The risk of having breast cancer a second time varies depending on the patient’s personal history, particularly, the characteristics of the first tumor. These relapses depend on several factors that are being analyzed by a multitude of experts. Now, a group of scientists from the University of Cambridge (United Kingdom) and Stanford University (USA), in collaboration with Canadian and Spanish research centers such as the University of Valladolid (UV), have created a statistical model capable of predict the reappearance of breast cancer in patients.
Oscar Rueda, a former UV student and researcher at the Cambridge Institute of Cancer Research, led for three years the group that studied the risk of relapse and mortality in 3,240 breast cancer patients. The results were published this week in the journal Nature.
For the study, the team of researchers took into account different characteristics of the disease for the statistical model, such as the distance between the new tumor and the original tumor, the time elapsed since surgery and relapse, and other factors known to influence mortality, such as the age and size of the cancer
In summary, they classified two types of breast tumors (triple negative breast cancer and RE+ tumors with a negative HER2 status) in several subgroups characterized by genetic alterations causing an increased risk of fast relapse.
High risk breast tumors
After analyzing high risk tumors in more than 3,000 patients from the United Kingdom and Canada diagnosed between 1977 and 2005, the authors divided the triple negative breast cancer group into two subgroups: IntClust10 and IntClust4ER-.
Oscar Rueda said: “One in three patients with IntClust10 breast cancer relapses in the first five years.” Those who exceed those five years “have a minimal risk of relapse after that period.”
On the other hand, the other subgroup IntClust4ER- have a lower risk in the short term, “but it persists after those five years“, researcher said.
With respect to ER+ tumors with a negative HER2 status, the scientists also identified four subtypes with a high risk of long-term relapse: IntClust1, IntClust2, IntClust6 and IntClust9.
According to the results, these four subtypes register a relapse risk of 47% to 62% for up to 20 years after diagnosis.
“The four represent about 26% of ER+ tumors and the vast majority of relapses,” said Christina Curtis, lead author of the study and a researcher at the Stanford School of Medicine.
More specific treatments
Each of these subgroups is characterized by specific molecular defects (alterations in different genes), which serve both as potential biomarkers and as therapeutic targets.
“Any characteristic of the tumor can provide us with information about the patient’s condition and how he will respond to the treatments,” explains Rueda.
However, according to the Spanish researcher, the most interesting of these new groups is their specificity, making them therapeutic targets.
“Each patient with one of these subtypes of breast cancer could be treated with specific treatments,” says Rueda.
“Although additional studies are needed to translate these findings to the clinic, we are optimistic that it may lead to new treatment options for breast cancer patients,” Curtis emphasizes.
The largest data obtained on breast cancer
Cristina Rueda, professor in the Department of Statistics of the UVa and participant in the design of the methodology used in this work, said that “there are more aspects that make this study pioneer.”
“It has been a very important and novel methodological development. We have contributed to the statistical methodology created specifically for the most efficient analysis of information,” said Rueda.
According to the researcher, the model incorporates different stages of the disease, different time scales. In addition, it adds tumor information that allows predicting the path of the disease and updating the data as the patient evolves.
“The largest set of data to date of breast cancer is made available to the scientific community with molecular information, long-term follow-up and detailed annotation of metastases and recurrences,” she concludes.