Anti-PGR monoclonal antibody, clone ZS96

Large oncology repositories have paired genomic and transcriptomic data for all patients. We used these data to perform two independent analyses: to identify gene expression changes related to a gene mutation and to identify mutations altering the expression of a selected gene. All data processing steps were performed in the R statistical environment. RNA-sequencing and mutation data were acquired from The Cancer Genome Atlas (TCGA). The DESeq2 algorithm was applied for RNA-seq normalization, and transcript variants were annotated with AnnotationDbi. MuTect2-identified somatic mutation data were utilized, and the MAFtools Bioconductor program was used to summarize the data. The Mann-WhitneyUtest was used for differential expression analysis. The established database contains 7876 solid tumors from 18 different tumor types with both somatic mutation and RNA-seq data. The utility of the approach is presented via three analyses in breast cancer: gene expression changes related to TP53 mutations, gene expression changes related to CDH1 mutations and mutations resulting in altered progesterone receptor (PGR) expression. The breast cancer database was split into equally sized training and test sets, and these data sets were analyzed independently. The highly significant overlap of the results (chi-square statistic = 16 719.7 andP < .00001) validates the presented pipeline. Finally, we set up a portal at enabling the rapid identification of novel mutational targets. By linking somatic mutations and gene expression, it is possible to identify biomarkers and potential therapeutic targets in different types of solid tumors. The registration-free online platform can increase the speed and reduce the development cost of novel personalized therapies.

Context. Poor uterine receptivity is one major factor leading to pregnancy loss and infertility. Understanding the molecular events governing successful implantation is hence critical in combating infertility. Objective. To define Progesterone Receptor (PGR)-regulated molecular mechanisms and epithelial roles in receptivity. Design. RNA-sequencing and PGR-ChIP-seq were conducted in parallel to identify PGR-regulated pathways during the Window of implantation (WOI) in endometrium of fertile women. Setting. Endometrial biopsies from the proliferative and mid-secretory phases were analyzed. Patients or Other Participants. Participants were fertile, reproductive aged (18-37 years) women with normal cycle length, and without any history of dysmenorrhea, infertility, or irregular cycles. In total, 42 endometrial biopsies obtained from 42 women were analyzed in this study. Interventions. There were no interventions during this study. Main Outcome Measures. Here we measured the alterations in gene expression and PGR occupancy in the genome during the WOI, based on the hypothesis that PGR binds uterine chromatin cycle dependently to regulate genes involved in uterine cell differentiation and function. Results. 653 genes were identified with regulated PGR binding and differential expression during the WOI. These were involved in regulating inflammatory response, xenobiotic metabolism, epithelial mesenchymal transition, cell death, interleukin/Signal Transducer And Activator Of Transcription (STAT) signaling, estrogen response, and Mammalian target of rapamycin complex 1 (MTORC1) response. Transcriptome of the epithelium identified 3052 differentially expressed genes, of which 658 were uniquely regulated. Transcription factors Interferon Regulatory Factor 8 (IRF8) and Myocyte Enhancer Factor 2C (MEF2C) were found to be regulated in the epithelium during the WOI at the protein level, suggesting potentially important functions that are previously unrecognized. Conclusion. PGR binds the genomic regions of genes regulating critical processes in uterine receptivity and function.