Breast cancer prevention with liquiritigenin from licorice through the inhibition of aromatase and protein biosynthesis in high-risk women's breast tissue.

Breast cancer risk continues to increase post menopause. Anti-estrogen therapies are available to prevent postmenopausal breast cancer in high-risk women. However, their adverse effects have reduced acceptability and overall success in cancer prevention. Natural products such as hops (Humulus lupulus) and three pharmacopeial licorice (Glycyrrhiza) species have demonstrated estrogenic and chemopreventive properties, but little is known regarding their effects on aromatase expression and activity as well as pro-proliferation pathways in human breast tissue. We show that Gycyrrhiza inflata (GI) has the highest aromatase inhibition potency among these plant extracts. Moreover, phytoestrogens such as liquiritigenin which is common in all licorice species have potent aromatase inhibitory activity, which is further supported by computational docking of their structures in the binding pocket of aromatase. In addition, GI extract and liquiritigenin suppress aromatase expression in the breast tissue of high-risk postmenopausal women. Although liquiritigenin has estrogenic effects in vitro, with preferential activity through estrogen receptor (ER)-ß, it reduces estradiol-induced uterine growth in vivo. It downregulates RNA translation, protein biosynthesis, and metabolism in high-risk women's breast tissue. Finally, it reduces the rate of MCF-7 cell proliferation, with repeated dosing. Collectively, these data suggest that liquiritigenin has breast cancer prevention potential for high-risk postmenopausal women.

Lipid exposure activates gene expression changes associated with estrogen receptor negative breast cancer.

Improved understanding of local breast biology that favors the development of estrogen receptor negative (ER-) breast cancer (BC) would foster better prevention strategies. We have previously shown that overexpression of specific lipid metabolism genes is associated with the development of ER- BC. We now report results of exposure of MCF-10A and MCF-12A cells, and mammary organoids to representative medium- and long-chain polyunsaturated fatty acids. This exposure caused a dynamic and profound change in gene expression, accompanied by changes in chromatin packing density, chromatin accessibility, and histone posttranslational modifications (PTMs). We identified 38 metabolic reactions that showed significantly increased activity, including reactions related to one-carbon metabolism. Among these reactions are those that produce S-adenosyl-L-methionine for histone PTMs. Utilizing both an in-vitro model and samples from women at high risk for ER- BC, we show that lipid exposure engenders gene expression, signaling pathway activation, and histone marks associated with the development of ER- BC.

Deep learning for cancer type classification and driver gene identification.

BACKGROUND: Genetic information is becoming more readily available and is increasingly being used to predict patient cancer types as well as their subtypes. Most classification methods thus far utilize somatic mutations as independent features for classification and are limited by study power. We aim to develop a novel method to effectively explore the landscape of genetic variants, including germline variants, and small insertions and deletions for cancer type prediction. RESULTS: We proposed DeepCues, a deep learning model that utilizes convolutional neural networks to unbiasedly derive features from raw cancer DNA sequencing data for disease classification and relevant gene discovery. Using raw whole-exome sequencing as features, germline variants and somatic mutations, including insertions and deletions, were interactively amalgamated for feature generation and cancer prediction. We applied DeepCues to a dataset from TCGA to classify seven different types of major cancers and obtained an overall accuracy of 77.6%. We compared DeepCues to conventional methods and demonstrated a significant overall improvement (p < 0.001). Strikingly, using DeepCues, the top 20 breast cancer relevant genes we have identified, had a 40% overlap with the top 20 known breast cancer driver genes. CONCLUSION: Our results support DeepCues as a novel method to improve the representational resolution of DNA sequencings and its power in deriving features from raw sequences for cancer type prediction, as well as discovering new cancer relevant genes.

Progesterone receptor antagonists reverse stem cell expansion and the paracrine effectors of progesterone action in the mouse mammary gland.

BACKGROUND: The ovarian hormones estrogen and progesterone (EP) are implicated in breast cancer causation. A specific consequence of progesterone exposure is the expansion of the mammary stem cell (MSC) and luminal progenitor (LP) compartments. We hypothesized that this effect, and its molecular facilitators, could be abrogated by progesterone receptor (PR) antagonists administered in a mouse model. METHODS: Ovariectomized FVB mice were randomized to 14 days of treatment: sham, EP, EP + telapristone (EP + TPA), EP + mifepristone (EP + MFP). Mice were then sacrificed, mammary glands harvested, and mammary epithelial cell lineages separated by flow cytometry using cell surface markers. RNA from each lineage was sequenced and differential gene expression was analyzed using DESeq. Quantitative PCR was performed to confirm the candidate genes discovered in RNA seq. ANOVA with Tukey post hoc analysis was performed to compare relative expression. Alternative splicing events were examined using the rMATs multivariate analysis tool. RESULTS: Significant increases in the MSC and luminal mature (LM) cell fractions were observed following EP treatment compared to control (p < 0.01 and p < 0.05, respectively), whereas the LP fraction was significantly reduced (p < 0.05). These hormone-induced effects were reversed upon exposure to TPA and MFP (p < 0.01 for both). Gene Ontology analysis of RNA-sequencing data showed EP-induced enrichment of several pathways, with the largest effect on Wnt signaling in MSC, significantly repressed by PR inhibitors. In LP cells, significant induction of Wnt4 and Rankl, and Wnt pathway intermediates Lrp2 and Axin2 (confirmed by qRTPCR) were reversed by TPA and MFP (p < 0.0001). Downstream signaling intermediates of these pathways (Lrp5, Mmp7) showed similar effects. Expression of markers of epithelial-mesenchymal transition (Cdh1, Cdh3) and the induction of EMT regulators (Zeb1, Zeb2, Gli3, Snai1, and Ptch2) were significantly responsive to progesterone. EP treatment was associated with large-scale alternative splicing events, with an enrichment of motifs associated with Srsf, Esrp, and Rbfox families. Exon skipping was observed in Cdh1, Enah, and Brd4. CONCLUSIONS: PR inhibition reverses known tumorigenic pathways in the mammary gland and suppresses a previously unknown effect of progesterone on RNA splicing events. In total, our results strengthen the case for reconsideration of PR inhibitors for breast cancer prevention.

Selective progesterone receptor blockade prevents BRCA1-associated mouse mammary tumors through modulation of epithelial and stromal genes.

Pharmacological approaches to breast cancer risk-reduction for BRCA1 mutation carriers would provide an alternative to mastectomy. BRCA1-deficiency dysregulates progesterone signaling, promoting tumorigenesis. Selective progesterone receptor (PR) modulators (SPRMs) are therefore candidate prevention agents. However, their efficacy varies in different BRCA1-deficient mouse models. We examined chemopreventive efficacy of telapristone acetate (TPA), ulipristal acetate (UPA) and mifepristone (MFP) in mice with a conditional knockout of the Brca1 C-terminal domain. The SPRMs displayed a spectrum of efficacy: UPA was most effective, TPA less, and MFP ineffective. Compared to no-treatment controls, UPA reduced tumorigenesis (p = 0.04), and increased tumor latency (p = 0.03). In benign mammary glands, UPA decreased Ki67 (p < 0.001) and increased PR expression (p < 0.0001). RNA sequencing analysis revealed distinct gene expression in response to UPA and MFP. UPA downregulated glycolysis and extracellular matrix-inflammation genes (Fn1, Ptgs2, Tgfb2, Tgfb3) whereas MFP downregulated claudin genes and upregulated amino acid metabolism and inflammation genes. The anti-glucocorticoid effects of MFP appeared not to be tumor-protective, while altering estrogen receptor signaling and NF-kB activation. Our study points to an important role of epithelial PR and its paracrine action on the microenvironment in BRCA1-deficient mammary tumorigenesis, and prevention.

Polygenic risk score for the prediction of breast cancer is related to lesser terminal duct lobular unit involution of the breast.

Terminal duct lobular units (TDLUs) are the predominant anatomical structures where breast cancers originate. Having lesser degrees of age-related TDLU involution, measured as higher TDLUs counts or more epithelial TDLU substructures (acini), is related to increased breast cancer risk among women with benign breast disease (BBD). We evaluated whether a recently developed polygenic risk score (PRS) based on 313-common variants for breast cancer prediction is related to TDLU involution in the background, normal breast tissue, as this could provide mechanistic clues on the genetic predisposition to breast cancer. Among 1398 women without breast cancer, higher values of the PRS were significantly associated with higher TDLU counts (P = 0.004), but not with acini counts (P = 0.808), in histologically normal tissue samples from donors and diagnostic BBD biopsies. Mediation analysis indicated that TDLU counts may explain a modest proportion (≤10%) of the association of the 313-variant PRS with breast cancer risk. These findings suggest that TDLU involution might be an intermediate step in the association between common genetic variation and breast cancer risk.

Somatic genetic aberrations in benign breast disease and the risk of subsequent breast cancer.

It is largely unknown how the development of breast cancer (BC) is transduced by somatic genetic alterations in the benign breast. Since benign breast disease is an established risk factor for BC, we established a case-control study of women with a history of benign breast biopsy (BBB). Cases developed BC at least one year after BBB and controls did not develop BC over an average of 17 years following BBB. 135 cases were matched to 69 controls by age and type of benign change: non-proliferative or proliferation without atypia (PDWA). Whole-exome sequencing (WES) was performed for the BBB. Germline DNA (available from n = 26 participants) was utilized to develop a mutation-calling pipeline, to allow differentiation of somatic from germline variants. Among the 204 subjects, two known mutational signatures were identified, along with a currently uncatalogued signature that was significantly associated with triple negative BC (TNBC) (p = 0.007). The uncatalogued mutational signature was validated in 109 TNBCs from TCGA (p = 0.001). Compared to non-proliferative samples, PDWA harbors more abundant mutations at PIK3CA pH1047R (p < 0.001). Among the 26 BBB whose somatic copy number variation could be assessed, deletion of MLH3 is significantly associated with the mismatch repair mutational signature (p < 0.001). Matched BBB-cancer pairs were available for ten cases; several mutations were shared between BBB and cancers. This initial study of WES of BBB shows its potential for the identification of genetic alterations that portend breast oncogenesis. In future larger studies, robust personalized breast cancer risk indicators leading to novel interception paradigms can be assessed.

Selective Progesterone Receptor Modulators in Early-Stage Breast Cancer: A Randomized, Placebo-Controlled Phase II Window-of-Opportunity Trial Using Telapristone Acetate.

PURPOSE: Selective progesterone receptor modulators (SPRMs) show preclinical activity against hormone-sensitive breast cancer, but have not been tested in patients with early, treatment-naïve tumors. PATIENTS AND METHODS: In a double-blind presurgical window trial of oral telapristone acetate (TPA) 12 mg daily versus placebo, 70 patients with early-stage breast cancer were randomized 1:1 (stratified by menopause) and treated for 2 to 10 weeks. The primary endpoint was change in Ki67 between diagnostic biopsy and surgical specimens. Gene expression pre- and posttherapy was assessed using RNA-sequencing and gene set enrichment analysis was performed to determine pathways enriched in response to TPA and placebo treatments. RESULTS: Among 61 evaluable women (29 placebo and 32 telapristone acetate), 91% of tumors were ER/PR positive. The mean Ki67 declined by 5.5% in all women treated with telapristone acetate (P = 0.003), and by 4.2% in all women treated with placebo (P = 0.04). After menopausal stratification, the Ki67 decline remained significant in 22 telapristone acetate-treated premenopausal women (P = 0.03). Differential gene expression analysis showed no significant modulation overall. However, in a subset of tumors that demonstrated ≥30% relative reduction in Ki67 in the telapristone acetate group, genes related to cell-cycle progression, and those in the HER2 amplicon were significantly downregulated. In contrast, no significantly enriched pathways were identified in the placebo group. CONCLUSIONS: Patients treated with telapristone acetate whose Ki67 decreased by ≥30% demonstrated a selective antiproliferative signal, with a potentially important effect on HER2 amplicon genes. Evaluation of SPRMs in a neoadjuvant trial is merited, with attention to predictors of response to SPRM therapy, and inclusion of pre- and postmenopausal women.

BRCA1 mutation influences progesterone response in human benign mammary organoids.

BACKGROUND: Women, who carry a germline BRCA1 gene mutation, have a markedly increased risk of developing breast cancer during their lifetime. While BRCA1 carriers frequently develop triple-negative, basal-like, aggressive breast tumors, hormone signaling is important in the genesis of BRCA1 mutant breast cancers. We investigated the hormone response in BRCA1-mutated benign breast tissue using an in vitro organoid system. METHODS: Scaffold-free, multicellular human breast organoids generated from benign breast tissues from non-carrier or BRCA1 mutation carriers were treated in vitro with a stepwise menstrual cycle hormone regimen of estradiol (E2) and progesterone (P4) over the course of 28 days. RESULTS: Breast organoids exhibited characteristics of the native breast tissue, including expression of hormone receptors, collagen production, and markers of luminal and basal epithelium, and stromal fibroblasts. RNA sequencing analysis revealed distinct gene expression in response to hormone treatment in the non-carrier and BRCA1-mutated organoids. The selective progesterone receptor modulator, telapristone acetate (TPA), was used to identify specifically PR regulated genes. Specifically, extracellular matrix organization genes were regulated by E2+P4+TPA in the BRCA1-mutated organoids but not in the non-carrier organoids. In contrast, in the non-carrier organoids, known PR target genes such as the cell cycle genes were inhibited by TPA. CONCLUSIONS: These data show that BRCA1 mutation influences hormone response and in particular PR activity which differs from that of non-carrier organoids. Our organoid model system revealed important insights into the role of PR in BRCA1-mutated benign breast cells and the critical paracrine actions that modify hormone receptor (HR)-negative cells. Further analysis of the molecular mechanism of BRCA1 and PR crosstalk is warranted using this model system.

Cancer classification and pathway discovery using non-negative matrix factorization.

OBJECTIVES: Extracting genetic information from a full range of sequencing data is important for understanding disease. We propose a novel method to effectively explore the landscape of genetic mutations and aggregate them to predict cancer type. DESIGN: We applied non-smooth non-negative matrix factorization (nsNMF) and support vector machine (SVM) to utilize the full range of sequencing data, aiming to better aggregate genetic mutations and improve their power to predict disease type. More specifically, we introduce a novel classifier to distinguish cancer types using somatic mutations obtained from whole-exome sequencing data. Mutations were identified from multiple cancers and scored using SIFT, PP2, and CADD, and collapsed at the individual gene level. nsNMF was then applied to reduce dimensionality and obtain coefficient and basis matrices. A feature matrix was derived from the obtained matrices to train a classifier for cancer type classification with the SVM model. RESULTS: We have demonstrated that the classifier was able to distinguish four cancer types with reasonable accuracy. In five-fold cross-validations using mutation counts as features, the average prediction accuracy was 80% (SEM = 0.1%), significantly outperforming baselines and outperforming models using mutation scores as features. CONCLUSION: Using the factor matrices derived from the nsNMF, we identified multiple genes and pathways that are significantly associated with each cancer type. This study presents a generic and complete pipeline to study the associations between somatic mutations and cancers. The proposed method can be adapted to other studies for disease status classification and pathway discovery.

Identifying Breast Cancer Distant Recurrences from Electronic Health Records Using Machine Learning.

Accurately identifying distant recurrences in breast cancer from the Electronic Health Records (EHR) is important for both clinical care and secondary analysis. Although multiple applications have been developed for computational phenotyping in breast cancer, distant recurrence identification still relies heavily on manual chart review. In this study, we aim to develop a model that identifies distant recurrences in breast cancer using clinical narratives and structured data from EHR. We applied MetaMap to extract features from clinical narratives and also retrieved structured clinical data from EHR. Using these features, we trained a support vector machine model to identify distant recurrences in breast cancer patients. We trained the model using 1,396 double-annotated subjects and validated the model using 599 double-annotated subjects. In addition, we validated the model on a set of 4,904 single-annotated subjects as a generalization test. In the held-out test and generalization test, we obtained F-measure scores of 0.78 and 0.74, area under curve (AUC) scores of 0.95 and 0.93, respectively. To explore the representation learning utility of deep neural networks, we designed multiple convolutional neural networks and multilayer neural networks to identify distant recurrences. Using the same test set and generalizability test set, we obtained F-measure scores of 0.79 ± 0.02 and 0.74 ± 0.004, AUC scores of 0.95 ± 0.002 and 0.95 ± 0.01, respectively. Our model can accurately and efficiently identify distant recurrences in breast cancer by combining features extracted from unstructured clinical narratives and structured clinical data.

Using natural language processing and machine learning to identify breast cancer local recurrence.

BACKGROUND: Identifying local recurrences in breast cancer from patient data sets is important for clinical research and practice. Developing a model using natural language processing and machine learning to identify local recurrences in breast cancer patients can reduce the time-consuming work of a manual chart review. METHODS: We design a novel concept-based filter and a prediction model to detect local recurrences using EHRs. In the training dataset, we manually review a development corpus of 50 progress notes and extract partial sentences that indicate breast cancer local recurrence. We process these partial sentences to obtain a set of Unified Medical Language System (UMLS) concepts using MetaMap, and we call it positive concept set. We apply MetaMap on patients' progress notes and retain only the concepts that fall within the positive concept set. These features combined with the number of pathology reports recorded for each patient are used to train a support vector machine to identify local recurrences. RESULTS: We compared our model with three baseline classifiers using either full MetaMap concepts, filtered MetaMap concepts, or bag of words. Our model achieved the best AUC (0.93 in cross-validation, 0.87 in held-out testing). CONCLUSIONS: Compared to a labor-intensive chart review, our model provides an automated way to identify breast cancer local recurrences. We expect that by minimally adapting the positive concept set, this study has the potential to be replicated at other institutions with a moderately sized training dataset.

Mechanism of Telapristone Acetate (CDB4124) on Progesterone Receptor Action in Breast Cancer Cells.

Progesterone is a steroid hormone that plays an important role in the breast. Progesterone exerts its action through binding to progesterone receptor (PR), a transcription factor. Deregulation of the progesterone signaling pathway is implicated in the formation, development, and progression of breast cancer. Next-generation selective progesterone receptor modulators (SPRMs) have potent antiprogestin activity and are selective for PR, reducing the off-target effects on other nuclear receptors. To date, there is limited information on how the newer generation of SPRMs, specifically telapristone acetate (TPA), affect PR function at the molecular level. In this study, T47D breast cancer cells were used to investigate the molecular mechanism by which TPA antagonizes PR action. Global profiling of the PR cistrome and interactome was done with chromatin immunoprecipitation sequencing (ChIP-seq) and rapid immunoprecipitation mass spectrometry. Validation studies were done on key genes and interactions. Our results demonstrate that treatment with the progestin (R5020) alone resulted in robust PR recruitment to the chromatin, and addition of TPA reduced PR recruitment globally. TPA significantly changed coregulator recruitment to PR compared with R5020. Upon conservative analysis, three proteins (TRPS1, LASP1, and AP1G1) were identified in the R5020+TPA-treated group. Silencing TRPS1 with small interfering RNA increased PR occupancy to the known PR regulatory regions and attenuated the inhibition of gene expression after TPA treatment. TRPS1 silencing alleviated the inhibition of proliferation by TPA. In conclusion, TPA decreases PR occupancy on chromatin and recruits coregulators such as TRPS1 to the PR complex, thereby regulating PR target gene expression and associated cellular responses.

Social Network, Surgeon, and Media Influence on the Decision to Undergo Contralateral Prophylactic Mastectomy.

OBJECTIVES: The rate of contralateral prophylactic mastectomy (CPM) has risen sharply in the past decade. The current study was designed to examine social network, surgeon, and media influence on patients' CPM decision-making, examining not only who influenced the decision, and to what extent, but also the type of influence exerted. METHODS: Patients (N=113) who underwent CPM at 4 Indiana University-affiliated hospitals between 2008 and 2012 completed structured telephone interviews in 2013. Questions addressed the involvement and influence of the social network (family, friends, and nonsurgeon health professionals), surgeon, and media on the CPM decision. RESULTS: Spouses, children, family, friends, and health professionals were reported as exerting a meaningful degree of influence on patients' decisions, largely in ways that were positive or neutral toward CPM. Most surgeons were regarded as providing options rather than encouraging or discouraging CPM. Media influence was present, but limited. CONCLUSIONS: Patients who choose CPM do so with influence and support from members of their social networks. Reversing the increasing choice of CPM will require educating these influential others, which can be accomplished by encouraging patients to include them in clinical consultations, and by providing patients with educational materials that can be shared with their social networks. Surgeons need to be perceived as having an opinion, specifically that CPM should be reserved for those patients for whom it is medically indicated.

Near-infrared remotely triggered drug-release strategies for cancer treatment.

Remotely controlled, localized drug delivery is highly desirable for potentially minimizing the systemic toxicity induced by the administration of typically hydrophobic chemotherapy drugs by conventional means. Nanoparticle-based drug delivery systems provide a highly promising approach for localized drug delivery, and are an emerging field of interest in cancer treatment. Here, we demonstrate near-IR light-triggered release of two drug molecules from both DNA-based and protein-based hosts that have been conjugated to near-infrared-absorbing Au nanoshells (SiO2 core, Au shell), each forming a light-responsive drug delivery complex. We show that, depending upon the drug molecule, the type of host molecule, and the laser illumination method (continuous wave or pulsed laser), in vitro light-triggered release can be achieved with both types of nanoparticle-based complexes. Two breast cancer drugs, docetaxel and HER2-targeted lapatinib, were delivered to MDA-MB-231 and SKBR3 (overexpressing HER2) breast cancer cells and compared with release in noncancerous RAW 264.7 macrophage cells. Continuous wave laser-induced release of docetaxel from a nanoshell-based DNA host complex showed increased cell death, which also coincided with nonspecific cell death from photothermal heating. Using a femtosecond pulsed laser, lapatinib release from a nanoshell-based human serum albumin protein host complex resulted in increased cancerous cell death while noncancerous control cells were unaffected. Both methods provide spatially and temporally localized drug-release strategies that can facilitate high local concentrations of chemotherapy drugs deliverable at a specific treatment site over a specific time window, with the potential for greatly minimized side effects.

Motivations for contralateral prophylactic mastectomy as a function of socioeconomic status.

BACKGROUND: Despite no demonstrated survival advantage for women at average risk of breast cancer, rates of contralateral prophylactic mastectomy (CPM) continue to increase. Research reveals women with higher socioeconomic status (SES) are more likely to select CPM. This study examines how indicators of SES, age, and disease severity affect CPM motivations. METHODS: Patients (N = 113) who underwent CPM at four Indiana University affiliated hospitals completed telephone interviews in 2013. Participants answered questions about 11 CPM motivations and provided demographic information. Responses to motivation items were factor analyzed, resulting in 4 motivational factors: reducing long-term risk, symmetry, avoiding future medical visits, and avoiding treatments. RESULTS: Across demographic differences, reducing long-term risk was the strongest CPM motivation. Lower income predicted stronger motivation to reduce long-term risk and avoid treatment. Older participants were more motivated to avoid treatment; younger and more-educated patients were more concerned about symmetry. Greater severity of diagnosis predicted avoiding treatments. CONCLUSIONS: Reducing long-term risk is the primary motivation across groups, but there are also notable differences as a function of age, education, income, and disease severity. To stop the trend of increasing CPM, physicians must tailor patient counseling to address motivations that are consistent across patient populations and those that vary between populations.

Understanding Resonant Light-Triggered DNA Release from Plasmonic Nanoparticles.

Nanoparticle-based platforms for gene therapy and drug delivery are gaining popularity for cancer treatment. To improve therapeutic selectivity, one important strategy is to remotely trigger the release of a therapeutic cargo from a specially designed gene- or drug-laden near-infrared (NIR) absorbing gold nanoparticle complex with NIR light. While there have been multiple demonstrations of NIR nanoparticle-based release platforms, our understanding of how light-triggered release works in such complexes is still limited. Here, we investigate the specific mechanisms of DNA release from plasmonic nanoparticle complexes using continuous wave (CW) and femtosecond pulsed lasers. We find that the characteristics of nanoparticle-based DNA release vary profoundly from the same nanoparticle complex, depending on the type of laser excitation. CW laser illumination drives the photothermal release of dehybridized single-stranded DNA, while pulsed-laser excitation results in double-stranded DNA release by cleavage of the Au-S bond, with negligible local heating. This dramatic difference in DNA release from the same DNA-nanoparticle complex has very important implications in the development of NIR-triggered gene or drug delivery nanocomplexes.

Association between breast cancer genetic susceptibility variants and terminal duct lobular unit involution of the breast.

Terminal duct lobular units (TDLUs) are the predominant source of future breast cancers, and lack of TDLU involution (higher TDLU counts, higher acini count per TDLU and the product of the two) is a breast cancer risk factor. Numerous breast cancer susceptibility single nucleotide polymorphisms (SNPs) have been identified, but whether they are associated with TDLU involution is unknown. In a pooled analysis of 872 women from two studies, we investigated 62 established breast cancer SNPs and relationships with TDLU involution. Poisson regression models with robust variance were used to calculate adjusted per-allele relative risks (with the non-breast cancer risk allele as the referent) and 95% confidence intervals between TDLU measures and each SNP. All statistical tests were two-sided; P < 0.05 was considered statistically significant. Overall, 36 SNPs (58.1%) were related to higher TDLU counts although this was not statistically significant (p = 0.25). Six of the 62 SNPs (9.7%) were nominally associated with at least one TDLU measure: rs616488 (PEX14), rs11242675 (FOXQ1) and rs6001930 (MKL1) were associated with higher TDLU count (p = 0.047, 0.045 and 0.031, respectively); rs1353747 (PDE4D) and rs6472903 (8q21.11) were associated with higher acini count per TDLU (p = 0.007 and 0.027, respectively); and rs1353747 (PDE4D) and rs204247 (RANBP9) were associated with the product of TDLU and acini counts (p = 0.024 and 0.017, respectively). Our findings suggest breast cancer SNPs may not strongly influence TDLU involution. Agnostic genome-wide association studies of TDLU involution may provide new insights on its biologic underpinnings and breast cancer susceptibility.

Ages at menarche- and menopause-related genetic variants in relation to terminal duct lobular unit involution in normal breast tissue.

Reduced levels of terminal duct lobular unit (TDLU) involution, as reflected by higher numbers of TDLUs and acini per TDLU, have been associated with higher breast cancer risk. Younger age at menarche and older age at menopause have been previously related to lower levels of TDLU involution. To determine a possible genetic link, we examined whether single-nucleotide polymorphisms (SNPs) previously established in genome-wide association studies (GWAS) for ages at menarche and menopause are associated with TDLU involution. We conducted a pooled analysis of 862 women from two studies. H&E tissue sections were assessed for numbers of TDLUs and acini/TDLU. Poisson regression models were used to estimate associations of 36 menarche- and 21 menopause-SNPs with TDLU counts, acini counts/TDLU, and the product of these two measures, adjusting for age and study site. Fourteen percent of evaluated SNPs (eight SNPs) were associated with TDLU counts at p < 0.05, suggesting an enrichment of associations with TDLU counts. However, only menopause-SNPs had >50 % that were either significantly or nonsignificantly associated with TDLU measures in the directions consistent with their relationships shown in GWAS. Among ten SNPs that were statistically significantly associated with at least one TDLU involution measure (p < 0.05), seven SNPs (rs466639: RXRG; rs2243803: SLC14A2; rs2292573: GAB2; rs6438424: 3q13.32; rs7606918: METAP1D; rs11668344: TMEM150B; rs1635501: EXO1) were associated in the consistent directions. Our data suggest that the loci associated with ages at menarche and menopause may influence TDLU involution, suggesting some shared genetic mechanisms. However, larger studies are needed to confirm the results.