GITR and TIGIT immunotherapy provokes divergent multicellular responses in the tumor microenvironment of gastrointestinal cancers.

BACKGROUND: Understanding the mechanistic effects of novel immunotherapy agents is critical to improving their successful clinical translation. These effects need to be studied in preclinical models that maintain the heterogenous tumor microenvironment (TME) and dysfunctional cell states found in a patient's tumor. We investigated immunotherapy perturbations targeting co-stimulatory molecule GITR and co-inhibitory immune checkpoint TIGIT in a patient-derived ex vivo system that maintains the TME in its near-native state. Leveraging single-cell genomics, we identified cell type-specific transcriptional reprogramming in response to immunotherapy perturbations. METHODS: We generated ex vivo tumor slice cultures from fresh surgical resections of gastric and colon cancer and treated them with GITR agonist or TIGIT antagonist antibodies. We applied paired single-cell RNA and TCR sequencing to the original surgical resections, control, and treated ex vivo tumor slice cultures. We additionally confirmed target expression using multiplex immunofluorescence and validated our findings with RNA in situ hybridization. RESULTS: We confirmed that tumor slice cultures maintained the cell types, transcriptional cell states and proportions of the original surgical resection. The GITR agonist was limited to increasing effector gene expression only in cytotoxic CD8 T cells. Dysfunctional exhausted CD8 T cells did not respond to GITR agonist. In contrast, the TIGIT antagonist increased TCR signaling and activated both cytotoxic and dysfunctional CD8 T cells. This included cells corresponding to TCR clonotypes with features indicative of potential tumor antigen reactivity. The TIGIT antagonist also activated T follicular helper-like cells and dendritic cells, and reduced markers of immunosuppression in regulatory T cells. CONCLUSIONS: We identified novel cellular mechanisms of action of GITR and TIGIT immunotherapy in the patients' TME. Unlike the GITR agonist that generated a limited transcriptional response, TIGIT antagonist orchestrated a multicellular response involving CD8 T cells, T follicular helper-like cells, dendritic cells, and regulatory T cells. Our experimental strategy combining single-cell genomics with preclinical models can successfully identify mechanisms of action of novel immunotherapy agents. Understanding the cellular and transcriptional mechanisms of response or resistance will aid in prioritization of targets and their clinical translation.

A spatially mapped gene expression signature for intestinal stem-like cells identifies high-risk precursors of gastric cancer.

Objective: Gastric intestinal metaplasia (GIM) is a precancerous lesion that increases gastric cancer (GC) risk. The Operative Link on GIM (OLGIM) is a combined clinical-histopathologic system to risk-stratify patients with GIM. The identification of molecular biomarkers that are indicators for advanced OLGIM lesions may improve cancer prevention efforts. Methods: This study was based on clinical and genomic data from four cohorts: 1) GAPS, a GIM cohort with detailed OLGIM severity scoring (N=303 samples); 2) the Cancer Genome Atlas (N=198); 3) a collation of in-house and publicly available scRNA-seq data (N=40), and 4) a spatial validation cohort (N=5) consisting of annotated histology slides of patients with either GC or advanced GIM. We used a multi-omics pipeline to identify, validate and sequentially parse a highly-refined signature of 26 genes which characterize high-risk GIM. Results: Using standard RNA-seq, we analyzed two separate, non-overlapping discovery (N=88) and validation (N=215) sets of GIM. In the discovery phase, we identified 105 upregulated genes specific for high-risk GIM (defined as OLGIM III-IV), of which 100 genes were independently confirmed in the validation set. Spatial transcriptomic profiling revealed 36 of these 100 genes to be expressed in metaplastic foci in GIM. Comparison with bulk GC sequencing data revealed 26 of these genes to be expressed in intestinal-type GC. Single-cell profiling resolved the 26-gene signature to both mature intestinal lineages (goblet cells, enterocytes) and immature intestinal lineages (stem-like cells). A subset of these genes was further validated using single-molecule multiplex fluorescence in situ hybridization. We found certain genes (TFF3 and ANPEP) to mark differentiated intestinal lineages, whereas others (OLFM4 and CPS1) localized to immature cells in the isthmic/crypt region of metaplastic glands, consistent with the findings from scRNAseq analysis. Conclusions: using an integrated multi-omics approach, we identified a novel 26-gene expression signature for high-OLGIM precursors at increased risk for GC. We found this signature localizes to aberrant intestinal stem-like cells within the metaplastic microenvironment. These findings hold important translational significance for future prevention and early detection efforts.

Follicular lymphoma evolves with a surmountable dependency on acquired glycosylation motifs in the B-cell receptor.

ABSTRACT: An early event in the genesis of follicular lymphoma (FL) is the acquisition of new glycosylation motifs in the B-cell receptor (BCR) due to gene rearrangement and/or somatic hypermutation. These N-linked glycosylation motifs (N-motifs) contain mannose-terminated glycans and can interact with lectins in the tumor microenvironment, activating the tumor BCR pathway. N-motifs are stable during FL evolution, suggesting that FL tumor cells are dependent on them for their survival. Here, we investigated the dynamics and potential impact of N-motif prevalence in FL at the single-cell level across distinct tumor sites and over time in 17 patients. Although most patients had acquired at least 1 N-motif as an early event, we also found (1) cases without N-motifs in the heavy or light chains at any tumor site or time point and (2) cases with discordant N-motif patterns across different tumor sites. Inferring phylogenetic trees of the patients with discordant patterns, we observed that both N-motif-positive and N-motif-negative tumor subclones could be selected and expanded during tumor evolution. Comparing N-motif-positive with N-motif-negative tumor cells within a patient revealed higher expression of genes involved in the BCR pathway and inflammatory response, whereas tumor cells without N-motifs had higher activity of pathways involved in energy metabolism. In conclusion, although acquired N-motifs likely support FL pathogenesis through antigen-independent BCR signaling in most patients with FL, N-motif-negative tumor cells can also be selected and expanded and may depend more heavily on altered metabolism for competitive survival.

Direct measurement of engineered cancer mutations and their transcriptional phenotypes in single cells.

Genome sequencing studies have identified numerous cancer mutations across a wide spectrum of tumor types, but determining the phenotypic consequence of these mutations remains a challenge. Here, we developed a high-throughput, multiplexed single-cell technology called TISCC-seq to engineer predesignated mutations in cells using CRISPR base editors, directly delineate their genotype among individual cells and determine each mutation's transcriptional phenotype. Long-read sequencing of the target gene's transcript identifies the engineered mutations, and the transcriptome profile from the same set of cells is simultaneously analyzed by short-read sequencing. Through integration, we determine the mutations' genotype and expression phenotype at single-cell resolution. Using cell lines, we engineer and evaluate the impact of >100 TP53 mutations on gene expression. Based on the single-cell gene expression, we classify the mutations as having a functionally significant phenotype.

Single-cell multi-gene identification of somatic mutations and gene rearrangements in cancer.

In this proof-of-concept study, we developed a single-cell method that provides genotypes of somatic alterations found in coding regions of messenger RNAs and integrates these transcript-based variants with their matching cell transcriptomes. We used nanopore adaptive sampling on single-cell complementary DNA libraries to validate coding variants in target gene transcripts, and short-read sequencing to characterize cell types harboring the mutations. CRISPR edits for 16 targets were identified using a cancer cell line, and known variants in the cell line were validated using a 352-gene panel. Variants in primary cancer samples were validated using target gene panels ranging from 161 to 529 genes. A gene rearrangement was also identified in one patient, with the rearrangement occurring in two distinct tumor sites.

Single-molecule methylation profiles of cell-free DNA in cancer with nanopore sequencing.

Epigenetic characterization of cell-free DNA (cfDNA) is an emerging approach for detecting and characterizing diseases such as cancer. We developed a strategy using nanopore-based single-molecule sequencing to measure cfDNA methylomes. This approach generated up to 200 million reads for a single cfDNA sample from cancer patients, an order of magnitude improvement over existing nanopore sequencing methods. We developed a single-molecule classifier to determine whether individual reads originated from a tumor or immune cells. Leveraging methylomes of matched tumors and immune cells, we characterized cfDNA methylomes of cancer patients for longitudinal monitoring during treatment.

Single cell and spatial alternative splicing analysis with long read sequencing.

Long-read sequencing has become a powerful tool for alternative splicing analysis. However, technical and computational challenges have limited our ability to explore alternative splicing at single cell and spatial resolution. The higher sequencing error of long reads, especially high indel rates, have limited the accuracy of cell barcode and unique molecular identifier (UMI) recovery. Read truncation and mapping errors, the latter exacerbated by the higher sequencing error rates, can cause the false detection of spurious new isoforms. Downstream, there is yet no rigorous statistical framework to quantify splicing variation within and between cells/spots. In light of these challenges, we developed Longcell, a statistical framework and computational pipeline for accurate isoform quantification for single cell and spatial spot barcoded long read sequencing data. Longcell performs computationally efficient cell/spot barcode extraction, UMI recovery, and UMI-based truncation- and mapping-error correction. Through a statistical model that accounts for varying read coverage across cells/spots, Longcell rigorously quantifies the level of inter-cell/spot versus intra-cell/ spot diversity in exon-usage and detects changes in splicing distributions between cell populations. Applying Longcell to single cell long-read data from multiple contexts, we found that intra-cell splicing heterogeneity, where multiple isoforms co-exist within the same cell, is ubiquitous for highly expressed genes. On matched single cell and Visium long read sequencing for a tissue of colorectal cancer metastasis to the liver, Longcell found concordant signals between the two data modalities. Finally, on a perturbation experiment for 9 splicing factors, Longcell identified regulatory targets that are validated by targeted sequencing.

GITR and TIGIT immunotherapy provokes divergent multi-cellular responses in the tumor microenvironment of gastrointestinal cancers.

Understanding the cellular mechanisms of novel immunotherapy agents in the human tumor microenvironment (TME) is critical to their clinical success. We examined GITR and TIGIT immunotherapy in gastric and colon cancer patients using ex vivo slice tumor slice cultures derived from cancer surgical resections. This primary culture system maintains the original TME in a near-native state. We applied paired single-cell RNA and TCR sequencing to identify cell type specific transcriptional reprogramming. The GITR agonist was limited to increasing effector gene expression only in cytotoxic CD8 T cells. The TIGIT antagonist increased TCR signaling and activated both cytotoxic and dysfunctional CD8 T cells, including clonotypes indicative of potential tumor antigen reactivity. The TIGIT antagonist also activated T follicular helper-like cells and dendritic cells, and reduced markers of immunosuppression in regulatory T cells. Overall, we identified cellular mechanisms of action of these two immunotherapy targets in the patients' TME.

Colorectal Cancer Metastases in the Liver Establish Immunosuppressive Spatial Networking between Tumor-Associated SPP1+ Macrophages and Fibroblasts.

PURPOSE: The liver is the most frequent metastatic site for colorectal cancer. Its microenvironment is modified to provide a niche that is conducive for colorectal cancer cell growth. This study focused on characterizing the cellular changes in the metastatic colorectal cancer (mCRC) liver tumor microenvironment (TME). EXPERIMENTAL DESIGN: We analyzed a series of microsatellite stable (MSS) mCRCs to the liver, paired normal liver tissue, and peripheral blood mononuclear cells using single-cell RNA sequencing (scRNA-seq). We validated our findings using multiplexed spatial imaging and bulk gene expression with cell deconvolution. RESULTS: We identified TME-specific SPP1-expressing macrophages with altered metabolism features, foam cell characteristics, and increased activity in extracellular matrix (ECM) organization. SPP1+ macrophages and fibroblasts expressed complementary ligand-receptor pairs with the potential to mutually influence their gene-expression programs. TME lacked dysfunctional CD8 T cells and contained regulatory T cells, indicative of immunosuppression. Spatial imaging validated these cell states in the TME. Moreover, TME macrophages and fibroblasts had close spatial proximity, which is a requirement for intercellular communication and networking. In an independent cohort of mCRCs in the liver, we confirmed the presence of SPP1+ macrophages and fibroblasts using gene-expression data. An increased proportion of TME fibroblasts was associated with the worst prognosis in these patients. CONCLUSIONS: We demonstrated that mCRC in the liver is characterized by transcriptional alterations of macrophages in the TME. Intercellular networking between macrophages and fibroblasts supports colorectal cancer growth in the immunosuppressed metastatic niche in the liver. These features can be used to target immune-checkpoint-resistant MSS tumors.

The Gastric Cancer Registry: A Genomic Translational Resource for Multidisciplinary Research in Gastric Cancer.

BACKGROUND: Gastric cancer is a leading cause of cancer morbidity and mortality. Developing information systems which integrate clinical and genomic data may accelerate discoveries to improve cancer prevention, detection, and treatment. To support translational research in gastric cancer, we developed the Gastric Cancer Registry (GCR), a North American repository of clinical and cancer genomics data. METHODS: Participants self-enrolled online. Entry criteria into the GCR included the following: (i) diagnosis of gastric cancer, (ii) history of gastric cancer in a first- or second-degree relative, or (iii) known germline mutation in the gene CDH1. Participants provided demographic and clinical information through a detailed survey. Some participants provided specimens of saliva and tumor samples. Tumor samples underwent exome sequencing, whole-genome sequencing, and transcriptome sequencing. RESULTS: From 2011 to 2021, 567 individuals registered and returned the clinical questionnaire. For this cohort 65% had a personal history of gastric cancer, 36% reported a family history of gastric cancer, and 14% had a germline CDH1 mutation. 89 patients with gastric cancer provided tumor samples. For the initial study, 41 tumors were sequenced using next-generation sequencing. The data was analyzed for cancer mutations, copy-number variations, gene expression, microbiome, neoantigens, immune infiltrates, and other features. We developed a searchable, web-based interface (the GCR Genome Explorer) to enable researchers' access to these datasets. CONCLUSIONS: The GCR is a unique, North American gastric cancer registry which integrates clinical and genomic annotation. IMPACT: Available for researchers through an open access, web-based explorer, the GCR Genome Explorer will accelerate collaborative gastric cancer research across the United States and world.

ALTEN: A High-Fidelity Primary Tissue-Engineering Platform to Assess Cellular Responses Ex Vivo.

To fully investigate cellular responses to stimuli and perturbations within tissues, it is essential to replicate the complex molecular interactions within the local microenvironment of cellular niches. Here, the authors introduce Alginate-based tissue engineering (ALTEN), a biomimetic tissue platform that allows ex vivo analysis of explanted tissue biopsies. This method preserves the original characteristics of the source tissue's cellular milieu, allowing multiple and diverse cell types to be maintained over an extended period of time. As a result, ALTEN enables rapid and faithful characterization of perturbations across specific cell types within a tissue. Importantly, using single-cell genomics, this approach provides integrated cellular responses at the resolution of individual cells. ALTEN is a powerful tool for the analysis of cellular responses upon exposure to cytotoxic agents and immunomodulators. Additionally, ALTEN's scalability using automated microfluidic devices for tissue encapsulation and subsequent transport, to enable centralized high-throughput analysis of samples gathered by large-scale multicenter studies, is shown.

KmerKeys: a web resource for searching indexed genome assemblies and variants.

K-mers are short DNA sequences that are used for genome sequence analysis. Applications that use k-mers include genome assembly and alignment. However, the wider bioinformatic use of these short sequences has challenges related to the massive scale of genomic sequence data. A single human genome assembly has billions of k-mers. As a result, the computational requirements for analyzing k-mer information is enormous, particularly when involving complete genome assemblies. To address these issues, we developed a new indexing data structure based on a hash table tuned for the lookup of short sequence keys. This web application, referred to as KmerKeys, provides performant, rapid query speeds for cloud computation on genome assemblies. We enable fuzzy as well as exact sequence searches of assemblies. To enable robust and speedy performance, the website implements cache-friendly hash tables, memory mapping and massive parallel processing. Our method employs a scalable and efficient data structure that can be used to jointly index and search a large collection of human genome assembly information. One can include variant databases and their associated metadata such as the gnomAD population variant catalogue. This feature enables the incorporation of future genomic information into sequencing analysis. KmerKeys is freely accessible at https://kmerkeys.dgi-stanford.org.

Single-cell characterization of CRISPR-modified transcript isoforms with nanopore sequencing.

We developed a single-cell approach to detect CRISPR-modified mRNA transcript structures. This method assesses how genetic variants at splicing sites and splicing factors contribute to alternative mRNA isoforms. We determine how alternative splicing is regulated by editing target exon-intron segments or splicing factors by CRISPR-Cas9 and their consequences on transcriptome profile. Our method combines long-read sequencing to characterize the transcript structure and short-read sequencing to match the single-cell gene expression profiles and gRNA sequence and therefore provides targeted genomic edits and transcript isoform structure detection at single-cell resolution.

Profiling diverse sequence tandem repeats in colorectal cancer reveals co-occurrence of microsatellite and chromosomal instability involving Chromosome 8.

We developed a sensitive sequencing approach that simultaneously profiles microsatellite instability, chromosomal instability, and subclonal structure in cancer. We assessed diverse repeat motifs across 225 microsatellites on colorectal carcinomas. Our study identified elevated alterations at both selected tetranucleotide and conventional mononucleotide repeats. Many colorectal carcinomas had a mix of genomic instability states that are normally considered exclusive. An MSH3 mutation may have contributed to the mixed states. Increased copy number of chromosome arm 8q was most prevalent among tumors with microsatellite instability, including a case of translocation involving 8q. Subclonal analysis identified co-occurring driver mutations previously known to be exclusive.

Integrative single-cell analysis of allele-specific copy number alterations and chromatin accessibility in cancer.

Cancer progression is driven by both somatic copy number aberrations (CNAs) and chromatin remodeling, yet little is known about the interplay between these two classes of events in shaping the clonal diversity of cancers. We present Alleloscope, a method for allele-specific copy number estimation that can be applied to single-cell DNA- and/or transposase-accessible chromatin-sequencing (scDNA-seq, ATAC-seq) data, enabling combined analysis of allele-specific copy number and chromatin accessibility. On scDNA-seq data from gastric, colorectal and breast cancer samples, with validation using matched linked-read sequencing, Alleloscope finds pervasive occurrence of highly complex, multiallelic CNAs, in which cells that carry varying allelic configurations adding to the same total copy number coevolve within a tumor. On scATAC-seq from two basal cell carcinoma samples and a gastric cancer cell line, Alleloscope detected multiallelic copy number events and copy-neutral loss-of-heterozygosity, enabling dissection of the contributions of chromosomal instability and chromatin remodeling to tumor evolution.

Single-cell analysis can define distinct evolution of tumor sites in follicular lymphoma.

Tumor heterogeneity complicates biomarker development and fosters drug resistance in solid malignancies. In lymphoma, our knowledge of site-to-site heterogeneity and its clinical implications is still limited. Here, we profiled 2 nodal, synchronously acquired tumor samples from 10 patients with follicular lymphoma (FL) using single-cell RNA, B-cell receptor (BCR) and T-cell receptor sequencing, and flow cytometry. By following the rapidly mutating tumor immunoglobulin genes, we discovered that BCR subclones were shared between the 2 tumor sites in some patients, but in many patients, the disease had evolved separately with limited tumor cell migration between the sites. Patients exhibiting divergent BCR evolution also exhibited divergent tumor gene-expression and cell-surface protein profiles. While the overall composition of the tumor microenvironment did not differ significantly between sites, we did detect a specific correlation between site-to-site tumor heterogeneity and T follicular helper (Tfh) cell abundance. We further observed enrichment of particular ligand-receptor pairs between tumor and Tfh cells, including CD40 and CD40LG, and a significant correlation between tumor CD40 expression and Tfh proliferation. Our study may explain discordant responses to systemic therapies, underscores the difficulty of capturing a patient's disease with a single biopsy, and furthers our understanding of tumor-immune networks in FL.

Characterization of the consensus mucosal microbiome of colorectal cancer.

Dysbioisis is an imbalance of an organ's microbiome and plays a role in colorectal cancer pathogenesis. Characterizing the bacteria in the microenvironment of a cancer through genome sequencing has advantages compared to culture-based profiling. However, there are notable technical and analytical challenges in characterizing universal features of tumor microbiomes. Colorectal tumors demonstrate microbiome variation among different studies and across individual patients. To address these issues, we conducted a computational study to determine a consensus microbiome for colorectal cancer, analyzing 924 tumors from eight independent RNA-Seq data sets. A standardized meta-transcriptomic analysis pipeline was established with quality control metrics. Microbiome profiles across different cohorts were compared and recurrently altered microbial shifts specific to colorectal cancer were determined. We identified cancer-specific set of 114 microbial species associated with tumors that were found among all investigated studies. Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria were among the four most abundant phyla for the colorectal cancer microbiome. Member species of Clostridia were depleted and Fusobacterium nucleatum was one of the most enriched bacterial species in tumors. Associations between the consensus species and specific immune cell types were noted. Our results are available as a web data resource for other researchers to explore (https://crc-microbiome.stanford.edu).

Joint single cell DNA-seq and RNA-seq of gastric cancer cell lines reveals rules of in vitro evolution.

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.

Whole genome analysis identifies the association of TP53 genomic deletions with lower survival in Stage III colorectal cancer.

DNA copy number aberrations (CNA) are frequently observed in colorectal cancers (CRC). There is an urgent need for CNA-based biomarkers in clinics,. n For Stage III CRC, if combined with imaging or pathologic evidence, these markers promise more precise care. We conducted this Stage III specific biomarker discovery with a cohort of 134 CRCs, and with a newly developed high-efficiency CNA profiling protocol. Specifically, we developed the profiling protocol for tumor-normal matched tissue samples based on low-coverage clinical whole-genome sequencing (WGS). We demonstrated the protocol's accuracy and robustness by a systematic benchmark with microarray, high-coverage whole-exome and -genome approaches, where the low-coverage WGS-derived CNA segments were highly accordant (PCC >0.95) with those derived from microarray, and they were substantially less variable if compared to exome-derived segments. A lasso-based model and multivariate cox regression analysis identified a chromosome 17p loss, containing the TP53 tumor suppressor gene, that was significantly associated with reduced survival (P = 0.0139, HR = 1.688, 95% CI = [1.112-2.562]), which was validated by an independent cohort of 187 Stage III CRCs. In summary, this low-coverage WGS protocol has high sensitivity, high resolution and low cost and the identified 17p-loss is an effective poor prognosis marker for Stage III patients.

Single-Cell Genomic Characterization Reveals the Cellular Reprogramming of the Gastric Tumor Microenvironment.

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. TME-exclusive 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.