Discovery may determine the best therapeutic option for breast cancer patients.
Scientists recently revealed how small, and often nearly imperceptible, structural changes in a key breast cancer receptor are directly linked to the regulation of molecules, creating predictable effects in cancer growth.
A study published in Molecular Systems Biology used a broad spectrum of analytical tools to perform their research.
In order to identify the root of estrogen receptor (ERα) cell signaling responsible for driving breast cancer cell proliferation, more than 240 estrogen receptor binding molecules (ligands) were synthesized to lead the cancer cells to proliferation. A structural analysis was then used to determine the basis for receptor activity.
Although there are several drugs that target signaling proteins like the estrogen receptor, they can increase the risk of uterine cancer.
“Drugs like tamoxifen can have different effects in different tissues because of structural changes often not discernable using traditional methods,” said first study co-author Sathish Srinivasan. “Our approach reveals some mechanisms associated with tissue specificity and several predictive structural features.”
Researchers continued to test the signaling models, solving the atomic structure of 76 different estrogen receptor-ligand complexes in order to gain better understanding of the responses.
“This is the first time we have been able to use these atomic structures to identify how very small changes from the ligands give different outcomes, leading us towards the goal of predicting which ligands are going to make the most effective treatments for breast cancer,” said lead study author Kendall Nettles.
Some of these effects can be predicted by measuring the distance between 2 specific carbon atoms of the estrogen receptor, according to the study.
“Our long-term goal is to be able to predict proliferative or anti-proliferative activity of receptor molecule complexes by identifying structural changes that lead to specific outcomes,” Nettles said. “In many cases, we can identify structural features that could help guide more effective drug development.”