The dream of monitoring a patient's physical condition through blood testing has long been realized. But how about detecting diseases in their very early stages, or evaluating how they are responding to treatment, with no more to work with than a drop of blood?
That dream is closer to realization than many of us think, according to several leading experts advocating a new approach known as systems biology. Writing in the current issue of the journal Science, Institute for Systems Biology immunologist and technologist Leroy Hood and California Institute of Technology chemist Jim Heath and their colleagues explain how a new approach to the way that biological information is gathered and processed could soon lead to breakthroughs in the prevention and early treatment of a number of diseases.
The lead author of the Science article is Leroy Hood, a former Caltech professor and now the founding director of the Institute for Systems Biology in Seattle. According to Hood, the focus of medicine in the next few years will shift from treating disease -- often after it has already seriously compromised the patient's health-to preventing it before it even sets in.
Hood explains that systems biology essentially analyzes a living organism as if it were an electronic circuit. This approach requires a gigantic amount of information to be collected and processed, including the sequence of the organism's genome, and the mRNAs and proteins that it generates. The object is to understand how all of these molecular components of the system are interrelated, and then predict how the mRNAs or proteins, for example, are affected by disturbances such as genetic mutations, infectious agents, or chemical carcinogens. Therefore, systems biology should be useful for diseases resulting from genetics as well as from the environment.
"Patients' individual genome sequences, or at least sections of them, may be part of their medical files, and routine blood tests will involve thousands of measurements to test for various diseases and genetic predispositions to other conditions," Hood says. "I'll guarantee you we'll see this predictive medicine in 10 years or so."
"In this paper, we first describe a predictive model of how a single-cell yeast organism works," Heath explains, adding that the model covers a metabolic process that utilizes copious amounts from data such as messenger RNA concentrations from all the yeast's 6,000 genes, protein-DNA interactions, and the like.
"The yeast model taught us many lessons for human disease," Heath says. "For example, when yeast is perturbed either genetically or through exposure to some molecule, the mRNAs and proteins that are generated by the yeast provide a fingerprint of the perturbation. In addition, many of those proteins are secreted. The lesson is that a disease, such as a very early-stage cancer, also triggers specific biological responses in people. Many of those responses lead to secreted proteins, and so the blood provides a powerful window for measuring the fingerprint of the early-stage disease."
Heath and his colleagues write in the Science article that, with a sufficient number of measurements, "one can presumably identify distinct patterns for each of the distinct types of a particular cancer, the various stages in the progression of each disease type, the partition of the disease into categories defined by critical therapeutic targets, and the measurement of how drugs alter the disease patterns. The key is that the more questions you want answered, the more measurements you need to make. It is the systems biology approach that defines what needs to be measured to answer the questions."
In other words, the systems biology approach should allow therapists to catch diseases much earlier and treat them much more effectively. "This allows you to imagine the pathway toward predictive medicine rather than reactive medicine, which is what we have now," Heath says.
About 100,000 measurements on yeast were required to construct a predictive network hypothesis. The authors write that 100,000,000 measurements do not yet enable such a hypothesis to be formulated for a human disease. In the conclusion of the Science article, the authors address the technologies that will be needed to fully realize the systems approach to medicine. Heath emphasizes that most of these technologies, ranging from microfluidics to nanotechnologies to molecular-imaging methods, have already been demonstrated, and some are already having a clinical impact.
"It's not just a dream that we'll be diagnosing multiple diseases, including early stage detection, from a fingerprick of blood," Heath says. "Early-stage versions of these technologies will be demonstrated very soon."
The other authors of the paper are Michael E. Phelps of the David Geffen School of Medicine at UCLA, and Biaoyang Lin of the Institute for Systems Biology.