scholarly journals Identification and Validation of a Tumor Microenvironment-Related Gene Signature for Prognostic Prediction in Advanced-Stage Non-Small-Cell Lung Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Xuening Zhang ◽  
Xuezhong Shi ◽  
Hao Zhao ◽  
Xiaocan Jia ◽  
Yongli Yang
Keyword(s):  
Tumor Microenvironment ◽  
Risk Score ◽  
Advanced Nsclc ◽  
Cox Regression ◽  
Gene Signature ◽  
Small Cell ◽  
Advanced Stage ◽  
Small Cell Lung ◽  
Score Model ◽  
Nsclc Patients

The development of immunotherapy has greatly changed the advanced-stage non-small-cell lung cancer (NSCLC) treatment landscape. The complexity and heterogeneity of tumor microenvironment (TME) lead to discrepant immunotherapy effects among patients at the same pathologic stages. This study is aimed at exploring potential biomarkers of immunotherapy and accurately predicting the prognosis for advanced NSCLC patients. RNA-seq data and clinical information on stage III/IV NSCLC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). In TCGA-NSCLC with stage III/IV ( n = 192 ), immune scores and stromal scores were calculated by using the ESTIMATE algorithms. Univariate, LASSO, and multivariate Cox regression analyses were performed to screen prognostic TME-related genes (TMERGs) and constructed a gene signature risk score model. It was validated in external dataset including GSE41271 ( n = 91 ) and GSE81089 ( n = 36 ). Additionally, a nomogram incorporating TMERG signature risk score and clinical characteristics was established. Further, we accessed the proportion of 22 types of tumor-infiltrating immune cells (TIIC) from the CIBERSORT website and analyzed the difference between two risk groups. OS of patients with high immune/stromal scores were higher (log-rank P = 0.044 /log-rank P = 0.048 ). Multivariate Cox regression identified six prognostic TMERGs, including CD200, CHI3L2, CNTN1, CTSL, FYB1, and SLC52A1. We developed a six-gene risk score model, which was validated as an independent prognostic factor for OS (HR: 3.32, 95% CI: 2.16-5.09). Time-ROC curves showed useful discrimination for TCGA-NSCLC cohort (1-, 2-, and 3-year AUCs were 0.718, 0.761, and 0.750). The predictive robustness was validated in the external dataset. The C-index and 1-, 2-, and 3-year AUCs of nomogram were the largest, which demonstrated the nomogram had the greatest predictive accuracy and effectiveness and could be used for clinical guidance. Besides, the increased infiltration of T cells regulatory (Tregs) and macrophages M2 in the high-risk group suggested that chronic inflammation may reduce survival probability in patients with advanced NSCLC. We conducted a comprehensive analysis of the tumor microenvironment and identified the TMERG signature, which could predict prognosis accurately and provide a reference for the personalized immunotherapy for advanced NSCLC patients.

scholarly journals Prediction of Overall Survival for Early-stage Non-small Cell Lung Cancer via a Novel 7-gene Prognostic Signature and Allele Frequency Deviation (AFD)

2020 ◽  
Author(s):  
Aisha AL-Dherasi ◽  
Yuwei Liao ◽  
Qi-Tian Huang ◽  
Yichen Wang ◽  
Rulin Hua ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Allele Frequency ◽  
Prognostic Model ◽  
Cox Regression ◽  
Gene Signature ◽  
Small Cell ◽  
Prognostic Signature ◽  
Small Cell Lung ◽  
Cox Regression Analysis ◽  
Nsclc Patients

Abstract Background Due to the late and poor prognosis of non-small lung cancer(NSCLC), the mortality of patients is high, underlines the need to identify a credible prognostic marker for NSCLC patients. The aim of our study is to examine the association of allele frequency deviation (AFD) with the patient's survival, as well as identification and validation of a new prognostic signature to predict NSCLC overall survival(OS).Methods First, we developed a new algorithm to calculate AFD from whole-exome sequencing(WES) data, then we compared the predictability of the patient's survival between AFD, tumor mutation burden (TMB) and change of variants allele frequency (dVAF). Second, we overlapped the differentially expressed genes (DEGs) from our data with the genes associated with the survival of The Cancer Genome Atlas (TCGA) database to confirm all genes significantly related to the survival of lung cancer. We identified 149 genes, 31 of which are new genes and have not been reported for lung cancer, that was used to develop a new prognostic model. Lung cancer adenocarcinoma (LUAD) data from the TCGA database was used to validate the gene-signature model. The prognostic model relating to the genes was established and validated in training and LUAD validation groups.Results There was a significant association found between the high AFD value and poor survival among non-small cell lung cancer (NSCLC) patients. A novel seven genes (UCN2, RIMS2, CAVIN2, GRIA1, PKHD1L1, PGM5, CLIC6) were obtained through multivariate Cox regression analysis and significantly associated with NSCLC patients survival. Cox regression analysis confirmed that AFD and 7-gene signature are an independent prognostic marker in NSCLC patients. The AUC for 5-year survival in AFD and the AUC for 3-year survival in both training and validation groups were greater than 0.7.Conclusion As a result, AFD and 7-gene signatures were identified as new independent predictive factors used for predicting the survival among NSCLC patients.

scholarly journals Identification and Prognostic Value Exploration of Radiotherapy Sensitivity-Associated Genes in Non-Small-Cell Lung Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Qing Ma ◽  
Kai Geng ◽  
Ping Xiao ◽  
Lili Zeng
Keyword(s):  
Lung Cancer ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Small Cell ◽  
Small Cell Lung ◽  
Cox Regression Analysis ◽  
Nsclc Patients

Background. Non-small-cell lung cancer (NSCLC) is a prevalent malignancy with high mortality and poor prognosis. The radiotherapy is one of the most common treatments of NSCLC, and the radiotherapy sensitivity of patients could affect the individual prognosis of NSCLC. However, the prognostic signatures related to radiotherapy response still remain limited. Here, we explored the radiosensitivity-associated genes and constructed the prognostically predictive model of NSCLC cases. Methods. The NSCLC samples with radiotherapy records were obtained from The Cancer Genome Atlas database, and the mRNA expression profiles of NSCLC patients from the GSE30219 and GSE31210 datasets were obtained from the Gene Expression Omnibus database. The Weighted Gene Coexpression Network Analysis (WGCNA), univariate, least absolute shrinkage and selection operator (LASSO), multivariate Cox regression analysis, and nomogram were conducted to identify and validate the radiotherapy sensitivity-related signature. Results. WGCNA revealed that 365 genes were significantly correlated with radiotherapy response. LASSO Cox regression analysis identified 8 genes, including FOLR3, SLC6A11, ALPP, IGFN1, KCNJ12, RPS4XP22, HIST1H2BH, and BLACAT1. The overall survival (OS) of the low-risk group was better than that of the high-risk group separated by the Risk Score based on these 8 genes for the NSCLC patients. Furthermore, the immune infiltration analysis showed that monocytes and activated memory CD4 T cells had different relative proportions in the low-risk group compared with the high-risk group. The Risk Score was correlated with immune checkpoints, including CTLA4, PDL1, LAG3, and TIGIT. Conclusion. We identified 365 genes potentially correlated with the radiotherapy response of NSCLC patients. The Risk Score model based on the identified 8 genes can predict the prognosis of NSCLC patients.

Radiotherapy with continued EGFR-TKIs for oligoprogressive disease in non-small cell lung cancer: A real-world study.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e20640-e20640
Author(s):  
Fang Wu ◽  
Sixuan Wu ◽  
Chunhong Hu
Keyword(s):  
Lung Cancer ◽  
Prognostic Factors ◽  
Advanced Nsclc ◽  
Cox Regression ◽  
Small Cell ◽  
Small Cell Lung ◽  
T790m Mutation ◽  
Nsclc Patients ◽  
Egfr Mutated ◽  
Egfr Tkis

e20640 Background: Almost all epidermal growth factor recept (EGFR)-mutated non-small-cell lung cancer (NSCLC) will develop tyrosine kinase inhibitors (TKIs) resistance. The treatment of oligoprogression is debatable after TKIs resistance. We conducted a real-world retrospective study to evaluate the efficacy of radiotherapy and continuation of TKIs in advanced NSCLC patients with oligoprogressive disease after EGFR-TKIs. Methods: From January 2011 to January 2019, we retrospectively analyzed EGFR-mutated NSCLC patients with oligoprogression in our institution. 33 patients were treated by radiotherapy and continuation of TKIs after oligoprogression. We used Kaplan-Meier and Cox regression model to analyse the prognostic factors of median progression-free survival (PFS) and median overall survival (OS) from the time of oligoprogression. Variables we selected for analyses included gender, age, smoking status, performance status (PS) score, stage at initial diagnosis, initial resectable, radiotherapy dose, EGFR mutation type, number of metastasis, sites of radiation, T790M status and the time of oligometastasis to radiotherapy. Results: 33 patients develop resistance to EGFR-TKIs at a median time of 11.0 months. The mPFS and mOS were 6.5 (95%CI, 1.4-11.6) and 21.8 (95%CI, 14.8-28.8) months, respectively. T790M mutation was tested in nine patients, four of which were mutation negative and five were positive. The mPFS was 15.5 (95%CI, 7.4-23.6) months in T790M mutation positive patients and 6.0 (95%CI, 0-14.0) months in negative patients. The mPFS in patients who started radiotherapy within or beyond 1 month after oligometastasis was 10.8 months (95% CI, 4.9-16.7 months) and 5.3 months (95% CI, 2.4-8.2 months). Cox regression model showed no variables significantly correlated with PFS difference. Univariate analysis identified female patients have longer OS (P = 0.038). The results of Multivariate analysis indicated that there was no OS-related prognostic factors. Conclusions: Radiotherapy with continued TKIs is an efficacious treatment option in advanced NSCLC patients with oligoprogressive disease after EGFR-TKIs. T790M mutation is a common mutation in NSCLC patients with oligoprogression. Gender was prognostic factors for OS. Moreover, there was a better prognosis in patients with T790M mutation positive or radiotherapy within 1 month after oligometastasis. However, this was not statistically significant. Larger sample size studies are needed to validate these clinical results.

scholarly journals A fourteen-lncRNA risk score system for prognostic prediction of patients with non-small cell lung cancer

2020 ◽  
Vol 29 (4) ◽  
pp. 493-508
Author(s):  
Jia-Yi Song ◽  
Xiao-Ping Li ◽  
Xiu-Jiao Qin ◽  
Jing-Dong Zhang ◽  
Jian-Yu Zhao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Small Cell Lung Cancer ◽  
Risk Score ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Cox Regression Analysis ◽  
Score Model ◽  
Nsclc Patients

Growing evidence has underscored long non-coding RNAs (lncRNAs) serving as potential biomarkers for cancer prognosis. However, systematic tracking of a lncRNA signature for prognosis prediction in non-small cell lung cancer (NSCLC) has not been accomplished yet. Here, comprehensive analysis with differential gene expression analysis, univariate and multivariate Cox regression analysis based on The Cancer Genome Atlas (TCGA) database was performed to identify the lncRNA signature for prediction of the overall survival of NSCLC patients. A risk-score model based on a 14-lncRNA signature was identified, which could classify patients into high-risk and low-risk groups and show poor and improved outcomes, respectively. The receiver operating characteristic (ROC) curve revealed that the risk-score model has good performance with high AUC value. Multivariate Cox’s regression model and stratified analysis indicated that the risk-score was independent of other clinicopathological prognostic factors. Furthermore, the risk-score model was competent for the prediction of metastasis-free survival in NSCLC patients. Moreover, the risk-score model was applicable for prediction of the overall survival in the other 30 caner types of TCGA. Our study highlighted the significant implications of lncRNAs as prognostic predictors in NSCLC. We hope the lncRNA signature could contribute to personalized therapy decisions in the future.

scholarly journals Prediction of Overall Survival for Early-stage Non-small Cell Lung Cancer via a Novel 7-gene Prognostic Signature and Allele Frequency Deviation (AFD)

2020 ◽  
Author(s):  
Aisha AL-Dherasi ◽  
Yuwei Liao ◽  
Qi-Tian Huang ◽  
Yichen Wang ◽  
Rulin Hua ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Allele Frequency ◽  
Prognostic Model ◽  
Cox Regression ◽  
Gene Signature ◽  
Small Cell ◽  
Prognostic Signature ◽  
Small Cell Lung ◽  
Cox Regression Analysis ◽  
Nsclc Patients

Abstract Background: Due to the late and poor prognosis of non-small lung cancer(NSCLC), the mortality of patients is high, underlines the need to identify a credible prognostic marker for NSCLC patients. The aim of our study is to examine the association of allele frequency deviation (AFD) with the patient's survival, as well as identification and validation of a new prognostic signature to predict NSCLC overall survival(OS).Methods: First, we developed a new algorithm to calculate AFD from whole-exome sequencing(WES) data, then we compared the predictability of the patient's survival between AFD, tumor mutation burden (TMB) and change of variants allele frequency (dVAF). Second, we overlapped the differentially expressed genes (DEGs) from our data with the genes associated with the survival of The Cancer Genome Atlas (TCGA) database to confirm all genes significantly related to the survival of lung cancer. We identified 149 genes, 31 of which are new genes and have not been reported for lung cancer, that was used to develop a new prognostic model. Lung cancer adenocarcinoma (LUAD) data from the TCGA database was used to validate the gene-signature model. The prognostic model relating to the genes was established and validated in training and LUAD validation groups. Results: There was a significant association found between the high AFD value and poor survival among non-small cell lung cancer (NSCLC) patients. A novel seven genes (UCN2, RIMS2, CAVIN2, GRIA1, PKHD1L1, PGM5, CLIC6) were obtained through multivariate Cox regression analysis and significantly associated with NSCLC patients survival. Cox regression analysis confirmed that AFD and 7-gene signature are an independent prognostic marker in NSCLC patients. The AUC for 5-year survival in AFD and the AUC for 3-year survival in both training and validation groups were greater than 0.7.Conclusion: As a result, AFD and 7-gene signatures were identified as new independent predictive factors used for predicting the survival among NSCLC patients.

scholarly journals Clinical impact of subclonal EGFR T790M mutations in advanced-stage EGFR-mutant non-small-cell lung cancers

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Tereza Vaclova ◽  
Ursula Grazini ◽  
Lewis Ward ◽  
Daniel O’Neill ◽  
Aleksandra Markovets ◽  
...  
Keyword(s):  
Kinase Inhibitor ◽  
Progression Free Survival ◽  
Small Cell ◽  
Phase Iii ◽  
Advanced Stage ◽  
Small Cell Lung ◽  
Nsclc Patients ◽  
The Impact ◽  
Egfr T790m

AbstractAdvanced non-small-cell lung cancer (NSCLC) patients with EGFR T790M-positive tumours benefit from osimertinib, an epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI). Here we show that the size of the EGFR T790M-positive clone impacts response to osimertinib. T790M subclonality, as assessed by a retrospective NGS analysis of 289 baseline plasma ctDNA samples from T790M‐positive advanced NSCLC patients from the AURA3 phase III trial, is associated with shorter progression-free survival (PFS), both in the osimertinib and the chemotherapy-treated patients. Both baseline and longitudinal ctDNA profiling indicate that the T790M subclonal tumours are enriched for PIK3CA alterations, which we demonstrate to confer resistance to osimertinib in vitro that can be partially reversed by PI3K pathway inhibitors. Overall, our results elucidate the impact of tumour heterogeneity on response to osimertinib in advanced stage NSCLC patients and could help define appropriate combination therapies in these patients.

scholarly journals Development of nomogram based on immune-related gene FGFR4 for advanced non-small cell lung cancer patients with sensitivity to immune checkpoint inhibitors

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Li Wang ◽  
Zhixuan Ren ◽  
Bentong Yu ◽  
Jian Tang
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Advanced Nsclc ◽  
Cancer Genomics ◽  
Checkpoint Inhibitors ◽  
Small Cell ◽  
Small Cell Lung ◽  
Nsclc Patients

Abstract Introduction Immune checkpoint inhibitors (ICIs) have become a frontier in the field of clinical technology for advanced non-small cell lung cancer (NSCLC). Currently, the predictive biomarker of ICIs mainly including the expression of PD-L1, TMB, TIICs, MMR and MSI-H. However, there are no official biomarkers to guide the treatment of ICIs and to determine the prognosis. Therefore, it is essential to explore a systematic nomogram to predict the prognosis of ICIs treatment in NSCLC Methods In this work, we obtained gene expression and clinical data of NSCLC patients from the TCGA database. Immune-related genes (IRGs) were downloaded from the ImmPort database. The detailed clinical annotation and response data of 240 advanced NSCLC patients who received ICIs treatment were obtained from the cBioPortal for Cancer Genomics. Kaplan–Meier survival analysis was used to perform survival analyses, and selected clinical variables to develop a novel nomogram. The prognostic significance of FGFR4 was validated by another cohort in cBioPortal for Cancer Genomics. Results 3% of the NSCLC patients harbored FGFR4 mutations. The mutation of FGFR4 were confirmed to be associated with PD-L1, and TMB. Patients harbored FGFR4 mutations were found to have a better prolonged progression-free survival (PFS) to ICIs treatment (FGFR4: P = 0.0209). Here, we built and verified a novel nomogram to predict the prognosis of ICIs treatment for NSCLC patients. Conclusion Our results showed that FGFR4 could serve as novel biomarkers to predict the prognosis of ICIs treatment of advanced NSCLC. Our systematic prognostic nomogram showed a great potential to predict the prognosis of ICIs for advanced NSCLC patients.

The role of social determinants on overall survival in advanced-stage non-small cell lung cancer patients.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e21117-e21117
Author(s):  
Andreas Bello ◽  
Neeharika Srivastava Makani
Keyword(s):  
Lung Cancer ◽  
Social Determinants ◽  
Small Cell ◽  
Financial Assistance ◽  
Advanced Stage ◽  
Small Cell Lung ◽  
Chi Square ◽  
Study Population ◽  
Nsclc Patients

e21117 Background: Non-small cell lung cancer (NSCLC) is the most prevalent form of lung cancer. Many studies have evaluated the association of social determinants with outcomes in early-stage NSCLC. These studies have shown statistically and clinically significant associations between overall survival (OS) and other social factors (e.g marital status, educational attainment). The aim of our study was to better understand the role of various social determinants of health (SDH) on OS in advanced-stage NSCLC patients in a community oncology practice in Florida. Methods: In this retrospective study, 125 patients with stage III and IV NSCLC were recruited between January 1st, 2014 until December 31st, 2018. We performed both categorical and continuous analyses (Pearson’s chi-square and Kruskal-Wallis test, respectively) to evaluate the association between median OS and several independent variables, including; gender, race, marital status, insurance status, living status, receiving financial assistance (FA), alcohol use, and smoking histories. OS is defined as the date of diagnosis up to the date of death. Other confounders that were analyzed included histology, treatment modality, comorbidities, and performance status of the patients. Results: Of the total study population (n = 125), 60% identified as male with a mean age of 73 years for the study population. The majority of patients (89%) identified as white; 56% were married, and 81% lived with someone. 66% of patients had an HMO insurance plan, and 51% of patients obtained FA to help with treatment care costs. 47% of patients identified as former smokers and 54% denied any alcohol use. The median OS for the patient population was 0.756 years. Chi-square analyses revealed that patients who received FA were more likely to live longer than median OS as opposed to patients that did not receive FA (OR = 2.41, 95% CI [1.18, 4.96], p = 0.050). Kruskal-Wallis analyses demonstrated that patients receiving FA had nearly a two-fold increase in median OS compared to patients without financial assistance (median OS = 1.01 years vs. 0.545 years, respectively; p = 0.013). However, other social determinants evaluated did not have a significant impact on relative OS in advanced-stage NSCLC. Conclusions: Ultimately, our study concludes that receiving FA has a significant association with increased OS in advanced-stage NSCLC patients. This study highlights the importance of reducing the financial burden of advanced-stage NSCLC patients and how FA impacts patient outcomes. Future prospective cohort studies with a larger sample size are warranted to identify other SDH, as well as the underlying mechanisms affecting median OS, in patients with advanced-stage NSCLC.

scholarly journals A Linear Discriminant Analysis Model Based on the Changes of 7 Proteins in Plasma Predicts Response to Anlotinib Therapy in Advanced Non-Small Cell Lung Cancer Patients

2022 ◽  
Vol 11 ◽  
Author(s):  
Fei Xu ◽  
Haiyan Xu ◽  
Zhiyi Wan ◽  
Guangjian Yang ◽  
Lu Yang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Discriminant Analysis ◽  
Advanced Nsclc ◽  
Predictive Marker ◽  
Small Cell ◽  
Small Cell Lung ◽  
Protein Biomarkers ◽  
Linear Discriminant ◽  
Model Based ◽  
Nsclc Patients

BackgroundAnlotinib is a multi-targeted tyrosine kinase inhibitor mainly targeting angiogenesis signaling. The predictive marker of anlotinib’s efficacy remains elusive. This study was designed to explore the predictive marker of anlotinib in non-small cell lung cancer (NSCLC).MethodsWe prospectively enrolled 52 advanced NSCLC patients who underwent at least one line of targeted therapy or chemotherapy between August 2018 and March 2020. Patients were divided into durable responders (DR) and non-durable responders (NDR) based on the median progression-free survival (PFS, 176 days). The Olink Immuno-Oncology panel (92 proteins) was used to explore the predictive protein biomarkers in plasma samples before treatment (baseline) and on the first treatment evaluation (paired).ResultsAt baseline, the response to anlotinib was not significantly associated with age, gender, smoke history, histology, oligo-metastases, EGFR mutations, and other clinical characteristics. The results of PFS-related protein biomarkers at baseline were all not satisfying. Then we assessed the changes of 92 proteins levels in plasma on the first treatment evaluation. We obtained a Linear discriminant analysis (LDA) model based on 7 proteins, with an accuracy of 100% in the original data and an accuracy of 89.2% in cross validation. The 7 proteins were CD70, MIC-A/B, LAG3, CAIX, PDCD1, MMP12, and PD-L2. Multivariate Cox analysis further showed that the changes of CD70 (HR 25.48; 95% CI, 4.90–132.41, P=0.000) and MIC-A/B (HR 15.04; 95% CI, 3.81–59.36, P=0.000) in plasma were the most significant prognostic factors for PFS.ConclusionWe reported herein a LDA model based on the changes of 7 proteins levels in plasma before and after treatment, which could predict anlotinib responders among advanced NSCLC patients with an accuracy of 100%. Further studies are warranted to verify the prediction performance of the LDA model.

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