Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer.

Each patient's cancer consists of multiple cell subpopulations that are inherently heterogeneous and may develop differing phenotypes such as drug sensitivity or resistance. A personalized treatment regimen should therefore target multiple oncoproteins in the cancer cell populations that are driving the treatment resistance or disease progression in a given patient to provide maximal therapeutic effect, while avoiding severe co-inhibition of non-malignant cells that would lead to toxic side effects. To address the intra- and inter-tumoral heterogeneity when designing combinatorial treatment regimens for cancer patients, we have implemented a machine learning-based platform to guide identification of safe and effective combinatorial treatments that selectively inhibit cancer-related dysfunctions or resistance mechanisms in individual patients. In this case study, we show how the platform enables prediction of cancer-selective drug combinations for patients with high-grade serous ovarian cancer using single-cell imaging cytometry drug response assay, combined with genome-wide transcriptomic and genetic profiles. The platform makes use of drug-target interaction networks to prioritize those combinations that warrant further preclinical testing in scarce patient-derived primary cells. During the case study in ovarian cancer patients, we investigated (i) the relative performance of various ensemble learning algorithms for drug response prediction, (ii) the use of matched single-cell RNA-sequencing data to deconvolute cell population-specific transcriptome profiles from bulk RNA-seq data, (iii) and whether multi-patient or patient-specific predictive models lead to better predictive accuracy. The general platform and the comparison results are expected to become useful for future studies that use similar predictive approaches also in other cancer types.

New Anti-Leukemic Effect of Carvacrol and Thymol Combination through Synergistic Induction of Different Cell Death Pathways.

Acute myeloid leukemia (AML) is a cancer of the myeloid lineage of blood cells, and treatment for AML is lengthy and can be very expensive. Medicinal plants and their bioactive molecules are potential candidates for improving human health. In this work, we studied the effect of Ptychotis verticillata (PV) essential oil and its derivatives, carvacrol and thymol, in AML cell lines. We demonstrated that a combination of carvacrol and thymol induced tumor cell death with low toxicity on normal cells. Mechanistically, we highlighted that different molecular pathways, including apoptosis, oxidative, reticular stress, autophagy, and necrosis, are implicated in this potential synergistic effect. Using quantitative RT-PCR, Western blotting, and apoptosis inhibitors, we showed that cell death induced by the carvacrol and thymol combination is caspase-dependent in the HL60 cell line and caspase-independent in the other cell lines tested. Further investigations should focus on improving the manufacturing of these compounds and understanding their anti-tumoral mechanisms of action. These efforts will lead to an increase in the efficiency of the oncotherapy strategy regarding AML.

Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy.

In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction.

LC-MS/MS analysis and network pharmacology of Trigonella foenum-graecum - A plant from Ayurveda against hyperlipidemia and hyperglycemia with combination synergy.

BACKGROUND: The seed of Trigonella foenum-graecum L. (Methika in Sanskrit) is a well known kaphahara (balancing kapha) herb in Ayurveda indicated in Prameha or early diabetes mellitus. It is also useful in obesity and reduces lipid level of blood. PURPOSE: We aimed to explore the metabolites present in the plant extract and to establish the combination synergy and the network pharmacology along with the underlying the mechanism of action involved. STUDY DESIGN: LC-MS/MS based metabolite screening followed by ADME screening and finally network pharmacology exploration of the mechanism of action involved against hyperlipidemia and hypolipidemia with neighbourhood based combination synergy approach. METHODS: Ethanolic extract of Trigonella foenum-graecum L. (TFHE) was subjected to LC-MS/MS analysis to identify the active constituents. Oral bioavailability and drug likeness was screened for all the compounds. Databases- Binding DB, DAVID, KEGG and STRING were used to gather information to develop the networks. The networks were constructed using Cytoscape 3.2.1. Combination synergy analysis was performed with the help of Cytoscape network analyzer tool with neighbourhood approach. RESULTS: The LC-MS/MS analysis identified 13 compounds which were found to be bio-available and drug like following the QED and Veber drug likeness parameters. The pathway analysis showed enrichment for different pathways like MAPK pathway (p-4.69E-07), JAK-STAT pathway (p-6.30E-05), Adipocytokine (p-0.00179), Type 2 Diabetes mellitus (0.00441), Insulin signalling pathway (p-0.0121), mTOR signalling pathway (p-0.000378), which are all connected to hyperlipidemia and hyperglycemia. The combination synergy network identified 23 targets interacting with 13 compounds based on a network neighbourhood approach. CONCLUSION: The network pharmacology analysis strongly suggested the multimode evidences that TFHE largely works on the insulin signalling pathway and mainly based on its antioxidant potential due to its interaction with carbonic anhydrase. Various compounds were found to be interacting with key proteins that activates EGFR/AKT/mTOR signalling cascade which has therapeutic implication in hyperglycemia and hyperlipidemia. The combination synergy network analysis based on neighbourhood approach can help us in further understanding mechanism of multi-molecular fixed dose combinations.