Genomic Biomarkers for Personalized Medicine in Breast Cancer

Breast cancer is the most common malignant disease in Western women. Historically, breast cancer was perceived as a single disease with various clinicalpathological features, and therefore, “one drug fits all” approaches drove the treatment regimens. The advent of genomics studies has led to a new paradigm in which breast cancer is heterogeneous consisting of different diseases from the same organ site. For example, gene expression profiling analysis revealed that estrogen receptor (ER)-positive and ER negative breast cancer are two distinct diseases with different risk factors, clinical presentations, outcomes, and responses to systemic therapies. Consequently, the new paradigm demands a personalized strategy in cancer medicine, in which the selection of treatment regimens for each cancer patient will largely rely on assessment by predictive biomarkers and study of the anatomical and pathological features of the cancer

An Efficient Source Model Selection Framework in Model Databases

With the explosive increase of big data, training a Machine Learning (ML) model becomes a computation-intensive workload, which would take days or even weeks. Thus, reusing an already trained model has received attention, which is called transfer learning. Transfer learning avoids training a new model from scratch by transferring knowledge from a source task to a target task. Existing transfer learning methods mostly focus on how to improve the performance of the target task through a specific source model, and assume that the source model is given. Although many source models are available, it is difficult for data scientists to select the best source model for the target task manually. Hence, how to efficiently select a suitable source model in a model database for model reuse is an interesting but unsolved problem. In this paper, we propose SMS, an effective, efficient, and flexible source model selection framework. SMS is effective even when the source and target datasets have significantly different data labels, and is flexible to support source models with any type of structure, and is efficient to avoid any training process. For each source model, SMS first vectorizes the samples in the target dataset into soft labels by directly applying this model to the target dataset, then uses Gaussian distributions to fit for clusters of soft labels, and finally measures the distinguishing ability of the source model using Gaussian mixture-based metric. Moreover, we present an improved SMS (I-SMS), which decreases the output number of the source model. I-SMS can significantly reduce the selection time while retaining the selection performance of SMS. Extensive experiments on a range of practical model reuse workloads demonstrate the effectiveness and efficiency of SMS

Computational models for predicting liver toxicity in the deep learning era

Drug-induced liver injury (DILI) is a severe adverse reaction caused by drugs and may result in acute liver failure and even death. Many efforts have centered on mitigating risks associated with potential DILI in humans. Among these, quantitative structure-activity relationship (QSAR) was proven to be a valuable tool for early-stage hepatotoxicity screening. Its advantages include no requirement for physical substances and rapid delivery of results. Deep learning (DL) made rapid advancements recently and has been used for developing QSAR models. This review discusses the use of DL in predicting DILI, focusing on the development of QSAR models employing extensive chemical structure datasets alongside their corresponding DILI outcomes. We undertake a comprehensive evaluation of various DL methods, comparing with those of traditional machine learning (ML) approaches, and explore the strengths and limitations of DL techniques regarding their interpretability, scalability, and generalization. Overall, our review underscores the potential of DL methodologies to enhance DILI prediction and provides insights into future avenues for developing predictive models to mitigate DILI risk in humans

Selecting a single model or combining multiple models for microarray-based classifier development? – A comparative analysis based on large and diverse datasets generated from the MAQC-II project

<p>Abstract</p> <p>Background</p> <p>Genomic biomarkers play an increasing role in both preclinical and clinical application. Development of genomic biomarkers with microarrays is an area of intensive investigation. However, despite sustained and continuing effort, developing microarray-based predictive models (i.e., genomics biomarkers) capable of reliable prediction for an observed or measured outcome (i.e., endpoint) of unknown samples in preclinical and clinical practice remains a considerable challenge. No straightforward guidelines exist for selecting a single model that will perform best when presented with unknown samples. In the second phase of the MicroArray Quality Control (MAQC-II) project, 36 analysis teams produced a large number of models for 13 preclinical and clinical endpoints. Before external validation was performed, each team nominated one model per endpoint (referred to here as 'nominated models') from which MAQC-II experts selected 13 'candidate models' to represent the best model for each endpoint. Both the nominated and candidate models from MAQC-II provide benchmarks to assess other methodologies for developing microarray-based predictive models.</p> <p>Methods</p> <p>We developed a simple ensemble method by taking a number of the top performing models from cross-validation and developing an ensemble model for each of the MAQC-II endpoints. We compared the ensemble models with both nominated and candidate models from MAQC-II using blinded external validation.</p> <p>Results</p> <p>For 10 of the 13 MAQC-II endpoints originally analyzed by the MAQC-II data analysis team from the National Center for Toxicological Research (NCTR), the ensemble models achieved equal or better predictive performance than the NCTR nominated models. Additionally, the ensemble models had performance comparable to the MAQC-II candidate models. Most ensemble models also had better performance than the nominated models generated by five other MAQC-II data analysis teams that analyzed all 13 endpoints.</p> <p>Conclusions</p> <p>Our findings suggest that an ensemble method can often attain a higher average predictive performance in an external validation set than a corresponding “optimized” model method. Using an ensemble method to determine a final model is a potentially important supplement to the good modeling practices recommended by the MAQC-II project for developing microarray-based genomic biomarkers.</p

Selective Adsorption of Ionic Species Using Macroporous Monodispersed Polyethylene Glycol Diacrylate/Acrylic Acid Microgels with Tunable Negative Charge

In this work, the possibility of fabricating composite magneto-optical ceramics by electrophoretic deposition (EPD) of nanopowders and high-temperature vacuum sintering of the compacts was investigated. Holmium oxide was chosen as a magneto-optical material for the study because of its transparency in the mid-IR range. Nanopowders of magneto-optical (Ho0.95La0.05)2O3 (HoLa) material were made by self-propagating high-temperature synthesis. Nanopowders of (Y0.9La0.1)2O3 (YLa) were made by laser synthesis for an inactive matrix. The process of formation of one- and two-layer compacts by EPD of the nanopowders from alcohol suspensions was studied in detail. Acetylacetone was shown to be a good dispersant to obtain alcohol suspensions of the nanopowders, characterized by high zeta potential values (+29–+80 mV), and to carry out a stable EPD process. One-layer compacts were made from the HoLa and YLa nanopowders with a density of 30–43%. It was found out that the introduction of polyvinyl butyral (PVB) into the suspension leads to a decrease in the mass and thickness of the green bodies deposited, but does not significantly affect their density. The possibility of making two-layer (YLa/HoLa) compacts with a thickness of up to 2.6 mm and a density of up to 46% was demonstrated. Sintering such compacts in a vacuum at a temperature of 1750 °C for 10 h leads to the formation of ceramics with a homogeneous boundary between the YLa/HoLa layers and a thickness of the interdiffused ion layer of about 30 μm

Cuproptosis-related gene FDX1 as a prognostic biomarker for kidney renal clear cell carcinoma correlates with immune checkpoints and immune cell infiltration

Background: Kidney renal clear cell carcinoma (KIRC) is not sensitive to radiotherapy and chemotherapy, and only some KIRC patients can benefit from immunotherapy and targeted therapy. Cuproptosis is a new mechanism of cell death, which is closely related to tumor progression, prognosis and immunity. The identification of prognostic markers related to cuproptosis in KIRC may provide targets for treatment and improve the prognosis of KIRC patients.Methods: Ten cuproptosis-related genes were analyzed for differential expression in KIRC-TCGA and a prognostic model was constructed. Nomogram diagnostic model was used to screen independent prognostic molecules. The screened molecules were verified in multiple datasets (GSE36895 and GSE53757), and in KIRC tumor tissues by RT-PCR and immunohistochemistry (IHC). Clinical correlation of cuproptosis-related independent prognostic molecules was analyzed. According to the molecular expression, the two groups were divided into high and low expression groups, and the differences of immune checkpoint and tumor infiltrating lymphocytes (TILs) between the two groups were compared by EPIC algorithm. The potential Immune checkpoint blocking (ICB) response of high and low expression groups was predicted by the “TIDE” algorithm.Results: FDX1 and DLAT were protective factors, while CDKN2A was a risk factor. FDX1 was an independent prognostic molecule by Nomogram, and low expressed in tumor tissues compared with adjacent tissues (p &lt; 0.05). FDX1 was positively correlated with CD274, HAVCR2, PDCD1LG2, and negatively correlated with CTLA4, LAG3, and PDCD1. The TIDE score of low-FDX1 group was higher than that of high-FDX1 group. The abundance of CD4+ T cells, CD8+ T cells and Endothelial cells in FDX1-low group was lower than that in FDX1-high group (p &lt; 0.05).Conclusion: FDX1, as a key cuproptosis-related gene, was also an independent prognostic molecule of KIRC. FDX1 might become an interesting biomarker and potential therapeutic target for KIRC