Biomedical subjects

Alan Sachs

Publications and source records attributed to Alan Sachs.

3 recordsLinked to original sources

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

An integrative genomics approach to infer causal associations between gene expression and disease.

A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene-perturbation experiments using gene-expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.

11-beta-Hydroxysteroid Dehydrogenase Type 1↗

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↗