://pubmed.ncbi.nlm.nih.gov/37139184/
If you are interested in this or similar spatial omic projects, contact:
We have ongoing and past experience in supervising short and long term projects for students at the Master program in Bioinformatics.