Whole transcriptome targeted gene quantification provides new insights on pulmonary sarcomatoid carcinomas

Alì, Greta; Bruno, Rossella; Poma, Anello Marcello; Affinito, Ornella; Monticelli, Antonella; Piaggi, Paolo; Ricciardi, Sara; Lucchi, Marco; Melfi, Franca; Chella, Antonio; Cocozza, Sérgio; Fontanini, Gabriella

doi:10.1038/s41598-019-40016-8

Cited by 12 publications

(6 citation statements)

References 51 publications

(67 reference statements)

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“…In recent studies, MET exon 14 skipping mutations were found to be enriched in PSC, and the incidence rate was as high as 31.8% (20), which could be a potential target for therapy using MET inhibitors, such as volitinib, capmatinib, and tepotinib (21)(22)(23). In addition, patients with PSC had an increased tendency for the high expression of tumor mutation burden (>20 mutations per Mb) compared with those with oNSCLC (20% vs 14%, P = 0.056) (22).…”

Section: Discussionmentioning

confidence: 99%

Survival Analysis and Prediction Model for Pulmonary Sarcomatoid Carcinoma Based on SEER Database

Chen

Qiao

et al. 2021

Front. Oncol.

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ObjectiveThis study aimed to investigate the incidence of the pulmonary sarcomatoid carcinoma (PSC), to compare the clinical characteristics and overall survival (OS) of patients with PSC and those with other non-small-cell lung cancer (oNSCLC), so as to analyze the factors affecting the OS of patients with PSC and construct a nomogram prediction model.MethodsData of patients with PSC and those with oNSCLC diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results database were collected. The age-adjusted incidence of PSC was calculated. The characteristics of patients with PSC and those with oNSCLC were compared, then the patients were matched 1:2 for further survival analysis. Patients with PSC were randomly divided into training set and testing set with a ratio of 7:3. The Cox proportional hazards model was used to identify the covariates associated with the OS. Significant covariates were used to construct the nomogram, and the C-index was calculated to measure the discrimination ability. The accuracy of the nomogram was compared with the tumor–node–metastasis (TNM) clinical stage, and the corresponding area under the curve was achieved.ResultsA total of 1049 patients with PSC were enrolled, the incidence of PSC was slowly decreased from 0.120/100,000 in 2004 to 0.092/100,000 in 2015. Before PSM, 793 PSC patients and 191356 oNSCLC patients were identified, the proportion of male, younger patients (<65 years), grade IV, TNM clinical stage IV was higher in the PSC. The patients with PSC had significantly poorer OS compared with those with oNSCLC. After PSM, PSC still had an extremely inferior prognosis. Age, sex, TNM clinical stage, chemotherapy, radiotherapy, and surgery were independent factors for OS. Next, a nomogram was established based on these factors, and the C-indexs were 0.775 and 0.790 for the training and testing set, respectively. Moreover, the nomogram model indicated a more comprehensive and accurate prediction than the TNM clinical stage.ConclusionsThe incidence of PSC was slowly decreased. PSC had a significantly poor prognosis compared with oNSCLC. The nomogram constructed in this study accurately predicted the prognosis of PSC, performed better than the TNM clinical stage.

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Section: Discussionmentioning

confidence: 99%

Survival Analysis and Prediction Model for Pulmonary Sarcomatoid Carcinoma Based on SEER Database

Chen

Qiao

et al. 2021

Front. Oncol.

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“…The read counts per gene were defined using feature count [ 23 ] as the reference transcriptome. Differential expression analysis was performed using edgeR R package [ 24 ], and the tumor samples were compared to their matched normal samples to identify DEGs. The selected genes are significantly differentially expressed between tumor and normal samples and their FDR < 0.05 and absolute log2 fold change (logFC) > 1.…”

Section: Methodsmentioning

confidence: 99%

A seven-gene prognostic signature predicts overall survival of patients with lung adenocarcinoma (LUAD)

et al. 2021

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Background Lung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients. Methods Raw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature. Results A prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041–39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779–3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis. Conclusion Our study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

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“…In a gene enrichment analysis, the Fanconi anemia (FA) pathway was indicated in addition to the above mentioned biological processes. Interestingly, this pathway was reported in connection with EGFR resistance by other groups [41,42,43,44]. Pfaffle et al explicitly described an FA phenotype in EGFR resistant cells and a possible susceptibility of these cells towards PARP inhibition [43].…”

Section: Discussionmentioning

confidence: 98%

Induction of Acquired Resistance towards EGFR Inhibitor Gefitinib in a Patient-Derived Xenograft Model of Non-Small Cell Lung Cancer and Subsequent Molecular Characterization

Schueler

Tschuch

Klingner

et al. 2019

Cells

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In up to 30% of non-small cell lung cancer (NSCLC) patients, the oncogenic driver of tumor growth is a constitutively activated epidermal growth factor receptor (EGFR). Although these patients gain great benefit from treatment with EGFR tyrosine kinase inhibitors, the development of resistance is inevitable. To model the emergence of drug resistance, an EGFR-driven, patient-derived xenograft (PDX) NSCLC model was treated continuously with Gefitinib in vivo. Over a period of more than three months, three separate clones developed and were subsequently analyzed: Whole exome sequencing and reverse phase protein arrays (RPPAs) were performed to identify the mechanism of resistance. In total, 13 genes were identified, which were mutated in all three resistant lines. Amongst them the mutations in NOMO2, ARHGEF5 and SMTNL2 were predicted as deleterious. The 53 mutated genes specific for at least two of the resistant lines were mainly involved in cell cycle activities or the Fanconi anemia pathway. On a protein level, total EGFR, total Axl, phospho-NFκB, and phospho-Stat1 were upregulated. Stat1, Stat3, MEK1/2, and NFκB displayed enhanced activation in the resistant clones determined by the phosphorylated vs. total protein ratio. In summary, we developed an NSCLC PDX line modelling possible escape mechanism under EGFR treatment. We identified three genes that have not been described before to be involved in an acquired EGFR resistance. Further functional studies are needed to decipher the underlying pathway regulation.

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Whole transcriptome targeted gene quantification provides new insights on pulmonary sarcomatoid carcinomas

Cited by 12 publications

References 51 publications

Survival Analysis and Prediction Model for Pulmonary Sarcomatoid Carcinoma Based on SEER Database

Survival Analysis and Prediction Model for Pulmonary Sarcomatoid Carcinoma Based on SEER Database

A seven-gene prognostic signature predicts overall survival of patients with lung adenocarcinoma (LUAD)

Induction of Acquired Resistance towards EGFR Inhibitor Gefitinib in a Patient-Derived Xenograft Model of Non-Small Cell Lung Cancer and Subsequent Molecular Characterization

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