Chemoprevention of pancreatic cancer: ready for the clinic?

Advances in our molecular, clinical, and epidemiologic understanding of the risk and development of pancreatic cancer offer hope for preventing this disease, which is largely intractable once developed. This perspective on provocative, genetically engineered mouse model work reported by Mohammed et al. (beginning on page 1417 in this issue of the journal) examines the prospects for pancreatic cancer chemoprevention with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI). Despite having limited value in advanced pancreatic cancer, EGFR TKIs show promise in the setting of early pancreatic carcinogenesis.     

Gene expression profiling: Does it add predictive accuracy to clinical characteristics in cancer prognosis?

It is widely accepted that gene expression classifiers need to be externally validated by showing that they predict the outcome well enough on other patients than those from whose data the classifier was derived. Unfortunately, the gain in predictive accuracy by the classifier as compared to established clinical prognostic factors often is not quantified. Our objective is to illustrate the application of appropriate statistical measures for this purpose. In order to compare the predictive accuracies of a model based on the clinical factors only and of a model based on the clinical factors plus the gene classifier, we compute the decrease in predictive inaccuracy and the proportion of explained variation. These measures have been obtained for three studies of published gene classifiers: for survival of lymphoma patients, for survival of breast cancer patients and for the diagnosis of lymph node metastases in head and neck cancer. For the three studies our results indicate varying and possibly small added explained variation and predictive accuracy due to gene classifiers. Therefore, the gain of future gene classifiers should routinely be demonstrated by appropriate statistical measures, such as the ones we recommend.     

High-throughput genomic technology in research and clinical management of breast cancer. Exploiting the potential of gene expression profiling: is it ready for the clinic?

Gene expression profiling is a relatively new technology for the study of breast cancers, but within the past few years there has been a rapid rise in interest in its potential to improve the clinical management of breast cancer. This technology has contributed to our knowledge of the molecular pathology of breast tumours and shows promise as a tool to predict response to therapy and outcome, such as risk of metastasis. Microarray technology is continually developing and it is becoming apparent that, despite the various platforms available, robust conclusions can still be drawn that apply across the different array types. Gene expression profiling is beginning to appear in the breast cancer clinic but it is not yet fully evaluated. This review explores the questions that must be addressed before this technology can become an everyday clinical tool.     

Gene expression profiling and pathway analysis identify the integrin signaling pathway to be altered by IL-1β in human pancreatic cancer cells: role of JNK

CYR61 over-expression promotes cell proliferation by inhibiting carboplatin-induced apoptosis, decreasing Bax expression, and increasing Bcl-xL, Mcl-1, and Bcl-2. At the same time, down-regulating p53 expression, while up-regulated NF-κB expression. Additionally, p21 and p53 promoter activities were reduced, while NF-κB and Bcl-2 activities increased. In parallel, CYR61-expressing cells, during carboplatin-induced apoptosis, resulted in an increase of Akt phosphorylation, while rapamycin-treated cells were not affected. Carboplatin effectively inhibited the activation of mTOR signaling cascade, which includes mTOR, 4E-BP1, p70S6K, HIF-1α, and VEGF. These results provide evidence that CYR61 promotes cell proliferation and inhibits apoptosis.     

Gene expression profiling of preneoplastic liver disease and liver cancer: a new era for improved early detection and treatment of these deadly diseases?

Hepatocellular carcinoma (HCC) is a multi-step process associated with changes in gene expression. Currently, several technologies enable global gene expression profiling. The number of studies to probe global gene expression profiles of HCC or preneoplastic chronic liver diseases has increased exponentially in recent years. These studies have quickly provided rich information and some additional clues to the genesis of liver cancer. The application of gene expression profiling to preneoplastic liver diseases and HCC is growing in importance and practicality. In this commentary, we review the recent advances in the utilization of global gene expression profiling to liver cancer, which have provided new insight into the molecular mechanisms underlying the development of HCC. We have also discussed the problems related to these new technologies, as well as their contributions and implications. By recognizing the shortcomings, we can reassess our current approaches, which allow us to better design and analyze global gene expression-based experiments. These new approaches will undoubtedly contribute to a better understanding of hepatocarcinogenesis.     

Gene expression profiling of ER+ breast cancers: to discriminate the poor prognosis tumors or to define the most suitable treatment?

The cDNA microarray technology allows the simultaneous analysis of all genes expressed in a tumor. This approach has already permitted to propose a molecular classification of breast cancers. Gene expression profiling is now expected to define prognosis signatures claiming the disease outcome as well as signatures predicting response to therapy. The over-expression of a cluster of genes that are implicated in cell cycle and mitosis control has been shown to discriminate a subgroup of ER+ breast cancers exhibiting a poor prognosis. This "proliferation cluster" is shown to be also present in signatures predicting relapse of ER+ breast cancers treated by the anti-estrogen tamoxifen, including the 36-gene classifier that we have recently identified. Consequently, it seems legitimate to wonder if this part of such predictive signature is really specific to the insensitivity to tamoxifen or rather indicates a poor disease outcome whatever the therapy applied. On the other hand, low expression of ER+ related genes and high expression of genes associated to ER- status or low ERalpha levels have been shown to sign a poor prognosis. Whether the estrogen-related genes that are present in our 36-gene classifier specify the clinical disease outcome or are really specific to the response to tamoxifen, remains to be determined. In any case, the specificity of our 36-gene classifier as those predicting the recurrence of ER+ breast cancer under one treatment or another should be demonstrated. In the same way, future studies should define molecular signatures that will be really predictive of the response to the treatment in order to define which one is the most suitable to decrease the risk of relapse of ER+ breast cancers. These studies should be based on neoadjuvant clinical trials that permit to evaluate the response to treatment in an objective manner.     

Does use of alternative medicine predict survival from cancer?

This study examines the association between alternative medicines (AM) and cancer survival. A national multicentre study was carried out in Norway in December 1992 to assess the prevalence of AM use among cancer patients. One of the aims of this study was to assess the association between AM and long-time survival. In January 2001, survival data were obtained with a follow-up of 8 years for 515 cancer patients. A total of 112 (22%) assessable patients used AM. During the follow-up period, 350 patients died. Death rates were higher in AM users (79%) than in those who did not use AM (65%). In a Cox regression model adjusted for demographic, disease and treatment factors, the hazard ratio of death for any use of AM compared with no use was 1.30, (95% Confidence Interval (CI) 0.99, 1.70; P=0.056), suggesting that AM use may predict a shorter survival. Sensitivity analyses strengthened the negative association between AM use and survival. AM use had the most detrimental effect in patients with an ECOG (Eastern Cooperative Oncology Group) performance status (PS) of 0 (hazard ratio for use=2.32, 95% CI, 1.44, 3.74, P=0.001), when compared with an ECOG PS of 1 or higher. The use of AM seems to predict a shorter survival from cancer. The effect appears predominantly in patients with a good PS.