The tumor microenvironment (TME) plays a critical role in cancer progression and therapeutic response. New single cell and imaging technologies provide unprecedented measurements of cell type composition, subtypes of common cell types in disparate states, interactions between neighboring cells, and T cell function. However, each of these components of the TME are captured by disparate measurement technologies. Both new computational methods and software for multi-platform data integration are essential to characterize the TME. Therefore, we propose a unified R/Bioconductor package TMEMap for multi-platform single cell data integration.
Aim 1 will integrate single cell RNA-sequencing and single cell TCR-sequencing data to distinguish T cell function in distinct T cell subtypes and states.
Aim 2 will integrate combined single cell RNA-sequencing and protein from CITE-seq with imaging proteomics for digital pathology to map cellular interactions in the TME.
Aim 3 will further the disseminate this software with through GenePattern Notebook. This workflow will be developed in collaboration with clinical investigators to ensure usability and interpretability of the visualization methods using clinical biospecimens from synergistic studies. Altogether, this software will provide a strong foundation for future work embedding these methods in a database for multi-platform single cell data to automatically perform comprehensive TME characterization in large scale NCI profiling efforts such as the Human Tumor Atlas Network.
The tumor microenvironment (TME) plays a critical role in cancer progression and therapeutic response. New single cell and imaging technologies yield unprecedented measurements to characterize these states. We propose software and workflows for single cell data integration to characterize the TME.
