Browsing by Author "Kleer, Celina G."

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    ESR1 and PGR polymorphisms are associated with estrogen and progesterone receptor expression in breast tumors
    (American Physiological Society, 2016-09-01) Hertz, Daniel L.; Henry, N. Lynn; Kidwell, Kelley M.; Thomas, Dafydd; Goddard, Audrey; Azzouz, Faouzi; Speth, Kelly; Li, Lang; Banerjee, Mousumi; Thibert, Jacklyn N.; Kleer, Celina G.; Stearns, Vered; Hayes, Daniel F.; Skaar, Todd C.; Rae, James M.; Medicine, School of Medicine
    Hormone receptor-positive (HR+) breast cancers express the estrogen (ERα) and/or progesterone (PgR) receptors. Inherited single nucleotide polymorphisms (SNPs) in ESR1, the gene encoding ERα, have been reported to predict tamoxifen effectiveness. We hypothesized that these associations could be attributed to altered tumor gene/protein expression of ESR1/ERα and that SNPs in the PGR gene predict tumor PGR/PgR expression. Formalin-fixed paraffin-embedded breast cancer tumor specimens were analyzed for ESR1 and PGR gene transcript expression by the reverse transcription polymerase chain reaction based Oncotype DX assay and for ERα and PgR protein expression by immunohistochemistry (IHC) and an automated quantitative immunofluorescence assay (AQUA). Germline genotypes for SNPs in ESR1 (n = 41) and PGR (n = 8) were determined by allele-specific TaqMan assays. One SNP in ESR1 (rs9322336) was significantly associated with ESR1 gene transcript expression (P = 0.006) but not ERα protein expression (P > 0.05). A PGR SNP (rs518162) was associated with decreased PGR gene transcript expression (P = 0.003) and PgR protein expression measured by IHC (P = 0.016), but not AQUA (P = 0.054). There were modest, but statistically significant correlations between gene and protein expression for ESR1/ERα and PGR/PgR and for protein expression measured by IHC and AQUA (Pearson correlation = 0.32–0.64, all P < 0.001). Inherited ESR1 and PGR genotypes may affect tumor ESR1/ERα and PGR/PgR expression, respectively, which are moderately correlated. This work supports further research into germline predictors of tumor characteristics and treatment effectiveness, which may someday inform selection of hormonal treatments for patients with HR+ breast cancer.
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    MammOnc-DB, an integrative breast cancer data analysis platform for target discovery
    (Springer Nature, 2025-04-18) Karthikeyan, Santhosh Kumar; Chandrashekar, Darshan S.; Sahai, Snigdha; Shrestha, Sadeep; Aneja, Ritu; Singh, Rajesh; Kleer, Celina G.; Kumar, Sidharth; Qin, Zhaohui S.; Nakshatri, Harikrishna; Manne, Upender; Creighton, Chad J.; Varambally, Sooryanarayana; Surgery, School of Medicine
    Breast cancer (BCa), a leading malignancy among women, is characterized by morphological and molecular heterogeneity. While early-stage, hormone receptor, and HER2-positive BCa are treatable, triple-negative BCa and metastatic BCa remains largely untreatable. Advances in sequencing and proteomic technologies have improved our understanding of the molecular alterations that occur during BCa initiation and progression and enabled identification of subclass-specific biomarkers and therapeutic targets. Despite the availability of abundant omics data in public repositories, user-friendly tools for multi-omics data analysis and integration are scarce. To address this, we developed a comprehensive BCa data analysis platform called MammOnc-DB ( http://resource.path.uab.edu/MammOnc-Home.html ), comprising data from more than 20,000 BCa samples. MammOnc-DB facilitates hypothesis generation and testing, biomarker discovery, and therapeutic targets identification. The platform also includes pre- and post-treatment data, which can help users identify treatment resistance markers and support combination therapy strategies, offering researchers and clinicians a comprehensive tool for BCa data analysis and visualization.