Genetic Signature for ER+ Breast Cancer Identified

A multi-institution team of researchers led by scientists at Baylor College of Medicine has discovered a genetic signature that can identify drivers of poor outcomes in advanced estrogen-receptor-positive (ER+) breast cancer, which could one day lead to personalized treatment for patients. Their findings, published in Cancer Research, provide a new strategy to refine breast cancer diagnosis that could help guide a more precise selection of tumor-specific treatments.

“One of the predominant ways ER+ breast cancer cells evade treatment is by creating mutant ERs that no longer can be recognized and targeted by ER-targeting cancer drugs,” said first author XuxuGou. The team has been studying ESR1 gene translocations, which refer to the ER gene swapping a part of its sequence with genetic information from another gene.  “Not all ER translocations were active—some drive metastasis and resistance to treatment, but others do not,” Gou added. “To be able to determine whether any particular ESR1 translocation can promote disease progression, we developed a diagnostic genetic signature that detects the presence of an active ESR1 chimeric protein.”

With support from the National Cancer Institute’s PDXnet program, the team used genomics and transcriptomics to annotate 20 mouse models of ER+ patient-derived tumors that demonstrated different degrees of dependence on estrogen for growth. In this data set, a 24-gene signature detected the presence of an active ESR1 fusion, but interestingly also common point mutations in ESR1. These findings were replicated in data from a human metastatic breast cancer cohort. The team, therefore, called their 24 gene signature the MOTERA score for “Mutant or Translocated Estrogen Receptor Alpha.”

“In the future, a patient’s cancer cells could be analyzed and, once the MOTERA score indicates the presence of an ER mutation or translocation, then the tumor cells would be further studied to more precisely determine what kind of ER mutant or translocation is present. This would help guide the selection of personalized, optimal treatment,” concluded Charles E. Foulds, co-author.