Identification of three immune molecular subtypes associated with immune profiles, immune checkpoints, and clinical outcome in multiple myeloma

Investigating the clinical role and prognostic value of genes related to insulin-like growth factor signaling pathway in thyroid cancer

BACKGROUND

Thyroid cancer (THCA) is the most common endocrine malignancy having a female predominance. The insulin-like growth factor (IGF) pathway contributed to the unregulated cell proliferation in multiple malignancies. We aimed to explore the IGF-related signature for THCA prognosis.

METHOD

The TCGA-THCA dataset was collected from the Cancer Genome Atlas (TCGA) for screening of key prognostic genes. The limma R package was applied for differentially expressed genes (DEGs) and the clusterProfiler R package was used for the Gene Ontology (GO) and KEGG analysis of DEGs. Then, the un/multivariate and least absolute shrinkage and selection operator (Lasso) Cox regression analysis was used for the establishment of RiskScore model. Receiver Operating Characteristic (ROC) analysis was used to verify the model's predictive performance. CIBERSORT and MCP-counter algorithms were applied for immune infiltration analysis. Finally, we analyzed the mutation features and the correlation between the RiskScore and cancer hallmark pathway by using the GSEA.

RESULT

We obtained 5 key RiskScore model genes for patient's risk stratification from the 721 DEGs. ROC analysis indicated that our model is an ideal classifier, the high-risk patients are associated with the poor prognosis, immune infiltration, high tumor mutation burden (TMB), stronger cancer stemness and stronger correlation with the typical cancer-activation pathways. A nomogram combined with multiple clinical features was developed and exhibited excellent performance upon long-term survival quantitative prediction.

CONCLUSIONS

We constructed an excellent prognostic model RiskScore based on IGF-related signature and concluded that the IGF signal pathway may become a reliable prognostic phenotype in THCA intervention.

Identification of an angiogenesis-related risk score model for survival prediction and immunosubtype screening in multiple myeloma

BACKGROUND

Multiple myeloma (MM) is an incurable B-cell malignancy, but with the emergence of immunotherapy, a potential cure is hopeful. The individualized interaction between the tumor and bone marrow (BM) microenvironment determines the response to immunotherapy. Angiogenesis is a constant hallmark of the BM microenvironment in MM. However, little is known about the potency ability of angiogenesis-associated genes (AAGs) to regulate the immune microenvironment of MM patients.

METHODS

We comprehensively dissected the associations between angiogenesis and genomic landscapes, prognosis, and the immune microenvironment by integrating 36 AAGs. Immunohistochemistry was performed to verify the correlation between angiogenic factor expression and patient prognosis. Single-sample gene set enrichment analysis was applied to quantify the relative abundance of 28 infiltrating cells. The AAG score was constructed using the least absolute shrinkage and selection operator Cox regression model.

RESULTS

Angiogenesis was closely correlated with MM patient prognosis, and the mutation intensity of the AAGs was low. Immunohistochemistry confirmed that high microvessel density predicted poor prognosis. Three AAG clusters and two gene clusters with distinct clinical outcomes and immune characteristics were identified. The established AAG_score model performed well in predicting patient prognosis and active immunotherapy response. The high-AAG_score subgroup was characterized by reduced immune cell infiltration, poor prognosis, and inactive immunotherapy response. Multivariate analyses indicated that the AAG_score was strongly robust and independent among the prognostic variables.

CONCLUSION

This study revealed that angiogenesis is significantly related to MM patient prognosis and immune phenotype. Evaluating the AAG signature was conducive to predicting patient response to immunotherapy and guiding more efficacious immunotherapy strategies.

CD74 is associated with inflamed tumor immune microenvironment and predicts responsiveness to PD-1/CTLA-4 bispecific antibody in patients with solid tumors

INTRODUCTION

Cadonilimab (AK104) is a first-in-class tetravalent bispecific antibody that targets both PD-1 and CTLA-4, showing a manageable safety profile and favorable clinical benefits. This study aimed to identify the biomarkers of clinical response and explore the immune response within the tumor microenvironment upon the AK104 therapy in advanced solid tumors.

MATERIAL AND METHODS

Gene expression profiles of paired pre- and post-treatment tumor tissues from twenty-one patients were analyzed. The association of gene expression levels with either clinical efficacy or prognosis was evaluated and subsequently validated with published datasets using log-rank for Kaplan-Meier estimates. Comparative immune profile analyses of tumor microenvironment before and after AK104 treatment were conducted. The visualization of tumor-infiltrating lymphocytes was performed using multiplex immunohistochemistry. The predictive value of CD74 was further validated with protein expression by immunohistochemistry.

RESULTS

Baseline CD74 gene expression was associated with favorable patient outcomes (overall survival [OS], HR = 0.33, 95% CI 0.11-1.03, p = 0.0463), which was further confirmed with the published datasets. Tumors with high CD74 gene expression at baseline were more likely to exhibit an immune-inflamed microenvironment. AK104 efficiently enhanced the infiltration of immune cells in the tumor microenvironment. Additionally, high CD74 protein expression (≥ 10% of the tumor area occupied by CD74 stained immune cells) at baseline was associated with better progressive-free survival (HR = 0.21, 95% CI 0.06-0.68, p = 0.0065) and OS (HR = 0.35, 95% CI 0.12-1.08, p = 0.0615).

CONCLUSIONS

Our findings demonstrate that CD74 is a promising predictive biomarker for AK104 therapeutic response in advanced solid tumors. Trial registration number NCT03261011.

Identification of three immune molecular subtypes associated with immune profiles, immune checkpoints, and clinical outcome in multiple myeloma

Purpose

To identify the immune molecular subtype for MM to help achieve individualized and precise targeted therapy.

Methods

The GDC API was used to download the TCGA‐MM profile dataset, which contains 859 samples in total, all of which were anterior to the standard treatment after diagnosis. Moreover, 282, 298, and 258 samples were stage I, stage II, and stage III separately. We used the immune gene expression profile for consistent clustering; and used the R software package ConsensusClusterPlus to sort the immune molecular subtypes. Correlation between subtypes and clinical features, immunity, and prognosis was then analyzed.

Results

A total of 859 tumor samples were separated into these three subtypes, which were not meaningfully related to age or sex but showed a remarkable association with stage. The results suggested that obvious differences in immune metagene expression and expression of 10 immune checkpoint genes appeared among the three subtypes.

Conclusion

The three subtypes are distinctly different in terms of immune metagenes, immune checkpoint molecules, and clinical prognosis. The discovery of the immune microenvironment of MM could further reveal the strategy for immunotherapy in MM and provide a promising candidate prognostic tool for survival.