Wang, L

Abstract Breast cancer is a heterogeneous disease, and unsupervised clustering approaches using gene expression data have identified 3-6 distinct subtypes of triple negative breast cancer (TNBC). A genomically and clinically distinct subtype of TNBC is referred to as LAR (Luminal Androgen Receptor). Tumors with this subtype typically express high levels of the AR and exhibit alterations within genes involved in the PI3K pathway (e.g. PIK3CA mutations). Prospective studies are underway using drugs that target the AR alone or in combination with PI3K and CDK 4/6 inhibitors. Given the importance of accurately identifying this subtype, we sought to develop an online tool that uses submitted gene expression data to confidently characterize LAR samples by corroborating the classification with previously published clustering approaches. Methods: We have investigated TNBC RNA-Seq data from The Cancer Genome Atlas (TCGA) breast cancer study (N=123 samples) by cluster analysis. Analysis of the average silhouette width in both biased and unbiased K-means clustering approaches demonstrated LAR and basal as two distinct and significant clusters. A shrunken centroid model of 426 differentially expressed genes, named as CABAL (Clustering Among BAsal and Luminal androgen receptor), was constructed by comparing LAR and basal subtypes. Results: We applied the CABAL model to classify the four TNBC microarray datasets that were previously used in clustering experiments as well as an independent RNA-Seq data cohort. Non-negative matrix factorization (NMF) and fuzzy clustering were applied to the samples (N=1046). Clustering similarity among the methods was assessed with the adjusted rand index, and CABAL demonstrated significant similarity with both fuzzy and NMF clustering methods. Similarly, hierarchical clustering analysis performed on the pooled cohort of 1046 samples recapitulated the CABAL classification with an area under the receiver operating curve of 0.91. Conclusions: Confident and robust identification of samples with the LAR phenotype is paramount in the assessment of clinical associations and therapeutic efficacy. To facilitate LAR identification, we have provided a web-based prediction tool of the CABAL classification, integrated with the NMF and fuzzy clustering results to identify candidate LAR samples. The end user is provided with the pair-wise adjusted rand indexes, thus reinforcing in the clustering characterizations. Further, our online LAR depiction tool provides a set of graphical and tabular summaries, which will be illustrated, while providing additional molecular characterizations of the PAM50 and Metabric classifications. The availability of this tool could advance the genomic research and treatment of TNBC patients. Citation Format: Thompson KJ, Alaparthi T, Sinnwell JP, Carlson EE, Tang X, Bockol M, Vedell PT, Ingle JN, Suman V, Weinshilboum RM, Wang L, Boughey JC, Kalari KR, Goetz MP. Molecular subtyping of androgen receptor-positive patients using gene expression profiles [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P1-03-04.

Abstract BACKGROUND: A GWAS for the MA.27 aromatase inhibitors (AIs) adjuvant trial (4,406 controls and 252 cases) identified variant (V) SNPs in a long noncoding (lnc) RNA, MIR2052HG, that were associated with longer breast cancer free interval (HR= 0.37, P= 2.15E-07). V SNPs (MAF= 0.32 to 0.42) were associated with lower MIR2052HG and ERα expression in the presence of AIs. MIR2052HG maintained ERα both by promoting AKT/FOXO3-mediated ESR1 transcription and by limiting ubiquitin-mediated ERα degradation. (Cancer Res 76:7012-23, 2016). Our goal was to further elucidate MIR2052HG's mechanism of action. METHODS: RNA-Binding Protein Immunoprecipitation (RBPI) assays were performed to demonstrate that the transcription factor, early growth response protein 1 (EGR1), worked together with MIR2052HG to regulate lemur tyrosine kinase-3 (LMTK3) transcription in MCF7/AC1 and CAMA-1 cells. The location of EGR1 on the LMTK3 gene locus was mapped using chromatin immunoprecipitation (ChIP) assays. The co-localization of MIR2052HG RNA and the LMTK3 gene locus was determined using RNA-DNA dual fluorescent in situ hybridization (FISH). SNP effects were evaluated using a panel of human lymphoblastoid cell lines. RESULTS: TCGA analysis revealed LMTK3 and MIR2052HG expression were highly correlated in ERα-positive breast cancer patients. We found that the MIR2052HG transcript was located in the LMTK3 gene locus by RNA-DNA FISH. Among all of the 12 potential LMTK3 transcription factors identified in the Encode database that were examined by RBPI, only EGR1 showed an interaction with MIR2052HG. CHIP assays confirmed EGR1 binding to the two putative EGR1 binding sites in LMTK3 gene.Depletion of MIR2052HG reduced the binding of EGR1 to the LMTK3 promoter and decreased LMTK3 expression, suggesting that it might function as a scaffold. Mechanistically, decreased LMTK3 levels further increased protein kinase C (PKC) activity and downstream AKT activity, leading to reduced ESR1 mRNA levels via increased pFOXO3. At the protein level, in MIR2052HG depleted cells, increased PKC activity increased the phosphorylation of MEK, ERK, and RSK1, leading to increased ERα phosphorylation at Ser167 and increased ERα degradation. Conversely, overexpression of LMTK3 in MIR2052HG depleted cells reversed these phenotypes. MIR2052HG regulated LMTK3 and ERα expression in a SNP- dependent fashion: the MIR2052HG V SNP, relative to wild-type (WT) genotype, increased LMTK3/ERα expression in response to androstenedione due to increased binding between EGR1 and the LMTK3 promoter in LCLs. However, AI treatment reduced this binding in MIR2052HG variant cells but increased binding in WT cells, resulting in decreased LMTK3/ERα in V cells and increased expression in WT cells. CONCLUSIONS: Our findings support a model in which the protective MIR2052HG variant genotype regulates LMTK3 via MIR2052HG/EGR1, and LMTK3 regulates ERα stability via the PKC/MEK/ERK/RSK1 axis. This regulation may explain the effect of the MIR2052HG variant genotype on cell proliferation and response to AIs in MA.27. These findings provide new insight into the mechanism of action of MIR2052HG and suggest that LMTK3 may be a new therapeutic target in ERα-positive breast cancer patients treated with AIs. Citation Format: Cairns J, Ingle JN, Shepherd LE, Kubo M, Goetz MP, Weinshilboum RM, Kalari KR, Wang L. LncRNA MIR2052HG regulates ERα level and endocrine resistance through LMTK3 by recruiting early growth response protein 1 [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 P5-07-01.