Immune-based combination therapy for esophageal cancer

A Correlation Evaluation Between the Peripheral Blood Index and the Prognosis of Advanced Esophageal Squamous Cell Carcinoma Patients Treated with Camrelizumab

Purpose

This study aimed to investigate the relationship between peripheral blood indices and the efficacy and prognosis of advanced esophageal squamous cell carcinoma (ESCC) patients treated with camrelizumab.

Patients and Methods

We retrospectively analyzed 64 patients who received camrelizumab for advanced ESCC at the Second People's Hospital of Lianyungang City between July 2020 and June 2022. The study included examination of the neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio (PLR), the systemic inflammation index (SII), the lymph-to-monocytes ratio (LMR), the absolute lymphocyte count (ALC), and lactate dehydrogenase (LDH). We used multivariate logistic regression analysis to explore the link existing between peripheral blood and the efficacy of treatment. Determination of potential prognostic factors for Progression-free survival (PFS) and Overall survival (OS) using Cox regression analysis. The nomogram model was developed based on the results of the Cox multivariate analysis. Patients were divided into three groups according to the reduction in LDH and LDL levels before treatment, and the Kaplan-Meier survival curves for the three groups were compared and ROC curves for LDH combined with PLR were plotted.

Results

Lower LDH (OR=6.237, 95% CI: 1.625-23.944) were independently associated with disease control rates(DCR). LDH was independently correlated with PFS (HR: 0.227 95% CI: 0.099-0.517). LDH and PLR were independently linked to OS. The C index of the nomogram model is 0.819, indicating good predictive performance. Kaplan-Meier Survival Curve suggested better OS in patients with reduced pretreatment LDH and PLR. The area under the ROC curve showed that the LDH index combined with the PLR index predicts patient survival better than the index alone.

Conclusion

LDH combined with PLR predicted prognosis in patients with ESCC treated with camrelizumab.

Prognostic and predictive value of angiogenesis-associated serum proteins for immunotherapy in esophageal cancer

BACKGROUND

Immune checkpoint inhibitors (ICIs) have significantly improved patient survival in multiple cancers. However, therapy response in esophageal cancer is limited to subgroups of patients and clinically useful predictive biomarkers are lacking.

METHODS

We collected a series of plasma samples from 91 patients with esophageal cancer before and after ICI treatment. The Olink Immuno-Oncology panel (92 proteins) with proximity extension assays was used to detect the dynamic changes in plasma and potential biomarkers associated with treatment outcomes. We screened all survival-related proteins and established a risk score model to better predict the prognosis and treatment response in patients with esophageal cancer immunotherapy.

RESULTS

We found that 47 out of 92 quantified proteins had significant changes in plasma levels during ICI treatment (p<0.050), and these changed proteins were involved in immune-related reactions, such as intercellular adhesion and T-cell activation. Notably, the baseline levels of three angiogenesis-related proteins (IL-8, TIE2, and HGF) were significantly associated with the survival outcomes of patients treated with ICIs (p<0.050). According to these prognostic proteins, we established an angiogenesis-related risk score, which could be a superior biomarker for ICI response prediction. In addition, antiangiogenic therapy combined with ICIs significantly improved overall survival compared with ICI monotherapy (p=0.044).

CONCLUSIONS

An angiogenesis-related risk score based on three proteins (IL-8, TIE2, and HGF) could predict ICI response and prognosis in patients with esophageal cancer, which warrants verification in the future. Our study highlights the potential application of combining ICIs and antiangiogenic therapy and supports Olink plasma protein sequencing as a liquid biopsy method for biomarker exploration.

Cancer Vaccines in the Immunotherapy Era: Promise and Potential

Therapeutic vaccines are a promising alternative for active immunotherapy for different types of cancers. Therapeutic cancer vaccines aim to prevent immune system responses that are not targeted at the tumors only, but also boost the anti-tumor immunity and promote regression or eradication of the malignancy without, or with minimal, adverse events. Clinical trial data have pushed the development of cancer vaccines forward, and the US Food and Drug Administration authorized the first therapeutic cancer vaccine. In the present review, we discuss the various types of cancer vaccines and different approaches for the development of therapeutic cancer vaccines, along with the current state of knowledge and future prospects. We also discuss how tumor-induced immune suppression limits the effectiveness of therapeutic vaccinations, and strategies to overcome this barrier to design efficacious, long-lasting anti-tumor immune responses in the generation of vaccines.

A SARS-CoV-2 related signature that explores the tumor microenvironment and predicts immunotherapy response in esophageal squamous cell cancer

BACKGROUND

The existing therapeutic approaches for combating tumors are insufficient in completely eradicating malignancy, as cancer facilitates tumor relapse and develops resistance to treatment interventions. The potential mechanistic connection between SARS-CoV-2 and ESCC has received limited attention. Therefore, our objective was to investigate the characteristics of SARS-CoV-2-related-genes (SCRGs) in esophageal squamous cancer (ESCC).

METHODS

Raw data were obtained from the TCGA and GEO databases. Clustering of SCRGs from the scRNA-seq data was conducted using the Seurat R package. A risk signature was then generated using Lasso regression, incorporating prognostic genes related to SCRGs. Subsequently, a nomogram model was developed based on the clinicopathological characteristics and the risk signature.

RESULTS

Eight clusters of SCRGs were identified in ESCC utilizing scRNA-seq data, of which three exhibited prognostic implications. A risk signature was then made up with bulk RNA-seq, which displayed substantial correlations with immune infiltration. The novel signature was verified to have excellent prognostic efficacy.

CONCLUSION

The utilization of risk signatures based on SCRGs can efficiently forecast the prognosis of ESCC. A thorough characterization of the SCRGs signature in ESCC could facilitate the interpretation of ESCC's response to immunotherapy and offer innovative approaches to cancer therapy.

Dendritic cell hybrid nanovaccine for mild heat inspired cancer immunotherapy

Cancer therapeutic vaccine can induce antigen-specific immune response, which has shown great potential in cancer immunotherapy. As the key factor of vaccine, antigen plays a central role in eliciting antitumor immunity. However, the insufficient antigen delivery and low efficiency of antigen presentation by dendritic cells (DCs) have greatly restricted the therapeutic efficiency of vaccine. Here we developed a kind of DC hybrid zinc phosphate nanoparticles to co-deliver antigenic peptide and photosensitive melanin. Owing to the chelating ability of Zn2+, the nanoparticles can co-encapsulate antigenic peptide and melanin with high efficiency. The nanovaccine showed good physiological stability with the hydration particle size was approximately 30 nm, and zeta potential was around - 10 mV. The nanovaccine showed homologous targeting effect to DCs in vivo and in vitro, efficiently delivering antigen to DCs. Meanwhile, the nanovaccine could effectively reflux to the tumor-draining lymph nodes. When combined with near-infrared irradiation, the nanovaccine induced effective mild heat in vitro and in vivo to promote antigen presentation. After administrating to MC38 tumor-bearing mice, the hybrid nanovaccine effectively promoted the maturation of DCs, the expansion of cytotoxic T lymphocytes and helper T cells, and the secretion of immunostimulatory cytokines, thereby significantly inhibiting tumor growth.

Identification of molecular pattern and prognostic risk model based on ligand-receptor pairs in liver cancer

Introduction

The tumor microenvironment of hepatocellular carcinoma is composed of multiple cells, and the interactive communication between cells drives tumor progression and characterizes the tumor. Communication between cells is mainly achieved through signal transduction between receptor ligands, and the rise of single-cell technology has made it possible to analyze the communication network between cells.

Methods

We applied a train of bioinformatic techniques and in vitro experiments. We analyzed the composition of the microenvironment of liver cancer by combining single-cell sequencing data and transcriptome sequencing data from liver cancer to construct molecular typing and risk models for LRs. Then, we analyzed association of it with prognosis, mutation, KEGG, tumor microenvironment (TME), immune infiltration, tumor mutational burden (TMB) and drug sensitivity in liver cancer. qPCR and was used to identify SLC1A5 expression in LIHC cell lines and CCK8, transwell and cell colony formation were performed to validate the function of SLC1A5. Meanwhile, we also performed polarization of macrophages.

Results

In this experiment, we found that liver cancer tissues are rich in immune and mesenchymal cells, and there is extensive signaling between individual cells, so we constructed molecular typing and risk models for LRs. Combining clinical data revealed significant differences in clinical characteristics, prognosis and mutated genes between the molecular typing of receptor-ligand pairs, as well as in sensitivity to drugs; similarly, there were significant prognostic differences between the risk models. There were also notable differences in activated signaling pathways, infiltrating immune cells and immune subtypes. Subsequently, we used siRNA to knock down SLC1A5 in hepatocellular carcinoma cells and found that cell proliferation, migration and invasion were diminished.

Conclusions

In conclusion, our LRs model may become a marker to guide clinical treatment and prognosis.