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Biomedical subjects

Stephen Friend

Publications and source records attributed to Stephen Friend.

5 recordsLinked to original sources
PubMed2006

Gene expression changes associated with progression and response in chronic myeloid leukemia.

Chronic myeloid leukemia (CML) is a hematopoietic stem cell disease with distinct biological and clinical features. The biologic basis of the stereotypical progression from chronic phase through accelerated phase to blast crisis is poorly understood. We used DNA microarrays to compare gene expression in 91 cases of CML in chronic (42 cases), accelerated (17 cases), and blast phases (32 cases). Three thousand genes were found to be significantly (P < 10(-10)) associated with phase of disease. A comparison of the gene signatures of chronic, accelerated, and blast phases suggest that the progression of chronic phase CML to advanced phase (accelerated and blast crisis) CML is a two-step rather than a three-step process, with new gene expression changes occurring early in accelerated phase before the accumulation of increased numbers of leukemia blast cells. Especially noteworthy and potentially significant in the progression program were the deregulation of the WNT/beta-catenin pathway, the decreased expression of Jun B and Fos, alternative kinase deregulation, such as Arg (Abl2), and an increased expression of PRAME. Studies of CML patients who relapsed after initially successful treatment with imatinib demonstrated a gene expression pattern closely related to advanced phase disease. These studies point to specific gene pathways that might be exploited for both prognostic indicators as well as new targets for therapy.

Antineoplastic Agents↗
PubMed2005

Embracing complexity, inching closer to reality.

Drugs designed against targets in presumably simple linear signaling pathways found to be associated with disease are often less effective than predicted. One reason for this is the overly simplistic view of the molecular mechanisms underlying common human diseases. This viewpoint is a consequence of biological reductionism, brought about by the need to form a basic understanding of the fundamental attributes of biological systems and by limitations in the set of tools available for analysis of biological systems. However, complex biological systems are best modeled as highly modular, fluid systems exhibiting a plasticity that allows them to adapt to a vast array of conditions. Historically, this viewpoint has long represented the ideal, but the tools needed to examine and describe this complexity were often lacking. Here we argue that the tools of biological science now allow for a more network-oriented view of biological systems and for explaining the underlying causes of disease, as well as the best ways to target disease. Ultimately, this will help to ensure that the right drug is administered to the right patient at the right time. Focusing on well-studied signaling pathways, refining the definition of disease, and identifying disease subtypes, we demonstrate a more holistic approach to elucidating common human diseases, with the potential to revolutionize treatment of these diseases.

Animals↗
PubMed2005

A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients.

Breast cancer comprises a group of distinct subtypes that despite having similar histologic appearances, have very different metastatic potentials. Being able to identify the biological driving force, even for a subset of patients, is crucially important given the large population of women diagnosed with breast cancer. Here, we show that within a subset of patients characterized by relatively high estrogen receptor expression for their age, the occurrence of metastases is strongly predicted by a homogeneous gene expression pattern almost entirely consisting of cell cycle genes (5-year odds ratio of metastasis, 24.0; 95% confidence interval, 6.0-95.5). Overexpression of this set of genes is clearly associated with an extremely poor outcome, with the 10-year metastasis-free probability being only 24% for the poor group, compared with 85% for the good group. In contrast, this gene expression pattern is much less correlated with the outcome in other patient subpopulations. The methods described here also illustrate the value of combining clinical variables, biological insight, and machine-learning to dissect biological complexity. Our work presented here may contribute a crucial step towards rational design of personalized treatment.

Age Factors↗
PubMed2002

Thirteenth annual pezcoller symposium: focusing analytical tools on complexity in cancer.

The physiopathology of cancer cells is the result of very complex signaling networks that represent in many cases distortions of the orderly networks regulating the physiology of normal cells. These networks are the consequence of the expression of, or the lack of expression of, genes, mutated or not, which represent the genomic profile of different types of or of individual cancers. The complex signaling pathways, the cross-talks among them, and the redundancies existing for several of them mediate not only the transmission of signals from the cell environment to the nucleus but also that from the nucleus to the other cellular components whose function is involved in cell proliferation, apoptosis, or differentiation. Modern approaches to cancer therapy and also prevention are aimed at identifying new molecular targets, pivotal to the life of the cancer cell, which would provide for specific sites of intervention. In the face of the enormous complexity of the phenomena on which the life of cancer cells is based, it is both difficult to identify unique specific target for intervention and important to develop analytical tools and approaches capable to identify them for further exploitation. This was the main subject of the Symposium. Consideration was given to: (a) tumor genotypic analysis through expression array evaluation and definition of cancer transcriptomes in studies aimed at identifying determinants of specific characteristics of cancer cells; (b) approaches based on the knowledge gained in this analysis that would lead to the visualization of new targets exploitable for antitumor action; and (c) multifactorial analysis of the complex interactions regulating cancer cells and methods to comprehend the complexity of molecular models and validate their functional relevance.

Animals↗