The Role of Molecular Profiling to Predict the Response to Immune Checkpoint Inhibitors in Lung Cancer

Elevated HSPB1 Expression Is Associated with a Poor Prognosis in Glioblastoma Multiforme Patients

BACKGROUND

 Glioblastoma multiforme (GBM) is a highly aggressive form of brain cancer. This study investigated the clinical predictive value of heat shock protein β1 (HSPB1) in patients with GBM.

METHODS

 A correlation was established between HSPB1 expression and GBM progression using data from The Cancer Genome Atlas (TCGA) dataset, Chinese Glioma Genome Atlas dataset, Gene Expression Omnibus dataset, and Human Protein Atlas database. A survival analysis was conducted and an HSPB1-based nomogram was constructed to evaluate the prognostic value of HSPB1 in patients with GBM.

RESULTS

 Based on TCGA data mining, we discovered that HSPB1 was significantly elevated in patients with GBM and may reflect their response to immunotherapy. In survival analysis, it appeared to have a predictive role in the prognosis of patients with GBM. Five signaling pathways were significantly enriched in the high HSPB1 expression phenotype according to the gene set enrichment analysis. In addition, a significant association was found between HSPB1 expression and immune checkpoints, tumor immune infiltration, tumor immune microenvironment, and immune cell markers in glioma. Overall, our results suggest that HSPB1 may regulate the function of immune cells, serve as a new immunotherapy target, and predict the response to immunotherapy in patients with GBM.

CONCLUSION

 HSPB1 appears to serve as a potential predictor of the clinical prognosis and response to immunotherapy in patients with GBM. It may be possible to identify patients who are likely to benefit from immunotherapy by assessing the expression level of HSPB1.

Tumor suppressor function of RBMS3 overexpression in EOC associated with immune cell infiltration

Objectives

Epithelial ovarian cancer (EOC) is considered to be a prevalent female malignancy with both high incidence and mortality. It is reported that RNA-binding protein 3 (RBMS3) executives a tumor suppressor function in different cancers. This investigation was designed to examine the expression of RBMS3 in epithelial ovarian cancer, the effects on EOC cells, and its connection to immune cells that infiltrate tumors in the EOC microenvironment.

Methods

The expression levels of RBMS3 in EOC tissues as well as their correlations with immune cell infiltration and clinical outcome were examined using bioinformatics approaches. Western blotting as well as immunohistochemistry were carried out to determine the protein levels in EOC tissues. In addition, qRT-PCR was employed to look at the expression of the mRNA. The role of RBMS3 in EOC cells was investigated, and an RBMS3 lentiviral vector was developed. The effects of RBMS3 on subcutaneous tumor development, the proliferation protein Ki-67, the tumor angiogenesis indicator CD31, and its function in controlling the tumor immune microenvironment were evaluated by in vivo tests.

Results

There was a considerable decrease in RBMS3 expression in EOC tissues, which was linked to a poor prognosis for patients and the infiltration of multiple immune cell. Given immunohistochemical studies, tissues with increased RBMS3 expression had decreased markers of myeloid-derived suppressor cells, regulatory T cells, and M2 macrophages, whereas M1 macrophage markers were elevated. RBMS3 appears to suppress the capabilities of proliferating, invading, and migrating in EOC cells according to in vitro tests, whereas tumors overexpressing RBMS3 developed more slowly in syngeneic mouse models. The overexpression of RBMS3 led to a decline in the levels of Ki-67 protein and CD31. Additionally, it showed a negatively correlation with markers of regulatory T cell, myeloid-derived suppressor cell, and M2 macrophage but a positive correlation with markers of M1 macrophage.

Conclusions

The findings revealed that elevated RBMS3 expression plays a tumor suppressor role in EOC and was connected to patient survival in EOC. The studies conducted in vitro and in vivo demonstrated a link between RBMS3 expression and the infiltration of certain immune cells, indicating a function for RBMS3 in the immunosuppressive tumor microenvironment and its promising efficiency as a novel target for immunotherapy against EOC.

Exploration of the Correlation Between GRHL1 Expression and Tumor Microenvironment in Endometrial Cancer and Immunotherapy

Introduction

GRHL1 belongs to the family of Grainyhead-like (GRHL). Previous studies have shown that dysregulation of growth and survival pathways is associated with the GRHL family of gene cancers. Immunotherapy with checkpoint inhibitors has changed the treatment paradigm for many tumors, including endometrial cancer (EC). However, the effect of GRHL1 on immunotherapy in EC and its relationship with immune cell infiltration are poorly understood.

Methods

Differential expression of GRHL1 between EC and normal EC tissues was analyzed by searching the TCGA database, and the results were verified utilizing immunohistochemistry analyses. Next, the relationship between GRHL1, CD8+ T cells and tumor microenvironment (TME) was also investigated, and the effect of GRHL1 expression on immunotherapy in EC was evaluated.

Results

According to the findings, EC tissues had elevated expression levels of GRHL1 relative to normal tissues. Patients with EC who expressed GRHL1 at high levels experienced worse overall survival (OS) and Progression-free survival (PFS) than those whose expression was lower. In addition, GRHL1 expression was negatively correlated with CD8+ T cells, and patients with high GRHL1 expression were less effective in receiving immunotherapy.

Conclusion

The expression of GRHL1 was high in EC patients, and high expression of GRHL1 inhibits the proliferation of CD8+ T cells in the tumor microenvironment of EC and affect the efficacy of immunotherapy.

Prognostic Model and Tumor Immune Microenvironment Analysis of Complement-Related Genes in Gastric Cancer

Introduction

The complement system is integral to the innate and adaptive immune response, helping antibodies eliminate pathogens. However, the potential role of complement and its modulators in the tumor microenvironment (TME) of gastric cancer (GC) remains unclear.

Methods

This study assessed the expression, frequency of somatic mutations, and copy number variations of complement family genes in GC derived from The Cancer Genome Atlas (TCGA). Lasso and Cox regression analyses were conducted to develop a prognostic model based on the complement genes family, with the training and validation sets taken from the TCGA-GC cohort (n=371) and the International Gene Expression Omnibus (GEO) cohort (n=433), correspondingly. The nomogram assessment model was used to predict patient outcomes. Additionally, the link between immune checkpoints, immune cells, and the prognostic model was investigated.

Results

In contrast to patients at low risk, those at high risk had a less favorable outcome. The prognostic model-derived risk score was shown to serve as a prognostic marker of GC independently, as per the multivariate Cox analysis. Nomogram assessment showed that the model had high reliability for predicting the survival of patients with GC in the 1, 3, 5 years. Additionally, the risk score was positively linked to the expression of immune checkpoints, notably CTLA4, LAG3, PDCD1, and CD274, according to an analysis of immune processes. The core gene C5aR1 in the prognostic model was found to be upregulated in GC tissues in contrast to adjoining normal tissues, and patients with elevated expressed levels of C5aR1 had lower 10-year overall survival (OS) rates.

Conclusion

Our work reveals that complement genes are associated with the diversity and complexity of TME. The complement prognosis model help improves our understanding of TME infiltration characteristics and makes immunotherapeutic strategies more effective.

Identification of SEC61G as a Diagnostic and Prognostic Biomarker in Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) is a heterogeneous malignancy originating from the oral mucosal epithelium. Detecting novel biomarkers can offer crucial information on disease aggressiveness and expected clinical outcomes for individual patients. SEC61G, an aberrantly expressed gene in various cancers, has been associated with negative clinical outcomes. However, its expression and clinical significance in OSCC is still unclear. In the present study, we investigated the SEC61G expression level in OSCC using bioinformatic and immunohistochemical analyses. Additionally, our findings revealed a significant correlation between SEC61G expression and clinicopathological characteristics, as well as a worse prognosis in OSCC patients. Notably, flow cytometry analysis on patient samples revealed that SEC61G expression was also linked to decreased immune infiltration in OSCC patients. In conclusion, our study provides evidence supporting SEC61G's role as a potential diagnostic, prognostic, and therapeutic marker in OSCC.

Progresses in biomarkers for cancer immunotherapy

Currently, checkpoint inhibitor-based immunotherapy has emerged as prevailing treatment modality for diverse cancers. However, immunotherapy as a first-line therapy has not consistently yielded durable responses. Moreover, the risk of immune-related adverse events increases with combination regimens. Thus, the development of predictive biomarkers is needed to optimize individuals benefit, minimize risk of toxicities, and guide combination approaches. The greatest focus has been on tumor programmed cell death-ligand 1 (PD-L1), microsatellite instability (MSI), and tumor mutational burden (TMB). However, there remains a subject of debate due to thresholds variability and significant heterogeneity. Major unmet challenges in immunotherapy are the discovery and validation of predictive biomarkers. Here, we show the status of tumor PD-L1, MSI, TMB, and emerging data on novel biomarker strategies with oncogenic signaling and epigenetic regulation. Considering the exploration of peripheral and intestinal immunity has served as noninvasive alternative in predicting immunotherapy, this review also summarizes current data in systemic immunity, encompassing solute PD-L1 and TMB, circulating tumor DNA and infiltrating lymphocytes, routine emerging inflammatory markers and cytokines, as well as gut microbiota. This review provides up-to-date information on the evolving field of currently available biomarkers in predicting immunotherapy. Future exploration of novel biomarkers is warranted.

COL5A2 is a prognostic-related biomarker and correlated with immune infiltrates in gastric cancer based on transcriptomics and single-cell RNA sequencing

BACKGROUND

There is still a therapeutic challenge in treating gastric cancer (GC) due to its high incidence and poor prognosis. Collagen type V alpha 2 (COL5A2) is increased in various cancers, yet it remains unclear how it contributes to the prognosis and immunity of GC.

METHODS

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to download transcriptome profiling (TCGA-STAD; GSE84437), single-cell RNA sequencing (scRNA-seq) data (GSE167297) and clinical information. COL5A2 expression and its relationship with clinicopathological factors were analyzed. We conducted survival analysis and Cox regression analysis to evaluate the prognosis and independent factors of GC. Co-expressed analysis was also performed. To identify the underlying mechanism, we conducted analyses of differentially expressed genes (DEGs) and functional enrichment. The correlations between COL5A2 expression and immune cell infiltration levels and immune infiltrate gene marker sets were further explored. Additionally, we analyzed the association of COL5A2 expression with immunological checkpoint molecules. Furthermore, the relationship between COL5A2 expression and immunotherapy sensitivity was also investigated.

RESULTS

COL5A2 expression was elevated in GC. More than this, the scRNA-seq analysis revealed that COL5A2 expression had a spatial gradient. The upregulated COL5A2 was associated with worse overall survival. A significant correlation was found between COL5A2 overexpression and age, T classification and clinical stage in GC. COL5A2 was found to be an independent factor for the unfortunate outcome in Cox regression analysis. The co-expressed genes of COL5A2 were associated with tumor stage or poor survival. Enrichment analysis revealed that the DEGs were mainly associated with extracellular matrix (ECM)-related processes, PI3K-AKT signaling pathway, and focal adhesion. GSEA analyses revealed that COL5A2 was associated with tumor progression-related pathways. Meanwhile, COL5A2 expression was correlated with tumor-infiltrating immune cells. Moreover, immunophenoscore (IPS) analysis and PRJEB25780 cohorts showed that patients with low COL5A2 expression were highly sensitive to immunotherapy.

CONCLUSIONS

COL5A2 might act as a prognostic biomarker of GC prognosis and immune infiltration and may provide a therapeutic intervention strategy.

Research progress of biomarkers in the prediction of anti-PD-1/PD-L1 immunotherapeutic efficiency in lung cancer

Currently, anti-PD-1/PD-L1 immunotherapy using immune checkpoint inhibitors is widely used in the treatment of multiple cancer types including lung cancer, which is a leading cause of cancer death in the world. However, only a limited proportion of lung cancer patients will benefit from anti-PD-1/PD-L1 therapy. Therefore, it is of importance to predict the response to immunotherapy for the precision treatment of patients. Although the expression of PD-L1 and tumor mutation burden (TMB) are commonly used to predict the clinical response of anti-PD-1/PD-L1 therapy, other factors such as tumor-specific genes, dMMR/MSI, and gut microbiome are also promising predictors for immunotherapy in lung cancer. Furthermore, invasive peripheral blood biomarkers including blood DNA-related biomarkers (e.g., ctDNA and bTMB), blood cell-related biomarkers (e.g., immune cells and TCR), and other blood-related biomarkers (e.g., soluble PD-L1 and cytokines) were utilized to predict the immunotherapeutic response. In this review, the current achievements of anti-PD-1/PD-L1 therapy and the potential biomarkers for the prediction of anti-PD-1/PD-L1 immunotherapy in lung cancer treatment were summarized and discussed.

Radiomics for the non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to non-small cell lung cancer

BACKGROUND

The expression level of the programmed cell death ligand 1 (PD-L1) appears to be a predictor for response to immunotherapy using checkpoint inhibitors in patients with non-small cell lung cancer (NSCLC). As differences in terms of PD-L1 expression levels in the extracranial primary tumor and the brain metastases may occur, a reliable method for the non-invasive assessment of the intracranial PD-L1 expression is, therefore of clinical value. Here, we evaluated the potential of radiomics for a non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to NSCLC.

PATIENTS AND METHODS

Fifty-three NSCLC patients with brain metastases from two academic neuro-oncological centers (group 1, n = 36 patients; group 2, n = 17 patients) underwent tumor resection with a subsequent immunohistochemical evaluation of the PD-L1 expression. Brain metastases were manually segmented on preoperative T1-weighted contrast-enhanced MRI. Group 1 was used for model training and validation, group 2 for model testing. After image pre-processing and radiomics feature extraction, a test-retest analysis was performed to identify robust features prior to feature selection. The radiomics model was trained and validated using random stratified cross-validation. Finally, the best-performing radiomics model was applied to the test data. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analyses.

RESULTS

An intracranial PD-L1 expression (i.e., staining of at least 1% or more of tumor cells) was present in 18 of 36 patients (50%) in group 1, and 7 of 17 patients (41%) in group 2. Univariate analysis identified the contrast-enhancing tumor volume as a significant predictor for PD-L1 expression (area under the ROC curve (AUC), 0.77). A random forest classifier using a four-parameter radiomics signature, including tumor volume, yielded an AUC of 0.83 ± 0.18 in the training data (group 1), and an AUC of 0.84 in the external test data (group 2).

CONCLUSION

The developed radiomics classifiers allows for a non-invasive assessment of the intracranial PD-L1 expression in patients with brain metastases secondary to NSCLC with high accuracy.

Exploring the Correlation Between GPR176, a Potential Target Gene of Gastric Cancer, and Immune Cell Infiltration

Introduction

GPR176, an orphan G protein-coupled receptor (GPCR), is essential for the progression of gastrointestinal cancers. However, it is still unclear how GPR176 affects tumor immunity and patient prognosis in gastric cancer (GC).

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were searched in this investigation to assess the expression patterns of GPR176 in GC tissues and normal gastric mucosa. The findings were further verified using immunohistochemical tests and quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). The Kaplan-Meier method, univariate logistic regression, and Cox regression were then used to investigate the relationship between GPR176 and clinical traits. Additionally, the potential correlation between GPR176, immune checkpoint genes, and immune cell infiltration levels was investigated.

Results

As per the research findings, GC tissues had higher levels of GPR176 than normal tissues. Additionally, individuals with high expression of GPR176 had a worse 10-year overall survival (OS), in contrast with those having a low expression of GPR176 (p < 0.001). The OS of GC can be predicted using a validated nomogram model. The expression of GPR176 demonstrated a negative correlation with CD8+ T cells. When compared to the low-expression group of GPR176, Tumor Immune Dysfunction and Exclusion (TIDE) analysis demonstrated that the high-expression group had a considerably higher risk of immune evasion. A remarkable difference (variation) was observed in the levels of GPR176 expression across both groups, ie, low and high-risk groups, as determined by the immune phenomenon scores (IPS) immunotherapy assessment.

Conclusion

By examining GPR176 from various biological perspectives, it was determined that GPR176 can act as a predictive biomarker for poor patient prognosis in GC. Additionally, it was observed that GPR176 is capable of suppressing the proliferation of CD8+ T cells and facilitating immune evasion.

Comprehensive analysis of 33 human cancers reveals clinical implications and immunotherapeutic value of the solute carrier family 35 member A2

Background

Solute carrier family 35 member A2 (SLC35A2), which belongs to the SLC35 solute carrier family of human nucleoside sugar transporters, has shown regulatory roles in various tumors and neoplasms. However, the function of SLC35A2 across human cancers remains to be systematically assessed. Insights into the prediction ability of SLC35A2 in clinical practice and immunotherapy response remains limited.

Materials and methods

We obtained the gene expression and protein levels of SLC35A2 in a variety of tumors from Molecular Taxonomy of Breast Cancer International Consortium, The Cancer Genome Atlas, Gene Expression Omnibus, Chinese Glioma Genome Atlas, and Human Protein Atlas databases. The SLC35A2 level was validated by immunohistochemistry. The predictive value for prognosis was evaluated by Kaplan-Meier survival and Cox regression analyses. Correlations between SLC35A2 expression and DNA methylation, genetic alterations, tumor mutation burden (TMB), microsatellite instability (MSI), and tumor microenvironment were performed using Spearman's correlation analysis. The possible downstream pathways of SLC35A2 in different human cancers were explored using gene set variation analysis. The potential role of SLC35A2 in the tumor immune microenvironment was evaluated via EPIC, CIBERSORT, MCP-counter, CIBERSORT-ABS, quanTIseq, TIMER, and xCell algorithms. The difference in the immunotherapeutic response of SLC35A2 under different expression conditions was evaluated by the tumor immune dysfunction and exclusion (TIDE) score as well as four independent immunotherapy cohorts, which includes patients with bladder urothelial carcinoma (BLCA, N = 299), non-small cell lung cancer (NSCLC, N = 72 and N = 36) and skin cutaneous melanoma (SKCM, N = 25). Potential drugs were identified using the CellMiner database and molecular docking.

Results

SLC35A2 exhibited abnormally high or low expression in 23 cancers and was significantly associated with the prognosis. In various cancers, SLC35A2 expression and mammalian target of rapamycin complex 1 signaling were positively correlated. Multiple algorithmic immune infiltration analyses suggested an inverse relation between SLC35A2 expression and infiltrating immune cells, which includes CD4+T cells, CD8+T cells, B cells, and natural killer cells (NK) in various tumors. Furthermore, SLC35A2 expression was significantly correlated with pan-cancer immune checkpoints, TMB, MSI, and TIDE genes. SLC35A2 showed significant predictive value for the immunotherapy response of patients with diverse cancers. Two drugs, vismodegib and abiraterone, were identified, and the free binding energy of cytochrome P17 with abiraterone was higher than that of SLC35A2 with abiraterone.

Conclusion

Our study revealed that SLC35A2 is upregulated in 20 types of cancer, including lung adenocarcinoma (LUAD), breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), and lung squamous cell carcinoma (LUSC). The upregulated SLC35A2 in five cancer types indicates a poor prognosis. Furthermore, there was a positive correlation between the overexpression of SLC35A2 and reduced lymphocyte infiltration in 13 cancer types, including BRCA and COAD. Based on data from several clinical trials, patients with LUAD, LUSC, SKCM, and BLCA who exhibited high SLC35A2 expression may experience improved immunotherapy response. Therefore, SLC35A2 could be considered a potential predictive biomarker for the prognosis and immunotherapy efficacy of various tumors. Our study provides a theoretical basis for further investigating its prognostic and therapeutic potentials.

Comprehensively prognostic and immunological analysis of snail family transcriptional repressor 2 in pan-cancer and identification in pancreatic carcinoma

Background

Snail family transcriptional repressor 2 (SNAI2) is a transcription factor that induces epithelial to mesenchymal transition in neoplastic epithelial cells. It is closely related to the progression of various malignancies. However, the significance of SNAI2 in human pan-cancer is still largely unknown.

Methods

The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases were taken to examine the SNAI2 expression pattern in tissues and cancer cells. The link between SNAI2 gene expression levels and prognosis, as well as immune cell infiltration, was investigated using the Kaplan-Meier technique and Spearman correlation analysis. We also explored the expression and distribution of SNAI2 in various tumor tissues and cells by the THPA (Human Protein Atlas) database. We further investigated the relationship between SNAI2 expression levels and immunotherapy response in various clinical immunotherapy cohorts. Finally, the immunoblot was used to quantify the SNAI2 expression levels, and the proliferative and invasive ability of pancreatic cancer cells was determined by colony formation and transwell assays.

Results

We discovered heterogeneity in SNAI2 expression in different tumor tissues and cancer cell lines by exploring public datasets. The genomic alteration of SNAI2 existed in most cancers. Also, SNAI2 exhibits prognosis predictive ability in various cancers. SNAI2 was significantly correlated with immune-activated hallmarks, cancer immune cell infiltrations, and immunoregulators. It's worth noting that SNAI2 expression is significantly related to the effectiveness of clinical immunotherapy. SNAI2 expression was also found to have a high correlation with the DNA mismatch repair (MMR) genes and DNA methylation in many cancers. Finally, the knockdown of SNAI2 significantly weakened the proliferative and invasive ability of pancreatic cancer cells.

Conclusion

These findings suggested that SNAI2 could be used as a biomarker in human pan-cancer to detect immune infiltration and poor prognosis, which provides a new idea for cancer treatment.

NNMT Is an Immune-Related Prognostic Biomarker That Modulates the Tumor Microenvironment in Pan-Cancer

Emerging evidence has revealed the significant roles of nicotinamide n-methyltransferase (NNMT) in cancer initiation, development, and progression; however, a pan-cancer analysis of NNMT has not been conducted. In this study, we first thoroughly investigated the expression and prognostic significance of NNMT and the relationship between NNMT and the tumor microenvironment using bioinformatic analysis. NNMT was significantly increased and associated with poor prognosis in many common cancers. NNMT expression correlated with the infiltration levels of cancer-associated fibroblasts and macrophages in pan-cancer. Function enrichment analysis discovered that NNMT related to cancer-promoting and immune pathways in various common cancers, such as colon adenocarcinoma, head and neck squamous cell carcinoma, ovarian serous cystadenocarcinoma, and stomach adenocarcinoma. NNMT expression was positively correlated with tumor-associated macrophages (TAMs), especially M2-like TAMs. The results suggest that NNMT might be a new biomarker for immune infiltration and poor prognosis in cancers, providing new direction on therapeutics of cancers.

Case report: Variable response to immunotherapy in ovarian cancer: Our experience within the current state of the art

Despite recent advances in ovarian cancer (OC) treatment, including the introduction of bevacizumab and PARP-inhibitors, OC remains a lethal disease. Other therapeutic options are being explored, such as immunotherapy (IT), which has been proved effective in many solid tumors. Findings about tumor-infiltrating cytotoxic and regulatory T cells, together with the expression of PD-1 on immune cells and of PD-L1 on tumor cells, gave the rationale for an attempt to the use of IT also in OC. We treated two patients with avelumab, an anti-PD-L1 monoclonal antibody, after the first line of chemotherapy: Patient A underwent 19 cycles of maintenance therapy with avelumab with a disease-free interval of 12 months, whereas patient B showed a slight progression of disease after only eight cycles. A higher PD-L1 expression in tumor cells of patient A was detected. She also underwent a genomic assessment that described the presence of a high Tumor Mutational Burden (TMB) and a status of Loss of Heterozygosity (LoH). This different response to the same treatment puts in evidence that some genomic and immune features might be investigated.

Overexpression of LncRNA SNHG14 as a biomarker of clinicopathological and prognosis value in human cancers: A meta-analysis and bioinformatics analysis

Background: SNGH14 is a newly discovered long non-coding RNA (lncRNA) highly associated with tumorigenesis. However, whether the level of SNHG14 is related to the prognosis of patients with different cancer types is unclear.

Methods: PubMed, Web of Science, Cochrane Library, and Embase were searched to identify eligible studies from inception to November 2021. The odds ratio (OR) and 95% confidence interval (CI) were utilized to analyze dichotomous variables, while the hazard ratio (HR) and 95% CI were used for survival outcomes. We also included trial sequential analysis (TSA) to assess whether the current evidence was sufficiently conclusive. Stata 15.0 and TSA 0.9 software were used for data analyses.

Results: A total of 21 studies involving 1,080 patients, mainly from China, were included. Our results revealed that high SNHG14 expression was associated significantly with poor overall survival (OS) [HR = 1.39; 95% CI: (1.06–1.83); p = 0.017]. In addition, elevated SNHG14 expression was related to tumor size (> 3.5 cm) [OR = 1.60; 95% CI: (1.20–2.14); p = 0.001], TNM staging [OR = 0.54; 95% CI: (0.40–0.71); p < 0.001], lymph node metastasis [OR = 1.86; 95% CI: (1.35–2.55); p < 0.001], differentiation grade [OR = 1.95; 95% CI: (1.36–2.80); p < 0.001], and distant metastasis [OR = 2.44; 95% CI: (1.30–4.58); p = 0.005]. However, no significant difference was observed between age [OR = 0.98; 95% CI: (0.72–1.35); p = 0.915] and gender [OR = 0.98; 95% CI: (0.72–1.35); p = 0.915] from the enhanced expression of SNHG14.

Conclusion: The current study revealed that overexpression of SNGH14 is associated with low OS rate and clinicopathological characteristics. SNGH14 can be a novel tumor marker that aids in tumor diagnosis, thereby improving patient prognosis.

CT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy

Objectives

In radiomics, high-throughput algorithms extract objective quantitative features from medical images. In this study, we evaluated CT-based radiomics features, clinical features, in-depth learning features, and a combination of features for predicting a good pathological response (GPR) in non-small cell lung cancer (NSCLC) patients receiving immunotherapy-based neoadjuvant therapy (NAT).

Materials and methods

We reviewed 62 patients with NSCLC who received surgery after immunotherapy-based NAT and collected clinicopathological data and CT images before and after immunotherapy-based NAT. A series of image preprocessing was carried out on CT scanning images: tumor segmentation, conventional radiomics feature extraction, deep learning feature extraction, and normalization. Spearman correlation coefficient, principal component analysis (PCA), and least absolute shrinkage and selection operator (LASSO) were used to screen features. The pretreatment traditional radiomics combined with clinical characteristics (before_rad_cil) model and pretreatment deep learning characteristics (before_dl) model were constructed according to the data collected before treatment. The data collected after NAT created the after_rad_cil model and after_dl model. The entire model was jointly constructed by all clinical features, conventional radiomics features, and deep learning features before and after neoadjuvant treatment. Finally, according to the data obtained before and after treatment, the before_nomogram and after_nomogram were constructed.

Results

In the before_rad_cil model, four traditional radiomics features ("original_shape_flatness," "wavelet hhl_firer_skewness," "wavelet hlh_firer_skewness," and "wavelet lll_glcm_correlation") and two clinical features ("gender" and "N stage") were screened out to predict a GPR. The average prediction accuracy (ACC) after modeling with k-nearest neighbor (KNN) was 0.707. In the after_rad_cil model, nine features predictive of GPR were obtained after feature screening, among which seven were traditional radiomics features: "exponential_firer_skewness," "exponential_glrlm_runentropy," "log- sigma-5-0-mm-3d_firer_kurtosis," "logarithm_skewness," "original_shape_elongation," "original_shape_brilliance," and "wavelet llh_glcm_clustershade"; two were clinical features: "after_CRP" and "after lymphocyte percentage." The ACC after modeling with support vector machine (SVM) was 0.682. The before_dl model and after_dl model were modeled by SVM, and the ACC was 0.629 and 0.603, respectively. After feature screening, the entire model was constructed by multilayer perceptron (MLP), and the ACC of the GPR was the highest, 0.805. The calibration curve showed that the predictions of the GPR by the before_nomogram and after_nomogram were in consensus with the actual GPR.

Conclusion

CT-based radiomics has a good predictive ability for a GPR in NSCLC patients receiving immunotherapy-based NAT. Among the radiomics features combined with the clinicopathological information model, deep learning feature model, and the entire model, the entire model had the highest prediction accuracy.

Validation of Patras Immunotherapy Score model for prediction and prognosis of patients with advanced NSCLC treated with nivolumab or pembrolizumab: results from a European multicentre study

Background:

Recently, the Patras Immunotherapy Score (PIOS) has been developed to estimate the survival benefit of patients with advanced non-small-cell lung cancer (aNSCLC) treated with nivolumab or pembrolizumab. The aim of this study was to validate the clinical value of PIOS in an external cohort of aNSCLC patients.

Methods:

PIOS is a baseline formula produced by the combination of performance status, body mass index, age and line of treatment. In this multicentre study, 626 patients with confirmed NSCLC pathology, who had been treated with nivolumab or pembrolizumab, as well as 444 patients with aNSCLC, who had been managed with chemotherapy alone, were retrospectively enrolled. Predictive and prognostic values of PIOS were finally evaluated.

Results:

Patients treated with immunotherapy and higher PIOS score had an improved progression-free survival not only in univariate [hazard ratio (HR) = 0.621, p = 0.001], but also in multivariable analysis (HR = 0.651, p = 0.003). In addition, improved overall survival with increasing PIOS score was also observed (HR = 0.608, p < 0.001) with this association remaining statistically significant after adjusting for programmed-cell death ligand 1 (PD-L1) expression (HR = 0.620, p < 0.001). In addition, patients with disease progression (PD) had lower scores compared to those with stable disease (SD), partial response (PR) or complete response (CR) in a two-tier model (p < 0.001) as well as in a four-tier model (PD, SD, PR and CR; p < 0.001). Prognostic significance of PIOS score also persisted using a binary logistic regression analysis, adjusted for disease stage and PD-L1 status (p = 0.002, odds ratio: 0.578). Contrarily, PIOS had no prognostic significance in the chemotherapy group; however, upon combined analysis of the two cohorts, PIOS was found to have a significant interaction with the type of treatment (HR = 0.066 with p < 0.001), confirming its predictive value for immunotherapy.

Conclusions:

This study provides further validation of PIOS in aNSCLC patients treated with anti-PD-1 monotherapy.

CDKN2A-mediated molecular subtypes characterize the hallmarks of tumor microenvironment and guide precision medicine in triple-negative breast cancer

Currently, breast cancer (BRCA) has become the most common cancer in the world, whose pathological mechanism is complex. Among its subtypes, triple-negative breast cancer (TNBC) has the worst prognosis. With the increasing number of diagnosed TNBC patients, the urgent need of novel biomarkers is also rising. Cyclin-dependent kinase inhibitor 2A (CDKN2A) has recently emerged as a key regulator associated with ferroptosis and cuproptosis (FAC) and has exhibited a significant effect on BRCA, but its detailed mechanism remains elusive. Herein, we conducted the first converge comprehensive landscape analysis of FAC-related gene CDKN2A in BRCA and disclosed its prognostic value in BRCA. Then, an unsupervised cluster analysis based on CDKN2A-correlated genes unveiled three subtypes, namely cold-immune subtype, IFN-γ activated subtype and FTL-dominant subtype. Subsequent analyses depicting hallmarks of tumor microenvironment (TME) among three subtypes suggested strong association between TNBC and CDKN2A. Given the fact that the most clinically heterogeneous TNBC always displayed the most severe outcomes and lacked relevant drug targets, we further explored the potential of immunotherapy for TNBC by interfering CDKN2A and constructed the CDKN2A-derived prognostic model for TNBC patients by Lasso-Cox. The 21-gene–based prognostic model showed high accuracy and was verified in external independent validation cohort. Moreover, we proposed three drugs for TNBC patients based on our model via targeting epidermal growth factor receptor. In summary, our study indicated the potential of CDKN2A as a pioneering prognostic predictor for TNBC and provided a rationale of immunotherapy for TNBC, and offered fresh perspectives and orientations for cancer treatment via inducing ferroptosis and cuproptosis to develop novel anti-cancer treatment strategies.

BESPOKE IO protocol: a multicentre, prospective observational study evaluating the utility of ctDNA in guiding immunotherapy in patients with advanced solid tumours

INTRODUCTION

Immunotherapy (IO) has transformed the treatment paradigm for a wide variety of solid tumours. However, assessment of response can be challenging with conventional radiological imaging (eg, iRECIST), which do not precisely capture the unique response patterns of tumours treated with IO. Emerging data suggest that circulating tumour DNA (ctDNA) can aid in response assessment in patients with solid tumours receiving IO. The short half-life of ctDNA puts it in a unique position for early treatment response monitoring. The BESPOKE IO study is designed to investigate the clinical utility of serial ctDNA testing to assess treatment response using a tumour-informed, bespoke ctDNA assay (Signatera) and to determine its impact on clinical decision-making with respect to continuation/discontinuation, or escalation/de-escalation of immunotherapy in patients with advanced solid tumours.

METHODS AND ANALYSIS

The BESPOKE IO is a multicentre, prospective, observational study with a goal to enroll over 1500 patients with solid tumours receiving IO in up to 100 US sites. Patients will be followed for up to 2 years with serial ctDNA analysis, timed with every other treatment cycle. The primary endpoint is to determine the percentage of patients who will have their treatment regimen changed as guided by post-treatment bespoke ctDNA results along with standard response assessment tools. The major secondary endpoints include progression-free survival, overall survival and overall response rate based on the ctDNA dynamics.

ETHICS AND DISSEMINATION

The BESPOKE IO study was approved by the WCG Institutional Review Board (Natera-20-043-NCP BESPOKE Study of ctDNA Guided Immunotherapy (BESPOKE IO)) on 22 February 2021. Data protection and privacy regulations will be strictly observed in the capturing, forwarding, processing and storing patients' data. Natera will approve the publication of any study results in accordance with the site-specific contract.

TRIAL REGISTRATION NUMBER

NCT04761783.

GIMAP7 as a Potential Predictive Marker for Pan-Cancer Prognosis and Immunotherapy Efficacy

Background

Methods

Results

Conclusion

GDPLichi: a DNA Damage Repair-Related Gene Classifier for Predicting Lung Adenocarcinoma Immune Checkpoint Inhibitors Response

Lung cancer is one of the most common and mortal malignancies, usually with a poor prognosis in its advanced or recurrent stages. Recently, immune checkpoint inhibitors (ICIs) immunotherapy has revolutionized the treatment of human cancers including lung adenocarcinoma (LUAD), and significantly improved patients' prognoses. However, the prognostic and predictive outcomes differ because of tumor heterogeneity. Here, we present an effective method, GDPLichi (Genes of DNA damage repair to predict LUAD immune checkpoint inhibitors response), as the signature to predict the LUAD patient's response to the ICIs. GDPLichi utilized only 7 maker genes from 8 DDR pathways to construct the predictive model and classified LUAD patients into two subgroups: low- and high-risk groups. The high-risk group was featured by worse prognosis and decreased B cells, CD8+ T cells, CD8+ central memory T cells, hematopoietic stem cells (HSC), myeloid dendritic cells (MDC), and immune scores as compared to the low-risk group. However, our research also suggests that the high-risk group was more sensitive to ICIs, which might be explained by increased TMB, neoantigen, immune checkpoint molecules, and immune suppression genes' expression, but lower TIDE score as compared to the low-risk group. This conclusion was verified in three other LUAD cohort datasets (GSE30219, GSE31210, GSE50081).

USH2A Mutation is Associated With Tumor Mutation Burden and Antitumor Immunity in Patients With Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is one of the diseases with the highest morbidity and mortality in the world. At present, immunotherapy has become a valuable method for the treatment of COAD. Tumor mutational burden (TMB) is considered to be the most common biomarker for predicting immunotherapy. According to reports, the mutation rate of COAD ranks third. However, whether these gene mutations are related to TMB and immune response is still unknown. Here, COAD somatic mutation data were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Bioinformatics methods were used to study the relationships among gene mutations, COAD survival prognosis, and tumor immune response. A total of 22 of the top 40 mutations in TCGA and ICGC databases were the same. Among them, the USH2A mutation was associated with high TMB and poor clinical prognosis. According to Gene Set Enrichment Analysis (GSEA) and the CIBERSORT algorithm, we determined that the USH2A mutation upregulates signaling pathways involved in the immune system and the antitumor immune response. In cases with a USH2A mutation, the immune score and MSI score of TCGA samples increased, the expression of immune checkpoint genes decreased significantly, and the TIDE score decreased significantly. Dependent on the presence or absence of a USH2A mutation, TCGA COAD samples were analyzed for differentially expressed genes, 522 of which were identified. Using a univariate Cox analysis and LASSO COX analysis of these differential genes, a prediction model was established, which established significant differences in the infiltration of immune cells, immune checkpoint gene expression, immune score, MSI score, TMB, and TIDE in patients in high- and low-risk groups. In conclusion, mutation of USH2A is frequent in COAD and is related to an increase in TMB and the antitumor immunity. The differential genes screened by USH2A mutation allowed the construction of a risk model for predicting the survival and prognosis of cancer patients, in addition to providing new ideas for COAD immunotherapy.

Identification of NSP3 (SH2D3C) as a Prognostic Biomarker of Tumor Progression and Immune Evasion for Lung Cancer and Evaluation of Organosulfur Compounds from Allium sativum L. as Therapeutic Candidates

The multi-domain non-structural protein 3 (NSP3) is an oncogenic molecule that has been concomitantly implicated in the progression of coronavirus infection. However, its oncological role in lung cancer and whether it plays a role in modulating the tumor immune microenvironment is not properly understood. In the present in silico study, we demonstrated that NSP3 (SH2D3C) is associated with advanced stage and poor prognoses of lung cancer cohorts. Genetic alterations of NSP3 (SH2D3C) co-occurred inversely with Epidermal Growth Factor Receptor (EGFR) alterations and elicited its pathological role via modulation of various components of the immune and inflammatory pathways in lung cancer. Our correlation analysis suggested that NSP3 (SH2D3C) promotes tumor immune evasion via dysfunctional T-cell phenotypes and T-cell exclusion mechanisms in lung cancer patients. NSP3 (SH2D3C) demonstrated a high predictive value and association with therapy resistance in lung cancer, hence serving as an attractive target for therapy exploration. We evaluated the in silico drug-likeness and NSP3 (SH2D3C) target efficacy of six organosulfur small molecules from Allium sativum using a molecular docking study. We found that the six organosulfur compounds demonstrated selective cytotoxic potential against cancer cell lines and good predictions for ADMET properties, drug-likeness, and safety profile. E-ajoene, alliin, diallyl sulfide, 2-vinyl-4H-1,3-dithiin, allicin, and S-allyl-cysteine docked well into the NSP3 (SH2D3C)-binding cavity with binding affinities ranging from -4.3~-6.70 Ă and random forest (RF) scores ranging from 4.31~5.26 pKd. However, S-allyl-cysteine interaction with NSP3 (SH2D3C) is unfavorable and hence less susceptible to NSP3 ligandability. In conclusion, our study revealed that NSP3 is an important onco-immunological biomarker encompassing the tumor microenvironment, disease staging and prognosis in lung cancer and could serve as an attractive target for cancer therapy. The organosulfur compounds from A. sativum have molecular properties to efficiently interact with the binding site of NSP3 and are currently under vigorous preclinical study in our laboratory.

Assessment of immune status of laryngeal squamous cell carcinoma can predict prognosis and guide treatment

BACKGROUND

In the past few years, immunotherapy has changed the way we treat solid tumors. People pay more and more attention to the immune microenvironment of laryngeal squamous cell carcinoma (LSCC). In this study, our immunotherapy research took advantage of the clinical database and focused our in-depth analysis on the tumor microenvironment (TME).

METHODS

This study evaluated the relationship between the clinical outcome and the local tissue and overall immune status in 412 patients with primary LSCC. We constructed and validated a risk model that could predict prognosis, assess immune status, identify high-risk patients, and develop personalized treatment plans through bioinformatics. In addition, through immunohistochemical analysis, we verified the differential expression of CTSL and KDM5D genes with the largest weight coefficients in the model in LSCC tissues and their influence on the prognosis and tumor-infiltrating lymphocytes (TILs).

RESULTS

We found that interstitial tumor-infiltrating lymphocytes, tumor parenchymal-infiltrating lymphocyte volume, tumor infiltrates lymphocytes of frontier invasion, and the platelet-to-lymphocyte ratio (PLR) were independent factors affecting the prognosis of patients with LSCC. A novel risk model can guide clinicians to accurately predict prognosis, identify high-risk patients, and formulate personalized treatment plans. The differential expression of genes such as CTSL and KDM5D has a significant correlation with the TILs of LSCC and the prognosis of patients.

CONCLUSION

Local and systemic inflammatory markers in patients with laryngeal squamous cell carcinoma are reliable prognostic factors. The risk model and CTSL, KDM5D gene have important potential research value.

Molecular predictors of response to pembrolizumab in thymic carcinoma

Thymic carcinoma is rare and has a poorer prognosis than thymomas. The treatment options are limited after failure of platinum-based chemotherapy. We previously performed a single-center phase II study of pembrolizumab in patients with advanced thymic carcinoma, showing a 22.5% response rate. Here, we characterize the genomic and transcriptomic profile of thymic carcinoma samples from 10 patients (5 non-responders versus 5 responders) in this cohort, with the main aim of identifying potential predictors of response to immunotherapy. We find that expression of PDL1 and alterations in genes or pathways that correlated with PD-L1 expression (CYLD and BAP1) could be potential predictors for response or resistance to immunotherapy in patients with advanced thymic carcinoma. Our study provides insights into potential predictive markers/pathways to select patients with thymic carcinoma for anti-PD-1 immunotherapy.

Pan-Cancer Analysis of Immune Complement Signature C3/C5/C3AR1/C5AR1 in Association with Tumor Immune Evasion and Therapy Resistance

Despite the advances in our understanding of the genetic and immunological basis of cancer, cancer remains a major public health burden with an ever-increasing incidence rate globally. Nevertheless, increasing evidence suggests that the components of the complement system could regulate the tumor microenvironment (TME) to promote cancer progression, recurrence, and metastasis. In the present study, we used an integrative multi-omics analysis of clinical data to explore the relationships between the expression levels of and genetic and epigenetic alterations in C3, C5, C3AR1, and C5AR1 and tumor immune evasion, therapy response, and patient prognosis in various cancer types. We found that the complements C3, C5, C3AR1, and C5AR1 have deregulated expression in human malignancies and are associated with activation of immune-related oncogenic processes and poor prognosis of cancer patients. Furthermore, we found that the increased expression levels of C3, C5, C3AR1, and C5AR1 were primarily predicted by copy number variation and gene methylation and were associated with dysfunctional T-cell phenotypes. Single nucleotide variation in the gene signature co-occurred with multiple oncogenic mutations and is associated with the progression of onco-immune-related diseases. Further correlation analysis revealed that C3, C5, C3AR1, and C5AR1 were associated with tumor immune evasion via dysfunctional T-cell phenotypes with a lesser contribution of T-cell exclusion. Lastly, we also demonstrated that the expression levels of C3, C5, C3AR1, and C5AR1 were associated with context-dependent chemotherapy, lymphocyte-mediated tumor killing, and immunotherapy outcomes in different cancer types. In conclusion, the complement components C3, C5, C3AR1, and C5AR1 serve as attractive targets for strategizing cancer immunotherapy and response follow-up.

N6-methyladenosine (m6A) RNA methylation regulator SNRPC is a prognostic biomarker and is correlated with immunotherapy in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world, and due to its complex pathogenic factors, its prognosis is poor. N6-methyladenosine (m⁶A) RNA methylation plays an important role in the tumorigenesis, progression, and prognosis of many tumors. The m⁶A RNA methylation regulator small nuclear ribonucleoprotein polypeptide C (SNRPC), which encodes one of the specific protein components of the U1 small nuclear ribonucleoprotein (snRNP) particle, has been proven to be related to the prognosis of patients with HCC. However, the effect of SNRPC on the tumor microenvironment and immunotherapy in HCC remains unclear.

Case presentation

The HCC RNA-seq profiles in The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases, including 421 LIHC and 440 LIRI-JP samples, respectively, were used in this study. Both the expression of SNRPC in HCC was upregulated in the TCGA and ICGC databases compared to normal tissues. Next, the expression of SNRPC was validated as a risk factor for prognosis by Kaplan-Meier analysis and employed to establish a nomogram with T pathologic stage. By gene set variation (GSVA) analysis and gene set enrichment (GSEA) analysis, we found that SNRPC was mainly related to protein metabolism and the immune process. Furthermore, the estimation of stromal and immune cells in malignant tumor tissues using expression (ESTIMATE), microenvironment cell population counter (MCP-counter), and single sample GSEA (ssGSEA) algorithms revealed that the high-SNRPC group had a lower stromal score, lower abundance of endothelial cells and fibroblasts, and lower immune infiltration. Ultimately, a tumor immune dysfunction and exclusion (TIDE) analysis revealed that patients in the low-SNRPC group may be more sensitive to immune checkpoint inhibitor therapy.

Conclusion

SNRPC could serve as a promising prognostic and immunotherapeutic marker in HCC and might contribute to new directions and strategies for HCC treatment.

Elevated Ras related GTP binding B (RRAGB) expression predicts poor overall survival and constructs a prognostic nomogram for colon adenocarcinoma

Currently, no articles have explored the roles of RRAGB gene in the occurrence and development of cancer. By means of The Cancer Genome Atlas (TCGA) data mining, we found that this gene might be a novel prognostic predictor for colon adenocarcinoma (COAD). Hence, this article was carried out to explore its roles in COAD and associations with immunity. RRAGB single-gene expression matrix and corresponding clinical information were extracted from TCGA database. Univariate/multivariate cox regression analyses and gene set enrichment analysis (GSEA) were utilized to identify independent prognostic factors and RRAGB related pathways, respectively. Relationships between RRAGB and immunity were also analyzed. Boxplot and K-M survival analysis indicated that RRAGB was not only differently expressed in COAD (P < 0.05), but also significantly associated with overall survival (OS; P < 0.05). Univariate and multivariate Cox hazard regression analyses indicated that RRAGB could serve as an independent prognostic factor for COAD (both P < 0.05). GSEA identified five signaling pathways significantly enriched in the high-RRAGB expression phenotype. Moreover, a RRAGB-based nomogram was successfully constructed and displayed a satisfactory performance. In addition, RRAGB expression was found to be significantly associated with microsatellite instability (MSI), tumor mutational burden (TMB) and immunity. Our results revealed that RRAGB could be a prognostic biomarker for COAD in terms of OS and markedly related to MSI, TMB, and immunity. We also constructed an RRAGB-based nomogram with a satisfactory performance. Further researches should be carried out to validate our findings.

METTL7B is a novel prognostic biomarker of lower-grade glioma based on pan-cancer analysis

Methyltransferase-like 7B (METTL7B) is a member of the methyltransferase-like protein family that plays an important role in the development and progression of tumors. However, its prognostic value and the correlation of METTL7B expression and tumor immunity in some cancers remain unclear. By analyzing online data, we found that METTL7B is abnormally overexpressed in multiple human tumors and plays an important role in the overall survival (OS) of patients with 8 cancer types and disease-free survival (DFS) of patients with 5 cancer types. Remarkably, METTL7B expression was positively correlated with the OS and DFS of patients with lower-grade glioma (LGG). In addition, a positive correlation between METTL7B expression and immune cell infiltration in LGG was observed. Moreover, we identified a strong correlation between METTL7B expression and immune checkpoint gene expression in kidney chromophobe (KICH), LGG and pheochromocytoma and paraganglioma (PCPG). Furthermore, METTL7B was involved in the extracellular matrix (ECM) and immune-related pathways in LGGs. Finally, in vitro experiments showed that knockdown of METTL7B inhibited the growth, migration, invasion and the epithelial–mesenchymal transition (EMT) of LGG cells. METTL7B expression potentially represents a novel prognostic biomarker due to its significant association with immune cell infiltration in LGG.

PD-L1 expression as a predictive biomarker for immune checkpoint inhibitors: between a dream and a nightmare

PD-L1 is an important predictive biomarker for treatment by immune checkpoint inhibitors (ICIs). ICIs are now indicated for the treatment of various cancer depending on the level of expression of PD-L1 on tumor cells. PD-L1 testing is done using immunohistochemistry with five different assays approved as companion diagnostic for ICIs. However, these assays have different score reporting methods and do not accurately measure PD-L1 expression. Exosomal PD-L1 testing has recently emerged as an alternative for cell-surface PD-L1 testing however studies are still premature and more extensive knowledge about this new potential biomarker is needed.

Pre-Immunotherapy Contrast-Enhanced CT Texture-Based Classification: A Useful Approach to Non-Small Cell Lung Cancer Immunotherapy Efficacy Prediction

Objective: To investigate the utility of the pre-immunotherapy contrast-enhanced CT-based texture classification in predicting response to non-small cell lung cancer (NSCLC) immunotherapy treatment.

Methods: Sixty-three patients with 72 lesions who received immunotherapy were enrolled in this study. We extracted textures including histogram, absolute gradient, run-length matrix, gray-level co-occurrence matrix, autoregressive model, and wavelet transform from pre-immunotherapy contrast-enhanced CT by using Mazda software. Three different methods, namely, Fisher coefficient, mutual information measure (MI), and minimization of classification error probability combined average correlation coefficients (POE + ACC), were performed to select 10 optimal texture feature sets, respectively. The patients were divided into non-progressive disease (non-PD) and progressive disease (PD) groups. t-test or Mann–Whitney U-test was performed to test the differences in each texture feature set between the above two groups. Each texture feature set was analyzed by principal component analysis (PCA), linear discriminant analysis (LDA), and non-linear discriminant analysis (NDA). The area under the curve (AUC) was used to quantify the predictive accuracy of the above three analysis models for each texture feature set, and the sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were also calculated, respectively.

Results: Among the three texture feature sets, the texture parameter differences of kurtosis (2.12 ± 3.92 vs. 0.78 ± 1.10, p = 0.047), “S(2,2)SumEntrp” (1.14 ± 0.31 vs. 1.24 ± 0.12, p = 0.036), and “S(1,0)SumEntrp” (1.18 ± 0.27 vs. 1.28 ± 0.11, p = 0.046) between the non-PD and PD group were statistically significant (all p < 0.05). The classification result of texture feature set selected by POE + ACC and analyzed by NDA was identified as the best model (AUC = 0.812, 95% CI: 0.706–0.919) with a sensitivity, specificity, accuracy, PPV, and NPV of 88.2, 76.3, 81.9, 76.9, and 87.9%, respectively.

Conclusion: Pre-immunotherapy contrast-enhanced CT-based texture provides a new method for clinical evaluation of the NSCLC immunotherapy efficacy prediction.

EVA1B to Evaluate the Tumor Immune Microenvironment and Clinical Prognosis in Glioma

Background

Previous research indicated that the tumor cells and microenvironment interactions are critical for the immunotherapeutic response. However, predicting the clinical response to immunotherapy remains a dilemma for clinicians. Hence, this study aimed to investigate the associations between EVA1B expression and prognosis and tumor-infiltrating immune cells in glioma.

Methods

Firstly, we detected the EVA1B expression in glioma tissues through biological databases. The chi-squared test, Kaplan-Meier, and univariate and multivariate Cox regression analyses were used to analyze the clinical significance of EVA1B expression. The correlation between EVA1B expression and levels of tumor-infiltrating immune cells in glioma tissues was investigated. Receiver operating characteristic (ROC) analysis was performed to compare the predictive power between EVA1B and other commonly immune-related markers.

Results

In the CGGA cohort of 325 glioma patients, we found that EVA1B was upregulated in glioma, and increased with tumor grade. High EVA1B expression was prominently associated with unfavorable clinicopathological features, and poorer survival of patients, which were further confirmed by TCGA (n=609) and GEO (n=74) cohorts. Furthermore, multivariate analysis indicated that EVA1B is an independent prognostic biomarker for glioma. Importantly, EVA1B overexpression was associated with a higher infiltration level of CD4⁺ T cells, CD8⁺ T cells, B cells, macrophages, and neutrophils in glioma. ROC curves showed that, compared with PD-L1, CTLA-4, and Siglec15, EVA1B presented a higher area under the curve (AUC) value (AUC=0.824) for predicting high immune infiltration levels in glioma.

Conclusions

We found that EVA1B was upregulated and could act as a poor prognostic biomarker in glioma. Importantly, EVA1B overexpression was associated with the immune infiltration levels of immune cells including B cells, CD4⁺ T cells, CD8⁺ T cells, macrophages, and neutrophils, and strongly with the overall immune infiltration levels of glioma. These findings suggested that EVA1B might be a potential biomarker for evaluating prognosis and immune infiltration in glioma.

Characterization of Tumor Microenvironment in Lung Adenocarcinoma Identifies Immune Signatures to Predict Clinical Outcomes and Therapeutic Responses

Background and Objective

Increasing evidence has elucidated the clinicopathological significance of individual TME component in predicting outcomes and immunotherapeutic efficacy in lung adenocarcinoma (LUAD). Therefore, we aimed to investigate whether comprehensive TME-based signatures could predict patient survival and therapeutic responses in LUAD, and to assess the associations among TME signatures, single nucleotide variations and clinicopathological characteristics.

Methods

In this study, we comprehensively estimated the TME infiltration patterns of 493 LUAD patients and systematically correlated the TME phenotypes with genomic characteristics and clinicopathological features of LUADs using two proposed computational algorithms. A TMEscore was then developed based on the TME signature genes, and its prognostic value was validated in different datasets. Bioinformatics analysis was used to evaluate the efficacy of the TMEscore in predicting responses to immunotherapy and chemotherapy.

Results

Three TME subtypes were identified with no prognostic significance exhibited. Among them, naïve B cells accounted for the majority in TMEcluster1, while M2 TAMs and M0 TAMs took the largest proportion in TMEcluster2 and TMEcluster3, respectively. A total of 3395 DEGs among the three TME clusters were determined, among which 217 TME signature genes were identified. Interestingly, these signature genes were mainly involved in T cell activation, lymphocyte proliferation and mononuclear cell proliferation. With somatic variations and tumor mutation burden (TMB) of the LUAD samples characterized, a genomic landscape of the LUADs was thereby established to visualize the relationships among the TMEscore, mutation spectra and clinicopathological profiles. In addition, the TMEscore was identified as not only a prognosticator for long-term survival in different datasets, but also a predictive biomarker for the responses to immune checkpoint blockade (ICB) and chemotherapeutic agents. Furthermore, the TMEscore exhibited greater accuracy than other conventional biomarkers including TMB and microsatellite instability in predicting immunotherapeutic response (p < 0.001).

Conclusion

In conclusion, our present study depicted a comprehensive landscape of the TME signatures in LUADs. Meanwhile, the TMEscore was proved to be a promising predictor of patient survival and therapeutic responses in LUADs, which might be helpful to the future administration of personalized adjuvant therapy.

Multi-Omics Data Analysis of Gene Expressions and Alterations, Cancer-Associated Fibroblast and Immune Infiltrations, Reveals the Onco-Immune Prognostic Relevance of STAT3/CDK2/4/6 in Human Malignancies

Simple Summary

Signal transducer and activator of transcription 3 (STAT3)/Cyclin-dependent kinases are multifunctional proteins that play instrumental roles in carcinogenesis. However, the genetic alterations of the STAT3/CDK2/4/6 signaling axis and its role in predicting immune infiltration and immunotherapeutic response remain unclear. Here, we used in silico analyses of multi-Omics data to map out the role of epigenetic and genetic alterations of STAT3/CDK2/4/6 in tumor immune infiltrations, immunotherapy response, and prognosis of cancer patients. Our study collectively suggested that STAT3/CDK2/4/6 are important onco-immune signatures that contribute to tumor immune invasion, poor prognoses, and immune therapy failure. Our finding may be clinically useful in designing therapeutic strategies, prognosis assessment, and follow-up management in patients receiving immunotherapy in multiple cancers.

Abstract

Signal transducer and activator of transcription 3 (STAT3)/Cyclin-dependent kinases are multifunctional proteins that play an important implicative role in cancer initiations, progression, drug resistance, and metastasis, and has been extensively explored in cancer therapy. However, the genetic alterations of STAT3/CDK2/4/6 and its role in predicting immune infiltration and immunotherapeutic response are yet to be well exploited. In this study, we use in silico methods to analyze differential expression, prognostic value, genetic and epigenetic alterations, association with tumor-infiltrating immune cells, and cancer-associated fibroblast (CAF) infiltrations of STAT3/CDK2/4/6 in multiple cancer types. Our results revealed that the expression of STAT3/CDK2/4/6 was altered in various cancers and is associated with poor overall and disease-free survival of the cohorts. Moreover, genetic alterations in STAT3/CDK2/4/6 co-occurred with a number of other genetic alterations and are associated with poorer prognoses of the cohorts. The protein-protein interaction (PPI) network analysis suggests CDK2/4/6/STAT3 may directly interact with factors that promote tumorigenesis and immune response. We found that STAT3/CDK2/4/6 expressions were associated with infiltrations of CAF and the various immune cells in multiple cancers and it’s associated with poor response to immunotherapy. Collectively, our study suggested that STAT3/CDK2/4/6 are important onco-immune signatures that play central roles in tumor immune invasion, poor prognoses and, immune therapy response. Findings from the present study may therefore be clinically useful in prognosis assessment and follow-up management of immunotherapy.

Prognostic value and immunological role of PDCD1 gene in pan-cancer

OBJECTIVE

Identify the prognostic value and investigate the association between programmed cell death 1 (PDCD1) gene expression and immune infiltration in pan-cancer.

METHODS

We used a series of bioinformatics methods to comprehensively analyze the relationship between PDCD1 gene and prognosis, tumor mutational burden (TMB), microsatellite instability (MSI), immune cell infiltration of various cancers from the existing public database, and try to find the potential prognostic value of PDCD1 for in pan-cancer.

RESULTS

High expression of PDCD1 was closely related to better overall survival (OS) and disease-specific survival (DSS) in breast invasive carcinoma, head and neck squamous cell carcinoma, skin cutaneous melanoma, and uterine corpus endometrial carcinoma; have a better disease-free interval (DFI) and progression-free interval (PFI) in several cancer types. Meanwhile, the high level of PDCD1 gene expression was associated with poorer OS, DSS, and PFI in brain lower grade glioma and uveal melanoma; poorer OS in acute myeloid leukemia and kidney renal papillary cell carcinoma; poorer OS and DSS in glioblastoma multiforme; poorer DSS in kidney renal clear cell carcinoma, by Kaplan-Meier and Cox survival analysis. PDCD1 gene expression was significantly correlated with TMB and MSI in 14 and 12 cancer types, respectively, and infiltrating levels of immune cells, especially Macrophages M0, M1, CD4-T-cells, CD8-T-cells, and T cells follicular helper, in most of eight cancer types.

CONCLUSION

PDCD1 can be used as a prognostic marker in multiple cancers, owing to it is closely associated with TMB, MSI, and immune cells infiltration.

Angiotensin-converting enzyme (ACE) inhibitor prescription affects non-small-cell lung cancer (NSCLC) patients response to PD-1/PD-L1 immune checkpoint blockers

Angiotensin-converting enzyme (ACE) inhibitors are frequently used to treat hypertension and congestive heart failure. Preclinical data show that ACE plays a role on both innate and adaptive immune responses. Since interactions between ACE inhibitors and immune checkpoint inhibitors (ICIs) have not been reported, the aim of this study is to investigate the influence of ACE inhibitors on non-small cell lung cancer (NSCLC) patients treated with programmed cell death-1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors. We conducted a retrospective cohort analysis of NSCLC patients treated with PD-1/PD-L1 inhibitors. Clinical and co-medication data as well as tumor biopsies were collected. Groups were defined according to patients' co-medications at the time of ICI initiation. Among the 178 patients included, 22 (13.1%) received ACE inhibitors. While baseline characteristics were similar in both groups, ACE inhibitors group had a shorter median PFS (Progression-Free Survival) compared to the control group: 1.97 vs. 2.56 months, p = .01 (HR = 1.8 CI95% 1.1-2.8). Using CIBERSORT, RNA sequencing suggested that tumors from the ACE inhibitors group had less M1 macrophages, activated mast cells, NK cells and memory activated T cells, thus suggesting an immunosuppressed state. In vitro, the ACE inhibitor, captopril, induced M2 marker at the cell surface of monocytes engaged into M1 differentiation. Thus, ACE inhibitors prescription concomitant to PD-1/PD-L1 inhibitors treatment seems to be associated with impaired outcome and with a tumor immunosuppressed state in patients with advanced NSCLC. These results should be validated in larger prospective cohorts.

Tumor Infiltrating Lymphocytes Signature as a New Pan-Cancer Predictive Biomarker of Anti PD-1/PD-L1 Efficacy

Tumor immune infiltrates are associated with tumor prognosis in many cancer types. However, their capacity to predict the efficacy of checkpoint inhibitors is poorly documented. We generate three signatures that evaluate in different ways these infiltrates: lymphoid- and myeloid-alone signatures, and a combined signature of both named the TIL (tumor-infiltrating lymphocyte) transcriptomic signature. We evaluate these signatures in The Cancer Genome Atlas Program (TCGA) Pan-Cancer cohort and four cohorts comprising patients with melanoma, lung, and head and neck cancer treated with anti-PD-1 or anti-CTLA-4 therapies. We observe using TCGA Pan-Cancer cohort that this TIL or lymphoid-alone signature accurately estimates prognosis in most cancer types and outperforms histological TIL evaluation or myeloid signature alone. Both TIL and lymphoid signatures are correlated with response rate to immunotherapy. Combining lymphoid signature or TIL with tumor mutational burden generates a score that is highly efficient in predicting response to immunotherapy. In different series of patients treated with checkpoint inhibitors for non-small cell lung cancer, head and neck cancer, and melanoma, we observed that TIL or lymphoid signature were associated with outcome. These data demonstrate that a simple TIL or lymphoid signature could be used as a Pan-Cancer prognostic and predictive biomarker to estimate patient survival under checkpoint inhibitors.

PD-L1 in Systemic Immunity: Unraveling Its Contribution to PD-1/PD-L1 Blockade Immunotherapy

The use of monoclonal antibodies targeting PD-1/PD-L1 axis completely changed anticancer treatment strategies. However, despite the significant improvement in overall survival and progression-free survival of patients undergoing these immunotherapy treatments, the only clinically accepted biomarker with some prediction capabilities for the outcome of the treatment is PD-L1 expression in tumor biopsies. Nevertheless, even when having PD-L1-positive tumors, numerous patients do not respond to these treatments. Considering the high cost of these therapies and the risk of immune-related adverse events during therapy, it is necessary to identify additional biomarkers that would facilitate stratifying patients in potential responders and non-responders before the start of immunotherapies. Here, we review the utility of PD-L1 expression not only in tumor cells but in immune system cells and their influence on the antitumor activity of immune cell subsets.

Role of pleural and peritoneal metastasis in immune checkpoint inhibitors efficacy patients with non-small cell lung cancer: real-world data from a large cohort in France

BACKGROUND

Checkpoint inhibitors (CKI) targeting PD-1 or PD-L1 are major therapies for the treatment of non-small cell lung cancer (NSCLC). Despite numerous studies of biological biomarkers, we currently lack a marker to predict CKI primary resistance. The aim of this study was to isolate clinical markers associated with the absence of efficacy of CKI used as monotherapy in NSCLC.

METHODS

We conducted a retrospective analysis of 172 patients treated with anti-PD1 or anti-PDL1 monoclonal antibodies (mAb) for advanced NSCLC at the Dijon Cancer Center. Baseline characteristics were compared using the Chi squared test between responders and non-responders. Survival curves were estimated by the Kaplan-Meier method and compared with the Log-rank test for univariate analysis. Cox regression models were used to determine hazard ratios and 95% confidence intervals for progression-free survival (PFS) and overall survival (OS).

RESULTS

Among 172 patients included, 149 (86.5%) received CKI after platinum chemotherapy. Response rate (RR) was 16%, median progression-free survival (PFS) was 2.5 months (95% CI 0.7-30 months) and median overall survival (OS) was 10 months (95% CI 0.7-46.8 months). By univariate analysis, WHO performance status ≥ 1, presence of bone, liver and pleuroperitoneal metastasis were associated with poor PFS and OS. Multivariate analysis showed that only pleuroperitoneal metastasis was independently associated with PFS and OS. Patients with pleuroperitoneal metastasis and WHO performance status ≥ 1 had a < 10% chance of yielding a benefit from CKI.

CONCLUSIONS

Our data support the hypothesis that pleuroperitoneal metastasis is a major predictive factor affecting CKI efficacy in NSCLC patients and may be used to avoid CKI monotherapy for such patients.

PIOS (Patras Immunotherapy Score) Score Is Associated with Best Overall Response, Progression-Free Survival, and Post-Immunotherapy Overall Survival in Patients with Advanced Non-Small-Cell Lung Cancer (NSCLC) Treated with Anti-Program Cell Death-1 (PD-1) Inhibitors

Immunotherapy with immune checkpoint inhibitors (ICIs) has changed the therapeutic management of advanced non-small cell lung cancer (aNSCLC) over the last decade. However, there is an unmet need for clinically useful biomarkers in this patient subgroup. The aim of this study was to combine baseline clinical characteristics of aNSCLC patients, in the form of a scoring system, and to investigate its predictive and prognostic value in NSCLC patients treated with ICIs. A total of 112 patients with advanced (stages IIIA to IV) NSCLC, treated with nivolumab or pembrolizumab, were enrolled in this study. Patras Immunotherapy Score (PIOS) was developed based on four of the studied parameters (performance status (PS), body mass index (BMI), age, and lines of treatment (LOT), which were incorporated into our formula (PS × BMI/ LOT × age). PIOS score was strongly associated with best overall responses (BOR), with those patients having benefit/good response (stable disease (SD) or partial (PR) or complete response (CR), achieving a higher score compared to patients who developed progressive disease (PD) (p < 0.001). Furthermore, PIOS score was associated with progression-free survival (PFS), since high-score patients had longer PFS (p < 0.001, hazard ratio (HR) = 0.469). Moreover, PIOS was associated with post-immunotherapy overall survival (OS), with high-score patients having improved OS (log-rank p = 0.019). This study suggests that a combination of baseline parameters, which give rise to PIOS score, may predict the best response of NSCLC patients treated with anti-program cell death -1 (PD-1) monotherapy as well as it may have a potent prognostic value for PFS and post immunotherapy OS.

LncRNA KCNQ1OT1 promotes cell proliferation, migration and invasion via regulating miR-129-5p/JAG1 axis in non-small cell lung cancer

Background

Non-small cell lung cancer (NSCLC) is the most deadly cancer worldwide. LncRNA KCNQ1OT1 has been reported to be involved in the progression of various tumors, including NSCLC. However, the precise mechanism of KCNQ1OT1 in NSCLC requires further investigation.

Methods

The expression levels of KCNQ1OT1, miR-129-5p and JAG1 were detected by qRT-PCR or western blot. Kaplan–Meier survival analysis was used to assess the correlation between KCNQ1OT1 expression and the overall survival of NSCLC patients. CCK-8 assay was used to measure cell viability. Cell migration and invasion were detected by transwell assay. The targets of KCNQ1OT1 and miR-129-5p were predicted by bioinformatics, which was confirmed by dual-luciferase reporter assay or pull-down assay.

Results

KCNQ1OT1 expression was significantly enhanced, while miR-129-5p expression was dramatically reduced in NSCLC tissues and cells. Higher KCNQ1OT1 shortened overall survival and was positively associated with tumor stage and lymph node metastasis. KCNQ1OT1 knockdown inhibited proliferation, migration and invasion of NSCLC cells. Inhibition of miR-129-5p attenuated the inhibition of NSCLC cell viability, migration and invasion induced by KCNQ1OT1 knockdown. In addition, JAG1 was confirmed as a target of miR-129-5p. Knockdown of JAG1 reversed the effects of miR-129-5p knockdown on NSCLC progression. KCNQ1OT1 regulated JAG1 expression by sponging miR-129-5p in NSCLC cells.

Conclusion

KCNQ1OT1 induced proliferation, migration and invasion of NSCLC cells by sponging miR-129-5p and regulating JAG1 expression, indicating that KCNQ1OT1 was a therapeutic target for NSCLC.

Effectiveness of PD-1/PD-L1 inhibitors in the treatment of lung cancer: Brightness and challenge

Immune checkpoint inhibitors (ICIs), especially inhibitors of the PD-1/PD-L1 axis, have significantly affected the outcomes of patients with lung cancer. Nivolumab and pembrolizumab have been approved as PD-1 blocking antibodies, whereas atezolizumab, avelumab, and durvalumab are approved as PD-L1 blocking antibodies by the United States Food and Drug Administration. However, which patient may benefit the most and how to identify patients at risk of primary or acquired resistance has not been completely defined. Meanwhile, close attention has been paid to the ongoing international and domestic clinical trials in Chinese patients with lung cancer. This review aimed to provide deep insight into the effectiveness of PD-1/PD-L1 inhibitors in patients with lung cancer, including the current settings for varied disease status, the predictive biomarkers, the resistance to ICIs, and the ongoing clinical trials in Chinese patients.

Analysis of immune-related signatures of lung adenocarcinoma identified two distinct subtypes: implications for immune checkpoint blockade therapy

Immune checkpoint blockade (ICB) therapies have revolutionized the treatment of human cancers including lung adenocarcinoma (LUAD). However, our understanding of the immune subtyping of LUAD and its association with clinical response of immune checkpoint inhibitor remains incomplete. Here we performed molecular subtyping and association analysis of LUAD from the Cancer Genome Atlas (TCGA) and validated findings from TCGA cohort in 9 independent validation cohorts. We conducted consensus molecular subtyping with nonnegative matrix factorization (NMF). Potential response of ICB therapy was estimated with Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. We identified 2 distinct subtypes of LUAD in TCGA cohort that were characterized by significantly different survival outcomes (i.e., high- and low-risk subtypes). The high-risk subtype was featured by lower TIDE score, upregulation of programmed death-ligand 1 (PD-L1) expression, and higher tumor mutation burden (TMB). The high-risk subtype also harbored significantly elevated cell cycle modulators CDK4/CDK6 and TP53 mutation. These observations were validated in 9 independent LUAD cohorts. Our findings suggest that immune checkpoint blockade therapy may be efficacious for high-risk subtype of LUAD patients.

LncRNA NNT-AS1 promotes non-small cell lung cancer progression through regulating miR-22-3p/YAP1 axis

Background

Lung cancer is the leading cause of cancer‐related mortality worldwide. Studies have demonstrated that long noncoding RNA nicotinamide nucleotide transhydrogenase‐antisense RNA1 (NNT‐AS1) functioned as an oncogene in most malignancies, including non‐small cell lung cancer (NSCLC). This study aimed to investigate the underlying mechanisms of NNT‐AS1 in NSCLC progression.

Methods

The levels of NNT‐AS1, miR‐22‐3p and Yes‐associated protein (YAP1) were detected by qRT‐PCR in NSCLC tissues and cells. Kaplan‐Meier analysis was conducted to analyze the correlation between NNT‐AS1 expression and overall survival of NSCLC patients. Cell proliferation was evaluated by MTT assay. Cell migration and invasion were assessed using transwell assay. The protein levels of YAP1 and EMT‐related proteins were detected by western blot. The molecular mechanism was predicted by starBase2.0 and validated by dual‐luciferase reporter assay or RNA pull‐down assay. Xenograft analysis was carried out to analyze tumor growth in vivo.

Results

We found that the levels of NNT‐AS1 and YAP1 were enhanced, while miR‐22‐3p expression was decreased in NSCLC tissues and cells. High NNT‐AS1 expression was correlated with poor prognosis. NNT‐AS1 knockdown impeded proliferation, migration, invasion and EMT of NSCLC cells. NNT‐AS1 targeted miR‐22‐3p, and YAP1 was a target of miR‐22‐3p in NSCLC cells. Furthermore, NNT‐AS1 facilitated the progression of NSCLC by regulating miR‐22‐3p/YAP1 axis. NNT‐AS1 knockdown repressed tumor growth in vivo.

Conclusion

NNT‐AS1 facilitated proliferation, migration, invasion and EMT of NSCLC cells by sponging miR‐22‐3p and regulating YAP1 expression, which might provide a potential biomarker and therapeutic target for NSCLC.

Incorporation of EGFR mutation status into M descriptor of new TNM classification influences survival curves in non-small cell lung cancer patients

Background The 8th edition of tumor node metastasis (TNM) staging system for lung cancer introduced a revision of M descriptor. The limitation of new classification to predict prognosis is its focus on anatomical extent of the disease only. Information on molecular status of the tumor significantly influences treatment response and survival; however, data addressing this issue is scarce. This report points to the impact of epidermal growth factor receptor (EGFR) mutation in non-small cell lung cancer (NSCLC) patients on survival in view of new M descriptors of TNM classification system. Patients and methods Medical records of 479 consecutive metastatic NSCLC patients treated between 2009 and 2011, all tested for EGFR mutations, were retrospectively reviewed. For 355 patients medical records included sufficient information to be appropriately categorized into one of the new subgroups according to the M descriptor in 8th TNM classification, of those 89 (25.1%) patients harboured EGFR mutations (EGFR-m). Results Median overall survival (mOS) of EGFR-m patients was significantly longer than mOS of patients without EGFR mutations (20.6 months vs. 8.3 months, p < 0.001). Patients with limited disease burden (M1b sub-group) had the longest mOS among EGFR wild type patients (EGFR-wt) and also among EGFR-m patients, 14.4 months and 39.2 month, respectively. In spite of widespread metastatic disease of M1c EGFR-m patients, their mOS (18.8 months) was longer than mOS of oligometastatic EGFR-wt patients (M1b), who had the lowest disease burden (14.4 months). Median follow up was 53.9 months. Conclusions Incorporation of EGFR mutation status in advanced NSCLC further differentiates survival curves of M categories in 8th TNM classification and more precisely predicts survival compared to number of metastasis or number of metastatic sites alone.

MIR155HG is a prognostic biomarker and associated with immune infiltration and immune checkpoint molecules expression in multiple cancers

In recent years, immune checkpoint inhibitor has achieved remarkable success in multiple cancer treatment. However, how to pre‐judge which patients are suitable for immune checkpoint inhibitor is a difficult problem. We use the existing public bioinformatics database to comprehensively analyze the relationship between clinical data of various cancers with immune checkpoint blocking molecules and long non‐coding RNAs (lncRNAs), and try to find the potential predictive value of lncRNA for immunotherapy with checkpoint inhibitors. In this study, we found that: (a) high expression of lncRNA MIR155 host gene (MIR155HG) was closely related to better overall survival (OS) in cholangiocarcinoma (CHOL), lung adenocarcinoma (LUAD), and skin cutaneous melanoma (SKCM), and have better disease‐free survival (DFS) in CHOL. Meanwhile, the high level of MIR155HG was associated with poorer OS in glioblastoma multiforme (GBM), kidney renal clear cell carcinoma (KIRC), brain lower grade glioma (LGG), and uveal melanoma (UVM). (b) The expression of MIR155HG was significantly correlated with infiltrating levels of immune cells and immune molecules, especially with immune checkpoint molecules such as programmed cell death protein 1 (PD‐1), PD‐1 ligand 1 (PD‐L1), and cytotoxic T lymphocyte‐associated antigen 4 (CTLA4) in most kinds of cancers. (c) Detection of clinical CHOL and liver hepatocellular carcinoma tissues confirmed that there was a strong positive correlation between MIR155HG expression and the levels of CTLA4 and PD‐L1. MIR155 host gene can be used as a prognostic marker in multiple cancers, and of great value in predicting the curative effect of immune checkpoint inhibitor therapy owing to it is closely related with immune cells infiltration and immune checkpoint molecules expression.

Possible Biomarkers for Cancer Immunotherapy

Immune checkpoint inhibitors (ICIs) have drastically changed the clinical care of cancer. Although cancer immunotherapy has shown promise in various types of malignancies, thus far, the proportion of patients who can benefit from ICIs is relatively small. Immune-related adverse events and high cost are unavoidable problems. Therefore, biomarkers defining patients that are most likely to benefit from ICIs are urgently needed. The expression of programmed cell death-ligand 1 (PD-L1) is a logical biomarker for the prediction of response to anti-PD1/PD-L1 immunotherapies. However, its usefulness is currently debatable because of its varied definition, threshold, and spatial/temporal heterogeneity. Recently, it was reported that the tumor mutational burden, expression of neoantigens, mismatch repair status, and specific gene mutations may be markers for the success of treatment with ICIs. Moreover, it was suggested that the fecal microbiota prior to immunotherapy may play an important role in predicting the efficacy of ICIs. In this review, we focused on these potential biomarkers for cancer immunotherapy reported in recent clinical articles. Further studies are warranted to develop a predictive model using these biomarkers, with the aim of practicing precision medicine in cancer immunotherapy.