Molecular Patterns of Breast Cancer May Predict Chance of Relapse Up to 20 Years Later

Screening a woman’s breast cancer for certain genetic and molecular patterns may be used to predict the risk of the cancer returning (relapsing) up to 20 years later, a study reports.

Looking at the molecular data of breast tumors of nearly 2,000 women, researchers identified specific tumor types and created a model to predict the risk of long-term cancer relapse, as well as when and where in the body a tumor was more likely to spread. 

The findings may provide clinicians with an improved predictive tool to help guide them through treatment choices and improve the development of new breast cancer therapies.

The study, “Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups,” was published in the journal Nature.

One of the great challenges in breast cancer management has been to identify which women are at high risk of having the disease return years later.

Doctors have relied primarily on characteristics such as size and grade of the tumor, the degree of lymph node involvement, and the presence or absence of hormone receptors for predicting disease course and choosing treatments.

More recently, tests that analyze the activity of cancer-associated genes have been used to profile breast cancers and help predict risk of recurrence and guide treatment decisions.

Now, researchers at Stanford University School of Medicine and the Cancer Research UK Cambridge Institute, together with other institutions, have joined to develop a more precise tool based on the molecular signature of each tumor to predict the risk of cancer relapse within a longer period than currently possible, up to 20 years after diagnosis.

Researchers’ goal was to use data already available from thousands of individual cancers to identify molecular information that could be indicative of the risk of a late relapse and pattern of cancer spread (metastasis).

The team analyzed long-term clinical data of 3,240 breast cancer patients diagnosed in the U.K. and Canada between 1977 and 2005. Together, the patients’ median follow-up time was 9.77 years, but for some, there was follow-up data as long as 20 years.

For 1,980 of the cases, there was molecular data available, including the activity of cancer-related genes, aberrations in the number of gene copies (common in cancer cells) and the expression of hormone receptors and HER2.

They combined the information to group tumors based on their molecular profile and create a computer model to predict whether and when a woman’s breast cancer could return.

“Once we compiled the rich, clinical follow-up data, it became strikingly apparent that distinct relapse trajectories characterized patients in each of the genomic subgroups we had previously defined,” Christina Curtis, PhD, professor at Stanford, co-director of the Molecular Tumor Board at the Stanford Cancer Institute, and senior co-author of the study, said in a news release.

Specifically, they identified four groups of women, accounting for 26% of the ER-positive, HER2- negative cancers, who were at a particularly high risk of seeing cancer return.

In these four groups, the mean chance of relapse was 42% to 62% up to 20 years after surgery.

“These are the women who seem to be cured but then present with systemic disease many years later. Until now, there has been no good way to identify this subset of women who might benefit from ongoing screening or treatment,” Curtis said.

The study also identifies a group of women with triple-negative breast cancer whose tumors rarely return after five years.

Moreover, the data also provide insights into when and where certain types of breast cancers might  spread.

Another result was the identification of genomic factors that are probably involved in the development of specific tumor types, which could serve as targets for drug development.

“In the future, this type of genomic classification should help us separate patients who remain at jeopardy — and might warrant additional or ongoing treatment — and those who do not,” said Harold Burstein, MD, PhD, a professor at Harvard Medical School.

Curtis said, “It will be important to take what we’ve learned here and determine whether we can similarly improve the outcomes of these patient subgroups at high risk of recurrence with new therapies that target their specific genomic drivers.” 

Now, she and her colleagues are planning clinical trials to test this possibility.

“We’ve shown that the molecular nature of a woman’s breast cancer determines how their disease could progress, not just for the first five years, but also later, even if it comes back,” said Oscar Rueda, PhD, researcher at the Cancer Research UK Cambridge Institute, and first author of the study. “We hope that our research tool can be turned into a test doctors can easily use to guide treatment recommendations.”