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Journal of Experimental & Clinical Cancer Research
Published in: Journal of Experimental & Clinical Cancer Research 1/2017

Open Access 01-12-2017 | Research

Nomogram integrating gene expression signatures with clinicopathological features to predict survival in operable NSCLC: a pooled analysis of 2164 patients

Authors: Jian Wu, Lizhi Zhou, Lixia Huang, Jincui Gu, Shaoli Li, Baomo Liu, Jinlun Feng, Yanbin Zhou

Published in: Journal of Experimental & Clinical Cancer Research | Issue 1/2017

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Abstract

Background

The current tumor-node-metastasis (TNM) staging system is insufficient to predict outcome of patients with operable Non-Small Cell Lung Cancer (NSCLC) owing to its phenotypic and genomic heterogeneity. Integrating genomic signatures with clinicopathological factors may provide more detailed evaluation of prognosis.

Methods

All 2164 clinically annotated NSCLC samples (1326 in the training set and 838 in the validation set) with corresponding microarray data from 17 cohorts were pooled to develop and validate a clinicopathologic-genomic nomogram based on Cox regression model. Two computational methods were applied to these samples to capture expression pattern of genomic signatures representing biological statuses. Model performance was measured by the concordance index (C-index) and calibration plot. Risk group stratification was proposed for the nomogram.

Results

Multivariable analysis of the training set identified independent factors including age, TNM stage, combined prognostic classifier, non-overlapping signature, and the ratio of neutrophil to plasma cells. The C-index of the nomogram for predicting survival was statistically superior to that of the TNM stage (training set, 0.686 vs 0.627, respectively; P < .001; validation set, 0.689 vs 0.638, respectively; P < .001). The calibration plots showed that the predicted 1-, 3- and 5-year survival probabilities agreed well with the actual observations. Stratifying patients into three risk groups detected significant differences among survival curves.

Conclusions

These findings offer preliminary evidence that genomic data provide independent and complementary prognostic information and incorporation of this information can refine prognosis in NSCLC. Prospective studies are required to further explore the value of this composite model for prognostic stratification and tailored therapeutic strategies.
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Metadata
Title
Nomogram integrating gene expression signatures with clinicopathological features to predict survival in operable NSCLC: a pooled analysis of 2164 patients
Authors
Jian Wu
Lizhi Zhou
Lixia Huang
Jincui Gu
Shaoli Li
Baomo Liu
Jinlun Feng
Yanbin Zhou
Publication date
01-12-2017
Publisher
BioMed Central
Published in
Journal of Experimental & Clinical Cancer Research / Issue 1/2017
Electronic ISSN: 1756-9966
DOI
https://doi.org/10.1186/s13046-016-0477-x

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