Dataset Information

A Two-Gene Prognostic Classifier for Early-Stage Lung Squamous Cell Carcinoma in Multiple Large-Scale and Geographically Diverse Cohorts.

ABSTRACT: INTRODUCTION:There are no validated molecular methods that prospectively identify patients with surgically resected lung squamous cell carcinoma (SCC) at high risk for recurrence. By focusing on the expression of genes with known functions in development of lung SCC and prognosis, we sought to develop a robust prognostic classifier of early-stage lung SCC. METHODS:The expression of 253 genes selected by literature search was evaluated in microarrays from 107 stage I/II tumors. Associations with survival were evaluated by Cox regression and Kaplan-Meier survival analyses in two independent cohorts of 121 and 91 patients with SCC, respectively. A classifier score based on multivariable Cox regression was derived and examined in six additional publicly available data sets of stage I/II lung SCC expression profiles (n = 358). The prognostic value of this classifier was evaluated in meta-analysis of patients with stage I/II (n = 479) and stage I (n = 326) lung SCC. RESULTS:Dual specificity phosphatase 6 gene (DUSP6) and actinin alpha 4 gene (ACTN4) were associated with prognostic outcome in two independent patient cohorts. Their expression values were utilized to develop a classifier that identified patients with stage I/II lung SCC at high risk for recurrence (hazard ratio [HR] = 4.7, p = 0.018) or cancer-specific mortality (HR = 3.5, p = 0.016). This classifier also identified patients at high risk for recurrence (HR = 2.7, p = 0.008) or death (HR = 2.2, p = 0.001) in publicly available data sets of stage I/II and in meta-analysis of stage I patients. CONCLUSIONS:We have established and validated a prognostic classifier to inform clinical management of patients with lung SCC after surgical resection.

SUBMITTER: Noro R

PROVIDER: S-EPMC6503970 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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A Two-Gene Prognostic Classifier for Early-Stage Lung Squamous Cell Carcinoma in Multiple Large-Scale and Geographically Diverse Cohorts.

Noro Rintaro R Ishigame Teruhide T Walsh Naomi N Shiraishi Kouya K Robles Ana I AI Ryan Bríd M BM Schetter Aaron J AJ Bowman Elise D ED Welsh Judith A JA Seike Masahiro M Gemma Akihiko A Skaug Vidar V Mollerup Steen S Haugen Aage A Yokota Jun J Kohno Takashi T Harris Curtis C CC

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 20160906 1

<h4>Introduction</h4>There are no validated molecular methods that prospectively identify patients with surgically resected lung squamous cell carcinoma (SCC) at high risk for recurrence. By focusing on the expression of genes with known functions in development of lung SCC and prognosis, we sought to develop a robust prognostic classifier of early-stage lung SCC.<h4>Methods</h4>The expression of 253 genes selected by literature search was evaluated in microarrays from 107 stage I/II tumors. Ass ...[more]

PMID: 27613525

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Project description:BackgroundHead and neck squamous cell carcinoma (HNSCC) is the most common type and accounts for 90% of all head and neck cancer cases. Despite advances in early diagnosis and treatment strategies-chemotherapy, surgical resection, and radiotherapy-5-year survival remains grim. For patients with early-stage HNSCC, accurately predicting clinical outcomes is challenging. Considering the pivotal role of the immune system in HNSCC, we developed a reliable immune-related gene signature (IRGS) and explored its predictive accuracy in patients with early-stage HNSCC.MethodsWe examined immune gene expression profiles and clinical information from 230 early-stage HNSCC specimens, including 100 cases from The Cancer Genome Atlas (TCGA), 49 cases from the Gene Expression Omnibus (GEO; GSE65858), and 81 cases from an independent clinical cohort. The prognostic signature was constructed using Kaplan-Meier analysis and the least absolute shrinkage and selection operator (LASSO) Cox algorithm. We also explored the IRGS-related biological pathways and immune landscape using bioinformatics analysis.ResultsA nine-immune-gene signature was generated to significantly stratify patients into high and low-risk groups. High risk patients exhibited shorter survival time [hazard ratio (HR) =13.795, 95% confidence interval (CI): 3.275-58.109, P<0.001]. The signature demonstrated robust prognostic ability in the training and validation sets and could independently predict overall survival (OS) and relapse-free survival (RFS). Subsequently, the receiver operating characteristic (ROC) curve and C-index confirmed the signature's predictive accuracy compared to clinical parameters. Additionally, cases classified as low risk showed more immune cell infiltration than high-risk cases.ConclusionsOur novel IRGS is a reliable and robust classifier for accurate patient stratification and prognostic evaluation. Future studies will attempt to affirm the signature's clinical application to early-stage HNSCC.

Project description:BACKGROUND: In ligand-based screening, as well as in other chemoinformatics applications, one seeks to effectively search large repositories of molecules in order to retrieve molecules that are similar typically to a single molecule lead. However, in some case, multiple molecules from the same family are available to seed the query and search for other members of the same family.Multiple-molecule query methods have been less studied than single-molecule query methods. Furthermore, the previous studies have relied on proprietary data and sometimes have not used proper cross-validation methods to assess the results. In contrast, here we develop and compare multiple-molecule query methods using several large publicly available data sets and background. We also create a framework based on a strict cross-validation protocol to allow unbiased benchmarking for direct comparison in future studies across several performance metrics. RESULTS: Fourteen different multiple-molecule query methods were defined and benchmarked using: (1) 41 publicly available data sets of related molecules with similar biological activity; and (2) publicly available background data sets consisting of up to 175,000 molecules randomly extracted from the ChemDB database and other sources. Eight of the fourteen methods were parameter free, and six of them fit one or two free parameters to the data using a careful cross-validation protocol. All the methods were assessed and compared for their ability to retrieve members of the same family against the background data set by using several performance metrics including the Area Under the Accumulation Curve (AUAC), Area Under the Curve (AUC), F1-measure, and BEDROC metrics.Consistent with the previous literature, the best parameter-free methods are the MAX-SIM and MIN-RANK methods, which score a molecule to a family by the maximum similarity, or minimum ranking, obtained across the family. One new parameterized method introduced in this study and two previously defined methods, the Exponential Tanimoto Discriminant (ETD), the Tanimoto Power Discriminant (TPD), and the Binary Kernel Discriminant (BKD), outperform most other methods but are more complex, requiring one or two parameters to be fit to the data. CONCLUSION: Fourteen methods for multiple-molecule querying of chemical databases, including novel methods, (ETD) and (TPD), are validated using publicly available data sets, standard cross-validation protocols, and established metrics. The best results are obtained with ETD, TPD, BKD, MAX-SIM, and MIN-RANK. These results can be replicated and compared with the results of future studies using data freely downloadable from http://cdb.ics.uci.edu/.

Dataset Information

A Two-Gene Prognostic Classifier for Early-Stage Lung Squamous Cell Carcinoma in Multiple Large-Scale and Geographically Diverse Cohorts.

Publications

A Two-Gene Prognostic Classifier for Early-Stage Lung Squamous Cell Carcinoma in Multiple Large-Scale and Geographically Diverse Cohorts.

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