Macrophage correlates with immunophenotype and predicts anti-PD-L1 response of urothelial cancer

A Pan-Cancer Analysis of CD161, a Potential New Immune Checkpoint

Background

CD161, encoded by killer cell lectin-like receptor B1 gene, is a newly reported candidate inhibitor of tumour-infiltrating T cells. Antibody-mediated CD161 blockade enhances T cell-mediated killing of cancer cells in vitro and in vivo in several tumour types. We evaluated the role of CD161 using The Cancer Genome Atlas (TCGA) Pan-Cancer Data.

Methods

CD161 expression was analysed using RNAseq data from TCGA and the Genotype-Tissue Expression (GTEx) database. HPA, GeneCards, and String database were used to explore the protein information of CD161. The prognostic value of CD161 was analysed using clinical survival data from the TCGA. Enrichment analysis of CD161 was conducted using the R package “clusterProfiler”. We downloaded the immune cell infiltration score of TCGA samples from published articles and online databases and performed a correlation analysis between immune cell infiltration levels and CD161 expression. We further assessed the association between CD161 and immune checkpoints, immune activating genes, immunosuppressive genes, chemokines, and chemokine receptors.

Findings

CD161 was differentially expressed and predicted better survival status in most tumour types in TCGA. In addition, CD161 expression was significantly associated with immunoregulatory interactions between lymphoid and non-lymphoid cells. CD161 expression was closely correlated with T cell infiltration, immune checkpoints, immune activating genes, immunosuppressive genes, chemokines, and chemokine receptors.

Interpretation

Our results suggest that CD161 is a potential cancer biomarker. CD161 might synergize with other immune checkpoints to regulate the immune microenvironment, which could be applied in the development of new-targeted drugs for immunotherapy.

Funding

This work was supported by the National Nature Science Foundation of China (grant numbers 81773008, 81672756, 81872399, 81972897), the Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2015), the Natural Science Foundation of Guangdong Province (grant number 2017A030311023), the Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program: 2017BT01S131 and the Guangzhou Technology Project (grant number 201804010044), National Key R&D Program of China (Grant Nos. 2020YFC2006400), Key-Area Research and Development Program of Guangdong Province (2019B020227004).

An N6-Methyladenosine-Related Gene Set Variation Score as a Prognostic Tool for Lung Adenocarcinoma

N6-methyladenosine (m6A) is the most prevalent type of RNA modification, and we hypothesized that patterns of m6A-related genes may be useful for estimating risk of lung adenocarcinoma (LUAD). An m6A-related gene set variation score (m6A-GSVS) was generated using RNA-sequencing data from LUAD patients in The Cancer Genome Atlas (TCGA). We investigated the association of m6A-GSVS with stemness, tumor mutational burden (TMB), expression of three immune checkpoints, levels of tumor-infiltrating lymphocytes (TILs), and patient prognosis. We found that m6A-GSVS was higher in LUAD than in healthy lung tissue, and it strongly correlated with stemness and TMB. Activated CD4 + T cells were more numerous in LUAD samples that had higher m6A-GSVS than in those with lower scores. Biological processes and pathways, including “Cell cycle,” “DNA replication,” and “RNA degradation,” were significantly enriched in samples with high scores. Furthermore, m6A-GSVS was an independent prognostic indicator in LUAD. In conclusion, we proposed an m6A-GSVS in LUAD. It is a putative indicator for evaluating the ability to RNA m6A, an independent prognostic indicator and associated with tumor stemness.

IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures

Recent advances in next-generation sequencing (NGS) technologies have triggered the rapid accumulation of publicly available multi-omics datasets. The application of integrated omics to explore robust signatures for clinical translation is increasingly emphasized, and this is attributed to the clinical success of immune checkpoint blockades in diverse malignancies. However, effective tools for comprehensively interpreting multi-omics data are still warranted to provide increased granularity into the intrinsic mechanism of oncogenesis and immunotherapeutic sensitivity. Therefore, we developed a computational tool for effective Immuno-Oncology Biological Research (IOBR), providing a comprehensive investigation of the estimation of reported or user-built signatures, TME deconvolution, and signature construction based on multi-omics data. Notably, IOBR offers batch analyses of these signatures and their correlations with clinical phenotypes, long non-coding RNA (lncRNA) profiling, genomic characteristics, and signatures generated from single-cell RNA sequencing (scRNA-seq) data in different cancer settings. Additionally, IOBR integrates multiple existing microenvironmental deconvolution methodologies and signature construction tools for convenient comparison and selection. Collectively, IOBR is a user-friendly tool for leveraging multi-omics data to facilitate immuno-oncology exploration and to unveil tumor-immune interactions and accelerating precision immunotherapy.

Robust Prediction of Immune Checkpoint Inhibition Therapy for Non-Small Cell Lung Cancer

Background

The development of immune checkpoint inhibitors (ICIs) is a revolutionary milestone in the field of immune-oncology. However, the low response rate is the major problem of ICI treatment. The recent studies showed that response rate to single-agent programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibition in unselected non-small cell lung cancer (NSCLC) patients is 25% so that researchers defined several biomarkers to predict the response of immunotherapy in ICIs treatment. Common biomarkers like tumor mutational burden (TMB) and PD-L1 expression have several limitations, such as low accuracy and inadequately validated cutoff value.

Methods

Two published and an unpublished ICIs treatment NSCLC cohorts with 129 patients were collected and divided into a training cohort (n = 53), a validation cohort (n = 22), and two independent test cohorts (n = 34 and n = 20). We identified six immune-related pathways whose mutational status was significantly associated with overall survival after ICIs treatment. Then these pathways mutational status combined with TMB, PD-L1 expression and intratumor heterogeneity were incorporated to build a Bayesian-regularization neural networks (BRNN) model to predict the ICIs treatment response.

Results

We firstly proved that TMB, PD-L1, and mutant-allele tumor heterogeneity (MATH) were independent biomarkers. The survival analysis of six immune-related pathways revealed the mutational status could distinguish overall survival after ICIs treatment. When predicting immunotherapy efficacy, the overall accuracy of area under curve (AUC) in validation cohort reaches 0.85, outperforming previous predictors in either sensitivity or specificity. And the AUC in two independent test cohorts reach 0.74 and 0.80.

Conclusion

We developed a pathway-model that could predict the efficacy of ICIs in NSCLC patients. Our study made a significant contribution to solving the low prediction accuracy of immunotherapy of single biomarker. With the accumulation of larger data sets, further studies are warranted to refine the predictive performance of the approach.

Identification of an Immune-Related Risk Signature Correlates With Immunophenotype and Predicts Anti-PD-L1 Efficacy of Urothelial Cancer

Immune checkpoint inhibitor (ICI) treatment has been used to treat advanced urothelial cancer. Molecular markers might improve risk stratification and prediction of ICI benefit for urothelial cancer patients. We analyzed 406 cases of bladder urothelial cancer from The Cancer Genome Atlas (TCGA) data set and identified 161 messenger RNAs (mRNAs) as differentially expressed immunity genes (DEIGs). Using the LASSO Cox regression model, an eight-mRNA-based risk signature was built. We validated the prognostic and predictive accuracy of this immune-related risk signature in 348 metastatic urothelial cancer (mUC) samples treated with anti-PD-L1 (atezolizumab) from IMvigor210. We built an immune-related risk signature based on the eight mRNAs: ANXA1, IL22, IL9R, KLRK1, LRP1, NRG3, SEMA6D, and STAP2. The eight-mRNA-based risk signature successfully categorizes patients into high-risk and low-risk groups. Overall survival was significantly different between these groups, regardless if the initial TCGA training set, the internal TCGA testing set, all TCGA set, or the ICI treatment set. The hazard ratio (HR) of the high-risk group to the low-risk group was 3.65 (p < 0.0001), 2.56 (p < 0.0001), 3.36 (p < 0.0001), and 2.42 (p = 0.0009). The risk signature was an independent prognostic factor for prediction survival. Moreover, the risk signature was related to immunity characteristics. In different tumor mutational burden (TMB) subgroups, it successfully categorizes patients into high-risk and low-risk groups, with significant differences of clinical outcome. Our eight-mRNA-based risk signature is a stable biomarker for urothelial cancer and might be able to predict which patients benefit from ICI treatment. It might play a role in precision individualized immunotherapy.

Paving the way for small-molecule drug discovery

Small-molecule drugs are organic compounds affecting molecular pathways by targeting important proteins, which have a low molecular weight, making them penetrate cells easily. Small-molecule drugs can be developed from leads derived from rational drug design or isolated from natural resources. As commonly used medications, small-molecule drugs can be taken orally, which enter cells to act on intracellular targets. These characteristics make small-molecule drugs promising candidates for drug development, and they are increasingly favored in the pharmaceutical market. Despite the advancements in molecular genetics and effective new processes in drug development, the drugs currently used in clinical practice are inadequate due to their poor efficacy or severe side effects. Therefore, developing new safe and efficient drugs is a top priority for disease control and curing.

Establishment of a Macrophage Phenotypic Switch Related Prognostic Signature in Patients With Pancreatic Cancer

Introduction

Macrophage phenotype switch plays a vital role in the progression of malignancies. We aimed to build a prognostic signature by exploring the expression pattern of macrophage phenotypic switch related genes (MRGs) in the Cancer Genome Atlas (TCGA)—pancreatic adenocarcinoma (PAAD), Genotype-Tissue Expression (GTEx)-Pancreas, and Gene Expression Omnibus (GEO) databases.

Methods

We identified the differentially expressed genes between the PAAD and normal tissues. We used single factor Cox proportional risk regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) analysis, and multivariate Cox proportional hazard regression analysis to establish the prognosis risk score by the MRGs. The relationships between the risk score and immune landscape, “key driver” mutations and clinicopathological factors were also analyzed. Gene-set enrichment analysis (GSEA) analysis was also performed.

Results

We detected 198 differentially expressed MRGs. The risk score was constructed based on 9 genes (KIF23, BIN1, LAPTM4A, ERAP2, ATP8B2, FAM118A, RGS16, ELMO1, RAPGEFL1). The median overall survival time of patients in the low-risk group was significantly longer than that of patients in the high-risk group (P < 0.001). The prognostic value of the risk score was validated in GSE62452 dataset. The prognostic performance of nomogram based on risk score was superior to that of TNM stage. And GSEA analysis also showed that the risk score was closely related with P53 signaling pathway, pancreatic cancer and T cell receptor signaling pathway. qRT-PCR assay showed that the expressions of the 9 MRGs in PDAC cell lines were higher than those in human pancreatic ductal epithelium cell line.

Conclusions

The nine gene risk score could be used as an independent prognostic index for PAAD patients. Further studies validating the prognostic value of the risk score are warranted.

PD-L1 correlated gene expression profiles and tumor infiltrating lymphocytes in pancreatic cancer

Objective: To study the expression and clinical value of PD-L1 gene in pancreatic cancer, and to predict the role of PD-L1 gene in the development of pancreatic cancer. Methods: The pancreatic cancer datasets were downloaded from the Cancer Genome Atlas (TCGA) and the Oncomine to obtain the PD-L1 gene expression profile and clinical information. Bioinformatics methods were used to analyze the correlation between the expression level of PD-L1 gene in pancreatic cancer and clinicopathological indicators, as well as its influence on prognosis. GSEA and WGCNA analysis was performed to predict the possible pathways of PD-L1 gene regulation in pancreatic cancer. TIMER and MCP-counter were used for PD-L1 with immune infiltration. The genes interact with PD-L1 were also investigated by STING and immunoco-precipitation combined with mass spectrometry analysis (IP-MS). Results: In TCGA database, the overall survival of patients with high expression of PD-L1 gene was significantly lower than that of patients with low expression of PD-L1 gene (χ² = 12.52, P < 0.001). The samples with high expression of PD-L1 gene showed enrichment of 8 pathways including toll-like receptor signaling pathway and NOD receptor signaling pathway (P < 0.01, FDR < 0.05). Immune infiltration analysis suggested that PD-L1 were associated with monocytic lineage (r = 0.5). The proteins interacting with PD-L1 are mainly concentrated in RNA binding, ribosome, spliceosome and other biological processes or pathways. Conclusion: PD-L1 gene may play an important role in the development of pancreatic cancer and is expected to be a prognostic indicator of pancreatic cancer.

m6A regulator-based methylation modification patterns characterized by distinct tumor microenvironment immune profiles in colon cancer

Recent studies have highlighted the biological significance of RNA N⁶-methyladenosine (m⁶A) modification in tumorigenicity and progression. However, it remains unclear whether m⁶A modifications also have potential roles in immune regulation and tumor microenvironment (TME) formation. Methods: In this study, we curated 23 m⁶A regulators and performed consensus molecular subtyping with NMF algorithm to determine m⁶A modification patterns and the m⁶A-related gene signature in colon cancer (CC). The ssGSEA and CIBERSORT algorithms were employed to quantify the relative infiltration levels of various immune cell subsets. An PCA algorithm based m⁶Sig scoring scheme was used to evaluate the m⁶A modification patterns of individual tumors with an immune response. Results: Three distinct m6A modification patterns were identified among 1307 CC samples, which were also associated with different clinical outcomes and biological pathways. The TME characterization revealed that the identified m⁶A patterns were highly consistent with three known immune profiles: immune-inflamed, immune-excluded, and immune-desert, respectively. Based on the m⁶Sig score, which was extracted from the m⁶A-related signature genes, CC patients can be divided into high and low score subgroups. Patients with lower m⁶Sig score was characterized by prolonged survival time and enhanced immune infiltration. Further analysis indicated that lower m⁶Sig score also correlated with greater tumor mutation loads, PD-L1 expression, and higher mutation rates in SMGs (e.g., PIK3CA and SMAD4). In addition, patients with lower m⁶Sig scores showed a better immune responses and durable clinical benefits in three independent immunotherapy cohorts. Conclusions: This study highlights that m⁶A modification is significantly associated with TME diversity and complexity. Quantitatively evaluating the m⁶A modification patterns of individual tumors will strengthen our understanding of TME characteristics and promote more effective immunotherapy strategies.

An Immune-Related Gene Prognostic Index for Head and Neck Squamous Cell Carcinoma

PURPOSE

To construct an immune-related gene prognostic index (IRGPI) for head and neck squamous cell carcinoma (HNSCC) and clarify the molecular and immune characteristics and the benefit of immune checkpoint inhibitor (ICI) therapy in IRGPI-defined subgroups of HNSCC.

EXPERIMENTAL DESIGN

On the basis of The Cancer Genome Atlas HNSCC immune dataset (n = 546), 22 immune-related hub genes were identified by weighted gene coexpression network analysis. Three genes were identified to construct an IRGPI by using the Cox regression method and validated with the Gene Expression Omnibus (GEO) dataset (n = 270). Afterward, the molecular and immune characteristics and the benefit of ICI therapy in IRGPI-defined subgroups were analyzed.

RESULTS

The IRGPI was constructed on the basis of SFRP4, CPXM1, and COL5A1 genes. IRGPI-high patients had a better overall survival than IRGPI-low patients, consistent with the results in the GEO cohort. The comprehensive results showed that a high IRGPI score was correlated with DNA repair-related pathways; low TP53 mutation rate; high infiltration of CD8 T cells, CD4 T cells, and M1 macrophages; active immunity and less aggressive phenotypes; and more benefit from ICI therapy. In contrast, a low IRGPI score was associated with cancer and metastasis-related pathways; high TP53 and PIK3CA mutation rate; high infiltration of B cells, M0 macrophages, and M2 macrophages; suppressive immunity and more aggressive phenotypes; and less benefit from ICI therapy.

CONCLUSIONS

IRGPI is a promising biomarker to distinguish the prognosis, the molecular and immune characteristics, and the immune benefit from ICI therapy in HNSCC.

FOXO1 promotes tumor progression by increased M2 macrophage infiltration in esophageal squamous cell carcinoma

Objective: The transcription factor forkhead box protein O1 (FOXO1) is critical for regulating cytokine and chemokine secretion. However, its function in the tumor microenvironment (TME) remains largely unexplored. In this study, we characterized the prognostic value of FOXO1 and the interaction between tumor-derived FOXO1 and M2 macrophages in esophageal squamous cell carcinoma (ESCC).

Methods: FOXO1 expression and macrophage infiltration in clinical samples and mouse models were quantified using quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry staining. Western blotting, qRT-PCR, and enzyme-linked immunosorbent assay were used to evaluate chemokine ligand 20 (CCL20) and colony stimulating factor 1 (CSF-1) expression in FOXO1(+) and FOXO1(-) tumor cells. Macrophage phenotypes were determined using qRT-PCR, flow cytometry, and RNA sequencing. Transcriptional activity was measured using chromatin immunoprecipitation (ChIP)-qPCR. Tumor viability was investigated using XTT proliferation and foci formation assays.

Results: FOXO1 upregulation in tumor tissues was found to drive the polarization of M0 macrophages and infiltration of M2 macrophages into the TME, resulting in worse prognosis in ESCC patients. CSF-1, a vital factor inducing M0-to-M2 polarization, was upregulated via a FOXO1-mediated mechanism. RNA sequencing results corroborated that the FOXO1-induced macrophages exhibited similar molecular signatures to the IL4-stimulated M2 macrophages. The transwell assays showed that FOXO1 promoted the migration of M2 macrophages via CCL20 secretion, which could be inhibited using an anti-CCL20 antibody. FOXO1(+) tumor-induced M2 macrophages promoted tumor proliferation via the FAK-PI3K-AKT pathway and the PI3K inhibitor could effectively impede the oncogenical process.

Conclusions: FOXO1 facilitated M0-to-M2 polarization and the recruitment of M2 macrophages in the TME via the transcriptional modulation of CCL20 and CSF-1. Our data deciphered the FOXO1-dependent mechanism in M2 macrophage infiltration in the TME of ESCC, which has implications for the development of novel prognostic and therapeutic targets to optimize the current treatment against ESCC.