The model that allows to predict what risk of relapse has a breast cancer

The model that allows to predict what risk of relapse has a breast cancer


The study revealed why in some cases, breast cancer returns years after the end of treatment.

The genetic and molecular characteristics of each malignant breast tumor contain information on how the disease will progress in the woman who suffers, according to a new study from the University of Cambridge in the United Kingdom and Stanford University in the United States.

The study, published in the journal Nature , examined patterns of genetic changes within the tumors of nearly 2,000 women and followed their progress over 20 years.

With these data, they created a statistical tool to predict what the odds are in each particular case that the disease returns after treatment, and in what time frame it will do so.

The objective of the study, the researchers say, is that this test allows in the future to identify which people who have had breast cancer need more constant monitoring and which can be more certain that the risk of a relapse is very low.

The British NGO Cancer Research considered the work of the scientists “incredibly encouraging”, but stressed that the test is not yet available to the general public.

Not one but 11

Currently, breast cancer is classified according to whether it responds to the hormone estrogen or to targeted therapies (such as is done for example with Herceptin).

However, after analyzing the genetic mutations within the tumor, the researchers created a new form of classification.

Woman looking at a mammogram
Copyright of the GETTY IMAGES image
Image caption Breast cancer is not a disease but 11 different.


Previous work of the team showed that breast cancer is actually 11 different diseases , each with a different cause and treatment.

After following the evolution of 2,000 women for two decades, they were able to establish which types of cancer are more likely to return.

The chances of the tumor returning are driven by biology, Carlos Caldas, lead author of the study, told the BBC. “(The explanation) is the molecular wiring of your tumor .”

“Once and for all we have to stop talking about breast cancer as a single disease, it is a constellation of 11 diseases.”

“This is a very significant step towards more precise medicine.”

Change of treatment

These 11 molecular subgroups (or 11 diseases) have a different “trajectory” , which can not be predicted solely by characteristics such as the size of the tumor or the stage of the disease.

These clinical trajectories vary considerably even among tumors that look similar.

For example, in women with a form of the disease known as triple negative breast cancer – one of the most difficult cancers to treat – they found that there were two different prognoses, because they observed that it was not one type of cancer but two.

In one group, if the cancer had not reappeared in five years, the women were probably cured, says Caldas.

But in the second group, there was a significant risk that the tumor would return, he adds.

Copyright of the GETTY IMAGES image
Image caption Statistical analysis allows to determine which women who have had breast cancer need to be monitored more closely.

Research can help provide more accurate information about future risks, but it can also help change the way the disease is treated.

The researchers found four subgroups in which breast cancer responded to estrogen and had a “markedly increased” risk of reoccurrence.

These women, for example, could benefit from longer treatment with drugs such as Tamoxifen.

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According to the BBC science and health correspondent James Gallagher, it has long been known that designating cancer according to the organ where it was first found – mamma, colon, prostate, lung, etc. – is not enough.

But this study shows where personalized medicine goes and the need to develop treatments that suit the specific causes of each cancer.

There are currently several studies underway that investigate which treatments may work best for each subgroup of cancer.

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