An Immune Cell Signature Is Associated With Disease-Free Survival and Adjuvant Chemosensitivity of Patients With Resectable Gastric Cancer

Prognostic prediction of gastric cancer based on H&E findings and machine learning pathomics

AIM

In this research, we aimed to develop a model for the accurate prediction of gastric cancer based on H&E findings combined with machine learning pathomics.

METHODS

Transcriptome data, pathological images, and clinical data from 443 cases were retrieved from TCGA (The Cancer Genome Atlas Program) for survival analysis. The images were segmented using the Otsu algorithm, and features were extracted using the PyRadiomics package. Subsequently, the cases were randomly divided into a training cohort of 165 cases and a validation cohort of 69 cases. Features selected via minimum Redundancy - Maximum Relevance (mRMR)- recursive feature elimination (RFE) screening were used to train a model using the Gradient Boosting Machine (GBM) algorithm. The model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curves. Additionally, the correlation between the Pathomics score (PS) and immune genes was examined.

RESULTS

In the multivariate analysis, heightened infiltration of activated CD4 memory T cells was strongly associated with improved overall survival (HR = 0.505, 95 % CI = 0.342-0.745, P < 0.001). The pathomic model, exhibiting robust predictive capability, demonstrated impressive AUC values of 0.844 and 0.750 in both study cohorts. The Decision Curve Analysis (DCA) unequivocally underscored the model's exceptional clinical utility. In a subsequent multivariate analysis, heightened infiltration of the PS also emerged as a significant protective factor for overall survival (HR = 0.506, 95 % CI = 0.329-0.777, P = 0.002).

CONCLUSION

The pathomic model based on H&E slides for predicting the infiltration degree of activated CD4 memory T cells, along with integrated bioinformatics analysis elucidating potential molecular mechanisms, offers novel prognostic indicators for the precise stratification and individualized prognosis of gastric cancer patients.

N6-methylation in the development, diagnosis, and treatment of gastric cancer

Gastric cancer (GC) ranks third among cancers in terms of mortality rate worldwide. A clear understanding of the mechanisms underlying the genesis and progression of GC will contribute to clinical decision making. N6-methyladenosine (m6A) is the most abundant among diverse mRNA modification types and regulates multiple facets of RNA metabolism. In recent years, emerging studies have shown that m6A modifications are involved in gastric carcinoma tumorigenesis and progression and can potentially be valuable new prospects for diagnosis and prognosis. This article reviews the recent progress regarding m6A in GC.

The molecular mechanism of actions and clinical utilities of tumor infiltrating lymphocytes in gastrointestinal cancers: a comprehensive review and future prospects toward personalized medicine

Gastrointestinal (GI) cancers remain a significant global health burden, accounting for a substantial number of cases and deaths. Regrettably, the inadequacy of dependable biomarkers hinders the precise forecasting of patient prognosis and the selection of appropriate therapeutic sequencing for individuals with GI cancers, leading to suboptimal outcomes for numerous patients. The intricate interplay between tumor-infiltrating lymphocytes (TILs) and the tumor immune microenvironment (TIME) has been shown to be a pivotal determinant of response to anti-cancer therapy and consequential clinical outcomes across a multitude of cancer types. Therefore, the assessment of TILs has garnered global interest as a promising prognostic biomarker in oncology, with the potential to improve clinical decision-making substantially. Moreover, recent discoveries in immunotherapy have progressively changed the landscape of cancer treatment and significantly prolonged the survival of patients with advanced cancers. Nonetheless, the response rate remains constrained within solid tumor sufferers, even when TIL landscapes appear comparable, which calls for the development of our understanding of cellular and molecular cross-talk between TIME and tumor. Hence, this comprehensive review encapsulates the extant literature elucidating the TILs' underlying molecular pathogenesis, prognostic significance, and their relevance in the realm of immunotherapy for patients afflicted by GI tract cancers. Within this review, we demonstrate that the type, density, and spatial distribution of distinct TIL subpopulations carries pivotal implications for the prediction of anti-cancer treatment responses and patient survival. Furthermore, this review underscores the indispensable role of TILs in modulating therapeutic responses within distinct molecular subtypes, such as those characterized by microsatellite stability or programmed cell death ligand-1 expression in GI tract cancers. The review concludes by outlining future directions in TIL-based personalized medicine, including integrating TIL-based approaches into existing treatment regimens and developing novel therapeutic strategies that exploit the unique properties of TILs and their potential as a promising avenue for personalized cancer treatment.

Gastric Cancer and the Immune System: The Key to Improving Outcomes?

Gastric adenocarcinoma is by far the most common form of gastric cancer (GC) and is a highly lethal form of cancer arising from the gastric epithelium. GC is an important area of focus of the medical community, given its often late-stage of diagnosis and associated high mortality rate. While surgery and chemotherapy remain the primary treatments, attention has been drawn to the use of immunologic therapies, which have shown promise in the treatment of other malignancies. The role for immune-based therapies has become clearer as we obtain a greater understanding of the role of the immune system in gastric cancer formation and growth. A variety treatment to augment the immune system are under evaluation in clinical trials, and these include immune checkpoint inhibitors, antibody-drug conjugates, and immune cell-based therapies. Here, we review the immune landscape and immune-based therapies for GC.

Immunoscore Combining CD8, FoxP3, and CD68-Positive Cells Density and Distribution Predicts the Prognosis of Head and Neck Cancer Patients

We assessed immune cell infiltrates to develop an immunoscore for prognosis and to investigate its correlation with the clinical data of patients with head and neck cancer. CD8, FoxP3, and CD68 markers were evaluated by immunohistochemistry in 258 carcinoma samples and positive cells were counted in stromal and intra-tumoral compartments. The RStudio software was used to assess optimal cut-offs to divide the population according to survival while the prognostic value was established by using Kaplan–Meier curves and Cox regression models for each immune marker alone and in combination. We found with univariate analysis that the infiltration of immune cells in both compartments was predictive for recurrence-free survival and overall survival. Multivariate analysis revealed that CD8+ density was an independent prognostic marker. Additionally, the combination of CD8, FoxP3, and CD68 in an immunoscore provided a significant association with overall survival (p = 0.002, HR = 9.87). Such an immunoscore stayed significant (p = 0.018, HR = 11.17) in a multivariate analysis in comparison to tumor stage and histological grade, which had lower prognostic values. Altogether, our analysis indicated that CD8, FoxP3, and CD68 immunoscore was a strong, independent, and significant prognostic marker that could be introduced into the landscape of current tools to improve the clinical management of head and neck cancer patients.

Dissecting the Role of Immune Checkpoint Regulation Patterns in Tumor Microenvironment and Prognosis of Gastric Cancer

Many studies suggest that immune checkpoint molecules play a vital role in tumor progression and immune responses. However, the impact of the comprehensive regulation pattern of immune checkpoint molecules on immune responses, tumor microenvironment (TME) formation, and patient prognosis is poorly understood. In this study, we evaluated immune checkpoint regulation patterns in 1,174 gastric cancer (GC) samples based on 31 immune checkpoint genes (ICGs). Three distinct immune checkpoint regulation patterns with significant prognostic differences were ultimately identified. Moreover, GC patients were divided into two subgroups according to immune checkpoint score (ICscore). Patients with lower ICscore were characterized by a favorable prognosis and enhanced immune infiltration as well as an increased tumor mutation burden, non-recurrence, and microsatellite instability-high. Collectively, this study indicated that immune checkpoint regulation patterns were essential to forming the diversity of TME and a better understanding of that will contribute to assessing the characteristics of TME in GC, which intends to improve the development of immunotherapy.

SMOX expression predicts the prognosis of non-small cell lung cancer

Background

The development of non-small cell lung cancer (NSCLC) is very rapid, and the effect of its treatment is often closely related to the diagnosis time of the disease. Therefore, simple and convenient tumor biomarkers are helpful for the timely diagnosis and prevention of NSCLC.

Methods

Through univariate and multivariate Cox regression analyses, SMOX was determined as an independent prognostic factor of GSE42127, GSE41271, GSE68465, and TCGA datasets. Furthermore, western blot, reverse transcription-polymerase chain reaction (RT-PCR), and immunohistochemical analysis were performed to confirm the predictive efficiency of SMOX expression in NSCLC.

Results

Patients were divided into high and low expression groups according to the median value of SMOX expression, and Kaplan-Meier curves of multiple datasets indicated that patients with low SMOX expression had a better survival rate. According to the analysis of immune infiltration, the immune microenvironment, and immune checkpoints, SMOX expression of the high and low groups showed differences in immunity in NSCLC. By comparing cancer and adjacent tissues using western blot analysis, RT-PCR and immunohistochemical analysis, we found that SMOX was highly expressed in tumor tissues and had low expression in adjacent tissues. Simultaneously, the Kaplan-Meier curve suggested that among the 155 NSCLC patients, those with low SMOX expression had better survival.

Conclusions

SMOX can be used as an effective predictive target for NSCLC.

Establishment of an Immune Cell Infiltration Score to Help Predict the Prognosis and Chemotherapy Responsiveness of Gastric Cancer Patients

The immune microenvironment plays a critical role in tumor biology. The molecular profiles of immune components and related genes are of tremendous value for the study of primary resistance to immune checkpoint blockers (ICBs) for gastric cancer (GC) and serve as prognostic biomarkers to predict GC survival. Recent studies have revealed that tumor immune cell infiltration (ICI) is an indicator of the survival and responsiveness to chemotherapy in GC patients. Here, we describe the immune cell landscape based on the ESTIMATE and CIBERSORT algorithms to help separate GC into 3 ICI clusters using the unsupervised clustering method. Further in-depth analyses, such as differential expression gene (DEG) analysis and principal component analysis (PCA), help to establish an ICI scoring system. A low ICI score is characterized by an increased tumor mutation burden (TMB). The combination of the ICI score and TMB score better predicts the survival of GC patients. Analyses based on public and our own database revealed that the ICI scoring system could also help predict the survival and chemotherapy responsiveness of GC patients. The present study demonstrated that the ICI score may be an effective prognostic biomarker and predictive indicator for chemotherapy and immunotherapy.