Array for cancer prognostics

Abstract

Much publicity has surrounded the human genome project and its promise of revolutionizing medical healthcare but, until recently, little direct benefit has emerged. In the past, many cancer prognoses had relied heavily on general parameters encompassed within the international prognostic index (IPI), such as tumour size and age of patient. The IPI is used to determine the level of treatment (e.g. chemo-, radio- and hormonal therapies) appropriate for the patient.In a recent article, Margaret Shipp and colleagues [1xDiffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised learning. Shipp, M.A. et al. Nat. Med. 2002; 8: 68–74Crossref | PubMed | Scopus (1501)See all References[1] take cancer prognosis a step further showing that gene activities that determine the biological behaviour of a tumour are more likely to reflect its aggressiveness. Oligonucleotide microarrays were used to determine the expression profile of 6817 genes in tissues of 58 patients with diffuse large B-cell lymphoma (DLBCL), the most common adult lymphoid malignancy with a mortality of >50%. Full treatment records and long-term follow-up were available for all 58 DLBCL patients in the study. When analysed using a ‘supervised learning’ prediction method, the gene expression patterns for the DLBCL patients could be divided into two categories; one with a 5 year survival rate of 70% and another with greatly reduced 5 year survival rates (12%). In short, some genes are over expressed in tissues from patients in remission from DLBCL (such as E2F) whereas others are over expressed in tissues from patients with fatal, or refractory, DLBCL (such as VEGF). The highest accuracy in prediction was obtained when 13 ‘key’ genes were used in the DLBCL outcome model. Given the success of these 13 key genes in predicting the clinical outcome of DLBCL, many might represent attractive therapeutic targets.The microarray classifier developed by Shipp et al. offers a refinement of tumour classification and therefore the technology could be used to improve the selection of patients for currently available treatments and should help in predicting the outcome of patients undergoing therapy. In a recent letter to Nature, the same technology was used to distinguish between embryonic tumours of the central nervous system, which had previously been difficult to diagnose [2xPrediction of central nervous system embryonal tumour outcome based on gene expression. Pomeroy, S.L. et al. Nature. 2002; 415: 436–442Crossref | PubMed | Scopus (1523)See all References[2].The enormous amount of data generated by the human genome project has, through necessity, brought about collaboration between clinicians, pathologists, molecular biologists and bioinformaticians and progress in cancer prognosis and treatment is the result.