Abstract
Prediction of the responses to neoadjuvant chemotherapy (NACT) can improve the treatment of patients with advanced breast cancer. Genes and proteins predictive of chemoresistance have been extensively studied in breast cancer tissues. However, noninvasive serum biomarkers capable of such prediction have been rarely exploited. Here, we performed profiling of N-glycosylated proteins in serum from fifteen advanced breast cancer patients (ten patients sensitive to and five patients resistant to NACT) to discover serum biomarkers of chemoresistance using a label-free liquid chromatography-tandem MS method. By performing a series of statistical analyses of the proteomic data, we selected thirteen biomarker candidates and tested their differential serum levels by Western blotting in 13 independent samples (eight patients sensitive to and five patients resistant to NACT). Among the candidates, we then selected the final set of six potential serum biomarkers (AHSG, APOB, C3, C9, CP, and ORM1) whose differential expression was confirmed in the independent samples. Finally, we demonstrated that a multivariate classification model using the six proteins could predict responses to NACT and further predict relapse-free survival of patients. In summary, global N-glycoproteome profile in serum revealed a protein pattern predictive of the responses to NACT, which can be further validated in large clinical studies.
Highlights
Breast cancer is the most common cause of cancer death in women [1]
We selected the final set of six potential serum biomarkers (AHSG, APOB, C3, C9, CP, and ORM1) whose differential expression was confirmed in the independent samples
Integration of Differential Expression Associated with Chemoresistance from Breast Cancer Tissues—During the selection of the 50 differentially expressed peptides, we investigated the differential expression of the mRNAs and proteins in the four sets of mRNA and proteomic data collected from breast cancer tissues and cell lines, as described above: 1 and 2) differential expression of the mRNAs associated with DC (GSE6434; supplemental Table S8) [46] and AC resistance (GSE1647; supplemental Table S9) [10] in breast cancer tissues and ZR751 cells, respectively; 3) differential expression of the proteins associated with DC ϩ AC resistance in breast cancer tissues; and 4) differential expression of the mRNAs associated with prognosis in breast cancer tissues [9]
Summary
Patients and Sample Collection—Patients with locally advanced breast cancer (i.e. tumor size greater than 2 cm) and clinically tumorpositive axillary lymph nodes were eligible for the study. To remove potential false positives coming from the statistical tests using the unbalanced sizes of CR and CS patients, we performed the following experiments: 1) from the ten CS patients, we generated 252 sets of five CS patients; 2) a set of differentially expressed peptides was identified by applying the above combined statistical test to each set of the five CS patients and the five CR patients, resulting in 252 sets of differentially expressed peptides; 3) for all the 558 peptides, the frequencies selected as differentially expressed peptides from the 252 comparisons were computed; 4) to evaluate the significance of the frequency, we generated a null hypothesis distribution of the frequency for nondifferentially expressed peptides by dividing the ten CS samples into two sets of five CS samples (252 cases) and performing Steps 2 and 3 for the 252 cases; 5) we computed the 95 percentile frequency value (i.e. 60 out of 252) from the null hypothesis distribution of the frequency (Step 4); and 6) among the 81 differentially expressed peptides, we selected 79 peptides that have their frequencies larger than the cutoff value of 60. The intensities of Coomassie staining of duplicate gels across samples were normalized to the same level [43]
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