Researchers have identified a gene signature that could be used to predict whether an estrogen receptor- positive (ER-positive) breast cancer patient will respond to the hormone therapy tamoxifen. The study is published in the July issue of Cancer Research.
Tamoxifen is among the most used treatments for ER-positive breast cancer, which makes up about 70% of all breast cancer cases. Although the hormone therapy is effective in many patients, resistance remains a clinical issue with about 30% of treated patients relapsing despite treatment. There are yet no biological explanations for this resistance to tamoxifen.
In the current study, researchers from the Netherlands Cancer Institute in Amsterdam identified a loss of function mutation in the USP9X gene that resulted in a lack of cell growth arrest to tamoxifen of a breast cancer tissue cell line. Then, based on the gene expression of the USP9X-mutated cell line in the presence of tamoxifen, the researchers identified a gene signature that was also present in patients who had poor outcomes after treatment with tamoxifen in the adjuvant setting. This gene signature was specific to tamoxifen-treated patients and not to those treated with other agents, such as fulvestrant or to ER-positive breast cancer patients, who received no endocrine therapy.
“This study shows that we can predict responses to specific therapies,” lead author RenÃ© Bernards, PhD, told OncoTherapy Network.
Loss of the USP9X, which encodes a deubiquitinase enzyme, resulted in increased gene expression of specific ER-alpha target genes and also cell proliferation in tissue culture. The researchers analyzed a public dataset from 250 mostly post-menopausal and mostly ER-positive breast cancer patients who had been treated with adjuvant tamoxifen for whom the outcomes of treatment were known. The data from these patients could be divided into two groups: those who had a good outcome and a defined gene signature, and another which had poor outcomes and a different gene signature. The team then validated their gene signature using an additional dataset of 134 ER-positive, postmenopausal breast cancer patients. From two additional datasets of patients who had not yet been treated with endocrine therapy, the researchers could identify a subset of patients who had the gene signature, most of which had luminal B type tumors.
"In principle, this gene signature could be applied in the clinic, although further validation is still required," said Bernards. "The gene signature is not prognostic, but it is predictive of tamoxifen treatment. The gold standard is to show that the signature is valid in a randomised clinical trial in which patients are treated with or without tamoxifen. We have such a series which has over 20 years of follow-up. We are currently validating the signature in that cohort of over 700 patients. If successful, (this tool’s) clinical implementation could be near term.”
Finding a gene signature to predict response to non-targeted therapies, such as chemotherapy or those that target a hormone pathway, is difficult because of the many genes that cooperate to produce a response--many of which are not known.
“The approach that we have used is indeed rather unconventional,” lead author RenÃ© Bernards, PhD, told OncoTherapy Network. The team used chromatin immunoprecipitation followed by sequencing and RNA sequencing, along with global RNA expression data from publically available datasets. The results were then translated from their cell culture experiments into a diagnostic tool that may be able to identify those patients who will benefit from tamoxifen.
Bernards and his team are also working on tools to predict responses to other breast cancer therapies such as PARP inhibitors, by going beyond just tumors that have a mutation in either BRCA1 or BRCA2. “The future of breast cancer treatment lies in a series of diagnostic signatures that predict the likelihood of recurrence and the likelihood of response to a host of specific drugs,” said Bernards. “This finding is one of multiple that hopefully will find a way to the clinic to help physicians select the best treatment for each individual patient.”