Genomic Markers for Decision Making: What is Preventing Us from Using Markers?

Overview

Journal Nat Rev Clin Oncol

Specialty Oncology

Date 2009 Dec 17

PMID 20010899

Citations 5

Authors

Vicky M Coyle

Patrick G Johnston

Affiliations

Soon will be listed here.

Abstract

The advent of novel genomic technologies that enable the evaluation of genomic alterations on a genome-wide scale has significantly altered the field of genomic marker research in solid tumors. Researchers have moved away from the traditional model of identifying a particular genomic alteration and evaluating the association between this finding and a clinical outcome measure to a new approach involving the identification and measurement of multiple genomic markers simultaneously within clinical studies. This in turn has presented additional challenges in considering the use of genomic markers in oncology, such as clinical study design, reproducibility and interpretation and reporting of results. This Review will explore these challenges, focusing on microarray-based gene-expression profiling, and highlights some common failings in study design that have impacted on the use of putative genomic markers in the clinic. Despite these rapid technological advances there is still a paucity of genomic markers in routine clinical use at present. A rational and focused approach to the evaluation and validation of genomic markers is needed, whereby analytically validated markers are investigated in clinical studies that are adequately powered and have pre-defined patient populations and study endpoints. Furthermore, novel adaptive clinical trial designs, incorporating putative genomic markers into prospective clinical trials, will enable the evaluation of these markers in a rigorous and timely fashion. Such approaches have the potential to facilitate the implementation of such markers into routine clinical practice and consequently enable the rational and tailored use of cancer therapies for individual patients.

Citing Articles

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References

Bueno-de-Mesquita J, van Harten W, Retel V, J van t Veer L, van Dam F, Karsenberg K . Use of 70-gene signature to predict prognosis of patients with node-negative breast cancer: a prospective community-based feasibility study (RASTER). Lancet Oncol. 2007; 8(12):1079-1087. DOI: 10.1016/S1470-2045(07)70346-7. View

Groene J, Mansmann U, Meister R, Staub E, Roepcke S, Heinze M . Transcriptional census of 36 microdissected colorectal cancers yields a gene signature to distinguish UICC II and III. Int J Cancer. 2006; 119(8):1829-36. DOI: 10.1002/ijc.22027. View

Scicchitano M, Dalmas D, Bertiaux M, Anderson S, Turner L, Thomas R . Preliminary comparison of quantity, quality, and microarray performance of RNA extracted from formalin-fixed, paraffin-embedded, and unfixed frozen tissue samples. J Histochem Cytochem. 2006; 54(11):1229-37. DOI: 10.1369/jhc.6A6999.2006. View

Matsuyama R, Togo S, Shimizu D, Momiyama N, Ishikawa T, Ichikawa Y . Predicting 5-fluorouracil chemosensitivity of liver metastases from colorectal cancer using primary tumor specimens: three-gene expression model predicts clinical response. Int J Cancer. 2006; 119(2):406-13. DOI: 10.1002/ijc.21843. View

Bibikova M, Talantov D, Chudin E, Yeakley J, Chen J, Doucet D . Quantitative gene expression profiling in formalin-fixed, paraffin-embedded tissues using universal bead arrays. Am J Pathol. 2004; 165(5):1799-807. PMC: 1618650. DOI: 10.1016/S0002-9440(10)63435-9. View

Pusztai L, Hess K . Clinical trial design for microarray predictive marker discovery and assessment. Ann Oncol. 2004; 15(12):1731-7. DOI: 10.1093/annonc/mdh466. View

McShane L, Altman D, Sauerbrei W, Taube S, Gion M, Clark G . REporting recommendations for tumor MARKer prognostic studies (REMARK). Nat Clin Pract Oncol. 2005; 2(8):416-22. View

Geyer F, Reis-Filho J . Microarray-based gene expression profiling as a clinical tool for breast cancer management: are we there yet?. Int J Surg Pathol. 2008; 17(4):285-302. DOI: 10.1177/1066896908328577. View

Moher D, Schulz K, Altman D . The CONSORT statement: revised recommendations for improving the quality of reports of parallel-group randomized trials. JAMA. 2001; 285(15):1987-91. DOI: 10.1001/jama.285.15.1987. View

10.

Simon R, Radmacher M, Dobbin K, McShane L . Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst. 2003; 95(1):14-8. DOI: 10.1093/jnci/95.1.14. View

11.

Mook S, Bonnefoi H, Pruneri G, Larsimont D, Jaskiewicz J, Sabadell M . Daily clinical practice of fresh tumour tissue freezing and gene expression profiling; logistics pilot study preceding the MINDACT trial. Eur J Cancer. 2009; 45(7):1201-1208. DOI: 10.1016/j.ejca.2009.01.004. View

12.

Simon R . The use of genomics in clinical trial design. Clin Cancer Res. 2008; 14(19):5984-93. DOI: 10.1158/1078-0432.CCR-07-4531. View

13.

Srivastava P, Kuffer S, Brors B, Shahi P, Li L, Kenzelmann M . A cut-off based approach for gene expression analysis of formalin-fixed and paraffin-embedded tissue samples. Genomics. 2008; 91(6):522-9. DOI: 10.1016/j.ygeno.2008.03.003. View

14.

Simon R . Challenges of microarray data and the evaluation of gene expression profile signatures. Cancer Invest. 2008; 26(4):327-32. DOI: 10.1080/07357900801971032. View

15.

Friederichs J, Rosenberg R, Mages J, Janssen K, Maeckl C, Nekarda H . Gene expression profiles of different clinical stages of colorectal carcinoma: toward a molecular genetic understanding of tumor progression. Int J Colorectal Dis. 2005; 20(5):391-402. DOI: 10.1007/s00384-004-0722-1. View

16.

Harris L, Fritsche H, Mennel R, Norton L, Ravdin P, Taube S . American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol. 2007; 25(33):5287-312. DOI: 10.1200/JCO.2007.14.2364. View

17.

Saltz L, Meropol N, Loehrer Sr P, Needle M, Kopit J, Mayer R . Phase II trial of cetuximab in patients with refractory colorectal cancer that expresses the epidermal growth factor receptor. J Clin Oncol. 2004; 22(7):1201-8. DOI: 10.1200/JCO.2004.10.182. View

18.

Bertucci F, Salas S, Eysteries S, Nasser V, Finetti P, Ginestier C . Gene expression profiling of colon cancer by DNA microarrays and correlation with histoclinical parameters. Oncogene. 2004; 23(7):1377-91. DOI: 10.1038/sj.onc.1207262. View

19.

Luzzi V, Holtschlag V, Watson M . Gene expression profiling of primary tumor cell populations using laser capture microdissection, RNA transcript amplification, and GeneChip microarrays. Methods Mol Biol. 2005; 293:187-207. DOI: 10.1385/1-59259-853-6:187. View

20.

Amado R, Wolf M, Peeters M, Van Cutsem E, Siena S, Freeman D . Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer. J Clin Oncol. 2008; 26(10):1626-34. DOI: 10.1200/JCO.2007.14.7116. View