Molecular serum portraits in patients with primary breast cancer predict the development of distant metastases

Abstract

The risk of distant recurrence in breast cancer patients is difficult to assess with current clinical and histopathological parameters, and no validated serum biomarkers currently exist. Using a recently developed recombinant antibody microarray platform containing 135 antibodies against 65 mainly immunoregulatory proteins, we screened 240 sera from 64 patients with primary breast cancer. This unique longitudinal sample material was collected from each patient between 0 and 36 mo after the primary operation. The velocity for each serum protein was determined by comparing the samples collected at the primary operation and then 3-6 mo later. A 21-protein signature was identified, using leave-one-out cross-validation together with a backward elimination strategy in a training cohort. This signature was tested and evaluated subsequently in an independent test cohort (prevalidation). The risk of developing distant recurrence after primary operation could be assessed for each patient, using her molecular portraits. The results from this prevalidation study showed that patients could be classified into high- versus low-risk groups for developing metastatic breast cancer with a receiver operating characteristic area under the curve of 0.85. This risk assessment was not dependent on the type of adjuvant therapy received by the patients. Even more importantly, we demonstrated that this protein signature provided an added value compared with conventional clinical parameters. Consequently, we present here a candidate serum biomarker signature able to classify patients with primary breast cancer according to their risk of developing distant recurrence, with an accuracy outperforming current procedures.

Fig. 1.

Flowchart describing the retrospective study design. Seven patients were excluded because of lack of samples collected at the relevant time points (preoperative and 3–6 mo later). BC, breast cancer.

Fig. 2.

SVM analysis for prediction of metastatic breast cancer. Analyte levels in samples collected 3–6 mo after surgery were compared with those in samples taken at the time of surgery. The differences in all analytes as identified on the arrays were fed to an SVM, which, using a leave-one-out cross-validation procedure, was calibrated to classify the patient as one who will develop metastatic cancer or one who will not. The analysis resulted in a decision value for each patient, significantly separating the groups (Wilcoxon P value of 1.7 × 10⁻⁵) and yielding an ROC AUC of 0.88. The heat map shows all analytes displaying a Wilcoxon P value <0.05, with red indicating an increase and green indicating a decrease in biomarker velocity.

Fig. 3.

Prevalidation of the classification of patients into high- or low-risk groups for breast cancer recurrence. (A) An SVM was calibrated on the discovery dataset and tested on the prevalidation dataset using antibody array data derived from the 21-biomarker signature generated using backward elimination, as described in SI Materials and Methods. The ROC AUC for this classification was 0.85. (B) An SVM was calibrated using conventional clinical data (yellow) giving an ROC AUC = 0.66. The combination of the 21-biomarker signature and conventional clinical parameters (red) resulted in an ROC AUC = 0.90, demonstrating the added value in the array data.

Fig. 4.

Chemotherapy was found not to be a confounding factor for our candidate biomarker signature. The SVM decision values for all of the patients are plotted, and patients receiving chemotherapy during the first 3–6 mo are indicated by arrows. When patients were categorized in risk groups based on the SVM decision value, 72% of patients in the high-risk group (red) but only 15% in the low-risk group (blue) later developed metastasis. When results were correlated to the patients who received adjuvant treatment (black arrows; 5 of 18 patents in the high-risk group and 7 of 20 patients in the low-risk group), it was evident that this factor did not bias the analysis.