Mutations in the estrogen receptor alpha (ERα) 1 gene (ESR1) are frequently detected in ER+ metastatic breast cancer, and there is increasing evidence that these mutations confer endocrine resistance in breast cancer patients with advanced disease. However, their functional role is not well-understood, at least in part due to a lack of ESR1 mutant models. Here, we describe the generation and characterization of genome-edited T47D and MCF7 breast cancer cell lines with the two most common ESR1 mutations, Y537S and D538G. Genome editing was performed using CRISPR and adeno-associated virus (AAV) technologies to knock-in ESR1 mutations into T47D and MCF7 cell lines, respectively. Various techniques were utilized to assess the activity of mutant ER, including transactivation, growth and chromatin-immunoprecipitation (ChIP) assays. The level of endocrine resistance was tested in mutant cells using a number of selective estrogen receptor modulators (SERMs) and degraders (SERDs). RNA sequencing (RNA-seq) was employed to study gene targets of mutant ER. Cells with ESR1 mutations displayed ligand-independent ER activity, and were resistant to several SERMs and SERDs, with cell line and mutation-specific differences with respect to magnitude of effect. The SERD AZ9496 showed increased efficacy compared to other drugs tested. Wild-type and mutant cell co-cultures demonstrated a unique evolution of mutant cells under estrogen deprivation and tamoxifen treatment. Transcriptome analysis confirmed ligand-independent regulation of ERα target genes by mutant ERα, but also identified novel target genes, some of which are involved in metastasis-associated phenotypes. Despite significant overlap in the ligand-independent genes between Y537S and D538G, the number of mutant ERα-target genes shared between the two cell lines was limited, suggesting context-dependent activity of the mutant receptor. Some genes and phenotypes were unique to one mutation within a given cell line, suggesting a mutation-specific effect. Taken together, ESR1 mutations in genome-edited breast cancer cell lines confer ligand-independent growth and endocrine resistance. These biologically relevant models can be used for further mechanistic and translational studies, including context-specific and mutation site-specific analysis of the ESR1 mutations.

Inflammatory breast cancer (IBC) is a difficult-to-treat disease with poor clinical outcomes due to high risk of metastasis and resistance to treatment. In breast cancer, CD44+CD24− cells possess stem cell-like features and contribute to disease progression, and we previously described a CD44+CD24−pSTAT3+ breast cancer cell subpopulation that is dependent on JAK2/STAT3 signaling. Here we report that CD44+CD24− cells are the most frequent cell type in IBC and are commonly pSTAT3+. Combination of JAK2/STAT3 inhibition with paclitaxel decreased IBC xenograft growth more than either agent alone. IBC cell lines resistant to paclitaxel and doxorubicin were developed and characterized to mimic therapeutic resistance in patients. Multi-omic profiling of parental and resistant cells revealed enrichment of genes associated with lineage identity and inflammation in chemotherapy-resistant derivatives. Integrated pSTAT3 chromatin immunoprecipitation sequencing and RNA sequencing (RNA-seq) analyses showed pSTAT3 regulates genes related to inflammation and epithelial-to-mesenchymal transition (EMT) in resistant cells, as well as PDE4A, a cAMP-specific phosphodiesterase. Metabolomic characterization identified elevated cAMP signaling and CREB as a candidate therapeutic target in IBC. Investigation of cellular dynamics and heterogeneity at the single cell level during chemotherapy and acquired resistance by CyTOF and single cell RNA-seq identified mechanisms of resistance including a shift from luminal to basal/mesenchymal cell states through selection for rare preexisting subpopulations or an acquired change. Finally, combination treatment with paclitaxel and JAK2/STAT3 inhibition prevented the emergence of the mesenchymal chemo-resistant subpopulation. These results provide mechanistic rational for combination of chemotherapy with inhibition of JAK2/STAT3 signaling as a more effective therapeutic strategy in IBC. Significance: Chemotherapy resistance in inflammatory breast cancer is driven by the JAK2/STAT3 pathway, in part via cAMP/PKA signaling and a cell state switch, which can be overcome using paclitaxel combined with JAK2 inhibitors.

KMT2C and KMT2D, encoding histone H3 lysine 4 methyltransferases, are among the most commonly mutated genes in triple-negative breast cancer (TNBC). However, how these mutations may shape epigenomic and transcriptomic landscapes to promote tumorigenesis is largely unknown. Here we describe that deletion of Kmt2c or Kmt2d in non-metastatic murine models of TNBC drives metastasis, especially to the brain. Global chromatin profiling and chromatin immunoprecipitation followed by sequencing revealed altered H3K4me1, H3K27ac and H3K27me3 chromatin marks in knockout cells and demonstrated enhanced binding of the H3K27me3 lysine demethylase KDM6A, which significantly correlated with gene expression. We identified Mmp3 as being commonly upregulated via epigenetic mechanisms in both knockout models. Consistent with these findings, samples from patients with KMT2C-mutant TNBC have higher MMP3 levels. Downregulation or pharmacological inhibition of KDM6A diminished Mmp3 upregulation induced by the loss of histone–lysine N-methyltransferase 2 (KMT2) and prevented brain metastasis similar to direct downregulation of Mmp3. Taken together, we identified the KDM6A–matrix metalloproteinase 3 axis as a key mediator of KMT2C/D loss-driven metastasis in TNBC.

BACKGROUND: HER2-targeting therapies have great efficacy in HER2-positive breast cancer, but resistance, in part due to HER2 heterogeneity (HET), is a significant clinical challenge. We previously described that in a phase II neoadjuvant trastuzumab emtansine (T-DM1) and pertuzumab (P) clinical trial in early-stage HER2-positive breast cancer, none of the patients with HER2-HET tumors had pathologic complete response (pCR). METHODS: To investigate cellular and molecular differences among tumors according to HER2 heterogeneity and pCR, we performed RNA sequencing and ERBB2 FISH of 285 pretreatment and posttreatment tumors from 129 patients in this T-DM1+P neoadjuvant trial. A subset of cases was also subject to NanoString spatial digital profiling. RESULTS: Pretreatment tumors from patients with pCR had the highest level of ERBB2 mRNA and ERBB signaling. HER2 heterogeneity was associated with no pCR, basal-like features, and low ERBB2 expression yet high ERBB signaling sustained by activation of downstream pathway components. Residual tumors showed decreased HER2 protein levels and ERBB2 copy number heterogeneity and increased PI3K pathway enrichment and luminal features. HET tumors showed minimal treatment-induced transcriptomic changes compared with non-HET tumors. Immune infiltration correlated with pCR and HER2-HET status. CONCLUSION: Resistance mechanisms in HET and non-HET tumors are distinct. HER2-targeting antibodies have limited efficacy in HET tumors. Our results support the stratification of patients based on HET status and the use of agents that target downstream components of the ERBB signaling pathway in patients with HET tumors. TRIAL REGISTRATION: ClinicalTrials.gov NCT02326974. FUNDING: This study was funded by Roche and the National Cancer Institute.

Indoor positioning, as often needed by mobile robots operating indoor and in other indoor navigation applications, usually has to be obtained without access to GNSS (Global Navigation Satellite Systems) signals. Many indoor localization techniques, such as RF-beacon-based ones and SLAM (Simultaneous Localization and Mapping) techniques, are either susceptible to tampering, environmental noise, or are costly. Alternatively, indoor landmarks, either permanent ones such as doors, windows and light fixtures or human-introduced ones such as pictures and QR codes, can be identified through image processing techniques and used to determine a user's location based on known landmark locations. This paper explored how to use the high-definition RGB camera and the depth sensor of the Kinect to detect the QR-code-based landmarks and measure the distance to the QR codes, so that it can assist a robot or other user to navigate in indoor environment. The RGB camera of the Kinect sensor was used to capture the images of the QR codes attached on the wall in different locations in the room. Each one of this QR codes has unique identifier and was coded to contain the indoor reference location information. Then the ZBar open-source barcode reading algorithm was applied to decode the QR codes and extract the reference coordinate information within the QR codes. After that, the depth sensor of the Kinect was initiated to measure the distance from the Kinect sensor to these QR codes. These raw data collected from the RGB camera and the depth sensor were calibrated and then used to locate the position of the Kinect within the indoor coordinate frame. In this paper, the preliminary results from testing the algorithm to locate the stationary position of the Kinect sensor using QR codes were presented.