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Triple-negative breast cancer (TNBC) lacks the expressions of estrogen receptor-α, progesterone receptor and human epidermal growth factor receptor-2. The treatment options for TNBC include anthracyclin/taxol based conventional chemotherapy and small molecular inhibitor based targeted therapy. However, the therapeutic efficacy is limited by systemic toxicity and acquired tumor resistance; identification of less toxic testable alternatives is urgently required. Non-toxic nutritional herbs are commonly used in traditional Chinese herbal medicine for general health management and may additionally represent a testable therapeutic alternative for TNBC. The present study examined the growth inhibitory efficacy of the nutritional herb Cornus officinalis (CO) in MDA-MB-231 cells, which represent a cell culture model for TNBC, and identified potential mechanistic leads. In MDA-MB-231 cells, CO induced dose-dependent cytostatic growth arrest [inhibitory concentration (IC)(50), 0.1% and IC(90), 0.5%], and inhibited anchorage independent colony formation. Mechanistically, CO inhibited G(1) to S phase transition leading to G(1) arrest and decreased the expression of cyclin D1 and phosphorylated-retinoblastoma proteins. CO additionally altered apoptosis specific BCL-2 associated X protein/B-cell lymphoma-2 expression and upregulated pro-apoptotic caspase-3/7 activity. Collectively, these data provided mechanistic evidence for the efficacy of CO, and validated a mechanism-based approach to prioritize efficacious nutritional herbs as testable alternatives for secondary prevention/treatment of TNBC.

The Luminal A subtype of breast cancer expresses the estrogen receptor (ER)-α and progesterone receptor (PR), but not the human epidermal growth factor receptor (HER)-2 oncogene. This subtype of breast cancer responds to endocrine therapy involving the use of selective estrogen receptor modulators and/or inhibitors of estrogen biosynthesis. However, these therapeutic agents are frequently associated with long-term systemic toxicity and acquired tumor resistance, emphasizing the need to identify non-toxic alternative treatments for chemo-endocrine therapy responsive breast cancer. The present study utilized the human mammary carcinoma-derived, ER+/PR+/HER-2− MCF-7 cell line as a model of the Luminal A subtype of breast cancer to examine the growth inhibitory effect of the Chinese nutritional herb Epimedium grandiflorum (EG) and determine the mechanisms underlying this effect. MCF-7 cells maintained in a serum-depleted culture medium retained their ability to grow in response to 17β-estradiol (E2). Treatment of the MCF-7 cells with EG resulted in dose-dependent inhibition of E2-promoted growth. Mechanistically, EG inhibited E2-promoted cell cycle progression through G1 stage arrest and modulated the cellular metabolism of E2, increasing the formation of the anti-proliferative metabolites 2-hydroxyestrone and estriol. Long-term treatment of MCF-7 cells with EG inhibited E2-promoted anchorage independent growth, a surrogate in vitro biomarker of tumorigenesis. In conclusion, the results of the present study demonstrate the growth inhibitory effects of EG on MCF-7 cells and identified clinically relevant mechanistic leads for its anti-tumorigenic efficacy.

Wong and Yu [Generalized MLE of a joint distribution function with multivariate interval-censored data, J. Multivariate Anal. 69 (1999) 155–166] discussed generalized maximum likelihood estimation of the joint distribution function of a multivariate random vector whose coordinates are subject to interval censoring. They established uniform consistency of the generalized MLE (GMLE) of the distribution function under the assumption that the random vector is independent of the censoring vector and that both of the vector distributions are discrete. We relax these assumptions and establish consistency results of the GMLE under a multivariate mixed case interval censorship model. van der Vaart and Wellner [Preservation theorems for Glivenko–Cantelli and uniform Glivenko–Cantelli class, in: E. Gine, D.M. Mason, J.A. Wellner (Eds.), High Dimensional Probability, vol. II, Birkhäuser, Boston, 2000, pp. 115–133] and Yu [Consistency of the generalized MLE with multivariate mixed case interval-censored data, Ph.D Dissertation, Binghamton University, 2000] independently proved strong consistency of the GMLE in the L1(μ)-topology, where μ is a measure derived from the joint distribution of the censoring variables. We establish strong consistency of the GMLE in the topologies of weak convergence and pointwise convergence, and eventually uniform convergence under appropriate distributional assumptions and regularity conditions.