Diagnostics of Ovarian Tumors in Postmenopausal Patients.

Early diagnosis of ovarian cancer remains an urgent issue owing to the continuing trend towards increasing incidence along with only marginal improvements in mortality and 5-year survival rates. Furthermore, there is a lack of a clear formulation of the concept of pathogenesis. The diagnostic values of tumor markers, their potential advantages and disadvantages, and their combination with radiation imaging methods and transvaginal sonography are discussed. More advanced imaging techniques, such as computed tomography and magnetic resonance imaging have proven too expensive for widespread use. According to the World Health Organization, more than half of the world's population does not have access to diagnostic imaging. Consequently, there is high demand for a low-cost, reliable, and safe imaging system for detecting and monitoring cancer. Currently, there is no clear algorithm available for examining and accurately diagnosing patients with postmenopausal ovarian tumors; moreover, reliable criteria allowing dynamic observation and for determining surgical access and optimal surgical intervention measures in postmenopausal patients are lacking. Medical microwave radiometry shows promising results yielding an accuracy of 90%.

Dynamic Weight Agnostic Neural Networks and Medical Microwave Radiometry (MWR) for Breast Cancer Diagnostics.

BACKGROUND AND OBJECTIVE: Medical microwave radiometry (MWR) is used to capture the thermal properties of internal tissues and has usages in breast cancer detection. Our goal in this paper is to improve classification performance and investigate automated neural architecture search methods. METHODS: We investigated extending the weight agnostic neural network by optimizing the weights using the bi-population covariance matrix adaptation evolution strategy (BIPOP-CMA-ES) once the topology was found. We evaluated and compared the model based on the F1 score, accuracy, precision, recall, and the number of connections. RESULTS: The experiments were conducted on a dataset of 4912 patients, classified as low or high risk for breast cancer. The weight agnostic BIPOP-CMA-ES model achieved the best average performance. It obtained an F1-score of 0.933, accuracy of 0.932, precision of 0.929, recall of 0.942, and 163 connections. CONCLUSIONS: The results of the model are an indication of the promising potential of MWR utilizing a neural network-based diagnostic tool for cancer detection. By separating the tasks of topology search and weight training, we can improve the overall performance.

Treatment and Companion Diagnostics of Lower Back Pain Using Self-Controlled Energo-Neuroadaptive Regulator (SCENAR) and Passive Microwave Radiometry (MWR).

Evaluation of the effectiveness of treatment of nonspecific lower back pain (LBP) is currently largely based on the patient's subjective feelings. The purpose of this study was to use passive microwave radiometry (MWR) as a tool for assessing the effectiveness of various treatment methods in patients with acute and subacute nonspecific LBP. Patients with a pain assessment on a visual analogue scale (VAS) of 6 to 10 points were divided into two groups: Group I included patients with pharmacological, syndrome-oriented treatment (n = 30, age 54.9 ± 2.3 years); Group II included a combination of pharmacotherapy with self-controlled energy-neuroadaptive regulation (SCENAR) (n = 25, age 52.8 ± 2.5 years). The analysis showed that the addition of SCENAR therapy (Group II) significantly potentiated the analgesic effect at the stages of treatment, and after 3 weeks, this had increased by more than two times, by 1.3 points on the VAS. There was also a significant decrease in the maximum internal temperature and normalization of the gradient of internal and skin temperatures, and a decrease in thermo-asymmetry, as assessed by temperature fields. Thermal asymmetry visualization allows the identification of the area of pathological muscle spasm and/or inflammation in the projection of the vertebral-motor segment for the possible targeted use of treatment methods such as percutaneous electro neurostimulation, massage, manual therapy, diagnostic and treatment blocks, etc. The MWR method also avoids unnecessary radiation exposure.

Passive Microwave Radiometry and microRNA Detection for Breast Cancer Diagnostics.

Breast cancer prevention is an important health issue for women worldwide. In this study, we compared the conventional breast cancer screening exams of mammography and ultrasound with the novel approaches of passive microwave radiometry (MWR) and microRNA (miRNA) analysis. While mammography screening dynamics could be completed in 3-6 months, MWR provided a prediction in a matter of weeks or even days. Moreover, MWR has the potential of being complemented with miRNA diagnostics to further improve its predictive quality. These novel techniques can be used alone or in conjunction with more established techniques to improve early breast cancer diagnosis.

Passive Microwave Radiometry for the Diagnosis of Coronavirus Disease 2019 Lung Complications in Kyrgyzstan.

The global spread of severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19), could be due to limited access to diagnostic tests and equipment. Currently, most diagnoses use the reverse transcription polymerase chain reaction (RT-PCR) and chest computed tomography (CT). However, challenges exist with CT use due to infection control, lack of CT availability in low- and middle-income countries, and low RT-PCR sensitivity. Passive microwave radiometry (MWR), a cheap, non-radioactive, and portable technology, has been used for cancer and other diseases' diagnoses. Here, we tested MWR use first time for the early diagnosis of pulmonary COVID-19 complications in a cross-sectional controlled trial in order to evaluate MWR use in hospitalized patients with COVID-19 pneumonia and healthy individuals. We measured the skin and internal temperature using 30 points identified on the body, for both lungs. Pneumonia and lung damage were diagnosed by both CT scan and doctors' diagnoses (pneumonia+/pneumonia-). COVID-19 was determined by RT-PCR (covid+/covid-). The best MWR results were obtained for the pneumonia-/covid- and pneumonia+/covid+ groups. The study suggests that MWR could be used for diagnosing pneumonia in COVID-19 patients. Since MWR is inexpensive, its use will ease the financial burden for both patients and countries. Clinical Trial Number: NCT04568525.

Use of Microwave Radiometry to Monitor Thermal Denaturation of Albumin.

This study monitored thermal denaturation of albumin using microwave radiometry. Brightness Temperature, derived from Microwave Emission (BTME) of an aqueous solution of bovine serum albumin (0.1 mM) was monitored in the microwave frequency range 3.8-4.2 GHz during denaturation of this protein at a temperature of 56°C in a conical polypropylene cuvette. This method does not require fluorescent or radioactive labels. A microwave emission change of 1.5-2°C in the BTME of aqueous albumin solution was found during its denaturation, without a corresponding change in the water temperature. Radio thermometry makes it possible to monitor protein denaturation kinetics, and the resulting rate constant for albumin denaturation was 0.2 ± 0.1 min-1, which corresponds well to rate constants obtained by other methods.

Model-based global sensitivity analysis as applied to identification of anti-cancer drug targets and biomarkers of drug resistance in the ErbB2/3 network.

High levels of variability in cancer-related cellular signalling networks and a lack of parameter identifiability in large-scale network models hamper translation of the results of modelling studies into the process of anti-cancer drug development. Recently global sensitivity analysis (GSA) has been recognised as a useful technique, capable of addressing the uncertainty of the model parameters and generating valid predictions on parametric sensitivities. Here we propose a novel implementation of model-based GSA specially designed to explore how multi-parametric network perturbations affect signal propagation through cancer-related networks. We use area-under-the-curve for time course of changes in phosphorylation of proteins as a characteristic for sensitivity analysis and rank network parameters with regard to their impact on the level of key cancer-related outputs, separating strong inhibitory from stimulatory effects. This allows interpretation of the results in terms which can incorporate the effects of potential anti-cancer drugs on targets and the associated biological markers of cancer. To illustrate the method we applied it to an ErbB signalling network model and explored the sensitivity profile of its key model readout, phosphorylated Akt, in the absence and presence of the ErbB2 inhibitor pertuzumab. The method successfully identified the parameters associated with elevation or suppression of Akt phosphorylation in the ErbB2/3 network. From analysis and comparison of the sensitivity profiles of pAkt in the absence and presence of targeted drugs we derived predictions of drug targets, cancer-related biomarkers and generated hypotheses for combinatorial therapy. Several key predictions have been confirmed in experiments using human ovarian carcinoma cell lines. We also compared GSA-derived predictions with the results of local sensitivity analysis and discuss the applicability of both methods. We propose that the developed GSA procedure can serve as a refining tool in combinatorial anti-cancer drug discovery.

Compensatory effects in the PI3K/PTEN/AKT signaling network following receptor tyrosine kinase inhibition.

Overcoming de novo and acquired resistance to anticancer drugs that target signaling networks is a formidable challenge for drug design and effective cancer therapy. Understanding the mechanisms by which this resistance arises may offer a route to addressing the insensitivity of signaling networks to drug intervention and restore the efficacy of anticancer therapy. Extending our recent work identifying PTEN as a key regulator of Herceptin sensitivity, we present an integrated theoretical and experimental approach to study the compensatory mechanisms within the PI3K/PTEN/AKT signaling network that afford resistance to receptor tyrosine kinase (RTK) inhibition by anti-HER2 monoclonal antibodies. In a computational model representing the dynamics of the signaling network, we define a single control parameter that encapsulates the balance of activities of the enzymes involved in the PI3K/PTEN/AKT cycle. By varying this control parameter we are able to demonstrate both distinct dynamic regimes of behavior of the signaling network and the transitions between those regimes. We demonstrate resistance, sensitivity, and suppression of RTK signals by the signaling network. Through model analysis we link the sensitivity-to-resistance transition to specific compensatory mechanisms within the signaling network. We study this transition in detail theoretically by variation of activities of PTEN, PI3K, AKT enzymes, and use the results to inform experiments that perturb the signaling network using combinatorial inhibition of RTK, PTEN, and PI3K enzymes in human ovarian carcinoma cell lines. We find good alignment between theoretical predictions and experimental results. We discuss the application of the results to the challenges of hypersensitivity of the signaling network to RTK signals, suppression of drug resistance, and efficacy of drug combinations in anticancer therapy.

Systems biology reveals new strategies for personalizing cancer medicine and confirms the role of PTEN in resistance to trastuzumab.

Resistance to targeted cancer therapies such as trastuzumab is a frequent clinical problem not solely because of insufficient expression of HER2 receptor but also because of the overriding activation states of cell signaling pathways. Systems biology approaches lend themselves to rapid in silico testing of factors, which may confer resistance to targeted therapies. Inthis study, we aimed to develop a new kinetic model that could be interrogated to predict resistance to receptor tyrosine kinase (RTK) inhibitor therapies and directly test predictions in vitro and in clinical samples. The new mathematical model included RTK inhibitor antibody binding, HER2/HER3 dimerization and inhibition, AKT/mitogen-activated protein kinase cross-talk, and the regulatory properties of PTEN. The model was parameterized using quantitative phosphoprotein expression data from cancer cell lines using reverse-phase protein microarrays. Quantitative PTEN protein expression was found to be the key determinant of resistance to anti-HER2 therapy in silico, which was predictive of unseen experiments in vitro using the PTEN inhibitor bp(V). When measured in cancer cell lines, PTEN expression predicts sensitivity to anti-HER2 therapy; furthermore, this quantitative measurement is more predictive of response (relative risk, 3.0; 95% confidence interval, 1.6-5.5; P < 0.0001) than other pathway components taken in isolation and when tested by multivariate analysis in a cohort of 122 breast cancers treated with trastuzumab. For the first time, a systems biology approach has successfully been used to stratify patients for personalized therapy in cancer and is further compelling evidence that PTEN, appropriately measured in the clinical setting, refines clinical decision making in patients treated with anti-HER2 therapies.

Dynamic computational modeling in the search for better breast cancer drug therapy.

Breast cancer is an excellent disease paradigm for systems biology. At the time of writing, a simple PubMed search for 'breast cancer' returns nearly 99,000 hits, compared with 51,000 or 16,000 for lung and colon cancer respectively, even though in terms of mortality lung and colon cancers are responsible for four-times more deaths per annum in the UK. These figures reflect the effort and money invested in breast cancer research. It is because breast cancer research is data-rich, crowded and competitive (often perceived as a negative for clinical and basic scientific researchers) that it is such an appealing area of research for systems biologists. For systems biologists, data is currency, and they scavenge diverse and multilayered datasets, from biochemical through genomics and transcriptomics to proteomics, in order to populate computational models. We discuss how dynamic modeling can be used as a tool for predicting responses to new and existing drugs, and what needs to be done to make systems biology a useful tool in the clinic.

The impact of the regulatory design on the response of epidermal growth factor receptor-mediated signal transduction towards oncogenic mutations.

Epidermal growth factor receptor (EGFR)-mediated signal transduction is often hyperactivated in tumour cells and therefore considered a promising target for cancer therapy. A number of computational models have been developed which describe the pathway in great detail. These models are similar in their description of the activation events. The deactivation of the EGFR signalling seems to be cell type-specific and is less understood. Deactivation via receptor internalization, feedback inhibition of son of sevenless (SOS) by double phosphorylated, extracellular signal-regulated kinase (ERKPP) or transiently activated Ras-GTPase activating protein (Ras-GAP) proteins is discussed to play a role. In this study we address the question of to what extent the effect of oncogenic perturbations on EGFR signalling depend on the specific regulation structure. This is investigated using a detailed pathway model under two regulatory modes: the negative feedback via ERKPP to SOS and feed-forward deactivation via transiently activated Ras-GAP proteins. We show that the effect of receptor overexpression differs qualitatively under both regulations. In the system with transiently activated Ras-GAP it may result in an attenuation of the ERK activation. Such a nonintuitive effect was also observed experimentally. In general we find the model with transiently activated Ras-GAP to have a higher robustness towards receptor overexpression and Ras mutations. In particular, we demonstrate that this model can compensate for these oncogenic perturbations if the regulation is strong. The negative feedback can not protect the system against Ras mutations. A general sensitivity analysis, however, shows a higher robustness of the model under negative feedback, indicating the limited significance of such analyses for the prediction of specific oncogenic perturbations.

Mathematical modeling of mitochondrial adenine nucleotide translocase.

We have developed a mathematical model of adenine nucleotide translocase (ANT) function on the basis of the structural and kinetic properties of the transporter. The model takes into account the effect of membrane potential, pH, and magnesium concentration on ATP and ADP exchange velocity. The parameters of the model have been estimated from experimental data. A satisfactory model should take into account the influence of the electric potential difference on both ternary complex formation and translocation processes. To describe the dependence of translocation constants on electric potential we have supposed that ANT molecules carry charged groups. These groups are shifted during the translocation. Using the model we have evaluated the translocator efficiency and predicted the behavior of ANT under physiological conditions.

Temperature dependence of the epidermal growth factor receptor signaling network can be accounted for by a kinetic model.

Stimulation of isolated hepatocytes with epidermal growth factor (EGF) causes rapid tyrosine phosphorylation of the EGF receptor (EGFR) and adapter/target proteins, which was monitored with 1 and 2 s resolution at 37, 20, and 4 degrees C. The temporal responses detected for multiple signaling proteins involve both transient and sustained phosphorylation patterns, which change dramatically at low temperatures. To account quantitatively for complex responses, we employed a mechanistic kinetic model of the EGFR pathway, formulated in molecular terms as cascades of protein interactions and phosphorylation and dephosphorylation reactions. Assuming differential temperature dependencies for different reaction groups, such as SH2 and PTB domain-mediated interactions, the EGFR kinase, and the phosphatases, good quantitative agreement was obtained between computer-simulated and measured responses. The kinetic model demonstrates that, for each protein-protein interaction, the dissociation rate constant, k(off), strongly decreases at low temperatures, whereas this decline may or may not be accompanied by a large decrease in the k(on) value. Temperature-induced changes in the maximal activities of the reactions catalyzed by the EGFR kinase were moderate, compared to such changes in the V(max) of the phosphatases. However, strong changes in both the V(max) and K(m) for phosphatases resulted in moderate changes in the V(max)/K(m) ratio, comparable to the corresponding changes in EGFR kinase activity, with a single exception for the receptor phosphatase at 4 degrees C. The model suggests a significant decrease in the rates of the EGF receptor dimerization and its dephosphorylation at 4 degrees C, which can be related to the phase transition in the membrane lipids. A combination of high-resolution experimental monitoring and molecular level kinetic modeling made it possible to quantitatively account for the temperature dependence of the integrative signaling responses.