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Gene status and clinicopathologic characteristics of lung adenocarcinomas with mediastinal lymph node metastasis.

ABSTRACT: Lung cancer with mediastinal lymph node metastasis is more likely to develop recurrence and metastasis after complete resection and targeted therapy is a promising treatment strategy. We performed amplification refractory mutation system (ARMS) fluorescence quantitative PCR to detect the gene status of EGFR, ALK, ROS1 and RET in resected samples from 280 patients who were confirmed to have primary lung adenocarcinomas with N1-N2 lymph node metastasis. Of the 280 patients enrolled, the frequency of EGFR mutations, ALK fusions, ROS1 fusions, RET fusions and no mutations was 42.9%, 10.7%, 1.8%, 3.6% and 42.9%, respectively. Five patients exhibited the coexistence of the EGFR and ALK alterations. ALK, ROS1 and RET fusions were mutually exclusive. The frequency of EGFR mutation was significantly lower among patients with poor differentiation, while the rates of ALK and ROS1 fusions were the opposite. RET fusions also tended to be more prevalent in poorly differentiated patients. EGFR and ALK double positive tumors were characterized by significantly smaller size compared with those had single gene alteration. Our study comprehensively analyzed the distinct and common clinicopathologic characteristics according to genotypes of the cohort, which should help in categorizing patients for efficient screening.

SUBMITTER: Zhang S

PROVIDER: S-EPMC5325401 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Gene status and clinicopathologic characteristics of lung adenocarcinomas with mediastinal lymph node metastasis.

Zhang Shumeng S Yan Bing B Zheng Jing J Zhao Jing J Zhou Jianying J

Oncotarget 20160901 39

Lung cancer with mediastinal lymph node metastasis is more likely to develop recurrence and metastasis after complete resection and targeted therapy is a promising treatment strategy. We performed amplification refractory mutation system (ARMS) fluorescence quantitative PCR to detect the gene status of EGFR, ALK, ROS1 and RET in resected samples from 280 patients who were confirmed to have primary lung adenocarcinomas with N1-N2 lymph node metastasis. Of the 280 patients enrolled, the frequency ...[more]

PMID: 27563816

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Project description:BackgroundEarly data has indicated that EGFR 19Del mutation and EGFR L585R mutation are two different types of non-small cell lung cancer (NSCLC). However, how the different molecular mechanisms participate in the process of mediastinal lymph node metastasis (MLNM) in lung adenocarcinoma (LA) harboring EGFR 19Del and EGFR L858R mutation remains unknown. We thus explored the genes responsible for MLNM in LA with EGFR 19Del or L858R mutation.MethodsWe performed transcriptome sequencing and bioinformatics analysis from 10 patients with LA resection specimens of primary tumors. Quantitative reverse transcription-polymerase chain reaction was used to validate gene expressions.ResultsThere were 69 mRNAs upregulated and 100 mRNAs downregulated in five samples with MLNM compared with samples without MLN metastasis. EEF1A2 and ERN2 were observed exhibiting different expression patterns in EGFR 19Del and EGFR L858R samples with MLNM. In samples harboring EGFR 19Del mutation, the expression of EEF1A2 gene in samples with MLNM was significantly lower compared with samples without MLN metastasis, and in samples with EGFR L858R, it was significantly higher in samples with MLNM. The expression pattern of ERN2 was opposite to EEF1A2. In addition, several other genes including SLC6A11, IGHV3-48, IGHV3-43, DUSP9, and HOXA9 were also shown to be associated with invasion and metastasis and exhibited an expression pattern similar to EEF1A2 and ERN2 in EGRF 19Del and L858R mutation tumors.ConclusionsEEF1A2 and ERN2 were for the first time observed exhibiting distinct expression patterns in MLNM in lung adenocarcinomas harboring EGFR 19Del and EGFR L858R mutation by interindividual DEGs analysis.Key pointsSignificant findings of the study In our study, we focused on the mechanisms of metastasis and invasion that different EGFR mutations conferred and identified two critical genes separately involved in this process in EGFR 19Del and L858R mutation tumors. What this study adds Our findings not only reinforced theoretical foundations that the EGFR 19Del and L858R mutation tumors should be considered as two kinds of diseases, but also laid the fundamentals for precise determination of the mediastinal lymph node radiation field and improvement of clinical outcome.

Project description:To identify patients in whom systematic lymph node dissection would be suitable, preoperative diagnosis of the biological invasiveness of lung adenocarcinomas through the classification of these T1aN0M0 lung adenocarcinomas into several subgroups may be warranted. In this retrospective study, we sought to determine predictive factors of lymph node status in clinical stage T1aN0M0 lung adenocarcinomas.We retrospectively reviewed the records of 273 consecutive patients undergone surgical resection of clinical stage T1aN0M0 lung adenocarcinomas at Shanghai Chest Hospital, from January 2011 to December 2012. Preoperative computed tomography findings of all 273 patients were reviewed and their tumors categorized as pure GGO, GGO with minimal solid components (<5 mm), part-solid (solid parts >5 mm), or purely solid. Relevant clinicopathologic features were investigated to identify predictors of hilar or mediastinal lymph node metastasis using univariate or multiple variable analysis.Among the 273 eligible clinical stage T1aN0M0 lung adenocarcinomas examined on thin-section CT, 103 (37.7%) were pure GGO, 118 (43.2%) GGO with minimal solid components, 13 (4.8%) part-solid (solid parts >5 mm, five GGO predominant and eight solid predominant), and 39 (14.3%) pure solid. There were 18 (6.6%) patients with lymph node metastasis. Incidence of N1 and N2 nodal involvement was 11 (6.6%) and seven (2.6%) patients, respectively. All patients with pure GGO and GGO with minimal solid components (<5 mm) tumors were pathologically staged N0. Multivariate analyses showed that the following factors significantly predicted lymph node metastasis for T1a lung adenocarcinomas: symptoms at presentation, GGO status, and abnormal carcinoembryonic antigen (CEA) titer. Multivariate analyses also showed that the following factors significantly predicted lymph node metastasis for pure solid tumors: air bronchogram sign, tumor size, symptoms at presentation, and abnormal CEA titer.The patients of clinical stage T1aN0M0 lung adenocarcinomas with pure GGO and GGO with minimal solid components tumors were pathologically staged N0 and systematic lymph node dissection should be avoided. But systematic lymph node dissection should be performed for pure solid tumors or part-solid, especially in patients with CEA greater than 5 ng/mL or symptoms at presentation, because of the high possibility of lymph node involvement.

Project description:Radiomics studies to predict lymph node (LN) metastasis has only focused on either primary tumor or LN alone. However, combining radiomics features from multiple sources may reflect multiple characteristic of the lesion thereby increasing the discriminative performance of the radiomic model. Therefore, the present study intends to evaluate the efficiency of integrative nomogram, created by combining clinical parameters and radiomics features extracted from gross tumor volume (GTV), peritumoral volume (PTV) and LN, for the preoperative prediction of LN metastasis in clinical cT1N0M0 adenocarcinoma. A primary cohort of 163 patients (training cohort, 113; and internal validation cohort, 50) and an external validation cohort of 53 patients with clinical stage cT1N0M0 were retrospectively included. Features were extracted from three regions of interests (ROIs): GTV; PTV (5.0 mm around the tumor) and LN on pre-operative contrast enhanced computed tomography (CT). LASSO logistic regression method was used to build radiomic signatures. Multivariable regression analysis was used to build a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The discriminative performance of nomogram was validated both internally and externally. The radiomic signatures using the features of GTV, PTV and LN showed a good ability in predicting LN metastasis with an area under the curve (AUC) of 0.74 (95% CI 0.60-0.88), 0.72 (95% CI 0.57-0.87) and 0.64 (95% CI 0.48-0.80) respectively in external validation cohort. The integration of different signature together further increases the discriminatory ability: GTV + PTV (GPTV): AUC 0.75 (95% CI 0.61-0.89) and GPTV + LN: AUC 0.76 (95% CI 0.61-0.91) in external validation cohort. An integrative nomogram of clinical parameters and radiomic features demonstrated further increase in discriminatory ability with AUC of 0.79 (95% CI 0.66-0.93) in external validation cohort. The nomogram showed good calibration. Decision curve analysis demonstrated that the radiomic nomogram was clinically useful. The integration of information from clinical parameters along with CT radiomics information from GTV, PTV and LN was feasible and increases the predictive performance of the nomogram in predicting LN status in cT1N0M0 adenocarcinoma patients suggesting merit of information integration from multiple sources in building prediction model.

Project description:BackgroundPreoperative evaluation of lymph node (LN) state is of pivotal significance for informing therapeutic decisions in gastric cancer (GC) patients. However, there are no non-invasive methods that can be used to preoperatively identify such status. We aimed at developing a genomic biosignature based model to predict the possibility of LN metastasis in GC patients.MethodsWe used the RNA profile retrieving strategy and performed RNA expression profiling in a large GC cohort (GSE62254, n = 300) from Gene Expression Ominus (GEO). In the exploratory stage, 300 GC patients from GSE62254 were involved and the differentially expressed RNAs (DERs) for LN-status were determined using the R software. GC samples in GSE62254 were randomly allocated into a learning set (n = 210) and a verification set (n = 90). By using the Least absolute shrinkage and selection operator (LASSO) regression approach, a set of 23-RNA signatures were established and the signature based nomogram was subsequently built for distinguishing LN condition. The diagnostic efficiency, as well as the clinical performance of this model were assessed using the decision curve analysis (DCA). Metascape was used for bioinformatic analysis of the DERs.ResultsBased on the genomic signature, we established a nomogram that robustly distinguished LN status in the learning (AUC = 0.916, 95% CI 0.833-0.999) and verification sets (AUC = 0.775, 95% CI 0.647-0.903). DCA demonstrated the clinical value of this nomogram. Functional enrichment analysis of the DERs was performed using bioinformatics methods which revealed that these DERs were involved in several lymphangiogenesis-correlated cascades.ConclusionsIn this study, we present a genomic signature based nomogram that integrates the 23-RNA biosignature based scores and Lauren classification. This model can be utilized to estimate the probability of LN metastasis with good performance in GC. The functional analysis of the DERs reveals the prospective biogenesis of LN metastasis in GC.

Dataset Information

Gene status and clinicopathologic characteristics of lung adenocarcinomas with mediastinal lymph node metastasis.

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Gene status and clinicopathologic characteristics of lung adenocarcinomas with mediastinal lymph node metastasis.

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