Identification and Validation of the Immune Subtypes of Lung Adenocarcinoma: Implications for Immunotherapy

Stem-like CD8 T cells in stage I lung adenocarcinoma as a prognostic biomarker: A preliminary study

OBJECTIVES

This study aimed to investigate the presence of stem-like CD8 T (CD8 TSL) cells in lung adenocarcinoma (LUAD) and explore their relationships with the clinical outcomes.

METHODS

Multiplex immunofluorescence (mIF) was performed to identify CD8 TSL and antigen-presenting cells (APC) in 76 LUAD patients. Differences in the number of CD8 TSL cells based on tumor stage and the spatial relationships between CD8 TSL cells and APC niches were determined. The optimal cutoff value of CD8 TSL cells for predicting survival in patients with stage I LUAD was calculated.

RESULTS

CD8 TSL cells were present in all tumors, and their numbers were significantly higher in stage I patients than in stage III patients (P = 0.010); CD8 TSL cells located in the APC niches accounted for 69.7% (53/76) of the hotspot fields. The optimal cutoff value for the number of CD8 TSL cells required to predict the overall survival (OS) in patients with stage I LUAD was 2.5 per 10000 μm2. The median OS and progression-free survival (PFS) in the high-level group (>2.5) were significantly (P < 0.001) longer than those in the low-level group (≤2.5). The number of CD8 TSL cells was an independent prognostic factor for stage I LUAD. Patients with more CD8 TSL cells had a lower risk of death and disease progression than those with less CD8 TSL cells.

CONCLUSION

CD8 TSL cells were observed in patients with stages I-III LUAD and might serve as prognostic biomarkers for stage I LUAD.

IGH repertoire analysis at scale: deciphering the complexity of B cell infiltration and migration in esophageal squamous cell carcinoma

Tumor-infiltrating B-lineage cells have become predictors of prognosis and immunotherapy responses in various cancers. However, limited knowledge about their infiltration and migration patterns has hindered the understanding of their anti-tumor functions. Here, we examined the immunoglobulin heavy chain (IGH) repertoires in 496 multi-regional tumor, 107 normal tissue, and 48 metastatic lymph node samples obtained from 107 patients with esophageal squamous cell carcinoma (ESCC). Our study revealed higher IgG-type B-lineage cells infiltration in tumors than in healthy tissue, which was associated with improved patient outcomes. Genes such as ACTN1, COL6A5, and pathways like focal adhesion, which shapes the physical structure of tumors, could affect B-lineage cell infiltration. Notably, the IGH sequence was used as an identity-tag to monitor B cell migration, and their infiltration schema within the tumor were depicted based on our multi-regional tumor specimens. This analysis revealed an escalation in B cell clones overlapped between metastatic lymph nodes and tumors. Therefore, the Lymph Node Activation Index was defined, which could predict the outcomes of patients with lymph node metastasis. This research introduces a novel framework for probing B cell infiltration and migration within the tumor microenvironment using large-scale transcriptome data, while simultaneously providing fresh perspectives on B cell immunology within ESCC.

Fusing WGCNA and Machine Learning for Immune-Related Gene Prognostic Index in Lung Adenocarcinoma: Precision Prognosis, Tumor Microenvironment Profiling, and Biomarker Discovery

Background

The objective is to create an IRGPI (Immune-related genes prognostic index), which could predict the survival and effectiveness of immune checkpoint inhibitor (ICI) treatment for lung adenocarcinoma (LUAD).

Methods

By applying weighted gene co-expression network analysis (WGCNA), we ascertained 13 genes associated with immune functions. An IRGPI was constructed using four genes through multicox regression, and its validity was assessed in the GEO dataset. Next, we explored the immunological and molecular attributes and advantages of ICI treatment in subcategories delineated by IRGPI. The model genes were also validated by the random forest tree, and functional experiments were conducted to validate it.

Results

The IRGPI relied on the genes CD79A, IL11, CTLA-4, and CD27. Individuals categorized as low-risk exhibited significantly improved overall survival in comparison to those classified as high-risk. Extensive findings indicated that the low-risk category exhibited associations with immune pathways, significant infiltration of CD8 T cells, M1 macrophages, and CD4 T cells, a reduced rate of gene mutations, and improved sensitivity to ICI therapy. Conversely, the higher-risk group displayed metabolic signals, elevated frequencies of TP53, KRAS, and KEAP1 mutations, escalated levels of NK cells, M0, and M2 macrophage infiltration, and a diminished response to ICI therapy. Additionally, our study unveiled that the downregulation of IL11 effectively impedes the proliferation and migration of lung carcinoma cells, while also inducing cell cycle arrest.

Conclusion

IRGPI is a biomarker with significant potential for predicting the effectiveness of ICI treatment in LUAD patients and is closely related to the microenvironment and clinicopathological characteristics.

Loss of Cadherin-11 in pancreatic ductal adenocarcinoma alters tumor-immune microenvironment

Pancreatic ductal adenocarcinoma (PDAC) is one of the top five deadliest forms of cancer with very few treatment options. The 5-year survival rate for PDAC is 10% following diagnosis. Cadherin 11 (Cdh11), a cell-to-cell adhesion molecule, has been suggested to promote tumor growth and immunosuppression in PDAC, and Cdh11 inhibition significantly extended survival in mice with PDAC. However, the mechanisms by which Cdh11 deficiency influences PDAC progression and anti-tumor immune responses have yet to be fully elucidated. To investigate Cdh11-deficiency induced changes in PDAC tumor microenvironment (TME), we crossed p48-Cre; LSL-KrasG12D/+; LSL-Trp53R172H/+ (KPC) mice with Cdh11+/- mice and performed single-cell RNA sequencing (scRNA-seq) of the non-immune (CD45-) and immune (CD45+) compartment of KPC tumor-bearing Cdh11 proficient (KPC-Cdh11+/+) and Cdh11 deficient (KPC-Cdh11+/-) mice. Our analysis showed that Cdh11 is expressed primarily in cancer-associated fibroblasts (CAFs) and at low levels in epithelial cells undergoing epithelial-to-mesenchymal transition (EMT). Cdh11 deficiency altered the molecular profile of CAFs, leading to a decrease in the expression of myofibroblast markers such as Acta2 and Tagln and cytokines such as Il6, Il33 and Midkine (Mdk). We also observed a significant decrease in the presence of monocytes/macrophages and neutrophils in KPC-Cdh11+/- tumors while the proportion of T cells was increased. Additionally, myeloid lineage cells from Cdh11-deficient tumors had reduced expression of immunosuppressive cytokines that have previously been shown to play a role in immune suppression. In summary, our data suggests that Cdh11 deficiency significantly alters the fibroblast and immune microenvironments and contributes to the reduction of immunosuppressive cytokines, leading to an increase in anti-tumor immunity and enhanced survival.

A Signature Constructed Based on the Integrin Family Predicts Prognosis and Correlates with the Tumor Microenvironment of Patients with Lung Adenocarcinoma

As an important element in regulating the tumor microenvironment (TME), integrin plays a key role in tumor progression. This study aimed to establish prognostic signatures to predict the overall survival and identify the immune landscape of patients with lung adenocarcinoma based on integrins. The Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD) and Gene Expression Omnibus datasets were used to obtain information on mRNA levels and clinical factors (GSE72094). The least absolute shrinkage and selection operator (LASSO) model was used to create a prediction model that included six integrin genes. The nomogram, risk score, and time-dependent receiver operating characteristic analysis all revealed that the signatures had a good prognostic value. The gene signatures may be linked to carcinogenesis and TME, according to a gene set enrichment analysis. The immunological and stromal scores were computed using the ESTIMATE algorithm, and the data revealed, the low-risk group had a higher score. We discovered that the B lymphocytes, plasma, CD4+ T, dendritic, and mast cells were much higher in the group with low-risk using the CiberSort. Inflammatory processes and several HLA family genes were upregulated in the low-risk group. The low-risk group with a better prognosis is more sensitive to immune checkpoint inhibitor medication, according to immunophenoscore (IPS) research. We found that the patients in the high-risk group were more susceptible to chemotherapy than other group patients, according to the prophetic algorithm. The gene signatures could accurately predict the prognosis, identify the immune status of patients with lung adenocarcinoma, and provide guidance for therapy.

Single-cell sequencing analysis and transcriptome analysis constructed the macrophage related gene-related signature in lung adenocarcinoma and verified by an independent cohort

BACKGROUND

Recent studies have emphasized the close relationship between macrophages and tumor immunity, and the prognosis of lung adenocarcinoma (LUAD) patients is intimately linked to this. Nonetheless, the prognostic signature and classification of different immune patterns in LUAD patients based on the macrophages is largely unexplored.

METHODS

Two sc-RNAseq datasets of LUAD patients were collected and reprocessed. The differentially expressed genes (DEGs) related to macrophages between LUAD tissues and normal lung tissues were then identified. Based upon the above genes, three distinct immune patterns in the TCGA-LUAD cohort were identified. The ssGSEA and CIBERSORT were applied for immune profiling and characterization of different subtypes. A four-gene prognostic signature for LUAD patients was established based on the DEGs between the subtypes using stepwise multi-Cox regression. TCGA-LUAD cohort was used as training set. Five GEO-LUAD datasets and an independent cohort containing 112 LUAD samples were used for validation. TIDE (tumor immune dysfunction and exclusion) and drug sensitivity analyses were also performed.

RESULTS

Macrophage-related differentially expressed genes were found out using the publicly available scRNA-seq data of LUAD. Three different immune patterns which were proved to have distinct immune infiltration characteristics in the TCGA-LUAD cohort were recognized based on the above macrophage-related genes. Thereafter, 174 DEGs among the above three different immune patterns were figured out; on the basis of this, a four-gene prognostic signature was constructed. This signature distinguished the prognosis of LUAD patients well in various GSE datasets as well as our independent cohort. Further analyses revealed that patients which had a higher risk score also accompanied with a lower immune infiltration level and a worse response to several immunotherapy biomarkers.

CONCLUSION

This study highlighted that macrophage were significantly associated with TME diversity and complexity. The four-gene prognostic signature could be used for predicting outcomes and immune landscapes for patients with LUAD.

Ttc39c is a potential target for the treatment of lung cancer

BACKGROUND

The novel TTC gene, tetratricopeptide repeat domain 39 C (Ttc39c), mainly mediates the interaction between proteins. It is involved in the progression of various tumors. In this study, we determined the effect of Ttc39c on lung adenocarcinoma and found that it might be used as a potential intervention target.

METHODS

We performed a difference analysis of Ttc39c samples from the TCGA database. Transwell experiments were conducted to determine the ability of cell metastasis. Celigo and MTT assays were performed to determine the effect of Ttc39c gene subtraction on cell proliferation. FACS was performed to determine the effect of Ttc39c gene subtraction on apoptosis. Clone-formation experiments were conducted to determine the effect of Ttc39c gene subtraction on cloning ability. Transcriptomics, proteomics, and metabolomics were used to elucidate the enrichment pathway of the Ttc39c gene in the progression of lung adenocarcinoma.

RESULTS

The expression of Ttc39c increased significantly in lung adenocarcinoma. The proliferation, metastasis, and cloning ability of human lung cancer cells were inhibited, while the apoptosis of cells increased significantly after the depletion of Ttc39c. Our results based on the transcriptomics, proteomics, and metabolomics analyses indicated that Ttc39c might be involved in the progression of lung adenocarcinoma (LUAD) mainly through the metabolic pathway and the p53 pathway.

CONCLUSION

To summarize, Ttc39c strongly regulates the proliferation and metastasis of lung adenocarcinoma cells. The main pathways involved in Ttc39c in lung adenocarcinoma include the energy metabolism and p53 pathways.

Development and Validation of a DNA Methylation-related Classifier of Circulating Tumour Cells to Predict Prognosis and to provide a therapeutic strategy in Lung Adenocarcinoma

Background: A significant factor influencing the prognosis of lung adenocarcinoma (LUAD) is tumor metastasis. Studies have shown that abnormal DNA methylation in circulating tumor cells (CTCs) is associated with tumour metastasis. Based on the genes expressed in CTCs that play an important role in DNA methylation, we hope to build a risk model to predict prognosis and provide a therapeutic strategy in LUAD.

Methods: The CTC sequencing data for LUAD were obtained from GSE74639, which contains 10 CTC samples and 6 primary tumour samples. To carefully assess the clinical value, functional status, involvement of the tumor microenvironment (TME) based on the risk model, and genetic variants based on based on data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), a reliable risk model was successfully built.

Results: Three differentially methylated genes (DMGs) of CTCs for LUAD, including mitochondrial ribosomal protein L51 (MRPL51), STE20-like kinase (SLK), and protein regulator of cytokinesis 1(PRC1), were effectively used to construct a risk model. Both the training and validation cohorts' stability and accuracy of the risk model were evaluated. Each patient in the TCGA-LUAD cohort received a risk score, and based on the median score, they were divided into high- and low-risk groups. The tumors in the high-risk group in this study were classified as "cold" and immunosuppressed, which may be linked to a poor prognosis. The tumors in the low-risk group, however, were deemed "hot" and had immune hyperfunction linked to a positive prognosis. Additionally, patients in the low-risk group showed greater sensitivity to immunotherapy than those in the high-risk group.

Conclusions: Based on DMGs of CTCs from LUAD, we successfully developed a predictive risk model and discovered differences in biological function, TME, genetic variation, and clinical outcomes between those at high and low risk group.

Exploration of Novel Immunological Terms in Lung Cancer With Large Populations: Implications for Immunotherapy

Background

Ideal biomarkers to predict the response to immunotherapy in lung cancer are still lacking. Therefore, there is a need to explore effective biomarkers in large populations.

Objective

The objective of this study is to explore novel immunological classifications that are associated with immunotherapy response through the ssGSEA algorithm.

Methods

Six independent lung cancer cohorts were collected for analysis including The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the EMBL-EBI database. The ssGSEA algorithm was performed to extract immune terms. Then, TCGA samples were involved as a training group and other cohorts were used as a validation group. After LASSO and Cox regression, prognostic associated immune terms were extracted and an immune-related risk score (IRS) signature was constructed. Furthermore, the association between IRS signature and clinical data, genome features, stemness indices analysis, tumor immune microenvironment, immunotherapy efficiency, and targeted therapy response was also investigated.

Results

A total of 1,997 samples were enrolled in this study including six large lung cancer cohorts. Fifty-four immune terms were calculated through the ssGSEA algorithm in combined cohorts. Then, a nine-immune-term risk score model named IRS signature was established to predict the prognosis in combined cohorts. We classified patients into high-risk and low-risk subgroups according to the cutoff point. Subsequently, analysis of clinical data and genome features indicated that the patients in the high-IRS group tend to have advanced clinical features (clinical stage and T classification), as well as a higher level of copy number variation burden, higher tumor burden mutation, and higher tumor stemness indices. Immune landscape analysis demonstrated that high-IRS groups exhibited lower immune cell infiltration and immune-suppressive state. More importantly, the predicted result of the Tumor Immune Dysfunction and Exclusion analysis showed that high-IRS groups might be more insensitive to immunotherapy. Meanwhile, we have also identified that high-IRS groups were associated with better efficiency of several targeted drugs.

Conclusion

To summarize, we identified a novel IRS model based on nine immune terms, which was quantified by the ssGSEA algorithm. This model had good efficacy in predicting overall survival and immunotherapy response in non-small cell lung cancer patients, which might be an underlying biomarker.

In vitro preliminary study on different anti-PD-1 antibody concentrations on T cells activation

Lung adenocarcinoma predominates among diagnosed nonsmall cell lung cancer subtypes in nonsmokers. The introduction of immune checkpoint inhibitors into clinical practice offered patients prolonged progression-free survival and overall survival times. However, the results demonstrate that the benefits do not apply to all patients. Nivolumab is a monoclonal antibody against the PD-1 protein expressed mainly on T lymphocytes and is widely used in cancer therapy in different settings. Tumor cells often express the PD-L1 molecule and can effectively block the action of PD-1-positive lymphocytes. A body of knowledge regarding the high expression of PD-L1 on tumor cells highlights that it does not always correlate with the effectiveness of anti-PD-1 therapy. The side effects of the therapy also constitute a significant issue. These side effects can occur at any time during anti-PD-1 treatment and lead to discontinuation and even the death of the patient. In these situations, it is possible to delay the dosage. Nevertheless, unfortunately, it is not possible to reduce the dose of anti-PD-1 antibody, which would undoubtedly minimize side effects, leaving the patient's immune system active. In our preliminary study, we analyzed the effect of different concentrations of nivolumab on the functioning of T lymphocytes. Activation and proliferation markers were investigated on T cells after being cultured with antigen-stimulated autologous dendritic cells. This process may indicate an appropriate concentration of nivolumab, which shows clinical activity with minimal side effects.

The Predictive Role of Immune Related Subgroup Classification in Immune Checkpoint Blockade Therapy for Lung Adenocarcinoma

Background: In lung adenocarcinoma (LUAD), the predictive role of immune-related subgroup classification in immune checkpoint blockade (ICB) therapy remains largely incomplete. Methods: Transcriptomics analysis was performed to evaluate the association between immune landscape and ICB therapy in lung adenocarcinoma and the associated underlying mechanism. First, the least absolute shrinkage and selection operator (LASSO) algorithm and K-means algorithm were used to identify immune related subgroups for LUAD cohort from the Cancer Genome Atlas (TCGA) database (n = 572). Second, the immune associated signatures of the identified subgroups were characterized by evaluating the status of immune checkpoint associated genes and the immune cell infiltration. Then, potential responses to ICB therapy based on the aforementioned immune related subgroup classification were evaluated via tumor immune dysfunction and exclusion (TIDE) algorithm analysis, and survival analysis and further Cox proportional hazards regression analysis were also performed for LUAD. In the end, gene set enrichment analysis (GSEA) was performed to explore the metabolic mechanism potentially responsible for immune related subgroup clustering. Additionally, two LUAD cohorts from the Gene Expression Omnibus (GEO) database were used as validation cohort. Results: A total of three immune related subgroups with different immune-associated signatures were identified for LUAD. Among them, subgroup 1 with higher infiltration scores for effector immune cells and immune checkpoint associated genes exhibited a potential response to IBC therapy and a better survival, whereas subgroup 3 with lower scores for immune checkpoint associated genes but higher infiltration scores for suppressive immune cells tended to be insensitive to ICB therapy and have an unfavorable prognosis. GSEA revealed that the status of glucometabolic reprogramming in LUAD was potentially responsible for the immune-related subgroup classification. Conclusion: In summary, immune related subgroup clustering based on distinct immune associated signatures will enable us to screen potentially responsive LUAD patients for ICB therapy before treatment, and the discovery of metabolism associated mechanism is beneficial to comprehensive therapeutic strategies making involving ICB therapy in combination with metabolism intervention for LUAD.

Construction of an immune-related lncRNA signature as a novel prognosis biomarker for LUAD

The tumor immune microenvironment of lung cancer is associated with prognosis and immunotherapy efficacy. Long noncoding RNAs are identified as prognostic biomarkers associated with immune functions. We constructed a signature comprising differentially expressed immune-related lncRNAs to predict the prognosis of patients with lung adenocarcinoma. We established the immune-related lncRNA signature by pairing immune-related lncRNAs regardless of expression level and lung adenocarcinoma patients were divided into high- and low-risk groups. The prognosis of patients in the two groups was significantly different; The immune-related lncRNA signature could serve as an independent lung adenocarcinoma prognostic indicator. The signature correlated negatively with B cell, CD4+ T cell, M2 macrophage, neutrophil, and monocyte immune infiltration. Patients with low risk scores had a higher abundance of immune cells and stromal cells around the tumor. Gene set enrichment analysis showed that samples from low-risk group were more active in the IgA production in intestinal immune network and the T and B cell receptor signaling pathway. High-risk groups had significant involvement of the cell cycle, DNA replication, adherens junction, actin cytoskeleton regulation, pathways in cancer, and TGF-β signaling pathways. High risk scores correlated significantly negatively with high CTLA-4 and HAVCR2 expression and higher median inhibitory concentration of common anti-tumor chemotherapeutics (e.g., cisplatin, paclitaxel, gemcitabine) and targeted therapy (e.g., erlotinib and gefitinib). We identified a reliable immune-related lncRNA lung adenocarcinoma prognosis model, and the immune-related lncRNA signature showed promising clinical prediction value.

Molecular subtypes based on CNVs related gene signatures identify candidate prognostic biomarkers in lung adenocarcinoma

The classical factors for predicting prognosis currently cannot meet the developing requirements of individualized and accurate prognostic evaluation in lung adenocarcinoma (LUAD). With the rapid development of high-throughput DNA sequencing technologies, genomic changes have been discovered. These sequencing data provide unprecedented opportunities for identifying cancer molecular subtypes. In this article, we classified LUAD into two distinct molecular subtypes (Cluster 1 and Cluster 2) based on Copy Number Variations (CNVs) and mRNA expression data from the Cancer Genome Atlas (TCGA) based on non-negative matrix factorization. Patients in Cluster 1 had worse outcomes than that in Cluster 2. Molecular features in subtypes were assessed to explain this phenomenon by analyzing differential expression genes expression pattern, which involved in cellular processes and environmental information processing. Analysis of immune cell populations suggested different distributions of CD4+ T cells, CD8+ T cells, and dendritic cells in the two subtypes. Subsequently, two novel genes, TROAP and RASGRF1, were discovered to be prognostic biomarkers in TCGA, which were confirmed in GSE31210 and Tianjin Medical University Cancer Institute and Hospital LUAD cohorts. We further proved their crucial roles in cancers by vitro experiments. TROAP mediates tumor cell proliferation, cycle, invasion, and migration, not apoptosis. RASGRF1 has a significant effect on tumor microenvironment. In conclusion, our study provides a novel insight into molecular classification based on CNVs related genes in LUAD, which may contribute to identify new molecular subtypes and target genes.