Meta-analysis of human cancer single cell RNA-seq datasets using the IMMUcan database.

The development of single cell RNA-sequencing (scRNA-seq) technologies has greatly contributed to deciphering the tumor microenvironment (TME). An enormous amount of independent scRNA-seq studies have been published representing a valuable resource that provides opportunities for meta-analysis studies. However, the massive amount of biological information, the marked heterogeneity and variability between studies, and the technical challenges in processing heterogeneous datasets create major bottlenecks for the full exploitation of scRNA-seq data. We have developed IMMUcan scDB (https://immucanscdb.vital-it.ch), a fully integrated scRNA-seq database exclusively dedicated to human cancer and accessible to non-specialists. IMMUcan scDB encompasses 144 datasets on 56 different cancer types, annotated in 50 fields containing precise clinical, technological and biological information. A data processing pipeline was developed and organized in 4 steps: 1) data collection; 2) data processing (quality control and sample integration); 3) supervised cell annotation with a cell ontology classifier of the TME; and 4) interface to analyze TME in a cancer type-specific or global manner. This framework was used to explore datasets across tumor locations in a gene centric (CXCL13) and cell-centric (B cells) manner as well as to conduct meta-analysis studies such as ranking immune cell types and genes correlated to malignant transformation. This integrated, freely accessible and user-friendly resource represents and unprecedented level of detailed annotation, offering vast possibilities for downstream exploitation of human cancer scRNAseq data for discovery and validation studies.