An antigen processing and presentation signature for prognostic evaluation and immunotherapy selection in advanced gastric cancer

Construction of a nomogram with IrAE and clinic character to predict the survival of advanced G/GEJ adenocarcinoma patients undergoing anti-PD-1 treatment

OBJECTIVE

This study aimed to develop and validate a survival prediction model and nomogram to predict survival in patients with advanced gastric or gastroesophageal junction (G/GEJ) adenocarcinoma undergoing treatment with anti-programmed cell death 1 receptor (PD-1). This model incorporates immune-related adverse events (irAEs) alongside common clinical characteristics as predictive factors.

METHOD

A dataset comprising 255 adult patients diagnosed with advanced G/GEJ adenocarcinoma was assembled. The irAEs affecting overall survival (OS) to a significant degree were identified and integrated as a candidate variable, together with 12 other candidate variables. These included gender, age, Eastern cooperative oncology group performance status (ECOG PS) score, tumor stage, human epidermal growth factor receptor 2 (HER2) expression status, presence of peritoneal and liver metastases, year and line of anti-PD-1 treatment, neutrophil-to-lymphocyte ratio (NLR), controlling nutritional status (CONUT) score, and Charlson comorbidity index (CCI). To mitigate timing bias related to irAEs, landmark analysis was employed. Variable selection was performed using the least absolute shrinkage and selection operator (LASSO) regression to pinpoint significant predictors, and the variance inflation factor was applied to address multicollinearity. Subsequently, a Cox regression analysis utilizing the forward likelihood ratio method was conducted to develop a survival prediction model, excluding variables that failed to satisfy the proportional hazards (PH) assumption. The model was developed using the entire dataset, then internally validated through bootstrap resampling and externally validated with a cohort from another Hospital. Furthermore, a nomogram was created to delineate the predictive model.

RESULTS

After consolidating irAEs from the skin and endocrine systems into a single protective irAE category and applying landmark analysis, variable selection was conducted for the prognostic prediction model along with other candidate variables. The finalized model comprised seven variables: ECOG PS score, tumor stage, HER2 expression status in tumor tissue, first-line anti-PD-1 treatment, peritoneal metastasis, CONUT score, and protective irAE. The overall concordance index for the model was 0.66. Calibration analysis verified the model's accuracy in aligning predicted outcomes with actual results. Clinical decision curve analysis indicated that utilizing this model for treatment decisions could enhance the net benefit regarding 1- and 2-year survival rates for patients.

CONCLUSION

This study developed a prognostic prediction model by integrating common clinical characteristics of irAEs and G/GEJ adenocarcinoma. This model exhibits good clinical practicality and possesses accurate predictive ability for overall survival OS in patients with advanced G/GEJ adenocarcinoma.

Gastric cancer prognosis: unveiling autophagy-related signatures and immune infiltrates

Background

Autophagy played a crucial regulatory role in tumor initiation and progression. Therefore, we aimed to comprehensively analyze autophagy-related genes (ARGs) in gastric cancer, focusing on their expression, prognostic value, and potential functions.

Methods

The gastric cancer gene chip datasets (GSE79973 and GSE54129) were collected from the Gene Expression Omnibus (GEO) database. Subsequently, the Limma package was employed to identify differentially expressed genes (DEGs) between the normal and disease groups. The selected ARGs were further authenticated using the Human Protein Atlas (HPA) database, The Cancer Genome Atlas (TCGA) database, and GSE19826 database.

Results

A total of 15 autophagy-related DEGs, eight of which were upregulated [FKBP1A, IL24, PEA15, HSP90AB1, cathepsin B (CTSB), ITGB1, SPHK1, HIF1A], while seven were downregulated (DAPK2, EIF2AK3, FKBP1B, PTK6, NKX2-3, NFE2L2, PRKCD). Analysis revealed that CTSB was specifically associated with the prognosis of gastric cancer patients. Gene set enrichment analysis (GSEA) showcased a significant enrichment of CTSB-related genes within immune-related pathways. Moreover, correlation analysis demonstrated a clear association between the expression of CTSB and immune infiltration. The upregulation of CTSB in gastric cancer was linked to poor survival and increased immune infiltration.

Conclusions

We conjectured that CTSB likely played a critical role in regulating immunity and autophagy in gastric cancer.

Targeting MHC-I molecules for cancer: function, mechanism, and therapeutic prospects

The molecules of Major histocompatibility class I (MHC-I) load peptides and present them on the cell surface, which provided the immune system with the signal to detect and eliminate the infected or cancerous cells. In the context of cancer, owing to the crucial immune-regulatory roles played by MHC-I molecules, the abnormal modulation of MHC-I expression and function could be hijacked by tumor cells to escape the immune surveillance and attack, thereby promoting tumoral progression and impairing the efficacy of cancer immunotherapy. Here we reviewed and discussed the recent studies and discoveries related to the MHC-I molecules and their multidirectional functions in the development of cancer, mainly focusing on the interactions between MHC-I and the multiple participators in the tumor microenvironment and highlighting the significance of targeting MHC-I for optimizing the efficacy of cancer immunotherapy and a deeper understanding of the dynamic nature and functioning mechanism of MHC-I in cancer.

Resistance to immune checkpoint inhibitors in gastric cancer

Gastric cancer (GC) is one of the most common gastrointestinal malignancies worldwide. In the past decade, with the development of early diagnostic techniques, a clear decline in GC incidence has been observed, but its mortality remains high. The emergence of new immunotherapies such as immune checkpoint inhibitors (ICIs) has changed the treatment of GC patients to some extent. However, only a small number of patients with advanced GC have a durable response to ICI treatment, and the efficacy of ICIs is very limited. Existing studies have shown that the failure of immunotherapy is mainly related to the development of ICI resistance in patients, but the understanding of the resistance mechanism is still insufficient. Therefore, clarifying the mechanism of GC immune resistance is critical to improve its treatment and clinical benefit. In this review, we focus on summarizing the mechanisms of primary or acquired resistance to ICI immunotherapy in GC from both internal and external aspects of the tumor. At the same time, we also briefly discuss some other possible resistance mechanisms in light of current studies.

Predictive Biomarkers for Immunotherapy in Gastric Cancer: Current Status and Emerging Prospects

Gastric cancer presents substantial management challenges, and the advent of immunotherapy has ignited renewed hope among patients. Nevertheless, a significant proportion of patients do not respond to immunotherapy, and adverse events associated with immunotherapy also occur on occasion, underscoring the imperative to identify suitable candidates for treatment. Several biomarkers, including programmed death ligand-1 expression, tumor mutation burden, mismatch repair status, Epstein-Barr Virus infection, circulating tumor DNA, and tumor-infiltrating lymphocytes, have demonstrated potential in predicting the effectiveness of immunotherapy in gastric cancer. However, the quest for the optimal predictive biomarker for gastric cancer immunotherapy remains challenging, as each biomarker carries its own limitations. Recently, multi-omics technologies have emerged as promising platforms for discovering novel biomarkers that may help in selecting gastric cancer patients likely to respond to immunotherapy. The identification of reliable predictive biomarkers for immunotherapy in gastric cancer holds the promise of enhancing patient selection and improving treatment outcomes. In this review, we aim to provide an overview of clinically established biomarkers of immunotherapy in gastric cancer. Additionally, we introduce newly reported biomarkers based on multi-omics studies in the context of gastric cancer immunotherapy, thereby contributing to the ongoing efforts to refine patient stratification and treatment strategies.

Cellular dynamics in tumour microenvironment along with lung cancer progression underscore spatial and evolutionary heterogeneity of neutrophil

BACKGROUND

The cellular dynamics in the tumour microenvironment (TME) along with non-small cell lung cancer (NSCLC) progression remain unclear.

METHODS

Multiplex immunofluorescence test detecting 10 immune-related markers on 553 primary tumour (PT) samples of NSCLC was conducted and spatial information in TME was assessed by the StarDist depth learning model. The single-cell transcriptomic atlas of PT (n = 4) and paired tumour-draining lymph nodes (TDLNs) (n = 5 for tumour-invaded, n = 3 for tumour-free) microenvironment was profiled. Various bioinformatics analyses based on Gene Expression Omnibus, TCGA and Array-Express databases were also used to validate the discoveries.

RESULTS

Spatial distances of CD4+ T cells-CD38+ T cells, CD4+ T cells-neutrophils and CD38+ T cells-neutrophils prolonged and they were replaced by CD163+ macrophages in PT along with tumour progression. Neutrophils showed unique stage and location-dependent prognostic effects. A high abundance of stromal neutrophils improved disease-free survival in the early-stage, whereas high intratumoural neutrophil infiltrates predicted poor prognosis in the mid-to-late-stage. Significant molecular and functional reprogramming in PT and TDLN microenvironments was observed. Diverse interaction networks mediated by neutrophils were found between positive and negative TDLNs. Five phenotypically and functionally heterogeneous subtypes of tumour-associated neutrophil (TAN) were further identified by pseudotime analysis, including TAN-0 with antigen-presenting function, TAN-1 with strong expression of interferon (IFN)-stimulated genes, the pro-tumour TAN-2 subcluster, the classical subset (TAN-3) and the pro-inflammatory subtype (TAN-4). Loss of IFN-stimulated signature and growing angiogenesis activity were discovered along the transitional trajectory. Eventually, a robust six neutrophil differentiation relevant genes-based model was established, showing that low-risk patients had longer overall survival time and may respond better to immunotherapy.

CONCLUSIONS

The cellular composition, spatial location, molecular and functional changes in PT and TDLN microenvironments along with NSCLC progression were deciphered, highlighting the immunoregulatory roles and evolutionary heterogeneity of TANs.