Menstrual Phase and Menopausal Status Classification of Benign Breast Tissue Using Hormone-Regulated Gene Expression and Histomorphology: A Validation Study

BACKGROUND

The validation of breast cancer risk biomarkers in benign breast samples (BBS) is a long-sought goal, hampered by the fluctuation of gene and protein expression with menstrual phase (MP) and menopausal status (MS). Previously, we identified hormone-related gene expression and histomorphology parameters to classify BBS by MS/MP. We now evaluate both together, to validate our prior results.

PATIENTS AND METHODS

BBS were obtained from consenting women (86 premenopausal, 55 postmenopausal) undergoing reduction mammoplasty (RM) or contralateral unaffected breast (CUB) mastectomy. MP/MS was defined using classical criteria for menstrual dates and hormone levels on the day of surgery. BBS gene expression was measured with reverse transcription quantitative polymerase chain reaction (RT-qPCR) for three luteal phase (LP) genes (TNFSF11, DIO2, MYBPC1) and four menopausal genes (PGR, GREB1, TIFF1, CCND1). Premenopausal samples were classified into LP or non-LP, using published histomorphology parameters. Logistic regression and receiver-operator curve analysis was performed to assess area under the curve (AUC) for prediction of MP/MS.

RESULTS

In all 131 women, menopausal genes plus age > 50 years predicted true MS [AUC 0.93, 95% confidence interval (CI) 0.89, 0.97]. Among premenopausal women, high TNFSF11 expression distinguished non-LP from LP samples (AUC 0.80, 95% CI 0.70, 0.91); the addition of histomorphology improved the prediction nonsignificantly (AUC 0.87, 95% CI 0.78, 0.96). In premenopausal subsets, addition of histomorphology improved LP prediction in RM (AUC 0.95, 95% CI 0.87, 1.0), but not in CUB (0.84, 95% CI 0.72, 0.96).

CONCLUSIONS

Expression of five-gene set accurately predicts menopausal status and menstrual phase in BBS, facilitating the development of breast cancer risk biomarkers using large, archived sample repositories.

Identification of metabolic pathways contributing to ER+ breast cancer disparities using a machine-learning pipeline

African American (AA) women in the United States have a 40% higher breast cancer mortality rate than Non-Hispanic White (NHW) women. The survival disparity is particularly striking among (estrogen receptor positive) ER+ breast cancer cases. The purpose of this study is to examine whether there are racial differences in metabolic pathways typically activated in patients with ER+ breast cancer. We collected pretreatment plasma from AA and NHW ER+ breast cancer cases (AA n = 48, NHW n = 54) and cancer-free controls (AA n = 100, NHW n = 48) to conduct an untargeted metabolomics analysis using gas chromatography mass spectrometry (GC-MS) to identify metabolites that may be altered in the different racial groups. Unpaired t-test combined with multiple feature selection and prediction models were employed to identify race-specific altered metabolic signatures. This was followed by the identification of altered metabolic pathways with a focus in AA patients with breast cancer. The clinical relevance of the identified pathways was further examined in PanCancer Atlas breast cancer data set from The Cancer Genome Atlas Program (TCGA). We identified differential metabolic signatures between NHW and AA patients. In AA patients, we observed decreased circulating levels of amino acids compared to healthy controls, while fatty acids were significantly higher in NHW patients. By mapping these metabolites to potential epigenetic regulatory mechanisms, this study identified significant associations with regulators of metabolism such as methionine adenosyltransferase 1A (MAT1A), DNA Methyltransferases and Histone methyltransferases for AA individuals, and Fatty acid Synthase (FASN) and Monoacylglycerol lipase (MGL) for NHW individuals. Specific gene Negative Elongation Factor Complex E (NELFE) with histone methyltransferase activity, was associated with poor survival exclusively for AA individuals. We employed a comprehensive and novel approach that integrates multiple machine learning and statistical methods, coupled with human functional pathway analyses. The metabolic profile of plasma samples identified may help elucidate underlying molecular drivers of disproportionately aggressive ER+ tumor biology in AA women. It may ultimately lead to the identification of novel therapeutic targets. To our knowledge, this is a novel finding that describes a link between metabolic alterations and epigenetic regulation in AA breast cancer and underscores the need for detailed investigations into the biological underpinnings of breast cancer health disparities.

A lysosome-targeted DNA nanodevice selectively targets macrophages to attenuate tumours

Activating CD8+ T cells by antigen cross-presentation is remarkably effective at eliminating tumours. Although this function is traditionally attributed to dendritic cells, tumour-associated macrophages (TAMs) can also cross-present antigens. TAMs are the most abundant tumour-infiltrating leukocyte. Yet, TAMs have not been leveraged to activate CD8+ T cells because mechanisms that modulate their ability to cross-present antigens are incompletely understood. Here we show that TAMs harbour hyperactive cysteine protease activity in their lysosomes, which impedes antigen cross-presentation, thereby preventing CD8+ T cell activation. We developed a DNA nanodevice (E64-DNA) that targets the lysosomes of TAMs in mice. E64-DNA inhibits the population of cysteine proteases that is present specifically inside the lysosomes of TAMs, improves their ability to cross-present antigens and attenuates tumour growth via CD8+ T cells. When combined with cyclophosphamide, E64-DNA showed sustained tumour regression in a triple-negative-breast-cancer model. Our studies demonstrate that DNA nanodevices can be targeted with organelle-level precision to reprogram macrophages and achieve immunomodulation in vivo.

Gene methylation and cytological atypia in random fine needle aspirates for assessment of breast cancer risk: 10-year follow up in average risk women

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Background: Alterations in DNA methylation occur early in tumorigenesis, and are a potential breast cancer risk biomarker. We previously reported a study where healthy volunteers underwent random fine needle aspiration (rFNA) of the breasts; cumulative methylation index of eight preselected tumor suppressor genes (CMI) was associated with the presence of cytological atypia in rFNA samples. We now report 10-year follow-up of this population, to evaluate whether increased CMI is associated with subsequent breast cancer development. Methods: 380 women, unselected by breast cancer risk, were enrolled. Demographics, breast cancer risk factors, lifetime Gail model risk estimates (Gail-LR), and %breast density were obtained at baseline. rFNA samples were assessed for cytopathology (Masood Score, MS) and CMI. Patients were contacted annually for 10 years to ascertain development of invasive or non-invasive breast cancer. In univariate analysis, log-rank test was used to compare breast cancer incidence rates between individuals with high and low baseline measures (separated by median). Area under the ROC curve was used to evaluate the cancer prediction accuracy. In multivariate analysis, the effect of CMI (after log-transformation to reduce skewness) was further studied using Cox regression model adjusting for confounding baseline variables. Results: 362 women participated in follow up. At a median follow up time of 9.5 years after rFNA sampling, 16 women developed invasive or non-invasive breast cancer. There were no significant differences between women who developed cancer and those that did not in regard to demographic factors, %breast density, MS, or Gail-LR. On univariate analysis, Gail-LR was higher in women who developed cancer (13.0 vs. 16.5, p=0.08). The largest hazard ratios were observed from high breast density (2.30, 95% CI 0.8, 6.6) and high CMI (2.26, 95% CI 0.8, 6.6, p=0.07). In breast cancer prediction, the AUC for CMI was 0.64 (95% CI 0.51, 0.77). In separate bivariable models that adjusted for age, Gail-LR, MS, and %breast density, the HR for log CMI was consistently above unity, with a p value consistently below 0.1, except for the model that included MS (see Table). Conclusions: Elevated CMI has potential as a robust predictor of future breast cancer occurrence in average risk women, even when adjusted for breast density or cytologic atypia. Our prior analysis established that CMI is not susceptible to variation with menstrual cycle phase and menopausal status. These features support its further evaluation in larger trials. Clinical trial information: NCT00896636 .[Table: see text]

Abstract 1176: Racial differences in circulating inflammatory cytokines and breast cancer disparities

Introduction

Racial disparities in breast cancer (BrCa) mortality are well documented. We previously reported that African American (AA) women with estrogen receptor-positive (ER+) BrCa have a 4-fold higher rate of death compared to non-Hispanic whites (white) after controlling for stage at diagnosis and treatment initiation. This suggests that racial differences in tumor biology may contribute to the survival disparity. In preliminary work we found that AA BrCa cases have higher serum levels of C-reactive protein (CRP) compared to whites, and that increased CRP was associated with worse survival, suggesting that increased systemic inflammation in AA women drives aggressive tumor biology and contributes to the racial disparity in survival. The goal of this study is to identify specific inflammatory pathways that may be involved.

Experimental Procedures

Pre-treatment blood samples from women with stage I-III ER+ BrCa (AA, n=40; white, n=55) and from healthy controls (AA, n=51; white, n=38) were analyzed with the Olink® targeted proteomic assay, which measures 92 inflammatory proteins. Normalized data was log2 transformed, and generalized linear models compared the normalized intensities with covariates of interest. The fit of the model was tested with empirical Bayes methods. All p-values are corrected for multiple testing with the false discovery rate (FDR) method of Benjamini and Hochberg.

Findings

After adjustment for site of enrollment, plasma levels of 13 inflammatory cytokines showed a log2 fold-change (log2FC) ≥ 1.0 for AA compared to white women, i.e. at least a two-fold increase in the mean value on a linear scale (Table). The racial difference was evident among both cases and controls, and remained after adjusting for body mass index (BMI). There were no differences between AA cases and controls, indicating that cytokine elevation in AA BrCa patients is not an effect of the tumor. Several of the cytokines are known to promote tumor progression and aggressive biological features in breast tumors.

ProteinLog2FCProteinLog2FCCXCL52.42**CCL81.13***CXCL12.06***EIF4EBP11.12**STAMBP1.91*CCL201.10**CXCL111.89**TNF141.06*CCL31.45**TGF beta1.04**CXCL61.40**CCL71.03***CCL131.16**FDR corrected p-values: * < 0.05, **< 0.01, ***< 0.001

Conclusion

Systemic elevation of biologically relevant inflammatory cytokines in AA women with ER+ BrCa does not appear to be an effect of the tumor. This inflammatory phenotype may activate inflammatory programs in ER+ breast tumors that develop in AA women, leading to disproportionately aggressive tumor biology and worse outcomes.

Supported by grants U54CA203000; U54CA2022995; and U54CA2022997 from the National Institutes of Health.

Citation Format: Oana C. Danciu, George Chlipala, Zeynep Madak-Erdogan, Hariyali Patel, Landan Banks, Ayesha Zaidi, Ashlie Santaliz-Casiano, Lauren Schulte, Lauren Weller, Deanna Taiym, Natalie Pulliam, Elona Liko Hazizi, Elonia Martin, Anita Fareeduddin, Archana Bargaje, Carlos Garcia, Scott Hegerty, Sarah Friedewald, Seema Khan, J Julie Kim, William Gradishar, Garth Rauscher, Jonna Frasor, Kent F. Hoskins. Racial differences in circulating inflammatory cytokines and breast cancer disparities [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1176.

Myeloma is characterized by stage-specific alterations in DNA methylation that occur early during myelomagenesis

Epigenetic changes play important roles in carcinogenesis and influence initial steps in neoplastic transformation by altering genome stability and regulating gene expression. To characterize epigenomic changes during the transformation of normal plasma cells to myeloma, we modified the HpaII tiny fragment enrichment by ligation-mediated PCR assay to work with small numbers of purified primary marrow plasma cells. The nano-HpaII tiny fragment enrichment by ligation-mediated PCR assay was used to analyze the methylome of CD138(+) cells from 56 subjects representing premalignant (monoclonal gammopathy of uncertain significance), early, and advanced stages of myeloma, as well as healthy controls. Plasma cells from premalignant and early stages of myeloma were characterized by striking, widespread hypomethylation. Gene-specific hypermethylation was seen to occur in the advanced stages, and cell lines representative of relapsed cases were found to be sensitive to decitabine. Aberrant demethylation in monoclonal gammopathy of uncertain significance occurred primarily in CpG islands, whereas differentially methylated loci in cases of myeloma occurred predominantly outside of CpG islands and affected distinct sets of gene pathways, demonstrating qualitative epigenetic differences between premalignant and malignant stages. Examination of the methylation machinery revealed that the methyltransferase, DNMT3A, was aberrantly hypermethylated and underexpressed, but not mutated in myeloma. DNMT3A underexpression was also associated with adverse overall survival in a large cohort of patients, providing insights into genesis of hypomethylation in myeloma. These results demonstrate widespread, stage-specific epigenetic changes during myelomagenesis and suggest that early demethylation can be a potential contributor to genome instability seen in myeloma. We also identify DNMT3A expression as a novel prognostic biomarker and suggest that relapsed cases can be therapeutically targeted by hypomethylating agents.