UMass Amherst biostatistician developing statistical tools to predict breast cancer survival and inform targeted therapies

Breast cancer is a complex disease, and its progression is difficult, yet important, to predict.

While many elements may contribute to a breast cancer prognosis, University of Massachusetts Amherst biostatistician Chi Hyun Lee has zeroed in on one risk factor that has emerged for its potential to predict the disease’s progression.

Lee will use a two-year, $154,791 grant from the National Institutes of Health (NIH) in an effort to develop statistical tools that will better predict breast cancer survival rates and survival time after breast cancer recurrence.

While the project focuses on breast cancer research, the proposed statistical methods will have a broad application for other chronic diseases, she notes.

Lee’s research involves an androgen receptor (AR), a biomarker that plays a role at the cellular level in regulating hormones, including in female sexual, somatic and behavioral functions. In excess, however, it has been linked to an increased risk of breast cancer.

“Our goal is that these novel statistical approaches will help us determine the prognostic values of AR and potentially lead to better targeted therapies for patients and advances in breast cancer survival,” Lee says.

For the project, Lee will work with data from the Nurses’ Health Study (NHS), one of the world’s largest prospective cohort studies investigating the risk factors for major chronic diseases in women, including breast cancer. Data from this study, established in 1976, contains such invaluable information for breast cancer research as lifestyle, hormonal and genetic risk factors, including AR, as well as clinical outcomes such as breast cancer diagnosis, recurrence and death.

“In many epidemiologic studies on breast cancer survival,” Lee explains, “researchers rely on the hazard ratio, or the likelihood of a harmful event such as death or disease progression compared to a control group. This ratio is determined by using a statistical method called the proportional hazard model.

“However, we have found in the NHS data that the assumption of the model on the association between AR expression and breast cancer survival is faulty. This means that the results of the hazard ratio are often misleading when it comes to assessing AR’s prognostic values.”

Lee notes that another statistical method, based on the restricted mean survival time (RMST), has much better prognostic value. RMST is a summary metric defined as the life expectancy up to a specific time point, eliminating assumptions of proportional hazards that may prove to be faulty. The RMST has many advantageous features, such as its straightforward interpretation and robustness.

“Specifically, we can assess the prognostic factor’s effects in terms of absolute effect, which is clinically more interpretable,” Lee says.

Lee’s funding will allow her to develop novel statistical methods based on RMST to fully utilize the rich data from the Nurses’ Health Study.

As a result, she expects to gain a better understanding of the complex effect of AR on breast cancer progression and survival. Ultimately, the funding will support two goals: to develop a flexible regression method based on RMST that will be used to elucidate the clinical significance of AR in survival by different subtypes of breast cancers; and to develop a model-free approach to compare survival rates after breast cancer recurrence between groups with different AR status.