Abstract
Various gene signatures of chemosensitivity in breast cancer have been discovered. One previous study employed t-test to find a signature of 31 probe sets (27 genes) from a group of patients who received weekly preoperative chemotherapy. Based on this signature, a 30-probe set diagonal linear discriminant analysis (DLDA-30) classifier of pathologic complete response (pCR) was constructed. In this study, we sought to uncover a signature that is much smaller than the 31 probe sets and yet has enhanced predictive performance. A signature of this nature could inform us what genes are essential in response prediction. Genetic algorithms (GAs) and sparse logistic regression (SLR) were employed to identify two such small signatures. The first had 13 probe sets (10 genes) selected from the 31 probe sets and was used to build a SLR predictor of pCR (SLR-13), and the second had 14 probe sets (14 genes) selected from the genes involved in Notch signaling pathway and was used to develop another SLR predictor of pCR (SLR-Notch-14). The SLR-13 and SLR-Notch-14 had a higher accuracy and a higher positive predictive value than the DLDA-30 with much lower P values, suggesting that our two signatures had their own discriminative power with high statistical significance. The SLR prediction model also suggested the dual role of gene RNUX1 in promoting residual disease (RD) or pCR in breast cancer. Our results demonstrated that the multivariable techniques such as GAs and SLR are effective in finding significant genes in chemosensitivity prediction. They have the advantage of revealing the interacting genes, which might be missed by single variable techniques such as t-test.
Highlights
The molecular and pathological characteristics observed in breast cancer patients implies that breast cancer is a heterogeneous disease
Our results demonstrated that the multivariable techniques such as Genetic algorithms (GAs) and sparse logistic regression (SLR) are effective in finding significant genes in chemosensitivity prediction
Recent studies suggest that gene expression profile correlates with responses to neoadjuvant chemotherapy, with tumors displaying the ER-positive gene signatures being less likely to respond than other types of breast cancer [17]
Summary
The molecular and pathological characteristics observed in breast cancer patients implies that breast cancer is a heterogeneous disease. Chemotherapy is applied empirically, and does not benefit all patients, illustrating the imperative needs for a more personalized approach in cancer treatment. The ability to predict whether an individual patient will benefit from a specific therapy is of great clinical significance. Single clinical or molecular indicators, such as tumor size, tumor grade, histology, hormone receptor or human epidermal growth factor receptor 2 (HER2) expression, does not always give reliable predictions of response to a treatment. It is possible to find multiple genes that can used to build an enhanced predictor of response to chemotherapy in breast cancer [1,2,3,4,5,6,7]
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