Of What Value Genomics in Colorectal Cancer? Opportunities and Challenges
Authors: Patrick G. Johnston
Journal: Journal of Clinical Oncology (2004)

Colorectal cancer (CRC) affects more than 160,000 patients each year in the United States, and approximately 60,000 patients die from this disease. Although it is a highly treatable and often curable disease when localized to the bowel, the prognosis for recurrent metastatic disease remains poor and most often is the ultimate cause of death. In the postoperative setting, there is clear evidence that adjuvant chemotherapy significantly improves outcome in patients with CRC. Chemotherapeutic drugs such as the fluoropyrimidines, irinotecan and oxaliplatin are now used as part of standard care and have been shown to significantly improve survival in patients with widespread metastatic disease. However, the management of patients with potentially curative, earlier stage disease (stage II and III) remains an area of debate, as the combined 5-year survival for these patients is 60%. Indeed, only one third of the 40% of patients at risk for relapse after primary surgical treatment derive any benefit from chemotherapeutic treatment. This means that giving adjuvant chemotherapy to stage III patients exposes more than 50% to treatment that may be unnecessary. In addition, the benefit of adjuvant chemotherapy in patients with stage II disease still remains very controversial, with a recent meta-analysis of prospective randomized clinical trials of adjuvant therapy not showing a significant survival benefit. Therefore, many more patients undergo chemotherapy for their disease who may not derive any significant clinical benefit. The identification of patients at high risk of relapse has traditionally depended on pathologic features, such as depth of bowel wall penetration, lymph node involvement, and adherence to, or invasion of, adjacent organs. These anatomic characteristics thus create populations with a similar clinical stage of disease, but with wide variability in cure rates. However, anatomic staging systems have been too imprecise to provide detailed predictions of individual patient outcome. Recent advances in our understanding of the molecular biology of CRC have led to the identification of other potential prognostic and predictive factors. Molecular diagnostic markers such as microsatellite instability, loss of heterozygosity, and thymidylate synthase level already identify patients with increased risk of relapse. However, cancer treatment and colorectal cancer clinical trials are still being based on the availability of new active compounds, rather than on an integrated approach that uses the genetic makeup of the tumor and the genotype of the patient. More recently, the application of microarray technology to the cancer field has created the potential to obtain large-scale gene expression profiles or genetic fingerprints of colorectal cancer. This has the potential to impact very significantly on the diagnostic capability and prognostic classification of tumors, which may aid in the prediction of response of individual patients to defined chemotherapeutic regimens. In this issue of the Journal of Clinical Oncology, Wang et al present the results of an important study using gene expression profiling to define a set of prognostic molecular markers in patients with stage II CRC. They harvested RNA from 74 patients with stage II CRC and performed gene expression profiling using an oligonucleotide microarray gene chip containing approximately 22,000 transcripts. To identify those genes that may identify patients with a high risk of relapse, the investigators used two supervised class prediction approaches to select markers from the 17,616 informative genes. The first involved dividing all 74 patients into a training set and a testing set, with equal numbers in each. The training set was used to select gene markers and to build a prognostic signature, and the testing set was used for independent validation. Using this approach, 60 genes were selected from the 38 patients in the training set, and a Cox regression model was then used to predict patient recurrence. The second step was to place JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 22 NUMBER 9 MAY 1 2004