Lau, M. C. | Search bioRxiv

bioRxiv2020

Single cell RNA-seq reveals immunosuppressive gastric stem-like cancer cells as a poor prognostic factor

ObjectiveGastric cancer (GC) tumors are highly heterogenous with different subpopulations of epithelial cells. We employed single cell RNA sequencing (scRNA-seq) to dissect the heterogeneity and identified subpopulations of cancer cells with stem-like properties. We further investigated their resistance to oxaliplatin chemotherapy and their contribution to gastric cancer outcome. DesignWe performed scRNA-seq on FACS sorted epithelial and immune cells from paired samples of GC tumors and normal adjacent tissues. We identified two epithelial subpopulations (STMN1+IQGAP3+ and STMN1+IQGAP3-) with stem-like properties. We characterized and compared them to known healthy gastric stem cell populations. We also cultivated GC derived organoids to study the chemoresistance of similarly marked populations. Lastly, we employed immunohistochemistry (IHC) staining to ascertain the predicted immunosuppressive interactions. ResultsThe STMN1+IQGAP3+ subpopulation showed a higher tumor mutation burden, upregulated proliferative pathways and transcriptomically resembled proliferative healthy gastric isthmus stem cells. The STMN1+IQGAP3- subpopulation were comparatively quiescent and transcriptomically resembled enteroendocrine cells. Both transcriptomic signatures were associated with worse mortality than other epithelial subpopulations with the quiescent being associated with the poorest patient survival. GC tissue derived organoids were dominated by STMN1+IQGAP3+ cells but the STMN1+IQGAP3- compartment was more resistant to chemotherapy. We also verified the likely suppression of CD8 T cell cytotoxicity by STMN1+IQGAP3+ cells through the NECTIN2/TIGIT interaction. ConclusionsCancer cells with stem-like characteristics are associated with poor survival through chemoresistance and immunosuppression. Reactivating the immune system through checkpoint blockade is an opportunity to eliminate these cells. What is already known on this topicMultiple gastric stem cell populations have been identified and linked to tumor initiation in rodent-based studies. However, none of them have been conclusively proven in human tumors. Isolating and characterizing tumor cells with stem-like properties will help shed light on their possible origin and possible mitigation strategies. What this study addsHere we identified two sets of stem-like gastric cancer cells that are associated with poorer patient prognosis. One set is highly proliferative and exhibits oxaliplatin susceptibility. It also engages in immunosuppressive interactions such as NECTIN2/TIGIT. The other set is quiescent and highly resistant to oxaliplatin. How this study might affect research, practice or policyThe transcriptome signatures of the identified stem-like cells can aid in patient prognosis and identify patients who can benefit from checkpoint blockade therapy to reactivate their immune response towards gastric cancer cells.

immunology↗

bioRxiv2020

Tumor-Immune Partitioning and Clustering (TIPC) algorithm reveals distinct signatures of tumor-immune cell interactions within the tumor microenvironment

Growing evidence supports the importance of understanding tumor-immune spatial relationship in the tumor microenvironment in order to achieve precision cancer therapy. However, existing methods, based on oversimplistic cell-to-cell proximity, are largely confounded by immune cell density and are ineffective in capturing tumor-immune spatial patterns. Here we developed a novel computational algorithm, termed Tumor-Immune Partitioning and Clustering (TIPC), to offer an effective solution for spatially informed tumor subtyping. Our method could measure the extent of immune cell partitioning between tumor epithelial and stromal areas as well as the degree of immune cell clustering. Using a U.S. nation-wide colorectal cancer database, we showed that TIPC could determine tumor subtypes with unique tumor-immune spatial patterns that were significantly associated with patient survival and key tumor molecular features. We also demonstrated that TIPC was robust to parameter settings and readily applicable to different immune cell types. The capability of TIPC in delineating clinically relevant patient subtypes that encapsulate tumor-immune spatial relationship, immune density, and tumor morphology is expected to shed light on underlying immune mechanisms. Hence, TIPC can be a useful bioinformatics tool for effective characterization of the spatial composition of the tumor-immune microenvironment to inform precision immunotherapy.

cancer biology↗