Identification of Potential Drug Targets in Cancer Signaling Pathways using Stochastic Logical Models

Radiogenomics and machine learning predict oncogenic signaling pathways in glioblastoma

BACKGROUND

Glioblastoma (GBM) is a highly aggressive brain tumor associated with a poor patient prognosis. The survival rate remains low despite standard therapies, highlighting the urgent need for novel treatment strategies. Advanced imaging techniques, particularly magnetic resonance imaging (MRI), are crucial in assessing GBM. Disruptions in various oncogenic signaling pathways, such as Receptor Tyrosine Kinase (RTK)-Ras-Extracellular signal-regulated kinase (ERK) signaling, Phosphoinositide 3- Kinases (PI3Ks), tumor protein p53 (TP53), and Neurogenic locus notch homolog protein (NOTCH), contribute to the development of different tumor types, each exhibiting distinct morphological and phenotypic features that can be observed at a microscopic level. However, identifying genetic abnormalities for targeted therapy often requires invasive procedures, prompting exploration into non-invasive approaches like radiogenomics. This study explores the utility of radiogenomics and machine learning (ML) in predicting these oncogenic signaling pathways in GBM patients.

METHODS

We collected post-operative MRI scans (T1w, T1c, FLAIR, T2w) from the BRATS-19 dataset, including scans from patients with both GBM and LGG, linked to genetic and clinical data via TCGA and CPTAC. Signaling pathway data was manually extracted from cBioPortal. Radiomic features were extracted from four MRI modalities using PyRadiomics. Dimensionality reduction and feature selection were applied and Data imbalance was addressed with SMOTE. Five ML models were trained to predict signaling pathways, with Grid Search optimizing hyperparameters and 5-fold cross-validation ensuring unbiased performance. Each model's performance was evaluated using various metrics on test data.

RESULTS

Our results showed a positive association between most signaling pathways and the radiomic features derived from MRI scans. The best models achieved high AUC scores, namely 0.7 for RTK-RAS, 0.8 for PI3K, 0.75 for TP53, and 0.4 for NOTCH, and therefore, demonstrated the potential of ML models in accurately predicting oncogenic signaling pathways from radiomic features, thereby informing personalized therapeutic approaches and improving patient outcomes.

CONCLUSION

We present a novel approach for the non-invasive prediction of deregulation in oncogenic signaling pathways in glioblastoma (GBM) by integrating radiogenomic data with machine learning models. This research contributes to advancing precision medicine in GBM management, highlighting the importance of integrating radiomics with genomic data to understand tumor behavior and treatment response better.

Physiological Indirect Response Model to Omics-Powered Quantitative Systems Pharmacology Model

Over the past several decades, mathematical modeling has been applied to increasingly wider scopes of questions in drug development. Accordingly, the range of modeling tools has also been evolving, as showcased by contributions of Jusko and colleagues: from basic pharmacokinetics/pharmacodynamics (PK/PD) modeling to today's platform-based approach of quantitative systems pharmacology (QSP) modeling. Aimed at understanding the mechanism of action of investigational drugs, QSP models characterize systemic effects by incorporating information about cellular signaling networks, which is often represented by omics data. In this perspective, we share a few examples illustrating approaches for the integration of omics into mechanistic QSP modeling. We briefly overview how the evolution of PK/PD modeling into QSP has been accompanied by an increase in available data and the complexity of mathematical methods that integrate it. We discuss current gaps and challenges of integrating omics data into QSP models and propose several potential areas where integrated QSP and omics modeling may benefit drug development.

Role of ZNF143 and Its Association with Gene Expression Patterns, Noncoding Mutations, and the Immune System in Human Breast Cancer

The function of noncoding sequence variations at ZNF143 binding sites in breast cancer cells is currently not well understood. Distal elements and promoters, also known as cis-regulatory elements, control the expression of genes. They may be identified by functional genomic techniques and sequence conservation, and they frequently show cell- and tissue-type specificity. The creation, destruction, or modulation of TF binding and function may be influenced by genetic modifications at TF binding sites that affect the binding affinity. Therefore, noncoding mutations that affect the ZNF143 binding site may be able to alter the expression of some genes in breast cancer. In order to understand the relationship among ZNF143, gene expression patterns, and noncoding mutations, we adopted an integrative strategy in this study and paid close attention to putative immunological signaling pathways. The immune system-related pathways ErbB, HIF1a, NF-kB, FoxO, JAK-STAT, Wnt, Notch, cell cycle, PI3K-AKT, RAP1, calcium signaling, cell junctions and adhesion, actin cytoskeleton regulation, and cancer pathways are among those that may be significant, according to the overall analysis.

In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model

Pleiotropy, which refers to the ability of different mutations on the same gene to cause different pathological effects in human genetic diseases, is important in understanding system-level biological diseases. Although some biological experiments have been proposed, still little is known about pleiotropy on gene–gene dynamics, since most previous studies have been based on correlation analysis. Therefore, a new perspective is needed to investigate pleiotropy in terms of gene–gene dynamical characteristics. To quantify pleiotropy in terms of network dynamics, we propose a measure called in silico Pleiotropic Scores (sPS), which represents how much a gene is affected against a pair of different types of mutations on a Boolean network model. We found that our model can identify more candidate pleiotropic genes that are not known to be pleiotropic than the experimental database. In addition, we found that many types of functionally important genes tend to have higher sPS values than other genes; in other words, they are more pleiotropic. We investigated the relations of sPS with the structural properties in the signaling network and found that there are highly positive relations to degree, feedback loops, and centrality measures. This implies that the structural characteristics are principles to identify new pleiotropic genes. Finally, we found some biological evidence showing that sPS analysis is relevant to the real pleiotropic data and can be considered a novel candidate for pleiotropic gene research. Taken together, our results can be used to understand the dynamics pleiotropic characteristics in complex biological systems in terms of gene–phenotype relations.

GPU Accelerated Drug Application on Signaling Pathways Containing Multiple Faults Using Boolean Networks

Cell growth is governed by the flow of information from growth factors to transcription factors. This flow involves protein-protein interactions known as a signaling pathway, which triggers the cell division. The biological network in the presence of malfunctions leads to a rapid cell division without any necessary input conditions. The effect of these malfunctions or faults can be observed if it is simulated explicitly in the Boolean derivative of the biological networks. The consequences thus produced can be nullified to a large extent, with the application of a reduced combination of drugs. This paper provides an insight into the behavior of the signaling pathway in the presence of multiple concurrent malfunctions. First, we simulate the behavior of malfunctions in the Boolean networks. Next, we apply the drug therapy to reduce the effects of malfunctions. In our approach, we introduce a parameter called probabilistic_score, which identifies the reduced drug combinations without prior knowledge of the malfunctions, and it is more beneficial in realistic cancerous conditions. The combinations of different custom drug inhibition points are chosen to produce more efficient results than known drugs. Our approach is significantly faster as GPU acceleration has been carried out during modeling the multiple faults/malfunctions in the Boolean networks.

Conformational perturbation, allosteric modulation of cellular signaling pathways, and disease in P23H rhodopsin

In this investigation we use THz spectroscopy and MD simulation to study the functional dynamics and conformational stability of P23H rhodopsin. The P23H mutation of rod opsin is the most common cause of human binding autosomal dominant retinitis pigmentosa (ADRP), but the precise mechanism by which this mutation leads to photoreceptor cell degeneration has not yet been elucidated. Our measurements confirm conformational instability in the global modes of the receptor and an active-state that uncouples the torsional dynamics of the retinal with protein functional modes, indicating inefficient signaling in P23H and a drastically altered mechanism of activation when contrasted with the wild-type receptor. Further, our MD simulations indicate that P23H rhodopsin is not functional as a monomer but rather, due to the instability of the mutant receptor, preferentially adopts a specific homodimerization motif. The preferred homodimer configuration induces structural changes in the receptor tertiary structure that reduces the affinity of the receptor for the retinal and significantly modifies the interactions of the Meta-II signaling state. We conjecture that the formation of the specific dimerization motif of P23H rhodopsin represents a cellular-wide signaling perturbation that is directly tied with the mechanism of P23H disease pathogenesis. Our results also support a direct role for rhodopsin P23H dimerization in photoreceptor rod death.

NOTCH2/NOTCH3/DLL3/MAML1/ADAM17 signaling network is associated with ovarian cancer

Notch signaling is well-known for its role in regulating cell self-renewal and differentiation. Within the cancer research field, it has been identified that dysregulated Notch signaling is involved directly with various types of cancer. Although Notch signaling is generally considered as oncogenic, it sometimes acts as a tumor suppressor, highlighting the complexity of the role of Notch in cancer. A number of studies have associated Notch signaling components with ovarian cancer, but the underlying molecular mechanisms are not well-elucidated. In the present study, the roles of main components of Notch signaling in ovarian cancer were systematically analyzed through large data portals, including Prediction of Clinical Outcomes from Genomic Profiles, Gene Expression across Normal and Tumor tissue, CSIOVDB, Broad Institute Cancer Cell Line Encyclopedia and cBioPortal. Upregulated expression of proteins in the Notch signaling pathway components in ovarian cancer was identified to be generally associated with poor overall and disease-free survival time, and more advanced cancer stages. In addition, Notch components were enriched in ovarian cancer tissues and cell lines. These results led to a proposed neurogenic locus notch homolog protein (NOTCH)2/NOTCH3/Delta-like protein 3/Mastermind-like protein 1/a disintegrin and metalloproteinase domain-containing protein 17 network. Anticancer drugs, developed to target this network, may have high specificity in treating Notch-associated ovarian cancer.

Computational discovery of dynamic cell line specific Boolean networks from multiplex time-course data

Protein signaling networks are static views of dynamic processes where proteins go through many biochemical modifications such as ubiquitination and phosphorylation to propagate signals that regulate cells and can act as feed-back systems. Understanding the precise mechanisms underlying protein interactions can elucidate how signaling and cell cycle progression occur within cells in different diseases such as cancer. Large-scale protein signaling networks contain an important number of experimentally verified protein relations but lack the capability to predict the outcomes of the system, and therefore to be trained with respect to experimental measurements. Boolean Networks (BNs) are a simple yet powerful framework to study and model the dynamics of the protein signaling networks. While many BN approaches exist to model biological systems, they focus mainly on system properties, and few exist to integrate experimental data in them. In this work, we show an application of a method conceived to integrate time series phosphoproteomic data into protein signaling networks. We use a large-scale real case study from the HPN-DREAM Breast Cancer challenge. Our efficient and parameter-free method combines logic programming and model-checking to infer a family of BNs from multiple perturbation time series data of four breast cancer cell lines given a prior protein signaling network. Because each predicted BN family is cell line specific, our method highlights commonalities and discrepancies between the four cell lines. Our models have a Root Mean Square Error (RMSE) of 0.31 with respect to the testing data, while the best performant method of this HPN-DREAM challenge had a RMSE of 0.47. To further validate our results, BNs are compared with the canonical mTOR pathway showing a comparable AUROC score (0.77) to the top performing HPN-DREAM teams. In addition, our approach can also be used as a complementary method to identify erroneous experiments. These results prove our methodology as an efficient dynamic model discovery method in multiple perturbation time course experimental data of large-scale signaling networks. The software and data are publicly available at https://github.com/misbahch6/caspo-ts.

Traditional canonical signaling pathways help to understand overall signaling processes inside the cell. Large scale phosphoproteomic data provide insight into alterations among different proteins under different experimental settings. Our goal is to combine the traditional signaling networks with complex phosphoproteomic time-series data in order to unravel cell specific signaling networks. In this study, we have applied the caspo time series (caspo-ts) approach which is a combination of logic programming and model checking, over the time series phosphoproteomic dataset of the HPN-DREAM challenge to learn cell specific BNs. The learned BNs can be used to identify the cell specific topology. Our analysis suggests that caspo-ts scales to real datasets, outputting networks that are not random with a lower fitness error than the models used by the 178 methods which participated in the HPN-DREAM challenge. On the biological side, we identified the cell specific and common mechanisms (logical gates) of the cell lines.

In silico models in drug development: where we are

The use and utility of computational models in drug development has significantly grown in the last decades, fostered by the availability of high throughput datasets and new data analysis strategies. These in silico approaches are demonstrating their ability to generate reliable predictions as well as new knowledge on the mode of action of drugs and the mechanisms underlying their side effects, altogether helping to reduce the costs of drug development. The aim of this review is to provide a panorama of developments in the field in the last two years.

Asteriscus graveolens Extract in Combination with Cisplatin/Etoposide/Doxorubicin Suppresses Lymphoma Cell Growth through Induction of Caspase-3 Dependent Apoptosis

Chemotherapy drugs action against cancer is not selective, lead to adverse reactions and drug resistance. Combination therapies have proven more effective in defeating cancers. We hypothesize that plant extract/fraction contains many/several compounds and as such can target multiple pathways as cytotoxic agent and may also have chemo sensitizing activities. We designed a study in which, Asteriscus graveolens (Forssk.) Less (A. graveolens)-derived fraction that contains sesquiterpene lactone asteriscunolide isomers (AS) will be tested in combination with known chemotherapy drugs. Successful combination will permit to reduce chemotherapy drugs concentration and still get the same impact on cancer cells. Sesquiterpene lactone such as asteriscunolide isomers is a naturally occurring compound found in a variety of fruits, vegetables, and medicinal plants with anti-cancer properties. The experiments presented here showed that adding plant fraction containing AS permit reducing the concentration of cisplatin/etoposide/doxorubicin in order to reduce mouse BS-24-1 lymphoma cells (BS-24-1 cells) survival. It involved enhancing the production of Reactive Oxygen Species (ROS), activation of caspase-3 and inhibition of Topoisomerase I activity. Taken together, the results suggest that A. graveolens fraction sensitized BS-24-1 cells to cisplatin/etoposide/doxorubicin through induction of ROS and caspase-3-dependent apoptosis.

Potential candidate treatment agents for targeting of cholangiocarcinoma identified by gene expression profile analysis

Cholangiocarcinoma (CCA) remains to be a major health problem in several Asian countries including Thailand. The molecular mechanism of CCA is poorly understood. Early diagnosis is difficult, and at present, no effective therapeutic drug is available. The present study aimed to identify the molecular mechanism of CCA by gene expression profile analysis and to search for current approved drugs which may interact with the upregulated genes in CCA. Gene Expression Omnibus (GEO) was used to analyze the gene expression profiles of CCA patients and normal subjects. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG), gene ontology enrichment analysis was also performed, with the KEGG pathway analysis indicating that pancreatic secretion, protein digestion and absorption, fat digestion and absorption, and glycerolipid metabolism may serve important roles in CCA oncogenesis. The drug signature database (DsigDB) was used to search for US Food and Drug Administration (FDA)-approved drugs potentially capable of reversing the effects of the upregulated gene expression in CCA. A total of 61 antineoplastic and 86 non-antineoplastic drugs were identified. Checkpoint kinase 1 was the most interacting with drug signatures. Many of the targeted protein inhibitors that were identified have been approved by the US-FDA as therapeutic agents for non-antineoplastic diseases, including cimetidine, valproic acid and lovastatin. The current study demonstrated an application for bioinformatics analysis in assessing the potential efficacy of currently approved drugs for novel use. The present results suggest novel indications regarding existing drugs useful for CCA treatment. However, further in vitro and in vivo studies are required to support the current predictions.

Hierarchical closeness-based properties reveal cancer survivability and biomarker genes in molecular signaling networks

Specific molecular signaling networks underlie different cancer types and quantitative analyses on those cancer networks can provide useful information about cancer treatments. Their structural metrics can reveal survivability of cancer patients and be used to identify biomarker genes for early cancer detection. In this study, we devised a novel structural metric called hierarchical closeness (HC) entropy and found that it was negatively correlated with 5-year survival rates. We also made an interesting observation that a network of higher HC entropy was likely to be more robust against mutations. This finding suggested that cancers of high HC entropy tend to be incurable because their signaling networks are robust to perturbations caused by treatment. We also proposed a novel core identification method based on the reachability factor in the HC measure. The cores were permitted to decompose such that the negative relationship between HC entropy and cancer survival rate was consistently conserved in every core level. Interestingly, we observed that many promising biomarker genes for early cancer detection reside in the innermost core of a signaling network. Taken together, the proposed analyses of the hierarchical structure of cancer signaling networks may be useful in developing future novel cancer treatments.

Boolean network modeling in systems pharmacology

Quantitative systems pharmacology (QSP) is an emerging discipline that aims to discover how drugs modulate the dynamics of biological components in molecular and cellular networks and the impact of those perturbations on human pathophysiology. The integration of systems-based experimental and computational approaches is required to facilitate the advancement of this field. QSP models typically consist of a series of ordinary differential equations (ODE). However, this mathematical framework requires extensive knowledge of parameters pertaining to biological processes, which is often unavailable. An alternative framework that does not require knowledge of system-specific parameters, such as Boolean network modeling, could serve as an initial foundation prior to the development of an ODE-based model. Boolean network models have been shown to efficiently describe, in a qualitative manner, the complex behavior of signal transduction and gene/protein regulatory processes. In addition to providing a starting point prior to quantitative modeling, Boolean network models can also be utilized to discover novel therapeutic targets and combinatorial treatment strategies. Identifying drug targets using a network-based approach could supplement current drug discovery methodologies and help to fill the innovation gap across the pharmaceutical industry. In this review, we discuss the process of developing Boolean network models and the various analyses that can be performed to identify novel drug targets and combinatorial approaches. An example for each of these analyses is provided using a previously developed Boolean network of signaling pathways in multiple myeloma. Selected examples of Boolean network models of human (patho-)physiological systems are also reviewed in brief.

Difatty Acyl-Conjugated Linear and Cyclic Peptides for siRNA Delivery

A number of amphiphilic difatty acyl linear and cyclic R5K2 peptide conjugates were synthesized by solid-phase peptide methods to enhance the interaction with the hydrophobic cellular phospholipid bilayer and to improve siRNA delivery and silencing. Binding to siRNA molecules was significantly less for the cyclic peptide conjugates. A gradual decrease was observed in the particle size of the complexes with increasing peptide/siRNA ratio for most of the synthesized peptides, suggesting the complex formation. Most of the complexes showed a particle size of less than 200 nm, which is considered an appropriate size for in vitro siRNA delivery. A number of fatty acyl-conjugated peptides, such as LP-C16 and LP-C18, displayed near complete protection against serum degradation. Flow cytometry studies demonstrated significantly higher internalization of fluorescence-labeled siRNA (FAM-siRNA) in the presence of LP-C16, LP-C18, and CP-C16 with 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE) addition. Confocal microscopy confirmed the cellular internalization of fluorescence-labeled siRNA in the presence of LP-C16 and LP-C18 with DOPE when compared with cells exposed to DOPE/FAM-siRNA. While C16- and C18-conjugated peptides (especially linear peptides) showed silencing against kinesin spindle protein (KSP) and janus kinase 2 (JAK2) proteins, the addition of DOPE enhanced the silencing efficiency significantly for all selected peptides, except for CP-C16. In conclusion, C16 and C18 difatty acyl peptide conjugates were found to enhance siRNA delivery and generate silencing of targeted proteins in the presence of DOPE. This study provides insights for the design and potential application of optimized difatty acyl peptide/lipid nanoparticles for effective siRNA delivery.

Towards targeted combinatorial therapy design for the treatment of castration-resistant prostate cancer

BACKGROUND

Prostate cancer is one of the most prevalent cancers in males in the United States and amongst the leading causes of cancer related deaths. A particularly virulent form of this disease is castration-resistant prostate cancer (CRPC), where patients no longer respond to medical or surgical castration. CRPC is a complex, multifaceted and heterogeneous malady with limited standard treatment options.

RESULTS

The growth and progression of prostate cancer is a complicated process that involves multiple pathways. The signaling network comprising the integral constituents of the signature pathways involved in the development and progression of prostate cancer is modeled as a combinatorial circuit. The failures in the gene regulatory network that lead to cancer are abstracted as faults in the equivalent circuit and the Boolean circuit model is then used to design therapies tailored to counteract the effect of each molecular abnormality and to propose potentially efficacious combinatorial therapy regimens. Furthermore, stochastic computational modeling is utilized to identify potentially vulnerable components in the network that may serve as viable candidates for drug development.

CONCLUSION

The results presented herein can aid in the design of scientifically well-grounded targeted therapies that can be employed for the treatment of prostate cancer patients.

Positively Charged Combinatory Drug Delivery Systems against Multi-Drug-Resistant Breast Cancer: Beyond the Drug Combination

The formation and development of cancer is usually accompanied by angiogenesis and is related to multiple pathways. The inhibition of one pathway by monotherapy might result in the occurrence of drug resistance, tumor relapse, or metastasis. Thus, a combinatory therapeutic system that targets several independent pathways simultaneously is preferred for the treatment. To this end, we prepared combinatory drug delivery systems consisting of cytotoxic drug SN38, pro-apoptotic KLAK peptide, and survivin siRNA with high drug loading capacity and reductive responsiveness for the treatment of multi-drug-resistant (MDR) cancer. With the help of positive charge and the synergistic effect of different drug, the combinatory systems inhibited the growth of doxorubicin-resistant breast cancer cells (MCF-7/ADR) efficiently. Interestingly, the systems without siRNA showed more superior in vivo anticancer efficacy than those with siRNA which exhibited enhanced in vitro cytotoxicity and pro-apoptotic ability. This phenomenon could be attributed to the preferential tumor accumulation, strong tumor penetration, and excellent tumor vasculature targeting ability of the combinatory micelles of SN38 and KLAK. As a result, a combinatory multitarget therapeutic system with positive charge induced tumor accumulation and vasculature targeting which can simultaneously inhibit the growth of both tumor cell and tumor vasculature was established. This work also enlightened us to the fact that the design of combinatory drug delivery systems is not just a matter of simple drug combination. Besides the cytotoxicity and pro-apoptotic ability, tumor accumulation, tumor penetration, or vascular targeting may also influence the eventual antitumor effect of the combinatory system.