Human epidermal growth factor receptor 2 (HER2)-positive is a particularly aggressive type of the breast cancer. Trastuzumab-based therapy is a standard treatment for HER2-positive breast cancer, but some patients are resistant to the therapy. Serum proteins have been used to predict therapeutic benefit for various cancers, but whether serum proteins can serve as biomarkers for HER2-positive breast cancer remains unclear. Using an isobaric Tandem Mass Tag (TMT) label-based quantitative proteomic, we discovered 18 differentially expressed proteins in the serum of trastuzumab-based therapy resistant patients before therapy. Then, four proteins were selected and validated using an LC-MS/MS-based multiple reaction monitoring quantification method, and it was confirmed that three proteins (SRGN, LDHA and CST3) were correlated with trastuzumab-based therapy resistance. Finally, the trastuzumab-based therapy resistance diagnostic score was calculated and acquired by means of a logistic regression pattern based on the level of these three proteins. In summary, we develop a serum-based protein signature that potentially predicts the therapeutic effects of trastuzumab-based therapy for HER2-positive breast cancer patients.

Association between the XRCC3 Thr241Met Polymorphism and Breast Cancer Risk: an Updated Meta-analysis of 36 Case-control Studies XRCC3;Thr241Met;polymorphism;breast cancer;DNA repair; Background: The X-ray repair cross-complementing group 3 (XRCC3) is a highly suspected candidate gene for cancer susceptibility. Attention has been drawn upon associations of the XRCC3 Thr241Met polymorphism with breast cancer risk. However, the previous published findings remain controversial. Hence, we performed a meta-analysis to accurately evaluate any association between breast cancer and XRCC3 T241M (23, 812 cases and 25, 349 controls) in different inheritance models. Materials and Methods: PubMed and Web of Science databases were searched systematically until December 31, 2013 to obtain all the records evaluating the association between the XRCC3 Thr241Met polymorphism and breast cancer risk. Crude odds ratios (ORs) together with 95% confidence intervals (CIs) were used to assess the strength of associations. Results: When all eligible studies were pooled into the meta analysis of XRCC3 T241M polymorphism, a significantly increased breast cancer risk was observed in heterozygote comparison (OR=1.06, 95%CI=1.01-1.12). No significant associations were found in other models. In subgroup analysis, this polymorphism seemed to be associated with elevated breast risk in Asians. No publication bias was detected. Conclusions: This meta-analysis suggests that the T241M polymorphism confers a weakly increased breast cancer risk. A study with the larger sample size is needed to further evaluate gene-gene and gene-environment interactions of the XRCC3 T241M polymorphism with breast cancer risk.

Background: Trastuzumab resistance strongly affects the prognosis of HER2‐positive breast cancer patients, and tryptophan metabolism plays a key role in cancer progression and cancer prognosis. However, the relationship between trastuzumab resistance and tryptophan metabolism in HER2‐positive breast cancer remains unclear. Methods: Four HER2‐positive breast cancer cell lines (TS: BT‐474, SK‐BR‐3; TR: HCC1954, JIMT‐1) were cultured in specific media. Metabolites from cells, tissues, and serum were extracted via pretreatment, centrifugation, drying, and redissolution. UPLC‐MS/MS was used for metabolite detection, while tryptophan metabolism‐related gene expression was measured using qRT‐PCR. T ‐test was used to calculate statistical differences. Data visualization, machine learning models construction (RF, LASSO logistic regression), and nomogram were achieved through R language. Results: PLS‐DA showed distinct separation between TS and TR breast cancer cell lines in tryptophan metabolism‐related gene expression (four differentially expressed genes: HAAO, KYNU, KMO, AFMID) and metabolite abundance (five differentially abundant metabolites). Integration analysis identified 3‐hydroxyanthranilic acid, cinnabarinic acid, kynurenine, and kynurenic acid as potential metabolic biomarkers. These four metabolites differed significantly in serum of HCs, TS and TR patients. Four predictors were selected by LASSO, and a nomogram was constructed based on logistic regression. Finally, biomarkers and related genes were validated in tissues. Conclusion: Our explored the changes induced by trastuzumab resistance in terms of metabolite abundance and gene transcription levels associated with tryptophan metabolism. Furthermore, this work provided new insights into novel diagnostic biomarkers and prediction models of trastuzumab resistance in HER2‐positive breast cancer patients.