Differentiation resistance through altered retinoblastoma protein function in acute lymphoblastic leukemia: in silico modeling of the deregulations in the G1/S restriction point pathway

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Correlation between the protein expression levels of A-kinase anchor protein95, p-retinoblastoma (Ser780), cyclin D2/3, and cyclin E2 in esophageal cancer tissues

AIM

This study aimed to investigate the correlation between the expression of A-kinase anchor protein95 (AKAP95), p-retinoblastoma (phosphorylated Rb, p-Rb), cyclin D2, cyclin D3 and cyclin E2 in esophageal cancer tissues and clinicopathological indexes.

METHOD

The protein expression levels of AKAP95, p-Rb, cyclin D2/3 and cyclin E2 in 40 esophageal cancer tissues were detected using immunohistochemistry, and the correlation between them was analyzed.

RESULT

The percentage of p-Rb (Ser780)-, cyclin D2-, cyclin D3- and cyclin E2-positive samples was 62.50%, 70.00%, 67.50% and 60.00%, respectively. Also, the positive expression did not correlate with the histological type, histological differentiation or lymph node metastasis. The expression of AKAP95 and p-Rb (Ser780), p-Rb (Ser780) and cyclin D2 and p-Rb (Ser780) and cyclin D3 in esophageal cancer tissues was found to be correlated (P < 0.05).

CONCLUSIONS

The expression of AKAP95 and p-Rb (Ser780), p-Rb(Ser780) and cyclin D2, and p-Rb (Ser780) and cyclin D3 in esophageal cancer tissue was correlated, suggesting that these proteins might play a synergistic role in cell-cycle progression. Cyclin D2/D3 and p-Rb (Ser780) were correlated whereas cyclin E2 and p-Rb (Ser780) were not, suggesting that p-Rb (Ser780) might be highly expressed and the Ser780 site of Rb protein might be phosphorylated in the early stage of the G1 phase. Ser780 was the site in the primary phosphorylation stage of several phosphorylation sites during stepwise phosphorylation (from primary to high phosphorylation).

High methylation of the 4-aminobutyrate aminotransferase gene predicts a poor prognosis in patients with myelodysplastic syndrome

In our previous study, the 4‑aminobutyrate aminotransferase (ABAT) gene was screened and selected as a target gene that may affect the prognosis of myelodysplastic syndrome (MDS). The present study aimed to determine the prognostic value of ABAT in 152 patients with MDS, 29 patients with acute myeloid leukemia (AML) and 40 controls, by detecting the expression and methylation levels of the ABAT gene. In patients with MDS, the expression levels of ABAT were significantly reduced compared with in the controls (P<0.0001), and the degree of DNA methylation was increased in MDS subjects (P<0.0001). Age, hemoglobin level, marrow blasts, International Prognostic Scoring System karyotype, and the expression and methylation levels of ABAT were associated with overall survival (OS), as determined by univariate analysis. Multivariate analysis revealed that older age, higher marrow blasts and higher methylation percentage were independent risk factors for OS. In addition, a functional study demonstrated that ABAT gene silencing increased cell apoptosis and blocked the G1/S phase in SKM‑1 and THP‑1 human leukemia cells. A γ‑aminobutyrate aminotransferase inhibitor also blocked the G1/S phase; however, it had no effect on cell apoptosis. In conclusion, the present study demonstrated that ABAT methylation served an essential role in the progression of MDS and therefore may be considered an indicator of poor prognosis for hematological malignancies.

Imidazolium ionic liquids as effective antiseptics and disinfectants against drug resistant S. aureus: In silico and in vitro studies

This paper describes Quantitative Structure-Activity Relationships (QSAR) studies, molecular docking and in vitro antibacterial activity of several potent imidazolium-based ionic liquids (ILs) against S. aureus ATCC 25923 and its clinical isolate. Small set of 131 ILs was collected from the literature and uploaded in the OCHEM database. QSAR methodologies used Associative Neural Networks and Random Forests (WEKA-RF) methods. The predictive ability of the models was tested through cross-validation, giving cross-validated coefficients q² = 0.82-0.87 for regression models and overall prediction accuracies of 80-82.1% for classification models. The proposed QSAR models are freely available online on OCHEM server at https://ochem.eu/article/107364 and can be used for estimation of antibacterial activity of new imidazolium-based ILs. A series of synthesized 1,3-dialkylimidazolium ILs with predicted activity were evaluated in vitro. The high activity of 7 ILs against S. aureus strain and its clinical isolate was measured and thereafter analyzed by the molecular docking to prokaryotic homologue of a eukaryotic tubulin FtsZ.

A study on the predictability of acute lymphoblastic leukaemia response to treatment using a hybrid oncosimulator

Efficient use of Virtual Physiological Human (VPH)-type models for personalized treatment response prediction purposes requires a precise model parameterization. In the case where the available personalized data are not sufficient to fully determine the parameter values, an appropriate prediction task may be followed. This study, a hybrid combination of computational optimization and machine learning methods with an already developed mechanistic model called the acute lymphoblastic leukaemia (ALL) Oncosimulator which simulates ALL progression and treatment response is presented. These methods are used in order for the parameters of the model to be estimated for retrospective cases and to be predicted for prospective ones. The parameter value prediction is based on a regression model trained on retrospective cases. The proposed Hybrid ALL Oncosimulator system has been evaluated when predicting the pre-phase treatment outcome in ALL. This has been correctly achieved for a significant percentage of patient cases tested (approx. 70% of patients). Moreover, the system is capable of denying the classification of cases for which the results are not trustworthy enough. In that case, potentially misleading predictions for a number of patients are avoided, while the classification accuracy for the remaining patient cases further increases. The results obtained are particularly encouraging regarding the soundness of the proposed methodologies and their relevance to the process of achieving clinical applicability of the proposed Hybrid ALL Oncosimulator system and VPH models in general.

Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway

Cellular heterogeneity, which plays an essential role in biological phenomena, such as drug resistance and migration, is considered to arise from intrinsic (i.e., reaction kinetics) and extrinsic (i.e., protein variability) noise in the cell. However, the mechanistic effects of these types of noise to determine the heterogeneity of signal responses have not been elucidated. Here, we report that the output of epidermal growth factor (EGF) signaling activity is modulated by cellular noise, particularly by extrinsic noise of particular signaling components in the pathway. We developed a mathematical model of the EGF signaling pathway incorporating regulation between extracellular signal-regulated kinase (ERK) and nuclear pore complex (NPC), which is necessary for switch-like activation of the nuclear ERK response. As the threshold of switch-like behavior is more sensitive to perturbations than the graded response, the effect of biological noise is potentially critical for cell fate decision. Our simulation analysis indicated that extrinsic noise, but not intrinsic noise, contributes to cell-to-cell heterogeneity of nuclear ERK. In addition, we accurately estimated variations in abundance of the signal proteins between individual cells by direct comparison of experimental data with simulation results using Apparent Measurement Error (AME). AME was constant regardless of whether the protein levels varied in a correlated manner, while covariation among proteins influenced cell-to-cell heterogeneity of nuclear ERK, suppressing the variation. Simulations using the estimated protein abundances showed that each protein species has different effects on cell-to-cell variation in the nuclear ERK response. In particular, variability of EGF receptor, Ras, Raf, and MEK strongly influenced cellular heterogeneity, while others did not. Overall, our results indicated that cellular heterogeneity in response to EGF is strongly driven by extrinsic noise, and that such heterogeneity results from variability of particular protein species that function as sensitive nodes, which may contribute to the pathogenesis of human diseases.

Individual cell behaviors are controlled by a variety of noise, such as fluctuations in biochemical reactions, protein variability, molecular diffusion, transcriptional noise, cell-to-cell contact, temperature, and pH. Such cellular noise often interferes with signal responses from external stimuli, and such heterogeneity functions in induction of drug resistance, survival, and migration of cells. Thus, heterogeneous cellular responses have positive and negative roles. However, the regulatory mechanisms that produce cellular heterogeneity are unclear. By mathematical modeling and simulations, we investigated how heterogeneous signaling responses are evoked in the EGF signaling pathway and influence the switch-like activation of nuclear ERK. This study demonstrated that cellular heterogeneity of the EGF signaling response is evoked by cell-to-cell variation of particular signaling proteins, such as EGFR, Ras, Raf, and MEK, which act as sensitive nodes in the pathway. These results suggest that signaling responses in individual cells can be predicted from the levels of proteins of sensitive nodes. This study also suggested that proteins of sensitive nodes may serve as cell survival mechanisms.