A serum protein profile predictive of the resistance to neoadjuvant chemotherapy in advanced breast cancers

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.

Fig. 1.

The overall scheme of the proposed integrative approach for identification of a serum protein profile predictive of the resistance to DC + AC NACT.

Fig. 2.

Differential expression of four peptides from APOB. A–B, The isotopic clusters of a differentially expressed peptide from APOB with the sequence K.SKPTVSSSM#EFKYDFN@SSM#LYSTAK.G in MS spectra at the retention time (66.40 min for a CS patient and 66.48 min for a CR patient) showing the maximum peak intensity during the course of elusion. The MS spectra show that the abundance of the differentially expressed peptide was increased by more than 2-fold in a CS patient, compared with that in a CR patient. C, Relative abundances of the four differentially expressed peptides from APOB in CS and CR samples. D, Higher abundances of all these peptides in CS patients than CR patients (p = 1.291 × 10⁻⁴ from two-tailed t test).

Fig. 3.

Clustering analysis of the differential serum protein profile associated with the resistance to DC + AC NACT. The columns and rows in the heat map represent samples and differentially expressed peptides, respectively. Red and blue colors indicate increased and decreased peptide expression levels, respectively, compared with the median intensity (white) of each peptide across all samples. The color bars denote the clusters of samples (CR [1–5], CS [1–4], and CS [5–10]) and peptides (Clusters 1 to 5). The relationships among the clusters were shown in the dendrograms. The bars on the right represent the peptides showing the shared differential expression between the serum proteomic data and the four tissue/cell line data sets. For clarity, only single representative overlapping peptide is labeled when there are multiple peptides for the same protein. See supplemental Table S7 for the cluster memberships of 50 differentially expressed peptides.

Fig. 4.

Selection of serum biomarkers and their prediction power for the resistance to DC+AC NACT. A, Separation between CS and CR patients that was achieved by PLS-DA using the 50 differentially expressed peptides. The line indicates a decision function between CS and CR patients. B, VIP values of the 50 differentially expressed peptides that represent their relative contribution to the separation. The VIP values of 19 peptides were larger than one. The 13 proteins including the 19 peptides were selected to be tested in the independent serum samples.

Fig. 5.

Testing of differential expression of the six selected biomarkers between CS and CR patients. A, Differential expression of the six proteins confirmed in the independent samples using Western blotting. B, The clear separation of discriminant scores between CS and CR patients achieved by PLS-DA when the PLS-DA model was constructed only using the selected six biomarkers. The low p value indicates a high prediction power for the response to NACT. C, Relapse-free survival analysis between high and low discriminant score groups, showing that survival rates of two groups were significantly different (p value ≤ 0.05). The low p value indicates that the prediction of the NACT resistance might also contribute to predicting survival and prognosis after the treatment.