Response In Gastric Cancer Research Articles

Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer

Background Cancer is characterized by its ability to resist cell death, and emerging evidence suggests a potential correlation between non-apoptotic regulated cell death (RCD), tumor progression, and therapy response. However, the prognostic significance of non-apoptotic RCD-related genes (NRGs) and their relationships with immune response in gastric cancer (GC) remain unclear. Methods In this study, RNA-seq gene expression and clinical information of GC patients were acquired from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Cox and LASSO regression analyses were used to construct the NRG signature. Moreover, we developed a deep learning model based on ResNet50 to predict the NRG signature from digital pathology slides. The expression of signature hub genes was validated using real-time quantitative PCR and single-cell RNA sequencing data. Results We identified 13 NRGs as signature genes for predicting the prognosis of patients with GC. The high-risk group, characterized by higher NRG scores, demonstrated a shorter overall survival rate, increased immunosuppressive cell infiltration, and immune dysfunction. Moreover, associations were observed between the NRG signature and chemotherapeutic drug responsiveness, as well as immunotherapy effectiveness in GC patients. Furthermore, the deep learning model effectively stratified GC patients based on the NRG signature by leveraging morphological variances, showing promising results for the classification of GC patients. Validation experiments demonstrated that the expression level of SERPINE1 was significantly upregulated in GC, while the expression levels of GPX3 and APOD were significantly downregulated. Conclusion The NRG signature and its deep learning model have significant clinical implications, highlighting the importance of individualized treatment strategies based on GC subtyping. These findings provide valuable insights for guiding clinical decision-making and treatment approaches for GC.

Unveiling the Power of Mitochondrial Fission and Fusion: A Five-Gene Signature for Personalized Prognosis in Gastric Cancer.

Mitochondrial fission and fusion play important roles in tumorigenesis, progression and therapy. Dysregulation of these processes may lead to tumor progression, and regulation of these processes may provide novel strategies for cancer therapy. The involvement of genes related to mitochondrial fission and fusion (MD) in gastric cancer (GC) remains poorly understood. The aim of this study was to establish an MD gene signature for GC patients and to investigate its association with prognosis, tumor microenvironment and treatment response in GC. We use the TCGA-GC database as the cohort, focusing specifically on genes associated with MD. We conducted identification and consistency clustering analysis of differentially expressed genes in MD, conducted MD gene mutation and copy number variation analysis, as well as correlation and functional enrichment analysis between MD gene cluster classification and immune infiltration. TCGA-GC and GSE15459 were used to construct training and validation cohorts for the model. We used various statistical methods, including Cox and Lasso regression, to develop the model. We validated the model using bulk transcriptome and single- cell transcriptome datasets (GSE13861, GSE26901, GSE66229, and GSE13450). We used GSEA enrichment, CIBERSORT algorithm, ESTIMATE, and TIDE to gain insight into the annotation of MD signature and the characterization of the tumor microenvironment. OncoPredict was used to analyze the relationship between the PRG signature and the drug sensitivity. We validated the expression of several key genes in MD signature on GC cell lines using quantitative real-time PCR (qRT-PCR). These MDs-related subtypes exhibited different prognosis and immune filtration patterns. A five-gene signature, comprising AGT, HCFC1, KIFC3, NOX4, and RIN1, was developed. There was a clear distinction in overall survival between low- and high-risk patients. The analyses showed further confirmation of the independent prognostic value of the gene signature. There was a notable correlation between the MD signature, immune infiltration and drug susceptibility. The expression levels of AGT, HCFC1, KIFC3, NOX4 and RIN1 mRNA were all increased in these GC cells. The MD signature has the capacity to significantly contribute to the prediction of personalized outcomes and the advancement of novel therapeutic strategies tailored for GC patients.

ZFP1 is a biomarker related to poor prognosis and immunity in gastric cancer

We aimed to determine the prognostic significance of ZFP1 in gastric cancer (GC), its role in the immune microenvironment, and its potential as a therapeutic target using data from The Cancer Genome Atlas (TCGA) database. ZFP1 overexpression was closely associated with tumour T stage and histological grade. Patients with GC and high ZFP1 expression had poor outcomes. Lower ZFP1 expression was associated with longer symptom-free intervals and disease-specific survival. Subgroup analyses of T3 and T4, N0, N1, and M0 patients showed that overall survival (OS), disease-specific survival, and progression-free interval (PFI) were worse in those with high ZFP1 expression. ZFP1 expression in GC was moderately to strongly positively correlated with the infiltration levels of effector central memory T cells and T helper cells and negatively correlated with Th17 cells and NK CD56bright cells. The lncRNA-miRNA-ZFP1 axis was predicted using a public database. CCK8, colony formation, and wound healing assays were conducted to investigate whether ZFP1 promoted the proliferation and migration of GC cells. Our study suggests that ZFP1 plays a key role in the prognosis, immune response, and progression of GC and is a significant factor in the diagnosis and treatment of this disease.

Neutrophil extracellular trap genes predict immunotherapy response in gastric cancer

BackgroundNeutrophil extracellular trap (NET) is associated with host response, tumorigenesis, and immune dysfunction. However, the link between NET and the tumor microenvironment (TME) of gastric cancer (GC) remains unclear. Our study aims to characterize the expression patterns of NET-related genes and their relationships with clinicopathological characteristics, prognosis, TME features, and immunotherapy efficacy in GC cohorts. MethodsTranscriptomic and single-cell RNA sequencing profiles of GC with annotated clinicopathological data were obtained from TCGA-STAD (n = 415), GSE62254 (n = 300), GSE15459 (n = 192), and GSE183904 (n = 26). The consensus cluster algorithm was used to classify tumor samples into different NET-related clusters. A NET-related signature was constructed using LASSO regression and verified in four immunotherapy cohorts. ROC and Kaplan-Meier analyses were conducted to evaluate the predictive and prognostic value of the model for immunotherapy efficacy. ResultsThis study identified two NET-related clusters with distinct clinicopathological features, prognosis, and TME landscapes. The high NET-related cluster, characterized by increased NET-related gene expression, exhibited more aggressive behavior and a worse prognosis (HR = 1.63, P = 0.004) than the low NET-related cluster. DEGs were primarily involved in the chemokine/cytokine-associated pathways. Moreover, the high NET-related cluster had significantly higher levels of TME scores, immune infiltration, and immune effectors (all P < 0.001). The NET-related signature displayed a high predictive accuracy for immunotherapy response (AUC = 0.939, P < 0.001). Furthermore, patients with high NET-related scores consistently harbored a more favorable prognosis in different immunotherapy cohorts (all P < 0.05). ConclusionsThis study identified the NET-related signature as a robust model for predicting immunotherapy response in GC, which can help clinicians make appropriate immunotherapy decisions.

Identification of an autophagy- and macropinocytosis-related prognostic signature for the prediction of prognosis and therapeutic response in gastric cancer.

Traditional liquid biopsy markers show a low rate of positivity and accurate in gastric cancer. With the rapid advancement of sequencing technology, scientists have identified promising research avenues in this field. Autophagy and macropinocytosis utilize diverse pathways and mechanisms to supply resources and fuel for tumor growth. Nonetheless, their potential interplay introduces an untapped avenue for the discovery of novel tumor biomarkers. To develop an innovative prognostic signature based on autophagy- and micropinocytosis-related genes, with the aim to predict the outcome and therapeutic response of gastric cancer patients. Additionally, to validate the prognostic impact of this signature, and elucidate the role of representative molecules in gastric cancer. To construct and validate a prognostic signature for gastric cancer, bioinformatics methods such as COX regression, LASSO regression, survival analysis, ROC curve, and nomogram were utilized based on the sequencing and clinical data of gastric cancer patients retrieved from the TCGA and GEO databases. GSEA functional enrichment analyses were employed to predict the biological functions. Meanwhile, qRT-PCR and Western blot experiments were utilized to quantify the mRNA and protein expression levels. Furthermore, the EdU assay and colony formation assay were utilized to examine the cell proliferation ability while the Transwell assays were conducted to assess the migration and invasion abilities of gastric cancer cells. Through consistency clustering and univariate COX analyses, potential prognostic genes involved in both autophagy and macropinocytosis were identified. Based on these genes, a 9-gene signature was constructed, which demonstrated high accuracy in predicting gastric cancer patients' survival period, immunotherapeutic response, and chemotherapy drug tolerance. Furthermore, qRT-PCR analyses of gastric cancer tissue samples showed that the representative genes of this signature were aberrantly overexpressed in gastric cancer, with MATN3, as the most notable molecule, exhibiting significant carcinogenic effects on cancer cells by actively regulating their proliferation, migration, and invasion abilities. Our newly created prognostic signature possesses significant potential as a biomarker for gastric cancer, while MATN3 is identified as an oncogenic factor in gastric cancer. This brings to light new perspectives, which can contribute to enhancing the diagnosis and treatment of gastric cancer.

Immune infiltration and prognosis in gastric cancer: role of NAD+ metabolism-related markers.

This study endeavored to develop a nicotinamide adenine dinucleotide (NAD+) metabolism-related biomarkers in gastric cancer (GC), which could provide a theoretical foundation for prognosis and therapy of GC patients. In this study, differentially expressed genes (DEGs1) between GC and paraneoplastic tissues were overlapped with NAD+ metabolism-related genes (NMRGs) to identify differentially expressed NMRGs (DE-NMRGs). Then, GC patients were divided into high and low score groups by gene set variation analysis (GSVA) algorithm for differential expression analysis to obtain DEGs2, which was overlapped with DEGs1 for identification of intersection genes. These genes were further analyzed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses to obtain prognostic genes for constructing a risk model. Enrichment and immune infiltration analyses further investigated investigate the different risk groups, and qRT-PCR validated the prognostic genes. Initially, we identified DE-NMRGs involved in NAD biosynthesis, with seven (DNAJB13, CST2, THPO, CIDEA, ONECUT1, UPK1B and SNCG) showing prognostic significance in GC. Subsequent, a prognostic model was constructed in which the risk score, derived from the expression profiles of these genes, along with gender, emerged as robust independent predictors of patient outcomes in GC. Enrichment analysis linked high-risk patients to synaptic membrane pathways and low-risk to the CMG complex pathway. Tumor immune infiltration analysis revealed correlations between risk scores and immune cell abundance, suggesting a relationship between NAD+ metabolism and immune response in GC. The prognostic significance of our identified genes was validated by qRT-PCR, which confirmed their upregulated expression in GC tissue samples. In this study, seven NAD+ metabolism-related markers were established, which is of great significance for the development of prognostic molecular biomarkers and clinical prognosis prediction for gastric cancer patients.

KPNA2 promotes the progression of gastric cancer by regulating the alternative splicing of related genes

RNA-binding proteins (RBPs) play critical roles in genome regulation. In this study, we explored the latent function of KPNA2, which is an essential member of the RBP family, in the regulation of alternative splicing (AS) in gastric cancer (GC). We analyzed the role of KPNA2 in regulating differential expression and AS via RNA sequencing (RNA-seq) and improved RNA immunoprecipitation sequencing (iRIP-seq). Clinical specimens were used to analyze the associations between KPNA2 expression and clinicopathological characteristics. CCK8 assays, transwell assays and wound healing assays were performed to explore the effect of KPNA2/WDR62 on GC cell progression. KPNA2 was shown to be highly expressed in GC cells and tissues and associated with lymph node metastases. KPNA2 promoted the proliferation, migration and invasion of GC cells and primarily regulated exon skipping, alternative 3's splice sites (A3SSs), alternative 5' splice sites (A5SSs), and cassette exons. We further revealed that KPNA2 participated in biological processes related to cell proliferation, and the immune response in GC via the regulation of transcription. In addition, KPNA2 preferentially bound to intron regions. Notably, KPNA2 regulated the A3SS AS mode of WDR62, and upregulation of WDR62 reversed the KPNA2 downregulation-induced inhibition of GC cell proliferation, migration and invasion. Finally, we discovered that the AS of immune-related molecules could be regulated by KPNA2. Overall, our results demonstrated for the first time that KPNA2 functions as an oncogenic splicing factor in GC that regulated the AS and differential expression of GC-related genes, and KPNA2 may be a potential target for GC treatment.

Identifying and validating angiogenesis-related genes remodeling tumor microenvironment and suppressing immunotherapy response in gastric cancer

Angiogenesis significantly correlates with tumor microenvironment remodeling and immunotherapy response. Our study aimed to construct a prognostic angiogenesis-related model for gastric cancer. Using public database, a angiogenetic related five-gene (FGF1, GRB14, PAK3, PDGFRA, and PRKD1) model was identified. The top 25 % of patients were defined as high-risk, and the remaining as low-risk. The area under the curve for 1-, 3-, and 5-year overall survival (OS) were 0.646, 0.711, and 0.793, respectively. Survival analysis showed a better 10-year OS in low-risk patients in the construction (HR = 0.57, p = 0.002) and validation cohorts. GO and GSEA revealed that DEGs were enriched in extracellular matrix receptor interactions, dendritic cell antigen processing/presentation regulation, and angiogenesis pathways. CIBERSORT analysis revealed abundant naïve B cells, resting mast cells, resting CD4+ memory T cells, M2 macrophages, and monocytes in high-risk subgroups. The TIMER database showed strong positive correlations between PAK3, FGF1, PRKD1, and PDGFRA expression levels and the infiltration of CD4+ T cells and macrophages. The IOBR analysis revealed an immunosuppressive environment in the high-risk subgroup. Low-risk patients show a higher response rate to anti-PD1 treatment. TMA showed that FGF1 overexpression was associated with poor prognosis and CD4+ T cells and macrophage infiltration. In vivo study based on the 615 mice indicated that inhibiting FGF1 function could suppress tumor growth and enhance anti-PD1 therapeutic efficacy. In summary, we established a five-angiogenesis-related gene model to predict survival outcomes and immunotherapy responses in patients with gastric cancer and identified FGF1 as a prognostic gene and potential target for improving immune treatment.

Patient-derived tumor organoids to model drug response in gastric cancer

Gastric cancer poses diverse treatment challenges due to its high tumor heterogeneity. Through the use of patient-derived tumor organoid (PDO) models, new research1 has identified genes and molecular signatures that are predictive of chemotherapeutic response, providing valuable insights for clinical management and translational advancements.

Ferroptosis-related alternative splicing signatures as potential biomarkers for predicting prognosis and therapy response in gastric cancer

Ferroptosis is linked to various tumor biological traits, and alternative splicing (AS), a crucial step in mRNA processing, plays a role in the post-transcriptional regulation of ferroptosis-related genes (FRGs). A least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis was utilized to build a prognostic signature based on 12 AS events (p < 0.05), which was validated in gastric cancer (GC) patients. The high-risk group (n = 203) showed enrichment in cancer and metastasis pathways (p < 0.05). Significant differences existed between the high- and low-risk groups in terms of tumor microenvironment (TME) cell infiltration and immune activities (p < 0.05). The low-risk group (n = 203) was characterized by immune activation and improved prognosis (p < 0.001). Additionally, targeted treatment and immunotherapy were more likely to benefit the low-risk group (p < 0.05). Correlation analysis was performed to detect related splicing factors (SF) (Cor>0.4, FDR<0.05). Furthermore, our functional assay results suggested that high SF3A2 expression might increase ferroptosis resistance and promote cell proliferation. In conclusion, the FRAs model we built has an advantage in predicting GC prognosis. The model's demonstration of variations in the immune microenvironment and drug response could potentially inform decisions regarding treatment strategies.

Identification of cancer-associated fibroblast-related Ectodysplasin-A as a novel indicator for prognosis and immune response in gastric cancer

Studies have indicated cancer-associated fibroblasts (CAFs) could have a significant impact in gastric cancer (GC) progression and chemotherapy resistance. However, the gene related to cancer fibroblasts that can be used as biomarkers to judge the occurrence of gastric cancer has not been fully explored. Based on two Gene Expression Omnibus (GEO) datasets, we focus on differentially expressed genes which may act as CAFs markers related to GC. Through COX regression, LASSO regression and Kaplan-Meier survival analysis, we discovered three upregulated genes (GLT8D2, GNAS and EDA) associated with poor GC patients’ survival. By single-cell analysis and nomogram, we found that EDA may affect fibroblast production and disease prognosis in GC patients. EDA expression showed a positive correlation with 5-Fluorouracil IC50 values. Immunohistochemistry (IHC) and real time PCR indicated elevated EDA levels in GC tissues and cells. Enrichment analysis revealed that EDA was closely linked to immune system regulation. IHC and single-cell analysis indicated that EDA gene was associated with cancer fibroblasts marker FGF12 and influence cell interferon-gamma response, which may play a role in regulating immune-related characteristics. In summary, we concluded that EDA may be used as a new therapeutic CAFs marker for GC.

Role of TAP1 in the identification of immune-hot tumor microenvironment and its prognostic significance for immunotherapeutic efficacy in gastric carcinoma.

Gastric cancer (GC), a multifaceted gastrointestinal malignancy, is the fourth most prevalent contributor to cancer-related fatalities globally. As a member of the ATP-binding cassette (ABC) family, transporter associated with antigen processing 1 (TAP1) is crucial for conveying antigen peptides from the cytoplasm to the lumen of the endoplasmic reticulum and subsequently loading them onto the major histocompatibility complex (MHC) class I molecules. Recent studies have established the biological significance of TAP1 in upholding tumor survival and facilitating immune evasion by remodeling the tumor microenvironment (TME) and orchestrating immune infiltration. The study was conducted to elucidate the association of TAP1 expression with immunological characteristics, and sought to exploit the value of TAP1 as a biomarker reflecting the inflamed TME and immunotherapeutic response. RNA-sequencing profiles and clinical annotations were obtained from The Cancer Genome Atlas-stomach adenocarcinoma (TCGA-STAD) cohort and Gene Expression Omnibus (GEO) portal. Preprocessing was conducting using the limma package. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules and TAP1 co-expressed genes (CEGs) based on correlation patterns. Consensus clustering and silhouette analysis determined the optimal number of TAP1-related groups. Gene expression profiles were integrated and classified using the pamr package. The Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm and single-sample gene set enrichment analysis (ssGSEA) were used to evaluate immunological characteristics. Differential expression analysis was conducted using the limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Single-cell RNA sequencing (scRNA-seq) datasets were analyzed using the Seurat toolkit to characterize cell types. Within this investigation, no significant differences in TAP1 expression were observed among patients exhibiting various clinicopathological features, indicating that TAP1 expression was not specific to molecular subtypes. Subsequent analysis revealed a positive correlation between TAP1 and diverse immunological traits, encompassing immunomodulators, tumor-infiltrating immune cells, as well as immune checkpoints across multiple datasets. Besides, within a GC immunotherapy cohort, individuals displaying high TAP1 expression demonstrated an increased likelihood of achieving complete remission (CR) post-treatment, suggesting heightened sensitivity to immunotherapy. In the clinical cohort, TAP1 overexpression in GC patients was positively correlated with CD8. TAP1 appears linked to an inflamed TME and serves as a prospective biomarker for discerning immunological attributes and gauging immunotherapeutic responses in GC, particularly in identifying immune-reactive tumors.

Evaluation of soluble co‐inhibitors and co‐stimulators levels of the immune response in gastric cancer

AbstractBackgroundCo‐inhibitor and co‐stimulator mediators trigger actions that result in immunological homeostasis and are being evaluated as potential therapeutic targets in gastric cancer (GC).ObjectiveTo evaluate the soluble levels of sPD‐1, sPD‐L1, sPD‐L2, sTIM‐3, sGal9, sGITR, and sGITRL in GC patients.MethodsThe cross‐sectional study was carried out at the Hospital de Cancer de Pernambuco, Brazil between 2017 and 2018. A total of 74 GC patients and 30 healthy controls were included.ResultsLow levels of sPD1 (p = 0.0179), sPDL2 (p = 0.0003), and sGal9 (p &lt; 0.0001), and higher levels of sPDL1 (p = 0.004), sTIM‐3 (p = 0.0072), sGITR (p = 0.0179), and sGITRL (p = 0.0055) compared to the control group. High sPD‐1, sTIM‐3, and sGal9 levels in stage IV compared I/II and III (p &lt; 0.05). High sPDL1, sGal9, and sGITRL levels in esophagogastric junction compared to body and Pylorus/Antrum groups (p &lt; 0.05). No significant differences were observed in sPD1, sPDL1, sPDL2, sTIM3, sGal9, sGITR, and sGITRL levels between the intestinal, diffuse, and mixed GC groups. Low sGITR levels in GC patients who died within the first 24 months compared to the who survived (p = 0.0332).ConclusionsThere is an association of sPD1, sTIM‐3, and sGal9 with disease progression and sGITR with death, these mediators may be potential prognostic biomarkers in GC.

Digital assessment of tertiary lymphoid structures and therapeutic responses in gastric cancer: A multi-centric retrospective study.

Tertiary lymphoid structures (TLSs) are associated with favorable prognosis and enhanced response to anti-cancer therapy. A digital assessment of TLSs could provide an objective alternative that mitigates variability inherent in manual evaluation. This study aimed to develop and validate a digital gene panel based on biological prior knowledge for assessment of TLSs, and further investigate its associations with survival and multiple anti-cancer therapies. The present study involved 1,704 patients with gastric cancer from seven cancer centers. TLSs were identified morphologically through hematoxylin-and-eosin staining. We further developed a digital score based on targeted gene expression profiling to assess TLSs status, recorded as gene signature of tertiary lymphoid structures (gsTLS). For enhanced interpretability, we employed the SHapley Additive exPlanation (SHAP) analysis to elucidate its contribution to the prediction. We next evaluated the signature's associations with prognosis, and investigated its predictive accuracy for multiple anti-cancer therapies, including adjuvant chemotherapy and immunotherapy. The gsTLS panel with nine gene features achieved high accuracies in predicting TLSs status in the training, internal and external validation cohorts (area under the curve, range: 0.729-0.791). In multivariable analysis, gsTLS remained an independent predictor of disease-free and overall survival (hazard ratio, range: 0.346-0.743, all P < 0.05) after adjusting for other clinicopathological variables. SHAP analysis highlighted gsTLS as the strongest predictor of TLSs status compared with clinical features. Importantly, patients with high gsTLS (but not those with low gsTLS) exhibited substantial benefits from adjuvant chemotherapy (P < 0.05). Furthermore, we found that the objective response rate to anti-programmed cell death protein 1 (anti-PD-1) immunotherapy was significantly higher in the high-gsTLS group (40.7%) versus the low-gsTLS group (5.6%, P = 0.036), and the diagnosis was independent from Epstein-Barr virus (EBV), tumor mutation burden (TMB), and programmed cell death-ligand 1 (PD-L1) expression. The gsTLS digital panel enables accurate assessment of TLSs status, and provides information regarding prognosis and responses to multiple therapies for gastric cancer.

439P Impact of neoadjuvant therapy-surgery interval on prognosis and tumor response in gastric cancer

439P Impact of neoadjuvant therapy-surgery interval on prognosis and tumor response in gastric cancer

Integrated multi-omics analysis and machine learning identify hub genes and potential mechanisms of resistance to immunotherapy in gastric cancer.

Patients with gastric cancer respond poorly to immunotherapy. There are still unknowns about the biomarkers associated with immunotherapy sensitivity and their underlying molecular mechanisms. Gene expression data for gastric cancer were gathered from TCGA and GEO databases. DEGs associated with immunotherapy response came from ICBatlas. KEGG and GO analyses investigated pathways. Hub genes identification employed multiple machine algorithms. Associations between hub genes and signaling pathways, disease genes, immune cell infiltration, drug sensitivity, and prognostic predictions were explored via multi-omics analysis. Hub gene expression was validated through HPA and CCLE. Multiple algorithms pinpointed Cancer-Associated Fibroblasts genes (CAFs), with ten machine-learning methods generating CAFs scores for prognosis. Model gene expression was validated at the single-cell level using the TISCH database. We identified 201 upregulated and 935 downregulated DEGs. Three hub genes, namely CDH6, EGFLAM, and RASGRF2, were unveiled. These genes are implicated in diverse disease-related signaling pathways. Additionally, they exhibited significant correlations with disease-associated gene expression, immune cell infiltration, and drug sensitivity. Exploration of the HPA and CCLE databases exposed substantial expression variations across patients and cell lines for these genes. Subsequently, we identified CAFs-associated genes and established a robust prognostic model. The analysis in the TISCH database showed that the genes in this model were highly expressed in CAFs. The results unveil an association between CDH6, EGFLAM, and RASGRF2 and the immunotherapeutic response in gastric cancer. These genes hold potential as predictive biomarkers for gastric cancer immunotherapy resistance and prognostic assessment.

Abstract 4262: AI-driven analysis of collagen characteristics in the tumor immune microenvironment predicts immune checkpoint inhibitors treatment responsiveness in gastric cancer

Abstract Background: Recent studies indicate a correlation between increased stromal composition in tumors and resistance to immune checkpoint inhibitors (ICIs) across various cancer types. Specifically, the extracellular matrix (ECM) within the stromal compartment significantly influence the tumor microenvironment, affecting cancer cell proliferation and immune cell recruitment. However, identifying and quantifying the ECM at the collagen fiber level in whole slide images (WSIs) is challenging due to its inherent heterogeneity and complexity. In this work, we utilized statistical analysis and a deep learning (DL)-based approach to identify and assess collagen in the ECM, examining its characteristics in relation to the ICIs treatment response in gastric cancer. Methods: We analyzed 116 WSIs of hematoxylin and eosin (H&amp;E) stained slides before ICIs treatment from three institutions. Responders were defined as those with a complete response or partial response (N=18); those with stable disease (SD) (N=34) or progressive disease (PD) (N=64) were considered non-responders (N=98). For non-responders, post-treatment H&amp;E slides were available for 21 patients; SD (N=5) and PD (N=16). We applied a DL-based second harmonic generation (SHG) synthesis algorithm to tumor regions identified by a tumor detection model. We then calculated various collagen properties, including volume, fiber orientation and alignment, waviness, straightness, and fiber thickness, based on collagen fiber centerlines extracted from the generated SHG images. An attention-based multiple instance learning (ABMIL) model was trained to automatically predict the treatment response solely based on collagen features. The dataset of 116 pre-treatment slides was divided into training and validation subsets using stratified 3-fold cross-validation. The area under the curve (AUC) was used to assess the performance of the trained models. Results: Collagen fiber features were significantly associated with ICIs response. Notably, ICIs responders have a decrease in collagen waviness and fiber orientation variation (p-value&amp;lt;0.05, by Mann-Whitney U test). Among non-responders, the PD group showed a statistical decrease in waviness after treatment (p-value&amp;lt;0.05, by Mann-Whitney U test). The ABMIL model achieved a mean AUC score of 0.823, with a standard deviation of 0.023. Conclusion: This study provides a comprehensive analysis of collagen in the ECM, and its properties in relation to gastric cancer ICIs response within Tumor Immune Microenvironment. We also developed a model using collagen features and demonstrated high accuracy in predicting treatment response. Citation Format: Minji Kim, Ekaterina Redekop, Yoonho Choi, Sung Hak Lee, Jae-Ho Cheong, Yu Aoki, Kohei Shitara, Tae Hyun Hwang. AI-driven analysis of collagen characteristics in the tumor immune microenvironment predicts immune checkpoint inhibitors treatment responsiveness in gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4262.

Abstract 2316: A deep Gaussian process (DGP)-based prediction model for immunecheckpoint inhibitor (ICI) response in gastric cancer (GC)

Abstract Background: Immune checkpoint inhibitors (ICIs) targeting PD-1 or PD-L1 have dramatically improved survival of patients with gastric cancer (GC). However, recent clinical trials, including KEYNOTE-059, KEYNOTE-061, and ATTRACTION-2, show that the objective response rate (ORR) for ICIs is still less than 20% in patients with advanced GC. Therefore, predicting who will respond to ICIs in advance is essential to make better treatment plans for GC patients. There are potential biomarkers to predict ICI response, but most of them can only be applied to a limited population of patients. For example, Kim ST., et al., (PMID: 30013197) show that patients with MSI-high or EBV-positive responded well to ICIs, but these subtypes account for less than 30 percent of GC patients (PMID:25079317). In this study, we propose a prediction model of ICI response using the 32 GC-specific genes that we identified from mutation data (PMID: 26635139) and showed their close association with survival and ICI response in GC (PMID: 35140202; PMID: 36508166). In addition, since the number of available GC-ICI samples is small, we design our prediction model in Bayesian and ensemble learning frameworks which are robust to small sample size problems. Methods: We collected mRNA sequencing data from GC-ICI treated patients from our institutions and publications (PMID: 30013197; PMID: 35254400; PMID: 33846173). We used 108 samples previously included in our previous work (PMID: 36508166) as the training data and 27 samples (13 responders and 14 non-responders) from (PMID: 3384617) as the test data. We built and trained a classifier to separate responders from non-responders using the expressions of the 32 genes. We proposed to use a Deep Gaussian process (GP) model, a multi-layered extension of a GP that is itself a Bayesian method. We optimized the parameters of each GP model in variational inference. To implement ensemble learning, we trained multiple DGP models with different initializations of the model parameters and data splitting (training/validation sets). For each training run, the best model was selected based on the prediction performance on the validation set, measured by AUC. The final prediction for a test sample was made by combining the prediction results of all the DGP models by taking the average. Results: We implemented the DGP-based prediction model using PyTorch in Python, and the codes will be publicly available on the GitHub repository (https://github.com/hwanglab/GC_ICI_response_prediction). We achieved a prediction performance of 0.813 AUC on the test data (sensitivity = 0.769 and specificity = 0.714). Conclusion: We propose the prediction model of ICI response, which can handle small sample size problems in the framework of Bayesian and ensemble learning. The experimental results using the GC-ICI data demonstrate that the 32 genes can be used as predictive markers for ICI response in GC. Citation Format: Sunho Park, Minji Kim, Changjin Hong, Tae Hyun Hwang. A deep Gaussian process (DGP)-based prediction model for immunecheckpoint inhibitor (ICI) response in gastric cancer (GC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2316.

Abstract 2528: Genomic and immune microenvironment features influencing chemoimmunotherapy response in gastric cancer with peritoneal metastasis: A retrospective cohort study

Abstract Background: Patients with peritoneal metastasis (PM) from gastric cancer (GC) exhibit poor prognosis. Chemoimmunotherapy offers promising clinical benefits; however, its efficacy and predictive biomarkers in a conversion therapy setting remain unclear. We aimed to retrospectively evaluate chemoimmunotherapy efficacy in a conversion therapy setting for GC patients with PM and establish a prediction model for assessing clinical benefits. Methods: A retrospective evaluation of clinical outcomes encompassed 55 GC patients with PM who underwent chemoimmunotherapy in a conversion therapy setting. Baseline PM specimens were collected for genomic and transcriptomic profiling. Clinicopathological factors, gene signatures, and tumor immune microenvironment were evaluated to identify predictive markers and develop a prediction model. Results: Chemoimmunotherapy achieved a 41.8% objective response rate and 72.4% R0 resection rate in GC patients with PM. Patients with conversion surgery showed better overall survival (OS) than those without the surgery (median OS: not reached vs 7.82m, P&amp;lt;0.0001). Responders to chemoimmunotherapy showed higher ERBB2 and ERBB3 mutation frequencies, CTLA4 and HLA-DQB1 expression, and CD8+ T cell infiltration, but lower CDH1 mutation and naïve CD4+ T cell infiltration, compared to non-responders. A prediction model was established integrating CDH1 and ERBB3 mutations, HLA-DQB1 expression, and naïve CD4+ T cell infiltration (AUC=0.918, 95%CI: 0.846-0.991), which were validated using an independent external cohort (AUC=0.785, 95%CI: 0.637-0.933). Conclusion: Our study comprehensively evaluated clinicopathological, genomic, and immune features and developed a novel prediction model, providing a rational basis for the selection of GC patients with PM for chemoimmunotherapy-involved conversion therapy. Citation Format: Pengfei Yu, Guangyu Ding, Xingmao Huang, Chenxuan Wang, Jingquan Fang, Ling Huang, Zeyao Ye, Qi Xu, Xiaoying Wu, Junrong Yan, Qiuxiang Ou, Haimeng Tang, Yian Du, Xiangdong Cheng. Genomic and immune microenvironment features influencing chemoimmunotherapy response in gastric cancer with peritoneal metastasis: A retrospective cohort study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2528.

Abstract 7400: AI-driven multimodal 3D tumor modeling with spatial molecular insights for predicting ICI response in gastric cancer

Abstract Traditional cancer research methodologies predominantly focus on 2D tumor modeling, including spatial profiling and AI-driven analysis at the 2D level. To advance beyond these limitations, we implemented Holotomography (HT), a 3D, label-free optical imaging method. This technique, leveraging refractive index contrasts, allows for enhanced visualization of tumor tissues, organoids, and cells in three dimensions. Using serial H&amp;E sections and unstained slides, we created detailed 3D tumor models in gastric cancer, providing insights into tumor, immune, and stromal composition, as well as cell-cell interactions and extracellular matrix characteristics. AI algorithms further enhanced our 3D tumor modeling, offering novel perspectives on the initiation and progression of tumor, immune, and stromal components at the subcellular level. Analyzing gastric tumors treated with immune checkpoint inhibitors before, during, and after treatment revealed dynamic changes in spatial composition over time, offering unique insights into the evolution of the 3D tumor ecosystem. Additionally, by integrating spatial transcriptomic and in situ molecular imaging data, we developed a novel Multimodal 3D Tumor Model. This model not only uncovers subcellular-level molecular characteristics but also elucidates cellular interactions during treatment, suggesting potential biomarkers and therapeutic approaches. Lastly, by constructing 3D cellular interaction networks incorporating true 3D interactions, we provided an in-depth view of cellular communication within the tumor microenvironment. This pioneering approach, representing the first Multimodal 3D Tumor Model at the subcellular level, offers novel insights into gastric cancer's spatial evolution, potentially revolutionizing our understanding and treatment of gastric cancer. Citation Format: Yoon Ho Choi, Minji Kim, Katya Redekop, Inyeop Jang, Jean R. Clemenceau, Isabel Barnfather, Sumanth Reddy Nakkireddy, Changjin Hong, Sunho Park, Ji-Youn Sung, Jeong Hwan Park, Shitara Kohei, Aoki Yu, Sung Hak Lee, Sam Wang, Jaeho Cheong, Tae Hyun Hwang. AI-driven multimodal 3D tumor modeling with spatial molecular insights for predicting ICI response in gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7400.