An immune gene expression signature distinguishes central nervous system metastases from primary tumours in non-small-cell lung cancer

Changes in the expression of cell interaction-related pathways during brain metastasis in lung adenocarcinoma: Gene expression and immunohistochemical analysis

BACKGROUND

Brain metastasis (BM) is a prevalent prognostic event in the development of lung adenocarcinoma (LUAD) with a poor prognosis. Alterations in gene or protein expression during various phases of BM remain unclear.

METHODS

We performed gene expression and pathway analyses using a metastasis-related gene panel on 12 lung tissues from patients with confirmed BM, 12 lung tissues from patients without BM, and 12 matched brain tissues from patients with confirmed BM during follow-up after LUAD surgery. The results of the gene expression analysis were validated by immunohistochemistry.

RESULTS

Cell interaction-related pathways (such as focal adhesion, extracellular matrix-receptor interaction, and proteoglycans in cancer) showed the greatest differences among the three groups. Expression of the cell interaction-related pathway was highest in the lung sample of BM group and lowest in the matched brain tissue. Using a machine learning model, a signature of 20 genes from cell interaction-related pathways accurately predicted BM (area under the curve score of 0.792 and an accuracy rate of 0.875). Immunohistochemical analysis showed higher expression of proteins associated with cell interaction-related genes and a mesenchymal phenotype in the lung sample of BM group than in those without BM or matched brain tissue.

CONCLUSIONS

LUAD acquires the characteristics of the cell interaction-related pathway that leads to the development of BM, with a significant decrease in expression following brain colonization.

Unlocking molecular mechanisms and identifying druggable targets in matched-paired brain metastasis of breast and lung cancers

Introduction

The incidence of brain metastases in cancer patients is increasing, with lung and breast cancer being the most common sources. Despite advancements in targeted therapies, the prognosis remains poor, highlighting the importance to investigate the underlying mechanisms in brain metastases. The aim of this study was to investigate the differences in the molecular mechanisms involved in brain metastasis of breast and lung cancers. In addition, we aimed to identify cancer lineage-specific druggable targets in the brain metastasis.

Methods

To that aim, a cohort of 44 FFPE tissue samples, including 22 breast cancer and 22 lung adenocarcinoma (LUAD) and their matched-paired brain metastases were collected. Targeted gene expression profiles of primary tumors were compared to their matched-paired brain metastases samples using nCounter PanCancer IO 360™ Panel of NanoString technologies. Pathway analysis was performed using gene set analysis (GSA) and gene set enrichment analysis (GSEA). The validation was performed by using Immunohistochemistry (IHC) to confirm the expression of immune checkpoint inhibitors.

Results

Our results revealed the significant upregulation of cancer-related genes in primary tumors compared to their matched-paired brain metastases (adj. p ≤ 0.05). We found that upregulated differentially expressed genes in breast cancer brain metastasis (BM-BC) and brain metastasis from lung adenocarcinoma (BM-LUAD) were associated with the metabolic stress pathway, particularly related to the glycolysis. Additionally, we found that the upregulated genes in BM-BC and BM-LUAD played roles in immune response regulation, tumor growth, and proliferation. Importantly, we identified high expression of the immune checkpoint VTCN1 in BM-BC, and VISTA, IDO1, NT5E, and HDAC3 in BM-LUAD. Validation using immunohistochemistry further supported these findings.

Conclusion

In conclusion, the findings highlight the significance of using matched-paired samples to identify cancer lineage-specific therapies that may improve brain metastasis patients outcomes.

Safety and efficacy of immune checkpoint blockade in patients with advanced nonsmall cell lung cancer and brain metastasis

The presence of brain metastases (BM) is a negative prognostic factor for patients with advanced nonsmall cell lung cancer (NSCLC). Their incidence seems to be higher in patients with oncogene-driven tumours, especially those with EGFR-mutated or ALK-rearranged tumours. Although targeted treatments demonstrate significant efficacy regarding BM, they only apply to a minority of NSCLC patients. On the other hand, systemic therapies for nononcogenic-driven NSCLC with BM have shown limited clinical benefit. In recent years, immunotherapy alone or combined with chemotherapy has been adopted as a new standard of care in first-line therapy. This approach seems to be beneficial to patients with BM in terms of efficacy and toxicity. Combined immune checkpoint inhibition as well as the combination of immunotherapy and radiation therapy show promising results with significant, but overall acceptable toxicity. A pragmatic approach of allowing enrolment of patients with untreated or symptomatic BM in randomised trials evaluating immune checkpoint inhibitors strategies, possibly coupled with central nervous system-related endpoints may be needed to generate data to refine treatment for this patient population.

Cerebrospinal fluid as a liquid biopsy for molecular characterization of brain metastasis in patients with non-small cell lung cancer

OBJECTIVES

Non-small cell lung cancer (NSCLC) with brain metastases (BM) is a challenging clinical issue with poor prognosis. No data exist regarding extensive genetic analysis of cerebrospinal fluid (CSF) and its correlation to associated tumor compartments.

MATERIALS AND METHODS

We designed a study across multiple NSCLC patients with matched material from four compartments; primary tumor, BM, plasma and CSF. We performed enrichment-based targeted next-generation sequencing analysis of ctDNA and exosomal RNA in CSF and plasma and compared the outcome with the solid tumor compartments.

RESULTS

An average of 105 million reads per sample was generated with fractions of mapped reads exceeding 99% in all samples and with a mean coverage above 10,000x. We observed a high degree of overlap in variants between primary lung tumor and BM. Variants specific for the BM/CSF compartment included in-frame deletions in AR, FGF10 and TSC1 and missense mutations in HNF1a, CD79B, BCL2, MYC, TSC2, TET2, NRG1, MSH3, NOTCH3, VHL and EGFR.

CONCLUSION

Our approach of combining ctDNA and exosomal RNA analyses in CSF presents a potential surrogate for BM biopsy. The specific variants that were only observed in the CNS compartments could serve as targets for individually tailored therapies in NSCLC patients with BM.

CGB5, INHBA and TRAJ19 Hold Prognostic Potential as Immune Genes for Patients with Gastric Cancer

BACKGROUND

Gastric cancer (GC) seriously threatens people's health and life quality worldwide.

AIM

The current study sought to explore prognostic immune genes and their regulatory network in GC.

METHODS

First, expression data in GC and normal samples were analyzed based on bioinformatics analysis. Immune-related genes were identified and confirmed with univariate/multivariate Cox analysis and receiver-operating characteristic curve. The upstream transcription factors of immune genes were subsequently predicted, and their regulatory network was constructed. GC and adjacent normal tissues were obtained from 76 patients with GC to determine the expression patterns of immune genes and their correlation with overall prognosis. CD8⁺ T-cell infiltration of patients with high or low risk was detected by means of immunohistochemistry.

RESULTS

Bioinformatics analysis highlighted 3689 differentially expressed genes in GC, including 87 immune genes, 8 of which were significantly associated with patient survival. CGB5 and INHBA were high-risk genes, while TRAJ19 was identified as a low-risk gene, all of which were found to be regulated by 11 different transcription factors. Furthermore, CGB5 and INHBA exhibited negative correlation with the prognosis of GC patients; however, TRAJ19 was positively correlated with GC patient prognosis. The incidence of lymph node metastasis was higher, the pathological stage was advanced and the infiltrated CD8⁺ T cells were fewer in the high-risk GC group.

CONCLUSIONS

Overall, our findings identified the key roles of CGB5, INHBA and TRAJ19 in prognosis GC patients, serving as an important gene set for prognostic prediction.

Advances in Brain Metastases Diagnosis: Non-coding RNAs As Potential Biomarkers

Brain metastasis is considered the most common brain tumor. They arise from different primary cancers. The most common primary tumors giving brain metastases include breast, colorectal, lung, melanoma, and renal cancer. Depending only on history, physical examination, and conventional imaging modalities makes brain tumors diagnosis difficult. Rapid and non-invasive promising modalities could diagnose and differentiate between different brain metastases without exposing the patients to unnecessary brain surgeries for biopsies. One of these promising modalities is non-coding RNAs (ncRNAs). NcRNAs can determine brain metastases' prognosis, chemoresistance, and radioresistance. It also helps us to understand the pathophysiology of brain metastases development. Additionally, ncRNAs may work as potential therapeutic targets for brain metastases treatment and prevention. Herein, we present deregulated ncRNAs in different brain metastases, including microRNAs and long non-coding RNAs (lncRNAs), such as gastric adenocarcinoma, colorectal, breast, melanoma, lung, and prostate cancer. Additionally, we focus on serum and cerebrospinal fluid (CSF) expression of these ncRNAs in patients with brain metastases compared to patients with primary tumors. Moreover, we discuss the role of ncRNAs in modulating the immune response in the brain microenvironment. More clinical studies are encouraged to assess the specificity and sensitivity of these ncRNAs.

Matched Analyses of Brain Metastases versus Primary Non-Small Cell Lung Cancer Reveal a Unique microRNA Signature

Distant spreading of tumor cells to the central nervous system in non-small cell lung cancer (NSCLC) occurs frequently and poses major clinical issues due to limited treatment options. RNAs displaying differential expression in brain metastasis versus primary NSCLC may explain distant tumor growth and may potentially be used as therapeutic targets. In this study, we conducted systematic microRNA expression profiling from tissue biopsies of primary NSCLC and brain metastases from 25 patients. RNA analysis was performed using the nCounter Human v3 miRNA Expression Assay, NanoString technologies, followed by differential expression analysis and in silico target gene pathway analysis. We uncovered a panel of 11 microRNAs with differential expression and excellent diagnostic performance in brain metastasis versus primary NSCLC. Five microRNAs were upregulated in brain metastasis (miR-129-2-3p, miR-124-3p, miR-219a-2-3p, miR-219a-5p, and miR-9-5p) and six microRNAs were downregulated in brain metastasis (miR-142-3p, miR-150-5p, miR-199b-5p, miR-199a-3p, miR-199b-5p, and miR-199a-5p). The differentially expressed microRNAs were predicted to converge on distinct target gene networks originating from five to twelve core target genes. In conclusion, we uncovered a unique microRNA profile linked to two target gene networks. Our results highlight the potential of specific microRNAs as biomarkers for brain metastasis in NSCLC and indicate plausible mechanistic connections.

Cancer driver gene and non-coding RNA alterations as biomarkers of brain metastasis in lung cancer: A review of the literature

Brain metastasis (BM) is the most common event in patients with lung cancer. Despite multimodal treatments and advances in systemic therapies, development of BM remains one of the main factors associated with poor prognosis and mortality in patients with lung cancer. Therefore, better understanding of mechanisms involved in lung cancer brain metastasis (LCBM) is of great importance to suppress cancer cells and to improve the overall survival of patients. Several cancer-related genes such as EGFR and KRAS have been proposed as potential predictors of LCBM. In addition, there is ample evidence supporting crucial roles of non-coding RNAs (ncRNAs) in mediating LCBM. In this review, we provide comprehensive information on risk assessment, predictive, and prognostic panels for early detection of BM in patients with lung cancer. Moreover, we present an overview of LCBM molecular mechanisms, cancer driver genes, and ncRNAs which may predict the risk of BM in lung cancer patients. Recent clinical studies have focused on determining mechanisms involved in LCBM and their association with diagnosis, prognosis, and treatment outcomes. These studies have shown that alterations in EGFR, KRAS, BRAF, and ALK, as the most frequent coding gene alterations, and dysregulation of ncRNAs such as miR-423, miR-330-3p, miR-145, piR-651, and MALAT1 can be considered as potential biomarkers of LCBM.

Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling

Simple Summary

It is important to be able to predict brain metastasis in lung adenocarcinoma patients; however, research in this area is still lacking. Much of the previous work on tumor microenvironments in lung adenocarcinoma with brain metastasis concerns the tumor immune microenvironment. The importance of the tumor nonimmune microenvironment (extracellular matrix (ECM), epithelial–mesenchymal transition (EMT) feature, and angiogenesis) has been overlooked with regard to brain metastasis. We evaluated tumor nonimmune-microenvironment-related gene expression signatures that could predict brain metastasis after the surgical resection of lung adenocarcinoma using a machine learning approach. We identified a tumor nonimmune-microenvironment-related 17-gene expression signature, and this signature showed high brain metastasis predictive power in four machine learning classifiers. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. Our tumor nonimmune-microenvironment-related gene expression signatures are important biological markers that can predict brain metastasis and provide patient-specific treatment options.

Abstract

Using a machine learning approach with a gene expression profile, we discovered a tumor nonimmune-microenvironment-related gene expression signature, including extracellular matrix (ECM) remodeling, epithelial–mesenchymal transition (EMT), and angiogenesis, that could predict brain metastasis (BM) after the surgical resection of 64 lung adenocarcinomas (LUAD). Gene expression profiling identified a tumor nonimmune-microenvironment-related 17-gene expression signature that significantly correlated with BM. Of the 17 genes, 11 were ECM-remodeling-related genes. The 17-gene expression signature showed high BM predictive power in four machine learning classifiers (areas under the receiver operating characteristic curve = 0.845 for naïve Bayes, 0.849 for support vector machine, 0.858 for random forest, and 0.839 for neural network). Subgroup analysis revealed that the BM predictive power of the 17-gene signature was higher in the early-stage LUAD than in the late-stage LUAD. Pathway enrichment analysis showed that the upregulated differentially expressed genes were mainly enriched in the ECM–receptor interaction pathway. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. The tumor nonimmune-microenvironment-related gene expression signatures found in this study are important biological markers that can predict BM and provide patient-specific treatment options.

Exploration of immune-related cells and ceRNA in squamous cell lung cancer

The treatment for squamous cell lung cancer (SqCLC) is limited, and the prognosis of SqCLC is poor. In this article, we aimed to analyze and identify immune-related cells and competition endogenous RNA (ceRNA) that influence the prognosis of SqCLC. SqCLC and lung adenocarcinoma data were downloaded from TCGA-GDC. A total of 22 types of immune cell fractions were estimated using CIBERSORT. R software was used to identify any significantly different transcriptome data, including mRNA, LncRNA, and miRNA. The univariate cox regression method was applied to screen for prognosis-related lncRNA, miRNA, mRNA and tumor-infiltrating immune cells. There were 504 patients included in this study. There was a higher proportion of memory activated CD4+ T cells and CD8+ T cells in younger women. Follicular helper T (Tfh) cells were predictive of a good prognosis and reflected immune activation in SqCLC. The SFTA1P/NKX2-1-AS1, hsa-mir-503, GREM2 ceRNA axes and NKX2-1-AS1, hsa-mir-96, PROK2 ceRNA axes were found to be important for the immune function, pathogenesis, and prognosis of SqCLC. Collectively, the immune-related ceRNA and tumor-infiltrating immune cells in SqCLC are likely important determinants of SqCLC pathogenesis, prognosis, and immune status.

Construction of Immune-Associated Nomogram for Predicting the Recurrence Survival Risk of Stage I Cervical Cancer

Background

Various studies reported that the prognosis of patients with cervical cancer (CC) was significantly associated with immunity, whereas limited studies have explored whether immune-associated genes could be classifiers for recurrence-free survival (RFS) of stage I CC. Thus, an improved immune-related gene signature for stage I CC patients' prognosis is urgently required.

Materials and Methods

We retrospectively analyzed the gene expression profiles of stage I CC patients in the GSE44001 set from the Gene Expression Omnibus (GEO) database. The stage I CC patients were randomly divided into the training group and the internal validation group. The training patients were adopted to develop a prognostic immune gene-based signature; meanwhile, the internal validation patients were used to validate the power of the selected immune gene-related signature using univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. The accuracy and reliability of the immune gene-related signature were evaluated based on Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curves.

Results

High power of the 8-immune gene signature was found on the basis of ROC analysis (AUC at 1, 3, and 5 years were exhibited in the internal validation group (0.702, 0.715, and 0.728, respectively), external validation group (0.702, 0.825, and 0.842, respectively), and entire GEO dataset (0.840, 0.894, and 0.852, respectively)). Besides, C-index, ROC, calibration plots, and decision curve analysis (DCA) also acted well in our nomogram, suggestive of a high ability of the nomogram to elevate the prognostic prediction of stage I CC patients.

Conclusions

In this study, we successfully constructed an integrated 8-immune gene-based signature which could accurately identify patients with low prognostic risk from those with high prognostic risk. In addition, we developed an immune-related nomogram which can elevate the prognostic prediction of stage I CC patients.

Combination of Immune Checkpoint Inhibitors and Anti-Angiogenic Agents in Brain Metastases From Non-Small Cell Lung Cancer

Brain metastases remain a critical issue in the management of non-small cell lung cancer (NSCLC) because of the high frequency and poor prognosis, with survival rates often measured in just months. The local treatment approach remains the current standard of care, but management of multiple asymptomatic brain metastases always involves systemic therapy. Given that anti-angiogenic agents and immune checkpoint inhibitors (ICIs) both target the tumor microenvironment (TME), this combination therapy has become a promising strategy in clinical practice. Increasing number of preclinical and clinical studies have shown remarkable anti-tumor activity of the combination therapy, but the efficacy in brain metastases is unclear due to the strict selection criteria adopted in most clinical trials. This review briefly summarizes the potential synergistic anti-tumor effect and clinical development of the combination of anti-angiogenic agents and ICIs in NSCLC brain metastases, and discusses the existing challenges and problems.

An immune-related gene expression signature predicts brain metastasis in lung adenocarcinoma patients after surgery: gene expression profile and immunohistochemical analyses

Background

Lung adenocarcinoma (LUAD) with brain metastasis (BM) occurs frequently and has a poor prognosis. In this study, we aimed to assess the correlation between gene expression signatures and the development of BM after surgical resection of LUAD.

Methods

We analyzed the immune-related gene expression profiles of 72 LUADs with and without BM after surgery and verified them using NanoString method and immunohistochemistry (IHC). We matched the Tumor, Node, Metastasis (TNM) stage in the groups with and without BM to minimize the effect of TNM stage. Pathway enrichment studies were also performed.

Results

In the NanoString results, we identified 11 upregulated immune-related gene signature that correlated specifically with BM in the discovery and validation sets [area under the curve (AUC) =0.750 and 0.787, respectively]. The discovery set achieved 94% sensitivity and 62% specificity and the validation set displayed 100% sensitivity and 50% specificity. Eight out of the 11 genes were verified by IHC and had profiles similar to the gene expression profile results (AUC =0.844 for the discovery set and AUC =0.795 for the validation set). Subgroup analysis revealed that 11 immune-related gene signature enabled prediction of BM at all TNM stages. There were no differences in the 11 immune-related gene expression signatures between the primary LUAD samples and the matched brain samples. Pathway enrichment analysis revealed that the cytokine-cytokine receptor interaction pathway was closely correlated with BM.

Conclusions

The 11 identified immune-related gene expression signatures may be potentially clinically useful predictors for BM and can provide patient-specific treatment options.

Outcome of Patients with NSCLC and Brain Metastases Treated with Immune Checkpoint Inhibitors in a 'Real-Life' Setting

Simple Summary

We identified non-small cell lung cancer (NSCLC) patients with brain metastases who were treated with immune checkpoint inhibitors at Karolinska University Hospital, Sweden and University Hospital of Heraklion, Greece from 2016 to 2019. We analyzed intracranial efficacies in the patients who had not received local treatment for their brain metastases less than three months prior to the initiation of immune checkpoint inhibitors and had adequate radiological evaluation. We demonstrated that immune checkpoint inhibitors are active in NSCLC patients with brain metastases regardless of the presence of neurological symptoms. This is a novel finding since, until now, this patient group has irrefutably been underrepresented in clinical studies and there is a clear scarcity of data. The results of our analyses suggest that symptomatic patients with active brain metastases (BM) may be considered for immunotherapy in routine clinical practice as well as clinical trials.

Abstract

There is lack of data addressing the intracranial (IC) efficacy of immune checkpoint inhibitors (ICIs) on brain metastases (BM) in non-small cell lung cancer (NSCLC). This patient category is underrepresented in randomized clinical trials. We retrospectively collected clinical data on patients with non-oncogenic driven NSCLC with BM who were treated with ICIs at two medical oncology institutes in Sweden and Greece from 2016 to 2019. IC efficacy was assessed in patients who had not received local treatment for BM less than three months prior to the initiation of ICIs and had adequate radiological evaluation. We screened 280 patients, of which 51 had BM. BM was an independent predictor for inferior PFS (HR = 2.27; 95% CI, 1.53–3.36) but not OS (HR = 1.58; 95% CI, 0.97–2.60) for the whole patient population. IC response assessment was done on 33 patients. IC objective response rate (ORR) was 24.2%. The presence of neurological symptoms related to BM did not affect IC ORR (p = 0.48). High PD-L1 levels from extracranial biopsies were not a predictive factor for IC ORR (p = 0.13). ICIs are active in NSCLC patients with BM regardless of the presence of neurological symptoms and can achieve durable IC disease stabilization in a subgroup of patients.