TCGA-LUAD Cohort Research Articles

Construction of machine learning models of lipid metabolism-related long non-coding RNA in lung adenocarcinoma is associated with microenvironmental heterogeneity and immunotherapy

Using various bioinformatics tools, we constructed a prognostic model integrating the expression profiles of lipid metabolization-related lncRNAs and clinical features. Our study discovered that various lipid metabolism-related lncRNAs were linked to the prognosis of lung adenocarcinoma. The link between immune cell infiltration in the tumour microenvironment and the expression level of lncRNAs involved with lipid metabolism was also investigated. Our findings suggest that there is a complex interplay between lipid metabolism, microenvironmental heterogeneity, and immunotherapy in lung adenocarcinoma. Furthermore, the study has significant clinical implications for the development of effective therapies for patients with lung adenocarcinoma by investigating the potential of these lncRNAs as biomarkers for anticipating the response to immunotherapy. Finally, our study emphasises the significance of continued analysis of lncRNAs associated with lipid metabolism in tumours to better understand the mechanisms behind the incidence and progression of lung adenocarcinoma. Several of the strengths of our work are the extensive analysis of the relationship between lipid metabolism and lncRNAs in lung adenocarcinoma and the utilization of a sizable sample size from the TCGA-LUAD cohort. However, there are also some limitations. Firstly, the mechanisms of how these lncRNAs interact with lipid metabolism pathways and immune response require further investigation. Secondly, our study was based on bioinformatics analysis and lacked experimental verification. Finally, our study was limited to the TCGA-LUAD cohort and further validation using other independent cohorts is required. In conclusion, our study provides a comprehensive and systematic analysis of lncRNAs associated with lipid metabolism in lung adenocarcinoma. Lung cancer patients may benefit from using identified lncRNAs as therapeutic targets and prognostic biomarkers. Validating these findings and confirming the potential therapeutic applications of these lncRNAs will require more mechanistic research.

A Risk Model Developed based on Homologous Recombination Deficiency Genes for Evaluating the Drug Sensitivity and Prognostic Prediction of Lung Adenocarcinoma.

This study was designed to construct a risk model based on homologous recombination deficiency (HRD) to evaluate the prognosis and drug sensitivity for patients with lung adenocarcinoma (LUAD). LUAD is a subtype of lung cancer with unfavorable overall survival (OS) and prognosis. HRD has been widely studied in various tumors, but its role in LUAD has not been fully understood. We aimed to construct an HRD-related risk model for predicting the prognosis and drug sensitivity of patients with LUAD. Gene expression data of the LUAD samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We extracted HRD genes from previous literature and performed univariate COX analysis to select those closely associated with LUAD prognosis. ConsensusClusterPlus was employed to stratify the samples in the TCGA-LUAD cohort into different subtypes. A RiskScore model was established applying random forest method. Furthermore, immunotherapy response and drug sensitivity were predicted using Tumor Immune Dysfunction and Exclusion (TIDE) software and pRRophytic R package, respectively. Finally, the clinical features between High- and Low- RiskScore groups were compared. A total of 16 HRD genes relevant to LUAD prognosis were selected and used to classify 3 LUAD clusters (C1, C2, and C3). Specifically, C1, with a lower TIDE score displayed higher immune infiltration and immunotherapy benefit and the optimal OS, while C2 was closely correlated with tumor-relevant pathways and had the worst OS. Finally, 4 HRD genes (RAD51AP1, BRCA1, H2AFX, and FANCL) were determined to develop a RiskScore signature. It was found that a higher RiskScore was related to more advanced stages, worse OS, and tumor development pathways. Additionally, the High-RiskScore group with a higher TIDE score was sensitive to 44 traditional chemotherapy drugs. A nomogram combined with RiskScore exhibited an accurate survival prediction ability. The HRD-based RiskScore played a crucial role in LUAD development, showing a strong potential to serve as a prognostic indicator for LUAD. Our findings contributed to the diagnosis of LUAD and its treatment.

Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study

IntroductionMolecular profiling of lung cancer is essential to identify genetic alterations that predict response to targeted therapy. While deep learning shows promise for predicting oncogenic mutations from whole tissue images, existing studies often face challenges such as limited sample sizes, a focus on earlier stage patients, and insufficient analysis of robustness and generalizability. MethodsThis retrospective study evaluates factors influencing mutation prediction accuracy using the large Heidelberg Lung Adenocarcinoma Cohort (HLCC), a cohort of 2356 late-stage FFPE samples. Validation is performed in the publicly available TCGA-LUAD cohort. ResultsModels trained on the larger HLCC cohort generalized well to the TCGA dataset for mutations in EGFR (AUC 0.76), STK11 (AUC 0.71) and TP53 (AUC 0.75), in line with the hypothesis that larger cohort sizes improve model robustness. Variation in performance due to pre-processing and modeling choices, such as mutation variant calling, affected EGFR prediction accuracy by up to 7 %. DiscussionModel explanations suggest that acinar and papillary growth patterns are critical for the detection of EGFR mutations, whereas solid growth patterns and large nuclei are indicative of TP53 mutations. These findings highlight the importance of specific morphological features in mutation detection and the potential of deep learning models to improve mutation prediction accuracy. ConclusionAlthough deep learning models trained on larger cohorts show improved robustness and generalizability in predicting oncogenic mutations, they cannot replace comprehensive molecular profiling. However, they may support patient pre-selection for clinical trials and deepen the insight in genotype-phenotype relationships.

TFEB controls sensitivity to chemotherapy and immuno-killing in non-small cell lung cancer

BackgroundIn non-small cell lung cancer (NSCLC) the efficacy of chemo-immunotherapy is affected by the high expression of drug efflux transporters as ABCC1 and by the low expression of ABCA1, mediating the isopentenyl pyrophosphate (IPP)-dependent anti-tumor activation of Vγ9Vδ2 T-lymphocytes. In endothelial cells ABCA1 is a predicted target of the transcription factor EB (TFEB), but no data exists on the correlation between TFEB and ABC transporters involved in the chemo-immuno-resistance in NSCLC.MethodsThe impact of TFEB/ABCC1/ABCA1 expression on NSCLC patients’ survival was analyzed in the TCGA-LUAD cohort and in a retrospective cohort of our institution. Human NSCLC cells silenced for TFEB (shTFEB) were analyzed for ABC transporter expression, chemosensitivity and immuno-killing. The chemo-immuno-sensitizing effects of nanoparticles encapsulating zoledronic acid (NZ) on shTFEB tumors and on tumor immune-microenvironment were evaluated in Hu-CD34+ mice by single-cell RNA-sequencing.ResultsTFEBlowABCA1lowABCC1high and TFEBhighABCA1highABCC1low NSCLC patients had the worst and the best prognosis, respectively, in the TCGA-LUAD cohort and in a retrospective cohort of patients receiving platinum-based chemotherapy or immunotherapy as first-line treatment. By silencing shTFEB in NSCLC cells, we demonstrated that TFEB was a transcriptional inducer of ABCA1 and a repressor of ABCC1. shTFEB cells had also a decreased activity of ERK1/2/SREBP2 axis, implying reduced synthesis and efflux via ABCA1 of cholesterol and its intermediate IPP. Moreover, TFEB silencing reduced cholesterol incorporation in mitochondria: this event increased the efficiency of OXPHOS and the fueling of ABCC1 by mitochondrial ATP. Accordingly, shTFEB cells were less immuno-killed by the Vγ9Vδ2 T-lymphocytes activated by IPP and more resistant to cisplatin. NZ, which increased IPP efflux but not OXPHOS and ATP production, sensitized shTFEB immuno-xenografts, by reducing intratumor proliferation and increasing apoptosis in response to cisplatin, and by increasing the variety of anti-tumor infiltrating cells (Vγ9Vδ2 T-lymphocytes, CD8+T-lymphocytes, NK cells).ConclusionsThis work suggests that TFEB is a gatekeeper of the sensitivity to chemotherapy and immuno-killing in NSCLC, and that the TFEBlowABCA1lowABCC1high phenotype can be predictive of poor response to chemotherapy and immunotherapy. By reshaping both cancer metabolism and tumor immune-microenvironment, zoledronic acid can re-sensitize TFEBlow NSCLCs, highly resistant to chemo- and immunotherapy.

Whole-Exome Sequencing and Experimental Validation Unveil the Roles of TMEM229A Q200del Mutation in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the major histopathological types of non-small cell lung cancer (NSCLC), including solid, acinar, lepidic, papillary and micropapillary subtypes. Increasing evidence has shown that micropapillary LUAD is positively associated with a higher percentage of driver gene mutations, a higher incidence of metastasis and a poorer prognosis, while lepidic LUAD has a relatively better prognosis. However, the novel genetic change and its underlying mechanism in the progression of micropapillary LUAD have not been exactly determined. A total of 181 patients with LUAD who underwent surgery at the First Affiliated Hospital of Huzhou University from January 2020 to December 2022 were enrolled. Three predominant lepidic and three predominant micropapillary LUAD tissue samples were carried out using whole-exome sequencing. Comprehensive analysis of genomic variations and the difference between lepidic and micropapillary LUAD was performed. In addition, the TMEM229A Q200del mutation was verified using our cohort and TCGA-LUAD datasets. The correlations between the TMEM229A Q200del mutation and the clinicopathological characteristics of patients with LUAD were further analyzed. The functions and mechanisms of TMEM229A Q200del on NSCLC cell proliferation and migration were also determined. The frequency of genomic changes in patients with micropapillary LUAD was higher than that in patients with lepidic LUAD. Mutations in EGFR, ATXN2, C14orf180, MUC12, NOTCH1, and PKD1L2 were concomitantly detected in three predominant micropapillary and three predominant lepidic LUAD cases. The TMEM229A Q200del mutation was only mutated in lepidic LUAD. Additionally, the TMEM229A Q200del mutation had occurred in 16 (8.8%) patients, and not found TMEM229A R76H and M346T mutations in our cohort, while TMEM229A mutations (R76H, M346T, and Q200del) occurred only in 1.0% of the TCGA-LUAD cohort. Further correlation analysis between the TMEM229A Q200del mutation and clinicopathological characteristics suggested that a lower frequency of the Q200del mutation was significantly associated with positive lymph node metastasis, advanced TNM stage, positive cancer thrombus, and pathological features. Finally, overexpression of TMEM229A Q200del suppressed NSCLC cell proliferation and migration invitro. Mechanistically, overexpression of TMEM229A and TMEM229A Q200del both reduced the expression level of phosphorylated (p)-ERK and p-AKT (Ser473), and the reduced protein level of p-ERK in the TMEM229A Q200del group was more pronounced compared to the TMEM229A group. Our results demonstrated that the TMEM229A Q200del mutant may play a protective role in the progression of LUAD via inactivating ERK pathway, providing a potential therapeutic target in LUAD.

RACGAP1 is a pivotal gene in lung adenocarcinoma-associated membranous nephropathy: Based on comprehensive bioinformatics analysis and machine learning

BackgroundThis study performs a detailed bioinformatics and machine learning analysis to investigate the genetic foundations of membranous nephropathy (MN) in lung adenocarcinoma (LUAD). MethodsIn this study, the gene expression profiles of MN microarray datasets (GSE99339) and LUAD dataset (GSE43767) were downloaded from the Gene Expression Omnibus database, common differentially expressed genes (DEGs) were obtained using the limma R package. The biological functions were analyzed with R Cluster Profiler package according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Machine learning algorithms, including LASSO regression, support vector machine (SVM), Random Forest, and Boruta analysis, were applied to identify hubgenes linked to LUAD-associated MN. These genes’ prognostic values were evaluated in the TCGA-LUAD cohort and validated through immunohistochemistry on renal biopsy specimens. ResultsA total of 36 DEGs in common were identified for downstream analyses. Functional enrichment analysis highlighted the involvement of the Toll-like receptor 4 pathway and several immune recognition pathways in LUAD-associated MN. COL3A1, PSENEN, RACGAP1, and TNFRSF10B were identified as hub genes in LUAD-associated MN using machine learning algorithms. ROC analysis demonstrated their effective discrimination of MN with high accuracy. Survival analysis showed that lung adenocarcinoma patients with higher expression of these genes had significantly reduced overall survival. In patients with lung adenocarcinoma-associated MN, RACGAP1, COL3A1, PSENEN, and TNFRSF10B were higher expressed in the glomerular, especially RACGAP1, indicating an important role in the pathogenesis of LUAD-associated membranous nephropathy. ConclusionsOur study underscores the critical role of RACGAP1, COL3A1, PSENEN, and TNFRSF10B in the development of LUAD-associated MN, providing important insights for future research and the development of potential therapeutic strategies.

Assessment of AURKA expression and prognosis prediction in lung adenocarcinoma using machine learning-based pathomics signature

ObjectiveThis study aimed to develop quantitative feature-based models from histopathological images to assess aurora kinase A (AURKA) expression and predict the prognosis of patients with lung adenocarcinoma (LUAD). MethodsA dataset of patients with LUAD was derived from the cancer genome atlas (TCGA) with information on clinical characteristics, RNA sequencing and histopathological images. The TCGA-LUAD cohort was randomly divided into training (n = 229) and testing (n = 98) sets. We extracted quantitative image features from histopathological slides of patients with LUAD using computational approaches, constructed a predictive model for AURKA expression in the training set, and estimated their predictive performance in the test set. A Cox proportional hazards model was used to assess whether the pathomic scores (PS) generated by the model independently predicted LUAD survival. ResultsHigh AURKA expression was an independent risk factor for overall survival (OS) in patients with LUAD (hazard ratio = 1.816, 95 % confidence intervals = 1.257–2.623, P = 0.001). The model based on histopathological image features had significant predictive value for AURKA expression: the area under the curve of the receiver operating characteristic curve in the training set and validation set was 0.809 and 0.739, respectively. Decision curve analysis showed that the model had clinical utility. Patients with high PS and low PS had different survival rates (P = 0.019). Multivariate analysis suggested that PS was an independent prognostic factor for LUAD (hazard ratio = 1.615, 95 % confidence intervals = 1.071–2.438, P = 0.022). ConclusionPathomics models based on machine learning can accurately predict AURKA expression and the PS generated by the model can predict LUAD prognosis.

A random forest model to predict the efficacy of anti-PD-1/PD-L1 monoclonal antibody in lung adenocarcinoma.

e20558 Background: Not all patients with lung adenocarcinoma (LUAD) could benefit from anti-PD-1/PD-L1 monoclonal antibody therapy, and existing biomarkers cannot explain all clinical benefits. Therefore, an effective model to predict the efficacy of anti-PD-1/PD-L1 monoclonal antibody is necessary. Methods: DNA methylation sequencing was used in LUAD patients’ FFPE samples, who received anti-PD-1/PD-L1 monoclonal antibody. The DNA methylation level of each gene was transformed into binary format according to its median value. The progression-free survival (PFS) was defined as the duration from the beginning of anti-PD-1/PD-L1 monoclonal antibody therapy to disease progression or death. Patients were randomly divided into training cohort (70%) and test cohort (30%). In the training cohort, uni-Cox regression was used to estimate the value in predicting PFS, and LASSO-Cox regression was performed to screened genes to build random forest model. The accuracy of this random forest model was validated in the test cohort. Finally, DNA methylation data were used to calculated the risk score of patients in the TCGA-LUAD cohort, and GSEA and GSVA were employed in RNA data to explore the mechanism and explain the efficacy of the prediction model. Results: In this study, 40 patients with LUAD were enrolled, and the median PFS of them was 7.57 months. A total of 29 genes with P < 0.05 and C-index > 0.7 were selected to perform LASSO-Cox regression, and 13 genes ( BTBD7, CAD, FBXL20, FKBP7, HBP1, IGFBP3, RAB5B, RASGRP1, RNF8, SOHLH1, TMEM223, UHRF1 and WWC2) were used to build prediction model. The C-index of risk score from this model was 0.96 and 0.93 in the training and test cohort, respectively. Patients were divided into the high and low risk groups according to the median risk score, and the median PFS of the high risk group was shorter than that of the low risk group (training cohort: 3.05 vs. 10.89 months, P < 0.01, HR = 2.51, 95% CI: 1.50-4.22; test cohort: 1.64 vs. 11.31 months, P = 0.03, HR = 1.80, 95% CI: 1.04-3.14). In the low and high risk group of the TCGA-LUAD cohort, there were 92.2% (388/421) and 0.5% (2/421) significant enriched ( P < 0.05 and FDR < 0.25) immunity-related pathways from results of GSEA, respectively, meanwhile 94.3% (397/421) and 1.9% (8/421) significant enriched immunity-related pathways from results of GSVA, respectively. Therefore, the lower risk score of this random forest model accompanied with the higher activation of immunity-related pathways which might help anti-PD-1/PD-L1 monoclonal antibody to express better efficacy. Conclusions: A random forest model based on DNA methylation data was constructed and showed a potential value in predicting the PFS of anti-PD-1/PD-L1 monoclonal antibody therapy in LUADs, and the risk score of this model is negatively correlated to the activation of immunity-related pathways, which could help to identify LUAD patients that would benefit from immunotherapy.

Identification of a methyltransferase-related long noncoding RNA signature as a novel prognosis biomarker for lung adenocarcinoma.

Lung adenocarcinoma (LUAD) accounts for a high proportion of tumor deaths globally, while methyltransferase-related lncRNAs in LUAD were poorly studied. In our study, we focused on two distinct cohorts, TCGA-LUAD and GSE3021, to establish a signature of methyltransferase-related long non-coding RNAs (MeRlncRNAs) in LUAD. We employed univariate Cox and LASSO regression analyses as our main analytical tools. The GSE30219 cohort served as the validation cohort for our findings. Furthermore, to explore the differential pathway enrichments between groups stratified by risk, we utilized Gene Set Enrichment Analysis (GSEA). Additionally, single-sample GSEA (ssGSEA) was conducted to assess the immune infiltration landscape within each sample. Reverse transcription quantitative PCR (RT-qPCR) was also performed to verify the expression of prognostic lncRNAs in both clinically normal and LUAD samples. In LUAD, we identified a set of 32 MeRlncRNAs. We further narrowed our focus to six prognostic lncRNAs to develop gene signatures. The TCGA-LUAD cohort and GSE30219 were utilized to validate the risk score model derived from these signatures. Our analysis showed that the risk score served as an independent prognostic factor, linked to immune-related pathways. Additionally, the analysis of immune infiltration revealed that the immune landscape in high-risk groups was suppressed, which could contribute to poorer prognoses. We also constructed a regulatory network comprising 6 prognostic lncRNAs, 19 miRNAs, and 21 mRNAs. Confirmatory RT-qPCR results aligned with public database findings, verifying the expression of these prognostic lncRNAs in the samples. The prognostic gene signature of LUAD associated with MeRlncRNAs that we provided, may offer us a comprehensive picture of the prognosis prediction for LUAD patients.

PCDH11X mutation as a potential biomarker for immune checkpoint therapies in lung adenocarcinoma.

Immune checkpoint inhibitors (ICIs) have achieved impressive success in lung adenocarcinoma (LUAD). However, the response to ICIs varies among patients, and predictive biomarkers are urgently needed. PCDH11X is frequently mutated in LUAD, while its role in ICI treatment is unclear. In this study, we curated genomic and clinical data of 151 LUAD patients receiving ICIs from three independent cohorts. Relations between PCDH11X and treatment outcomes of ICIs were examined. A melanoma cohort collected from five published studies, a pan-cancer cohort, and non-ICI-treated TCGA-LUAD cohort were also examined to investigate whether PCDH11X mutation is a specific predictive biomarker for LUAD ICI treatment. Among the three ICI-treated LUAD cohorts, PCDH11X mutation (PCDH11X-MUT) was associated with better clinical response compared to wild-type PCDH11X (PCDH11X-WT). While in ICI-treated melanoma cohort, the pan-cancer cohort excluding LUAD, and the non-ICI-treated TCGA-LUAD cohort, no significant differences in overall survival (OS) were observed between the PCDH11X-MUT and PCDH11X-WT groups. PCDH11X mutation was associated with increased PD-L1 expression, tumor mutation burden (TMB), neoantigen load, DNA damage repair (DDR) mutations, and hot tumor microenvironment in TCGA-LUAD cohort. Our findings suggested that the PCDH11X mutation might serve as a specific biomarker to predict the efficacy of ICIs for LUAD patients. Considering the relatively small sample size of ICI-treated cohorts, future research with larger cohorts and prospective clinical trials will be essential for validating and further exploring the role of PCDH11X mutation in the context of immunotherapy outcomes in LUAD. KEY MESSAGES: PCDH11X mutation is associated with better clinical response compared to wild type PCDH11X in three ICIs-treated LUAD cohorts. In ICIs-treated melanoma cohort, the pan-cancer cohort excluding LUAD, and non-ICIs-treated TCGA-LUAD cohortsPCDH11X mutation is not associated with better clinical response, suggesting PCDH11X mutation might be a specific biomarker to predict the efficacy of ICIs treatment for LUAD patients. PCDH11X mutation is associated with increased PD-L1 expression, tumor mutation burden, and neoantigen load in TCGA-LUAD cohort. PCDH11X mutation is associated with hot tumor microenvironment in TCGA-LUAD cohort.

Comprehensive molecular analyses of cuproptosis-related genes with regard to prognosis, immune landscape, and response to immune checkpoint blockers in lung adenocarcinoma

BackgroundRecent studies have emphasized the importance of the biological processes of different forms of cell death in tumor heterogeneity and anti-tumor immunity. Nonetheless, the relationship between cuproptosis and lung adenocarcinoma (LUAD) remains largely unexplored.MethodsData for 793 LUAD samples and 59 normal lung tissues obtained from TCGA-LUAD cohort GEO datasets were used in this study. A total of 165 LUAD tissue samples and paired normal lung tissue samples obtained from our hospital were used to verify the prognostic value of dihydrolipoamide S-acetyltransferase (DLAT) and dihydrolipoamide branched chain transacylase E2 (DBT) for LUAD. The cuproptosis-related molecular patterns of LUAD were identified using consensus molecular clustering. Recursive feature elimination with random forest and a tenfold cross-validation method was applied to construct the cuproptosis score (CPS) for LUAD.ResultsBioinformatic and immunohistochemistry (IHC) analyses revealed that 13 core genes of cuproptosis were all significantly elevated in LUAD tissues, among which DBT and DLAT were associated with poor prognosis (DLAT, HR = 6.103; DBT, HR = 4.985). Based on the expression pattern of the 13 genes, two distinct cuproptosis-related patterns have been observed in LUAD: cluster 2 which has a relatively higher level of cuproptosis was characterized by immunological ignorance; conversely, cluster 1 which has a relatively lower level of cuproptosis is characterized by TILs infiltration and anti-tumor response. Finally, a scoring scheme termed the CPS was established to quantify the cuproptosis-related pattern and predict the prognosis and the response to immune checkpoint blockers of each individual patient with LUAD.ConclusionCuproptosis was found to influence tumor microenvironment (TME) characteristics and heterogeneity in LUAD. Patients with a lower CPS had a relatively better prognosis, more abundant immune infiltration in the TME, and an enhanced response to immune checkpoint inhibitors.

Abstract 180: LINC00261 is functionally linked to tumor DNA mutational burden and tumor immune microenvironment composition in lung adenocarcinoma

Abstract Immunotherapy has emerged as a breakthrough in the improvement of survival outcomes for lung adenocarcinoma (LUAD), however effective response requires the combination of blocking inhibitory signals on the tumor surface and antigen presentation to the tumor surface for proper immune recognition. Several commercially available and robust methods exist for identification of tumors displaying immune-inhibitory surface receptors, such as PD-L1, however it is currently difficult to predict effectiveness of antigen presentation on the cell surface. To address this, we utilized clinical and next-generation sequencing data from The Cancer Genome Atlas (TCGA) to identify gene signatures that are correlated to tumor mutational burden (TMB) within cancers of epithelial origins as a surrogate for neoantigen signatures. We identified LINC00261 as a top gene correlated to TMB, whose expression activates DNA damage response pathways in vitro along with resistance to cisplatin. LINC00261 expression was also significantly correlated to MHC class I and II genes involved in endogenous neoantigen presentation expression within the TCGA-LUAD cohort. This relationship was confirmed in vitro through ectopic reintroduction of LINC00261 for key MHC class II presentation genes. Interferon gamma-induced MHC gene activation in vitro was also able to induce endogenous expression of LINC00261. Staining of primary human lung cancer sections suggested that loss of LINC00261 is associated with an immunosuppressive tumor microenvironment. Taken together, our results suggest there is a mechanistic relationship in the silencing of LINC00261 in LUAD and compromised DNA repair, accumulation of mutations, and reduced antitumor immune response. Citation Format: Jonathan Castillo, Tianchun Xue, Mayela Norwood, Samantha Joseph, Alan L. Epstein, William D. Wallace, Anthony W. Kim, Crystal N. Marconett. LINC00261 is functionally linked to tumor DNA mutational burden and tumor immune microenvironment composition in lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 180.

Machine learning framework develops neutrophil extracellular traps model for clinical outcome and immunotherapy response in lung adenocarcinoma.

Neutrophil extracellular traps (NETs) are novel inflammatory cell death in neutrophils. Emerging studies demonstrated NETs contributed to cancer progression and metastases in multiple ways. This study intends to provide a prognostic NETs signature and therapeutic target for lung adenocarcinoma (LUAD) patients. Consensus cluster analysis performed by 38 reported NET-related genes in TCGA-LUAD cohorts. Then, WGCNA network was conducted to investigate characteristics genes in clusters. Seven machine learning algorithms were assessed for training of the model, the optimal model was picked by C-index and 1-, 3-, 5-year ROC value. Then, we constructed a NETs signature to predict the overall survival of LUAD patients. Moreover, multi-omics validation was performed based on NETs signature. Finally, we constructed stable knockdown critical gene LUAD cell lines to verify biological functions of Phospholipid Scramblase 1 (PLSCR1) in vitro and in vivo. Two NETs-related clusters were identified in LUAD patients. Among them, C2 cluster was provided as "hot" tumor phenotype and exhibited a better prognosis. Then, WGCNA network identified 643 characteristic genes in C2 cluster. Then, Coxboost algorithm proved its optimal performance and provided a prognostic NETs signature. Multi-omics revealed that NETs signature was involved in an immunosuppressive microenvironment and predicted immunotherapy efficacy. In vitro and in vivo experiments demonstrated that knockdown of PLSCR1 inhibited tumor growth and EMT ability. Besides, cocultural assay indicated that the knockdown of PLSCR1 impaired the ability of neutrophils to generate NETs. Finally, tissue microarray (TMA) for LUAD patients verified the prognostic value of PLSCR1 expression. In this study, we focus on emerging hot topic NETs in LUAD. We provide a prognostic NETs signature and identify PLSCR1 with multiple roles in LUAD. This work can contribute to risk stratification and screen novel therapeutic targets for LUAD patients.

Development of the ARDS-derived gene panel for lung adenocarcinoma prognosis stratification and experiment validation of CCL20 expression.

Acute respiratory distress syndrome (ARDS) is a life-threatening condition characterized by lung inflammation and high mortality rates. Lung cancer, specifically lung adenocarcinoma (LUAD), is a major cause of cancer-related deaths worldwide. Patients with LUAD, particularly those undergoing chemotherapy, are more likely to develop ARDS. ARDS inflicts major malfunctioning in the immune system. We suspected a certain shared pathogenic mechanism between these diseases. This study analyzed 503 LUAD patients from the TCGA-LUAD cohort as the training set, 85 LUAD cases from the GSE30219 cohort as the validation set, and 24 RNA-seq samples from ARDS mice model and control groups in the GSE2411 cohort. The differentially expressed genes (DEGs) of ARDS were analyzed using the limma package and screened by Cox and Lasso analysis. ssGSEA and xCell algorithms were utilized for immune landscaping. RT-qPCR analysis was used to determine the mRNA levels of key genes in both the LPS-induced ARDS model and human LUAD cell lines. We identified DEGs between ARDS and control groups, which were highly associated with cytokine production and leukocyte migration. A prognosis model for LUAD patients was developed based on the expressions of the key genes in the ARDS-derived DEGs, including FMO3, IL1R2, CCL20, CFTR, and GADD45G. A satisfactory efficacy was observed in both the training and validation cohorts. The model demonstrated increased effectiveness in predicting the intratumor immune profile and mutation status of LUAD. Moreover, we utilized LPS to induce the ARDS model, which resulted in elevated expressions of IL1R2 and CCL20. Additionally, CCL20 was upregulated in cancerous LUAD cell lines. We developed an ARDS-based model for stratifying LUAD prognosis. CCL20 was found to be elevated in both the ARDS model and LUAD, suggesting a shared underlying mechanism of these two diseases.

PLAU and GREM1 are prognostic biomarkers for predicting immune response in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a common malignant tumor. Identification of biomarkers and understanding their potential functions will facilitate the treatment and diagnosis in LUAD patients. The yellow module (cor = 0.31, P = 2e-6) was selected as the core module based on weighted gene co-expression network analysis (WGCNA) by integrating RNA-seq data and tumor stage. Two upregulated genes (PLAU and GREM1) in yellow module were identified to be biomarkers. Kaplan-Meier curve analysis displayed that high expression levels of them had a poor overall survival (OS). And, their high expression levels revealed higher tumor stage and relapse possibility in LUAD patients, and could be a prognostic parameter. Both biomarkers showed similar immune cell expression profiles in low- and high-expression groups. Strongly positive correlation between both biomarkers and biomarkers of tumor-infiltrating lymphocytes were also clarified in TCGA-LUAD cohort. Importantly, single gene GSEA showed that transcriptional mis-regulation in cancer and microRNAs in cancer were enriched in LUAD patients. Therefore, a miRNA-mRNA-transcription factors (TFs) co-expression regulatory networks was constructed for each biomarker, various miRNAs and TFs were related to PLAU and GREM1. Among which, 6 downstream TFs were overlapped genes for both biomarkers. Notably, 2 of these TFs (FOXF1 and TFAP2A) exhibited significantly abnormal expression levels. Among which, FOXF1 was downregulated and TFAP2A was upregulated in TCGA-LUAD cohort. Both TFs showed a significantly positive correlation with the expression level of PLAU. In conclusion, we identified 2 biomarkers related to immune response and achieved a good accuracy in predicting OS in patients with LUAD.

Abstract A016: LINC00261 is functionally linked to tumor DNA mutational burden and tumor immune microenvironment composition in lung adenocarcinoma

Abstract Immunotherapy has emerged as a breakthrough in the improvement of survival outcomes for lung adenocarcinoma (LUAD), however effective response requires the combination of blocking inhibitory signals on the tumor surface and antigen presentation to the tumor surface for proper immune recognition. Several commercially available and robust methods exist for identification of tumors displaying immune-inhibitory surface receptors, such as PD-L1, however it is currently difficult to predict effectiveness of antigen presentation on the cell surface. To address this, we utilized clinical and next-generation sequencing data from The Cancer Genome Atlas (TCGA) to identify gene signatures that are correlated to tumor mutational burden (TMB) within cancers of epithelial origins as a surrogate for neoantigen signatures. We identified LINC00261 as a top gene correlated to TMB, whose expression activates DNA damage response pathways in vitro along with resistance to cisplatin. LINC00261 expression was also significantly correlated to MHC class I and II genes involved in endogenous neoantigen presentation expression within the TCGA-LUAD cohort. This relationship was confirmed in vitro through ectopic reintroduction of LINC00261 for key MHC class II presentation genes. Interferon gamma-induced MHC gene activation in vitro was also able to induce endogenous expression of LINC00261. The staining of primary human lung cancer sections suggested that loss of LINC00261 is associated with an immunosuppressive tumor microenvironment. Taken together, our results suggest there is a mechanistic relationship in the silencing of LINC00261 in LUAD and compromised DNA repair, accumulation of mutations, and reduced antitumor immune response. Citation Format: Jonathan Castillo, Tianchun Xue, Mayela Norwood, Samantha Joseph, Alan L. Epstein, W. Dean Wallace, Anthony W. Kim, Crystal N. Marconett. LINC00261 is functionally linked to tumor DNA mutational burden and tumor immune microenvironment composition in lung adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Damage Repair: From Basic Science to Future Clinical Application; 2024 Jan 9-11; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2024;84(1 Suppl):Abstract nr A016.

Leveraging diverse cell-death patterns to predict the clinical outcome of immune checkpoint therapy in lung adenocarcinoma: Based on muti-omics analysis and vitro assay.

Advanced LUAD shows limited response to treatment including immune therapy. With the development of sequencing omics, it is urgent to combine high-throughput multi-omics data to identify new immune checkpoint therapeutic response markers. Using GSE72094 (n = 386) and GSE31210 (n = 226) gene expression profile data in the GEO database, we identified genes associated with lung adenocarcinoma (LUAD) death using tools such as "edgeR" and "maftools" and visualized the characteristics of these genes using the "circlize" R package. We constructed a prognostic model based on death-related genes and optimized the model using LASSO-Cox regression methods. By calculating the cell death index (CDI) of each individual, we divided LUAD patients into high and low CDI groups and examined the relationship between CDI and overall survival time by principal component analysis (PCA) and Kaplan-Meier analysis. We also used the "ConsensusClusterPlus" tool for unsupervised clustering of LUAD subtypes based on model genes. In addition, we collected data on the expression of immunomodulatory genes and model genes for each cohort and performed tumor microenvironment analyses. We also used the TIDE algorithm to predict immunotherapy responses in the CDI cohort. Finally, we studied the effect of PRKCD on the proliferation and migration of LUAD cells through cell culture experiments. The study utilized the TCGA-LUAD cohort (n = 493) and identified 2,901 genes that are differentially expressed in patients with LUAD. Through KEGG and GO enrichment analysis, these genes were found to be involved in a wide range of biological pathways. The study also used univariate Cox regression models and LASSO regression analyses to identify 17 candidate genes that were best associated with mortality prognostic risk scores. By comparing the overall survival (OS) outcomes of patients with different CDI values, it was found that increased CDI levels were significantly associated with lower OS rates. In addition, the study used unsupervised cluster analysis to divide 115 LUAD patients into two distinct clusters with significant differences in OS timing. Finally, a prognostic indicator called CDI was established and its feasibility as an independent prognostic indicator was evaluated by Cox proportional risk regression analysis. The immunotherapy efficacy was more sensitive in the group with high expression of programmed cell death models. Relationship between programmed cell death (PCD) signature models and drug reactivity. After evaluating the median inhibitory concentration (IC50) of various drugs in LUAD samples, statistically significant differences in IC50 values were found in cohorts with high and low CDI status. Specifically, Gefitinib and Lapatinib had higher IC50 values in the high-CDI cohort, while Olaparib, Oxaliplatin, SB216763, and Axitinib had lower values. These results suggest that individuals with high CDI levels are sensitive to tyrosine kinase inhibitors and may be resistant to conventional chemotherapy. Therefore, this study constructed a gene model that can evaluate patient immunotherapy by using programmed cell death-related genes based on muti-omics. The CDI index composed of these programmed cell death-related genes reveals the heterogeneity of lung adenocarcinoma tumors and serves as a prognostic indicator for patients.

Identification and validation of eight lysosomes-related genes signatures and correlation with immune cell infiltration in lung adenocarcinoma

Lung cancer is the leading cause of cancer-related death. Lysosomes are key degradative compartments that maintain protein homeostasis. In current study, we aimed to construct a lysosomes-related genes signature to predict the overall survival (OS) of patients with Lung Adenocarcinoma (LUAD). Differentially expressed lysosomes-related genes (DELYs) were analyzed using The Cancer Genome Atlas (TCGA-LUAD cohort) database. The prognostic risk signature was identified by Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox proportional hazards regression and multivariate Cox analysis. The predictive performance of the signature was assessed by Kaplan–Meier curves and Time-dependent receiver operating characteristic (ROC) curves. Gene set variant analysis (GSVA) was performed to explore the potential molecular biological function and signaling pathways. ESTIMATE and single sample gene set enrichment analysis (ssGSEA) were applied to estimate the difference of tumor microenvironment (TME) between the different risk subtypes. An eight prognostic genes (ACAP3, ATP8B3, BTK, CAV2, CDK5R1, GRIA1, PCSK9, and PLA2G3) signature was identified and divided patients into high-risk and low-risk groups. The prognostic signature was an independent prognostic factor for OS (HR > 1, p < 0.001). The molecular function analysis suggested that the signature was significantly correlated with cancer-associated pathways, including angiogenesis, epithelial mesenchymal transition, mTOR signaling, myc-targets. The low-risk patients had higher immune cell infiltration levels than high-risk group. We also evaluated the response to chemotherapeutic, targeted therapy and immunotherapy in high- and low-risk patients with LUAD. Furthermore, we validated the expression of the eight gene expression in LUAD tissues and cell lines by qRT-PCR. LYSscore signature provide a new modality for the accurate diagnosis and targeted treatment of LUAD and will help expand researchers’ understanding of new prognostic models.

Determining the prognosis of Lung cancer from mutated genes using a deep learning survival model: a large multi-center study

BackgroundGene status has become the focus of prognosis prediction. Furthermore, deep learning has frequently been implemented in medical imaging to diagnose, prognosticate, and evaluate treatment responses in patients with cancer. However, few deep learning survival (DLS) models based on mutational genes that are directly associated with patient prognosis in terms of progression-free survival (PFS) or overall survival (OS) have been reported. Additionally, DLS models have not been applied to determine IO-related prognosis based on mutational genes. Herein, we developed a deep learning method to predict the prognosis of patients with lung cancer treated with or without immunotherapy (IO).MethodsSamples from 6542 patients from different centers were subjected to genome sequencing. A DLS model based on multi-panels of somatic mutations was trained and validated to predict OS in patients treated without IO and PFS in patients treated with IO.ResultsIn patients treated without IO, the DLS model (low vs. high DLS) was trained using the training MSK-MET cohort (HR = 0.241 [0.213–0.273], P < 0.001) and tested in the inter-validation MSK-MET cohort (HR = 0.175 [0.148–0.206], P < 0.001). The DLS model was then validated with the OncoSG, MSK-CSC, and TCGA-LUAD cohorts (HR = 0.420 [0.272–0.649], P < 0.001; HR = 0.550 [0.424–0.714], P < 0.001; HR = 0.215 [0.159–0.291], P < 0.001, respectively). Subsequently, it was fine-tuned and retrained in patients treated with IO. The DLS model (low vs. high DLS) could predict PFS and OS in the MIND, MSKCC, and POPLAR/OAK cohorts (P < 0.001, respectively). Compared with tumor-node-metastasis staging, the COX model, tumor mutational burden, and programmed death-ligand 1 expression, the DLS model had the highest C-index in patients treated with or without IO.ConclusionsThe DLS model based on mutational genes can robustly predict the prognosis of patients with lung cancer treated with or without IO.

A prognostic model of drug tolerant persister-related genes in lung adenocarcinoma based on single cell and bulk RNA sequencing data

BackgroundAcquired resistance to targeted drugs is a major challenge in cancer. The drug-tolerant state has been proposed to be an initial step towards acquisition of real drug-resistance. Drug tolerant persister (DTP) cells are purported to survive during treatment and stay dormant for several years. Single cell sequencing can provide a comprehensive landscape of gene expression in DTP cells, which can facilitate investigation of heterogeneity of a drug tolerant state and identification of new anticancer targets. MethodsThe genetic profiling of DTPs was explored by integrating Gene Expression Omnibus (GEO) datasets, and a prognostic signature of DTP-related genes (DTPRGs) in lung adenocarcinoma of TCGA LUAD cohort was constructed. The scores of infiltrating immune cells were calculated and activity of immune-related pathways was evaluated by single-sample gene set enrichment analysis (ssGSEA). Functional enrichment analysis of the DTPRGs between low- and high-risk groups was performed. Immune cell subtypes and immune-related pathways were analyzed. ResultsAn 11-gene panel (MT2A, UBE2S, CLTB, KRT7, IGFBP3, CTSH, NPC2, HMGA1, HNRNPAB, DTYMK, and IHNA) was established. DTPRGs were mainly correlated with nuclear division, chromosome segregation, and cell cycle pathways. Infiltration of immune cells was lower in the high-risk group while the inflammation-promoting and MCH-class I response pathway had higher activity in the high-risk group. A nomogram was generated with prognostic accuracy, further validated using clinical outcomes following therapy with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). DiscussionA prognostic model of lung adenocarcinoma based on DTPRGs was constructed. Targeting DTP cells is a potential therapeutic approach to prevent a drug tolerant state.