Anti-tumor effects of retinoids combined with trastuzumab or tamoxifen in breast cancer cells: induction of apoptosis by retinoid/trastuzumab combinations

INTRODUCTION: HER2 and estrogen receptor (ER) are important in breast cancer and are therapeutic targets of trastuzumab (Herceptin) and tamoxifen, respectively. Retinoids inhibit breast cancer growth, and modulate signaling by HER2 and ER. We hypothesized that treatment with retinoids and simultaneous targeting of HER2 and/or ER may have enhanced anti-tumor effects. METHODS: The effects of retinoids combined with trastuzumab or tamoxifen were examined in two human breast cancer cell lines in culture, BT474 and SKBR3. Assays of proliferation, apoptosis, differentiation, cell cycle distribution, and receptor signaling were performed. RESULTS: In HER2-overexpressing/ER-positive BT474 cells, combining all-trans retinoic acid (atRA) with tamoxifen or trastuzumab synergistically inhibited cell growth, and altered cell differentiation and cell cycle. Only atRA/trastuzumab-containing combinations induced apoptosis. BT474 and HER2-overexpressing/ER-negative SKBR3 cells were treated with a panel of retinoids (atRA, 9-cis-retinoic acid, 13-cis-retinoic acid, or N-(4-hydroxyphenyl) retinamide (fenretinide) (4-HPR)) combined with trastuzumab. In BT474 cells, none of the single agents except 4-HPR induced apoptosis, but again combinations of each retinoid with trastuzumab did induce apoptosis. In contrast, the single retinoid agents did cause apoptosis in SKBR3 cells; this was only modestly enhanced by addition of trastuzumab. The retinoid drug combinations altered signaling by HER2 and ER. Retinoids were inactive in trastuzumab-resistant BT474 cells. CONCLUSIONS: Combining retinoids with trastuzumab maximally inhibits cell growth and induces apoptosis in trastuzumab-sensitive cells. Treatment with such combinations may have benefit for breast cancer patients

Quantitative Assessment of Tissue Biomarkers and Construction of a Model to Predict Outcome in Breast Cancer Using Multiple Imputation

Missing data pose one of the greatest challenges in the rigorous evaluation of biomarkers. The limited availability of specimens with complete clinical annotation and quality biomaterial often leads to underpowered studies. Tissue microarray studies, for example, may be further handicapped by the loss of data points because of unevaluable staining, core loss, or the lack of tumor in the histospot. This paper presents a novel approach to these common problems in the context of a tissue protein biomarker analysis in a cohort of patients with breast cancer. Our analysis develops techniques based on multiple imputation to address the missing value problem. We first select markers using a training cohort, identifying a small subset of protein expression levels that are most useful in predicting patient survival. The best model is obtained by including both protein markers (including COX6C, GATA3, NAT1, and ESR1) and lymph node status. The use of either lymph node status or the four protein expression levels provides similar improvements in goodness-of-fit, with both significantly better than a baseline clinical model. Using the same multiple imputation strategy, we then validate the results out-of-sample on a larger independent cohort. Our approach of integrating multiple imputation with each stage of the analysis serves as an example that may be replicated or adapted in future studies with missing values

Is there a correlation between infection control performance and other hospital quality measures?

Quality measures are increasingly reported by hospitals to the Centers for Medicare and Medicaid Services (CMS), yet there may be tradeoffs in performance between infection control (IC) and other quality measures. Hospitals that performed best on IC measures did not perform well on most CMS non–IC quality measures

Recursive SVM Feature Selection and Sample Classification for Mass-Spectrometry and Microarray Data

Background: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. Results: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. Conclusion: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features.Statistic

Low-level expression of HER2 and CK19 in normal peripheral blood mononuclear cells: relevance for detection of circulating tumor cells

<p>Abstract</p> <p>Background</p> <p>Detection of circulating tumor cells (CTC) in the blood of cancer patients may have prognostic and predictive significance. However, background expression of 'tumor specific markers' in peripheral blood mononuclear cells (PBMC) may confound these studies. The goal of this study was to identify the origin of Cytokeratin 19 (CK19) and HER-2 signal in PBMC and suggest an approach to enhance techniques involved in detection of CTC in breast cancer patients.</p> <p>Methods</p> <p>PBMC from healthy donors were isolated and fractionated into monocytes, lymphocytes, natural killer cells/granulocytes and epithelial populations using immunomagnetic selection and fluorescent cell-sorting for each cell type. RNA isolated from each fraction was analyzed for CK19, HER2 and Beta 2 microglobulin (B2M) using real-time qRT-PCR. Positive selection for epithelial cells and negative selection for NK/granulocytes were used in an attempt to reduce background expression of CK19 and HER2 markers.</p> <p>Results</p> <p>In normal PBMC, CK19 was expressed in the lymphocyte population while HER-2 expression was highest in the NK/granulocyte population. Immunomagnetic selection for epithelial cells reduced background CK19 signal to a frequency of <5% in normal donors. Using negative selection, the majority (74–98%) of HER2 signal could be removed from PBMC. Positive selection methods are variably effective at reducing these background signals.</p> <p>Conclusion</p> <p>We present a novel method to improve the specificity of the traditional method of detecting CTC by identifying the source of the background signals and reducing them by negative immunoselection. Further studies are warranted to improve sensitivity and specificity of methods of detecting CTC will prove to be useful tools for clinicians in determining prognosis and monitoring treatment responses of breast cancer patients.</p

Optimal tumor sampling for immunostaining of biomarkers in breast carcinoma

IntroductionBiomarkers, such as Estrogen Receptor, are used to determine therapy and prognosis in breast carcinoma. Immunostaining assays of biomarker expression have a high rate of inaccuracy; for example, estimates are as high as 20% for Estrogen Receptor. Biomarkers have been shown to be heterogeneously expressed in breast tumors and this heterogeneity may contribute to the inaccuracy of immunostaining assays. Currently, no evidence-based standards exist for the amount of tumor that must be sampled in order to correct for biomarker heterogeneity. The aim of this study was to determine the optimal number of 20X fields that are necessary to estimate a representative measurement of expression in a whole tissue section for selected biomarkers: ER, HER-2, AKT, ERK, S6K1, GAPDH, Cytokeratin, and MAP-Tau.MethodsTwo collections of whole tissue sections of breast carcinoma were immunostained for biomarkers. Expression was quantified using the Automated Quantitative Analysis (AQUA) method of quantitative immunofluorescence. Simulated sampling of various numbers of fields (ranging from one to thirty five) was performed for each marker. The optimal number was selected for each marker via resampling techniques and minimization of prediction error over an independent test set.ResultsThe optimal number of 20X fields varied by biomarker, ranging between three to fourteen fields. More heterogeneous markers, such as MAP-Tau protein, required a larger sample of 20X fields to produce representative measurement.ConclusionsThe optimal number of 20X fields that must be sampled to produce a representative measurement of biomarker expression varies by marker with more heterogeneous markers requiring a larger number. The clinical implication of these findings is that breast biopsies consisting of a small number of fields may be inadequate to represent whole tumor biomarker expression for many markers. Additionally, for biomarkers newly introduced into clinical use, especially if therapeutic response is dictated by level of expression, the optimal size of tissue sample must be determined on a marker-by-marker basis

Local CpG density affects the trajectory and variance of age-associated DNA methylation changes

Acknowledgements We thank Riccardo Marioni, Chris Haley, Ailith Ewing, David Porteous, Chris Ponting, Rob Illingworth, Tamir Chandra, Sara Hagg, Yunzhang Wang, Chantriolnt-Andreas Kapourani, Nick Gilbert, Hannes Becher and members of the Sproul lab for helpful discussions about the study and the manuscript. This work has made use of the resources provided by the University of Edinburgh digital research services and the MRC IGC compute cluster. We are grateful to all the families who took part in the Generation Scotland study along with the general practitioners and the Scottish School of Primary Care for their help in recruiting them, and the entire Generation Scotland team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, healthcare assistants, and nurses. Peer review information Anahita Bishop and Kevin Pang were the primary editors of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Review history The review history is available as Additional file 3. Funding DS is a Cancer Research UK Career Development fellow (reference C47648/A20837), and work in his laboratory is also supported by an MRC university grant to the MRC Human Genetics Unit. LK is a cross-disciplinary postdoctoral fellow supported by funding from the University of Edinburgh and Medical Research Council (MC_UU_00009/2). S.R.C. and I.J.D. were supported by a National Institutes of Health (NIH) research grant R01AG054628, and S.R.C is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (221890/Z/20/Z). AMM is supported by the Wellcome Trust (104036/Z/14/Z, 216767/Z/19/Z, 220857/Z/20/Z) and UKRI MRC (MC_PC_17209, MR/S035818/1). PMV acknowledges support from the Australian National Health and Medical Research Council (1113400) and the Australian Research Council (FL180100072). DMH is supported by a Sir Henry Wellcome Postdoctoral Fellowship (Reference 213674/Z/18/Z). We thank the LBC1936 participants and team members who contributed to the study. Further study information can be found at https://www.ed.ac.uk/lothian-birth-cohorts. The LBC1936 is supported by a jointly funded grant from the BBSRC and ESRC (BB/W008793/1), and also by Age UK (Disconnected Mind project), the Medical Research Council (G0701120, G1001245, MR/M013111/1, MR/R024065/1), and the University of Edinburgh. Genotyping of LBC1936 was funded by the BBSRC (BB/F019394/1), and methylation typing of LBC1936 was supported by Centre for Cognitive Ageing and Cognitive Epidemiology (Pilot Fund award), Age UK, The Wellcome Trust Institutional Strategic Support Fund, The University of Edinburgh, and The University of Queensland. Work on Generation Scotland was supported by a Wellcome Strategic Award “STratifying Resilience and Depression Longitudinally” (STRADL; 104036/Z/14/Z) to AMM, KLE, and others, and an MRC Mental Health Data Pathfinder Grant (MC_PC_17209) to AMM. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). DNA methylation profiling and analysis of the GS:SFHS samples was supported by Wellcome Investigator Award 220857/Z/20/Z and Grant 104036/Z/14/Z (PI: AM McIntosh) and through funding from NARSAD (Ref: 27404; awardee: Dr DM Howard) and the Royal College of Physicians of Edinburgh (Sim Fellowship; Awardee: Dr HC Whalley).Peer reviewedPublisher PD