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An Engineering Approach to Translational Medicine


Michael Liebman








Disease Is a Process, Not a State

For the purposes of diagnosis, analysis and experimentation, academic physicians tend to focus on disease at a single point in time. But disease needs to be treated as a process that evolves over time through the interaction of genetic, environmental and lifestyle factors. This view puts a premium on understanding the complex history of a patient, and it acknowledges that most disease cannot be tied to a single cause.

When physicians make a diagnosis, it's natural to focus on the patient and symptoms at the time of presentation. The doctor's knowledge of a patient's past is typically limited to major illnesses, allergies and family history. Yet clinical assessments could be much more meaningful if we understood the way that genes and environment interact to produce disease. For example, we know that certain biomarkers, such as mutations in the genes BRCA1 or BRCA2, indicate higher risks of breast cancer. But the fact that a woman has a mutation in BRCA1 doesn't mean that she will develop breast cancer—it only indicates that she needs to be monitored more closely.

Likewise, smoking, high alcohol consumption and obesity are correlated with an increased risk of breast cancer, but we know little about how each factor raises the risk—much less about how two or more might work in concert to increase risk. This situation leaves us with a circular argument: To justify the cost of collecting a comprehensive patient history, we need proof that such data are relevant, but we can't evaluate which data are relevant because we don't have a database of comprehensive patient histories.

We in the CBCP think that detailed information will prove useful, although we don't know exactly what connections will emerge from the mass of variables. We are collecting from each patient a lengthy history that includes her exposures to tobacco and alcohol, details about pregnancy, childbirth and breastfeeding, and a record of changes in her body mass. We also try to include information about the timing of these events in a person's life. The chronology is particularly relevant for breast cancer because the breast develops continuously from birth through old age. This lifetime of changes also presents an additional challenge: Not only do these factors influence risk differently over time, but their interactions with one another also vary with age.

An engineering perspective treats the patient as a system, or a set of subsystems, that has been acted on, differentially, by many elements that influence its state at critical points over time. Our job is to identify these critical points so that they might be controlled. Whereas many current studies identify correlations between isolated variables, we hope that the wider scope of the CBCP's  systems-based approach will help us determine causality, thereby improving diagnosis and treatment.

Aging as a Background to Disease

The breast changes between a woman's time in utero and her post-menopausal years. This maturation process is different for women who have had children than for those who have not, and it also varies under the influence of several variables: age of menarche, use of hormonal birth control, number and timing of children, the practice of breastfeeding, age of menopause and use of hormone-replacement therapy. Thus, our definition of "normal" varies with age and experience, and an optimal diagnosis must use a systems-based approach to compare an individual cancer patient's baseline (which we must guess at) to her disease state (which we can measure during diagnosis and treatment). The immediate aim of our project is to determine background levels of gene and protein expression in breast tissue and to find out how these numbers vary in a healthy population. This information will be a significant step in the development of molecular diagnostics.

Note that a woman's life stages are not separated by fixed boundaries. Rather, each represents a unique intersection of a woman's age and an event. Given this complexity, it was crucial to sieve the scientific literature for data that we could integrate into a systems approach. This was more difficult than one might think. Scientists have studied these stages for decades, producing a tremendous body of work in physiology and pathology—more than any one scientist can master. Furthermore, we recognized that even the most encyclopedic and fair-minded review article cannot escape the inherent bias of its author. Thus, we have harnessed some computing power, employing text data-mining to cull the literature. This effort has two aims: to refine the definitions of these stages and to extract information about the underlying physiological and developmental changes. This information becomes the foundation for our molecular analyses and helps integrate clinical and molecular data. We plan to augment this computational approach with a community-based longitudinal study that includes molecular and behavioral components.

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Heterogeneity of Breast Disease



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