Composite anatomical–clinical–molecular prognostic model in nonsmall cell lung cancer

The objective of the present study was to elaborate a survival model that integrates anatomic factors, according to the 2010 seventh edition of the tumour, node and metastasis (TNM) staging system, with clinical and molecular factors. Pathologic TNM descriptors (group A), clinical variables (group B), laboratory parameters (group C) and molecular markers (tissue microarrays; group D) were collected from 512 early-stage nonsmall cell lung cancer (NSCLC) patients with complete resection. A multivariate analysis stepped supervised learning classification algorithm was used. The prognostic performance by groups was: areas under the receiver operating characteristic curve (C-index): 0.67 (group A), 0.65 (Group B), 0.57 (group C) and 0.65 (group D). Considering all variables together selected for each of the four groups (integrated group) the C-index was 0.74 (95% CI 0.70–0.79), with statistically significant differences compared with each isolated group (from p = 0.006 to p<0.001). Variables with the greatest prognostic discrimination were the presence of another ipsilobar nodule and tumour size >3 cm, followed by other anatomical and clinical factors, and molecular expressions of phosphorylated mammalian target of rapamycin (phospho-mTOR), Ki67cell proliferation index and phosphorylated acetyl-coenzyme A carboxylase. This study on early-stage NSCLC shows the benefit from integrating pathological TNM, clinical and molecular factors into a composite prognostic model. The model of the integrated group classified patients with significantly higher accuracy compared to the TNM 2010 staging.

[1]  B. Solomon,et al.  Class IA phosphatidylinositol 3-kinase signaling in non-small cell lung cancer. , 2009, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[2]  P. Sprent Statistics in medical research. , 2003, Swiss medical weekly.

[3]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[4]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[5]  Zhifu Sun,et al.  Non-overlapping and non-cell-type-specific gene expression signatures predict lung cancer survival. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[6]  Elisabeth Brambilla,et al.  Pathology and genetics of tumours of the lung , pleura, thymus and heart , 2004 .

[7]  Igor Jurisica,et al.  Gene expression–based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study , 2008, Nature Medicine.

[8]  P. Kvale,et al.  Smoking and lung cancer survival: the role of comorbidity and treatment. , 2004, Chest.

[9]  M. Tsao,et al.  Immunohistochemical markers of prognosis in non-small cell lung cancer: a review and proposal for a multiphase approach to marker evaluation , 2006, Journal of Clinical Pathology.

[10]  S. Lester,et al.  Manual of Surgical Pathology , 2005 .

[11]  L. Angel Survival of 2,991 Patients With Surgical Lung Cancer: The Denominator Effect in Survival , 2005 .

[12]  J. Crowley,et al.  The IASLC Lung Cancer Staging Project: Proposals for the Revision of the TNM Stage Groupings in the Forthcoming (Seventh) Edition of the TNM Classification of Malignant Tumours , 2007, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[13]  Yong Qian,et al.  Confirmation of Gene Expression–Based Prediction of Survival in Non–Small Cell Lung Cancer , 2008, Clinical Cancer Research.

[14]  Marcin Skrzypski,et al.  An Immune Response Enriched 72-Gene Prognostic Profile for Early-Stage Non–Small-Cell Lung Cancer , 2009, Clinical Cancer Research.

[15]  Nam Huh,et al.  Prediction of Recurrence-Free Survival in Postoperative Non–Small Cell Lung Cancer Patients by Using an Integrated Model of Clinical Information and Gene Expression , 2008, Clinical Cancer Research.

[16]  L. Tanoue,et al.  The new lung cancer staging system. , 2009, Chest.

[17]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[18]  M. Brundage,et al.  Prognostic factors in non-small cell lung cancer: a decade of progress. , 2002, Chest.

[19]  R. R. Porta,et al.  Survival of 2,991 patients with surgical lung cancer: the denominator effect in survival. , 2005, Chest.

[20]  Jeremy J. W. Chen,et al.  A five-gene signature and clinical outcome in non-small-cell lung cancer. , 2007, The New England journal of medicine.

[21]  Yi Hu,et al.  Three immunomarker support vector machines-based prognostic classifiers for stage IB non-small-cell lung cancer. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[22]  Xianglin Shi,et al.  Constructing Molecular Classifiers for the Accurate Prognosis of Lung Adenocarcinoma , 2006, Clinical Cancer Research.

[23]  F. López-Ríos,et al.  Specific pattern of LKB1 and phospho-acetyl-CoA carboxylase protein immunostaining in human normal tissues and lung carcinomas. , 2007, Human pathology.

[24]  N. Nagelkerke,et al.  A note on a general definition of the coefficient of determination , 1991 .

[25]  Richard Simon,et al.  Gene expression-based prognostic signatures in lung cancer: ready for clinical use? , 2010, Journal of the National Cancer Institute.

[26]  E. Berg,et al.  World Health Organization Classification of Tumours , 2002 .

[27]  J. Lafitte,et al.  Ki-67 expression and patients survival in lung cancer: systematic review of the literature with meta-analysis , 2004, British Journal of Cancer.

[28]  Igor Jurisica,et al.  Three-gene prognostic classifier for early-stage non small-cell lung cancer. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[29]  S. Mukherjee,et al.  A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer. , 2006, The New England journal of medicine.