Therapy For Lung Adenocarcinoma Patients Research Articles

An original aneuploidy-related gene model for predicting lung adenocarcinoma survival and guiding therapy.

Aneuploidy is a hallmark of cancers, but the role of aneuploidy-related genes in lung adenocarcinoma (LUAD) and their prognostic value remain elusive. Gene expression and copy number variation (CNV) data were enrolled from TCGA and GEO database. Consistency clustering analysis was performed for molecular cluster. Tumor microenvironment was assessed by the xCell and ESTIMATE algorithm. Limma package was used for selecting differentially expressed genes (DEGs). LASSO and stepwise multivariate Cox regression analysis were used to establish an aneuploidy-related riskscore (ARS) signature. GDSC database was conducted to predict drug sensitivity. A nomogram was designed by rms R package. TCGA-LUAD patients were stratified into 3 clusters based on CNV data. The C1 cluster displayed the optimal survival advantage and highest inflammatory infiltration. Based on integrated intersecting DEGs, we constructed a 6-gene ARS model, which showed effective prediction for patient's survival. Drug sensitivity test predicted possible sensitive drugs in two risk groups. Additionally, the nomogram exhibited great predictive clinical treatment benefits. We established a 6-gene aneuploidy-related signature that could effectively predict the survival and therapy for LUAD patients. Additionally, the ARS model and nomogram could offer guidance for the preoperative estimation and postoperative therapy of LUAD.

A disulfidptosis-related lncRNA prognostic model to predict survival and response to immunotherapy in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is the major subtype of lung cancer and has a poor prognosis. Disulfidptosis is a novel regulated cell death form characterized by aberrant disulfide stress and actin network collapse. This study aimed to identify disulfidptosis-related lncRNAs, and predict LUAD patients' prognosis and response to antitumor therapies by establishing a disulfidptosis-related lncRNA model. Methods: Transcriptome and clinical data of LUAD patients were obtained from the TCGA database. Pearson correlation and Cox regression analysis was used to identify disulfidptosis-related lncRNAs associated with overall survival. LASSO regression analysis was adopted to construct the prognostic model. GO, KEGG and GSEA analysis was used to identify cellular pathways related to this model. Immune cell infiltration was investigated by ESTIMATE and CIBERSORT algorithms. Tumor mutational burden (TMB) and its association with model-derived risk score were analyzed using simple nucleotide variation data. Patients' response to immunotherapy and other antineoplastic drugs was predicted by the TIDE algorithm and GDSC tool, respectively. Results: We identified 127 disulfidptosis-related lncRNAs, and a prognostic model that consists eight of them (KTN1-AS1, AL365181.3, MANCR, LINC01352, AC090559.1, AC093673.1, AP001094.3, and MHENCR) was established and verified. The prognostic model could stratify LUAD patients into two distinct risk-score groups. A high risk score was an independent prognosis factor indicating poor overall survival, and correlated with reduced immune cell infiltration, high TMB, and lower activity of tumor immune response. Immune checkpoint blockade might bring more survival benefits to the high-risk LUAD patients, whereas low-risk patients might be more responsive to targeted therapy and diverse kinase inhibitors. Conclusion: We established a disulfidptosis-related lncRNA model that can be exploited to predict the prognosis, tumor mutational burden, immune cell infiltration landscape, and response to immunotherapy and targeted therapy in LUAD patients.

Osimertinib resistance prognostic gene signature: STRIP2 is associated with immune infiltration and tumor progression in lung adenocarcinoma.

Although the use of osimertinib can significantly improve the survival time of lung adenocarcinoma (LUAD) patients with epithelial growth factor receptor mutation, eventually drug resistance will limit the survival benefit of most patients. This study aimed to develop a novel prognostic predictive signature based on genes associated with osimertinib resistance. The differentially expressed genes (DEGs) associated with osimertinib resistance in LUAD were screened from Gene Expression Omnibus datasets and The Cancer Genome Atlas datasets. Multivariate cox regression was used to establish a prognostic signature, and then a nomogram was developed to predict the survival probability of LUAD patients. We used ROC curve and DCA curve to evaluate its clinical prediction accuracy and net benefit.In addition, the differentially expressed genes significantly associated with prognosis were selected for immune infiltration analysis and drug sensitivity analysis, and their roles in the progression of lung adenocarcinoma were verified by in vitro experiments. Our evaluation results indicated that the new nomogram had higher clinical prediction accuracy and net benefit value than the TN nomogram. Further analysis showed that patients with low STRIP2 expression had a higher level of immune response, and may be more likely to benefit from immune checkpoint inhibitors and conventional antitumor drugs. This may help to select more precise and appropriate therapy for LUAD patients with osimertinib resistance. Furthermore, in vitro experiments showed that STRIP2 promoted the LUAD cells proliferation, migration and invasion. This further demonstrates the importance of this gene signature for prognostic prediction. We developed a reliable prognostic model based on DEGs associated with osimertinib resistance and screened for biomarker that can predict the immune response in LUAD patients, which may help in the selection of treatment regimens after osimertinib resistance.

THORACIC CT-SCAN WITH EGFR MUTATIONS IN PULMONARY ADENOCARCINOMA PATIENTS WITH PULMONARY METASTASES AT DR. MOEWARDI HOSPITAL, SURAKARTA, INDONESIA

Highlights The thoracic CT scan can be an alternative evaluation before administering EGFR-targeting medicine to patients. EGFR is now a therapeutic target for lung cancer. Abstract Background: EGFR mutation is one of the molecular markers associated with prognosis and therapy in lung adenocarcinoma patients, including with lung metastases. The predominant imaging modality for assessing lung cancer is a chest CT scan, which correlates with prognosis. Objective: This study aimed to explore the association between characteristics of lung adenocarcinoma based on chest CT-Scan and EGFR mutation in lung adenocarcinoma with lung metastases. Material and Method: This study involved 65 lung adenocarcinoma patients in Dr. Moewardi Hospital, Surakarta, Indonesia, from January 2018-December 2020. Result: Chest CT-Scan and EGFR mutation were assessed in all subjects. EGFR mutation was found in 32 (49.8%) subjects. The risk of EGFR mutation was found to be higher in patients with lung adenocarcinoma who had lung metastases in a peripheral location (OR=11.56; 95%CI 3.57-37.33; p<0.001), tumor size <4 cm (OR=8.00; 95%CI 2.64-24.22; p<0.001), and semi-solid density (OR=5.21; 95%CI 1.79-15.19; p=0.002). Conclusion: EGFR mutation is associated with tumor location, size, and thickness based on chest CT-Scan in lung adenocarcinoma with lung metastases patients.

Bioinformatics analysis of an immunotherapy responsiveness-related gene signature in predicting lung adenocarcinoma prognosis.

Immune therapy has become first-line treatment option for patients with lung cancer, but some patients respond poorly to immune therapy, especially among patients with lung adenocarcinoma (LUAD). Novel tools are needed to screen potential responders to immune therapy in LUAD patients, to better predict the prognosis and guide clinical decision-making. Although many efforts have been made to predict the responsiveness of LUAD patients, the results were limited. During the era of immunotherapy, this study attempts to construct a novel prognostic model for LUAD by utilizing differentially expressed genes (DEGs) among patients with differential immune therapy responses. Transcriptome data of 598 patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) database, which included 539 tumor samples and 59 normal control samples, with a mean follow-up time of 29.69 months (63.1% of patients remained alive by the end of follow-up). Other data sources including three datasets from the Gene Expression Omnibus (GEO) database were analyzed, and the DEGs between immunotherapy responders and nonresponders were identified and screened. Univariate Cox regression analysis was applied with the TCGA cohort as the training set and GSE72094 cohort as the validation set, and least absolute shrinkage and selection operator (LASSO) Cox regression were applied in the prognostic-related genes which fulfilled the filter criteria to establish a prognostic formula, which was then tested with time-dependent receiver operating characteristic (ROC) analysis. Enriched pathways of the prognostic-related genes were analyzed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and tumor immune microenvironment (TIME), tumor mutational burden, and drug sensitivity tests were completed with appropriate packages in R (The R Foundation of Statistical Computing). Finally, a nomogram incorporating the prognostic formula was established. A total of 1,636 DEGs were identified, 1,163 prognostic-related DEGs were extracted, and 34 DEGs were selected and incorporated into the immunotherapy responsiveness-related risk score (IRRS) formula. The IRRS formula had good performance in predicting the overall prognoses in patients with LUAD and had excellent performance in prognosis prediction in all LUAD subgroups. Moreover, the IRRS formula could predict anticancer drug sensitivity and immunotherapy responsiveness in patients with LUAD. Mechanistically, immune microenvironments varied profoundly between the two IRRS groups; the most significantly varied pathway between the high-IRRS and low-IRRS groups was ribonucleoprotein complex biogenesis, which correlated closely with the TP53 and TTN mutation burdens. In addition, we established a nomogram incorporating the IRRS, age, sex, clinical stage, T-stage, N-stage, and M-stage as predictors that could predict the prognoses of 1-year, 3-year, and 5-year survival in patients with LUAD, with an area under curve (AUC) of 0.718, 0.702, and 0.68, respectively. The model we established in the present study could predict the prognosis of LUAD patients, help to identify patients with good responses to anticancer drugs and immunotherapy, and serve as a valuable tool to guide clinical decision-making.

Construction and Validation of a Tumor Microenvironment-Based Scoring System to Evaluate Prognosis and Response to Immune Checkpoint Inhibitor Therapy in Lung Adenocarcinoma Patients.

Background: Lung cancer is among the most dangerous malignant tumors to human health. Lung adenocarcinoma (LUAD) accounts for about 40% of all lung cancers. Accumulating evidence suggests that the tumor microenvironment (TME) is a crucial regulator of carcinogenesis and therapeutic efficacy in LUAD. However, the impact of tumor microenvironment-related signatures (TMERSs) representing the TME characteristics on the prognosis and therapeutic outcome of LUAD patients remains to be further explored. Materials and methods: Gene expression files and clinical information of 1630 LUAD samples and 275 samples with immunotherapy information from different databases such as The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Cancer Research Institute (CRI) iAtlas were downloaded and analyzed. Three hundred tumor microenvironment-related signatures (TMERS) based on a comprehensive collection of marker genes were quantified by single sample gene set enrichment analysis (ssGSEA), and then eight significant signatures were selected to construct the tumor microenvironment-related signature score (TMERSscore) by performing Least Absolute Shrinkage and Selection Operator (LASSO)-Cox analysis. Results: In this study, we constructed a TME-based prognostic stratification model for patients with LUAD and validated it in several external datasets. Furthermore, the TMERSscore was found to be positively correlated with tumor malignancy and a high TMERSscore predicted a poor prognosis. Moreover, the TMERSscore of responders treated with Immune Checkpoint Inhibitor (ICI) therapies was significantly lower than that of non-responders, and the TMERSscore was positively correlated with the tumor immune dysfunction and exclusion (TIDE) score, implying that a low TMERSscore predicts a better response to ICI treatment and may provide independent and incremental predictive value over current biomarkers. Conclusions: Overall, we constructed a TMERSscore that can be used for LUAD patient prognosis stratification as well as ICI therapeutic efficacy evaluation, supportive results from independent external validation sets showed its robustness and effectiveness.

Analysis of the metabolic proteome of lung adenocarcinomas by reverse-phase protein arrays (RPPA) emphasizes mitochondria as targets for therapy

Lung cancer is the leading cause of cancer-related death worldwide despite the success of therapies targeting oncogenic drivers and immune-checkpoint inhibitors. Although metabolic enzymes offer additional targets for therapy, the precise metabolic proteome of lung adenocarcinomas is unknown, hampering its clinical translation. Herein, we used Reverse Phase Protein Arrays to quantify the changes in enzymes of glycolysis, oxidation of pyruvate, fatty acid metabolism, oxidative phosphorylation, antioxidant response and protein oxidative damage in 128 tumors and paired non-tumor adjacent tissue of lung adenocarcinomas to profile the proteome of metabolism. Steady-state levels of mitochondrial proteins of fatty acid oxidation, oxidative phosphorylation and of the antioxidant response are independent predictors of survival and/or of disease recurrence in lung adenocarcinoma patients. Next, we addressed the mechanisms by which the overexpression of ATPase Inhibitory Factor 1, the physiological inhibitor of oxidative phosphorylation, which is an independent predictor of disease recurrence, prevents metastatic disease. We highlight that IF1 overexpression promotes a more vulnerable and less invasive phenotype in lung adenocarcinoma cells. Finally, and as proof of concept, the therapeutic potential of targeting fatty acid assimilation or oxidation in combination with an inhibitor of oxidative phosphorylation was studied in mice bearing lung adenocarcinomas. The results revealed that this therapeutic approach significantly extended the lifespan and provided better welfare to mice than cisplatin treatments, supporting mitochondrial activities as targets of therapy in lung adenocarcinoma patients.

A Risk Model Including Eight Tumor Microenvironment-Related Genes for Prediction of Lung Adenocarcinoma Prognosis

Background: The tumor microenvironment (TME) plays a crucial role in lung cancer development and outcome. The objective of this study was to construct a prognostic model using TME-related genes. Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) provided transcriptome profile and clinical characteristics. We examined and selected the key genes to obtain a predictive risk model for lung adenocarcinoma (LUAD) prognosis. We also assessed the efficiency of the model and the interaction of key genes with immune-related genes. Findings: After screening 760 TME-related genes, we established a risk model using ANGPTL4, FUT4, CDC25C, FLNC, KRT6A, NEIL3 , HS3ST2, and DAAM2 to independently predict LUAD prognosis using TCGA data. The ROC curve and C-index confirmed the usefulness of this risk model, and a nomogram that integrated this predictive risk model with age and TNM stages was more effective on prediction of LUAD prognosis. The risk model was further confirmed using the GEO data. Furthermore, the risk model of genes interacted with 11 types of immune cells and three immune checkpoint proteins (LAG3, PDL1 and TDO2) in LUAD. Interpretation: Our predictive risk model was effective on survival prediction and helpful for immune targeted therapy in LUAD patients. Funding Information: This study was supported in part by grants from the National Natural Science Foundation of China (#81703087, #81903185, and #81972916), the United Fund of the Second Hospital of Dalian Medical University and Dalian Institute of Chemical Physics, Chinese Academy of Sciences (#UF-ZD-202011), and Project of Education Department of Liaoning Province (#LZ2020009). Declaration of Interests: The authors declared that there is no conflict of interest in this work. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Target Therapy in Lung Cancer.

Lung cancer is an extremely heterogeneous disease, with well over 50 different histological variants recognized under the fourth revision of the World Health Organization (WHO) typing system. Because these variants have differing genetic and biological properties correct classification of lung cancer is necessary to assure that lung cancer patients receive optimum management. Due to the recent understanding that histologic typing and EGFR mutation status are important for target the therapy in lung adenocarcinoma patients there was a great need for a new classification that addresses diagnostic issues and strategic management to allow for molecular testing in small biopsy and cytology specimens. For this reason and in order to address advances in lung cancer treatment an international multidisciplinary classification was proposed by the International Association for the Study of Lung Cancer (IASLC), American Thoracic Society (ATS), and European Respiratory Society (ERS), further increasing the histological heterogeneity and improving the existing WHO-classification. Is now the beginning of personalized therapy era that is ideally finalized to treat each individual case of lung cancer in different way.

Concurrence of EGFR amplification and sensitizing mutations indicate a better survival benefit from EGFR-TKI therapy in lung adenocarcinoma patients.

Tumor heterogeneity, which causes different EGFR mutation abundance, is believed to be responsible for varied progression-free survival (PFS) in lung adenocarcinoma (ADC) patients receiving EGFR-TKI treatment. Frequent EGFR amplification and its common affection in EGFR mutant allele promote the hypothesis that EGFR mutant abundance might be determined by EGFR copy number variation and therefore examination of EGFR amplification status in EGFR mutant patients could predict the efficacy of EGFR-TKI treatment. In this study, 86 lung ADC patients, who harbored EGFR activating mutations and received EGFR-TKI treatment, were examined for EGFR amplification and expression by Dual-color Silver in situ Hybridization (DISH) and immunohistochemistry analysis, respectively. Forty-one of 86 (47.7%) samples with EGFR activating mutations were identified with EGFR amplification. Patients with EGFR gene amplification had a significantly longer PFS than those without (16.3 vs. 9.1 months, p=0.004). The EGFR expression was then examined by immunohistochemistry analysis. Thirty-nine of 86 (45%) tumors had EGFR overexpression, which was significantly correlated with EGFR amplification (p=0.000). However, patients with EGFR overexpression exhibited no difference in PFS (14.1 vs. 13.3 months, p=0.797). In conclusion, EGFR amplification occurs frequently in lung ADC patients harboring EGFR activating mutations, and could serve as an indicator for better response from EGFR-TKI treatment.

The Genomics of Lung Adenocarcinoma: Opportunities for Targeted Therapies

Standard cytotoxic chemotherapy is effective for some cancers, but for many others, available treatments offer only a limited survival benefit. Lung adenocarcinoma is one such cancer, responsible for approximately half of lung cancer deaths each year. Development of targeted therapies is thought to hold the most promise for successfully treating this disease, but a targeted approach is dependent on understanding the genomic state of the tumor cells. Exon-directed sequencing of large numbers of lung adenocarcinoma tumor samples has provided an initial low-resolution image of the somatic mutation profile of these tumors. Such cancer sequencing studies have confirmed the high frequency of TP53 and KRAS mutations in lung adenocarcinoma, have found inactivating mutations in known tumor suppressor genes not previously associated with lung adenocarcinoma, and have identified oncogenic mutations of EGFR upon which the first targeted therapy for lung adenocarcinoma patients was based. Additional candidate oncogenes await functional validation. It is anticipated that upcoming whole-exome and whole-genome lung adenocarcinoma sequencing experiments will reveal a more detailed landscape of somatic mutations that can be exploited for therapeutic purposes.