Levels of Hormones Help Better Predict Breast Cancer

Levels of key hormones can help better predict a woman’s risk for breast cancer, according to a study at Harvard Medical School presented at a conference hosted by the American Association for Cancer Research at the end of October.

The research team, led by Harvard Medical School professor Shelley S. Tworoger, found that higher blood concentrations of certain hormones led to a greater chance of developing breast cancer. The findings indicate that a simple blood test could prove useful for predicting whether women will develop the disease.

Tworoger said that it is important to identify women who are at a high risk of developing breast cancer as early as possible so that doctors can intervene with chemoprevention or even more intensive screening.


For the study, Tworoger and her colleagues analyzed the hormone levels of 473 postmenopausal women diagnosed with invasive breast cancer, as well as 770 women without the disease, none of whom were using postmenopausal hormones at the time of blood draw.

Tworoger said the study found there were three specific hormones—estrone sulfate, testosterone, and prolactin—that increased the accuracy of current models that predict the risk of breast cancer.


Most methods of assessing breast cancer risk rely solely on factors that can be investigated through a questionnaire, which as questions such as how many children a woman has, at what age she had her first child, whether she has family history of breast cancer, and what her personal history with breast disease.

However, Tworoger and her colleagues’ approach is unique in that it also takes into account hormone levels.

Though Tworoger said that while improvements in breast cancer prediction will help women make more informed decisions about health care, further research is still needed.

“It is important for future studies to confirm that the hormones that we selected as the best subset replicates in other populations,” Tworoger said.

Tworoger said that other factors such as genetic risk and mammographic density should also be examined and added to risk prediction models.

These two factors are not correlated with hormone levels and therefore offer potentially significant improvements, she said.  “The next step for research is adding all hormones, genetics, and mammographic density together to look at improvement in risk prediction,” Tworoger said.