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

A Personalized Therapeutics Approach Using an In Silico Drosophila Patient Model Reveals Optimal Chemo- and Targeted Therapy Combinations for Colorectal Cancer

In silico models of biomolecular regulation in cancer, annotated with patient-specific gene expression data, can aid in the development of novel personalized cancer therapeutic strategies. Drosophila melanogaster is a well-established animal model that is increasingly being employed to evaluate such preclinical personalized cancer therapies. Here, we report five Boolean network models of biomolecular regulation in cells lining the Drosophila midgut epithelium and annotate them with colorectal cancer patient-specific mutation data to develop an in silico Drosophila Patient Model (DPM). We employed cell-type-specific RNA-seq gene expression data from the FlyGut-seq database to annotate and then validate these networks. Next, we developed three literature-based colorectal cancer case studies to evaluate cell fate outcomes from the model. Results obtained from analyses of the proposed DPM help: (i) elucidate cell fate evolution in colorectal tumorigenesis, (ii) validate cytotoxicity of nine FDA-approved CRC drugs, and (iii) devise optimal personalized treatment combinations. The personalized network models helped identify synergistic combinations of paclitaxel-regorafenib, paclitaxel-bortezomib, docetaxel-bortezomib, and paclitaxel-imatinib for treating different colorectal cancer patients. Follow-on therapeutic screening of six colorectal cancer patients from cBioPortal using this drug combination demonstrated a 100% increase in apoptosis and a 100% decrease in proliferation. In conclusion, this work outlines a novel roadmap for decoding colorectal tumorigenesis along with the development of personalized combinatorial therapeutics for preclinical translational studies.

Bayesian Network Analysis of Lysine Biosynthesis Pathway in Rice

Lysine is the first limiting essential amino acid in rice because it is present in the lowest quantity compared to all the other amino acids. Amino acids are the building block of proteins and play an essential role in maintaining the human body’s healthy functioning. Rice is a staple food for more than half of the global population; thus, increasing the lysine content in rice will help improve global health. In this paper, we studied the lysine biosynthesis pathway in rice (Oryza sativa) to identify the regulators of the lysine reporter gene LYSA (LOC_Os02g24354). Genetically intervening at the regulators has the potential to increase the overall lysine content in rice. We modeled the lysine biosynthesis pathway in rice seedlings under normal and saline (NaCl) stress conditions using Bayesian networks. We estimated the model parameters using experimental data and identified the gene DAPF(LOC_Os12g37960) as a positive regulator of the lysine reporter gene LYSA under both normal and saline stress conditions. Based on this analysis, we conclude that the gene DAPF is a potent candidate for genetic intervention. Upregulating DAPF using methods such as CRISPR-Cas9 gene editing strategy has the potential to upregulate the lysine reporter gene LYSA and increase the overall lysine content in rice.

Targeting oncogenic mutations in colorectal cancer using cryptotanshinone

Colorectal cancer (CRC) is one of the most prevalent types of cancer in the world and ranks second in cancer deaths in the US. Despite the recent improvements in screening and treatment, the number of deaths associated with CRC is still very significant. The complexities involved in CRC therapy stem from multiple oncogenic mutations and crosstalk between abnormal pathways. This calls for using advanced molecular genetics to understand the underlying pathway interactions responsible for this cancer. In this paper, we construct the CRC pathway from the literature and using an existing public dataset on healthy vs tumor colon cells, we identify the genes and pathways that are mutated and are possibly responsible for the disease progression. We then introduce drugs in the CRC pathway, and using a boolean modeling technique, we deduce the drug combinations that produce maximum cell death. Our theoretical simulations demonstrate the effectiveness of Cryptotanshinone, a traditional Chinese herb derivative, achieved by targeting critical oncogenic mutations and enhancing cell death. Finally, we validate our theoretical results using wet lab experiments on HT29 and HCT116 human colorectal carcinoma cell lines.

A modern era of personalized medicine in the diagnosis, prognosis, and treatment of prostate cancer

The present era is witnessing rapid advancements in the field of medical informatics and modern healthcare management. The role of translational bioinformatics (TBI), an infant discipline in the field of medical informatics, is pivotal in this revolution. The development of high-throughput technologies [e.g., microarrays, next-generation sequencing (NGS)] has propelled TBI to the next stage in this modern era of medical informatics. In this review, we assess the promising translational outcomes of microarray- and NGS-based discovery of genes, proteins, micro RNAs, and other active biological compounds aiding in the diagnosis, prognosis, and therapy of prostate cancer (PCa) to improve treatment strategies at the localized and/or metastatic stages in patients. Several promising candidate biomarkers in circulating blood (miR-25-3p and miR-18b-5p), urine (miR-95, miR-21, miR-19a, and miR-19b), and prostatic secretions (miR-203) have been identified. AURKA and MYCN, novel candidate biomarkers, were found to be specifically expressed in neuroendocrine PCa. The use of BTNL2 gene mutations and inflammasomes as biomarkers in immune function-mediated, inherited PCa has also been elucidated based on NGS data. Although TBI discoveries can benefit clinical performance metrics, the translational potential and the in vivo performance of TBI outcomes need to be verified. In conclusion, TBI aids in the effective clinical management of PCa; furthermore, the fate of personalized/precision medicine mostly relies on the enhanced diagnostic, prognostic, and therapeutic potential of TBI.

In Silico Design and Experimental Validation of Combination Therapy for Pancreatic Cancer

The number of deaths associated with Pancreatic Cancer has been on the rise in the United States making it an especially dreaded disease. The overall prognosis for pancreatic cancer patients continues to be grim because of the complexity of the disease at the molecular level involving the potential activation/inactivation of several diverse signaling pathways. In this paper, we first model the aberrant signaling in pancreatic cancer using a multi-fault Boolean Network. Thereafter, we theoretically evaluate the efficacy of different drug combinations by simulating this boolean network with drugs at the relevant intervention points and arrive at the most effective drug(s) to achieve cell death. The simulation results indicate that drug combinations containing Cryptotanshinone, a traditional Chinese herb derivative, result in considerably enhanced cell death. These in silico results are validated using wet lab experiments we carried out on Human Pancreatic Cancer (HPAC) cell lines.

Cryptotanshinone Induces Cell Death in Lung Cancer by Targeting Aberrant Feedback Loops

Signaling pathways oversee highly efficient cellular mechanisms such as growth, division, and death. These processes are controlled by robust negative feedback loops that inhibit receptor-mediated growth factor pathways. Specifically, the ERK, the AKT, and the S6K feedback loops attenuate signaling via growth factor receptors and other kinase receptors to regulate cell growth. Irregularity in any of these supervised processes can lead to uncontrolled cell proliferation and possibly Cancer. These irregularities primarily occur as mutated genes, and an exhaustive search of the perfect drug combination by performing experiments can be both costly and complex. Hence, in this paper, we model the Lung Cancer pathway as a Modified Boolean Network that incorporates feedback. By simulating this network, we theoretically predict the drug combinations that achieve the desired goal for the majority of mutations. Our theoretical analysis identifies Cryptotanshinone, a traditional Chinese herb derivative, as a potent drug component in the fight against cancer. We validated these theoretical results using multiple wet lab experiments carried out on H2073 and SW900 lung cancer cell lines.

MiRNA therapeutics based on logic circuits of biological pathways

BACKGROUND

In silico experiments, with the aid of computer simulation, speed up the process of in vitro or in vivo experiments. Cancer therapy design is often based on signalling pathway. MicroRNAs (miRNA) are small non-coding RNA molecules. In several kinds of diseases, including cancer, hepatitis and cardiovascular diseases, they are often deregulated, acting as oncogenes or tumor suppressors. miRNA therapeutics is based on two main kinds of molecules injection: miRNA mimics, which consists of injection of molecules that mimic the targeted miRNA, and antagomiR, which consists of injection of molecules inhibiting the targeted miRNA. Nowadays, the research is focused on miRNA therapeutics. This paper addresses cancer related signalling pathways to investigate miRNA therapeutics.

RESULTS

In order to prove our approach, we present two different case studies: non-small cell lung cancer and melanoma. KEGG signalling pathways are modelled by a digital circuit. A logic value of 1 is linked to the expression of the corresponding gene. A logic value of 0 is linked to the absence (not expressed) gene. All possible relationships provided by a signalling pathway are modelled by logic gates. Mutations, derived according to the literature, are introduced and modelled as well. The modelling approach and analysis are widely discussed within the paper. MiRNA therapeutics is investigated by the digital circuit analysis. The most effective miRNA and combination of miRNAs, in terms of reduction of pathogenic conditions, are obtained. A discussion of obtained results in comparison with literature data is provided. Results are confirmed by existing data.

CONCLUSIONS

The proposed study is based on drug discovery and miRNA therapeutics and uses a digital circuit simulation of a cancer pathway. Using this simulation, the most effective combination of drugs and miRNAs for mutated cancer therapy design are obtained and these results were validated by the literature. The proposed modelling and analysis approach can be applied to each human disease, starting from the corresponding signalling pathway.

Formononetin: A Review of Its Anticancer Potentials and Mechanisms

Cancer, a complex yet common disease, is caused by uncontrolled cell division and abnormal cell growth due to a variety of gene mutations. Seeking effective treatments for cancer is a major research focus, as the incidence of cancer is on the rise and drug resistance to existing anti-cancer drugs is major concern. Natural products have the potential to yield unique molecules and combinations of substances that may be effective against cancer with relatively low toxicity/better side effect profile compared to standard anticancer therapy. Drug discovery work with natural products has demonstrated that natural compounds display a wide range of biological activities correlating to anticancer effects. In this review, we discuss formononetin (C₁₆H₁₂O₄), which originates mainly from red clovers and the Chinese herb Astragalus membranaceus. The compound comes from a class of 7-hydroisoflavones with a substitution of methoxy group at position 4. Formononetin elicits antitumorigenic properties in vitro and in vivo by modulating numerous signaling pathways to induce cell apoptosis (by intrinsic pathway involving Bax, Bcl-2, and caspase-3 proteins) and cell cycle arrest (by regulating mediators like cyclin A, cyclin B1, and cyclin D1), suppress cell proliferation [by signal transducer and activator of transcription (STAT) activation, phosphatidylinositol 3-kinase/protein kinase-B (PI3K/AKT), and mitogen-activated protein kinase (MAPK) signaling pathway], and inhibit cell invasion [by regulating growth factors vascular endothelial growth factor (VEGF) and Fibroblast growth factor 2 (FGF2), and matrix metalloproteinase (MMP)-2 and MMP-9 proteins]. Co-treatment with other chemotherapy drugs such as bortezomib, LY2940002, U0126, sunitinib, epirubicin, doxorubicin, temozolomide, and metformin enhances the anticancer potential of both formononetin and the respective drugs through synergistic effect. Compiling the evidence thus far highlights the potential of formononetin to be a promising candidate for chemoprevention and chemotherapy.

Fault detection and therapeutic intervention in gene regulatory networks using SAT solvers

Random somatic mutations disrupt homeostasis of the cell resulting in various undesirable phenotypes including proliferation. One of the most important questions in systems medicine research is the therapeutic intervention design, which requires the knowledge of these mutations. A single or multiple mutations can occur in the diseases like cancer. These mutations have been successfully modeled as stuck-at faults in the Boolean network model of the underlying regulatory system. Identification of these fault types for multiple stuck-at faults is a non-trivial problem and requires some system theoretic introspection. This manuscript addresses the dual problem of the fault identification and the therapeutic intervention. Both the problems are mapped to the Boolean satisfiability (SAT) problem. The underlying problems are solved using a fast SAT solver. The synthetic and biological examples elucidate the effectiveness of the mapping.

Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients

Logical models of cancer pathways are typically built by mining the literature for relevant experimental observations. They are usually generic as they apply for large cohorts of individuals. As a consequence, they generally do not capture the heterogeneity of patient tumors and their therapeutic responses. We present here a novel framework, referred to as PROFILE, to tailor logical models to a particular biological sample such as a patient tumor. This methodology permits to compare the model simulations to individual clinical data, i.e., survival time. Our approach focuses on integrating mutation data, copy number alterations (CNA), and expression data (transcriptomics or proteomics) to logical models. These data need first to be either binarized or set between 0 and 1, and can then be incorporated in the logical model by modifying the activity of the node, the initial conditions or the state transition rates. The use of MaBoSS, a tool based on Monte-Carlo kinetic algorithm to perform stochastic simulations on logical models results in model state probabilities, and allows for a semi-quantitative study of the model phenotypes and perturbations. As a proof of concept, we use a published generic model of cancer signaling pathways and molecular data from METABRIC breast cancer patients. For this example, we test several combinations of data incorporation and discuss that, with these data, the most comprehensive patient-specific cancer models are obtained by modifying the nodes' activity of the model with mutations, in combination or not with CNA data, and altering the transition rates with RNA expression. We conclude that these model simulations show good correlation with clinical data such as patients' Nottingham prognostic index (NPI) subgrouping and survival time. We observe that two highly relevant cancer phenotypes derived from personalized models, Proliferation and Apoptosis, are biologically consistent prognostic factors: patients with both high proliferation and low apoptosis have the worst survival rate, and conversely. Our approach aims to combine the mechanistic insights of logical modeling with multi-omics data integration to provide patient-relevant models. This work leads to the use of logical modeling for precision medicine and will eventually facilitate the choice of patient-specific drug treatments by physicians.

Bayesian Inference Identifies Combination Therapeutic Targets in Breast Cancer

OBJECTIVE

Breast cancer is the second leading cause of cancer death among US women; hence, identifying potential drug targets is an ever increasing need. In this paper, we integrate existing biological information with graphical models to deduce the significant nodes in the breast cancer signaling pathway.

METHODS

We make use of biological information from the literature to develop a Bayesian network. Using the relevant gene expression data we estimate the parameters of this network. Then, using a message passing algorithm, we infer the network. The inferred network is used to quantitatively rank different interventions for achieving a desired phenotypic outcome. The particular phenotype considered here is the induction of apoptosis.

RESULTS

Theoretical analysis pinpoints to the role of Cryptotanshinone, a compound found in traditional Chinese herbs, as a potent modulator for bringing about cell death in the treatment of cancer.

CONCLUSION

Using a mathematical framework, we showed that the combination therapy of mTOR and STAT3 genes yields the best apoptosis in breast cancer.

SIGNIFICANCE

The computational results we arrived at are consistent with the experimental results that we obtained using Cryptotanshinone on MCF-7 breast cancer cell lines and also by the past results of others from the literature, thereby demonstrating the effectiveness of our model.