Dataset Information

Validation of survival prognostic models for non-small-cell lung cancer in stage- and age-specific groups.

ABSTRACT: PURPOSE:Prognostic models have been proposed to predict survival for non-small-cell lung cancer (NSCLC). It is important to evaluate whether these models perform better than performance status (PS) alone in stage- and age-specific subgroups. PATIENTS AND METHODS:The validation cohort included 2060 stage I and 1611 stage IV NSCLC patients from 23CALGB studies. For stage I, Blanchon (B), Chansky (C) and Gail (G) models were evaluated along with the PS only model. For stage IV, Blanchon (B) and Mandrekar (M) models were compared with the PS only model. The c-index was used to assess the concordance between survival and risk scores. The c-index difference (c-difference) and the integrated discrimination improvement (IDI) were used to determine the improvement of these models over the PS only model. RESULTS:For stage I, B and PS have better survival separation. The c-index for B, PS, C and G are 0.61, 0.58, 0.57 and 0.52, respectively, and B performs significantly better than PS with c-difference=0.034. For stage IV, B, M and PS have c-index 0.61, 0.64 and 0.60, respectively; B and M perform significantly better than PS with c-difference=0.015 and 0.033, respectively. CONCLUSION:Although some prognostic models have better concordance with survival than the PS only model, the absolute improvement is small. More accurate prognostic models should be developed; the inclusion of tumor genetic variants may improve prognostic models.

SUBMITTER: Wang X

PROVIDER: S-EPMC4619143 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Validation of survival prognostic models for non-small-cell lung cancer in stage- and age-specific groups.

Wang Xiaofei X Gu Lin L Zhang Ying Y Sargent Daniel J DJ Richards William W Ganti Apar Kishor AK Crawford Jeffery J Cohen Harvey Jay HJ Stinchcombe Thomas T Vokes Everett E Pang Herbert H

Lung cancer (Amsterdam, Netherlands) 20150818 2

<h4>Purpose</h4>Prognostic models have been proposed to predict survival for non-small-cell lung cancer (NSCLC). It is important to evaluate whether these models perform better than performance status (PS) alone in stage- and age-specific subgroups.<h4>Patients and methods</h4>The validation cohort included 2060 stage I and 1611 stage IV NSCLC patients from 23CALGB studies. For stage I, Blanchon (B), Chansky (C) and Gail (G) models were evaluated along with the PS only model. For stage IV, Blanc ...[more]

PMID: 26319317

Similar Datasets

Project description:Importance:The prevalence of early-stage non-small cell lung cancer (NSCLC) is expected to increase with recent implementation of annual screening programs. Reliable prognostic biomarkers are needed to identify patients at a high risk for recurrence to guide adjuvant therapy. Objective:To develop a robust, individualized immune signature that can estimate prognosis in patients with early-stage nonsquamous NSCLC. Design, Setting, and Participants:This retrospective study analyzed the gene expression profiles of frozen tumor tissue samples from 19 public NSCLC cohorts, including 18 microarray data sets and 1 RNA-Seq data set for The Cancer Genome Atlas (TCGA) lung adenocarcinoma cohort. Only patients with nonsquamous NSCLC with clinical annotation were included. Samples were from 2414 patients with nonsquamous NSCLC, divided into a meta-training cohort (729 patients), meta-testing cohort (716 patients), and 3 independent validation cohorts (439, 323, and 207 patients). All patients underwent surgery with a negative surgical margin, received no adjuvant or neoadjuvant therapy, and had publicly available gene expression data and survival information. Data were collected from July 22 through September 8, 2016. Main Outcomes and Measures:Overall survival. Results:Of 2414 patients (1205 men [50%], 1111 women [46%], and 98 of unknown sex [4%]; median age [range], 64 [15-90] years), a prognostic immune signature of 25 gene pairs consisting of 40 unique genes was constructed using the meta-training data set. In the meta-testing and validation cohorts, the immune signature significantly stratified patients into high- vs low-risk groups in terms of overall survival across and within subpopulations with stage I, IA, IB, or II disease and remained as an independent prognostic factor in multivariate analyses (hazard ratio range, 1.72 [95% CI, 1.26-2.33; P?<?.001] to 2.36 [95% CI, 1.47-3.79; P?<?.001]) after adjusting for clinical and pathologic factors. Several biological processes, including chemotaxis, were enriched among genes in the immune signature. The percentage of neutrophil infiltration (5.6% vs 1.8%) and necrosis (4.6% vs 1.5%) was significantly higher in the high-risk immune group compared with the low-risk groups in TCGA data set (P?<?.003). The immune signature achieved a higher accuracy (mean concordance index [C-index], 0.64) than 2 commercialized multigene signatures (mean C-index, 0.53 and 0.61) for estimation of survival in comparable validation cohorts. When integrated with clinical characteristics such as age and stage, the composite clinical and immune signature showed improved prognostic accuracy in all validation data sets relative to molecular signatures alone (mean C-index, 0.70 vs 0.63) and another commercialized clinical-molecular signature (mean C-index, 0.68 vs 0.65). Conclusions and Relevance:The proposed clinical-immune signature is a promising biomarker for estimating overall survival in nonsquamous NSCLC, including early-stage disease. Prospective studies are needed to test the clinical utility of the biomarker in individualized management of nonsquamous NSCLC.

Project description:ObjectiveThe purpose of this study is to screen for microRNAs (miRNAs) associated with the prognosis of lung adenocarcinoma (LUAD) and to explore its prognosis and effects on the tumor microenvironment in patients with LUAD.MethodsGene expression data, miRNA expression data, and clinical data for two different databases, TCGA-LUAD and CPTAC-3 LUAD, were downloaded from the GDC database. The miRNA prognosis of LUAD was filtered by the Cox proportional hazard model and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. The performance of the model was validated by time-dependent receiver operating characteristics (ROC) curves. Possible biological processes associated with the miRNAs target gene were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, the prognostic model was scored by risk, divided into high- and low-risk groups by median, and the differences in the immersion level of 21 immune cells in the high- and low-risk groups were assessed. To gain a deeper understanding of the underlying mechanism behind the model, the two most important miRNAs in the model, miR-195-3p and miR-5571-5p, were selected for HPA database validation and ceRNA network construction.ResultsOf the 209 variance expressions identified in the screening analysis, 145 were upregulated and 64 were downregulated by miRNAs. The prognostic models of six miRNA genes were obtained: miR-195-3p, miR-5571-5p, miR-584-3p, miR-494-3p, miR-4664-3p, and miR-1293. These six genes were significantly associated with survival rates in LUAD patients. In particular, miR-1293, miR-195-3p, and miR-5571-5p are highly correlated with OS. The higher expression of miR-195-3p and miR-5571-5p, the better survival of LUAD OS is, and these two miRNA expressions contribute the most to the model. Finally, after sorting the risk scores calculated from low to high using the prognostic model, the patients with higher scores had shorter survival time and higher frequency of death, and there were significant differences in the immersion levels of 21 immune cells in the high- and low-risk groups. ceRNA network analysis found that TM9SF3 was regulated by miR-195-3p and was highly expressed in the tissues of LUAD patients, and the prognosis of the patients was poor.ConclusionsmiR-195-3p, miR-5571-5p, miR-584-3p, miR-494-3p, miR-4664-3p, and miR-1293 may be used as new biomarkers for prognosis prediction of LUAD. Our results also identified a lncRNA MEG3/miR-195-3p/RAB1A/TM9SF3 regulatory axis, which may also play an important role in the progression of LUAD. Further study needs to be conducted to verify this result.

Dataset Information

Validation of survival prognostic models for non-small-cell lung cancer in stage- and age-specific groups.

Publications

Validation of survival prognostic models for non-small-cell lung cancer in stage- and age-specific groups.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets