Phase II randomized trial of neoadjuvant metformin plus letrozole versus placebo plus letrozole for estrogen receptor positive postmenopausal breast cancer (METEOR)

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Abstract Background Neoadjuvant endocrine therapy with an aromatase inhibitor has shown efficacy comparable to that of neoadjuvant chemotherapy in patients with postmenopausal breast cancer. Preclinical and clinical studies have shown that the antidiabetic drug metformin has anti-tumor activity. This prospective, multicenter, phase II randomized, placebo controlled trial was designed to evaluate the direct anti-tumor effect of metformin in non-diabetic postmenopausal women with estrogen-receptor (ER) positive breast cancer. Methods/Design Patients meeting the inclusion criteria and providing written informed consent will be randomized to 24 weeks of neoadjuvant treatment with letrozole (2.5 mg/day) and either metformin (2000 mg/day) or placebo. Target accrual number is 104 patients per arm. The primary endpoint will be clinical response rate, as measured by calipers. Secondary endpoints include pathologic complete response rate, breast conserving rate, change in Ki67 expression, breast density change, and toxicity profile. Molecular assays will be performed using samples obtained before treatment, at week 4, and postoperatively. Discussion This study will provide direct evidence of the anti-tumor effect of metformin in non-diabetic, postmenopausal patients with ER-positive breast cancer. Trial registration ClinicalTrials.gov Identifier NCT0158936

Phase II randomized trial of neoadjuvant metformin plus letrozole versus placebo plus letrozole for estrogen receptor positive postmenopausal breast cancer (METEOR)

This study is being supported by grant no 04-2012-0290 from the SNUH Research fund and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP)(No. 2013005540). Letrozole and metformin are being supplied by the pharmaceutical company, Shin Poong Pharm. Co., Ltd.Background : Neoadjuvant endocrine therapy with an aromatase inhibitor has shown efficacy comparable to that of neoadjuvant chemotherapy in patients with postmenopausal breast cancer. Preclinical and clinical studies have shown that the antidiabetic drug metformin has anti-tumor activity. This prospective, multicenter, phase II randomized, placebo controlled trial was designed to evaluate the direct anti-tumor effect of metformin in non-diabetic postmenopausal women with estrogen-receptor (ER) positive breast cancer. Methods/Design : Patients meeting the inclusion criteria and providing written informed consent will be randomized to 24 weeks of neoadjuvant treatment with letrozole (2.5 mg/day) and either metformin (2000 mg/day) or placebo. Target accrual number is 104 patients per arm. The primary endpoint will be clinical response rate, as measured by calipers. Secondary endpoints include pathologic complete response rate, breast conserving rate, change in Ki67 expression, breast density change, and toxicity profile. Molecular assays will be performed using samples obtained before treatment, at week 4, and postoperatively. Discussion : This study will provide direct evidence of the anti-tumor effect of metformin in non-diabetic, postmenopausal patients with ER-positive breast cancer. Trial registration : ClinicalTrials.gov Identifier NCT01589367Peer Reviewe

Multi-Fidelity Surrogate Models for Predicting Averaged Heat Transfer Coefficients on Endwall of Turbine Blades

This paper proposes a multi-fidelity surrogate (MFS) model for predicting the heat transfer coefficient (HTC) on the turbine blades. First, the low-fidelity (LF) and high-fidelity (HF) surrogates were built using LF-data from numerical simulation and HF-data from an experiment. To evaluate the prediction by these two surrogates, the averaged HTC distribution on the endwall of the gas turbine blade predicted by these two surrogates was compared for input variables as Reynolds number (Re) and boundary layer (BL) thickness. This shows that the prediction by LF surrogate is saturated with an increase in Re, while has monotonic behavior with an increase in BL thickness, which is contrary in general. The prediction by HF surrogate is linear with Re and is increased with BL thickness up to 30 mm and then decreased after 30 mm. Following this, a one-dimensional projection of the two-dimensional HTC distribution was presented to show the prediction tendency of the surrogates by varying the Re and fixing the BL thickness, and vice versa. Second, the MFS was built by combining the LF and HF data. The HTC distribution by the MFS model for the same input variables was shown with the HF data points. It is observed that the prediction by MFS is agreed well with the high-fidelity data. The MFS’s one-dimensional projection of the two-dimensional HTC distribution was compared with the LF surrogate prediction by varying the Re and fixing the BL thickness, and vice versa. This shows that the MFS model has more variations due to the included LF data. It is worth to mention that the averaged HTC distribution with an increase in boundary layer thickness predicted by the MFS is agreed well with the LF and HF data in the available dataset and has a large confidence interval between 30 and 50 mm due to the unavailable data in the specified range. To check the MFS accuracy, the root-mean-square error (RMSE) and error rate were evaluated to compare with the experimental uncertainty for a wide range of high-fidelity data. The present study shows that MFS would be expected to be an effective model for saving computing time and experimental costs

Multi-Fidelity Surrogate Models for Predicting Averaged Heat Transfer Coefficients on Endwall of Turbine Blades

This paper proposes a multi-fidelity surrogate (MFS) model for predicting the heat transfer coefficient (HTC) on the turbine blades. First, the low-fidelity (LF) and high-fidelity (HF) surrogates were built using LF-data from numerical simulation and HF-data from an experiment. To evaluate the prediction by these two surrogates, the averaged HTC distribution on the endwall of the gas turbine blade predicted by these two surrogates was compared for input variables as Reynolds number (Re) and boundary layer (BL) thickness. This shows that the prediction by LF surrogate is saturated with an increase in Re, while has monotonic behavior with an increase in BL thickness, which is contrary in general. The prediction by HF surrogate is linear with Re and is increased with BL thickness up to 30 mm and then decreased after 30 mm. Following this, a one-dimensional projection of the two-dimensional HTC distribution was presented to show the prediction tendency of the surrogates by varying the Re and fixing the BL thickness, and vice versa. Second, the MFS was built by combining the LF and HF data. The HTC distribution by the MFS model for the same input variables was shown with the HF data points. It is observed that the prediction by MFS is agreed well with the high-fidelity data. The MFS’s one-dimensional projection of the two-dimensional HTC distribution was compared with the LF surrogate prediction by varying the Re and fixing the BL thickness, and vice versa. This shows that the MFS model has more variations due to the included LF data. It is worth to mention that the averaged HTC distribution with an increase in boundary layer thickness predicted by the MFS is agreed well with the LF and HF data in the available dataset and has a large confidence interval between 30 and 50 mm due to the unavailable data in the specified range. To check the MFS accuracy, the root-mean-square error (RMSE) and error rate were evaluated to compare with the experimental uncertainty for a wide range of high-fidelity data. The present study shows that MFS would be expected to be an effective model for saving computing time and experimental costs

Enhanced Prediction and Determination of Hydrological Drought at Ungauged River Intake Stations under Changing Climate

Droughts, which are expected to worsen under global climate change, have major impacts on human life and the natural environment. In this study, an analysis system was established for predicting and determining hydrological drought conditions at ungauged water stations and in watersheds connected to municipal river water intake facilities. The aim was to help prevent drought damage or minimize its effects based on an immediate response to severe drought events. A system is presented for the selection of ungauged watersheds that take in river water, and three methodologies are proposed for identifying and forecasting hydrological drought conditions. Two South Korean pilot sites among the numerous ungauged water intake plants that lack local data collection facilities were selected as study areas. In addition, a roadmap for the establishment of standards for the determination of drought conditions in ungauged river basins was proposed. The methodologies introduced in this study assume nationwide expansion and construction. Their utilization can facilitate effective drought responses, based on drought forecasting and restricted water supply criteria for each phase of water intake, at local (and other) waterworks

Liquid Metal Embrittlement Cracking in Uncoated Transformation-Induced Plasticity Steel during Consecutive Resistance Spot Welding

In the automotive production line, a single pair of electrodes is employed to produce hundreds of consecutive welds before undergoing dressing or replacement. In consecutive resistance spot welding (RSW) involving Zn-coated steels, the electrodes undergo metallurgical degradation, characterized by Cu-Zn alloying, which impacts the susceptibility to liquid metal embrittlement (LME) cracking. In the present investigation, the possibility of LME crack formation in uncoated TRIP steel joints during consecutive RSW (involving 400 welds in galvannealed and uncoated TRIP steels) was investigated. The results have shown that different Cu-Zn phases were formed on the electrode surface because of its contamination with Zn from the galvannealed coating. Therefore, during the welding of the uncoated TRIP steel, the heat generated at the electrode/sheet interface would result in the melting of the Cu-Zn phases, thereby exposing the uncoated steel surface to molten Zn and Cu, leading to LME cracking. The cracks exhibited a maximum length of approximately 30 µm at Location A (weld center) and 50 µm at Location B (shoulder of the weld). The occurrence and characteristics of the cracks differed depending on the location as the number of welds increased due to the variation in Zn content. Type A cracks did not form when the number of welds was less than 280. Several cracks with a total length of approximately 30 μm were suddenly formed between 280 and 400 welds. On the other hand, type B cracks began to appear after 40 welds. However, the number and size of these exhibited inconsistency as the number of welds increased. Overall, the results have shown that small LME cracks can form even in uncoated steels during consecutive welding of Zn-coated and uncoated steel joints

Modification of the Association between Visual Impairment and Mortality by Physical Activity: A Cohort Study among the Korean National Health Examinees

The association between visual impairment and higher mortality remains unclear. In addition, evidence is lacking on the interaction between visual function and physical activity on mortality. We used data of individuals with no disability or with visual impairment among those who participated in the National Health Screening Program in Korea in 2009 or 2010. We constructed Cox proportional hazard models adjusted for potential confounders to evaluate the independent association between visual impairment and mortality. More severe visual impairment was associated with higher all-cause mortality (p-value for trend = 0.03) and mortality due to cardiovascular diseases (p-value for trend = 0.02) and that due to other diseases (p-value for trend = 0.01). We found an interaction on an additive scale between visual impairment and no physical activity on all-cause mortality (relative excess risk due to interaction = 1.34, 95% confidence interval: 0.37, 2.30, p-value = 0.01). When we stratified the study population by physical activity, the association between visual impairment and mortality was only found among individuals who did not engage in regular physical activity (p-value for trend = 0.01). We found an independent association between visual impairment and mortality and modification of this association by physical activity

Optokinetically Encoded Nanoprobe-Based Multiplexing Strategy for MicroRNA Profiling

Multiplexed real-time analysis on multiple interacting molecules and particles is needed to obtain information on binding patterns between multiple ligands and receptors, specificity of bond formations, and interacting pairs in a complex medium, often found in chemical and biological systems, and difference in binding affinity and kinetics for different binding pairs in one solution. In particular, multiplexed profiling of microRNA (miRNA) in a reliable, quantitative manner is of great demand for the use of miRNA in cell biology, biosensing, and clinical diagnostic applications, and accurate diagnosis of cancers with miRNA is not possible without detecting multiple miRNA sequences in a highly specific manner. Here, we report a multiplexed molecular detection strategy with optokinetically (OK) coded nanoprobes (NPs) that show high photostability, distinct optical signals, and dynamic behaviors on a supported lipid bilayer (SLB) (OK-NLB assay). Metal NPs with three distinct dark-field light scattering signals [red (R), green (G), and blue (B)] and three different target miRNA half-complements were tethered to a two dimensionally fluidic SLB with mobile (M) or immobile (I) state. <i>In situ</i> single-particle monitoring and normalized RGB analysis of the optokinetically combinatorial assemblies among three M-NPs and three I-NPs with dark-field microscopy (DFM) allow for differentiating and quantifying 9 different miRNA targets in one sample. The OK-NP-based assay enables simultaneous detection of multiple miRNA targets in a highly quantitative, specific manner within 1 h and can be potentially used for diagnosis of different cancer types. We validated the OK-NLB assay with single-base mismatched experiments and HeLa cell-extracted total RNA samples by comparing the assay results to the quantitative reverse transcription polymerase chain reaction (qRT-PCR) results

Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells

Background Intra-tumoral genetic and functional heterogeneity correlates with cancer clinical prognoses. However, the mechanisms by which intra-tumoral heterogeneity impacts therapeutic outcome remain poorly understood. RNA sequencing (RNA-seq) of single tumor cells can provide comprehensive information about gene expression and single-nucleotide variations in individual tumor cells, which may allow for the translation of heterogeneous tumor cell functional responses into customized anti-cancer treatments. Results We isolated 34 patient-derived xenograft (PDX) tumor cells from a lung adenocarcinoma patient tumor xenograft. Individual tumor cells were subjected to single cell RNA-seq for gene expression profiling and expressed mutation profiling. Fifty tumor-specific single-nucleotide variations, including KRAS G12D, were observed to be heterogeneous in individual PDX cells. Semi-supervised clustering, based on KRAS G12D mutant expression and a risk score representing expression of 69 lung adenocarcinoma-prognostic genes, classified PDX cells into four groups. PDX cells that survived in vitro anti-cancer drug treatment displayed transcriptome signatures consistent with the group characterized by KRAS G12D and low risk score. Conclusions Single-cell RNA-seq on viable PDX cells identified a candidate tumor cell subgroup associated with anti-cancer drug resistance. Thus, single-cell RNA-seq is a powerful approach for identifying unique tumor cell-specific gene expression profiles which could facilitate the development of optimized clinical anti-cancer strategies