Postoperative serum proteomic profiles and identification of biomarkers with prognosis value in high-risk early breast cancer patients

Background

A significant number of early breast cancer (EBC) patients considered as high risk using standard prognostic factors develop metastasis despite standard adjuvant therapy. A better prediction of clinical outcome is needed to optimize and individualize therapeutic decisions.

Methods

To identify a protein signature correlating with metastatic relapse, we performed surface-enhanced laser desorption/ionization-time of flight mass spectrometry profiling of early postoperative serum from 81 high-risk EBC patients. Denatured serum samples were fractionated and the resulting fractions were incubated with ProteinChip arrays (Ciphergen Biosystems, Fremont, CA, USA).

Results

Several protein peaks were differentially expressed according to clinical outcome (long-term metastasis-free survival versus metastatic relapse). By combining partial least squares and logistic regression methods, we built a multiprotein model that correctly predicted outcome in 83% of patients. Consistency and robustness of the model were verified using leave-one-out cross-validation. Five-year metastasis-free survival in 'good prognosis' and 'poor prognosis' patients as defined using the multiprotein index were strikingly different (83% vs 22%, P < 0.0001, log-rank test). In a multivariate Cox regression including conventional pathological factors and multiprotein index, only the latter retained independent prognosis significance for metastatic relapse. Major components of the multiprotein index were identified and included haptoglobin, C3a complement fraction, transferrin, apolipoprotein C₁ and apolipoprotein A₁.

Conclusions

Postoperative serum protein pattern may have an important prognostic value in high-risk EBC. In addition, it may reveal new insights on the metastatic process while providing new targets for future therapeutics.