Predicting potential therapeutic targets and small molecule drugs for early-stage lung adenocarcinoma.

Lung cancer is a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) constituting the majority, and its main subtype being lung adenocarcinoma (LUAD). Despite substantial advances in LUAD diagnosis and treatment, early diagnostic biomarkers inadequately fulfill clinical requirements. Thus, we conducted bioinformatics analysis to identify potential biomarkers and corresponding therapeutic drugs for early-stage LUAD patients. Here we identified a total of 10 differentially expressed genes (DEGs) with survival significance through the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Subsequently, we identified a promising small molecule drug, Aminopurvalanol A, based on the 10 key genes using the L1000FWD application, which was validated by molecular docking followed by in vivo and in vitro experiments. The results highlighted TOP2A, CDH3, ASPM, CENPF, SLC2A1, and PRC1 as potential detection biomarkers for early LUAD. We confirmed the efficacy and safety of Aminopurvalanol A, providing valuable insights for the clinical management of LUAD.

High-resolution transcriptomics analysis of CXCL13+ EPSTI1+ CDK1+ cells with a specific focus on lung adenocarcinoma.

Background: Programmed cell death ligand 1 (PD-L1) blocking therapy has transformed the treatment of lung adenocarcinoma (LUAD), which has significantly changed the landscape of immunotherapy. We aimed to explore specific cell subpopulations to understand tumor progression and identify markers of response to PD-L1 blocking therapy. Methods: Bulk, fluorescence-activated cell sorting (FACS), and single-cell RNA (scRNA) sequencing were used to profile CXCL13, EPSTI1, and CDK1. The gene set variation analysis (GSVA) R package was utilized for score calculation, and prognostic analyses included receiver operating characteristic (ROC) curves, Cox proportional hazard models, and meta-analysis. Additionally, we analyzed tumor microenvironment (TME), genomics, compound perturbations, and clinical indicators. The high-dimensional analysis captured the intrinsic characteristics of the subpopulation. Furthermore, subpopulation differential genes were used for enrichment analysis of transcription factors and compounds. Results: Literature and website analyses supported the essential role of CXCL13, CDK1, and EPSTI1 in immunotherapy. This led us to focus specifically on LUAD by representing a pan-cancer profile of immune-sensitive genes. Logically, the high-characteristic population may consist of samples positive for CXCL13, EPSTI1, and CDK1. The three-gene signature was a favorable indicator of immunotherapy response in the Stand Up to Cancer-Mark Foundation (SU2C-MARK) LUAD cohort but showed a poor prognosis before treatment in the Lung Cancer Explorer (LCE) database. Further mechanistic exploration revealed specific mutations associated with the three-gene signature in SU2C-MARK LUAD, such as STK11. In The Cancer Genome Atlas (TCGA)-LUAD cohort, the high-scoring group exhibited a higher tumor mutational burden (TMB) and global methylation but a lower fraction genome altered (FGA) and estimated tumor purity. Moreover, dasatinib demonstrated sensitivity in the high-scoring group. The co-localization of the CXCL13, EPSTI1, and CDK1 subpopulation was validated through spatial transcriptome and immunohistochemical databases. Assessment of the subpopulation depicted high-resolution intercellular communication. Maintenance of specific pathways, such as TNF, CD74, and CD44, contributed to immunotherapy sensitivity. Finally, the subpopulation-enriched targets and drugs were confirmed through ConnectivityMap (CMAP) analysis and multi-omics, respectively. Conclusions: In this study, positive samples for CXCL13, EPSTI1, and CDK1 exhibited poor prognostic significance in treatment-naïve LUAD cases but demonstrated benefits from PD-L1 blockade and dasatinib therapies.

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.

YAP1 as a Novel Negative Biomarker of Immune Checkpoint Inhibitors for EGFR-Mutant Non-Small-Cell Lung Cancer.

Background: Immune checkpoint inhibitors (ICIs) have become a standard care in non-small-cell lung cancer (NSCLC). However, its application to epidermal growth factor receptor (EGFR)-mutant NSCLC patients is confronted with drug resistance. This study aimed to clarify the potential role of Yes1-associated transcriptional regulator (YAP1) in ICIs treatment for EGFR-mutant NSCLC population. Methods: All the clinical data of NSCLC were downloaded from Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) for GSE11969 and GSE72094. Based on YAP1 expression, all the NSCLC patients including the EGFR-mutant and EGFR-wildtype (WT) patients were divided into two groups, YAP1_High and YAP1_Low. Using cBioPortal, genetic alterations were analyzed for identification of immunogenicity in EGFR-mutant NSCLC. MR analysis was used to analyze the hub gene of EGFR. The infiltration of immune cells and the expression of the identified tumor-associated antigens were identified with TIMER. By graph learning-based dimensionality reduction analysis, the immune landscape was visualized. Moreover, survival analysis was performed to verify the predictive value of YAP1 in ICIs treatment for EGFR-mutant NSCLC population using Ren's research data (NCT03513666). Results: YAP1 was a poor prognostic factor of EGFR-mutant NSCLC population rather than lung adenocarcinoma (LUAD) patients. MR analysis revealed that the EGFR gene regulated YAP1 expression. YAP1 was identified as a hub gene closely associated with immunosuppressive microenvironment and poor prognosis in EGFR-mutant NSCLC population in TCGA LUAD. Tumors with YAP1_High showed an immune-"cold" and immunosuppressive phenotype, whereas those with YAP1_Low demonstrated an immune-"hot" and immunoactive phenotype. More importantly, it was verified that YAP1_High subpopulation had a significantly shorter progression-free survival (PFS) and overall survival (OS) after ICIs treatment in EGFR-mutant NSCLC patients in the clinical trial. Conclusions: YAP1 mediates immunosuppressive microenvironment and poor prognosis in EGFR-mutant NSCLC population. YAP1 is a novel negative biomarker of ICIs treatment in EGFR-mutant NSCLC population. Clinical Trials. This trial is registered with NCT03513666.

Correlation of preclinical and clinical biomarkers with efficacy and toxicity of cancer immunotherapy.

Immune checkpoint inhibitors (ICIs) have revealed significant clinical values in different solid tumors and hematological malignancy, changing the landscape for the treatment of multiple types of cancer. However, only a subpopulation of patients has obvious tumor response and long-term survival after ICIs treatment, and many patients may experience other undesirable clinical features. Therefore, biomarkers are critical for patients to choose exact optimum therapy. Here, we reviewed existing preclinical and clinical biomarkers of immunotherapeutic efficacy and immune-related adverse events (irAEs). Based on efficacy prediction, pseudoprogression, hyperprogressive disease, or irAEs, these biomarkers were divided into cancer cell-derived biomarkers, tumor microenvironment-derived biomarkers, host-derived biomarkers, peripheral blood biomarkers, and multi-modal model and artificial intelligence assessment-based biomarkers. Furthermore, we describe the relation between ICIs efficacy and irAEs. This review provides the overall perspective of biomarkers of immunotherapeutic outcome and irAEs prediction during ICIs treatment.

Clinical features and death risk factors in COVID-19 patients with cancer: a retrospective study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spread around the world. This retrospective study aims to analyze the clinical features of COVID-19 patients with cancer and identify death outcome related risk factors. METHODS: From February 10th to April 15th, 2020, 103 COVID-19 patients with cancer were enrolled. Difference analyses were performed between severe and non-severe patients. A propensity score matching (PSM) analysis was performed, including 103 COVID-19 patients with cancer and 206 matched non-cancer COVID-19 patients. Next, we identified death related risk factors and developed a nomogram for predicting the probability. RESULTS: In 103 COVID-19 patients with cancer, the main cancer categories were breast cancer, lung cancer and bladder cancer. Compared to non-severe patients, severe patients had a higher median age, and a higher proportion of smokers, diabetes, heart disease and dyspnea. In addition, most of the laboratory results between two groups were significantly different. PSM analysis found that the proportion of dyspnea was much higher in COVID-19 patients with cancer. The severity incidence in two groups were similar, while a much higher mortality was found in COVID-19 patients with cancer compared to that in COVID-19 patients without cancer (11.7% vs. 4.4%, P = 0.028). Furthermore, we found that neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) were related to death outcome. And a nomogram based on the factors was developed. CONCLUSION: In COVID-19 patients with cancer, the clinical features and laboratory results between severe group and non-severe group were significantly different. NLR and CRP were the risk factors that could predict death outcome.

A randomized, controlled phase II trial of maxillofacial and oral massage in attenuating severe radiotherapy-induced oral mucositis and lipid metabolite changes in nasopharyngeal carcinoma.

PURPOSE: This randomized controlled phase II study investigated the efficacy, safety and underlying mechanism of maxillofacial and oral massage (MOM) in nasopharyngeal carcinoma (NPC) patients receiving intensity-modulated radiotherapy. METHODS: A total of 158 NPC patients were randomly assigned 1:1 to routine oral care and medication (the control group) or that with additional MOM (the treatment group). The primary endpoint was the incidence of severe radiotherapy-induced oral mucositis (SRTOM). In addition, the time of initiation and duration of RTOM and SRTOM, adverse events, dynamic changes of lipid metabolites in peripheral blood were analyzed. RESULTS: Seventy-six patients in the treatment group and seventy-nine in the control group completed the trial. The incidence of SRTOM in the treatment group was lower than the control (26.3% vs. 46.8%, P = 0.008). The median initiation time to RTOM and SRTOM was significantly longer in the treatment group than the control (RTOM:12 vs 10 days, hazard ratio [HR] 0.52, P < 0.001; SRTOM: 28.5 vs 19 days, HR 0.5579, P = 0.002). While the median duration time of RTOM and SRTOM in the treatment group was shorter (RTOM: 20.7 vs 24.7 days, P = 0.001; SRTOM: 8.05 vs 13.08 days, P < 0.001). Only 1.3% of patients obtained grade 3 or higher adverse events during MOM. The anti-inflammatory lipids increased significantly after MOM, especially with 10.6 Gy or higher. CONCLUSION: MOM significantly attenuated the incidence of SRTOM in NPC patients. The adverse events of MOM were slight and tolerant. MOM enhanced anti-inflammatory lipid metabolites, which might be an underlying mechanism.

Label-free and specific detection of soluble programmed death ligand-1 using a localized surface plasmon resonance biosensor based on excessively tilted fiber gratings.

Programmed death ligand-1 (PD-L1) plays an important role in tumor evasion from the host immune system. The level of soluble PD-L1 (sPD-L1) in serum is closely related to tumor aggressiveness and outcomes. This study aimed to propose a localized surface Plasmon resonance (LSPR) biosensor based on excessively tilted fiber grating (ExTFG) coated with large-sized (∼160 nm) gold nanoshells for label-free and specific detection of sPD-L1. The experimental results showed that the limit of detection (LOD) of the immunosensor for sPD-L1 in buffer solutions was ∼1 pg/mL due to the enhanced LSPR effect resulting from the interaction between sPD-L1 molecules and anti-sPD-L1 monoclonal antibodies. The detection of sPD-L1 in complex serum media, such as fetal bovine serum, confirmed that the label-free immunosensor was extremely specific to sPD-L1 and could identify it at a concentration as low as 5 pg/mL. Therefore, it can be potentially applied in clinic for the fast and early diagnosis of cancer.