This year, according to the American Cancer Society, some 232,340 new cases of invasive breast cancer will be diagnosed in women. Some of these patients will succumb to the disease, while others survive and live healthy lives for decades.

Part of the enormous disparity in outcome has to do with the differing ways diseases like cancer affect individuals based on age, ethnicity, lifestyle, environmental conditions, genetic predisposition and other factors.

Garrick Wallstrom is an Assistant Professor in the Biodesign Institute, Center for Personalized Diagnostics
Photo by: The Biodesign Institute at Arizona State University

According to Garrick Wallstrom, a researcher at Arizona State University's Biodesign Institute, how we study an illness can also depend on a feature of the disease itself-one known as heterogeneity.

Heterogeneous diseases are those composed of multiple molecular subgroups, each producing distinct manifestations of illness, differing in severity, prognosis and recurrence. Breast cancer is one such example of a heterogeneous disease.

"Our ability to differentiate and understand subgroups of disease is fundamental to personalized medicine," Wallstrom says. But disease heterogeneity presents a real challenge in medical research because a set of patients in a study may actually have very different diseases at the molecular level. What we've shown is that researchers need to carefully consider heterogeneity early on, when they are designing their studies."

In new research appearing in the journal Cancer Epidemiology, Biomarkers and Prevention, Wallstrom and colleagues evaluate the statistical reliability of biomarkers-protein factors used to pinpoint the presence of disease at an early, pre-symptomatic stage. Their work reveals for the first time that disease heterogeneity profoundly affects biomarker performance.

While multiple subtypes of diseases like breast cancer have long been recognized, the implications for biomarker discovery and validation have not been systematically evaluated prior to the current study. Wallstrom and his colleagues determined that a two-fold larger sample size is typically required to establish strong biomarker candidates for heterogeneous diseases, compared with monotypic diseases-those with just a single underlying molecular pathology.

The study also established that specific statistical tests used to screen biomarkers differ markedly in their predictive reliability, depending on whether the disease under study is monotypic or heterogeneous.

The work has implications for the design of experiments aimed at identifying new biomarkers, as well as for drug-discovery studies and drug trials. (Certain anti-cancer drugs are already recognized for their preferential effectiveness depending on disease subtype. Herceptin for example, is an effective drug for breast cancer patients who test positive for the HER-2/neu biomarker. For others, it is ineffective.)

A persistent scourge

Among women, breast cancer is the most frequently occurring malignancy and the second leading cause of cancer-related death in the United States. About 1 in 8 women in the US (12 percent) will develop invasive breast cancer during their lifetime. Currently, there are novalidated plasma/serum biomarkers for the disease. Only a few biomarkers (such as HER-2/neu, estrogen receptor, and progesterone receptor) have so far shown clinical effectiveness for diagnosis and prognosis. The need for new diagnostic biomarkers is therefore acute.

Mammography remains the most effective clinical screening method for breast cancer, though lesions less than .5 cm in size remain undetectable. Further, mammography has a fairly low ratio of sensitivity to specificity. This accounts for the fact that roughly four times as many women undergo biopsy for benign breast lesions as those with actual malignancy.

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