Studying the emergence of invasiveness in tumours using game theory

Tumour cells have to acquire a number of capabilities if a neoplasm is to become a cancer. One of these key capabilities is increased motility which is needed for invasion of other tissues and metastasis. This paper presents a qualitative mathematical model based on game theory and computer simulations using cellular automata. With this model we study the circumstances under which mutations that confer increased motility to cells can spread through a tumour made of rapidly proliferating cells. The analysis suggests therapies that could help prevent the progression towards malignancy and invasiveness of benign tumours.     

Evaluation of drug candidature: In silico ADMET,binding interactions with CDK7 and normal cell line studies of potentially anti-breast cancer enamidines

We have recently explored novel class of potentially anti-breast cancer active enamidines in which four molecules 4a-c and 4h showed higher anticancer activity compared to standard drug doxorubicin. As a part of extension of this work, we have further evaluated in silico cheminformatic studies on bioactivity prediction of synthesized series of enamidines using mole information. The normal cell line study of four lead compounds 4a-c and 4h against African green monkey kidney vero strain further revealed that the compounds complemented good selectivity in inhibition of cancer cells. The in silico bioactivity and molecular docking studies also revealed that the compounds have significant interactions with the drug targets. The results reveal that enamidine moieties are vital for anti-breast cancer activity as they possess excellent drug-like characteristics, being potentially good inhibitors of cyclin dependent kinases7 (CDK7).     

Efficient Gaussian sample specific network marker discovery and drug enrichment analysis validation

Identifying stable gene markers at an individual level can help to understand the genetic mechanisms of each individual patient and accomplish personalized medicine. In this paper, we propose an efficient framework to identify sample-specific markers. Gene expression data first is transformed to a corresponding likelihood matrix to alleviate inherent noise besides adding population information to each sample. Then those significantly differential genes or gene pairs are further mapped to a STRING network for analysis by assuming that the likelihood of each gene or gene pairs in the control group follows a Gaussian distribution. The proposed method is applied to three benchmark datasets including lung adenocarcinoma, kidney renal clear cell carcinoma, and uterine corpus endometrial carcinoma. It is found that disease gene markers identified by the proposed methods outperform the previous sample-specific network (SSN) method in both subtyping and survival analysis. Furthermore, we exploit the application of the subtype markers in following drug selection. The difference of the enriched drug set may reflect some underlying mechanisms of the subtypes and shed light on selecting appropriate drugs for each cancer subtype.     

In silico identification of natural products with anticancer activity using a chemo-structural database of Brazilian biodiversity

Cancer is one of the leading causes of death worldwide, and the number of patients has only increased each year, despite the considerable efforts and investments in scientific research. Since natural products (NPs) may serve as suitable sources for drug development, the cytotoxicity against cancer cells of 2221 compounds from the Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBE_DB) was predicted using CDRUG algorithm. Molecular modeling, chemoinformatics, and chemometric tools were then used to analyze the structural and physicochemical properties of these compounds. We compared the positive NPs with FDA-approved anticancer drugs and predicted the molecular targets involved in the anticancer activity. In the present study, 46 families comprising potential anticancer compounds and at least 19 molecular targets involved in oncogenesis. To the best of our knowledge, this is the first large-scale study conducted to evaluate the potentiality of NPs sourced from Brazilian biodiversity as anticancer agents, using in silico approaches. Our results provided interesting insights about the mechanism of action of these compounds, and also suggested that their structural diversity may aid structure-based optimization strategies for developing novel drugs for cancer therapy.     

Synthesis,anticancer activity,toxicity evaluation and molecular docking studies of novel phenylaminopyrimidine—(thio)urea hybrids as potential kinase inhibitors

Thirty-two novel urea/thiourea compounds as potential kinase inhibitor were designed, synthesized and evaluated for their cytotoxic activity on breast (MCF7), colon (HCT116) and liver (Huh7) cancer cell lines. Compounds 10, 19 and 30 possessing anticancer activity with IC₅₀ values of 0.9, 0.8 and 1.6 μM respectively on Huh7 cells were selected for further studies. These hit compounds were tested against liver carcinoma panel. Real time cell electronic sensing assay was used to evaluate the effects of the compounds 10, 19 and 30 on the growth pattern of liver cancer cells. Apoptotic cell death and cell cycle analysis upon treatment of liver carcinoma cells with hit compounds were determined. A significant apoptotic cell death was detected upon treatment of Huh7 and Mahlavu cells with compound 30 after 48 h of treatment. Additionally, compound 10 caused cell cycle arrest at G0/G1 phase. Mutagenicity of hit compounds was evaluated. Assertively, these compounds were not found to be mutagenic on Salmonella typhimurium strains TA98 and TA100. To understand the binding modes of the synthesized compounds, molecular docking studies were performed using the crystal data of VEGFR and Src-kinase enzymes in correlation with anticancer activities.     

Identifying mutated driver pathways in cancer by integrating multi-omics data

Since the driver pathway in cancer plays a crucial role in the formation and progression of cancer, it is very imperative to identify driver pathways, which will offer important information for precision medicine or personalized medicine. In this paper, an improved maximum weight submatrix problem model is proposed by integrating such three kinds of omics data as somatic mutations, copy number variations, and gene expressions. The model tries to adjust coverage and mutual exclusivity with the average weight of genes in a pathway, and simultaneously considers the correlation among genes, so that the pathway having high coverage but moderate mutual exclusivity can be identified. By introducing a kind of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm PGA-MWS is presented to solve the model. Experimental comparisons among algorithms GA, MOGA, iMCMC and PGA-MWS were performed on biological and simulated data sets. The experimental results show that, compared with the other three algorithms, the PGA-MWS one based on the improved model can identify the gene sets with high coverage but moderate mutual exclusivity and scales well. Many of the identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in literatures. The experimental results indicate that the proposed approach may become a useful complementary tool for detecting cancer pathways.     

Chemical biology of salinomycin

Cancer stem cells (CSC) have been shown to be refractory to conventional therapeutic agents, can promote metastasis, and have been linked to cancer relapse. The natural product Salinomycin has been identified by means of high throughput phenotypic screening as a selective killer of CSC in vitro and in vivo. In this article we comprehensively review the chemistry of Salinomycin, documenting early total syntheses, along with strategies that have been developed over the years to effectively modify this natural product at key positions with the view to establish a robust structure-activity-relationship and to delineate the complex mechanism of action of this fascinating molecule in the context of cancer research. Then, we document the biology of Salinomycin, putting forward phenotypic alterations that have been observed in the relevant biological models and highlighting how chemistry has been instrumental in discovering unprecedented physiological features of cancer stem cells that can be exploited for therapeutic benefits.     

Reprogramming the chemical reactivity of iron in cancer stem cells

Cancer stem cells (CSCs) have been shown to be refractory to conventional therapeutic agents, can promote metastasis, and have been linked to cancer relapse. Salinomycin can selectively kill CSCs. We have shown that salinomycin derivatives accumulate in lysosomes and sequester iron in this organelle. As a result, accumulation of iron leads to the production of reactive oxygen species and lysosomal membrane permeabilization, which in turn promotes cell death by ferroptosis. These findings have revealed the prevalence of iron homeostasis in CSCs and paved the way toward the development of next-generation therapeutics.     

Rapid and quantitative method for evaluating the personal therapeutic potential of cancer drugs

An in vitro, rapid, and quantitative cell-based assay is needed to predict the efficacy of cancer drugs in individual patients, because a cancer patient may have unconventional aspects of tumor development. Here we report a rapid and label-free quantitative method for verifying apoptosis in living cancer cells cultured on a sensor chip with a newly developed high-precision surface plasmon resonance (SPR) sensor. The time-course cell reaction was monitored as the SPR angle change rate for 5 min during a 35-min cell culture of pancreatic cancer lines with a drug. The time-course cell reaction was significantly related to cell viability counted after 48 h as assessed by caspase-3 activity assay of apoptosis. Furthermore, the detected SPR signal was derived from the decrease in inner mitochondrial membrane potential. The results obtained are universally valid for various cancer drugs mediating apoptosis through different cell-signaling pathways and even for combined use in various pancreatic cancer cell lines. This system can be applied in a clinical setting to evaluate the personal therapeutic potential of drugs including pharmacodynamic interactions.