Combining Biomarkers and Clinicopathologic Factors for Prediction of Response to Adjuvant Chemotherapy for Breast Cancer: Cox Model and Support Vector Machine (SVM) Methods

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Liu, Xudong
Breast Cancer , Predictive Marker , Machine Learning , Supporting Vector
Background: Breast cancer is a complex disease, both phenotypically and etiologically. Accordingly, the responses to various treatments in the adjuvant setting among individuals vary considerably. There is a demand for tools that can distinguish patients who may benefit or may suffer from particular systemic treatments. We hypothesized that combination of data on genetic biomarkers with data from traditional clinical and pathophysiological (clinicopathologic) factors using traditional Cox model or Support Vector Machine (SVM) method, a new machine learning method, may provide a better tool for prediction of benefits to chemotherapy for the treatment of early breast cancer than using either biomarker or clinicopathologic data alone. Methods: This project included 531 patients from NCIC-CTG MA.5 trial who had data on both clinicopathologic factors, such as age, tumor size, ER status, type of surgery, tumor grade and lymph node involvement, and biomarkers assayed on tissue microarrays (TMAs), including HER2, p53, CA9, MEP21, clusterin, pAKT, COX2 and TOP2A. The Cox model and SVM methods were used to develop prognostic indices for relapse-free or overall survival with either data from TMAs and clinicopathologic assessments alone or their combination. The prognostic indices developed were then examined for their value as predictive classifiers for benefits from CEF treatment. The power of the predictive classifiers derived was evaluated and compared using the bootstrap approach. Results: None of the prognostic indices developed were found to have significant predictive value, although the prognostic index developed using SVM method based on only biomarkers yielded a marginal significant p-value (p=0.0527) for the interaction between classifier and treatment. In accordance with results published previously, the interaction between the classifier developed based on HER2 or TOP2A and treatment was significant (p=0.02 and 0.04 respectively). Comparisons based on the bootstrap approach indicate classifiers developed based on SVM performed better than those based on the Cox model method. Conclusions: Combination of data using biomarkers and clinical-pathological factors, and using either the traditional COX model method or the new machine learning method was not shown to perform better than two single previously known biomarkers in prediction of response to CEF treatment for early breast cancer.
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