Clinical outcomes of immune checkpoint blockades and the underlying immune escape mechanisms in squamous and adenocarcinoma NSCLC

The association of azole antifungals with overall survival in patients with non-small cell lung cancer receiving immune checkpoint inhibitors

BACKGROUND

Preclinical data suggest antifungal azole derivatives have antitumor efficacy that may modulate response to immune checkpoint inhibitors (ICIs). We aimed to evaluate the association of azole drugs with overall survival (OS) in a population of patients with non-small cell lung cancer (NSCLC) treated with ICI within the Veterans Health Administration (VHA).

METHODS

In this retrospective study, the VA Corporate Data Warehouse was queried for patients diagnosed with NSCLC and treated with ICI from 2010 to 2018. Concomitant oral azole use was defined as dispensation by a VA pharmacy within 90 days of the first ICI infusion. Patients who received azole after 30 days were excluded from the analysis to mitigate immortal time bias. OS was measured from the start of ICI. Cox regression and propensity score matching were used to adjust for confounders.

RESULTS

We identified 3413 patients with NSCLC receiving ICI; 324 (9.5%) were exposed to concomitant azoles. As a group, azole use was not associated with OS (hazard ratio [HR] = 0.96; 95% CI, 0.84-1.09; P = .51). After stratification by azole type, clotrimazole had an association with better OS on univariable (HR = 0.75; 95% CI, 0.59-0.96; P = .024) and multivariable analysis (HR = 0.71; 95% CI, 0.56-0.91; P = .007). Propensity score matching of patients who received clotrimazole vs no azole yielded 101 patients per matched cohort. Clotrimazole was associated with improved OS, although this did not meet the threshold for statistical significance (HR = 0.74; 0.54-1.01; P = .058).

CONCLUSION

This observational study demonstrated an association between clotrimazole and OS among patients with advanced NSCLC receiving ICI. These findings build upon preclinical evidence and support further investigation into the potential for clotrimazole as a repurposed FDA drug to improve cancer outcomes.

Identification of a signature of histone modifiers in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is a cancer that is closely associated with epigenetic alterations, and histone modifiers (HMs) are closely related to epigenetic regulation. Therefore, this study aimed to comprehensively explore the function and prognostic value of HMs-based signature in KIRC. HMs were first obtained from top journal. Then, the mRNA expression profiles and clinical information in KIRC samples were downloaded from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (Lasso) analysis were implemented to find prognosis-related HMs and construct a risk model related to the prognosis in KIRC. Kaplan-Meier analysis was used to determine prognostic differences between high- and low-risk groups. Immune infiltration and drug sensitivity analysis were also performed between high- and low-risk groups. Eventually, 8 HMs were successfully identified for the construction of a risk model in KIRC. The results of the correlation analysis between risk signature and the prognosis showed HMs-based signature has good prognostic value in KIRC. Results of immune analysis of risk models showed there were significant differences in the level of immune cell infiltration and expression of immune checkpoints between high- and low-risk groups. The results of the drug sensitivity analysis showed that the high-risk group was more sensitive to several chemotherapeutic agents such as Sunitinib, Tipifarnib, Nilotinib and Bosutinib than the low-risk group. In conclusion, we successfully constructed HMs-based prognostic signature that can predict the prognosis of KIRC.

Transcription Factor ETV4 Activates AURKA to Promote PD-L1 Expression and Mediate Immune Escape in Lung Adenocarcinoma

INTRODUCTION

The occurrence and progression of lung adenocarcinoma (LUAD) impair T-cell immune responses, causing immune escape and subsequently affecting the efficacy of immunotherapy in patients. Aurora kinase A (AURKA) is upregulated in varying cancers, but its role in LUAD immune escape is elusive. This work attempted to explore molecular mechanisms of AURKA regulation in LUAD immune escape.

METHODS

Through bioinformatics analysis, AURKA level in LUAD was evaluated, and potential upstream transcription factors of AURKA were predicted using hTFtarget. ETS variant transcription factor 4 (ETV4) expression in LUAD was analyzed through The Cancer Genome Atlas. Pearson's correlation analysis was then utilized to test the correlation between AURKA and ETV4. Interaction and binding between AURKA and ETV4 were validated through dual-luciferase assay and chromatin immunoprecipitation. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) tested relative mRNA expression of AURKA and ETV4 in LUAD cells, cell counting kit-8 assayed cell viability, and Western blot analysis was conducted to determine the protein level of programmed death-ligand 1 (PD-L1). Coculture of LUAD cells with activated CD8+ T cells was carried out, and an LDH assay was used to assess the cytotoxicity of CD8+ T cells against LUAD cells. Interferon-γ (IFN-γ), interleukin-2 (IL-2), and tumor necrosis factor-α (TNF-α) levels in the coculture system were assessed by enzyme-linked immunosorbent assay (ELISA). Western blot assessed protein levels of JAK2, p-JAK2, STAT3, and p-STAT3.

RESULTS

Compared to normal tissues, AURKA and ETV4 were upregulated in tumor tissues, and AURKA presented a negative association with CD8+ T-cell immune infiltration but a positive association with PD-L1. qRT-PCR unveiled significantly upregulated mRNA of AURKA and ETV4 in LUAD cells compared to normal lung epithelial cells. Knockdown of AURKA significantly decreased cell viability and PD-L1 protein level in LUAD cells, enhanced cytotoxicity of CD8+ T cells against LUAD cells and IFN-γ, IL-2, and TNF-α expression, while overexpression of AURKA yielded opposite results. Furthermore, the knockdown of ETV4 could reverse the oncogenic characteristics of cells caused by AURKA overexpression.

CONCLUSION

Our study illustrated that ETV4/AURKA axis promoted PD-L1 expression, suppressed CD8+ T-cell activity, and mediated immune escape in LUAD by regulating the JAK2/STAT3 signaling pathway.

Qingfei mixture modulates the immune responses in lung cancer through modulating mTOR signaling and gut microbiota-derived short-chain fatty acids

Lung cancer ranks among the primary contributors to cancer-related fatalities on a global scale. Multiple research investigations have demonstrated that there exists a dysbiosis within the intestinal bacteria and short-chain fatty acids (SCFAs) is linked with immune responses in lung cancer. Qingfei mixture (QFM) has been widely used in treating lung cancer, yet the active ingredients and roles of the QFM on immune responses by targeting gut microbiota remain to be elucidated. The chemical constituents of QFM were qualitatively examined by UPLC/Q-TOF-MS. Additionally, we evaluated the therapeutic impact of the organic substance QFM on lung cancer, aiming to elucidate its mechanisms for improving the tumor-immune microenvironment. Herein, we constructed a Lewis lung carcinoma (LLC)-bearing mice model with QFM treatment to observe tumor growth and immune cell changes. Then, the feces were collected and a combinatory study using metagenomes, non-targeted metabonomics, and targeted metabonomics of SCFAs was performed. In vitro experiments have been conducted to estimate the roles of acetate and sodium propionate in CD8+ T cells. Furthermore, we treated tumor-bearing mice with QFM, QFM + MHY1485 (an mTOR activator), and QFM + an antibiotic mixture (ABX) to explore the potential therapeutic benefit of regulation of the tumor microenvironment. A total of 96 compounds were obtained from QFM by UPLC/Q-TOF-MS. Besides, the findings demonstrated that QFM exhibited significant efficacy against lung cancer, manifesting in reduced tumor growth and improved immune responses. In investigating its mechanisms, we integrated gut microbiota sequencing and fecal metabolomics, revealing that QFM effectively restored disruptions in gut microbiota and SCFAs in mice with lung cancer. QFM, acetate, or sodium propionate contributed to the up-regulation of IFN-γ, Gzms-B, perforin, IL-17, IL-6, IL-12, TNF-α expressions and decreased HDAC and IL-10 levels in vitro and in vivo. Moreover, MHY1485 and ABX weakened the effects of QFM on immunomodulation. Collectively, these results suggest that QFM may facilitate immune responses in the LLC-bearing mice via regulating the gut microbiota-derived SCFAs at least partially through targeting the mTOR signaling pathway.

Cordyceps sinensis relieves non-small cell lung cancer by inhibiting the MAPK pathway

OBJECTIVE

To determine the pharmacodynamic mechanism underlying Cordyceps sinensis relief in a murine model of non-small cell lung cancer (NSCLC).

METHODS

We created a murine model of NSCLC and studied the potential molecular mechanism by which C. sinensis relieved NSCLC using a combination of transcriptomics, proteomics, and experimental validation.

RESULTS

C. sinensis markedly suppressed the fluorescence values in mice with NSCLC, improved the pathologic morphology of lung tissue, ameliorated inflammatory cytokines (tumor necrosis factor-alpha, interleukin-6, interleukin-10, and the oxidative stress indicators superoxide dismutase, malondialdehyde, and glutathione peroxidase). Transcriptomics results showed that the therapeutic effect of C. sinensis was primarily involved in the differentiation and activation of T cells. Based on the proteomic results, C. sinensis likely exerted a protective effect by recruiting immune cells and suppressing tumor cell proliferation via the MAPK pathway. Finally, the experimental validation results indicated that C. sinensis significantly decreased the VEGF and Ki67 expression, downregulated RhoA, Raf-1, and c-fos expression, which are related to cell migration and invasion, increased the serum concentration of hematopoietic factors (EPO and GM-CSF), and improved the percentage of immune cells (natural killer cells, dendritic cells, and CD4+ and CD8+ lymphocytes), which enhanced immune function.

CONCLUSIONS

Based on our preclinical study, C. sinensis was shown to exert a protective effect on NSCLC, primarily by inhibiting the MAPK pathway.

Radiomics model based on intratumoral and peritumoral features for predicting major pathological response in non-small cell lung cancer receiving neoadjuvant immunochemotherapy

Objective

To establish a radiomics model based on intratumoral and peritumoral features extracted from pre-treatment CT to predict the major pathological response (MPR) in patients with non-small cell lung cancer (NSCLC) receiving neoadjuvant immunochemotherapy.

Methods

A total of 148 NSCLC patients who underwent neoadjuvant immunochemotherapy from two centers (SRRSH and ZCH) were retrospectively included. The SRRSH dataset (n=105) was used as the training and internal validation cohort. Radiomics features of intratumoral (T) and peritumoral regions (P1 = 0-5mm, P2 = 5-10mm, and P3 = 10-15mm) were extracted from pre-treatment CT. Intra- and inter- class correlation coefficients and least absolute shrinkage and selection operator were used to feature selection. Four single ROI models mentioned above and a combined radiomics (CR: T+P1+P2+P3) model were established by using machine learning algorithms. Clinical factors were selected to construct the combined radiomics-clinical (CRC) model, which was validated in the external center ZCH (n=43). The performance of the models was assessed by DeLong test, calibration curve and decision curve analysis.

Results

Histopathological type was the only independent clinical risk factor. The model CR with eight selected radiomics features demonstrated a good predictive performance in the internal validation (AUC=0.810) and significantly improved than the model T (AUC=0.810 vs 0.619, p<0.05). The model CRC yielded the best predictive capability (AUC=0.814) and obtained satisfactory performance in the independent external test set (AUC=0.768, 95% CI: 0.62-0.91).

Conclusion

We established a CRC model that incorporates intratumoral and peritumoral features and histopathological type, providing an effective approach for selecting NSCLC patients suitable for neoadjuvant immunochemotherapy.

In silico analyses of pan-squamous cell carcinoma unveiled the immunological implications of MRPL13, which had previously been under-recognized

The involvement of the mitochondrial ribosomal protein 13 (MRPL13) gene in the development of adenocarcinoma has been previously reported. However, the clinicopathological significance of MRPL13 in squamous cell carcinoma (SCC) remains poorly understood. To gain insight into the clinicopathological and immunological implications of MRPL13 expression in SCC, we conducted a bioinformatic analysis utilizing various available databases, including TIMER 2.0, Xiantao academic tool and TISIDB, attempting to evaluate the abnormal expression, prognosis and immunological correlation of MRPL13 in the pan-SCC setting. Subsequently, we conducted experimental verification using an esophageal squamous cell carcinoma (ESCC) tissue array subjected to multiplexed immunofluorescent (mIF) staining. The ESCC tissue array we used consists of 93 dots of ESCC and 86 dots of matched adjacent normal tissues (ANT). Data from in silico analyses showed that MRPL13 mRNA is significantly up-regulated and correlated with infiltration of CD8+ T cells in pan-SCC. However, in silico analyses did not support the prognostic role of MRPL13 in SCC. Consistently, data from the ESCC tissue array showed that MRPL13 was remarkably elevated in ESCC tissues relative to ANT in stroma, which was controlled by pan-cytokeratin (pan-CK) staining. In the epithelia, no significant difference was identified between ESCC and ANT. Furthermore, MRPL13 expression markedly correlated with the infiltration of CD8+ T cells in the stromal region but not in the epithelial region. Prognostically, no significant association was observed between MRPL13 expression and overall survival, regardless of epithelial or stromal section. Through these pan-SCC analyses, we have expanded the understanding of MRPL13 previously reported, in particular, underscoring the immunological involvement of MRPL13 in the tumor microenvironment of SCC that has been under-recognized before, suggesting that MRPL13 may regulate the infiltration of CD8+ T cells into the SCC microenvironment.

Clinical characteristics and novel strategies of immune checkpoint inhibitor rechallenge therapy for non-small cell lung cancer: a comprehensive review

Treatment of non-small-cell lung cancer (NSCLC) has entered the immunotherapy era, marked by significant survival improvements due to the use of immune checkpoint inhibitors (ICIs). However, owing to factors, such as disease progression, long-term use, and side effects, some patients discontinue immunotherapy, resulting in limited subsequent treatment option and a negative impact on their survival and quality of life. We have collected relevant data which reveal that ICI rechallenge may be an effective clinical strategy. However, many factors affect the efficacy of rechallenge, including patient characteristics, initial treatment drugs, treatment duration, efficacy, toxicity, and side effects. Additionally, the side effects of rechallenge and mechanisms of reversing drug resistance play crucial roles. Identifying suitable candidates, optimizing treatment plans and duration, enhancing treatment efficacy, and minimizing toxicity and adverse effects in rechallenges are pressing clinical needs. Addressing these issues can provide guidance for the clinical use of immunotherapy rechallenges to better serve patients. This review focuses on the clinical considerations and strategies for immune therapy rechallenges in NSCLC.

Population-based high-dimensional analyses identify multiple intrinsic characters for cancer vaccines against lung squamous cell carcinoma

In lung squamous cell carcinoma (LUSC), current cancer vaccines show promising effects, despite a lack of benefit for a large number of patients. We first identified the tumor antigens into shared and private antigens, and determined the population by clustering analysis in public datasets. For vaccine development, The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were collected. WGCNA method was furthermore applied to construct a consensus gene co-expression network based on TCGA and CPTAC datasets. The main analyses in bulk sequencing included survival, clinical features, tumor microenvironment (TME), and pathways enrichment. In addition, single-cell RNA (scRNA) analysis of cancer epithelium dissected consensus subtype. We identified the ideal population for cancer vaccines, and candidate neoantigens including AOC1, COL5A2, LGI2, and POSTN. According to subtype analysis, Lung squamous 1 (LSQ1) type exhibited a higher tumor mutational load (TMB) and copy number but no immune infiltration, whereas lung squamous 2 (LSQ2) tumors had a higher global methylation level and more fibroblasts but had less stemness. Meanwhile, trajectory analysis further revealed that the evolution of TME influenced prognosis. We emphasized specific pathways or targets with the potential for combination immunotherapy by consensus network and single-cell RNA analyses. Anti-androgen therapy has been validated in vitro experiments of LUSC as proof of concept. In conclusion, LSQ1 was linked to immune exclusion and might be utilized for vaccination, while LSQ2 was linked to immune dysfunction and could be used for programmed cell death protein 1 (PD1) blocking therapy.

The Era of Immunotherapy in Small-Cell Lung Cancer: More Shadows Than Light?

Small-cell lung cancer is an extremely chemo-sensitive disease; the addition of immunotherapy to chemotherapy has demonstrated a slight clinical benefit in pivotal trials, even with a statistically significant difference in terms of survival outcomes when compared to chemotherapy alone. In this scenario, the role of radiotherapy as a consolidation treatment in thoracic disease or as a prophylactic therapy in the brain should be clarified. In addition, due to the frailty and the poor prognostic characteristics of these patients, the need for predictive biomarkers that could support the use of immunotherapy is crucial. PD-L1 and TMB are not actually considered definitive biomarkers due to the heterogeneity of results in the literature. A new molecular classification of small-cell lung cancer based on the expression of key transcription factors seems to clarify the disease behavior, but the knowledge of this molecular subtype is still insufficient and the application in clinical practice far from reality; this classification could lead to a better understanding of SCLC disease and could provide the right direction for more personalized treatment. The aim of this review is to investigate the current knowledge in this field, evaluating whether there are predictive biomarkers and clinical patient characteristics that could help us to identify those patients who are more likely to respond to immunotherapy.

Prognostic Impact of the Immune-Cell Infiltrate in N1-Positive Non-Small-Cell Lung Cancer

INTRODUCTION

The tumoral immune milieu plays a crucial role for the development of non-small-cell lung cancer (NSCLC) and may influence individual prognosis. We analyzed the predictive role of immune cell infiltrates after curative lung cancer surgery.

MATERIALS AND METHODS

The tumoral immune-cell infiltrate from 174 patients with pN1 NSCLC and adjuvant chemotherapy was characterized using immunofluorescence staining. The density and distribution of specific immune cells in tumor center (TU), invasive front (IF) and normal tissue (NORM) were correlated with clinical parameters and survival data.

RESULTS

Tumor specific survival (TSS) of all patients was 69.9% at 5 years. The density of tumor infiltrating lymphocytes (TIL) was higher in TU and IF than in NORM. High TIL density in TU (low vs. high: 62.0% vs. 86.7%; p = .011) and the presence of cytotoxic T-Lymphocytes (CTLs) in TU and IF were associated with improved TSS (positive vs. negative: 90.6% vs. 64.7% p = .024). High TIL-density correlated with programmed death-ligand 1 expression levels ≥50% (p < .001). Multivariate analysis identified accumulation of TIL (p = .016) and low Treg density (p = .003) in TU as negative prognostic predictors in squamous cell carcinoma (p = .025), whereas M1-like tumor- associated macrophages (p = .019) and high programmed death-ligand 1 status (p = .038) were associated with better survival in adenocarcinoma.

CONCLUSION

The assessment of specific intratumoral immune cells may serve as a prognostic predictor in pN1 NSCLC. However differences were observed related to adenocarcinoma or squamous cell carcinoma histology. Prospective assessment of the immune-cell infiltrate and further clarification of its prognostic relevance could assist patient selection for upcoming perioperative immunotherapies.

A novel necroptosis signature for predicting survival in lung adenocarcinoma

BACKGROUND

To explore the necroptosis-related genes (NRGs) signature and its predictive values in lung adenocarcinoma (LUAD).

METHODS

The training cohort consisted of tumor samples from The Cancer Genome Atlas, and the validation set comprised data from the Gene Expression Omnibus. Univariate and multivariate Cox regression analyses were applied to identify the prognostic NRG signature as an independent molecular indicator. Correlation analysis was used for the association assessment between the NRG signature and immune checkpoint molecules.

RESULTS

NRGs involved in necroptosis and immune NOD-like receptor signaling. The NRG signature based on eight NRGs can divide tumors into high-risk and low-risk groups, which was significantly associated with worse survival. Multivariate Cox regression analysis showed that this NRG signature remained an independent prognostic indicator. Stratification analyses demonstrated that this NRG signature was still effective for predicting survival in each stratum of age, gender, and tumor stage. The ROC curve showed a good predictive ability using the NRG signature in the validation cohort (AUC = 0.81). The NRG signature was related to immune checkpoint molecules PD - 1, PD-L1, and PD-L2.

CONCLUSIONS

The NRG signature could be a novel predictor of the prognosis and may become a potential therapeutic target in LUAD.

Therapies Targeting Immune Cells in Tumor Microenvironment for Non-Small Cell Lung Cancer

The tumor microenvironment (TME) plays critical roles in immune modulation and tumor malignancies in the process of cancer development. Immune cells constitute a significant component of the TME and influence the migration and metastasis of tumor cells. Recently, a number of therapeutic approaches targeting immune cells have proven promising and have already been used to treat different types of cancer. In particular, PD-1 and PD-L1 inhibitors have been used in the first-line setting in non-small cell lung cancer (NSCLC) with PD-L1 expression ≥1%, as approved by the FDA. In this review, we provide an introduction to the immune cells in the TME and their efficacies, and then we discuss current immunotherapies in NSCLC and scientific research progress in this field.

Immunotherapeutic targets in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is one of the most common types of cancer in the world and has a 5-year survival rate of ~20%. Immunotherapies have shown promising results leading to durable responses, however, they are only effective for a subset of patients. To determine the best therapeutic approach, a thorough and in-depth profiling of the tumour microenvironment (TME) is required. The TME is a complex network of cell types that form an interconnected network, promoting tumour cell initiation, growth and dissemination. The stroma, immune cells and endothelial cells that comprise the TME generate a plethora of cytotoxic or cytoprotective signalling pathways. In this review, we discuss immunotherapeutic targets in NSCLC tumours and how the TME may influence patients' response to immunotherapy.

Effects of Lipid Metabolism-Related Genes PTGIS and HRASLS on Phenotype, Prognosis, and Tumor Immunity in Lung Squamous Cell Carcinoma

Background

Lipid metabolism reprogramming played an important role in cancer occurrence, development, and immune regulation. The aim of this study was to identify and validate lipid metabolism-related genes (LMRGs) associated with the phenotype, prognosis, and immunological characteristics of lung squamous cell carcinoma (LUSC).

Methods

In the TCGA cohort, bioinformatics and survival analysis were used to identify lipid metabolism-related differentially expressed genes (DEGs) associated with the prognosis of LUSC. PTGIS/HRASLS knockdown and overexpression effects on the LUSC phenotype were analyzed in vitro experiments. Based on the expression distribution of PTGIS/HRASLS, LUSC patients were divided into two clusters by consensus clustering. Clinical information, prognosis, immune infiltration, expression of immune checkpoints, and tumor mutation burden (TMB) level were compared between the TCGA and GSE4573 cohorts. The genes related to clustering and tumor immunity were screened by weighted gene coexpression network analysis (WGCNA), and the target module genes were analyzed by functional enrichment analysis, protein-protein interaction (PPI) analysis, and immune correlation analysis.

Results

191 lipid metabolism-related DEGs were identified, of which 5 genes were independent prognostic genes of LUSC. PTGIS/HRASLS were most closely related to LUSC prognosis and immunity. RT-qPCR, western blot (WB) analysis, and immunohistochemistry (IHC) showed that the expression of PTGIS was low in LUSC, while HRASLS was high. Functionally, PTGIS promoted LUSC proliferation, migration, and invasion, while HRASLS inhibited LUSC proliferation, migration, and invasion. The two clusters' expression and distribution of PTGIS/HRASLS had the opposite trend. Cluster 1 was associated with lower pathological staging (pT, pN, and pTNM stages), better prognosis, stronger immune infiltration, higher expression of immune checkpoints, and higher TMB level than cluster 2. WGCNA found that 28 genes including CD4 and IL10RA were related to the expression of PTGIS/HRASLS and tumor immune infiltration. PTGIS/HRASLS in the GSE4573 cohort had the same effect on LUSC prognosis and tumor immunity as the TCGA cohort.

Conclusions

PTGIS and HRASLS can be used as new therapeutic targets for LUSC as well as biomarkers for prognosis and tumor immunity, which has positive significance for guiding the immunotherapy of LUSC.

Development and validation of a radiomics-based nomogram for predicting a major pathological response to neoadjuvant immunochemotherapy for patients with potentially resectable non-small cell lung cancer

Introduction

The treatment response to neoadjuvant immunochemotherapy varies among patients with potentially resectable non-small cell lung cancers (NSCLC) and may have severe immune-related adverse effects. We are currently unable to accurately predict therapeutic response. We aimed to develop a radiomics-based nomogram to predict a major pathological response (MPR) of potentially resectable NSCLC to neoadjuvant immunochemotherapy using pretreatment computed tomography (CT) images and clinical characteristics.

Methods

A total of 89 eligible participants were included and randomly divided into training (N=64) and validation (N=25) sets. Radiomic features were extracted from tumor volumes of interest in pretreatment CT images. Following data dimension reduction, feature selection, and radiomic signature building, a radiomics-clinical combined nomogram was developed using logistic regression analysis.

Results

The radiomics-clinical combined model achieved excellent discriminative performance, with AUCs of 0.84 (95% CI, 0.74-0.93) and 0.81(95% CI, 0.63-0.98) and accuracies of 80% and 80% in the training and validation sets, respectively. Decision curves analysis (DCA) indicated that the radiomics-clinical combined nomogram was clinically valuable.

Discussion

The constructed nomogram was able to predict MPR to neoadjuvant immunochemotherapy with a high degree of accuracy and robustness, suggesting that it is a convenient tool for assisting with the individualized management of patients with potentially resectable NSCLC.

RASAL3 predicts overall survival and CD8+ T lymphocyte infiltration in lung adenocarcinoma

RAS-activating protein-like 3 (RASAL3) is a synaptic Ras GTPase-activating protein (SynGAP) and a potential novel biomarker of CD8+ T cell infiltration in lung adenocarcinoma (LUAD). This study explored RASAL3 expression in LUAD, the prognostic impact of RASAL3 and the relationship with immune cell infiltration. RASAL3 expression in LUAD tissues was considerably low, with high RASAL3 expression associated with better overall survival, whereas the low expression was linked to advanced T, N, M classifications, TNM stage and lower grade. Furthermore, RASAL3 expression positively correlated with CD8+ T lymphocyte infiltration. In conclusion, RASAL3 expression is a potential prognostic and immunological biomarker of LUAD.

Development and clinical validation of a necroptosis-related gene signature for prediction of prognosis and tumor immunity in lung adenocarcinoma

Necroptosis is a new programmed formation of necrotizing cell death, which plays important role in tumor biological regulation, including tumorigenesis and immunity. In this study, we aimed to establish and validate a prediction model based on necroptosis-related genes (NRGs) for lung adenocarcinoma (LUAD) prognosis and tumor immunity. The training set consisted of samples from The Cancer Genome Atlas (TCGA) dataset (n = 334), and the validation sets consisted of samples from the Gene Expression Omnibus (GEO) (n = 439) and clinical (n = 20) datasets. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that 28 necroptosis-related differentially expressed genes (DEGs) were enriched in cell death and immune regulation. RT-qPCR and western blot results showed the low expression of necroptosis markers in LUAD cells. A prognostic gene signature based on 6 NRGs (PYGB, IL1A, IFNAR2, BIRC3, H2AFY2, and H2AFX) was constructed and the risk score was calculated. Multivariate Cox regression analysis showed that the risk score was an independent risk factor [hazard ratio (HR) = 1.220, 95% confidence interval (CI): 1.154-1.290, P<0.001]. In the TCGA cohort, a high-risk score was associated with poor prognosis, weak immune infiltration, and low expression at immune checkpoints, which was validated in the GEO and clinical cohorts. Our findings showed that the patients in the low-risk group had a better progression-free survival (PFS) [not reached vs. 8.5 months, HR = 0.18, 95% CI: 0.04-0.72, P<0.001] than those in the high-risk score group. Immunotherapy tolerance was found to be correlated with the high-risk score, and the risk score combined with PD-L1 (AUC = 0.808, 95% CI: 0.613-1.000) could better predict the immunotherapy response of LUAD. A nomogram was shown to have a strong ability to predict the individual survival rate of patients with LUAD in the TCGA and GSE68465 cohorts. We constructed and validated a potential prognostic signature consisting of 6 NRGs to predict the prognosis and tumor immunity of LUAD, which may be helpful to guide the individualized immunotherapy of LUAD.

Effect of histology on the efficacy of immune checkpoint inhibitors in advanced non-small cell lung cancer: A systematic review and meta-analysis

BACKGROUND

Little is known about the effect of histology on the efficacy of immune checkpoint inhibitors (ICI) in non-small-cell lung cancer (NSCLC). We conducted a systematic review and meta-analysis to assess the potential differences in the efficacy of ICIs between squamous NSCLC (SQ-NSCLC) and non-squamous NSCLC (non-SQ-NSCLC).

METHODS

Systematic searches of PubMed, Embase, Scopus, and Cochrane Library databases were conducted. All randomized clinical trials of ICIs with available hazard ratios (HR) for progression-free survival (PFS) or overall survival (OS) according to histology were included. The primary endpoint was to assess the difference in the efficacy of ICIs between SQ-NSCLC and non-SQ-NSCLC, measured by the ratio of the HR in SQ-NSCLC to the HR in non-SQ-NSCLC (RHR).

RESULTS

A total of 40 trials were included in the meta-analysis. ICI monotherapy could improve OS in both SQ-NSCLC (OS-HR 0.71, 95% CI 0.65-0.77) and non-SQ-NSCLC (OS-HR 0.80, 95% CI 0.73-0.87) while OS benefit was larger in SQ-NSCLC (OS-RHR 0.89, 95% CI 0.80-0.99). In terms of PFS, ICI monotherapy could reduce the risk of progression by 35% (PFS-HR 0.65, 95% CI 0.56-0.77) in SQ-NSCLC while the PFS benefit was smaller (10%) and not statistically significant in non-SQ-NSCLC (PFS-HR 0.90, 95% CI 0.76-1.07). Similarly, ICI-based combination treatments could reduce the risk of both progression and death in SQ-NSCLC (OS-HR 0.70, 95% CI 0.61-0.80; PFS-HR 0.56, 95% CI 0.48-0.65) and non-SQ-NSCLC (OS-HR 0.78, 95% CI 0.74-0.83; PFS-HR 0.63, 95% CI 0.57-0.69) while the survival benefits were larger in SQ-NSCLC (OS-RHR 0.83, 95% CI 0.70-0.99; PFS-RHR 0.82, 95% CI 0.70-0.96).

CONCLUSIONS

ICIs could deliver survival benefits in both SQ-NSCLC and non-SQ-NSCLC while the magnitude of survival benefits was histology-dependent. Future researches should consider the effect of histology on the efficacy of ICIs.

SYSTEMATIC REVIEW REGISTRATION

https://www.crd.york.ac.uk/prospero/, identifier [CRD42022299603].

Plasma autoantibodies IgG and IgM to PD1/PDL1 as potential biomarkers and risk factors of lung cancer

Antibodies targeting programmed cell death-1 (PD1) and its ligand (PDL1) have transformed current cancer therapy while little is known about the expression of anti-PD1/PDL1 autoantibodies between lung cancer (LC) patients and normal controls (NC). The expression level of anti-PD1/PDL1 IgG and IgM was detected in plasma of 325 LC and 324 NC by indirect enzyme-linked immune sorbent assay (ELISA). Western blot and indirect immunofluorescence (IIF) were used to verify the ELISA results. The association analysis was used to evaluate the odds ratio (OR) of LC. The expression of anti-PD1/PDL1 IgG in LC samples was significantly higher than NC (P < 0.001 and P < 0.05, respectively). The positive rate of anti-PD1/PDL1 IgG in LC was significantly higher than NC and significant difference was also shown in LC samples of different clinical characteristics, such as clinical stage, nodules diameter, lymph node metastasis and distant metastasis (P < 0.001). Moreover, PD1/PDL1 expression in tissues showed no significant relation with that in plasma (P > 0.05). Anti-PD1/PDL1 IgG were the risk factors related to LC (OR (95% CI): 22.433 (5.426-92.745) and 5.051 (1.316-19.386)), while anti-PD1/PDL1 IgM were the risk factors for LC with ≤ 60 years (OR (95% CI): 6.122 (1.365-27.455) and 7.664 (1.715-34.251)) and anti-PD1 IgM was also the risk factor for male LC cases(OR (95% CI): 6.948 (1.076-44.868)). Plasma anti-PD1/PDL1 IgG and IgM might serve as potential biomarkers and risk predictors for LC.

Correlation between PD-L1 expression and radiomic features in early-stage lung adenocarcinomas manifesting as ground-glass nodules

Background

Immunotherapy might be a promising auxiliary or alternative systemic treatment for early-stage lung adenocarcinomas manifesting as ground-glass nodules (GGNs). This study intended to investigate the PD-L1 expression in these patients, and to explore the non-invasive prediction model of PD-L1 expression based on radiomics.

Methods

We retrospectively analyzed the PD-L1 expression of patients with postoperative pathological diagnosis of lung adenocarcinomas and with imaging manifestation of GGNs, and divided patients into positive group and negative group according to whether PD-L1 expression ≥1%. Then, CT-based radiomic features were extracted semi-automatically, and feature dimensions were reduced by univariate analysis and LASSO in the randomly selected training cohort (70%). Finally, we used logistic regression algorithm to establish the radiomic models and the clinical-radiomic combined models for PD-L1 expression prediction, and evaluated the prediction efficiency of the models with the receiver operating characteristic (ROC) curves.

Results

A total of 839 “GGN-like lung adenocarcinoma” patients were included, of which 226 (26.9%) showed positive PD-L1 expression. 779 radiomic features were extracted, and 9 of them were found to be highly corelated with PD-L1 expression. The area under the curve (AUC) values of the radiomic models were 0.653 and 0.583 in the training cohort and test cohort respectively. After adding clinically significant and statistically significant clinical features, the efficacy of the combined model was slightly improved, and the AUC values were 0.693 and 0.598 respectively.

Conclusions

GGN-like lung adenocarcinoma had a fairly high positive PD-L1 expression rate. Radiomics was a hopeful noninvasive method for predicting PD-L1 expression, with better predictive efficacy in combination with clinical features.

The influence of baseline characteristics on the efficacy of immune checkpoint inhibitors for advanced lung cancer: A systematic review and meta-analysis

Purpose: To investigate the impact of different baseline characteristics on the efficacy of immune checkpoint inhibitors (ICIs) for advanced lung cancer.

Methods: In order to identify eligible randomized controlled trials (RCTs), a systematic search was conducted in PubMed, Embase, Web of Science, and Scopus Cochrane Library databases. The primary outcomes were hazard ratios (HRs) and 95% confidence intervals (CIs) for overall survival (OS). To explore the potential interaction during the administration of ICI, patients were stratified by baseline characteristics.

Results: The meta-analysis included 24 RCTs. ① Compared with non-ICI therapy, patients with lung cancer benefitted more from immunotherapy (HR, 0.78; p &lt; 0.0001). ② Patients without liver metastases could get more survival benefits than those with liver metastases (HR, 1.20; p = 0.0139). Similar outcomes were also observed in the following subgroups: small-cell lung cancer (HR, 1.20; p = 0.0433), subsequent line (HR, 1.40; p = 0.0147), and ICI monotherapy (HR, 1.40; p = 0.0147). ③ Subgroup analysis showed that tumor type affected the efficacy of immunotherapy in patients with brain metastases (HR, 0.72 vs. 1.41; interaction, p &lt; 0.01). Among patients with smoking history (HR, 0.87 vs. 1.23; interaction, p = 0.05) and brain metastases (HR, 0.69 vs. 1.21; interaction, p = 0.05), the type of therapy (i.e., monotherapy or combination therapy) had potential influences on the efficacy of immunotherapy.

Conclusion: Some critical baseline characteristics could indicate the efficacy of ICI therapy. Liver metastasis status could predict the efficacy of ICI therapy for lung cancer. Compared with small-cell lung cancer, patients with brain metastases might have durable OS in non-small-cell lung cancer. The smoking history or brain metastasis status of patients could indicate the potential clinical benefits of monotherapy or combination therapy.

N6-Methyladenosine (m6A)-Related lncRNAs Are Potential Signatures for Predicting Prognosis and Immune Response in Lung Squamous Cell Carcinoma

Background

Despite increasing understanding of m⁶A-related lncRNAs in lung cancer, the role of m⁶A-related lncRNAs in the prognosis and treatment of lung squamous cell carcinoma is poorly understood to date. Thus, the current study aims to elucidate its role and build a model to predict the prognosis of LUSC patients.

Materials and Methods

The data of the current study were accessed from the TCGA database. Pearson correlation analysis was performed to identify lncRNAs correlated to m⁶A. Next, an m⁶A-related lncRNAs risk model was built using a single factor, least absolute association, selection operator, and multivariate Cox regression analysis.

Results

The relevance between 23 m⁶A genes and 14,056 lncRNAs is shown by Pearson correlation analysis by Sankey diagram. Multivariate Cox regression analysis determined that 11 m⁶A-lncRNAs show predictive potential in prognosis, which is confirmed by the consistency index, Kaplan–Meier analysis, principal component analysis, and ROC curve. Additionally, the immune analysis showed that the enrichment of immune cells, major histocompatibility complex molecules, and immune checkpoints in the high and low-risk subgroups were markedly disparate, with the high-risk group showing a stronger immune escape ability and a worse response to immunotherapy.

Conclusion

In conclusion, the risk model based on m⁶A-related lncRNAs showed great promise in predicting the prognosis and the efficacy of immunotherapy.

An immune gene signature to predict prognosis and immunotherapeutic response in lung adenocarcinoma

Lung adenocarcinoma is one of the most common malignant tumors worldwide. The purpose of this study was to construct a stable immune gene signature for prediction of prognosis (IGSPP) and response to immune checkpoint inhibitors (ICIs) therapy in LUAD patients. Five genes were screened by weighted gene coexpression network analysis, Cox regression and LASSO regression analyses and were used to construct the IGSPP. The survival rate of the IGSPP low-risk group was higher than that of the IGSPP high-risk group. Multivariate Cox regression analysis showed that IGSPP could be used as an independent prognostic factor for the overall survival of LUAD patients. IGSPP genes were enriched in cell cycle pathways. IGSPP gene mutation rates were higher in the high-risk group. CD4 memory-activated T cells, M0 and M1 macrophages had higher infiltration abundance in the high-risk group, which was associated with poor overall survival. In contrast, the abundance of resting CD4 memory T cells, monocytes, resting dendritic cells and resting mast cells associated with a better prognosis was higher in the low-risk group. TIDE scores and the expressions of different immune checkpoints showed that patients in the high-risk IGSPP group benefited more from ICIs treatment. In short, an IGSPP of LUAD was constructed and characterized. It could be used to predict the prognosis and benefits of ICIs treatment in LUAD patients.

Multi-Level Analysis and Identification of Tumor Mutational Burden Genes across Cancer Types

Tumor mutational burden (TMB) is considered a potential biomarker for predicting the response and effect of immune checkpoint inhibitors (ICIs). However, there are still inconsistent standards of gene panels using next-generation sequencing and poor correlation between the TMB genes, immune cell infiltrating, and prognosis. We applied text-mining technology to construct specific TMB-associated gene panels cross various cancer types. As a case exploration, Pearson’s correlation between TMB genes and immune cell infiltrating was further analyzed in colorectal cancer. We then performed LASSO Cox regression to construct a prognosis predictive model and calculated the risk score of each sample for receiver operating characteristic (ROC) analysis. The results showed that the assessment of TMB gene panels performed well with fewer than 500 genes, highly mutated genes, and the inclusion of synonymous mutations and immune regulatory and drug-target genes. Moreover, the analysis of TMB differentially expressed genes (DEGs) suggested that JAKMIP1 was strongly correlated with the gene expression level of CD8⁺ T cell markers in colorectal cancer. Additionally, the prognosis predictive model based on 19 TMB DEGs reached AUCs of 0.836, 0.818, and 0.787 in 1-, 3-, and 5-year OS models, respectively (C-index: 0.810). In summary, the gene panel performed well and TMB DEGs showed great potential value in immune cell infiltration and in predicting survival.

FCGBP Is a Prognostic Biomarker and Associated With Immune Infiltration in Glioma

The Fc Fragment of IgG Binding Protein (FCGBP) has been proven to participate in intestinal tumor immunity. However, the biological role of FCGBP has remained unclear in glioma. The differential expression of FCGBP was explored by Oncomine and GEPIA databases. The effect of FCGBP on prognosis was analyzed via Kaplan–Meier plotter and GEPIA. The Tumor Immune Estimation Resource (TIMER) tool was used to determine the correlations of FCGBP expression with tumor immune infiltration. Firstly, FCGBP was highly expressed in glioma and correlated with a worse prognosis. Gene Ontology (GO) and KEGG pathway enrichment analyses revealed that the differentially expressed genes (DEGs) and co-expression genes of FCGBP were mainly involved in the immune response. Furthermore, FCGBP expression was positively associated with multiple immune cells infiltrates as well as the expression levels of multiple immune markers in glioma. FCGBP co-expression networks mostly participated in the regulation of immune response. Finally, immunohistochemistry (IHC) assays were conducted to explore the expression of FCGBP, PD-L1, CCL2 and CD8 in glioma and correlations between them. We found that PDL1 and FCGBP were synchronously upregulated in glioma tissues. These findings revealed a new mechanism by which FCGBP participates in the immune tolerance of glioma, and implied the potential of FCGBP as a therapeutic target or predictive marker for patients.

Identification of Key Genes Related to CD8+ T-Cell Infiltration as Prognostic Biomarkers for Lung Adenocarcinoma

Background

CD8+ T cells are one of the central effector cells in the immune microenvironment. CD8+ T cells play a vital role in the development and progression of lung adenocarcinoma (LUAD). This study aimed to explore the key genes related to CD8+ T-cell infiltration in LUAD and to develop a novel prognosis model based on these genes.

Methods

With the use of the LUAD dataset from The Cancer Genome Atlas (TCGA), the differentially expressed genes (DEGs) were analyzed, and a co-expression network was constructed by weighted gene co-expression network analysis (WGCNA). Combined with the CIBERSORT algorithm, the gene module in WGCNA, which was the most significantly correlated with CD8+ T cells, was selected for the subsequent analyses. Key genes were then identified by co-expression network analysis, protein–protein interactions network analysis, and least absolute shrinkage and selection operator (Lasso)-penalized Cox regression analysis. A risk assessment model was built based on these key genes and then validated by the dataset from the Gene Expression Omnibus (GEO) database and multiple fluorescence in situ hybridization experiments of a tissue microarray.

Results

Five key genes (MZT2A, ALG3, ATIC, GPI, and GAPDH) related to prognosis and CD8+ T-cell infiltration were identified, and a risk assessment model was established based on them. We found that the risk score could well predict the prognosis of LUAD, and the risk score was negatively related to CD8+ T-cell infiltration and correlated with the advanced tumor stage. The results of the GEO database and tissue microarray were consistent with those of TCGA. Furthermore, the risk score was higher significantly in tumor tissues than in adjacent lung tissues and was correlated with the advanced tumor stage.

Conclusions

This study may provide a novel risk assessment model for prognosis prediction and a new perspective to explore the mechanism of tumor immune microenvironment related to CD8+ T-cell infiltration in LUAD.

Development and Validation of a TNF Family-Based Signature for Predicting Prognosis, Tumor Immune Characteristics, and Immunotherapy Response in Colorectal Cancer Patients

In this study, a comprehensive analysis of TNF family members in colorectal cancer (CRC) was conducted and a TNF family-based signature (TFS) was generated to predict prognosis and immunotherapy response. Using the expression data of 516 CRC patients from The Cancer Genome Atlas (TCGA) database, TNF family members were screened to construct a TFS by using the univariate Cox proportional hazards regression and the least absolute shrinkage and selection operator- (LASSO-) Cox proportional hazards regression method. The TFS was then validated in a meta-Gene Expression Omnibus (GEO) cohort (n = 1162) from the GEO database. Additionally, the tumor immune characteristics and predicted responses to immune checkpoint blockade in TFS-based risk subgroups were analyzed. Eight genes (TNFRSF11A, TNFRSF10C, TNFRSF10B, TNFSF11, TNFRSF25, TNFRSF19, LTBR, and NGFR) were used to construct the TFS. Compared to the high-risk patients, the low-risk patients had better overall survival, which was verified by the GEO data. In addition, a high TFS risk score was associated with high infiltration of regulatory T cells (Tregs), nonactivated macrophages (M0), natural killer cells, immune escape phenotypes, poor immunotherapy response, and tumorigenic and metastasis-related pathways. Conversely, a low TFS risk score was related to high infiltration of resting CD4 memory T cells and resting dendritic cells, few immune escape phenotypes, and high sensitivity to immunotherapy. Thus, the eight gene-based TFS is a promising index to predict the prognosis, immune characteristics, and immunotherapy response in CRC, and our results also provide new understanding of the role of the TNF family members in the prognosis and treatment of CRC.

EVA1C Is a Potential Prognostic Biomarker and Correlated With Immune Infiltration Levels in WHO Grade II/III Glioma

Background

Immunotherapy is an effective therapeutic approach for multiple human cancer types. However, the correlations between EVA1C and patients’ prognosis as well as immune infiltration remain obscure. Herein, we employed transcriptomic and clinical data extracted from two independent databases to systematically investigate the role of EVA1C in the oncological context.

Methods

The differential expression of EVA1C was analyzed via TCGA and Oncomine databases. We evaluated the influence of EVA1C on clinical prognosis using Kaplan-Meier plotter. We then used the expression profiler to calculate stromal score, immune score, and ESTIMATE score based on the ESTIMATE algorithm. The abundance of infiltrating immune cells was calculated via TIMER. The correlations between EVA1C expression and immune infiltration levels were analyzed in two independent cohorts.

Results

In patients with World Health Organization (WHO) grade II/III glioma, high EVA1C expression was associated with malignant clinicopathological features and poor overall survival in both cohorts. EVA1C expression was positively associated with immune infiltration levels of B cell, CD4+ T cell, neutrophil, macrophage, and dendritic cells (DCs). Besides, EVA1C expression strongly correlated with diverse immune marker sets. And the predictive power of EVA1C was better than that of other indicators in predicting high immune infiltration levels in glioma.

Conclusions

For the first time, we identified the overexpression of EVA1C in glioma, which was tightly correlated with the high infiltration levels of multiple immune cells as well as poor prognosis. Meanwhile, EVA1C might be a potential biomarker for predicting high immune infiltration in WHO grade II/III gliomas.