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Gene-expression profiles predict survival of patients with lung adenocarcinoma

Nature Medicine volume 8, pages 816–824 (2002)Cite this article

Abstract

Histopathology is insufficient to predict disease progression and clinical outcome in lung adenocarcinoma. Here we show that gene-expression profiles based on microarray analysis can be used to predict patient survival in early-stage lung adenocarcinomas. Genes most related to survival were identified with univariate Cox analysis. Using either two equivalent but independent training and testing sets, or 'leave-one-out' cross-validation analysis with all tumors, a risk index based on the top 50 genes identified low-risk and high-risk stage I lung adenocarcinomas, which differed significantly with respect to survival. This risk index was then validated using an independent sample of lung adenocarcinomas that predicted high- and low-risk groups. This index included genes not previously associated with survival. The identification of a set of genes that predict survival in early-stage lung adenocarcinoma allows delineation of a high-risk group that may benefit from adjuvant therapy.

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Figure 1: Unsupervised classification analysis of lung adenocarcinomas.
Figure 2: Validation analyses of gene-expression profiling.
Figure 3: Gene-expression profiles and patient survival.
Figure 4: Gene expression patterns of top survival genes
Figure 5: Gene amplification and protein expression of survival-related genes.

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References

  1. Fry, W.A., Phillips, J.L. & Menck, H.R. Ten-year survey of lung cancer treatments and survival in hospitals in the United States. Cancer 86, 1867–1876 (1999).

    Article  CAS  Google Scholar 

  2. Williams, D.E. et al. Survival of patients surgically treated for stage I lung cancer. J. Thorac. Cardiovasc. Surg. 82, 70–76 (1981).

    CAS  PubMed  Google Scholar 

  3. Pairolero, P.C. et al. Postsurgical stage I bronchogenic carcinoma: Morbid implications of recurrent disease. Ann. Thorac. Surg. 38, 331–338 (1984).

    Article  CAS  Google Scholar 

  4. Naruke, T. et al. Prognosis and survival in resected carcinoma based on the new international staging system. J. Thorac. Cardiovasc. Surg. 96, 440–447 (1988).

    CAS  PubMed  Google Scholar 

  5. Kaisermann, M.C. et al. Evolving features of lung adenocarcinoma in Rio de Janeiro, Brazil. Oncol. Rep. 8, 189–192 (2001).

    CAS  PubMed  Google Scholar 

  6. Roggli, V.L. et al. Lung cancer heterogeneity: A blinded and randomized study of 100 consecutive cases. Hum. Pathol. 16, 569–579 (1985).

    Article  CAS  Google Scholar 

  7. Gail, M.H. et al. Prognostic factors in patients with resected stage I non-small cell lung cancer: A report from the Lung Cancer Study Group. Cancer 54, 1802–1813 (1984).

    Article  CAS  Google Scholar 

  8. Takise, A. et al. Histopathologic prognostic factors in adenocarcinomas of the peripheral lung less than 2 cm in diameter. Cancer 61, 2083–2088 (1988).

    Article  CAS  Google Scholar 

  9. Ichinose, Y. et al. Is T factor of the TMN staging system a predominant prognostic factor in pathologic stage I non-small cell lung cancer. J. Thorac. Cardiovasc. Surg. 106, 90–94 (1993).

    CAS  PubMed  Google Scholar 

  10. Harpole, D.H. et al. A prognostic model of recurrence and death in stage I non-small cell lung cancer utilizing presentation, histopathology, and oncoprotein expression. Cancer Res. 55, 51–56 (1995).

    CAS  PubMed  Google Scholar 

  11. Rodenhuis, S. et al. Mutational activation of the K-ras oncogene: A possible pathogenic factor in adenocarcinoma of the lung. N. Engl. J. Med. 317, 929–935 (1987).

    Article  CAS  Google Scholar 

  12. Slebos, R.J.C. et al. K-ras oncogene activation as a prognostic marker in adenocarcinoma of the lung. N. Engl. J. Med. 323, 561–565 (1990).

    Article  CAS  Google Scholar 

  13. Horio, Y. et al. Prognostic significance of p53 mutations and 3p deletions in primary resected non-small cell lung cancer. Cancer Res. 53, 1–4 (1993).

    CAS  PubMed  Google Scholar 

  14. Kern, J.A. et al. C-erbB-2 expression and codon 12 K-ras mutations both predict shortened survival for patients with pulmonary adenocarcinomas. J. Clin. Invest. 93, 516–520 (1994).

    Article  CAS  Google Scholar 

  15. Ebina, M. et al. Relationship of p53 overexpresson and up-regulation of proliferating cell nuclear antigen with the clinical course of non-small cell lung cancer. Cancer Res. 54, 2496–2503 (1994).

    CAS  PubMed  Google Scholar 

  16. Mehdi, S.A. et al. Prognostic markers in resected stage I and II non-small cell lung cancer: an analysis of 260 patients with 5 year follow-up. Clin. Lung Cancer 1, 59–67 (1997).

    Article  Google Scholar 

  17. Schneider, P.M. et al. Multiple molecular marker testing (p53, c-Ki-ras, c-erbB-2) improves estimation of prognosis in potentially curative resected non-small cell lung cancer. Br. J. Cancer 83, 473–479 (2000).

    Article  CAS  Google Scholar 

  18. Herbst, R.S. et al. Differential expression of E-cadherin and type IV collagenase genes predicts outcome in patients with stage I non-small cell lung carcinoma. Clin. Can. Res. 6, 790–797 (2000).

    CAS  Google Scholar 

  19. Liotta, L. & Petricion, E. Molecular profiling of human cancer. Nature Rev. Genet. 1, 48–56 (2000).

    Article  CAS  Google Scholar 

  20. Golub, T.R. Editorial: Genome-wide views of cancer. N. Engl. J. Med. 344, 601–602 (2001).

    Article  CAS  Google Scholar 

  21. Bhattacharjee, A. et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc. Natl. Acad. Sci. USA 98, 13790–13795 (2001).

    Article  CAS  Google Scholar 

  22. Garber, M.E. et al. Diversity of gene expression in adenocarcinoma of the lung. Proc. Natl. Acad. Sci. USA 98, 13784–13789 (2001).

    Article  CAS  Google Scholar 

  23. Mills, N.E. et al. Increased prevalence of K-ras oncogene mutations in lung adenocarcinoma. Cancer Res. 55, 1444–1447 (1995).

    CAS  PubMed  Google Scholar 

  24. Giordano T.J. et al. Organ-specific molecular classification of lung, colon and ovarian adenocarcinomas using gene expression profiles. Am. J. Pathol. 159, 1231–1238 (2001).

    Article  CAS  Google Scholar 

  25. Albertson, D.G. et al. Quantitative mapping of amplicon structure by array CGH identifies CYP24 as a candidate oncogene. Nature Genet. 25, 144–146 (2000).

    Article  CAS  Google Scholar 

  26. Kansra, S. et al. IGFBP-3 mediates TGF β1 proliferative response in colon cancer cells. Int. J. Cancer 87, 373–378 (2000).

    Article  CAS  Google Scholar 

  27. Vadgama J.V. et al. Plasma insulin-like growth factor-I and serum IGF-binding protein 3 can be associated with the progression of breast cancer, and predict the risk of recurrence and the probability of survival in African-American and Hispanic women. Oncology 57, 330–340 (1999).

    Article  CAS  Google Scholar 

  28. Volm, M., Mattern, J. & Stammler, G. Up-regulation of heat shock protein 70 in adenocarcinoma of the lung in smokers. Anticancer Res. 15, 2607–2609 (1995).

    CAS  PubMed  Google Scholar 

  29. Ciocca, D.R. et al. Heat shock protein hsp70 in patients with auxillary lymph node-positive breast cancer:prognostic implications. J. Natl. Cancer. Inst. 85, 570–574 (1993).

    Article  CAS  Google Scholar 

  30. Rotenberg, Z. et al. Total lactate dehydrogenase and its isoenzymes in serum of patients with non-small cell lung cancer. Clin. Chem. 34, 668–670 (1988).

    CAS  PubMed  Google Scholar 

  31. Krepela, E. et al. Cysteine proteases and cysteine protease inhibitors in non-small cell lung cancer. Neoplasma 45, 318–331 (1998).

    CAS  PubMed  Google Scholar 

  32. Kos, J. et al. Cysteine proteinases and their inhibitors in extracellular fluids: Markers for diagnosis and prognosis in cancer. Int. J. Biol. Markers 15, 84–89 (2000).

    Article  CAS  Google Scholar 

  33. Golub, T.R. et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999).

    Article  CAS  Google Scholar 

  34. Hedenfalk, I. et al. Gene-expression profiles in hereditary breast cancer. N. Engl. J. Med. 344, 539–548 (2001).

    Article  CAS  Google Scholar 

  35. Ohta, Y. et al. Vascular endothelial growth factor and lymph node metastasis in primary lung cancer. Br. J. Cancer. 76, 1041–1045 (1997).

    Article  CAS  Google Scholar 

  36. Shibusa, T., Shijubo, N. & Abe, S. Tumor angiogenesis and vascular endothelial growth factor expression in stage I lung adenocarcinoma. Clin. Cancer Res. 4, 1483–1487 (1998).

    CAS  PubMed  Google Scholar 

  37. Girardin, S.E. & Yaniv, M. A direct interaction between JNK1 and CrkII is critical for Rac1-induced JNK activation. EMBO J. 20, 3437–3446 (2001).

    Article  CAS  Google Scholar 

  38. Liu, E. et al. The Ras-mitogen-activated protein kinase pathway is critical for the activation of matrix metalloproteinase secretion and the invasiveness in v-crk-transformed 3Y1. Cancer Res. 60, 2361–64 (2000).

    CAS  PubMed  Google Scholar 

  39. Hanahan, D. & Weinberg, R.A. The hallmarks of cancer. Cell 100, 57–70 (2000).

    Article  CAS  Google Scholar 

  40. Hanson, L.A. et al. Expression of the glucocorticoid receptor and K-ras genes in urethan-induced mouse lung tumors and transformed cell lines. Exp. Lung. Res. 17, 371–387 (1991).

    Article  CAS  Google Scholar 

  41. Lin, L. et al. A minimal critical region of the 8p22-23 amplicon in esophageal adenocarcinomas defined using STS-amplification mapping and quantitative PCR includes the GATA-4 gene. Cancer Res. 60, 1341–1347 (2000).

    CAS  PubMed  Google Scholar 

  42. Kononen, J. et al. Tissue microarrays for high throughput molecular profiling of tumor specimens. Nature Med. 4, 844–847 (1998).

    Article  CAS  Google Scholar 

  43. Johnson, R. & Wichern, D.W. Applied Multivariate Statistical Analysis. 543–578 (Prentice Hall, New Jersey, 1988).

    Google Scholar 

  44. Stone, M. Asymptomics for and against cross-validation. Biometrika 64, 29–38 (1977).

    Article  Google Scholar 

  45. Cox, D.R. Regression models and life tables. J.R. Stat. Soc. 34, 187–220 (1972).

    Google Scholar 

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Acknowledgements

We thank D. Sanders for technical assistance; D. Sing for assistance with the figures; and G. Omenn for critical reading of this manuscript. This work was supported by National Cancer Institute grant: U19 CA-85953 and the Tissue Core of the University of Michigan Comprehensive Cancer Center (NIH CA-46952).

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Authors and Affiliations

  1. Department of Surgery, University of Michigan, Ann Arbor, Michigan, USA

    David G. Beer, Lin Lin, Guoan Chen, Tarek G. Gharib, Mark D. Iannettoni & Mark B. Orringer

  2. Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA

    Sharon L.R. Kardia & Albert M. Levin

  3. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA

    Chiang-Ching Huang, Satoru Hayasaka & Jeremy M.G. Taylor

  4. Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA

    Thomas J. Giordano, Dafydd G. Thomas & Michelle L. Lizyness

  5. Department of Pediatrics, University of Michigan, Ann Arbor, Michigan, USA

    David E. Misek, Rork Kuick & Samir Hanash

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  1. David G. Beer
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Correspondence to David G. Beer.

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Beer, D., Kardia, S., Huang, CC. et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med 8, 816–824 (2002). https://doi.org/10.1038/nm733

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