Lung Cancer Samples Research Articles

3D cultivation of non-small-cell lung cancer cell lines using four different methods

PurposeThe aim of this study was to set up reliable and reproducible culture conditions for 3D tumoroids derived from non-small cell lung cancer (NSCLC) cell lines to enable greater opportunity for successful cultivation of patient-derived samples.MethodsFour NSCLC cell lines, two adenocarcinomas (A549, NCI-H1975) and two squamous cell carcinomas (HCC-95, HCC-1588), were first cultured in traditional 2D settings. Their expected expression profiles concerning TTF-1, CK7, CK5, and p40 status were confirmed by immunohistochemistry (IHC) before the generation of 3D cultures. Tumoroids were established in the hydrogel GrowDex®-T, Nunclon™ Sphera™ flasks, BIOFLOAT™ plates, and Corning® Elplasia® plates. Western blot was used to verify antigen protein expression. Hematoxylin-eosin staining was used to evaluate the cell morphology in the 2D and 3D cultures. Mutational analysis of KRAS and EGFR by PCR on extracted DNA from 3D tumoroids generated from cells with known mutations (A549; KRAS G12S mutation, NCI-H1975; EGFR L858R/T790M mutations).ResultsWe successfully established 3D cultures from A549, NCI-H1975, HCC-95, and HCC-1588 with all four used cultivation methods. The adenocarcinomas (A549, NCI-H1975) maintained their original IHC features in the tumoroids, while the squamous cell carcinomas (HCC-95, HCC-1588) lost their unique markers in the cultures. PCR analysis confirmed persistent genetic changes where expected.ConclusionThe establishment of tumoroids from lung cancer cell lines is feasible with various methodologies, which is promising for future tumoroid growth from clinical lung cancer samples. However, analysis of relevant markers is a prerequisite and may need to be validated for each model and cell type.

ADAMTS4 exacerbates lung cancer progression via regulating c-Myc protein stability and activating MAPK signaling pathway

BackgroundLung cancer is one of the most frequent cancers and the leading cause of cancer-related deaths worldwide with poor prognosis. A disintegrin and metalloproteinase with thrombospondin motifs 4 (ADAMTS4) is crucial in the regulation of the extracellular matrix (ECM), impacting its formation, homeostasis and remodeling, and has been linked to cancer progression. However, the specific involvement of ADAMTS4 in the development of lung cancer remains unclear.MethodsADAMTS4 expression was identified in human lung cancer samples by immunohistochemical (IHC) staining and the correlation of ADAMTS4 with clinical outcome was determined. The functional impact of ADAMTS4 on malignant phenotypes of lung cancer cells was explored both in vitro and in vivo. The mechanisms underlying ADAMTS4-mediated lung cancer progression were explored by ubiquitination-related assays.ResultsOur study revealed a significant upregulation of ADAMTS4 at the protein level in lung cancer tissues compared to para-carcinoma normal tissues. High ADAMTS4 expression inversely correlated with the prognosis of lung cancer patients. Knockdown of ADAMTS4 inhibited the proliferation and migration of lung cancer cells, as well as the tubule formation of HUVECs, while ADAMTS4 overexpression exerted opposite effects. Mechanistically, we found that ADAMTS4 stabilized c-Myc by inhibiting its ubiquitination, thereby promoting the in vitro and in vivo growth of lung cancer cells and inducing HUVECs tubule formation in lung cancer. In addition, our results suggested that ADAMTS4 overexpression activated MAPK signaling pathway.ConclusionsWe highlighted the promoting role of ADAMTS4 in lung cancer progression and proposed ADAMTS4 as a promising therapeutic target for lung cancer patients.

Interpretable spatially aware dimension reduction of spatial transcriptomics with STAMP.

Spatial transcriptomics produces high-dimensional gene expression measurements with spatial context. Obtaining a biologically meaningful low-dimensional representation of such data is crucial for effective interpretation and downstream analysis. Here, we present Spatial Transcriptomics Analysis with topic Modeling to uncover spatial Patterns (STAMP), an interpretable spatially aware dimension reduction method built on a deep generative model that returns biologically relevant, low-dimensional spatial topics and associated gene modules. STAMP can analyze data ranging from a single section to multiple sections and from different technologies to time-series data, returning topics matching known biological domains and associated gene modules containing established markers highly ranked within. In a lung cancer sample, STAMP delineated cell states with supporting markers at a higher resolution than the original annotation and uncovered cancer-associated fibroblasts concentrated on the tumor edge's exterior. In time-series data of mouse embryonic development, STAMP disentangled the erythro-myeloid hematopoiesis and hepatocytes developmental trajectories within the liver. STAMP is highly scalable and can handle more than 500,000 cells.

Comparing the concentration of radon and uranium, with the concentration of some elements in biological samples of lung cancer: A comparative study

Comparing the concentration of radon and uranium, with the concentration of some elements in biological samples of lung cancer: A comparative study

FAS gene expression, prognostic significance and molecular interactions in lung cancer.

FAS has been implicated in the development of various cancers, but its involvement in lung cancer has not been systematically characterized. In this study, we performed data mining in online tumor databases to investigate the expression, methylation, alterations, protein interactions, co-expression and prognostic significance of FAS in lung cancer. The expression, prognostic significance and molecular interactions of FAS in lung cancer was mined and analyzed using GENT2, GEPIA2, UALCAN, cBioPortal, STRING, GeneMANIA, UCSC Xena, Enrichr, and OSluca databases. FAS expression was subsequently investigated at the protein level in samples from 578 lung cancer patients to understand its protein-level expression. In vitro validation of FAS gene expression was performed on H1299, H1993, A549 and HBE cell lines. We found that the expression of FAS was significantly downregulated in both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) compared to normal lung tissue. In addition, we observed a higher level of FAS promoter methylation in LUSC tissue than in normal tissue. FAS alterations were rare (1.9%) in lung cancer samples, with deep deletions being more common than missense mutations, which occurred mainly in the TNFR-like cysteine-rich domain and the death domain. We also identified a list of proteins interacting with FAS and genes co-expressed with FAS, with LUAD having 11 co-expressed genes and LUSC having 90 co-expressed genes. Our results also showed that FAS expression has limited prognostic significance (HR=1.302, 95% CI=0.935-1.139, P=0.530). Protein level investigation revealed that FAS expression varied among individuals, with nTPM values ranging from 5.2 to 67.2. This study provides valuable insights into the involvements and characteristics of FAS in lung cancer. Further studies are needed to investigate the clinical significance of FAS alterations in lung cancer and to explore the potential of targeting FAS for therapeutic intervention.

Inflammatory‐associated myeloid dendritic cells reveals associations between chronic lung diseases and lung cancer

ABSTRACTThe high risk of patients with chronic lung diseases in developing into lung cancer has been recognised, but the key factors driving such procedure are still barely known. Dendritic cells (DCs) as major antigen presenting cells take part in the immune response in the very upstream, and myeloid DCs regulate the inflammation in pulmonary diseases. In this article, we performed single‐cell RNA sequencing (scRNA‐seq) analyses on DCs from pulmonary diseases. We explore the DC characteristics in chronic lung diseases, lung cancer and healthy control samples. We discover that a special type of DC, which is highly associated with inflammatory, inf‐DC, is abundant in lung cancer samples. Furthermore, we find that there are about 10% patients with chronic lung diseases also has such inf‐DC‐rich pattern. Such proportion is consistent to the fact that about 10% chronic lung disease patients finally developed into cancer. Our findings indicate inf‐DC could be a potential factor to predict the risk of chronic lung disease developing into cancer.

O-GlcNAcylation promotes malignancy and cisplatin resistance of lung cancer by stabilising NRF2.

The transcription factor NRF2 plays a significant role in regulating genes that protect cells from oxidative damage. O-GlcNAc modification, a type of posttranslational modification, is crucial for cellular response to stress. Although the involvement of both NRF2 and O-GlcNAc in maintaining cellular redox balance and promoting cancer malignancy has been demonstrated, the potential mechanisms remain elusive. The immunoblotting, luciferase reporter, ROS assay, co-immunoprecipitation, and immunofluorescence was used to detect the effects of global cellular O-GlcNAcylation on NRF2. Mass spectrometry was utilised to map the O-GlcNAcylation sites on NRF2, which was validated by site-specific mutagenesis and O-GlcNAc enzymatic labelling. Human lung cancer samples were employed to verify the association between O-GlcNAc and NRF2. Subsequently, the impact of NRF2 O-GlcNAcylation in lung cancer malignancy and cisplatin resistance were evaluated in vitro and in vivo. NRF2 is O-GlcNAcylated at Ser103 residue, which hinders its binding to KEAP1 and thus enhances its stability, nuclear localisation, and transcription activity. Oxidative stress and cisplatin can elevate the phosphorylation of OGT at Thr444 through the activation of AMPK kinase, leading to enhanced binding of OGT to NRF2 and subsequent elevation of NRF2 O-GlcNAcylation. Both in cellular and xenograft mouse models, O-GlcNAcylation of NRF2 at Ser103 promotes the malignancy of lung cancer. In human lung cancer tissue samples, there was a significant increase in global O-GlcNAcylation, and elevated levels of NRF2 and its O-GlcNAcylation compared to paired adjacent normal tissues. Chemotherapy promotes NRF2 O-GlcNAcylation, which in turn decreases cellular ROS levels and drives lung cancer cell survival. Our findings indicate that OGT O-GlcNAcylates NRF2 at Ser103, and this modification plays a role in cellular antioxidant, lung cancer malignancy, and cisplatin resistance.

EP.04C.09 Liquid Biopsy Assay to Detect Low Levels of ctDNA in Early-Stage Lung Cancer Samples

EP.04C.09 Liquid Biopsy Assay to Detect Low Levels of ctDNA in Early-Stage Lung Cancer Samples

Identification of potential core genes in lung cancer and therapeutic traditional Chinese medicine compounds using bioinformatics analysis.

Lung cancer (LC) remains the leading cause of cancer-related death. We identified potential therapeutic targets and traditional Chinese medicine (TCM) compounds for LC treatment. GSE43346 and GSE18842 were derived from the Gene Expression Omnibus (GEO) database and used to identify differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using The Database for Annotation, Visualization and Integrated Discovery (DAVID). Protein-protein interactions were analyzed using STRING and Cytoscape software. Hub gene expression was validated using Gene Expression Profiling Interactive Analysis and the Human Protein Atlas. Kaplan-Meier survival analysis was conducted to evaluate the prognostic value of hub genes in patients with LC. Therapeutic TCM compounds were screened using the Comparative Toxicogenomics Database, and DEGs were largely enriched in biological processes, including cell division and mitotic nuclear division, such as the cell cycle and p53 signaling pathways. Elevated expression of hub genes was observed in LC samples. Overexpression of CDC20, CCNB2, and TOP2A is an unfavorable prognostic factor for postprogressive survival in patients with LC. Paclitaxel, quercetin, and rotenone have been identified as active substances in TCM. CDC20, CCNB2, and TOP2A are novel hub genes associated with LC. Paclitaxel, quercetin, and rotenone can be used as therapeutic agents in TCM.

MRNA expression insights: Unraveling the relationship between COPD and lung cancer.

Lung cancer is a prevalent form of cancer worldwide. A possible link between lung cancer and chronic obstructive pulmonary disease (COPD) has been suggested by recent studies. The objective of our research was to analyze the mRNA expression patterns in both situations, with a specific emphasis on their biological functions and the pathways they are linked to. Data on COPD mRNA expression was collected from the NCBI-GEO database, while information regarding lung cancer mRNA was acquired from The Cancer Genome Atlas database. To examine the association of COPD-related scores in lung cancer patients, we utilized the ssGSEA algorithm for single sample gene set enrichment analysis. The possible routes were examined through the utilization of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. Risk models were developed using Coxand least absolute shrinkage and selection operator (LASSO) regression analyses. Moreover, a GSEA was performed to investigate significant pathways among various risk groups. After identifying 17 genes that were differentially expressed and linked to COPD, we found that they met the criteria of having a false discovery rate < 0.05 and an absolute log2 fold change > 0.585. By utilizing the ssGSEA algorithm, it became possible to classify individuals with lung cancer into two distinct groups based on their COPD status. Consequently, a seven-gene risk model was developed specifically for these patients. The risk score was determined by applying the given formula: risk score = AC022784.1 × 0.0423737993775888 + CRISP3× 0.0415322046890524 + MELTF × 0.0661848418476596 + MT2P1 × 0.111843227536117 + FAM83A-AS1 × 0.045295939710361 + ZNF506 × -0.309489953363417 + ITGA6 × 0.01813978449589. The risk model associated with COPD showed a notable connection with different immune cells found in the lung cancer sample, including macrophages of M0/M1/M2 types, hematopoietic stem cells, mast cells, NK T cells and regulatory T cells. Overexpression of crucial genes was seen to enhance cell proliferation and invasive potential in the lung cancer sample. In the lung cancer sample, it was observed that an increase in ZNF506 expression enhanced both cell proliferation and invasion. In conclusion, this study effectively examines the potential correlation between COPD and lung cancer. A prognostic model based on seven COPD-associated genes demonstrated robust predictive potential in the lung cancer sample. Our analysis offers comprehensive insights for lung cancer patients.

A preliminary study on the diagnostic performance of the uPath PD-L1 (SP263) artificial intelligence (AI) algorithm in patients with NSCLC treated with PD-1/PD-L1 checkpoint blockade.

The uPath PD-L1 (SP263) is an AI-based platform designed to aid pathologists in identifying and quantifying PD-L1 positive tumor cells in non-small cell lung cancer (NSCLC) samples stained with the SP263 assay. In this preliminary study, we explored the diagnostic performance of the uPath PD-L1 algorithm in defining PD-L1 tumor proportion score (TPS) and predict clinical outcomes in a series of patients with advanced stage NSCLC treated with single agent PD-1/PD-L1 checkpoint blockade previously assessed with the SP263 assay in clinical practice. 44 patients treated from August 2015 to January 2019 were included, with baseline PD-L1 TPS of ≥ 50%, 1-49% and < 1% in 38.6%, 25.0% and 36.4%, respectively. The median uPath PD-L1 score was 6 with a significant correlation with the baseline PD-L1 TPS (r: 0.83, p < 0.01). However, only 27 cases (61.4%) were scored within the same clinically relevant range of expression (≥ vs < 50%). In the study population the baseline PD-L1 TPS was not significantly associated with clinical outcomes, while the uPath PD-L1 score showed a good diagnostic ability for the risk of death at the ROC curve analysis [AUC: 0.81 (95%CI: 0.66-0.91), optimal cut-off of ≥ 3.2], resulting in 19 patients (43.2%) being u-Path low and 25 patients (56.8%) being uPath high. The objective response rate in uPath high and low was 51.6% and 25.0% (p = 0.1), respectively, although the uPath was significantly associated with overall survival (OS, HR 2.45, 95%CI: 1.19-5.05) and progression free survival (PFS, HR 3.04, 95%CI: 1.51-6.14). At the inverse probability of treatment weighting analysis used to balance baseline covariates, the uPath categories confirmed to be independently associated with OS and PFS. This preliminary analysis suggests that AI-based, digital pathology tools such as uPath PD-L1 (SP263) can be used to optimize already available biomarkers for immune-oncology treatment in patients with NSCLC.

Metabolic reprogramming-related gene classifier distinguishes malignant from the benign pulmonary nodules

The current existing classifiers for distinguishing malignant from benign pulmonary nodules is limited by effectiveness or clinical practicality. In our study, we aimed to develop and validate a gene classifier for lung cancer diagnosis. To identify the genes involved in this process, we used the weighted gene co-expression network analysis to analyze gene expression datasets from Gene Expression Omnibus (GEO). We identified the three most relevant modules associated with malignant nodules and performed functional enrichment analysis on them. The results indicated significant involvement in metabolic, immune-related, cell cycle, and viral-related processes. All three modules showed enrichment in metabolic reprogramming pathways. Based on these genes, we intersected genes from the three modules with metabolic reprogramming-related genes and further intersected with differentially expressed genes to get 78 genes. After machine learning algorithms and manual selection, we finally got a nine-gene classifier consisting of SEC24D, RPSA, PSME3, PSMD8, PSMB7, NCOA1, MED12, LPCAT1, and AKR1C3. Our developed and validated classifier-based model demonstrated good discrimination, with an area under the curve (AUC) of 0.763 in the development cohort, 0.744 in the internal validation cohort, and 0.718 in the external validation cohort, and outperformed previous clinical models. Moreover, the addition of nodule size improved the predictive capability of the classifier. We further verify the expression of the gene in the classifier using TCGA lung cancer samples and found eight of the genes showed significant differential expression in lung adenocarcinoma while all nine genes showed significant differential expression in lung squamous carcinoma. Our findings underscore the significance of metabolic reprogramming pathways in patients with malignant pulmonary nodules, and our gene classifier can assist clinicians in differentiating benign from malignant pulmonary nodules in clinical settings.

Prognostic significance of a 3-gene ferroptosis-related signature in lung cancer via LASSO analysis and cellular functions of UBE2Z

Ferroptosis is a newly identified form of non-apoptotic programmed cell death resulting from iron-dependent lipid peroxidation. It is controlled by integrated oxidation and antioxidant systems. Ferroptosis exerts a crucial effect on the carcinogenesis of several cancers, including pulmonary cancer. Herein, a ferroptosis-associated gene signature for lung cancer prognosis and diagnosis was identified using integrative bioinformatics analyses. From the FerrDB database, 256 ferroptotic regulators and markers were identified. Of these, 25 exhibited differential expression between lung cancer and non-cancerous samples, as evidenced by the GSE19804 and GSE7670 datasets from the GEO database. Utilizing LASSO Cox regression analysis on TCGA-LUAD data, a potent 3-gene risk signature comprising CAV1, RRM2, and EGFR was established. This signature adeptly differentiates various survival outcomes in lung cancer patients, including overall survival and disease-specific intervals. Based on the 3-gene risk signature, lung cancer patients were categorized into high-risk and low-risk groups. Comparative analysis revealed 69 differentially expressed genes between these groups, with UBE2Z significantly associated with overall survival in TCGA-LUAD. UBE2Z was found to be upregulated in LUAD tissues and cells compared to normal controls. Functionally, the knockdown of UBE2Z curtailed aggressive behaviors in LUAD cells, including viability, migration, and invasion. Moreover, this knockdown led to a decrease in the mesenchymal marker vimentin while elevating the epithelial marker E-cadherin within LUAD cell lines. In conclusion, the ferroptosis-associated 3-gene risk signature effectively differentiates prognosis and clinical features in patients with lung cancer. UBE2Z was identified through this model, and it is upregulated in LUAD samples. Its knockdown inhibits aggressive cellular behaviors, suggesting UBE2Z's potential as a therapeutic target for lung cancer treatment.

Effect on Non-Small Cell Lung Cancer after Combination of Driver Gene Mutations and Anti-PD-1/PD-L1 Immunotherapy as Well as Chemotherapy.

We aimed to reveal the correlation between pathological indicators and PD-L1, between gene mutation status in lung cancer through clinico-pathological data and lung cancer-related gene mutation and PD-L1 expression analysis. The study was conducted in Jinhua Municipal Central Hospital, Zhejiang, China from 2017 to 2022. PD-L1 testing and targeted gene mutations detection were evaluated. The clinical characteristics of these non-small cell lung cancer (NSCLC) samples have been obtained. The groups (LUAD, n=142; LUSC, n=143) were grouped according to clinico-pathological features and PD-L1 expression (Yes/No or High/Low), and the clinico-pathological and genetic and molecular features and correlation with PD-L1 expression were compared across the above groups. Comparisons and analyses were made between different treatment schemes. Lung adenocarcinoma (LUAD, n=142) and lung squamous carcinoma (LUSC, n=143) samples were enrolled (median age: 64 years old). Pleural invasion and M staging were significantly different from PD-L1 alterations (P<0.05). The percentage of patients with PD-L1 tumor proportion score (TPS)≥50% was 36.24% and the percentage of patients with PD-L1 TPS<50% was 29.53%. The percentage of patients with PD-L1 high-expressed and treated by immunotherapy was 75.93% and 63.41% experienced Partial Response/Complete Response. The mutations ratio of EGFR, ALK, KRAS, MET, RET and TP53 were 28.86%, 1.34%, 6.04%, 0.67%, 1.34% and 0.67%, respectively. KRAS mutation was significantly different from PD-L1 alterations (P<0.01). There are individual differences in PD-L1 expression, which can also vary depending on the different clinical features. Specific molecular features correlate with differential PD-L1 expression and may influence the response to therapy.

High success rate of first proficiency testing for RET fusions and RET mutations in lung and thyroid cancer samples by various methods.

This study describes the external quality assessment (EQA) scheme for molecular testing of RET alterations in non-small cell lung cancer (NSCLC), medullary thyroid carcinomas (MTC), and non-MTC. The lead panel institute and Quality Assurance Initiative in Pathology (Qualitätssicherungs-Initiative Pathologie [QuIP] GmbH) selected formalin-fixed paraffin-embedded (FFPE) tissue from MTC for RET mutation testing by next-generation sequencing (NGS) methods and FFPE tissue from NSCLC and non-MTC for RET gene fusion testing using either in situ hybridisation (ISH) or NGS methods, forming 3 sub-schemes of the EQA scheme. Tissue material underwent an internal validation phase followed by an external testing phase. The internal validation phase served as a cross-validation step conducted by panel institutes. In the external testing phase, the number of participating institutes in the RET point mutation sub-scheme, RET fusion (ISH) sub-scheme, and RET fusion (NGS) sub-scheme was 32, 24, and 38, respectively. The reported success rates for external testing were 96.0%, 89.5%, and 93.5% for the RET point mutation, the ISH RET fusion, and the NGS RET fusion EQA sub-schemes, respectively. These findings confirm the reliability of the NGS method in detecting RET alterations and align with current screening recommendations. Overall, 31 institutes were certified for RET point mutation testing by NGS methods, 22 institutes were certified for RET fusion testing by ISH, and 36 institutes were certified for RET fusion testing by NGS methods. Results can be employed to inform real-world diagnostic decisions in Germany, Austria, and Switzerland.

Highly consistency of PIK3CA mutation spectrum between circulating tumor DNA and paired tissue in lung cancer patients

BackgroundPhosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) mutations are associated with drug resistance and prognosis in lung cancer; however, the consistency and clinical value of PIK3CA mutations between tissue and liquid samples are unknown. MethodsCirculating tumor DNA (ctDNA) and matched tumor tissue samples from 405 advanced lung cancer patients were collected at Jilin Cancer Hospital between 2018 and 2022, and the PIK3CA mutation status was sequenced using next-generation sequencing based on a 520 gene panel. The viability of different mutant lung cancer cells was detected using MTT assay. ResultsPIK3CA mutations were detected in 46 (5.68 %) of 810 lung cancer samples, with 21 (5.19 %) of 405 plasma samples and 25 (6.17 %) of 405 matched tissues. p.Glu542Lys, p.Glu545Lys, and p.His1047Arg were the most common mutation types of PIK3CA in both the ctDNA and tissue samples. The concordance of PIK3CA mutations was 97.53 % between ctDNA and matched tissues (kappa: 0.770, P = 0.000), with sensitivity/true positive rate of 72.0 %, specificity/true negative rate of 99.2 %, and negative predictive value and positive predictive value of 0.982 and 0.857, respectively (AUC = 0.856, P = 0.000). Furthermore, the concordance of PIK3CA mutations was 98.26 % in lung adenocarcinoma and 96.43 % in lung squamous cell carcinoma. TP53 and EGFR were the most common concomitant mutations in ctDNA and tissues. Patients with PIK3CA mutations showed a high tumor mutational burden (TMB) (P < 0.001) and a significant correlation between bTMB and tTMB (r = 0.5986, P = 0.0041). For the tPIK3CAmut/ctDNA PIK3CAmut cohort, PI3K pathways alteration was associated with male sex (P = 0.022), old age (P = 0.007), and smoking (P = 0.001); tPIK3CAmut/ctDNA PIK3CAwt patients harbored clinicopathological factors of adenocarcinoma stage IV, with low PS score (≤1) and TMB. ConclusionThis study showed that ctDNA is highly concordant and sensitive for identifying PIK3CA mutations, suggesting that PIK3CA mutation detection in liquid samples may be an alternative clinical practice for tissues.

The prognostic value of immune escape-related genes in lung adenocarcinoma.

Lung cancer is one of the most common cancers in humans, and lung adenocarcinoma (LUAD) has become the most common histological type of lung cancer. Immune escape promotes progression of LUAD from the early to metastatic late stages and is one of the main obstacles to improving clinical outcomes for immunotherapy targeting immune detection points. Our study aims to explore the immune escape related genes that are abnormally expressed in lung adenocarcinoma, providing assistance in predicting the prognosis of lung adenocarcinoma and targeted. RNA data and related clinical details of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database. Through weighted gene coexpression network analysis (WGCNA), 3112 key genes were screened and intersected with 182 immune escape genes obtained from a previous study to identify the immune escape-related genes (IERGs). The role of IERGs in LUAD was systematically explored through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses, which were used to enrich the relevant pathways of IERGs. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis were used to identify the key prognostic genes, and a prognostic risk model was constructed. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) and microenvironment cell populations (MCP) counter methods (which can accurately assess the amount of eight immune cell populations and two stromal cell groups) were used to analyze the tumor immune status of the high and low risk subgroups. The protein expression level of the differentially expressed genes in lung cancer samples was determined by using the Human Protein Atlas (HPA) database. A nomogram was constructed, and the prognostic risk model was verified via the Gene Expression Omnibus (GEO) datasets GSE72094 and GSE30219. Twenty differentially expressed IERGs were obtained. GO analysis of these 20 IERGs revealed that they were mainly associated with the regulation of immune system processes, immune responses, and interferon-γ enrichment in mediating signaling pathways and apoptotic signaling pathways; meanwhile, KEGG analysis revealed that IERGs were associated with necroptosis, antigen processing and presentation, programmed cell death ligand 1 (PD-L1) expression and programmed cell death 1 (PD-1) pathway in tumors, cytokine-cytokine receptor interactions, T helper cell 1 (Th1) and Th2 differentiation, and tumor necrosis factor signaling pathways. Using LASSO and Cox regression analysis, we constructed a four-gene model that could predict the prognosis of patients with LUAD, and the model was validated with a validation cohort. The immunohistochemical results of the HPA database showed that AHSA1 and CEP55 had low expression in normal lung tissue but high expression in lung cancer tissue. We constructed an IERG-based model for predicting the prognosis of LUAD. Among the genes identified, CEP55 and AHSA1 may be potential prognostic and therapeutic targets, and reducing their expression may represent a novel approach in the treatment of LUAD.

The Comparison of Real-Time PCR and Mutation-Specific Immunohistochemistry in EGFR Mutation Analysis of Non-Small Cell Lung Carcinomas

Objective: This study aims to identify activating mutations in the epidermal growth factor receptor (EGFR) gene in patients with non-small cell lung cancer (NSCLC) and to evaluate their correlation with responses to EGFR-tyrosine kinase inhibitors (TKI) treatment. This study aims to identify activating mutations in the epidermal growth factor receptor (EGFR) gene in patients with non-small cell lung cancer (NSCLC) and to evaluate their correlation with responses to EGFR-tyrosine kinase inhibitors (TKI) treatment. We conducted a comparative analysis of Real-Time PCR and immunohistochemistry to detect EGFR mutation status in non-small cell lung cancer patients, focusing on the sensitivity, specificity, and predictive values of immunohistochemistry. Material and Method: We evaluated 788 non-small cell lung cancer samples which were analyzed for EGFR mutation status by RT-PCR. We detected 126 EGFR mutated cases among these patients. We evaluated mutation-specific EGFR immunohistochemistry directed towards the exon 19 deletions (15 bp E746-A750) and exon 21 point mutation (L858R) to the 47 EGFR mutated patients histologic material and cell blocks of cytologic specimens. Results: 32 of the 47 cases (68%) had exon 19 deletion, 14 of them (30%) had point mutation in exon 21, and one of them (2%) showed exon 18 mutation. EGFR exon 19 (15 bp E746-A750 deletion) antibody showed a sensitivity of 100%, specificity of 40%, negative predictive value of 100%, and positive predictive value of 78%. The sensitivity of the exon 21 (L858R point mutation) antibody was 93%, specificity was 91%, negative predictive value was 97% and positive predictive value was 82%. Conclusion: Our investigation indicates that mutation-specific EGFR immunohistochemistry has demonstrated a notable sensitivity and specificity for exon 21. However, while sensitive, the exon 19 (15 bp E746-A750 deletion) antibody lacked specificity. While positive immunohistochemical staining may suggest the presence of an EGFR mutation, making the patient potentially eligible for TKI treatment, it should not be the sole determinant. If immunohistochemistry results are negative, it is essential to resort to molecular tests to ensure accurate diagnosis and appropriate therapeutic guidance. With evolving diagnostic landscapes, it is crucial to harness both IHC and molecular techniques judiciously for optimal patient care.

Exploring the prognostic analysis of autophagy and tumor microenvironment based on monocyte cells in lung cancer.

A deep understanding of the biological mechanisms of lung cancer offers more precise treatment options for patients. In our study, we integrated data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) to investigate lung adenocarcinoma. Analyzing 538 lung cancer samples and 31 normal samples, we focused on 3076 autophagy-related genes. Using Seurat, dplyr, tidyverse, and ggplot2, we conducted single-cell data analysis, assessing the quality and performing Principal Component Analysis (PCA) and t-SNE analyses. Differential analysis of TCGA data using the "Limma" package, followed by immune infiltration analysis using the CIBERSORT algorithm, led us to identify seven key genes. These genes underwent further scrutiny through consensus clustering and gene set variation analysis (GSVA). We developed a prognostic model using Lasso Cox regression and multivariable Cox analysis, which was then validated with a nomogram, predicting survival rates for lung adenocarcinoma. The model's accuracy and universality were corroborated by ROC curves. Additionally, we explored the relationship between immune checkpoint genes and immune cell infiltration and identified two key genes, HLA-DQB1 and OLR1. This highlighted their potential as therapeutic targets. Our comprehensive approach sheds light on the molecular landscape of lung adenocarcinoma and offers insights into potential treatment strategies, emphasizing the importance of integrating single-cell and genomic data in cancer research.

Abstract PR006: DNA repair inhibition via targeting ATR activates cGAS/STING signaling, promotes anti-tumor immunity, and is a synthetic lethal strategy to augment immunotherapy response in preclinical models and clinical samples of small cell lung cancer

Abstract Introduction: Small cell lung cancer (SCLC) is the most aggressive subtype of lung cancer. Clinical trials of immune checkpoint blockade (ICB) combined with chemotherapy delivered only very modest benefits. In this study, we investigated the effect of targeting ataxia telangiectasia and rad3-related (ATR), the primary activator of the replication stress response, in SCLC. Clinical samples from treatment naïve and treated SCLC patients were analyzed by single cell and bulk RNA sequencing to ascertain the intratumoral transcriptional heterogeneity and the surrounding tumor microenvironment (TME). Methods: In this study, we investigated the effect of ATR inhibition either alone or in combination with PD-L1 blockade in multiple immunocompetent mouse models of SCLC. The downstream effects of ATR inhibition were assessed by bulk RNA sequencing, and multicolor flow cytometry. The SCLC clinical samples from treatment naïve and patients treated were analyzed by single cell and bulk RNA sequencing. Results: In multiple immunocompetent SCLC mouse models, ATR inhibition (ATRi) remarkably enhanced the anti-tumor effect of PD-L1 blockade. We next tested the ATR inhibition either alone or in combination with PD-L1 in the second-line regimen for SCLC. We observed that ATR inhibition in combination with PD-L1 blockade significantly reduced tumor volume and prolonged survival of aggressive mice models when compared to PD-L1 alone. Targeting ATR enhanced the expression of PD-L1, activated the cGAS/STING pathway, induced the expression of Type I and II interferon pathways, and caused significant infiltration of cytotoxic and memory/effector T-cells into tumors. Interestingly, ATRi also led to significant induction of MHC class I in SCLC in vitro and in vivo models. Single-cell RNA sequencing data provided a deep annotation of SCLC phenotypes and the surrounding immune TME. Treatment and subtype are associated with substantial phenotypic changes in the SCLC immune microenvironment, with greater T-cell dysfunction in SCLC-N than in SCLC-A. Moreover, a recurrent highly metastatic SCLC subset is associated with exhausted CD8+ T-cells and a pro-fibrotic, immunosuppressive monocyte/macrophage population, suggesting possible tumor-immune coordination to promote metastasis. Specifically, analysis of pre-and post-treatment clinical samples from a proof-of-concept study of a first-in-class ATR inhibitor, VX970 (berzosertib), and topotecan, in patients with relapsed SCLCs, validated the induction of MHC class I and interferon pathway genes, for the first time in this disease. Conclusion: We provide insight into tumor and immune biology in SCLC at single-cell resolution. Further, we show ATRi as a potentially transformative vulnerability of SCLC. Given the increasing importance of immunotherapy for the management of SCLC and that ATR inhibitors are already in clinical trials, combining ATR inhibitors with PD-L1-blockade offers an attractive strategy for the treatment of SCLC and contributes to the rapid translation of this combination into the clinic. Citation Format: Triparna Sen. DNA repair inhibition via targeting ATR activates cGAS/STING signaling, promotes anti-tumor immunity, and is a synthetic lethal strategy to augment immunotherapy response in preclinical models and clinical samples of small cell lung cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr PR006.