Joseph Jonathan Zhao

Figure S1: Quality-control parameters and sensitivity analyses of CLDN18.2 microenvironmental and pathway changes Figure S2: Additional sensitivity survival analyses of the YCC cohort and CheckMate 649 trial Figure S3: Features of cellular neighbourhoods and correlation with progression-free survival Figure S4: Supplementary analyses to digital spatial profiling Figure S5: B/plasma cell enrichment scores across the tumor-stroma interface

Supplementary Tables 1-4 from Spatial Organization of Tumor-Infiltrating B Cells Informs Immune Checkpoint Inhibitor Response in Claudin 18.2–Expressing Gastric Cancer

Malignant ascites is caused by the peritoneal dissemination of cancer and has a poor prognosis. Here, we performed multiomic analyses on ascites samples from patients with cancer and identified RORC -expressing dendritic cells ( RORC DCs) enriched in malignant ascites. RORC DCs expressed PIGR and were transcriptionally similar to murine RORγt ⁺ DCs/Thetis cells implicated in immune tolerance. We devised a high-purity sorting strategy to enable in vitro experiments with patient-derived RORC DCs, which indicated that these cells were phenotypically stable and stimulated T cell proliferation while attenuating IFNG expression. Multiplexed immunofluorescence identified RORC DCs in primary tumors and demonstrated their enrichment in regional lymph node metastases. These results associate human RORC DCs with advanced malignancies and suggest a role for these cells in the tumor microenvironment.

Purpose: Claudin 18.2 (CLDN18.2), a tight junction protein normally expressed in the gastric epithelium and rendered accessible during malignant transformation, has emerged as a key therapeutic target. Here, we seek to characterize the immune microenvironment of CLDN18.2-expressing gastric cancer and identify immune features associated with CLDN18.2 expression status. Experimental Design: Tumor-immune features were profiled in 103 patients with HER2-neg/low advanced gastric cancer treated with first-line immune checkpoint inhibitor (ICI) using multiplex immunohistochemistry [7,423,483 cells across 2,315 regions of interest (ROI)]. The findings were validated in five published whole-transcriptome sequencing/microarray cohorts (1,521 samples), including the landmark CheckMate 649 phase III trial. Spatial immune architecture was assessed by cellular neighborhood analysis. Digital spatial profiling (DSP) was performed on 480 ROIs from a CLDN18.2-annotated tissue microarray (75 patients). Results: CLDN18.2high tumors showed reproducible enrichment of humoral pathways, with higher CD20+ B-cell density and enrichment of B-cell/plasma-cell signatures. CLDN18.2 status alone was not associated with response or survival on first-line ICI therapy; in CLDN18.2low tumors, B-cell enrichment was associated with ICI benefit, unlike CLDN18.2high tumors. Spatial analyses indicated that CLDN18.2high tumors preferentially harbored tumor-infiltrating B cells, rather than tertiary lymphoid structure/lymphoid aggregate–like neighborhoods, and this tumor-compartment localization was associated with diminished ICI benefit. DSP confirmed that CD20+ B-cell enrichment was most pronounced within tumor regions, alongside compartment-specific activation of humoral and extrafollicular response programs and interferon-associated and remodeling signatures. Conclusions: CLDN18.2high gastric cancer exhibits a humoral-enriched microenvironment with increased tumor-infiltrating B cells and spatial immune organization associated with reduced ICI benefit. Spatial and humoral-state biomarkers may inform ongoing CLDN18.2-directed combination trials.

(1) Supplementary Figure 1 shows morphologies of different ROI categories and their representative genes, pathways and similarities with paired scRNA-seq data. (2) Supplementary Figure 2 shows validation of findings from the discovery GeoMx DSP cohort in the whole-section GeoMx DSP cohort and also the TMA GeoMx DSP cohort. (3) Supplementary Figure 3 shows experiments validating that intratumor G1/G2 subregion expression is tumor-intrinsic using multiplexed IHC, Stereoseq spatial transcriptomics, and in vitro Stereoseq drug treatment profiling. (4) Supplementary Figure 4 shows expression of individual immune checkpoints, chemokines, cytokines, molecular signatures (immune exhaustion, intracellular signaling) and emerging therapeutic targets in G1 and G2 subregions in the discovery and validation cohort. (5) Supplementary Figure 5 shows experiments validating that G2 RNA-ITH subregions have higher CNV burdens than G1 subregions using signature scores, CNV calling from scRNA-seq, and validation using WES and Stereoseq data. (6) Supplementary Figure 6 shows phylogenetic analysis and trajectory analysis of scRNA-seq tumor subgroups using various methods. (7) Supplementary Figure 7 shows survival analysis and multi-variate analysis validating that internal diaspora GC is associated with worse prognosis in various cohorts. (8) Supplementary Figure 8 shows functional validation experiments for the potential G2 markers SOX9, AGR2 and TSPAN8 using CellOracle, RNAi and CRISPR knock out assays. (9) Supplementary Figure 9 shows typical pathways, markers and cell types for tumor-stroma interface ROIs.

(1) Supplementary Table 1 shows GeoMx DSP ROI and scRNA-seq characteristics of samples profiled; (2) Supplementary Table 2 shows clinical characteristics of GeoMx DSP ROI and scRNA-seq samples profiled; (3) Supplementary Table 3A shows consistency between before and after batch effect removal on top enriched pathways in various ROI categories, 3B shows consistency between before and after batch effect removal on G1 and G2 labels on Tumor ROIs; (4) Supplementary Table 4 shows Silhouette scores of using different number of clusters in Tumor ROI hierarchical clustering; (5) Supplementary Table 5 shows detailed information of GeoMx ROIs in the TMA cohort; (6) Supplementary Table 6 shows top DEGs for endothelial cell subtypes (Endo1-Endo3); (7) Supplementary Table 7 shows top DEGs for macrophage cell subtypes (TAM1-TAM6); (8) Supplementary Table 8 shows top DEGs for fibroblast cell subtypes (Fib1-Fib4).

Supplementary Table 4. Comparisons of histological features between SC1 and SC2.

Supplementary Methods 1. Supplementary Methods, DNA extraction and whole exome sequencing, Mutation calling and mutational signatures, Whole transcriptome sequencing, Spatial profiling, Methods of A5 Discovery and Validation, Other statistical methods and reproducibility.

Supplementary Figure 1. Quality control parameters for WES data utilized in the study.