Protein biosignature identified by LC-MS/MS in classifying breast cancer and predicting tumor response to neoadjuvant therapy.

Abstract

e21050 Background: In this study, we investigated proteomic biosignature by LC-MS/MS to classify tumor subtypes and to predict tumor response to neoadjuvant therapy. Methods: Baseline breast cancer specimens of 39 patients who were enrolled in an IRB-approved clinical trial including 28 HER2-positive and 11 triple negative (TNBC) tumors were studied. The protein extracts of all tumors were subjected to abundant protein depletion including albumin, hemoglobulin and immunoglobulin removal. Samples were digested by trypsin and analyzed by Orbitrap LC-MS/MS. Bioworks Software (Thermo-Fisher) was used to study the mass spectra against a human trypsin injected protein database. Positive protein identification was defined if the X corr was ≥ 2.0 for doubly charged ions and ≥ 2.5 for triply charged ions. Scaffold software was used to verify the proteins identified by SEQUEST. Results: In this study, we identified 20 proteins by LS-MS/MS that correctly classified all HER2 positive tumors and 7 of the 11 TNBC. We found that ALDH A1, galactin 3-binding protein, L-plastin and moesin were elevated in TNBC and CK19, transferin and transketolase were over-expressed in HER2 positive cancers. In HER2 positive breast cancer, heat shock proteins 10KD and 70 KD, annexin V and peroxiredoxin 5 were associated with poor tumor response to docetaxel, carboplatin ± trastuzumab (TC ± H). In TNBC annexin I, periostin precursor, tubulin α1/β5 and caldesmon were associated with drug resistance to neoadjuvant TC. Conclusions: Proteomic biosignatures may differentiate tumor types and predict tumor response to chemotherapy. When sufficiently validated, candidate biomarkers may be developed into diagnostic tools for cancer screening and for tailored therapy. No significant financial relationships to disclose.