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Originally published as JCO Early Release 10.1200/JCO.2004.05.931 on July 12 2004 © 2004 American Society of Clinical Oncology.
Assessing Prognosis in Non–Small-Cell Lung Cancer: Avenues to a More Complete Picture?
UCLA Lung Cancer Research Program, Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA, Los Angeles, CA Lung cancer is the leading cause of cancer mortality, with more than 1 million deaths worldwide in 2000.1, 2 With an overall 5-year survival rate of 15%, investigations are proceeding on multiple fronts to improve prognosis. Studies in prevention, early detection, risk assessment, surgical staging, and genetic and proteomic profiling are attempting to define new opportunities in lung cancer detection and therapy. The discovery of new targets and the application of targeted therapies are beginning to shape our understanding of the complex pathways that operate in this disease, leading to the development of pathogenesis-based therapies.3 Numerous studies have focused attention on improving survival in early stage non–small-cell lung cancer (NSCLC). Recently, cisplatin-based adjuvant chemotherapy has been found to improve survival among patients with completely resected NSCLC.4 This group of patients constitutes an important population for intervention, with as many as 200,000 individuals worldwide potentially eligible for adjuvant therapy each year. However, it has been suggested that NSCLC, particularly adenocarcinomas, can be considered to be a group of heterogeneous clinical disorders that share some molecular and cellular characteristics, but that have variable clinical behaviors and prognoses.5, 6 Can more sophisticated measures than histopathology alone be utilized to understand the heterogeneity that leads to these diverse clinical outcomes, and then to identify high-risk groups suitable for intervention? Prognostic assessments in the postsurgical patient population have been the focus of many investigations.5 Two recent studies published in the Journal of Clinical Oncology (JCO) have taken different approaches for assessment of prognostic indicators for patients in this setting. A variety of specific proteins have been suggested as markers of prognosis in NSCLC. The first approach focuses on evaluation of a single protein, TIMP-1, to determine its prognostic significance in the postsurgical setting. TIMP-1 is a member of a family of proteins characterized for their capacity to inhibit metalloproteinase (MP) activity.7 Despite being named for their MP inhibitory activities, these proteins are multifunctional. TIMP-1 has the capacity to enhance apoptosis resistance, promote cell proliferation, and facilitate angiogenesis.7 A recent study by Rhee et al8 implicates TIMP-1 as an important element in epithelial neoplastic progression. In the current issue of the JCO, Aljada et al9 focus on TIMP-1 as a marker associated with progression. TIMP-1 protein expression was examined by immunohistochemistry in 160 patients with primary resectable NSCLC. Detected in 27% of tumors, elevated TIMP-1 protein was associated with adverse outcome. In multivariate analyses, patients with high TIMP-1 expression had a significantly increased risk of death when compared to those with low expression. Thus, the findings of Aljada et al are consistent with the previously documented functions of TIMP-1, as well as with clinical investigations suggesting that elevated TIMP-1 in the serum or tumor constitutes a marker of poor prognosis in lung cancer and other malignancies.10, 11 The study by Aljada et al9 also addresses some of the difficulties inherent in the evaluation of individual proteins in this setting, including the difficulty in determining the biologic and statistical interactions of TIMP-1 with other factors that could impact patient outcome. Recent advances in molecular and protein profiling offer the capacity to better define risk of progression by assessment of global patterns of gene and protein expression.6 The genomic or proteomic approach to biomarker discovery, which have the capacity to utilize the patterns of gene or protein expression rather than thresholds of individual markers, opens a window of enormous opportunity in clinical lung cancer research.4 The initial assessments of NSCLC prognosis in the postsurgical patient by gene array profiling and proteomics demonstrate that these technologies can provide a broader and more complete picture than that provided by assessment of individual proteins.12-16 These molecular and protein prediction profiles open important possibilities for new methods to classify NSCLC. These classifications could then be exploited in patient treatment strategies. Several studies have now shown that patient survival can be correlated with lung cancer microarray expression profiles.13-15 Current studies are utilizing gene expression profiling with application of hierarchical clustering, an unsupervised class discovery approach, to classify lung adenocarcinomas.6 These studies have been extended by a recent article published in the JCO by Endoh et al,17 who utilized quantitative real-time reverse transcriptase polymerase chain reaction to establish a prognostic model of pulmonary adenocarcinoma by expression profiling. From those identified in the original gene expression profiling studies,12, 14 Endoh et al first selected 44 genes to test whether their expression patterns were relevant to prognosis in a patient cohort. Following initial evaluation of these genes, in order to have a set of markers that could be used in clinical practice, eight genes were selected that jointly predict patient prognosis. Importantly, both unsupervised clustering and supervised methods were useful in predicting survival. These authors are applauded for validating a specific subset of genes identified in earlier studies. This article is an example of what should be the second level of microarray-based science. The first level, of course, is gene discovery, which is exploratory and generally only validated by reverse transcriptase polymerase chain reaction within the same patient set. The second level, selecting candidate genes from the exploratory work and validating them in a new cohort, is of vital importance. What makes this report from Endoh et al a superior second level analysis is that it not only validates and refines the earlier work by finding a prognostic model based on a subset of the original genes, but it includes a third level validation of this prognostic model using a separate independent patient cohort. This strategy of gene discovery and refinement and validation of the refined model appears to be a powerful methodology for uncovering true relationships and overcoming the principle problems with microarray analysis, which are multiple comparisons and false-positives. Because they appear to be highly correlated with patient outcome, the first sets of comprehensive expression-based classifications for lung adenocarcinoma raise the promise of novel gene or protein-based subsets.12-16, 18 Although substantial work has been done in the field of gene array and proteomic profiling in lung cancer, routine clinical applications of these methods are not yet in place. Additional larger studies are underway to refine and validate profiles so that they can be used in daily practice. Many unanswered questions remain, and new avenues are open for the application of these technologies in lung cancer.19 Assessment of prognosis in the postsurgical setting is only one of several end points that can be evaluated. Other areas, such as early detection and prediction of response to targeted therapies,20-22 may also benefit from the power of global gene and protein expression evaluations in lung cancer patients and individuals at risk.6 These approaches hold substantial promise for clinical impact in lung cancer. Authors' Disclosures of Potential Conflicts of Interest The following authors or their immediate family members have indicated a financial interest. No conflict exists for drugs or devices used in a study if they are not being evaluated as part of the investigation. Consultant/Advisory Role: Matthew Meyerson, Novartis. Research Funding: Matthew Meyerson, Novartis. For a detailed description of these categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration form and the Disclosures of Potential Conflicts of Interest section of Information for Contributors found in the front of every issue. REFERENCES 1. Parkin DM, Bray FI, Devesa SS: Cancer burden in the year 2000. The global picture. Eur J Cancer 37:S4-66, 2001 (suppl 8) 2. Proctor RN: Tobacco and the global lung cancer epidemic. Nat Rev Cancer 1:82-86, 2001[CrossRef][Medline]
3. Dy GK, Adjei AA: Novel targets for lung cancer therapy: Part I. J Clin Oncol 20:2881-2894, 2002
4. Liu ET, Karuturi KR: Microarrays and clinical investigations. N Engl J Med 350:1595-1597, 2004
5. Brundage MD, Davies D, Mackillop WJ: Prognostic factors in non-small cell lung cancer: A decade of progress. Chest 122:1037-1057, 2002 6. Meyerson M, Franklin WA, Kelley MJ: Molecular classification and molecular genetics of human lung cancers. Semin Oncol 31:4-19, 2004 7. Jiang Y, Goldberg ID, Shi YE: Complex roles of tissue inhibitors of metalloproteinases in cancer. Oncogene 21:2245-2252, 2002[CrossRef][Medline]
8. Rhee JS, Diaz R, Korets L, et al: TIMP-1 alters susceptibility to carcinogenesis. Cancer Res 64:952-961, 2004
9. Aljada IS, Ramnath N, Donohue K, et al: Upregulation of the Tissue Inhibitor of Metalloproteinase-1 Protein Is Associated With Progression of Human Non–Small-Cell Lung Cancer. J Clin Oncol 22:3218-3229, 2004 10. Thomas P, Khokha R, Shepherd FA, et al: Differential expression of matrix metalloproteinases and their inhibitors in non-small cell lung cancer. J Pathol 190:150-156, 2000[CrossRef][Medline]
11. Ylisirnio S, Hoyhtya M, Makitaro R, et al: Elevated serum levels of type I collagen degradation marker ICTP and tissue inhibitor of metalloproteinase (TIMP) 1 are associated with poor prognosis in lung cancer. Clin Cancer Res 7:1633-1637, 2001
12. Bhattacharjee A, Richards WG, Staunton J, et al: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci U S A 98:13790-13795, 2001
13. Chen G, Gharib TG, Wang H, et al: Protein profiles associated with survival in lung adenocarcinoma. Proc Natl Acad Sci U S A 100:13537-13542, 2003
14. Garber ME, Troyanskaya OG, Schluens K, et al: Diversity of gene expression in adenocarcinoma of the lung. Proc Natl Acad Sci U S A 98:13784-13789, 2001 15. Ramaswamy S, Ross KN, Lander ES, et al: A molecular signature of metastasis in primary solid tumors. Nat Genet 33:49-54, 2003[CrossRef][Medline] 16. Yanagisawa K, Shyr Y, Xu BJ, et al: Proteomic patterns of tumour subsets in non-small-cell lung cancer. Lancet 362:433-439, 2003[CrossRef][Medline]
17. Endoh H, Tomida S, Yatabe Y, et al: Prognostic model of pulmonary adenocarcinoma by expression profiling of eight genes as determined by quantitative real-time reverse transcriptase polymerase chain reaction. J Clin Oncol 22:811-819, 2004
18. Wigle DA, Jurisica I, Radulovich N, et al: Molecular profiling of non-small cell lung cancer and correlation with disease-free survival. Cancer Res 62:3005-3008, 2002 19. Wigle DA, Tsao M, Jurisica I: Making sense of lung-cancer gene-expression profiles. Genome Biol 5:309, 2004[CrossRef][Medline]
20. Lynch TJ, Bell DW, Sordella R, et al: Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 350:2129-2139, 2004
21. Paez JG, Janne PA, Lee JC, et al: EGFR mutations in lung cancer: Correlation with clinical response to gefitinib therapy. Science 304:1497-1500, 2004 22. Dancey JE: Predictive factors for epidermal growth factor receptor inhibitors—The bull's-eye hits the arrow. Cancer Cell 5:411-415, 2004[CrossRef][Medline]
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Copyright © 2004 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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