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Purpose: The tumor microenvironment (TME) consists of a heterogenous cellular milieu that can influence cancer cell behavior. Its characteristics have an impact on treatments such as immunotherapy. These features can be revealed with single-cell RNA sequencing (scRNA-seq). We hypothesized that scRNA-seq analysis of gastric cancer together with paired normal tissue and peripheral blood mononuclear cells (PBMC) would identify critical elements of cellular deregulation not apparent with other approaches.Experimental Design: scRNA-seq was conducted on seven patients with gastric cancer and one patient with intestinal metaplasia. We sequenced 56,167 cells comprising gastric cancer (32,407 cells), paired normal tissue (18,657 cells), and PBMCs (5,103 cells). Protein expression was validated by multiplex immunofluorescence.Results: Tumor epithelium had copy number alterations, a distinct gene expression program from normal, with intratumor heterogeneity. Gastric cancer TME was significantly enriched for stromal cells, macrophages, dendritic cells (DC), and Tregs. TMEexclusive stromal cells expressed distinct extracellular matrix components than normal. Macrophages were transcriptionally heterogenous and did not conform to a binary M1/M2 paradigm. Tumor DCs had a unique gene expression program compared to PBMC DCs. TME-specific cytotoxic T cells were exhausted with two heterogenous subsets. Helper, cytotoxic T, Treg, and NK cells expressed multiple immune checkpoint or co-stimulatory molecules. Receptor-ligand analysis revealed TME-exclusive intercellular communication.Conclusions: Single-cell gene expression studies revealed widespread reprogramming across multiple cellular elements in the gastric cancer TME. Cellular remodeling was delineated by changes in cell numbers, transcriptional states, and intercellular interactions. This characterization facilitates understanding of tumor biology and enables identification of novel targets including for immunotherapy.

In this study, we present a highly customizable method for quantifying copy number and point mutations utilizing a single-color, droplet digital PCR platform. Droplet digital polymerase chain reaction (ddPCR) is rapidly replacing real-time quantitative PCR (qRT-PCR) as an efficient method of independent DNA quantification. Compared to quantative PCR, ddPCR eliminates the needs for traditional standards; instead, it measures target and reference DNA within the same well. The applications for ddPCR are widespread including targeted quantitation of genetic aberrations, which is commonly achieved with a two-color fluorescent oligonucleotide probe (TaqMan) design. However, the overall cost and need for optimization can be greatly reduced with an alternative method of distinguishing between target and reference products using the nonspecific DNA binding properties of EvaGreen (EG) dye. By manipulating the length of the target and reference amplicons, we can distinguish between their fluorescent signals and quantify each independently. We demonstrate the effectiveness of this method by examining copy number in the proto-oncogene FLT3 and the common V600E point mutation in BRAF. Using a series of well-characterized control samples and cancer cell lines, we confirmed the accuracy of our method in quantifying mutation percentage and integer value copy number changes. As another novel feature, our assay was able to detect a mutation comprising less than 1% of an otherwise wild-type sample, as well as copy number changes from cancers even in the context of significant dilution with normal DNA. This flexible and cost-effective method of independent DNA quantification proves to be a robust alternative to the commercialized TaqMan assay.

Cancer cell lines are not homogeneous nor are they static in their genetic state and biological properties. Genetic, transcriptional and phenotypic diversity within cell lines contributes to the lack of experimental reproducibility frequently observed in tissue-culture-based studies. While cancer cell line heterogeneity has been generally recognized, there are no studies which quantify the number of clones that coexist within cell lines and their distinguishing characteristics. We used a single-cell DNA sequencing approach to characterize the cellular diversity within nine gastric cancer cell lines and integrated this information with single-cell RNA sequencing. Overall, we sequenced the genomes of 8824 cells, identifying between 2 and 12 clones per cell line. Using the transcriptomes of more than 28 000 single cells from the same cell lines, we independently corroborated 88% of the clonal structure determined from single cell DNA analysis. For one of these cell lines, we identified cell surface markers that distinguished two subpopulations and used flow cytometry to sort these two clones. We identified substantial proportions of replicating cells in each cell line, assigned these cells to subclones detected among the G0/G1 population and used the proportion of replicating cells per subclone as a surrogate of each subclone's growth rate.

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