Non-coding single nucleotide variants affecting estrogen receptor binding and activity

Whole-Genome Genotyping Using DNA Microarrays for Population Genetics

The field of population genetics has exploded in the last two decades following the sequencing of the human genome in 2001 (Green et al. Nature 526:29-31, 2015). Tools to measure genetic variation have matured significantly throughout this advancement in knowledge (Lenoir and Giannella. J Biomed Discov Collab 1:11, 2006; Marzancola et al. Methods Mol Biol 1368:161-178, 2016). In this chapter, the focus is on the laboratory methods developed to perform genome-wide genotyping utilizing DNA microarrays, which is one of the most commonly used molecular techniques to assess global genetic variation (Heller MJ, Annu Rev Biomed Eng 4:129-153, 2002). DNA microarrays allow for the interrogation of hundreds of thousands of SNPs (single nucleotide polymorphisms) at once utilizing array-based technology in conjunction with fluorescent molecular labels in a process referred to as genotyping (Marzancola et al. Methods Mol Biol 1368:161-178, 2016). Genotype data can be utilized to associate certain phenotypes in relation with specific genetic variants within a population in a process known as genome-wide association studies or GWAS (Charlesworth and Charlesworth. Heredity (Edinb) 118(1):2-9, 2017; Casillas and Barbadilla. Genetics 205(3):1003-1035, 2017). This experimental technique is a multiple-day process involving the combination of DNA extraction, amplification, fragmentation, binding, and staining (Illumina Infinium HTS Assay Protocol Guide, 2013). Many vendors supply platforms and products to assess global genetic variation using DNA microarrays (Illumina Infinium HTS Assay Protocol Guide, 2013). In this chapter, the focus is on the methods utilized to generate high-quality genotype data with the Illumina^® Infinium Global Screening Array. Although data analysis and quality control are not the focus for this chapter, they are also briefly addressed.

ESR1 PvuII polymorphism: from risk factor to prognostic and predictive factor of the success of primary systemic therapy in advanced breast cancer

BACKGROUND

The ESR1 gene encodes Estrogen Receptor alpha (ERα), which plays a role in the tumourigenesis of breast cancer. A single nucleotide polymorphism (SNP) in intron 1 of this gene called ESR1 PvuII (rs2234693) has been reported to increase the risk of breast cancer. This study aimed to investigate the ESR1 PvuII polymorphism as a prognostic and predictive factor guiding the choice of therapy for advanced breast cancer.

METHODS

This retrospective study was conducted in 104 advanced breast cancer patients at Dharmais Cancer Hospital from 2011 to 2018. The ESR1 PvuII polymorphism was analysed by Sanger sequencing of DNA from primary breast tumour samples.

RESULTS

The percentages of patients with ESR1 PvuII genotypes TT, TC, and CC were 42.3, 39.4, and 18.3%, respectively. Looking at prognosis, patients with ESR1 PvuII TC + CC had shorter overall survival than those with the TT genotype [HR = 1.79; 95% CI 1.05-3.04; p = 0.032]. As a predictive marker, TC + CC was associated with shorter survival (p = 0.041), but TC + CC patients on primary hormonal therapy had a median overall survival longer than TC + CC patients on primary chemotherapy (1072 vs 599 days).

CONCLUSION

The ESR1 PvuII TC + CC genotypes confer poor prognosis in advanced breast cancer, but these genotypes could be regarded as a good predictor of the therapeutic effect of hormonal treatment.

Decoding the Inversion Symmetry Underlying Transcription Factor DNA-Binding Specificity and Functionality in the Genome

Understanding why a transcription factor (TF) binds to a specific DNA element in the genome and whether that binding event affects transcriptional output remains a great challenge. In this study, we demonstrate that TF binding in the genome follows inversion symmetry (IS). In addition, the specific DNA elements where TFs bind in the genome are determined by internal IS within the DNA element. These DNA-binding rules quantitatively define how TFs select the appropriate regulatory targets from a large number of similar DNA elements in the genome to elicit specific transcriptional and cellular responses. Importantly, we also demonstrate that these DNA-binding rules extend to DNA elements that do not support transcriptional activity. That is, the DNA-binding rules are obeyed, but the retention time of the TF at these non-functional DNA elements is not long enough to initiate and/or maintain transcription. We further demonstrate that IS is universal within the genome. Thus, IS is the DNA code that TFs use to interact with the genome and dictates (in conjunction with known DNA sequence constraints) which of those interactions are functionally active.