Molecular signals associated with intraperitoneal chemotherapy resistance in gastric cancer with peritoneal metastasis through PIPS GC trial integrated translational research

Abstract

Purpose Recent studies have investigated intraperitoneal paclitaxel (IP PTX) combined with systemic chemotherapy for the treatment of gastric cancer peritoneal metastases (GCPM). This clinical trial integrated translational study was conducted to identify biomarkers that can predict patient responses to IP PTX plus systemic systemic S-1 plus oxaliplatin (SOX) chemotherapy. Materials and methods Patients from the PIPS-GC phase Ib/II clinical trial, enrolled at the Korea University Guro Hospital, were included in analyses. Whole exome and transcriptome sequencing were performed on formalin-fixed, paraffin-embedded gastric tumor, normal gastric, and peritoneal tumor tissues. A metastatic tumor-specific gene set was analyzed using The Cancer Genome Atlas (TCGA) gastric cancer data and publicly available single-cell RNA sequencing (scRNA-seq) datasets. Spatial transcriptomic analysis of primary and peritoneal tumors from a non-responder was performed to validate candidate biomarkers. Results Nine patients with gastric cancer were enrolled; six in the response group and three in the non-response group. Candidate genes for predicting IP PTX plus systemic SOX chemotherapy response were identified and compared with TCGA-GC and scRNA-seq datasets. Spatial transcriptomics revealed higher expression of thrombospondin type 1 domain-containing protein 4 (THSD4) in peritoneal tumors, which was associated with chemotherapy resistance via midkine and epithelial–mesenchymal transition pathways. Conclusion This PIPS-GC clinical trial identified THSD4 as a potential biomarker for predicting IP PTX plus systemic SOX response in gastric cancer peritoneal metastasis. Further research is required to elucidate the mechanism of action by which THSD4 affects GCPM and validate its clinical utility as a predictive biomarker for IP PTX response.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-19515-4.

Introduction

Gastric cancer (GC) is the fifth most common cancer globally, with over one million new cases annually, and the fourth leading cause of cancer-related deaths¹. Its high mortality rate is primarily due to distant metastasis, particularly peritoneal metastasis (PM), which occurs in 2–15% of GC cases and up to 40% among surgical cohorts². Median survival for stage IV GC is 13–16 months but decreases to 2–9 months when PM is present^2,3.

Systemic chemotherapy, using fluoropyrimidine, platinum, or taxanes^4–6, is standard for stage IV GC, but its efficacy is limited in PM due to the peritoneal plasma barrier impeding drug distribution into the abdominal cavity⁷. As an alternative, intraperitoneal (IP) chemotherapy is being investigated^8–11. IP Paclitaxel (PTX) combined with systemic chemotherapy offers survival benefits. Repeated IP administration of PTX achieves high concentrations in the abdomen with low systemic toxicity^12,13. Furthermore, IP PTX demonstrates high tissue penetration in murine models, eliminating the need for pre-chemotherapeutic cytoreduction¹⁴.

The perioperative intraperitoneal and systemic chemotherapy for gastric cancer (PIPS-GC) study group designed a regimen of IP PTX plus systemic S-1 plus oxaliplatin (SOX) for patients with PM from GC^8,9. The phase I portion of the trial established the recommended dose of IP PTX at 80 mg/m²⁸. In phase II, progression-free survival (PFS) rates at 6 and 12 months were reported as 82.6% and 69.6%, respectively⁹. The overall response rate of IP PTX with systemic chemotherapy in the PIPS-GC and other trials exceeded 70%^9,10. However, a significant proportion of patients did not experience benefit from IP PTX plus systemic chemotherapy.

Chemoresistance in GC arises from multiple complex mechanisms, including overexpression of drug efflux transporters, altered drug metabolism and detoxification, protective tumor microenvironment signaling, and disrupted apoptotic pathways¹⁵. Meanwhile, the molecular mechanisms underlying resistance to IP PTX plus systemic chemotherapy in cases of gastric cancer peritoneal metastasis (GCPM) are not fully understood. Thus, further research is required to determine whether the mechanisms of chemotherapy resistance in GC contribute to IP PTX resistance for PM, or if alternative pathways are involved.

This PIPS-GC clinical trial integrated translational research, aimed to identify potential biomarkers capable of predicting responses to IP PTX plus systemic SOX in patients with GCPM. The findings may support the development of personalized therapeutic strategies for patients with GCPM.

Materials and methods

Patients and tissue samples

The PIPS-GC phase Ib/II clinical trial was conducted between June 2020 and May 2022. Nine patients participated in Phase Ib and 24 in Phase II. This study included patients from the trial who had tissue samples available at Korea University Guro Hospital. All participants received 80 mg/m² IP PTX with systemic oxaliplatin (100 mg/m²) and S-1 (80 mg/m²). The detailed clinical trial protocol was described previously⁸. Whole exome sequencing (WES) and whole transcriptome sequencing (WTS) were performed on formalin-fixed paraffin-embedded (FFPE) tumor, normal stomach, and peritoneal tumor tissue blocks. The treatment responder group (R) was defined as having a decrease in the Peritoneal Cancer Index (PCI) score during diagnostic laparoscopy, both before and after chemotherapy, without the appearance of new lesions in radiological evaluations. Conversely, the treatment non-responder group (NR) was characterized by the development of new lesions, an increase in ascites on radiological evaluations, or an increase in the PCI score. The study protocol was approved by the Institutional Review Board of Korea University Guro Hospital (2022GR0367), and informed consent was obtained from all participants.

Whole exome sequencing (WES) analysis

The study employed FFPE samples from surgical specimens, which were confirmed as tumor samples through histological examination, with normal gastric mucosa as the control. A pathologist reviewed the histology of all the samples. Genomic DNA was extracted from macro-dissected, unstained sections of tumor-enriched areas. Sequencing libraries were prepared using the Agilent SureSelect All Exon V6 kit (Agilent Technologies, Santa Clara, CA, USA) and sequenced on the IlluminaTM NovaSeq 6000 system (Illumina, San Diego, CA, USA), achieving 2 × 100 bp read lengths with an average depth of 143× (range: 137× to 155×). The paired-end sequencing data were aligned to the human genome (hg38) using Burrows-Wheeler Aligner (BWA 0.7.17). Duplicate reads were removed, and base quality recalibration and multiple sequence alignment were processed using Picard and Genome Analysis Toolkit (GATK v4.2.2) through bcbio-nextgen (v.1.2.3)¹⁶. Somatic analysis used paired-normal sequence as a reference; Mutect2 (v2.2) detected mutations and indels annotated via SnpEff v4.3.1^17,18. Variants were filtered according to GATK best practices, selecting high-quality variants based on specific thresholds¹⁹.

The tumor’s copy number (CN) alterations were compared to paired-normal samples using CNVkit (v0.9.9)²⁰. Microsatellite status was estimated using microsatellite instability (MSI) sensor-pro (v1.2.0), with scores > 5 indicating MSI-high (h)²¹. The presence of Epstein-Barr virus (EBV) in tumor sequences was examined against the EBV reference sequence from the Genomic Data Commons to classify EBV-positive GCs²².

Whole transcriptomic sequencing (WTS) analysis

Total RNA was extracted from FFPE tissue samples using the RNeasy FFPE kit (QIAGEN, Hilden, German), following the manufacturer’s guidelines. RNA integrity and quality were evaluated using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), with a DNA integrity number value (DV) of 200 ≥ 40% for subsequent analysis. Library preparation and sequencing were conducted by Macrogen Inc. (Seoul, Korea), employing the TruSeq Stranded Total RNA Library Prep Kit (Illumina, San Diego, CA, USA) and Illumina NovaSeq6000 system (Illumina) to generate paired-end 101 bp reads. Sequence alignment to the hg38 reference genome was conducted using the spliced transcript alignment to a reference aligner²³, followed by RNA sequencing analysis via the bcbio-nextgen pipeline. Gene expression quantification was carried out using Salmon, with tximport employed for transcript-to-gene aggregation^24,25. Differential gene expression analysis between Rs and NRs to IP chemotherapy was performed using differential expression for sequence count data (DESeq2), applying a false discovery rate (FDR) threshold of < 0.05 to identify significantly differentially expressed genes (DEGs). To address dynamic changes in disease burden, correlations between gene expression levels and variations in PCI scores were examined using Pearson’s correlation coefficients; genes showing significant correlations (p < 0.05) were included in subsequent analysis. This approach facilitated the identification of genes associated with the degree of peritoneal disease response beyond the binary R/NR classification. Overrepresentation analysis of the DEGs was performed to identify enriched biological pathways utilizing enrichR software²⁶, focusing on the Molecular Signatures Database (MSigDB)²⁷. Statistical significance was assessed using hypergeometric testing, with a p-value threshold of 0.05.

The cancer cancer genome atlas (TCGA) data analysis

This study analyzed biological and genomic features of a metastatic tumor-specific gene set in a TCGA GC (STAD) dataset²⁸, encompassing genomic data from 415 patients. Data was obtained from the National Cancer Institute Genomic Data Commons using TCGAbiolinks²⁹. Unsupervised clustering with non-negative matrix factorization (NMF), using the “NMF” package, determined the optimal cluster number based on cophenetic values³⁰, and cluster genomic characteristics were compared using “maftools”³¹.

Single-cell RNA (scRNA) sequencing

For our scRNA-seq analysis, public data from primary and peritoneal GC tumors (GSE183904) and ascites samples (PRJNA992126) were analyzed³². Single-cell 3′ gene expression and feature barcode Libraries were processed using standard protocols and 10× Genomics Cell Ranger 7.2.0 software with default settings for demultiplexing, hg38 for genome alignment, and unique molecular identifier (UMI) collapsing. Gene expression matrices were generated by aligning sequencing reads to the reference genome, filtering and correcting barcodes, and quantifying UMIs using Cell Ranger.

Spatial transcriptomics analysis

For spatial transcriptomics analysis, the Visium CytAssist Spatial Gene Expression Assay (10c Genomics, Pleasanton, CA, USA) for FFPE tissue samples was utilized. This method enabled mRNA expression profiling while preserving spatial information within the tissue sections. Libraries were prepared using the Visium Spatial Gene Expression for FFPE Library v2 Human kit (10× Genomics), following the manufacturer’s protocols. The Libraries comprised Read 1, which included a 16-bp spatial barcode and a 12-bp UMI, and Read 2, featuring a 50-bp ligated probe.

Sequencing was performed on the Illumina NextSeq2000 platform (Illumina, San Diego, CA, USA), using paired-end sequencing to capture spatial and gene expression data. After sequencing, RNA reads were aligned to the hg38 human genome build. Sequencing data and high-resolution histological images (TIFF format) were processed using 10× Genomics Space Ranger 2.1.0 software to generate gene expression matrices corresponding to specific tissue spots. This method created a comprehensive spatial gene expression map of the tissue samples, facilitating analysis of gene expression distribution within the FFPE samples and enabling assessment of the tumor microenvironment’s molecular characteristics while maintaining spatial information.

Data processing and quality control

We processed the gene expression data using Seurat (version 5.0.0). As part of quality control, we filtered out genes expressed in fewer than 10 cells and excluded cells with fewer than 200 detected genes, fewer than 2,500 total counts, or mitochondrial content exceeding 5%. Gene expression was normalized to 10,000 counts per cell. Batch effects were corrected using Seurat and sctransform^33,34. Doublets (~ 1% multiplet rate) were identified and removed using DoubletFinder 2.0³⁵.

Dimensional reduction and clustering

The dimensionality reduction approach focused on 1000 highly variable genes selected based on expression variance. Principal component analysis was applied, and the top 50 components were retained for subsequent analysis. Clustering was performed using graph-based methods, specifically the Louvain algorithm. For data visualization, t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation (UMAP) were used to generate 2D representations. Further sub-cluster analysis utilized the Seurat package with the “FindAllMarkers” function to extract key markers for each cluster, which were then annotated using immune cell-specific methodologies for accurate identification and characterization of sub-clusters.

Cell type annotation

We identified marker genes for each cluster using the Wilcoxon Rank-Sum Test through Seurat’s “FindMarkers” function. Significant differential expression was defined as a log2 fold change > 1.2 and an FDR < 0.01. Cell types were assigned based on differential expression signatures derived from log-likelihood ratio tests and refined using SingleR, sctype, and scATOMIC to adjust for robust transcriptomic signals from immune and cancer cells^36–38. To improve accuracy, a single-cell transcriptome classification algorithm was applied³⁹, which further annotated cells by predefined marker genes and captured biological diversity within the samples.

Cellular communication analysis

CellChatDB version 2, a computational tool for scRNA-seq data with over 1,000 protein and non-protein interactions⁴⁰, was used to analyze intercellular communication by identifying ligand–receptor interactions and calculating communication probability scores. Only statistically significant major signaling pathways were considered, using the triMean method for robust estimates by integrating multiple measures of central tendency and variability. Signals were filtered to require at least 20 cells in each communicating group to ensure reliability. This threshold ensured that the communication signals analyzed were representative and reliable. Network analysis, visualized using circular plots showing cell group sizes and interaction strengths, enabled clear interpretation of complex cell–cell communication within the samples.

Over-representation analysis

enrichR was used to perform over-representation analysis (ORA) and identify enriched biological pathways and functional categories associated with significant gene sets²⁶, with a specific focus on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways⁴¹. Enrichment scores were calculated using Fisher’s exact test to determine the significance of the overlap between the input gene list and each gene set in the selected libraries. Adjusted p-values were calculated using the Benjamini–Hochberg method. The log of the nominal p-value from Fisher’s exact test was multiplied by the z-score of the deviation from the expected rank. Pathways and functional categories with an adjusted p-value < 0.05 were considered significantly enriched, and results were ranked by significance.

Statistical analysis

The Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria for measurable tumors identified during radiological assessments were applied to evaluate the overall response. For unmeasurable PM, direct PCI scores were compared before and after chemotherapy through diagnostic laparoscopy. Tumor responses were further assessed by constructing waterfall plots that showed percentage changes in PCI scores before and after chemotherapy. All statistical analyses and visualizations were performed using R software (version 4.3.0, R Foundation for Statistical Computing, Vienna, Austria), with statistical significance set at p < 0.05.

Results

Patients and samples

This study analyzed a subset of patients from the PIPS-GC phase Ib/II trials, including six in the R group and three in the NR group. Two NR cases had diagnostic laparoscopy for PCI evaluation, while one was classified as a non-responder based on radiological tumor progression, without undergoing diagnostic laparoscopy (Fig. 1a). No significant trends were observed between the two groups in terms of age, sex, or initial disease presentation (Table 1, Supplementary Table S1).

Fig. 1.

IP chemotherapy response and genomic alterations in patient tumors. (a) Waterfall plot showing changes in PCI (Peritoneal Cancer Index) scores in response to IP chemotherapy. (b) Oncoplot depicting mutations and copy number alterations in the analyzed tumors. IP: intraperitoneal; PCI: Peritoneal Cancer Index.

Table 1.

Patient baseline characteristics.

Patient ID	Age	Sex	Disease presentation	Histology	Lauren classification	Pre CTx. PCI score	Post CTx. PCI score	Combined metastasis	Response
PIPS_R1	52	Male	Metachronous	Adenocarcinoma, Poorly differentiated	NA	37	10	Hematogenous	Responder
PIPS_R2	42	Female	Synchronous	Poorly cohesive carcinoma	Diffuse	21	0	Distant Lymph node	Responder
PIPS_R3	57	Male	Synchronous	Adenocarcinoma, Poorly differentiated	Diffuse	34	7	None	Responder
PIPS_R4	63	Male	Synchronous	NA	NA	19	6	Distant Lymph node, Hematogenous	Responder
PIPS_R5	40	Female	Synchronous	Adenocarcinoma, Poorly differentiated	NA	14	1	None	Responder
PIPS_R6	69	Female	Synchronous	Adenocarcinoma, Poorly differentiated	NA	4	0	None	Responder
PIPS_NR1	57	Female	Synchronous	Adenocarcinoma, Poorly differentiated	Mixed	2	NA	Distant Lymph node	Non-Responder
PIPS_NR2	58	Male	Synchronous	Poorly cohesive carcinoma	Mixed	11	14	Distant Lymph node	Non-Responder
PIPS_NR3	34	Female	Synchronous	Adenocarcinoma, Poorly differentiated	Diffuse	2	10	Distant Lymph node	Non-Responder

NA, not available data.

The median PCI score before IP chemotherapy was 6.5 (range: 2–11) in the NR group and 14 (range: 4–37) in the R group. Post-treatment, the median PCI score increased to 12 (range: 10–14) in the NR group and decreased to 6.5 (range: 0–10) in the R group, indicating a differential response to therapy. All patients in the NR group had distant lymph node metastases in addition to intra-abdominal metastases. In contrast, in the R group, three patients had metastases other than PM: one with distant lymph node metastasis, one with hematogenous metastasis, and one with both types.

A total of 18 samples were collected from the nine patients (R: n = 6, NR: n = 3; Supplementary Table S2). After quality control, WTS was performed on 13 tumor samples, including four paired primary gastric tumors and corresponding PM. WES was conducted on these 13 samples—eight tumor and five normal tissues—to identify somatic mutations via tumor–normal comparisons.

Molecular subtypes and mutations of tumors

Analysis of the tumor samples revealed no EBV-positive subtypes. However, one case of MSI-H was identified in an NR patient (NR1 patient, Supplementary Table S2). Most tumors did not exhibit CN gains or losses, indicating a genomic stable (GS) subtype of GC, except for the primary tumor of the R4 case (Fig. 1b). No recurrent genes or variants were found among known cancer driver genes (Supplementary Table S3), consistent with previous findings reporting that GCPM, a typical diffuse type, is highly associated with GS subtypes and rarely contains variants or cancer driver genes²⁸. Furthermore, no pathogenic variants were observed in genes associated with hereditary GC, such as cadherin 1 or mismatch repair-associated genes.

Differential gene expression analysis

To identify signals associated with IP chemotherapy responses, gene expression was compared between the two groups. A total of 682 significantly DEGs were identified using DESeq2. Further analysis of expression levels based on PCI scores before and after IP chemotherapy revealed 400 genes of interest: 333 upregulated and 67 downregulated in the NR group compared to the R group.

Over-representation analysis revealed significant upregulation of the Hedgehog signaling pathway in the NR group. Conversely, oxidative phosphorylation, adipogenesis, and fatty acid metabolism pathways were downregulated (Fig. 2a and b, Supplementary Tables S4 and S5).

Fig. 2.

Over-representation analysis of IP chemotherapy response and associated gene expression patterns in TCGA cohort. (a) Over-representation analysis of highly expressed genes in the IP chemotherapy non-responsive (NR) group. (b) Over-representation analysis of highly expressed genes in the IP chemotherapy-responsive (R) group. (c) Expression of differentially expressed genes (DEGs) associated with IP chemotherapy response (R vs. NR group) in TCGA cohort. Non-negative matrix factorization (NMF) analysis identified two distinct clusters: NMF_NR1 and NMF_NR2. The NMF_NR2 group shows higher expression of NR-associated genes and is predominantly composed of the genomically stable (GS) TCGA subtype. (d) Relationship between NMF-derived groups and molecular subtypes in TCGA cohort. Most cases with typically favorable prognosis (MSI and EBV subtypes) are clustered in the NMF_NR1 group. (e) Kaplan–Meier survival analysis comparing NMF_NR1 and NMF_NR2 groups. The NMF_NR2 group, characterized by high expression of genes associated with IP chemotherapy resistance, shows significantly worse prognosis (log-rank p = 0.01). IP: intraperitoneal; NR: non-responsive; R: responsive; DEG: differentially expressed gene; TCGA: The Cancer Genome Atlas; NMF: non-negative matrix factorization; GS: genomically stable; MSI: microsatellite instability; EBV: Epstein-Barr virus.

To investigate the biological implications of these genes in GC, their expression in the GC TCGA cohort was examined (n = 415). Expression data were available for 221 of the 400 genes. Using the NMF method, GCs were stratified into two major subgroups: NMF_NR1 and NMF_NR2 (Fig. 2c). On analyzing the association with TCGA molecular subtypes, most EBV and MSI-H types belonged to the NMF_NR1 subgroup, while 76% (38 out of 50) of the GS subtype fell into the NMF_NR2 category (Fig. 2d). As expected, patients classified in the NMF_NR2 subgroup, characterized by higher expression of these genes, exhibited significantly poorer prognosis compared to those in the NMF_NR1 subgroup (log-rank p = 0.01, Fig. 2e). Based on these results, potential biomarkers for predicting IP chemotherapy resistance were identified by selecting significant DEGs from the NMF_NR2 subgroup (poor prognosis) compared with the NMF_NR1 subgroups (t-test, FDR < 0.05) with an absolute log2 expression difference > 1. Finally, 45 candidate genes were identified (Supplementary Table S6).

Origin of IP chemotherapy resistance genes at the single-cell level

To trace the origin of signals from the 45 candidate genes associated with IP chemotherapy resistance, scRNA-seq data from primary tumors (n = 26), PM tumors (n = 3, GSE183904)⁴², and malignant ascites of GC (n = 4; PRJNA992126) were evaluated⁴³. A total of 100,001 cells were identified in primary tumors, 5,147 cells in PM tumors, and 30,252 cells in malignant ascites (Fig. 3a–c).

Fig. 3.

Identification of IP chemotherapy resistance genes at the single-cell level. (a) Top: UMAP plot of single-cell RNA-seq data from primary gastric tumors (n = 26, GSE183904). Bottom: Expression of 45 IP chemotherapy resistance-associated genes in primary tumors. (b) Top: UMAP plot of single-cell RNA-seq data from peritoneal metastatic tumors (n = 3, GSE183904). Bottom: Heatmap displaying the expression of the 45 genes in peritoneal metastatic tumors. (c) Top: UMAP plot of single-cell RNA-seq data from malignant ascites of gastric cancer patients (n = 4, PRJNA992126). Bottom: Expression of the 45 genes in malignant ascites. The heatmaps demonstrate that most IP chemotherapy resistance genes are expressed in cancer-associated fibroblasts (CAFs). Notably, THSD4 (Thrombospondin Type 1 Domain Containing 4) shows consistent expression in CAFs across primary tumors, peritoneal metastatic tumors, and malignant ascites. IP: intraperitoneal; UMAP: Uniform Manifold Approximation and Projection; CAF: cancer-associated fibroblast; THSD4: Thrombospondin Type 1 Domain Containing 4.

The candidate genes were expressed in cancer cells and the tumor microenvironment, particularly in endothelial cells and cancer-associated fibroblasts (CAFs) of primary tumors. Given their association with IP chemotherapy resistance, their expression was assessed in PM tumors and malignant ascites of GC. Notably, more genes were identified in CAFs within PM tumors and ascites. Among these, thrombospondin type 1 domain containing 4 (THSD4) was consistently expressed in CAFs across primary tumors, PM tumors, and malignant ascites, suggesting its potential association with IP chemotherapy resistance in patients with GCPM.

THSD4 expression in IP chemotherapy-resistant primary and PM tumors

Spatial transcriptomics were performed on primary tumor and chemotherapy-naive PM tissue samples from the patient with the poorest IP chemotherapy response (PIPS_NR3). This facilitated the evaluation of THSD4 expression in relation to histological locations. THSD4 expression was significantly higher in peritoneal tumors than in the corresponding primary tumor (Fig. 4a, p < 0.001).

Fig. 4.

Spatial transcriptomic analysis of IP chemotherapy-resistant primary and paired peritoneal metastatic tumors. Visium spatial transcriptomics performed on primary and peritoneal metastatic tumors (chemotherapy naïve) from patient PIPS_NR3, with the poorest response to IP chemotherapy. (a) Comparison of THSD4 gene expression between peritoneal metastatic tumors and primary tumors. (b) THSD4 expression at the cellular level: desmoplastic CAFs (dCAFs) and myofibroblastic CAFs (myoCAFs, indicated by arrows). (c) Spatial pattern of THSD4 expression in the primary tumor. (d) Spatial pattern of THSD4 expression in peritoneal metastatic tumor. IP: intraperitoneal; CAF: cancer-associated fibroblast; dCAF: desmoplastic CAF; myoCAF: myofibroblastic CAF; THSD4: Thrombospondin Type 1 Domain Containing 4.

THSD4 was predominantly expressed in spots associated with the microenvironment of peritoneal tumors, particularly in myofibroblast cancer-associated fibroblasts (myoCAFs) and desmoplastic CAFs (dCAFs; Fig. 4b). In comparing THSD4 expression patterns between primary and peritoneal tumors, its pattern was similar to myoCAFs in the primary tumor and dCAFs in the peritoneal tumor (Fig. 4c and d), indicating different roles for THSD4 in primary versus peritoneal tumors.

Additionally, cell-to-cell interactions of myoCAFs in the primary tumor and dCAFs in the peritoneal tumor, where THSD4 was mainly expressed, were investigated. MyoCAFs in the primary tumor predominantly interacted with tumor spots, inflammatory CAFs, and dCAFs. In contrast, dCAFs in the peritoneal tumors interacted with spots of myoCAFs and mesothelial cells (Fig. 5a and b).

Fig. 5.

Cell-to-cell interaction and pathway analysis of IP chemotherapy-resistant primary and peritoneal tumors (PIPS_NR3 case). (a) Analysis of myoCAFs in IP chemotherapy-resistant primary tumors: Left: Cell-to-cell interaction analysis among myoCAFs, tumor cells, inflammatory CAFs, and dCAFs. Middle: Enrichment of fibroblast growth factor (FGF) pathways in myoCAFs of primary tumors. Right: Over-representation analysis revealing enrichment of ECM-modulating pathways in myoCAFs of primary tumors. (b) Analysis of dCAFs in IP chemotherapy resistant peritoneal tumors: Left: Cell-to-cell interaction analysis between dCAFs, myoCAFs, and mesothelial cells. Middle: Enrichment of midkine (MK) pathways in dCAFs of peritoneal tumors. Right: Enrichment of TGF-beta and Hedgehog signaling pathways in peritoneal tumors. IP: intraperitoneal; CAFs: cancer-associated fibroblasts; myoCAFs: myofibroblastic CAFs; dCAFs: detached CAFs; ECM: extracellular matrix; FGF: fibroblast growth factor; MK: midkine; TGF: transforming growth factor.

Signaling pathway analysis indicated that fibroblast growth factor (FGF) pathways were enriched in primary tumor myoCAFs, while midkine (MK) pathways were enriched in peritoneal tumor dCAFs. Over-representation analysis revealed that extracellular matrix (ECM) modulating pathways were enriched in primary tumor myoCAFs, while epithelial–mesenchymal transition (EMT)-associated pathways were enriched in peritoneal tumor dCAFs. These findings suggest that THSD4 may have distinct functions in primary versus peritoneal tumors, and could contribute to IP chemotherapy resistance through activation of MK and EMT-associated signals.

Discussion

This study is the first comprehensive genomic investigation into resistance mechanisms associated with IP PTX plus systemic SOX chemotherapy in patients with GCPM. THSD4 upregulation was associated with resistance to IP PTX combined with systemic chemotherapy. THSD4 is hypothesized to play a significant role in ECM organization and elastic fiber assembly, with its genetic variation recognized as being associated with thoracic aortic aneurysms⁴⁴. Although its precise oncological role has not been thoroughly examined, ECM remodeling reportedly contributes to chemoresistance across various cancers⁴⁵. In GC, THSD4 overexpression is Linked to tumor progression and resistance to chemotherapeutic agents, such as 5-FU, oxaliplatin, and docetaxel⁴⁶. Meanwhile, in the current study, THSD4 was predominantly expressed in CAFs, with particularly high expression in dCAFs within PM tumors. The presence of THSD4-expressing dCAFs surrounding PM tumors may create a significant barrier to drug efficacy, even with IP chemotherapy. Thus, it is hypothesized that these dCAFs form a protective niche, limiting drug penetration and enhancing resistance mechanisms.

A novel association was also detected between high THSD4 expression in dCAFs from chemoresistant PM and increased MK and EMT-related signaling, suggesting a complex mechanism of chemoresistance in GC. MK, a heparin-binding growth factor, has been implicated in various aspects of tumor progression, including EMT induction and chemoresistance development^47,48. The results of the current study underscore MK’s involvement in chemoresistance in GC cells⁴⁹, highlighting its potential involvement in dCAF-mediated processes. MK drives drug resistance through drug efflux induction and immune suppression^50,51. Additionally, higher dCAF counts are linked to poorer prognosis and greater chemotherapeutic resistance in various cancers, including ovarian cancer⁵². Furthermore, CAF–tumor cell interactions, potentially mediated by MK and other factors, may drive EMT that enhance chemoresistance⁵³. THSD4, as a protein involved in elastic fiber assembly and ECM organization, may aid in remodeling the tumor microenvironment to create a niche that supports chemoresistant tumor cells. Thus, targeting THSD4, MK, or key EMT pathways in dCAFs may help overcome chemoresistance in PM.

GCPM poses a significant therapeutic challenge, primarily due to the peritoneal plasma barrier restricting drug delivery⁷. Despite advancements in systemic chemotherapy, many cases remain resistant to treatment⁵⁴, with conventional drugs proving minimally effective and genomically stable subtypes in PM limiting targeted therapies for specific genomic amplifications²⁸. Immunotherapies have also demonstrated reduced efficacy in this patient population^55,56. The current study identifies THSD4 as a potential negative biomarker or surrogate for chemoresistance in GCPM. Furthermore, its predominant localization in CAFs, particularly dCAFs, within PM tumors, suggests a critical role for these stromal cells in mediating chemoresistance. These findings highlight the importance of targeting THSD4 or dCAFs in combination with IP chemotherapy. Approaches may include direct THSD4 inhibition, targeting dCAF-specific pathways, or modulating the tumor microenvironment to reduce chemoresistance^52,57. Although treating GCPM remains challenging, THSD4, as a potential biomarker and therapeutic target in dCAFs, offers a promising avenue for personalized treatment strategies in patients with this refractory form of GC.

This study is the first to integrate biomarker research with a clinical trial for peritoneal chemotherapy in GC; however, certain limitations must be acknowledged. First, the sample size was relatively small, potentially limiting statistical power and the generalizability of the results, largely due to the rarity of patients with GCPM who undergo surgery and intraperitoneal chemotherapy. Despite this, the translational analysis of WES/WTS data from a prospective clinical trial (PIPS-GC) constitutes a significant resource for this rare disease subset. The absence of in vitro and in vivo functional validation for THSD4 was mitigated through multi-platform data triangulation, including TCGA-STAD, single-cell, and spatial transcriptomics datasets. Nevertheless, other relevant biomarkers may not have reached significance due to limited statistical power (type II error), underscoring the need for larger cohorts. Second, the number of paired primary-PM cases was limited, constraining the comprehensive analysis of changes in THSD4 expression between primary tumors and their corresponding PM. A larger set of paired samples would facilitate a more detailed examination of THSD4 expression during metastasis. Finally, the study design did not include direct comparisons of pre- and post-chemotherapy samples, which limited the ability to observe dynamic THSD4 expression changes in response to chemotherapy. These factors warrant caution when interpreting the study’s results. Future studies, including the PIPS-GC Phase III clinical trial, are required to validate and expand these results, informing more effective management strategies for GCPM.

In conclusion, this study found that THSD4 may serve as a biomarker for predicting resistance to IP PTX plus systemic SOX chemotherapy in GCPM. The predominant expression of THSD4 in dCAFs within PM tumors suggests a complex interplay between the tumor microenvironment and treatment response. Future research is needed to explore the specific mechanisms through which THSD4 and dCAFs contribute to chemotherapy resistance and investigate potential strategies to target this resistance pathway.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

GC

Gastric cancer

PM

Peritoneal metastasis

IP

Intraperitoneal

PTX

Paclitaxel

WES

Whole exome sequencing

WTS

Whole transcriptome sequencing

FFPE

Formalin-fixed paraffin-embedded

R group

Responder group

PCI

Peritoneal cancer index

NR group

Non-responder group

DNA

Deoxyribonucleic acid

BWA

Burrows-wheeler aligner

GATK

Genome analysis toolkit

CN

Copy number

EBV

Epstein-barr virus

RNA

Ribonucleic acid

FDR

False discovery rate

TCGA

The cancer genome atlas

NMF

Non-negative matrix factorization

sc

Single-cell

seq

Sequencing

UMI

Unique molecular identifier

t-SNE

t-distributed stochastic neighbor embedding

UMAP

Uniform manifold approximation

ORA

Over-representation analysis

KEGG

Kyoto encyclopedia of genes and genomes

RECIST

Response evaluation criteria in solid tumors

MSI-H

Microsatellite instability-high

GS

Genomic stable

MMR

Mismatch repair

CAF

Cancer-associated fibroblast

THSD4

Thrombospondin type 1 domain containing 4

myoCAFs

Myofibroblast cancer-associated fibroblasts

dCAFs

desmoplastic CAFs

FGF

Fibroblast growth factor

MK

Midkine

ECM

Extracellular matrix

EMT

Epithelial-mesenchymal transition

Author contributions

Conceptualization: Seo WJ, Kim KT, Jang Y, Choi YY, Kim JMethodology: Seo WJ, Kim KT, Choi YY, Kim JInvestigation: Seo WJ, Kim KT, Jang Y, Choi YY, Kim J Visualization: Seo WJ, Kim KT, Choi Y Funding acquisition: Seo WJ, Choi Y Project administration: Choi YY, Kim J Supervision: Jang Y, Choi YY, Kim JWriting – original draft: Seo WJ, Kim KT, Choi Y Writing – review & editing: Seo WJ, Kim KT, Jang Y, Choi YY, Kim J.

Funding

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (2022R1A2C2092005), by the Soonchunhyang University Research Fund and Korea University Guro Hospital grant (KOREA RESEARCH-DRIVEN HOSPITAL) funded by the Korea University College of Medicine (No. O2208101).

Data availability

Raw data for the whole exome sequencing (WES), whole transcriptome sequencing (WTS), and spatial transcriptomic data (visium) reported in this manuscript have been deposited into a sequence read archive (SRA) database with BioProject accession number PRJNA1146495 (WES, n = 8; WTS, n = 13; Visium, n = 2; https://dataview.ncbi.nlm.nih.gov/object/PRJNA1146495?reviewer=c52iigf0ba637s453f88h69gll). The supported data which analyzed in this manuscript were publicly available from the GEO (www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE183904 (scRNA-seq of GC), and SRA under accession numbers PRJNA992126 (scRNA-seq of malignant ascites of GC, https://dataview.ncbi.nlm.nih.gov/object/PRJNA992126). Additionally, the code utilized for this analysis is not proprietary. All software and algorithms used in this study are freely accessible and are detailed in the Methods section. For more detailed information, please contact the corresponding author. (YYC, JK)

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of Korea University Guro Hospital (2022GR0367), and informed consent was obtained from all participants.