Quantitative proteomics landscape and association with BASP1 and breast cancer metastasis.

Abstract

1090 Background: Tumor recurrence and metastatic progression remain the leading cause for breast cancer related mortalities. The study aimed to identify differences in proteomics landscape in serum between (i) healthy controls and breast cancer patients, (ii) baseline samples at time of diagnosis from patients that later developed metastases versus those that did not and, (iii) baseline samples presented after completed treatment versus the samples collected before patients developed metastasis. Methods: We performed mass spectrometery-based proteome profiling of 100 serum samples from 51 breast cancer patients and 27 healthy donors. Of the 51-breast cancer patients, 29 patients did not metastasize, and 22 patients had a metastatic recurrence. Each of the 22 breast cancer patients with metastasis had 2 samples-one collected within a year of diagnosis and second one collected within a year before the patient developed metastases. Intensity-based absolute quantification (iBAQ) method was employed to convert recovered peptide information to quantificational gene protein product and then normalized to final quantificational value using an in-housed software. Protein values were normalized using z-score before differential expression analyses. FDR < 0.1 and a p-value < 0.05 was used as the cutoff to identify differentially expressed proteins. Results: We identified 1177 proteins in total from 100 serum samples of healthy women and breast cancer patients. PCA analysis revealed a complete separation of the breast cancer and healthy control samples. However, we found overlapping but distinct groups of metastatic and non-metastatic samples. We found 179 proteins to be differentially expressed between normal healthy control samples and baseline breast cancer samples irrespective of their metastatic status. Upon comparing baseline breast cancer samples that metastasized with breast cancer samples that did not metastasize, we found BASP1 as the top-ranked gene that was significantly upregulated in metastatic samples. We did not find any significant differences between paired baseline samples collected at diagnosis and pre-metastatic samples collected before the patient developed metastasis. Conclusions: Our results show distinct proteomic profiles exist between breast cancer and normal healthy control samples. Further studies are required to confirm if serum BASP1 can be used as a putative biomarker for predicting metastatic risk in breast cancer patients.