Association Between Adrenal-Restrictive HSD3B1 Inheritance and Hormone-Independent Subtypes of Endometrial and Breast Cancer.

BACKGROUND: The germline variant rs1047303 (HSD3B1[1245A/C]), restricting or enabling production of potent androgens and estrogens from adrenal precursors, affects outcomes of castration-resistant prostate cancer and is associated with estrogen receptor positivity in postmenopausal breast cancer. Like breast cancer, endometrial cancer is another malignancy with hormone-dependent and hormone-independent subtypes. We hypothesized that adrenal-restrictive HSD3B1 genotype would associate with hormone-independent cancer subtypes. METHODS: We employed a previously described classification of tumors in The Cancer Genome Atlas into genomic clusters. We determined HSD3B1 genotype frequencies by endometrial cancer genomic cluster and calculated the odds per adrenal-restrictive A allele for the largely hormone-independent copy-number (CN) high subtype vs other subtypes. An equivalent analysis was performed for the genomically similar, hormone-independent basal breast cancer subtype. Last, we performed survival analyses for UK Biobank participants with endometrial cancer by HSD3B1 genotype. All statistical tests were 2-sided. RESULTS: The adrenal-restrictive HSD3B1(1245A) allele was associated with the CN-high endometrial cancer subtype (odds ratio [OR] = 1.63, 95% confidence interval [CI] = 1.14 to 2.32; P = .007). Similarly, HSD3B1(1245A) was associated with the basal breast cancer subtype (OR = 1.54, 95% CI = 1.13 to 2.08; P = .006). In the UK Biobank, endometrial cancer patients homozygous for HSD3B1(1245A) had worse overall (hazard ratio [HR] = 1.39, 95% CI = 1.16 to 1.68; P < .001) and cancer-specific (HR = 1.39, 95% CI = 1.14 to 1.70; P = .001) survival, consistent with the A allele being enriched in the more aggressive CN-high subtype. CONCLUSIONS: These findings suggest roles for adrenal-restrictive vs adrenal-permissive steroidogenesis, by way of rs1047303 genotype, in the development of and/or outcomes from at least 3 commonly hormone-associated types of cancer: prostate, breast, and endometrial.

Adrenal-permissive HSD3B1 genetic inheritance and risk of estrogen-driven postmenopausal breast cancer.

BACKGROUNDGenetics of estrogen synthesis and breast cancer risk has been elusive. The 1245A→C missense-encoding polymorphism in HSD3B1, which is common in White populations, is functionally adrenal permissive and increases synthesis of the aromatase substrate androstenedione. We hypothesized that homozygous inheritance of the adrenal-permissive HSD3B1(1245C) is associated with postmenopausal estrogen receptor-positive (ER-positive) breast cancer.METHODSA prospective study of postmenopausal ER-driven breast cancer was done for determination of HSD3B1 and circulating steroids. Validation was performed in 2 other cohorts. Adrenal-permissive genotype frequency was compared between postmenopausal ER-positive breast cancer, the general population, and postmenopausal ER-negative breast cancer.RESULTSProspective and validation studies had 157 and 538 patients, respectively, for the primary analysis of genotype frequency by ER status in White female breast cancer patients who were postmenopausal at diagnosis. The adrenal-permissive genotype frequency in postmenopausal White women with estrogen-driven breast cancer in the prospective cohort was 17.5% (21/120) compared with 5.4% (2/37) for ER-negative breast cancer (P = 0.108) and 9.6% (429/4451) in the general population (P = 0.0077). Adrenal-permissive genotype frequency for estrogen-driven postmenopausal breast cancer was validated using Cambridge and The Cancer Genome Atlas data sets: 14.4% (56/389) compared with 6.0% (9/149) for ER-negative breast cancer (P = 0.007) and the general population (P = 0.005). Circulating androstenedione concentration was higher with the adrenal-permissive genotype (P = 0.03).CONCLUSIONAdrenal-permissive genotype is associated with estrogen-driven postmenopausal breast cancer. These findings link genetic inheritance of endogenous estrogen exposure to estrogen-driven breast cancer.FUNDINGNational Cancer Institute, NIH (R01CA236780, R01CA172382, and P30-CA008748); and Prostate Cancer Foundation Challenge Award.

Polycomb protein EZH2 regulates tumor invasion via the transcriptional repression of the metastasis suppressor RKIP in breast and prostate cancer.

Epigenetic modifications such as histone methylation play an important role in human cancer metastasis. Enhancer of zeste homolog 2 (EZH2), which encodes the histone methyltransferase component of the polycomb repressive complex 2 (PRC2), is overexpressed widely in breast and prostate cancers and epigenetically silences tumor suppressor genes. Expression levels of the novel tumor and metastasis suppressor Raf-1 kinase inhibitor protein (RKIP) have been shown to correlate negatively with those of EZH2 in breast and prostate cell lines as well as in clinical cancer tissues. Here, we show that the RKIP/EZH2 ratio significantly decreases with the severity of disease and is negatively associated with relapse-free survival in breast cancer. Using a combination of loss- and gain-of-function approaches, we found that EZH2 negatively regulated RKIP transcription through repression-associated histone modifications. Direct recruitment of EZH2 and suppressor of zeste 12 (Suz12) to the proximal E-boxes of the RKIP promoter was accompanied by H3-K27-me3 and H3-K9-me3 modifications. The repressing activity of EZH2 on RKIP expression was dependent on histone deacetylase promoter recruitment and was negatively regulated upstream by miR-101. Together, our findings indicate that EZH2 accelerates cancer cell invasion, in part, via RKIP inhibition. These data also implicate EZH2 in the regulation of RKIP transcription, suggesting a potential mechanism by which EZH2 promotes tumor progression and metastasis.

snoTARGET shows that human orphan snoRNA targets locate close to alternative splice junctions.

Among thousands of non-protein-coding RNAs which have been found in humans, a significant group represents snoRNA molecules that guide other types of RNAs to specific chemical modifications, cleavages, or proper folding. Yet, hundreds of mammalian snoRNAs have unknown function and are referred to as "orphan" molecules. In 2006, for the first time, it was shown that a particular orphan snoRNA (HBII-52) plays an important role in the regulation of alternative splicing of the serotonin receptor gene in humans and other mammals. In order to facilitate the investigation of possible involvement of snoRNAs in the regulation of pre-mRNA processing, we developed a new computational web resource, snoTARGET, which searches for possible guiding sites for snoRNAs among the entire set of human and rodent exonic and intronic sequences. Application of snoTARGET for finding possible guiding sites for a number of human and rodent orphan C/D-box snoRNAs showed that another subgroup of these molecules (HBII-85) have statistically elevated guiding preferences toward exons compared to introns. Moreover, these energetically favorable putative targets of HBII-85 snoRNAs are non-randomly associated with genes producing alternatively spliced mRNA isoforms. The snoTARGET resource is freely available at: (http://hsc.utoledo.edu/depts/bioinfo/snotarget.html).

Synergistic use of compound properties and docking scores in neural network modeling of CYP2D6 binding: predicting affinity and conformational sampling.

Cytochrome P450 2D6 (CYP2D6) is used to develop an approach for predicting affinity and relevant binding conformation(s) for highly flexible binding sites. The approach combines the use of docking scores and compound properties as attributes in building a neural network (NN) model. It begins by identifying segments of CYP2D6 that are important for binding specificity, based on structural variability among diverse CYP enzymes. A family of distinct, low-energy conformations of CYP2D6 are generated using simulated annealing (SA) and a collection of 82 compounds with known CYP2D6 affinities are docked. Interestingly, docking poses are observed on the backside of the heme as well as in the known active site. Docking scores for the active site binders, along with compound-specific attributes, are used to train a neural network model to properly bin compounds as strong binders, moderate binders, or nonbinders. Attribute selection is used to preselect the most important scores and compound-specific attributes for the model. A prediction accuracy of 85+/-6% is achieved. Dominant attributes include docking scores for three of the 20 conformations in the ensemble as well as the compound's formal charge, number of aromatic rings, and AlogP. Although compound properties were highly predictive attributes (12% improvement over baseline) in the NN-based prediction of CYP2D6 binders, their combined use with docking score attributes is synergistic (net increase of 23% above baseline). Beyond prediction of affinity, attribute selection provides a way to identify the most relevant protein conformation(s), in terms of binding competence. In the case of CYP2D6, three out of the ensemble of 20 SA-generated structures are found to be the most predictive for binding.