Plasma Proteome Signature to Predict the Outcome of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy

Abstract

The plasma proteome of 51 non-metastatic breast cancer patients receiving neoadjuvant chemotherapy (NCT) was prospectively analyzed by high-resolution mass spectrometry coupled with nano-flow liquid chromatography using blood drawn at the time of diagnosis. Plasma proteins were identified as potential biomarkers, and their correlation with clinicopathological variables and survival outcomes was analyzed. Of 51 patients, 20 (39.2%) were HR+/HER2-, five (9.8%) were HR+/HER2+, five (9.8%) were HER2+, and 21 (41.2%) were triple-negative subtype. During a median follow-up of 52.0 months, there were 15 relapses (29.4%) and eight deaths (15.7%). Four potential biomarkers were identified among differentially expressed proteins: APOC3 had higher plasma concentrations in the pathological complete response (pCR) group, whereas MBL2, ENG, and P4HB were higher in the non-pCR group. Proteins statistically significantly associated with survival and capable of differentiating low- and high-risk groups were MBL2 and P4HB for disease-free survival, P4HB for overall survival, and MBL2 for distant metastasis-free survival (DMFS). In the multivariate analysis, only MBL2 was a consistent risk factor for DMFS (HR: 9.65, 95% CI 2.10-44.31). The results demonstrate that the proteomes from non-invasive sampling correlate with pCR and survival in breast cancer patients receiving NCT. Further investigation may clarify the role of these proteins in predicting prognosis and thus their therapeutic potential for the prevention of recurrence.

Keywords: LC-MS/MS; breast cancer; liquid biopsy; neoadjuvant chemotherapy; proteome.

Figure 1

Analysis workflow and partial least-squares discriminant analysis (PLS-DA) of plasma proteomes in 51 breast cancer (BC) patients. (A) The analysis method is shown at the top, the number of proteins is shown in the middle, and the meaning of the step is shown at the bottom. PLS-DA score plot (B) and top26 variable importance in projection (VIP) score (>1.5) plot derived from PLS-DA analysis (C) in 15 patients with pathological complete response (pCR; green) after neoadjuvant therapy and 36 patients with non-pCR (red).

Figure 2

Volcano plot for DAPs altered by pathological complete response (pCR) and functional interpretation. (A) Log₂ fold changes and the corresponding p-values of all proteins between pCR group (n = 15) and non-pCR group (n = 36) are presented as volcano plot. Proteins upregulated with more than a twofold change with a p-value < 0.05 are depicted in red circles and those downregulated with identical fold change and p-value are in green circles. Gray circles show plasma proteins that did not show statistically significant differences. (B) Association between WikiPathways and proteins, and violin plots of the corresponding proteins between the two groups (pCR: green; non-pCR: red).

Figure 3

ROC curves of SVM and RF classifiers for three selected proteins (ENG, MBL2, and P4HB). Capability of the two classifiers in a set of 51 samples, 15 from patients with pCR and 36 from patients with non-pCR. (A) ROC curves of SVM classifiers generated through 100 repeats of threefold cross-validation steps. (B) ROC curves of SVM classifiers generated through 100 repeats of threefold cross-validation steps. ROC curves were obtained by plotting the 25th, 50th, and 75th quantiles of the sensitivities for each value of 1-specificity. (B) Violin plots of 100 area under the curve (AUC) values in the SVM model. (C) ROC curves of RF classifiers generated through 100 repeats of threefold cross-validation steps. ROC curves were obtained by plotting the 25th, 50th, and 75th quantiles of the sensitivities for each value of 1-specificity. (D) Violin plots of 100 AUC values in the RF model.

Figure 4

Kaplan–Meier plots of pathological complete response (pCR) and three proteins, MBL2, ENG, and P4HB. (A) Categorization of patients into pCR and non—pCR risk groups (pCR, n = 15, 6.7%; non-pCR, n = 36, 38.9%; p = 2.59 × 10⁻²). Classification of patients into risk groups according to (B) MBL2 abundance: low abundance group (n = 34, 17.6%) and high abundance group (n = 17, 52.9%), p = 4.21 × 10⁻³; (C) ENG abundance: low abundance group (n = 12, 8.3%) and high abundance group (n = 39, 35.9%), p = 7.34 × 10⁻²; and (D) P4HB abundance: low abundance group (n = 36, 6.7%) and high abundance group (n = 15, 38.9%), p = 2.44 × 10⁻². Statistical significance was determined using the log-rank test. p-values < 0.05 are displayed in bold.