A family of proteins has been identified that could potentially be used as biomarkers to predict resistance to chemotherapy in breast cancer patients.

Protein biomarkers have been identified that are associated with response to neoadjuvant chemotherapy using comparative high-throughput processes based on antibodies and using mass spectrometry, and proteomic analysis of estrogen receptor-positive (ER-positive) breast tumor samples.

Scientists at the University of Hull (UK) conducted comparative proteomic studies using the two dimensional gel electrophoresis, matrix-assisted laser desorption/ionization time-of-flight mass spectrometer (2-DE MALDI TOF/TOF MS) on fresh tumor samples. They identified 132 unique proteins that were significantly differently expressed, more than two-fold, in chemotherapy resistant samples, 57 of which were identified in at least two studies.

Five of the proteins in the 57 candidates belong to the 14-3-3 protein family, namely the isoforms: theta/tau, gamma, epsilon, beta/alpha and zeta/delta, which have previously been associated with chemotherapy resistance in breast cancer. The team says their findings confirm the 14-3-3 protein family as a strong candidate for a predictive test for chemotherapy resistance. Without a means to predict whether chemotherapy will work, some patients with resistant cancers undergo much hardship while suffering the side effects of ineffective chemotherapy options without the benefits, plus they lose valuable time until an effective therapy is found.

The Ultraflex III MALDI TOF/TOF MS used in the study was manufactured by Bruker Daltonics (Billerica, MA, USA). Lynn Cawkwell, PhD, the senior author of the study, said, "If we're correct, we hope that by testing for these proteins, doctors will be able to anticipate a patient's response to different chemotherapies, and decide which course of treatment is most appropriate for them." The team is also investigating radiotherapy resistance in a number of different cancers. The study was published on April 3, 2012, in the Journal of Proteomics.

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