Treating triple negative breast cancer cells with erlotinib plus a select antioxidant overcomes drug resistance by targeting cancer cell heterogeneity

Among breast cancer patients, those diagnosed with the triple-negative breast cancer (TNBC) subtype have the worst prog-nosis. TNBC does not express estrogen receptor-alpha, progesterone receptor, or the HER2 oncogene; therefore, TNBC lacks targets for molecularly-guided therapies. The concept that EGFR oncogene inhibitor drugs could be used as targeted treatment against TNBC has been put forth based on estimates that 30-60% of TNBC express high levels of EGFR. However, results from clinical trials testing EGFR inhibitors, alone or in combination with cytotoxic chemotherapy, did not improve patient outcomes. Results herein offer an explanation as to why EGFR inhibitors failed TNBC patients and support how combining a select antioxidant and an EGFR-specific small molecule kinase inhibitor (SMKI) could be an effective, novel therapeutic strategy. Treatment with CAT-SKL-a re-engineered protein form of the antioxidant enzyme catalase-inhibited cancer stem-like cells (CSCs), and treatment with the EGFR-specific SMKI erlotinib inhibited non-CSCs. Thus, combining the antioxidant CAT-SKL with erlotinib targeted both CSCs and bulk cancer cells in cultures of EGFR-expressing TNBC-derived cells. We also report evidence that the mechanism for CAT-SKL inhibition of CSCs may depend on antioxidant-induced downregulation of a short alternative mRNA splicing variant of the methyl-CpG binding domain 2 gene, isoform MBD2c

Optimization Algorithms for Computational Systems Biology

Computational systems biology aims at integrating biology and computational methods to gain a better understating of biological phenomena. It often requires the assistance of global optimization to adequately tune its tools. This review presents three powerful methodologies for global optimization that fit the requirements of most of the computational systems biology applications, such as model tuning and biomarker identification. We include the multi-start approach for least squares methods, mostly applied for fitting experimental data. We illustrate Markov Chain Monte Carlo methods, which are stochastic techniques here applied for fitting experimental data when a model involves stochastic equations or simulations. Finally, we present Genetic Algorithms, heuristic nature-inspired methods that are applied in a broad range of optimization applications, including the ones in systems biology

Modeling time-dependent transcription effects of HER2 oncogene and discovery of a role for E2F2 in breast cancer cell-matrix adhesion

MOTIVATIONS: Oncogenes are known drivers of cancer phenotypes and targets of molecular therapies; however, the complex and diverse signaling mechanisms regulated by oncogenes and potential routes to targeted therapy resistance remain to be fully understood. To this end, we present an approach to infer regulatory mechanisms downstream of the HER2 driver oncogene in SUM-225 metastatic breast cancer cells from dynamic gene expression patterns using a succession of analytical techniques, including a novel MP grammars method to mathematically model putative regulatory interactions among sets of clustered genes.RESULTS: Our method highlighted regulatory interactions previously identified in the cell line and a novel finding that the HER2 oncogene, as opposed to the proto-oncogene, upregulates expression of the E2F2 transcription factor. By targeted gene knockdown we show the significance of this, demonstrating that cancer cell-matrix adhesion and outgrowth were markedly inhibited when E2F2 levels were reduced. Thus, validating in this context that upregulation of E2F2 represents a key intermediate event in a HER2 oncogene-directed gene expression-based signaling circuit. This work demonstrates how predictive modeling of longitudinal gene expression data combined with multiple systems-level analyses can be used to accurately predict downstream signaling pathways. Here, our integrated method was applied to reveal insights as to how the HER2 oncogene drives a specific cancer cell phenotype, but it is adaptable to investigate other oncogenes and model systems

Metabolinės P sistemos įgyvendinimo lauku programuojamomis loginėmis matricomis tyrimas

The advancement in the fields of electronics and nature inspired computing, including metabolic P (MP) systems, presents new possible solutions to existing problems,however there are still no implementations of MP systems in field programmable gate arrays (FPGA). Therefore, in this work the problem of lack of knowledge about the quality of MP systems implementation in FPGA together with absence of implementation technique for multiple and effective MP systems is solved. The object of the research is specialized MP system implementations in FPGA that operate in real-time. The main aspects of the research object investigated in the thesis are: implementation quality and techniques. The aim of the thesis is to offer original FPGA based MP system solutions by creating and investigating real-time metabolic process electronic system used for imitation and testing. In order to solve the stated problem and reach the aim of the thesis the following objectives are formulated: using theoretical results of MP systems and other best practices, offer original solutions for MP system transformation to FPGA structural elements and signal processing schemes; reveal quality characteristics of the transformation based on throughput, complexity and power consumption; create real-time metabolic process imitation and testing electronic system and perform its evaluation experiments. The dissertation consists of an introduction, four chapters and general conclusions.The first chapter reveals the fundamental knowledge on nature inspired computing,MP system definition and application, and FPGA implementation quality estimation.In the second chapter the quality criteria of calculation accuracy, throughput,resource usage, power consumption and interface complexity are selected for the evaluation of MP system FPGA implementation. New combined MP system quality metric ant its visualisation is also proposed. In the third chapter the common FPGA implementation techniques are adapted for MP systems and new unified technique is proposed. The evaluation of the developed MP system implementations in FPGA is presented in the fourth chapter. The experiments consist of a single MP system implementation using three different techniques and a multiple MP system implementation using two new developed unified implementation techniques. The main results of the thesis were published in 5 scientific publications: three of them were printed in peer-reviewed scientific journals, one of them in Clarivate Analytics Web of Science database, two articles – in conference proceedings. The research results were presented in 6 scientific conferences