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Prediction of lymph node metastasis by analysis of gene expression profiles in primary lung adenocarcinomas.

ABSTRACT: Lymph node status is a strong predictor of outcome for lung cancer patients. Recently, several reports have hinted that gene expression profiles of primary tumor may be able to predict node status. The goals of this study were to determine if microarray data could be used to accurately classify patients with regard to pathologic lymph node status, and to determine if this analysis could identify patients at risk for occult disease and worse survival.Two previously published lung adenocarcinoma microarray data sets were reanalyzed. Patients were separated into two groups based on pathologic lymph node positive (pN+) or negative (pN0) status, and prediction analysis of microarray (PAM) was used for training and validation to classify nodal status. Overall survival analysis was performed based on PAM classifications.In the training phase, a 318-gene set gave classification accuracy of 88.4% when compared with pathology. Survival was significantly worse in PAM-positive compared with PAM-negative patients overall (P < 0.0001) and also when confined to pN0 patients only (P = 0.0037). In the validation set, classification accuracy was again 94.1% in the pN+ patients but only 21.2% in the pN0 patients. However, among the pN0 patients, recurrence rates and overall survival were significantly worse in the PAM-positive compared with PAM-negative patients (P = 0.0258 and 0.0507).Analysis of gene expression profiles from primary tumor may predict lymph node status but frequently misclassifies pN0 patients as node positive. Recurrence rates and overall survival are worse in these "misclassified" patients, implying that they may in fact have occult disease spread.

SUBMITTER: Xi L

PROVIDER: S-EPMC2211271 | biostudies-literature | 2005 Jun

REPOSITORIES: biostudies-literature

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Prediction of lymph node metastasis by analysis of gene expression profiles in primary lung adenocarcinomas.

Xi Liqiang L Lyons-Weiler James J Coello Michael C MC Huang Xin X Gooding William E WE Luketich James D JD Godfrey Tony E TE

Clinical cancer research : an official journal of the American Association for Cancer Research 20050601 11

<h4>Purpose</h4>Lymph node status is a strong predictor of outcome for lung cancer patients. Recently, several reports have hinted that gene expression profiles of primary tumor may be able to predict node status. The goals of this study were to determine if microarray data could be used to accurately classify patients with regard to pathologic lymph node status, and to determine if this analysis could identify patients at risk for occult disease and worse survival.<h4>Experimental design</h4>Tw ...[more]

PMID: 15930348

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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: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.

Dataset Information

Prediction of lymph node metastasis by analysis of gene expression profiles in primary lung adenocarcinomas.

Publications

Prediction of lymph node metastasis by analysis of gene expression profiles in primary lung adenocarcinomas.

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