The Effect of Polymorphism in UGT1A4 on Clinical Outcomes of Adjuvant Tamoxifen Therapy for Patients With Breast Cancer in China

Recent advances in the personalized treatment of estrogen receptor-positive breast cancer with tamoxifen: a focus on pharmacogenomics

Introduction: Tamoxifen is still an important drug in hormone-dependent breast cancer therapy. Personalization of its clinical use beyond hormone receptor positivity could improve the substantial variability of the treatment response.Areas covered: The overview of the current evidence for the treatment personalization using therapeutic drug monitoring, or using genetic biomarkers including CYP2D6 is provided. Although many studies focused on the PK aspects or the impact of CYP2D6 variability the translation into clinical routine is not clearly defined due to the inconsistent clinical outcome data.Expert opinion: We believe that at least the main candidate factors, i.e. CYP2D6 polymorphism, CYP2D6 inhibition, endoxifen serum levels may become important predictors of clinical relevance for tamoxifen treatment personalization in the future. To achieve this aim, however, further research should take into consideration more precise characterization of the disease, epigenetic factors and also utilize an appropriately powered multifactorial approach instead of a single gene evaluating studies.

Genome-wide DNA copy number profiling and bioinformatics analysis of ovarian cancer reveals key genes and pathways associated with distinct invasive/migratory capabilities

Ovarian cancer (OC) metastasis presents major hurdles that must be overcome to improve patient outcomes. Recent studies have demonstrated copy number variations (CNVs) frequently contribute to alterations in oncogenic drivers. The present study used a CytoScan HD Array to analyse CNVs and loss of heterozygosity (LOH) in the entire genomes of 6 OC patients and human OC cell lines to determine the genetic target events leading to the distinct invasive/migratory capacities of OC. The results showed that LOH at Xq11.1 and Xp21.1 and gains at 8q21.13 were novel, specific CNVs. Ovarian cancer-related CNVs were then screened by bioinformatics analysis. In addition, transcription factors-target gene interactions were predicted with information from PASTAA analysis. As a result, six genes (i.e., GAB2, AKT1, EGFR, COL6A3, UGT1A1 and UGT1A8) were identified as strong candidates by integrating the above data with gene expression and clinical outcome data. In the transcriptional regulatory network, 4 known cancer-related transcription factors (TFs) interacted with 6 CNV-driven genes. The protein/DNA arrays revealed 3 of these 4 TFs as potential candidate gene-related transcription factors in OC. We then demonstrated that these six genes can serve as potential biomarkers for OC. Further studies are required to elucidate the pathogenesis of OC.