Citations

Abstract Purpose: Although high glucocorticoid receptor (GR) expression in early-stage estrogen receptor (ER)-negative breast cancer is associated with shortened relapse-free survival (RFS), how associated GR transcriptional activity contributes to aggressive breast cancer behavior is not well understood. Using potent GR antagonists and primary tumor gene expression data, we sought to identify a tumor-relevant gene signature based on GR activity that would be more predictive than GR expression alone. Experimental Design: Global gene expression and GR ChIP-sequencing were performed to identify GR-regulated genes inhibited by two chemically distinct GR antagonists, mifepristone and CORT108297. Differentially expressed genes from MDA-MB-231 cells were cross-evaluated with significantly expressed genes in GR-high versus GR-low ER-negative primary breast cancers. The resulting subset of GR-targeted genes was analyzed in two independent ER-negative breast cancer cohorts to derive and then validate the GR activity signature (GRsig). Results: Gene expression pathway analysis of glucocorticoid-regulated genes (inhibited by GR antagonism) revealed cell survival and invasion functions. GR ChIP-seq analysis demonstrated that GR antagonists decreased GR chromatin association for a subset of genes. A GRsig that comprised n = 74 GR activation-associated genes (also reversed by GR antagonists) was derived from an adjuvant chemotherapy-treated Discovery cohort and found to predict probability of relapse in a separate Validation cohort (HR = 1.9; P = 0.012). Conclusions: The GRsig discovered herein identifies high-risk ER-negative/GR-positive breast cancers most likely to relapse despite administration of adjuvant chemotherapy. Because GR antagonism can reverse expression of these genes, we propose that addition of a GR antagonist to chemotherapy may improve outcome for these high-risk patients. Clin Cancer Res; 24(14); 3433–46. ©2018 AACR.

5 Background: Triple-negative breast cancer (TNBC) is an immunogenic breast cancer subtype, with a greater percentage of tumors containing tumor-infiltrating lymphocytes (TILs) compared to non-TNBC. TILs are prognostic for recurrence and predictive for chemotherapy benefit in TNBC, but not in estrogen receptor (ER)-positive tumors. A subset of TNBC expresses the androgen receptor (AR), and resembles ER-positive breast cancer in terms of demographics (older age) and clinical course (later recurrences that tend to involve bone). However, little is known regarding the association between TILs and AR expression in TNBC. Methods: From a cohort of 9982 women with surgically-treated non-metastatic breast cancer, patients who met criteria for TNBC (ER/PR < 1% and HER2-negative) by centralized-pathology review were included as previously published [Leon-Ferre et al, Breast Cancer Res Treat 2017]. Stromal TILs in these tumors were quantified on full-face hematoxylin and eosin sections from the surgical specimen, following the 2014 TIL Working Group recommendations [Salgado et al, Ann Oncol 2014]. The expression of AR [EPR1535(2), Abcam] was scored as a continuous variable (1% increments), and categorized as absent (0%), low (1-25% ), low-moderate (26-50%) moderate (51-75%) and high (> 75%) and correlated with TIL density. Results: 605 patients met criteria for TNBC. The median age was 56 years. Most tumors were T1-2 (95%), N0-1 (86%), and high grade (88%). The median stromal TIL content was 20% (0-90%). Tumor tissue was available for AR staining in 509/605 patients. 165 (32%) tumors expressed AR as follows: low: 12%, low-moderate: 7%, moderate: 5%, and high: 8%. Apocrine tumors (n = 30) expressed AR most frequently (86%) and at higher levels (AR high: 50%). For all 509 tumors, the median stromal TIL content according to AR expression was AR absent: 20%, AR low: 20%, AR low-moderate: 15%, AR high: 10%. AR and stromal TILs (both as continuous variables) demonstrated a weak inverse correlation (correlation coefficient -0.1064, p = 0.017). Conclusions: AR expression in TNBC is associated with lower levels of stromal TILs. Additional data correlating the AR, stromal TILs and outcomes in this cohort will be presented at the meeting.

Abstract Motivation RNA sequencing of single cells enables characterization of transcriptional heterogeneity in seemingly homogeneous cell populations. Single-cell sequencing has been applied in a wide range of researches fields. However, few studies have focus on characterization of isoform-level expression patterns at the single-cell level. In this study, we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of isoform pairs from the same gene in single-cell isoform-level expression data. Results We define six principal patterns of isoform expression relationships and describe a method for differential-pattern analysis. We demonstrate ISOP through analysis of single-cell RNA-sequencing data from a breast cancer cell line, with replication in three independent datasets. We assigned the pattern types to each of 16 562 isoform-pairs from 4929 genes. Among those, 26% of the discovered patterns were significant (P&lt;0.05), while remaining patterns are possibly effects of transcriptional bursting, drop-out and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differential-pattern analysis were not detected by differential-expression analysis. Finally, the effects of drop-out events and expression levels of isoforms on ISOP's performances were investigated through simulated datasets. To conclude, ISOP provides a novel approach for characterization of isoform-level preference, commitment and heterogeneity in single-cell RNA-sequencing data. Availability and implementation The ISOP method has been implemented as a R package and is available at https://github.com/nghiavtr/ISOP under a GPL-3 license. Supplementary information Supplementary data are available at Bioinformatics online.

Abstract Purpose: Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype with substantial risks of disease recurrence. While cytotoxic chemotherapy is commonly administered and reduces recurrence, disease outcomes vary considerably and few prognostic tools are available for risk stratification for TNBC patients. We constructed and validated clinical calculators for invasive-disease free survival (IDFS) and overall survival (OS) for TNBC and compared their performance against AJCC-based models which include only tumor size and nodal status. Methods: From a surgical cohort of 9,982 patients who underwent breast cancer surgery at Mayo Clinic between January 1985 and December 2012, 605 centrally reviewed TNBC patients were identified and used to construct Cox models for IDFS and OS. Patients treated with neoadjuvant chemotherapy were excluded. Variables considered included age, menopausal status, tumor size, nodal status, Nottingham grade, type of breast surgery (mastectomy vs. lumpectomy), adjuvant radiation therapy, adjuvant chemotherapy, Ki67, stromal tumor infiltrating lymphocytes (sTILs), and neutrophil-to-lymphocyte ratio (NLR). Missing values were imputed using single imputation with all variables (including outcomes) included in the imputation model. Backward step-down procedure was used for model selections. The final models were internally validated for calibration and discrimination using bootstrapping methods and compared with AJCC-based models. Results: For both IDFS and OS, higher sTIL's, less extensive nodal involvement, use of adjuvant chemotherapy, and lower NLR were significant predictors of improved clinical outcomes. Premenopausal status and younger age were additionally predictive of improved IDFS and OS, respectively. Models for IDFS and OS have good calibration and are associated with bias-corrected C-indices of 0.68 and 0.71, respectively, as compared with C-indices of 0.59 and 0.62 for AJCC-based models. Conclusions: Our data indicate that a clinical calculator that includes sTIL's, NLR, menopausal status, age, nodal involvement as well as chemotherapy use can provide significantly greater prediction of clinical risk than tumor size and nodal status alone. These tools may be used to identify TNBC patients at elevated risk of disease relapse and to aid physician's communication with patients regarding their long-term disease outlook and planning treatment strategies. External validation is required to further evaluate broader applicability of this tool, which was developed utilizing a single-institutional experience. Citation Format: Polley M-YC, Leon-Ferre RA, Liu H, Gilbert J, Cafourek V, Hillman DW, Negron V, Boughey JC, Liu MC, Ingle JN, Kalari K, Couch F, Visscher DW, Goetz MP. Mayo clinic TNBC outcome calculator: A clinical calculator to predict disease relapse and survival in women with triple-negative breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P1-06-07.

Abstract BACKGROUND: Based on prospective clinical trials, there is no evidence for differences in efficacy between the 3 aromatase inhibitors (AIs) anastrozole, exemestane, and letrozole. The purpose of this study was to identify germline genetic variants associated with response to AIs and to help identify novel mechanisms associated with drug disease efficacy. METHODS: A genome-wide association study (GWAS) was performed for 624 patients (Steroids 2015;99:32-38) to identify SNPs associated with estrogen level change in women with estrogen receptor (ER) positive breast cancer treated with anastrozole. Replication of associated SNPs was performed in a GWAS from the MA.27 trial that compared adjuvant anastrozole and exemestane treatment of post-menopausal women with ER+ breast cancer. Functional studies were subsequently performed to determine SNP effects and underlying mechanisms. RESULTS: Our initial GWAS identified SNPs within CSMD1 that were associated with changes in estrogen levels during anastrozole therapy. An additional SNP in CSMD1 was also associated with breast cancer events in CCTG MA.27. Functionally, we showed that CSMD1 regulates CYP19 expression in a SNP-, and in an anastrozole- dependent fashion. These phenomena were not observed for either letrozole or exemestane. In MA.27, an anastrozole- specific effect was also seen with the minor allele having a protective effect on time to distant metastasis (HR=0.49, p=0.00259), but this was not the case for exemestane (HR=0.71, p=0.111). Our in vitro functional studies indicated that overexpression of CSMD1 sensitized anastrozole or letrozole resistant cells to anastrozole but not to the other two AIs. The SNP in CSMD1 that was associated with increased CSMD1 and CYP19 expression levels increased anastrozole sensitivity, but not letrozole or exemestane in lymphoblastoid cell lines (LCLs) homozygous for either WT or variant CSMD1 SNP genotypes. Based on these observations, we explored whether anastrozole has additional mechanisms beyond its function as a CYP19 inhibitor. Utilizing an estrogen response element (ERE) luciferase reporter assay in a CYP19 CRISPR knockout breast cancer T47D cell line and a surface plasmon resonance (SPR) assay, we found that anastrozole can also function as an ERα agonist, and can bind to, and result in, proteasome dependent ERα degradation, especially in the presence of E2. Treatment of these CYP19 CRISPR knockout cells with anastrozole in the presence of increasing concentrations of E2 results in greater sensitivity compared with anastrozole alone, while the addition of E2, as expected, does not improve letrozole or exemestane sensitivity. These same observations were also seen in letrozole and anastrazole resistant cells. CONCLUSIONS: Our findings suggest that anastrozole might be more effective than letrozole or exemestane in patients with the CSMD1 SNP. Furthermore, anastrozole can function as an ERα agonist, binding to ERα and resulting in its degradation, especially in the presence of E2. These findings should help to make it possible to develop precision endocrine therapies for women who are candidates for AIs. Citation Format: Cairns J, Ingle J, Dudenkov T, Kalari K, Buzdar A, Kubo M, Robson M, Ellis M, Goss P, Shepherd L, Goetz M, Weinshilboum R, Wang L. CSMD1 SNPs selectively affect anastrozole response in postmenopausal breast cancer patients [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr PD1-04.

Abstract Genetic risks in breast cancer remain only partly understood. Here, we report the results of a genome-wide association study of germline DNA from 4,658 women, including 252 women experiencing a breast cancer recurrence, who were entered on the MA.27 adjuvant trial comparing the aromatase inhibitors (AI) anastrozole and exemestane. Single-nucleotide polymorphisms (SNP) of top significance were identified in the gene encoding MIR2052HG, a long noncoding RNA of unknown function. Heterozygous or homozygous individuals for variant alleles exhibited a ∼40% or ∼63% decrease, respectively, in the hazard of breast cancer recurrence relative to homozygous wild-type individuals. Functional genomic studies in lymphoblastoid cell lines and ERα-positive breast cancer cell lines showed that expression from MIR2052HG and the ESR1 gene encoding estrogen receptor-α (ERα) was induced by estrogen and AI in a SNP-dependent manner. Variant SNP genotypes exhibited increased ERα binding to estrogen response elements, relative to wild-type genotypes, a pattern that was reversed by AI treatment. Further, variant SNPs were associated with lower expression of MIR2052HG and ERα. RNAi-mediated silencing of MIR2052HG in breast cancer cell lines decreased ERα expression, cell proliferation, and anchorage-independent colony formation. Mechanistic investigations revealed that MIR2052HG sustained ERα levels both by promoting AKT/FOXO3-mediated ESR1 transcription and by limiting ubiquitin-mediated, proteasome-dependent degradation of ERα. Taken together, our results define MIR2052HS as a functionally polymorphic gene that affects risks of breast cancer recurrence in women treated with AI. More broadly, our results offer a pharmacogenomic basis to understand differences in the response of breast cancer patients to AI therapy. Cancer Res; 76(23); 7012–23. ©2016 AACR.

Abstract INTRODUCTION: The tumor microenvironment consists of a complicated interplay between a heterogeneous mixture of tumor cells and the surrounding tissue and there is a burgeoning interest in the role of the tissue microbiota within this microenvironment. Our group has investigated the breast tumor microbiome using the unaligned (non-human) reads from 796 RNASequencing data obtained from The Cancer Genome Atlas (TCGA) repository of Breast Cancer. METHODS: Unmapped sequences for 720 TCGA tumor samples and 76 paired normal samples were analyzed with Kraken v0.10.5 using the RefSeq 16S and standard genomes. To minimize false discoveries Kraken results were filtered to species that have bacterial genomic and 16S read support. Cluster analysis of the T-distributed Stochastic Neighbor Embedding (tsne) was performed and consensus voting of 26 maximum scoring metrics identified 3 distinct clusters. Differential abundance analysis was performed with the edgeR package. Covariate association analysis was performed for tumor clinical characteristics, as well as sample heterogeneity characteristics. RESULTS: There were three microbiome clusters we identified using TCGA breast samples. The average silhouette width of the third cluster was deemed statistically insignificant (0.071), suggesting further stratification of this cluster is necessary. The remaining two TCGA clusters (467) shared an average silhouette width of 0.39, using Bray's dissimilarity index. Differential abundance analysis of the two clusters after Bonferroni correction identified 450 significant species. The biomes were represented by 63 triple negative, 93 HER2+, 258 Luminal, and 50 tumor adjacent normals. Concordant cluster assignment was observed in 93.7 +/- 7.3% of the normal-tumor pairings, averaged across clinical subtypes. We observed clear delineation of biome populations at the order taxonomic level, which was not associated with the clinical subtype or the tumor/tumor adjacent pairings. We found enrichment of Burkholderiales in the first biome and enrichment of Bacillales and Lactobacillales within the second biome. The second biome appears to be deplete of representatives from the Asian population (p value 0.005) and survival associations were observed to be significantly reduced for HER2 patients associated with Biome 1 (p value 0.025). Geneset enrichment analysis of the expression signatures indicated drug metabolism and chemical carcinogenesis pathways to be associated with biome 2. CONCLUSIONS: In our study, we observed two significantly different microbiomes among TCGA breast cancer samples that were not associated with the clinical subtypes. Current efforts include validating these biomes, using 16S sequencing on matching fresh frozen paraffin embedded samples. Additionally, we are evaluating the influence of the biomes in independent adjuvant and neo-adjuvant studies with next generation sequencing data. Citation Format: Kevin J. Thompson, Jason Sinnwell, Xiaojia Tang, James N. Ingle, Matthew P. Goetz, Krishna R. Kalari. Distinct microbiome populations within breast cancer microenvironments. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3298.