"skipMainNavigation"
closeDrawerMenuopenDrawerMenu
Search
Quick Search in Journals
Quick Search anywhere
Advanced Search
Radiology
Menu
  • Information
    • Previous
    Next
    Original Research

    Non–Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data—Methods and Preliminary Results

    Published Online:Aug 1 2012https://doi.org/10.1148/radiol.12111607

    Abstract

    A radiogenomics strategy to accelerate the identification of prognostically important imaging biomarkers is presented, and preliminary results were demonstrated in a small cohort of patients with non-small cell lung cancer for whom CT and PET images and gene expression microarray data were available but for whom survival data were not available.

    Purpose

    To identify prognostic imaging biomarkers in non–small cell lung cancer (NSCLC) by means of a radiogenomics strategy that integrates gene expression and medical images in patients for whom survival outcomes are not available by leveraging survival data in public gene expression data sets.

    Materials and Methods

    A radiogenomics strategy for associating image features with clusters of coexpressed genes (metagenes) was defined. First, a radiogenomics correlation map is created for a pairwise association between image features and metagenes. Next, predictive models of metagenes are built in terms of image features by using sparse linear regression. Similarly, predictive models of image features are built in terms of metagenes. Finally, the prognostic significance of the predicted image features are evaluated in a public gene expression data set with survival outcomes. This radiogenomics strategy was applied to a cohort of 26 patients with NSCLC for whom gene expression and 180 image features from computed tomography (CT) and positron emission tomography (PET)/CT were available.

    Results

    There were 243 statistically significant pairwise correlations between image features and metagenes of NSCLC. Metagenes were predicted in terms of image features with an accuracy of 59%–83%. One hundred fourteen of 180 CT image features and the PET standardized uptake value were predicted in terms of metagenes with an accuracy of 65%–86%. When the predicted image features were mapped to a public gene expression data set with survival outcomes, tumor size, edge shape, and sharpness ranked highest for prognostic significance.

    Conclusion

    This radiogenomics strategy for identifying imaging biomarkers may enable a more rapid evaluation of novel imaging modalities, thereby accelerating their translation to personalized medicine.

    © RSNA, 2012

    Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12111607/-/DC1

    References

    • 1 Sotiriou C. Molecular biology in oncology and its influence on clinical practice: gene expression profiling [abstr]. Ann Oncol 2009;20(Suppl 4):v10. Medline, Google Scholar
    • 2 Pao W, Kris MG, Iafrate AJ, et al.. Integration of molecular profiling into the lung cancer clinic. Clin Cancer Res 2009;15(17):5317–5322. Crossref, Medline, Google Scholar
    • 3 Gevaert O, De Moor B. Prediction of cancer outcome using DNA microarray technology: past, present and future. Expert Opin Med Diagn 2009;3(2):157–165. Crossref, Medline, Google Scholar
    • 4 Segal E, Sirlin CB, Ooi C, et al.. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 2007;25(6):675–680. Crossref, Medline, Google Scholar
    • 5 Diehn M, Nardini C, Wang DS, et al.. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A 2008;105(13):5213–5218. Crossref, Medline, Google Scholar
    • 6 Kuo MD, Gollub J, Sirlin CB, Ooi C, Chen X. Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. J Vasc Interv Radiol 2007;18(7):821–831. Crossref, Medline, Google Scholar
    • 7 Rutman AM, Kuo MD. Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol 2009;70(2):232–241. Crossref, Medline, Google Scholar
    • 8 Jemal A, Siegel R, Xu J, Ward E. Cancer statistics, 2010. CA Cancer J Clin 2010;60(5):277–300. Google Scholar
    • 9 National Lung Screening Trial Research Team, Aberle DR, Berg CD, et al.. The National Lung Screening Trial: overview and study design. Radiology 2011;258(1):243–253. Link, Google Scholar
    • 10 National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al.. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011;365(5):395–409. Crossref, Medline, Google Scholar
    • 11 Barrett T, Troup DB, Wilhite SE, et al.. NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res 2009;37(Database issue):D885–D890. Crossref, Medline, Google Scholar
    • 12 Parkinson H, Sarkans U, Kolesnikov N, et al.. ArrayExpress update—an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res 2011;39(Database issue):D1002–D1004. Crossref, Medline, Google Scholar
    • 13 Lee ES, Son DS, Kim SH, 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. Clin Cancer Res 2008;14(22):7397–7404. Crossref, Medline, Google Scholar
    • 14 Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001;98(9):5116–5121. Crossref, Medline, Google Scholar
    • 15 Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010;33(1):1–22. Crossref, Medline, Google Scholar
    • 16 Inoue A, Nukiwa T. Gene mutations in lung cancer: promising predictive factors for the success of molecular therapy. PLoS Med 2005;2(1):e13. Crossref, Medline, Google Scholar
    • 17 Riely GJ, Marks J, Pao W. KRAS mutations in non-small cell lung cancer. Proc Am Thorac Soc 2009;6(2):201–205. Crossref, Medline, Google Scholar
    • 18 Brose MS, Volpe P, Feldman M, et al.. BRAF and RAS mutations in human lung cancer and melanoma. Cancer Res 2002;62(23):6997–7000. Medline, Google Scholar
    • 19 Bild AH, Yao G, Chang JT, et al.. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006;439(7074):353–357. Crossref, Medline, Google Scholar
    • 20 Manalo DJ, Rowan A, Lavoie T, et al.. Transcriptional regulation of vascular endothelial cell responses to hypoxia by HIF-1. Blood 2005;105(2):659–669. Crossref, Medline, Google Scholar
    • 21 Elvidge GP, Glenny L, Appelhoff RJ, Ratcliffe PJ, Ragoussis J, Gleadle JM. Concordant regulation of gene expression by hypoxia and 2-oxoglutarate-dependent dioxygenase inhibition: the role of HIF-1alpha, HIF-2alpha, and other pathways. J Biol Chem 2006;281(22):15215–15226. Crossref, Medline, Google Scholar
    • 22 Sweet-Cordero A, Mukherjee S, Subramanian A, et al.. An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis. Nat Genet 2005;37(1):48–55. Crossref, Medline, Google Scholar
    • 23 Mazumdar J, Hickey MM, Pant DK, et al.. HIF-2alpha deletion promotes Kras-driven lung tumor development. Proc Natl Acad Sci U S A 2010;107(32):14182–14187. Crossref, Medline, Google Scholar
    • 24 Bergamaschi A, Tagliabue E, Sørlie T, et al.. Extracellular matrix signature identifies breast cancer subgroups with different clinical outcome. J Pathol 2008;214(3):357–367. Crossref, Medline, Google Scholar
    • 25 Charafe-Jauffret E, Ginestier C, Monville F, et al.. Gene expression profiling of breast cell lines identifies potential new basal markers. Oncogene 2006;25(15):2273–2284. Crossref, Medline, Google Scholar
    • 26 Seo DC, Sung JM, Cho HJ, et al.. Gene expression profiling of cancer stem cell in human lung adenocarcinoma A549 cells. Mol Cancer 2007;6:75. Crossref, Medline, Google Scholar
    • 27 Xi LQ, Lyons-Weiler J, Coello MC, et al.. Prediction of lymph node metastasis by analysis of gene expression profiles in primary lung adenocarcinomas. Clin Cancer Res 2005;11(11):4128–4135. Crossref, Medline, Google Scholar
    • 28 Port JL, Kent MS, Korst RJ, Libby D, Pasmantier M, Altorki NK. Tumor size predicts survival within stage IA non-small cell lung cancer. Chest 2003;124(5):1828–1833. Crossref, Medline, Google Scholar
    • 29 Massarelli E, Varella-Garcia M, Tang X, et al.. KRAS mutation is an important predictor of resistance to therapy with epidermal growth factor receptor tyrosine kinase inhibitors in non-small-cell lung cancer. Clin Cancer Res 2007;13(10):2890–2896. Crossref, Medline, Google Scholar
    • 30 Mascaux C, Iannino N, Martin B, et al.. The role of RAS oncogene in survival of patients with lung cancer: a systematic review of the literature with meta-analysis. Br J Cancer 2005;92(1):131–139. Crossref, Medline, Google Scholar
    • 31 Yoshino I, Nakanishi R, Kodate M, et al.. Pleural retraction and intra-tumoral air-bronchogram as prognostic factors for stage I pulmonary adenocarcinoma following complete resection. Int Surg 2000;85(2):105–112. Medline, Google Scholar
    • 32 Ikehara M, Saito H, Kondo T, et al.. Comparison of thin-section CT and pathological findings in small solid-density type pulmonary adenocarcinoma: prognostic factors from CT findings. Eur J Radiol 2012;81(1):189–194. Crossref, Medline, Google Scholar
    • 33 Travis WD, Brambilla E, Noguchi M, et al.. International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol 2011;6(2):244–285. Crossref, Medline, Google Scholar
    • 34 Rubin DL, Rodriguez C, Shah P, Beaulieu C. iPad: Semantic annotation and markup of radiological images. AMIA Annu Symp Proc 2008:626–630. Google Scholar

    Article History

    Received August 5, 2011; revision requested September 30; revision received November 26; accepted December 29; final version accepted February 21, 2012.
    Published online: Aug 2012
    Published in print: Aug 2012