How can one take the measure of a cell? What most characterizes it? In this genomic age, one possible answer is that a cell can be defined by a profile of all of the genes it is expressing. Microarray technology and next-generation sequencing methods have enabled us to see all of the genes expressed by a given cell type at any particular time, or under a particular circumstance. Such profiles can then be compared to those of other cell types, or the same cell type at other times or under other circumstances.
These profiles measure mRNA, not protein, so further studies are needed to corroborate that any detected changes in gene expression actually have meaning in the life of the cell. But even so, gene-expression profiling can be enormously valuable in finding biomarkers, highlighting druggable targets, revealing the metabolic pathways important in a biological process, and identifying subsets of a population that might respond to a certain therapy or be more susceptible to a certain clinical outcome. Researchers around the globe are harnessing gene-expression profiling studies in a variety of clinical settings to prevent and treat a number of diseases.
Identifying At-Risk Patients
At the “Critical Assessment of Massive Data Analysis” conference held recently in Vienna, John Storey, from the Lewis-Sigler Institute for Integrative Genomics at Princeton University, gave a talk on dissecting the inflammatory complications in critically injured patients by within-patient gene-expression changes.
Trauma is a major killer, as it can lead to sepsis, infection, and multiple organ failure. But it has been difficult to determine which hospitalized patients will succumb to these severe sequelae and which will have a smoother recovery. Dr. Storey participated in a longitudinal study to try to identify which genetic pathways are associated with more direct clinical outcomes as well as exactly when these molecular signatures must be detected to be useful in determining clinical treatments.
Using mRNA from total blood leukocytes, they found that a full one-quarter of the human genome exhibits changes in expression levels during the early stages of post-trauma (40–80 hours post-trauma). Dr. Storey specializes in quantitative genomics; his statistical challenge in this study was to correlate gene expression over multiple time points, in multiple patients, with multiple and different clinical presentations.
“It took me a long time to appreciate just how extensively population heterogeneity and other unmeasured sources of variation introduce unwanted and systematic biases into the statistical analysis of gene-expression studies,” Dr. Storey noted.
“I view this problem as being as or more problematic than the population stratification problem in genome-wide association studies (referring to allelic differences in subpopulations that may be due to ancestral, rather than disease associated, differences).”
His innovation was to correlate the within-patient change in gene expression with the severity of the clinical outcome, rather than using absolute expression values as has traditionally been done. By measuring the change in expression within each patient, regardless of the patient’s baseline values, this method leads more easily to a clinical translation of the results.
Hara Levy, professor of physiology at the Human and Molecular Genetics Center of the Medical College of Wisconsin, studies cystic fibrosis (CF). Although CF is caused by mutations in the CF transmembrane conductance regulator gene, patients with identical genetic mutations in this gene can suffer variable levels of lung disease severity.
Dr. Levy is harnessing a functional genomics technique initially used to determine when people at high risk for type 1 diabetes—such as those who have siblings with the disease—might go on to develop the disease. The technique relies on the observation that inflammatory cytokines are often upregulated in those CF patients who will go on to have more severe pulmonary disease, but at levels too low to detect.
When serum from those patients is cultured with peripheral blood mononuclear cells from healthy individuals, however, it induces an inflammatory signature that can be readily detected, thus distinguishing CF patients at risk for more severe lung disease.
“We have validated this technology and plan to use it to correlate gene-expression status to disease status and pulmonary function,” Dr. Levy said. As progressive pulmonary disease is the major cause of morbidity and mortality in CF patients, identifying markers of such disease should help plan appropriate, individualized courses of treatment.
In 1997, researchers in Norway found that trees with diseased roots exhibited altered gene expression in their leaves. DiaGenic was founded in 1998 to take advantage of this idea that diseases cause changes all over the body, not just in the affected area. Erik Christensen, CEO, explained that they chose to look for signatures of CNS disease in gene-expression profiles from blood cells because “it is difficult to have access to brain tissues while people are living, and spinal tapping is not very convenient. Blood is a convenient sample for large-scale use.”
Degenerative CNS diseases like Alzheimer and Parkinson are usually diagnosed only upon the appearance of symptoms, and current treatments can only allay those symptoms but cannot delay disease development. Yet often, changes in gene expression are detectable in blood cells decades before clinical symptoms appear.
“DiaGenic is the first company worldwide to develop a diagnostic test for prodromal (very early stage) Alzheimer disease in the blood,” said Dr. Christensen, and this test has been licensed to Pfizer. “If you can measure disease progression without cognitive measurements, but rather with objective measurements, you can develop new drugs to delay disease progression. This can prevent it from getting to the costly stage.”
“Gene-expression studies are more advanced in cancer, because you take out, and therefore, have access to the tumor tissue,” pointed out Pierluigi Tricoci, a cardiologist at the Duke Clinical Research Institute. Echoing the philosophy set out by Dr. Christensen, he said, “In coronary disease, you don’t have the coronary artery wall available. We have reason to believe that although it is indirect, taking blood may be a good way to study acute coronary syndrome (ACS) since mononuclear cells are involved in atherosclerosis.”
Thus, he and his colleagues have collected RNA samples for the first large-scale clinical trial characterizing gene-expression profiles in patients with ACS. This study is ambitious, with many aims: to better understand the molecular mechanisms involved in developing ACS; to develop new diagnostic tools and identify new therapeutic targets; to identify gene-expression signatures associated with adverse outcomes; and to enhance our ability to distinguish between those who respond to current therapies and those who don’t.
Preliminary studies have already suggested that gene-expression profiles taken from blood cells can help to predict the onset of ACS. It is hoped that this large-scale study, reportedly the first of its kind, will achieve some, if not all, of its lofty goals.