Background:Vasculogenic mimicry (VM) is an adaptive biological phenomenon wherein cancer cells spontaneously self-organize into 3-dimensional (3D) branching network structures. This emergent behavior is considered central in promoting an invasive, metastatic, and therapy resistance molecular signature to cancer cells. The quantitative analysis of such complex phenotypic systems could require the use of computational approaches including machine learning algorithms originating from complexity science.Procedures:In vitro 3D VM was performed with SKOV3 and ES2 ovarian cancer cells cultured on Matrigel. Diet-derived catechins disruption of VM was monitored at 24 hours with pictures taken with an inverted microscope. Three computational algorithms for complex feature extraction relevant for 3D VM, including 2D wavelet analysis, fractal dimension, and percolation clustering scores were assessed coupled with machine learning classifiers.Results:These algorithms demonstrated the structure-to-function galloyl moiety impact on VM for each of the gallated catechin tested, and shown applicable in quantifying the drug-mediated structural changes in VM processes.Conclusions:Our study provides evidence of how appropriate 3D VM compression and feature extractors coupled with classification/regression methods could be efficient to study in vitro drug-induced perturbation of complex processes. Such approaches could be exploited in the development and characterization of drugs targeting VM.

Background:Colorectal cancer is the third largest cause of cancer-related mortality worldwide. Although current treatments with chemotherapeutics have allowed for management of colorectal cancer, additional novel treatments are essential. Intervening with the metabolic reprogramming observed in cancers called “Warburg Effect,” is one of the novel strategies considered to combat cancers. In the metabolic reprogramming pathway, pyruvate dehydrogenase kinase (PDK1) plays a pivotal role. Identification and characterization of a PDK1 inhibitor is of paramount importance. Further, for efficacious treatment of colorectal cancers, combinatorial regimens are essential. To this end, we opted to identify a PDK1 inhibitor using computational structure-based drug design FINDSITEcomb and perform combinatorial studies with 5-FU for efficacious treatment of colorectal cancers.Methods:Using computational structure-based drug design FINDSITEcomb, stearic acid (SA) was identified as a possible PDK1 inhibitor. Elucidation of the mechanism of action of SA was performed using flow cytometry, clonogenic assays.Results:When the growth inhibitory potential of SA was tested on colorectal adenocarcinoma (DLD-1) cells, a 50% inhibitory concentration (IC50) of 60 µM was recorded. Moreover, SA inhibited the proliferation potential of DLD-1 cells as shown by the clonogenic assay and there was a sustained response even after withdrawal of the compound. Elucidation of the mechanism of action revealed, that the inhibitory effect of SA was through the programmed cell death pathway. There was increase in the number of apoptotic and multicaspase positive cells. SA also impacted the levels of the cell survival protein Bcl-2. With the aim of achieving improved treatment for colorectal cancer, we opted to combine 5-fluorouracil (5-FU), the currently used drug in the clinic, with SA. Combining SA with 5-FU, revealed a synergistic effect in which the IC50 of 5-FU decreased from 25 to 6 µM upon combination with 60 µM SA. Further, SA did not inhibit non-tumorigenic NIH-3T3 proliferation.Conclusions:We envision that this significant decrease in the IC50 of 5-FU could translate into less side effects of 5-FU and increase the efficacy of the treatment due to the multifaceted action of SA. The data generated from the current studies on the inhibition of colorectal adenocarcinoma by SA discovered by the use of the computational program as well as synergistic action with 5-FU should open up novel therapeutic options for the management of colorectal adenocarcinomas.

Bisphenol-A (BPA) is a synthetic chemical that has widely been used in the production of polycarbonate plastic and epoxy resins in the manufacture of consumer products. The most common path of human exposure to BPA is by oral intake that involves genotoxicity, oxidative stress, endocrine disruption, mutagenicity, and carcinogenicity in both in vitro and in vivo models. Melatonin is known as a free-radical scavenger and a powerful antioxidant agent. This study aimed to investigate the effects of melatonin on viability and genetic disorders of normal Human Gingival Fibroblasts (HGF), colon cancer (MKN45), and bone marrow stem cell (MSC) lines exposed to BPA. For this purpose, MTT and Comet assays were performed to evaluate the cytotoxicity and genotoxicity properties of BPA and the role of melatonin. The results showed that BPA exposure resulted in increased oxidative stress parameters including MDA and ROS, and decreased GSH content. The current study demonstrated the cytotoxicity and genotoxicity effects of BPA and the protective role of melatonin in preventing cytotoxicity and DNA damage are induced by BPA.

In recent years, due to an increase in the incidence of different cancers, various data sources are available in this field. Consequently, many researchers have become interested in the discovery of useful knowledge from available data to assist faster decision-making by doctors and reduce the negative consequences of such diseases. Data mining includes a set of useful techniques in the discovery of knowledge from the data: detecting hidden patterns and finding unknown relations. However, these techniques face several challenges with real-world data. Particularly, dealing with inconsistencies, errors, noise, and missing values requires appropriate preprocessing and data preparation procedures. In this article, we investigate the impact of preprocessing to provide high-quality data for classification techniques. A wide range of preprocessing and data preparation methods are studied, and a set of preprocessing steps was leveraged to obtain appropriate classification results. The preprocessing is done on a real-world breast cancer dataset of the Reza Radiation Oncology Center in Mashhad with various features and a great percentage of null values, and the results are reported in this article. To evaluate the impact of the preprocessing steps on the results of classification algorithms, this case study was divided into the following 3 experiments:Breast cancer recurrence prediction without data preprocessingBreast cancer recurrence prediction by error removalBreast cancer recurrence prediction by error removal and filling null valuesThen, in each experiment, dimensionality reduction techniques are used to select a suitable subset of features for the problem at hand. Breast cancer recurrence prediction models are constructed using the 3 widely used classification algorithms, namely, naïve Bayes, k-nearest neighbor, and sequential minimal optimization. The evaluation of the experiments is done in terms of accuracy, sensitivity, F-measure, precision, and G-mean measures. Our results show that recurrence prediction is significantly improved after data preprocessing, especially in terms of sensitivity, F-measure, precision, and G-mean measures.

Prognostic markers for cancer can assist in the evaluation of survival probability of patients and help clinicians to assess the available treatment modalities. Gallbladder cancer (GBC) is a rare tumor that causes 165 087 deaths in the world annually. It is the most common cancer of the biliary tract and has a particularly high incidence in Chile, Japan, and northern India. Currently, there is no accurate diagnosis test or effective molecular markers for GBC identification. Several studies have focused on the discovery of genetic alterations in important genes associated with GBC to propose novel diagnosis pathways and to create prognostic profiles. To achieve this, we performed data-mining of GBC in public repositories, harboring 133 samples of GBC, allowing us to describe relevant somatic mutations in important genes and to propose a genetic alteration atlas for GBC. In our results, we reported the 14 most altered genes in GBC: arid1a, arid2, atm, ctnnb1, erbb2, erbb3, kmt2c, kmt2d, kras, pik3ca, smad4, tert, tp53, and znf521 in samples from Japan, the United States, Chile, and China. Missense mutations are common among these genes. The annotations of many mutations revealed their importance in cancer development. The observed annotations mentioned that several mutations found in this repository are probably oncogenic, with a putative loss-of-function. In addition, they are hotspot mutations and are probably linked to poor prognosis in other cancers. We identified another 11 genes, which presented a copy number alteration in gallbladder database samples, which are ccnd1, ccnd3, ccne1, cdk12, cdkn2a, cdkn2b, erbb2, erbb3, kras, mdm2, and myc. The findings reported here can help to detect GBC cancer through the development of systems based on genetic alterations, for example, the development of a mutation panel specifically for GBC diagnosis, as well as the creation of prognostic profiles to accomplish the development of GBC and its prevalence.

Introduction:The emergence of new omics approaches, such as genomic algorithms to identifytumor mutations and molecular modeling tools to predict thethree-dimensional structure of proteins, has facilitated the understandingof the dynamic mechanisms involved in the pathogenesis of low-grade gliomasincluding oligodendrogliomas and astrocytomas.Methods:In this study, we targeted known mutations involved in low-grade gliomas,starting with the sequencing of genomic regions encompassing exon 4 ofisocitrate dehydrogenase 1 (IDH1) and isocitratedehydrogenase 2 (IDH2) and the four exons (5-6 and 7-8) ofTP53 from 32 samples, followed by computationalanalysis to study the impact of these mutations on the structure andfunction of 3 proteins IDH1, IDH2, andp53.Results:We obtain a mutation that has an effect on the catalytic site of the proteinIDH1 as R132H and on the catalytic site of the proteinIDH2 as R172M. Other mutations at p53have been identified as K305N, which is a pathogenic mutation; R175 H, whichis a benign mutation; and R158G, which disrupts the structural conformationof the tumor suppressor protein.Conclusion:In low-grade gliomas, mutations in IDH1, IDH2, andTP53 may be the key to tumor progression because theyhave an effect on the function of the protein such as mutations R132H inIDH1 and R172M in IDH2, which changethe function of the enzyme alpha-ketoglutarate, or R158G inTP53, which affects the structure of the generatedprotein, thus their importance in understanding gliomagenesis and for moreaccurate diagnosis complementary to the anatomical pathology tests.

[This corrects the article DOI: 10.1177/1176935120948474.].

In diffuse large B-cell lymphoma (DLBCL), predictive modeling may contribute to targeted drug development by enrichment of the study populations enrolled in clinical trials of DLBCL investigational drugs to include patients with lower likelihood of responding to standard of care. In clinical practice, predictive modeling has the potential to optimize therapy choices in DLBCL. The objectives of this study were to create a model for predicting health outcomes in patients with DLBCL treated with standard of care and determine informative predictors of health outcomes for patients with DLBCL. This was a retrospective observational study using data extracted from the IMS Health Database between September 2007 and April 2015. Patients were ⩾18 years of age with a DLBCL diagnosis. The index date was the date of the first DLBCL diagnosis. Patients were followed until outcome occurrence, defined as progression to a later line of therapy after ⩾60 days from the end of a previous therapy or stem cell transplantation. Patients were categorized into three cohorts depending on the post-index observation period: ⩽1 year, ⩽3 years, or ⩽5 years. Lasso logistic regression (LASSO), Naive Bayes, gradient-boosting machine (GBM), random forest (RF), and neural network models were performed for each cohort. The best-performing algorithms were predictive models based on GBM and observation periods ⩽1 and ⩽3 years after index date. Informative predictors included myocardial imaging, DLBCL stage IV, bronchiolar and renal disease, a chemotherapy regimen, and exposure to diphenhydramine and vasoprotectives on or before the first DLBCL diagnosis. These predictive models may be applied to targeted drug development and have the potential to optimize therapy choices in DLBCL. They were generated efficiently using a large number of independent variables readily available in standard insurance claims or electronic health record data systems.

KRAS-activation mutations occur in 25% to 40% of lung adenocarcinomas and are a known mechanism of epidermal growth factor receptor inhibitor (EGFRI) resistance. There are currently no targeted therapies approved specifically for the treatment of KRAS-active non–small cell lung cancers (NSCLC). Attempts to target mutant KRAS have failed in clinical studies leaving no targeted therapy option for these patients. To circumvent targeting KRAS directly, we hypothesized that targeting proteins connected to KRAS function rather than targeting KRAS directly could induce cell death in KRAS-active NSCLC cells. To identify potential targets, we leveraged 2 gene expression data sets derived from NSCLC cell lines either resistant and sensitive to EGFRI treatment. Using a Feasible Solutions Algorithm, we identified genes with deregulated expression in KRAS-active cell lines and used STRING as a source for known protein-protein interactions. This process generated a network of 385 deregulated proteins including KRAS and other known mechanisms of EGFRI resistance. To identify candidate drug targets from the network for further study, we selected proteins with the greatest number of connections within the network and possessed an enzymatic activity that could be inhibited with an existing pharmacological agent. Of the potential candidates, the pharmacological impact of targeting casein kinase 2 (CK2) as a single target was tested, and we found a modest reduction in viability in KRAS-active NSCLC cells. MEK was chosen as a second target from outside the network because it lies downstream of KRAS and MEK inhibition can overcome resistance to CK2 inhibitors. We found that CK2 and MEK inhibition demonstrates moderate synergy in inducing apoptosis in KRAS-active NSCLC cells. These results suggest promise for a combination inhibitor strategy for treating KRAS-active NSCLC.

Long-term outcome of cancer therapy is often severely perturbed by the acquisition of drug resistance. Recent evidence point toward the survival of a subpopulation of tumor cells under acute drug stress that over time can re-populate the tumor. These transiently existing, weakly proliferative, drug-tolerant cells facilitate tumor cell survival until more stable resistance mechanisms are acquired. From a therapeutic perspective, understanding the molecular features of the tolerant cells is critical to attenuation of resistance. In this article, we discuss the transcriptomic features of drug-tolerant osteosarcoma cells that survive a high dose of cisplatin shock. We present the unique transcriptome of the minimally dividing tolerant cells in comparison with the proliferative persisters or resistant cells derived from the tolerant cells. Targeting the tolerant cells can represent an efficient therapeutic strategy impeding tumor recurrence.