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Estrogen receptor alpha (ER)-positive breast cancer is commonly treated with endocrine therapies, including antiestrogens that bind and inhibit ER activity, and aromatase inhibitors that suppress estrogen biosynthesis to inhibit estrogen-dependent ER activity. Paradoxically, treatment with estrogens such as 17b-estradiol can also be effective against ER+ breast cancer. Despite the known efficacy of estrogen therapy, the lack of a predictive biomarker of response and understanding of the mechanism of action have contributed to its limited clinical use. Herein, we demonstrate that ER overexpression confers resistance to estrogen deprivation through ER activation in human ER+ breast cancer cells and xenografts grown in mice. However, ER overexpression and the associated high levels of ER transcriptional activation converted 17b-estradiol from a growth-promoter to a growth-suppressor, offering a targetable therapeutic vulnerability and a potential means of identifying patients likely to benefit from estrogen therapy. Since ER+ breast cancer cells and tumors ultimately developed resistance to continuous estrogen deprivation or continuous 17b-estradiol treatment, we tested schedules of alternating treatments. Oscillation of ER activity through cycling of 17b-estradiol and estrogen deprivation provided long-term control of patient-derived xenografts, offering a novel endocrine-only strategy to manage ER+ breast cancer.

Although synergy is a pillar of modern pharmacology, toxicology, and medicine, there is no consensus on its definition despite its nearly one hundred-year history. Moreover, methods for statistical determination of synergy that account for variation of response to treatment are underdeveloped and if exist are reduced to the traditional t-test, but do not comply with the normal distribution assumption. We offer statistical models for estimation of synergy using an established definition of Bliss drugs' independence. Although Bliss definition is well-known, it remains a theoretical concept and has never been applied for statistical determination of synergy with various forms of treatment outcome. We rigorously and consistently extend the Bliss definition to detect statistically significant synergy under various designs: (1) in vitro, when the outcome of a cell culture experiment with replicates is the proportion of surviving cells for a single dose or multiple doses, (2) dose-response methodology, (3) in vivo studies in organisms, when the outcome is a longitudinal measurement such as tumor volume, and (4) clinical studies, when the outcome of treatment is measured by survival. For each design, we developed a specific statistical model and demonstrated how to test for independence, synergy, and antagonism, and compute the associated p-value.

During a first-in-humans clinical trial investigating electron paramagnetic resonance tumor oximetry, a patient injected with the particulate oxygen sensor Printex ink was found to have unexpected fluorodeoxyglucose (FDG) uptake in a dermal nodule via positron emission tomography (PET). This nodule co-localized with the Printex ink injection; biopsy of the area, due to concern for malignancy, revealed findings consistent with ink and an associated inflammatory reaction. Investigations were subsequently performed to assess the impact of oxygen sensors on FDG-PET/CT imaging. A retrospective analysis of three clinical tumor oximetry trials involving two oxygen sensors (charcoal particulates and LiNc-BuO microcrystals) in 22 patients was performed to evaluate FDG imaging characteristics. The impact of clinically used oxygen sensors (carbon black, charcoal particulates, LiNc-BuO microcrystals) on FDG-PET/CT imaging after implantation in rat muscle (n = 12) was investigated. The retrospective review revealed no other patients with FDG avidity associated with particulate sensors. The preclinical investigation found no injected oxygen sensor whose mean standard uptake values differed significantly from sham injections. The risk of a false-positive FDG-PET/CT scan due to oxygen sensors appears low. However, in the right clinical context the potential exists that an associated inflammatory reaction may confound interpretation.

Praise for the First Edition \"This book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend including it in one's personal library.\" —Journal of the American Statistical Association    Mixed modeling is a crucial area of statistics, enabling the analysis of clustered and longitudinal data. Mixed Models: Theory and Applications with R, Second Edition fills a gap in existing literature between mathematical and applied statistical books by presenting a powerful examination of mixed model theory and application with special attention given to the implementation in R. The new edition provides in-depth mathematical coverage of mixed models' statistical properties and numerical algorithms, as well as nontraditional applications, such as regrowth curves, shapes, and images. The book features the latest topics in statistics including modeling of complex clustered or longitudinal data, modeling data with multiple sources of variation, modeling biological variety and heterogeneity, Healthy Akaike Information Criterion (HAIC), parameter multidimensionality, and statistics of image processing. Mixed Models: Theory and Applications with R, Second Edition features unique applications of mixed model methodology, as well as: * Comprehensive theoretical discussions illustrated by examples and figures * Over 300 exercises, end-of-section problems, updated data sets, and R subroutines * Problems and extended projects requiring simulations in R intended to reinforce material * Summaries of major results and general points of discussion at the end of each chapter * Open problems in mixed modeling methodology, which can be used as the basis for research or PhD dissertations Ideal for graduate-level courses in mixed statistical modeling, the book is also an excellent reference for professionals in a range of fields, including cancer research, computer science, and engineering.

There is growing frustration with the concept of the p-value. Besides having an ambiguous interpretation, the p-value can be made as small as desired by increasing the sample size, n. The p-value is outdated and does not make sense with big data: Everything becomes statistically significant. The root of the problem with the p-value is in the mean comparison. We argue that statistical uncertainty should be measured on the individual, not the group, level. Consequently, standard deviation (SD), not standard error (SE), error bars should be used to graphically present the data on two groups. We introduce a new measure based on the discrimination of individuals/objects from two groups, and call it the D-value. The D-value can be viewed as the n-of-1 p-value because it is computed in the same way as p while letting n equal 1. We show how the D-value is related to discrimination probability and the area above the receiver operating characteristic (ROC) curve. The D-value has a clear interpretation as the proportion of patients who get worse after the treatment, and as such facilitates to weigh up the likelihood of events under different scenarios. [Received January 2015. Revised June 2015.]

MicroRNAs (miRNAs) regulate gene expression. It has been suggested that obtaining miRNA expression profiles can improve classification, diagnostic, and prognostic information in oncology. Here, we sought to comprehensively identify the miRNAs that are overexpressed in lung cancer by conducting miRNA microarray expression profiling on normal lung versus adjacent lung cancers from transgenic mice. We found that miR-136, miR-376a, and miR-31 were each prominently overexpressed in murine lung cancers. Real-time RT-PCR and in situ hybridization (ISH) assays confirmed these miRNA expression profiles in paired normal-malignant lung tissues from mice and humans. Engineered knockdown of miR-31, but not other highlighted miRNAs, substantially repressed lung cancer cell growth and tumorigenicity in a dose-dependent manner. Using a bioinformatics approach, we identified miR-31 target mRNAs and independently confirmed them as direct targets in human and mouse lung cancer cell lines. These targets included the tumor-suppressive genes large tumor suppressor 2 (LATS2) and PP2A regulatory subunit B alpha isoform (PPP2R2A), and expression of each was augmented by miR-31 knockdown. Their engineered repression antagonized miR-31-mediated growth inhibition. Notably, miR-31 and these target mRNAs were inversely expressed in mouse and human lung cancers, underscoring their biologic relevance. The clinical relevance of miR-31 expression was further independently and comprehensively validated using an array containing normal and malignant human lung tissues. Together, these findings revealed that miR-31 acts as an oncogenic miRNA (oncomir) in lung cancer by targeting specific tumor suppressors for repression.

Realizing the full potential of iron oxide nanoparticles (IONP) for cancer diagnosis and therapy requires selective tumor cell accumulation. Here, we report a systematic analysis of two key determinants for IONP homing to human breast cancers: (i) particle size and (ii) active vs passive targeting. In vitro, molecular targeting to the HER2 receptor was the dominant factor driving cancer cell association. In contrast, size was found to be the key determinant of tumor accumulation in vivo, where molecular targeting increased tumor tissue concentrations for 30 nm but not 100 nm IONP. Similar to the in vitro results, PEGylation did not influence in vivo IONP biodistribution. Thus, the results reported here indicate that the in vitro advantages of molecular targeting may not consistently extend to pre-clinical in vivo settings. These observations may have important implications for the design and clinical translation of advanced, multifunctional, IONP platforms.

Estrogens have been shown to elicit anticancer effects against estrogen receptor α (ER)‐positive breast cancer. We sought to determine the mechanism underlying the therapeutic response. Response to 17β‐estradiol was assessed in ER+ breast cancer models with resistance to estrogen deprivation: WHIM16 patient‐derived xenografts, C7‐2‐HI and C4‐HI murine mammary adenocarcinomas, and long‐term estrogen‐deprived MCF‐7 cells. As another means to reactivate ER, the anti‐estrogen fulvestrant was withdrawn from fulvestrant‐resistant MCF‐7 cells. Transcriptional, growth, apoptosis, and molecular alterations in response to ER reactivation were measured. 17β‐estradiol treatment and fulvestrant withdrawal induced transcriptional activation of ER, and cells adapted to estrogen deprivation or fulvestrant were hypersensitive to 17β‐estradiol. ER transcriptional response was followed by an unfolded protein response and apoptosis. Such apoptosis was dependent upon the unfolded protein response, p53, and JNK signaling. Anticancer effects were most pronounced in models exhibiting genomic amplification of the gene encoding ER (ESR1), suggesting that engagement of ER at high levels is cytotoxic. These data indicate that long‐term adaptation to estrogen deprivation or ER inhibition alters sensitivity to ER reactivation. In such adapted cells, 17β‐estradiol treatment and anti‐estrogen withdrawal hyperactivate ER, which drives an unfolded protein response and subsequent growth inhibition and apoptosis. 17β‐estradiol treatment should be considered as a therapeutic option for anti‐estrogen‐resistant disease, particularly in patients with tumors harboring ESR1 amplification or ER overexpression. Furthermore, therapeutic strategies that enhance an unfolded protein response may increase the therapeutic effects of ER reactivation. Estrogens typically induce growth of estrogen receptor α (ER)‐positive breast tumor cells. Cells adapted to estrogen deprivation, as is induced by aromatase inhibitor therapy, can exhibit upregulation of ER levels. Estrogen retreatment can induce an ER‐dependent unfolded protein response, growth inhibition, and apoptosis. Strategies that enhance an unfolded protein response may increase the therapeutic effects of estrogens.

Small-cell lung cancer (SCLC) has a poor prognosis since there is currently no effective therapy for commonly recurring disease. In our previous study, both primary and recurrent human tumors have been shown to express functional -methyl-D-aspartate (NMDA) receptors, and blockade of these receptors with GluN1 and GluN2B antagonists decreased tumor cell viability in vitro, and growth of tumor xenografts in nu/nu mice. In this study, we examine the influence of the GluN2B antagonist ifenprodil and the channel-blocker antagonist memantine, on cell viability and growth of tumor xenografts of recurrent SCLC (rSCLC) in mice. Both antagonists significantly reduced cell viability and levels of components of the ERK1/2 pathway, increased apoptosis, and at very safe levels significantly reduced the growth of tumors in mice. Each antagonist and topotecan had additive effects to reduce cell viability with significant synergy demonstrated for the case of memantine. More significantly, combination treatments of xenografts in mice with ifenprodil and the chemotherapeutic agent topotecan produced clear additive effects that completely stopped tumor growth. Moreover, the ifenprodil and topotecan combination showed excellent supra-addition or synergy of inhibition for tumors ≤300 mm in size ( =4.7E-4). Combination treatment of memantine with topotecan also showed clear addition but, unlike ifenprodil, no synergy for the doses chosen. Since topotecan is a drug of choice for treatment of rSCLC, our findings suggest that combining this agent with NMDA receptor blockade using the GluN2B antagonist, ifenprodil, will significantly improve patient outcomes.

The exact density distribution of the non-linear least squares estimator in the one-parameter regression model is derived in closed form and expressed through the cumulative distribution function of the standard normal variable. Several proposals to generalize this result are discussed. The exact density is extended to the estimating equation (EE) approach and the non-linear regression with an arbitrary number of linear parameters and one intrinsically non-linear parameter. For a very special non-linear regression model, the derived density coincides with the distribution of the ratio of two normally distributed random variables previously obtained by Fieler almost a century ago, unlike other approximations previously suggested by other authors. Approximations to the density of the EE estimators are discussed in the multivariate case. Numerical complications associated with the non-linear least squares are illustrated, such as non-existence and/or multiple solutions, as major factors contributing to poor density approximation. The non-linear Markov–Gauss theorem is formulated on the basis of the near exact EE density approximation.