Unveiling the relationship between the UGT2B gene family and neoadjuvant treatment response in patients with HER2-positive breast cancer.

e12651 Background: Achieving a pathological complete response (pCR) following neoadjuvant treatment (NAT) in HER2-positive tumors significantly enhances patient survival, resulting in a remarkable 92% reduction in mortality risk. However, it is crucial to identify biomarkers capable of predicting the NAT response, moving towards more personalized medicine. This approach would enable the identification of patients likely to respond to specific therapies, thereby facilitating the implementation of targeted treatment strategies. Our study aims to identify potential molecular biomarkers predictive of NAT response through a comprehensive analysis of differential gene expression in tissue samples from patients with HER2-positive, hormone receptor (HR)-negative breast cancer. Methods: Total RNA was extracted from FFPE tissue samples from two independent cohorts of women diagnosed with localized or locally advanced HER2-positive, HR-negative breast cancer. These patients underwent NAT in several hospitals in Andalusia, Spain. Standard chemotherapy, including taxanes and/or anthracyclines, and targeted anti-HER2 treatment such as trastuzumab and pertuzumab were administered. The patients were stratified into responders (R), achieving pCR, and non-responders (nR). In a discovery cohort (n=20), RNA hybridization using Clariom D microarray was conducted to discern differentially expressed transcripts between R and nR. Subsequently, gene expression patterns, and related pathways involved in NAT response, were analyzed using Transcriptome Analysis Console (TAC). The selected transcripts then underwent validation through quantitative PCR (qPCR) in an independent cohort (n=40). The area under the receiver operating characteristic (ROC) curve was used to evaluate the sensitivity and specificity of the genes as predictive biomarkers. Results: Microarray analysis of the discovery cohort, revealed 954 differentially expressed transcripts between R and nR, following criteria of fold change greater than 1.5 or less than -1.5 and a p-value below 0.05. Among these, 643 were upregulated, while 311 were downregulated in nR compared to R. Bioinformatic analysis and a comprehensive literature review guided the selection of genes UGT2B10, UGT2B11, UGT2B15, UGT2B17, and UGT2B28 for further investigation. Subsequent qPCR analysis in the validation cohort, demonstrated the overexpression of these genes in non-responders, with statistical significance achieved only for UGT2B15. Conclusions: The UGT2B family genes encode enzymes involved in glucuronidation and metabolic pathways associated with drug detoxification and elimination. Our findings suggest that variations in the UGT2B15 expression may impact the metabolism rate of specific drugs, underscoring the necessity for additional research in this area.