Literature DB >> 19138936

A prediction model for lung cancer diagnosis that integrates genomic and clinical features.

Jennifer Beane1, Paola Sebastiani, Theodore H Whitfield, Katrina Steiling, Yves-Martine Dumas, Marc E Lenburg, Avrum Spira.   

Abstract

Lung cancer is the leading cause of cancer death due, in part, to lack of early diagnostic tools. Bronchoscopy represents a relatively noninvasive initial diagnostic test in smokers with suspect disease, but it has low sensitivity. We have reported a gene expression profile in cytologically normal large airway epithelium obtained via bronchoscopic brushings, which is a sensitive and specific biomarker for lung cancer. Here, we evaluate the independence of the biomarker from other clinical risk factors and determine the performance of a clinicogenomic model that combines clinical factors and gene expression. Training (n = 76) and test (n = 62) sets consisted of smokers undergoing bronchoscopy for suspicion of lung cancer at five medical centers. Logistic regression models describing the likelihood of having lung cancer using the biomarker, clinical factors, and these data combined were tested using the independent set of patients with nondiagnostic bronchoscopies. The model predictions were also compared with physicians' clinical assessment. The gene expression biomarker is associated with cancer status in the combined clinicogenomic model (P < 0.005). There is a significant difference in performance of the clinicogenomic relative to the clinical model (P < 0.05). In the test set, the clinicogenomic model increases sensitivity and negative predictive value to 100% and results in higher specificity (91%) and positive predictive value (81%) compared with other models. The clinicogenomic model has high accuracy where physician assessment is most uncertain. The airway gene expression biomarker provides information about the likelihood of lung cancer not captured by clinical factors, and the clinicogenomic model has the highest prediction accuracy. These findings suggest that use of the clinicogenomic model may expedite more invasive testing and definitive therapy for smokers with lung cancer and reduce invasive diagnostic procedures for individuals without lung cancer.

Entities:  

Mesh:

Substances:

Year:  2008        PMID: 19138936      PMCID: PMC4167688          DOI: 10.1158/1940-6207.CAPR-08-0011

Source DB:  PubMed          Journal:  Cancer Prev Res (Phila)        ISSN: 1940-6215


  26 in total

Review 1.  Phases of biomarker development for early detection of cancer.

Authors:  M S Pepe; R Etzioni; Z Feng; J D Potter; M L Thompson; M Thornquist; M Winget; Y Yasui
Journal:  J Natl Cancer Inst       Date:  2001-07-18       Impact factor: 13.506

2.  Solitary pulmonary nodules: clinical prediction model versus physicians.

Authors:  S J Swensen; M D Silverstein; E S Edell; V F Trastek; G L Aughenbaugh; D M Ilstrup; C D Schleck
Journal:  Mayo Clin Proc       Date:  1999-04       Impact factor: 7.616

3.  Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes.

Authors:  Jennifer Pittman; Erich Huang; Holly Dressman; Cheng-Fang Horng; Skye H Cheng; Mei-Hua Tsou; Chii-Ming Chen; Andrea Bild; Edwin S Iversen; Andrew T Huang; Joseph R Nevins; Mike West
Journal:  Proc Natl Acad Sci U S A       Date:  2004-05-19       Impact factor: 11.205

4.  Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy.

Authors:  Andrew J Stephenson; Alex Smith; Michael W Kattan; Jaya Satagopan; Victor E Reuter; Peter T Scardino; William L Gerald
Journal:  Cancer       Date:  2005-07-15       Impact factor: 6.860

5.  Bronchoscopy for lung cancer.

Authors:  Pieter E Postmus
Journal:  Chest       Date:  2005-07       Impact factor: 9.410

6.  A risk model for prediction of lung cancer.

Authors:  Margaret R Spitz; Waun Ki Hong; Christopher I Amos; Xifeng Wu; Matthew B Schabath; Qiong Dong; Sanjay Shete; Carol J Etzel
Journal:  J Natl Cancer Inst       Date:  2007-05-02       Impact factor: 13.506

7.  Modeling lung cancer risk in case-control studies using a new dose metric of smoking.

Authors:  Sally W Thurston; Geoffrey Liu; David P Miller; David C Christiani
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2005-10       Impact factor: 4.254

Review 8.  Molecular epidemiology of lung cancer.

Authors:  P G Shields
Journal:  Ann Oncol       Date:  1999       Impact factor: 32.976

9.  A method of comparing the areas under receiver operating characteristic curves derived from the same cases.

Authors:  J A Hanley; B J McNeil
Journal:  Radiology       Date:  1983-09       Impact factor: 11.105

10.  Estimating the probability of malignancy in solitary pulmonary nodules. A Bayesian approach.

Authors:  S R Cummings; G A Lillington; R J Richard
Journal:  Am Rev Respir Dis       Date:  1986-09
View more
  42 in total

1.  Characterizing the impact of smoking and lung cancer on the airway transcriptome using RNA-Seq.

Authors:  Jennifer Beane; Jessica Vick; Frank Schembri; Christina Anderlind; Adam Gower; Joshua Campbell; Lingqi Luo; Xiao Hui Zhang; Ji Xiao; Yuriy O Alekseyev; Shenglong Wang; Shawn Levy; Pierre P Massion; Marc Lenburg; Avrum Spira
Journal:  Cancer Prev Res (Phila)       Date:  2011-06

2.  Airway PI3K pathway activation is an early and reversible event in lung cancer development.

Authors:  Adam M Gustafson; Raffaella Soldi; Christina Anderlind; Mary Beth Scholand; Jun Qian; Xiaohui Zhang; Kendal Cooper; Darren Walker; Annette McWilliams; Gang Liu; Eva Szabo; Jerome Brody; Pierre P Massion; Marc E Lenburg; Stephen Lam; Andrea H Bild; Avrum Spira
Journal:  Sci Transl Med       Date:  2010-04-07       Impact factor: 17.956

Review 3.  Molecular targets for cancer chemoprevention.

Authors:  William N William; John V Heymach; Edward S Kim; Scott M Lippman
Journal:  Nat Rev Drug Discov       Date:  2009-03       Impact factor: 84.694

Review 4.  Translating the COPD transcriptome: insights into pathogenesis and tools for clinical management.

Authors:  Julie E Zeskind; Marc E Lenburg; Avrum Spira
Journal:  Proc Am Thorac Soc       Date:  2008-12-01

5.  Similarities and differences between smoking-related gene expression in nasal and bronchial epithelium.

Authors:  Xiaoling Zhang; Paola Sebastiani; Gang Liu; Frank Schembri; Xiaohui Zhang; Yves Martine Dumas; Erika M Langer; Yuriy Alekseyev; George T O'Connor; Daniel R Brooks; Marc E Lenburg; Avrum Spira
Journal:  Physiol Genomics       Date:  2009-12-01       Impact factor: 3.107

Review 6.  Evolving concepts in lung carcinogenesis.

Authors:  Brigitte N Gomperts; Avrum Spira; Pierre P Massion; Tonya C Walser; Ignacio I Wistuba; John D Minna; Steven M Dubinett
Journal:  Semin Respir Crit Care Med       Date:  2011-04-15       Impact factor: 3.119

Review 7.  Integrating omics technologies to study pulmonary physiology and pathology at the systems level.

Authors:  Ravi Ramesh Pathak; Vrushank Davé
Journal:  Cell Physiol Biochem       Date:  2014-04-28

Review 8.  Airway gene expression in chronic obstructive pulmonary disease.

Authors:  Katrina Steiling; Marc E Lenburg; Avrum Spira
Journal:  Proc Am Thorac Soc       Date:  2009-12

9.  Comparison of proteomic and transcriptomic profiles in the bronchial airway epithelium of current and never smokers.

Authors:  Katrina Steiling; Aran Y Kadar; Agnes Bergerat; James Flanigon; Sriram Sridhar; Vishal Shah; Q Rushdy Ahmad; Jerome S Brody; Marc E Lenburg; Martin Steffen; Avrum Spira
Journal:  PLoS One       Date:  2009-04-09       Impact factor: 3.240

10.  Determining relative importance of variables in developing and validating predictive models.

Authors:  Joseph Beyene; Eshetu G Atenafu; Jemila S Hamid; Teresa To; Lillian Sung
Journal:  BMC Med Res Methodol       Date:  2009-09-14       Impact factor: 4.615

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.