High expression of orphan nuclear receptor NR4A1 in a subset of ovarian tumors with worse outcome.

OBJECTIVE: Nuclear receptors (NRs) play a vital role in the development and progression of several cancers including breast and prostate. Using TCGA data, we sought to identify critical nuclear receptors in high grade serous ovarian cancers (HGSOC) and to confirm these findings using in vitro approaches. METHODS: In silico analysis of TCGA data was performed to identify relevant NRs in HGSOC. Ovarian cancer cell lines were screened for NR expression and functional studies were performed to determine the significance of these NRs in ovarian cancers. NR expression was analyzed in ovarian cancer tissue samples using immunohistochemistry to identify correlations with histology and stage of disease. RESULTS: The NR4A family of NRs was identified as a potential driver of ovarian cancer pathogenesis. Overexpression of NR4A1 in particular correlated with worse progression free survival. Endogenous expression of NR4A1 in normal ovarian samples was relatively high compared to that of other tissue types, suggesting a unique role for this orphan receptor in the ovary. Expression of NR4A1 in HGSOC cell lines as well as in patient samples was variable. NR4A1 primarily localized to the nucleus in normal ovarian tissue while co-localization within the cytoplasm and nucleus was noted in ovarian cancer cell lines and patient tissues. CONCLUSIONS: NR4A1 is highly expressed in a subset of HGSOC samples from patients that have a worse progression free survival. Studies to target NR4A1 for therapeutic intervention should include HGSOC.

Large-scale Pan-cancer Cell Line Screening Identifies Actionable and Effective Drug Combinations.

Oncology drug combinations can improve therapeutic responses and increase treatment options for patients. The number of possible combinations is vast and responses can be context-specific. Systematic screens can identify clinically relevant, actionable combinations in defined patient subtypes. We present data for 109 anticancer drug combinations from AstraZeneca's oncology small molecule portfolio screened in 755 pan-cancer cell lines. Combinations were screened in a 7 × 7 concentration matrix, with more than 4 million measurements of sensitivity, producing an exceptionally data-rich resource. We implement a new approach using combination Emax (viability effect) and highest single agent (HSA) to assess combination benefit. We designed a clinical translatability workflow to identify combinations with clearly defined patient populations, rationale for tolerability based on tumor type and combination-specific "emergent" biomarkers, and exposures relevant to clinical doses. We describe three actionable combinations in defined cancer types, confirmed in vitro and in vivo, with a focus on hematologic cancers and apoptotic targets. SIGNIFICANCE: We present the largest cancer drug combination screen published to date with 7 × 7 concentration response matrices for 109 combinations in more than 750 cell lines, complemented by multi-omics predictors of response and identification of "emergent" combination biomarkers. We prioritize hits to optimize clinical translatability, and experimentally validate novel combination hypotheses. This article is featured in Selected Articles from This Issue, p. 695.

Cardiac Arrest Following the Administration of Intravenous Diphenhydramine for Sedation to an Infant With Congenital Heart Disease.

Diphenhydramine (Benadryl) is a first-generation antihistamine that is used primarily to treat allergic reactions including anaphylaxis, urticaria, and allergic rhinitis. Despite its availability as an over-the-counter medication, toxicity may occur with its use especially when administered in large doses or via the intravenous route. We present a 3-month-old infant with Trisomy 21 who suffered a cardiac arrest immediately following administration of a single 1.25 mg/kg dose of intravenous diphenhydramine, prescribed for sedation in the Pediatric ICU setting. The potential cardiovascular and respiratory effects of diphenhydramine are presented, previous reports of life-threatening adverse effects reviewed, and options to limit these effects discussed.

Targeting melanoma's MCL1 bias unleashes the apoptotic potential of BRAF and ERK1/2 pathway inhibitors.

BRAF and MEK1/2 inhibitors are effective in melanoma but resistance inevitably develops. Despite increasing the abundance of pro-apoptotic BIM and BMF, ERK1/2 pathway inhibition is predominantly cytostatic, reflecting residual pro-survival BCL2 family activity. Here, we show that uniquely low BCL-XL expression in melanoma biases the pro-survival pool towards MCL1. Consequently, BRAF or MEK1/2 inhibitors are synthetic lethal with the MCL1 inhibitor AZD5991, driving profound tumour cell death that requires BAK/BAX, BIM and BMF, and inhibiting tumour growth in vivo. Combination of ERK1/2 pathway inhibitors with BCL2/BCL-w/BCL-XL inhibitors is stronger in CRC, correlating with a low MCL1:BCL-XL ratio; indeed the MCL1:BCL-XL ratio is predictive of ERK1/2 pathway inhibitor synergy with MCL1 or BCL2/BCL-w/BCL-XL inhibitors. Finally, AZD5991 delays acquired BRAFi/MEKi resistance and enhances the efficacy of an ERK1/2 inhibitor in a model of acquired BRAFi + MEKi resistance. Thus combining ERK1/2 pathway inhibitors with MCL1 antagonists in melanoma could improve therapeutic index and patient outcomes.

The Evolution of Estrogen Receptor Signaling in the Progression of Endometriosis to Endometriosis-Associated Ovarian Cancer.

To investigate changes in estrogen receptor alpha (ERα) signaling during progression of endometriosis to endometriosis-associated ovarian cancer (EAOC) as a driver of malignant transformation. We procured tissue samples of normal endometrium, endometriosis (benign, atypical, concurrent with EAOC), and EAOC. We evaluated expression of a 236-gene signature of estrogen signaling. ANOVA and unsupervised clustering were used to identify gene expression profiles across disease states. These profiles were compared to profiles of estrogen regulation in cancer models from the Gene Expression Omnibus (GEO). Gene Set Enrichment Analysis (GSEA) was performed to determine whether gene expression in EAOC was consistent with ERα activity. ANOVA revealed 158 differentially expressed genes (q < 0.05) and unsupervised clustering identified five distinct gene clusters. The estrogen signaling profile of EAOC was not consistent with activated ERα in pre-clinical models. Gene set enrichment analysis did not identify signatures of activated ERα in EAOC but instead identified expression patterns consistent with loss of ERα function and development of endocrine resistance. Gene expression data suggest that ERα signaling becomes inactivated throughout the progression of endometriosis to EAOC. The gene expression pattern in EAOC is more consistent with profiles of endocrine resistance.

Active Estrogen Receptor-alpha Signaling in Ovarian Cancer Models and Clinical Specimens.

Purpose: High-grade serous ovarian cancer (HGSOC) is an aggressive disease with few available targeted therapies. Despite high expression of estrogen receptor-alpha (ERα) in approximately 80% of HGSOC and some small but promising clinical trials of endocrine therapy, ERα has been understudied as a target in this disease. We sought to identify hormone-responsive, ERα-dependent HGSOC.Experimental Design: We characterized endocrine response in HGSOC cells across culture conditions [ two-dimensional (2D), three-dimensional (3D), forced suspension] and in patient-derived xenograft (PDX) explants, assessing proliferation and gene expression. Estrogen-regulated transcriptome data were overlapped with public datasets to develop a comprehensive panel of ERα target genes. Expression of this panel and ERα H-score were assessed in HGSOC samples from patients who received endocrine therapy. Time on endocrine therapy was used as a surrogate for clinical response.Results: Proliferation is ERα-regulated in HGSOC cells in vitro and in vivo, and is partly dependent on 3D context. Transcriptomic studies identified genes shared by cell lines and PDX explants as ERα targets. The selective ERα downregulator (SERD) fulvestrant is more effective than tamoxifen in blocking ERα action. ERα H-score is predictive of efficacy of endocrine therapy, and this prediction is further improved by inclusion of target gene expression, particularly IGFBP3Conclusions: Laboratory models corroborate intertumor heterogeneity of endocrine response in HGSOC but identify features associated with functional ERα and endocrine responsiveness. Assessing ERα function (e.g., IGFBP3 expression) in conjunction with H-score may help select patients who would benefit from endocrine therapy. Preclinical data suggest that SERDs might be more effective than tamoxifen. Clin Cancer Res; 23(14); 3802-12. ©2017 AACR.

Treating gynecologic malignancies with selective estrogen receptor downregulators (SERDs): promise and challenges.

Endometrial and ovarian cancers are estrogen-dependent gynecologic malignancies. Although many are estrogen receptor (ER) positive, treatment with the selective estrogen receptor modulator (SERM) tamoxifen, a tissue selective partial-agonist, has demonstrated only modest clinical benefit. Selective estrogen receptor downregulators (SERDs) are pure ER antagonists showing a benefit for advanced ER positive breast cancer, which has bolstered their potential use for ER positive gynecologic malignancies. We summarize these preclinical and clinical data, suggesting that a subpopulation of patients with endometrial or ovarian cancer exists in which treatment with SERDs results in improved outcome. However, the full potential of SERDs for a gynecologic malignancies will be realized only when the appropriate predictive biomarkers are identified. Additionally, a further understanding ER signaling in the context of ovarian and endometrial tissues that appear to involve c-Src and other kinase pathways is needed to successfully address the emergence of resistance with rationally designed combination therapies.

Hormone response in ovarian cancer: time to reconsider as a clinical target?

Ovarian cancer is the sixth most common cancer worldwide among women in developed countries and the most lethal of all gynecologic malignancies. There is a critical need for the introduction of targeted therapies to improve outcome. Epidemiological evidence suggests a critical role for steroid hormones in ovarian tumorigenesis. There is also increasing evidence from in vitro studies that estrogen, progestin, and androgen regulate proliferation and invasion of epithelial ovarian cancer cells. Limited clinical trials have shown modest response rates; however, they have consistently identified a small subset of patients that respond very well to endocrine therapy with few side effects. We propose that it is timely to perform additional well-designed trials that should include biomarkers of response.