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Background: Many cancer genomes have been known to contain more than one subclone inside one tumor, the phenomenon of which is called intra-tumor heterogeneity (ITH). Characterizing ITH is essential in designing treatment plans, prognosis as well as the study of cancer progression. Single-cell DNA sequencing (scDNAseq) has been proven effective in deciphering ITH. Cells corresponding to each subclone are supposed to carry a unique set of mutations such as single nucleotide variations (SNV). While there have been many studies on the cancer evolutionary tree reconstruction, not many have been proposed that simply characterize the subclonality without tree reconstruction. While tree reconstruction is important in the study of cancer evolutionary history, typically they are computationally expensive in terms of running time and memory consumption due to the huge search space of the tree structure. On the other hand, subclonality characterization of single cells can be converted into a cell clustering problem, the dimension of which is much smaller, and the turnaround time is much shorter. Despite the existence of a few state-of-the-art cell clustering computational tools for scDNAseq, there lacks a comprehensive and objective comparison under different settings. Results: In this paper, we evaluated six state-of-the-art cell clustering tools–SCG, BnpC, SCClone, RobustClone, SCITE and SBMClone–on simulated data sets given a variety of parameter settings and a real data set. We designed a simulator specifically for cell clustering, and compared these methods' performances in terms of their clustering accuracy, specificity and sensitivity and running time. For SBMClone, we specifically designed an ultra-low coverage large data set to evaluate its performance in the face of an extremely high missing rate. Conclusion: From the benchmark study, we conclude that BnpC and SCG's clustering accuracy are the highest and comparable to each other. However, BnpC is more advantageous in terms of running time when cell number is high (> 1500). It also has a higher clustering accuracy than SCG when cluster number is high (> 16). SCClone's accuracy in estimating the number of clusters is the highest. RobustClone and SCITE's clustering accuracy are the lowest for all experiments. SCITE tends to over-estimate the cluster number and has a low specificity, whereas RobustClone tends to under-estimate the cluster number and has a much lower sensitivity than other methods. SBMClone produced reasonably good clustering (V-measure > 0.9) when coverage is > = 0.03 and thus is highly recommended for ultra-low coverage large scDNAseq data sets. Author summary: Cancer cells evolve by gaining new mutations. Different cancer cells may gain different mutations. Thus even inside the tumor of one cancer patient, there could be multiple clones of cancer cells, each having its unique set of mutations. Characterization of the clonality of a tumor can improve cancer treatment and prognosis. Single-cell DNA sequencing, or scDNAseq, refers to the DNA sequencing technology that can sequence each cell separately. ScDNAseq has been widely used to characterize the clonality of a tumor. In this study, we benchmarked six existing computational tools that characterize the clonality of the cancer cells, which are SCG, SCClone, BnpC, RobustClone, SCITE and SBMClone. To accomplish this study, we modified our simulator and simulated eight sets of simulation data for testing the first five methods, and one set of simulation data for testing SBMClone considering that SBMClone was specifically designed for highly sparse data set. We also tested SCG, SCClone, BnpC and RobubstClone on a real data set CRC2. We recommend SCG and BnpC due to their high accuracy and low running time. In the face of highly sparse data, we highly recommend SBMClone, the only method that can deal with such type of data. [ABSTRACT FROM AUTHOR]

The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample taken from the oral cavity. This process is time-consuming and more invasive than an alternative approach of acquiring a brush sample followed by cytological analysis. Using a microscope, skilled cytotechnologists are able to detect changes due to malignancy; however, introducing this approach into clinical routine is associated with challenges such as a lack of resources and experts. To design a trustworthy OC detection system that can assist cytotechnologists, we are interested in deep learning based methods that can reliably detect cancer, given only per-patient labels (thereby minimizing annotation bias), and also provide information regarding which cells are most relevant for the diagnosis (thereby enabling supervision and understanding). In this study, we perform a comparison of two approaches suitable for OC detection and interpretation: (i) conventional single instance learning (SIL) approach and (ii) a modern multiple instance learning (MIL) method. To facilitate systematic evaluation of the considered approaches, we, in addition to a real OC dataset with patient-level ground truth annotations, also introduce a synthetic dataset—PAP-QMNIST. This dataset shares several properties of OC data, such as image size and large and varied number of instances per bag, and may therefore act as a proxy model of a real OC dataset, while, in contrast to OC data, it offers reliable per-instance ground truth, as defined by design. PAP-QMNIST has the additional advantage of being visually interpretable for non-experts, which simplifies analysis of the behavior of methods. For both OC and PAP-QMNIST data, we evaluate performance of the methods utilizing three different neural network architectures. Our study indicates, somewhat surprisingly, that on both synthetic and real data, the performance of the SIL approach is better or equal to the performance of the MIL approach. Visual examination by cytotechnologist indicates that the methods manage to identify cells which deviate from normality, including malignant cells as well as those suspicious for dysplasia. We share the code as open source. [ABSTRACT FROM AUTHOR]

Determining sensitive drugs for a patient is one of the most critical problems in precision medicine. Using genomic profiles of the tumor and drug information can help in tailoring the most efficient treatment for a patient. In this paper, we proposed a classification machine learning approach that predicts the sensitive/resistant drugs for a cell line. It can be performed by using both drug and cell line similarities, one of the cell line or drug similarities, or even not using any similarity information. This paper investigates the influence of using previously defined as well as two newly introduced similarities on predicting anti-cancer drug sensitivity. The proposed method uses max concentration thresholds for assigning drug responses to class labels. Its performance was evaluated using stratified five-fold cross-validation on cell line-drug pairs in two datasets. Assessing the predictive powers of the proposed model and three sets of methods, including state-of-the-art classification methods, state-of-the-art regression methods, and off-the-shelf classification machine learning approaches shows that the proposed method outperforms other methods. Moreover, The efficiency of the model is evaluated in tissue-specific conditions. Besides, the novel sensitive associations predicted by this model were verified by several supportive evidence in the literature and reliable database. Therefore, the proposed model can efficiently be used in predicting anti-cancer drug sensitivity. Material and implementation are available at https://github.com/fahmadimoughari/CDSML. [ABSTRACT FROM AUTHOR]

A new form of cell death has recently been proposed involving copper-induced cell death, termed cuproptosis. This new form of cell death has been widely studied in relation to a novel class of copper ionophores, including elesclomol and disulfiram. However, the exact mechanism leading to cell death remains contentious. The oldest and most widely accepted biological mechanism is that the accumulated intracellular copper leads to excessive build-up of reactive oxygen species and that this is what ultimately leads to cell death. Most of this evidence is largely based on studies using N-acetylcysteine (NAC), an antioxidant, to relieve the oxidative stress and prevent cell death. However, here we have demonstrated using inductively coupled mass-spectrometry, that NAC pretreatment significantly reduces intracellular copper uptake triggered by the ionophores, elesclomol and disulfiram, suggesting that reduction in copper uptake, rather than the antioxidant activity of NAC, is responsible for the diminished cell death. We present further data showing that key mediators of reactive oxygen species are not upregulated in response to elesclomol treatment, and further that sensitivity of cancer cell lines to reactive oxygen species does not correlate with sensitivity to these copper ionophores. Our findings are in line with several recent studies proposing the mechanism of cuproptosis is instead via copper mediated aggregation of proteins, resulting in proteotoxic stress leading to cell death. Overall, it is vital to disseminate this key piece of information regarding NAC's activity on copper uptake since new research attributing the effect of NAC on copper ionophore activity to quenching of reactive oxygen species is being published regularly and our studies suggest their conclusions may be misleading. [ABSTRACT FROM AUTHOR]

Background: HOTAIR, a newly discovered long intergenic noncoding RNA (lincRNA), has been reported to be aberrantly expressed in many types of cancers. This meta-analysis summarizes its potential role as a biomarker in malignancy. Methods: A quantitative meta-analysis was performed through a systematic search in Pubmed, Medline and Web of Science for eligible papers on the prognostic impact of HOTAIR in cancer from inception to Feb. 28, 2014. Pooled hazard ratios (HRs) with 95% confidence interval (95% CI) were calculated to summarize the effect. Results: Nineteen studies were included in the study, with a total of 2033 patients. A significant association was observed between high HOTAIR expression and poor overall survival (OS) in patients with cancer (pooled HR 2.22, 95% CI: 1.68–2.93). Place of residence (Asian or Western countries), type of cancer (digestive or non-digestive disease), sample size (more or less than 100), and paper quality (score more or less than 85%) did not alter the significant predictive value of HOTAIR in OS from various kinds of cancer but preoperative status did. By combining HRs from Cox multivariate analyses, we found that HOTAIR expression was an independent prognostic factor for cancer patients (pooled HR 2.26, 95% CI: 1.62–3.15). Subgroup analysis showed that HOTAIR abundance was an independent prognostic factor for cancer metastasis (HR 3.90, 95% CI: 2.25–6.74). For esophageal carcinoma, high HOTAIR expression was significantly associated with TNM stage (III/IV vs. I/II: OR 6.90, 95% CI: 2.81–16.9) without heterogeneity. In gastric cancer, HOTAIR expression was found to be significantly associated with lymph node metastases (present vs. absent: OR 4.47, 95% CI: 1.88–10.63) and vessel invasion (positive vs. negative: OR 2.88, 95% CI: 1.38–6.04) without obvious heterogeneity. Conclusions: HOTAIR abundance may serve as a novel predictive factor for poor prognosis in different types of cancers in both Asian and Western countries. [ABSTRACT FROM AUTHOR]

Sialyltransferase, an enzyme responsible for attaching sialic acid to the cell surface, is reported to play a key role in cancer, making sialyltransferase a potential therapeutic target in drug development. Several methods have been developed to quantify sialic acids in biological samples however limitations exists and quantification in complex cell matrices lack investigation. Hence, this paper outlines a simple method to detect and quantify sialic acids in cancer cells for evaluating sialyltransferase activity of potential therapeutic compounds. An efficient method was developed using a reverse-phase ion-pairing HPLC-UV using triisopropanolamine as the ion-pairing agent with a C18 column. Neu5Ac was successfully eluted with the retention time 6.344 min with a flow rate of 0.4 mL/min. The proposed method was validated appropriately according to the AOAC guidelines (2013). This work demonstrates that the proposed method is not only relatively simple but also cost and time effective compared to pre-existing methods to successfully determine both free and protein-bound Neu5Ac in a complex cancer cell matrix. Furthermore, by applying the proposed method, a statistically significant decrease was observed for both HeLa and HuCCT1 cell lines with the application of deoxycholic acid–a known sialyltransferase inhibitor. Hence, the proposed method seems promisingly applicable to evaluate the effectiveness of potential sialyltransferase inhibitors. [ABSTRACT FROM AUTHOR]

A recent result obtained by means of an in vitro experiment with cancer cultured cells has configured the endoplasmic reticulum as the preferential site for the accumulation of 2-deoxy-2-[18F]fluoro-D-glucose (FDG). Such a result is coherent with cell biochemistry and is made more significant by the fact that the reticular accumulation rate of FDG is dependent upon extracellular glucose availability. The objective of the present paper is to confirm in vivo the result obtained in vitro concerning the crucial role played by the endoplasmic reticulum in FDG cancer metabolism. This study utilizes data acquired by means of a Positron Emission Tomography scanner for small animals in the case of CT26 models of cancer tissues. The recorded concentration images are interpreted within the framework of a three-compartment model for FDG kinetics, which explicitly assumes that the endoplasmic reticulum is the dephosphorylation site for FDG in cancer cells. The numerical reduction of the compartmental model is performed by means of a regularized Gauss-Newton algorithm for numerical optimization. This analysis shows that the proposed three-compartment model equals the performance of a standard Sokoloff's two-compartment system in fitting the data. However, it provides estimates of some of the parameters, such as the phosphorylation rate of FDG, more consistent with prior biochemical information. These results are made more solid from a computational viewpoint by proving the identifiability and by performing a sensitivity analysis of the proposed compartment model. [ABSTRACT FROM AUTHOR]

Many different physiological processes affect the growth of malignant lesions and their response to therapy. Each of these processes is spatially and genetically heterogeneous; dynamically evolving in time; controlled by many other physiological processes, and intrinsically random and unpredictable. The objective of this paper is to show that all of these properties of cancer physiology can be treated in a unified, mathematically rigorous way via the theory of random processes. We treat each physiological process as a random function of position and time within a tumor, defining the joint statistics of such functions via the infinite-dimensional characteristic functional. The theory is illustrated by analyzing several models of drug delivery and response of a tumor to therapy. To apply the methodology to precision cancer therapy, we use maximum-likelihood estimation with Emission Computed Tomography (ECT) data to estimate unknown patient-specific physiological parameters, ultimately demonstrating how to predict the probability of tumor control for an individual patient undergoing a proposed therapeutic regimen. [ABSTRACT FROM AUTHOR]

Kaempferol (kae) and its glycosides are widely distributed in nature and show multiple bioactivities, yet few reports have compared them. In this paper, we report the antitumor, antioxidant and anti-inflammatory activity differences of kae, kae-7-O-glucoside (kae-7-O-glu), kae-3-O-rhamnoside (kae-3-O-rha) and kae-3-O-rutinoside (kae-3-O-rut). Kae showed the highest antiproliferation effect on the human hepatoma cell line HepG2, mouse colon cancer cell line CT26 and mouse melanoma cell line B16F1. Kae also significantly inhibited AKT phosphorylation and cleaved caspase-9, caspase-7, caspase-3 and PARP in HepG2 cells. A kae-induced increase in DPPH and ABTS radical scavenging activity, inhibition of concanavalin A (Con A)-induced activation of T cell proliferation and NO or ROS production in LPS-induced RAW 264.7 macrophage cells were also seen. Kae glycosides were used to produce kae via environment-friendly enzymatic hydrolysis. Kae-7-O-glu and kae-3-O-rut were hydrolyzed to kae by β-glucosidase and/or α-L-rhamnosidase. This paper demonstrates the application of enzymatic catalysis to obtain highly biologically active kae. This work provides a novel and efficient preparation of high-value flavone-related products. [ABSTRACT FROM AUTHOR]

One of the hallmarks of cancer is the extremely high mutability and genetic instability of tumor cells. Inherent heterogeneity of intra-tumor populations manifests itself in high variability of clone instability rates. Analogously to fitness landscapes, the instability rates of clonal populations form their mutability landscapes. Here, we present MULAN (MUtability LANdscape inference), a maximum-likelihood computational framework for inference of mutation rates of individual cancer subclones using single-cell sequencing data. It utilizes the partial information about the orders of mutation events provided by cancer mutation trees and extends it by inferring full evolutionary history and mutability landscape of a tumor. Evaluation of mutation rates on the level of subclones rather than individual genes allows to capture the effects of genomic interactions and epistasis. We estimate the accuracy of our approach and demonstrate that it can be used to study the evolution of genetic instability and infer tumor evolutionary history from experimental data. MULAN is available at https://github.com/compbel/MULAN. Author summary: Cancer is a dynamical evolutionary process that unfolds in populations of tumor cells. Combinations of genomic alterations of these cells affect their replication and survival. In particular, intra-tumor rates of mutation and genetic instability are often significantly higher than the normal rate. The impact of combinations of gene alterations on the genetic instability of cancer cells could be highly non-linear. In this paper, we present a computational approach called MULAN, that allows for estimation of instability rates inside heterogeneous intra-tumor populations shaped by such non-linear genetic interactions. To achieve this, we make use of single-cell sequencing, that allows to capture exact cancer clones rather than just individual mutations. We demonstrate the accuracy of our approach and show how it could be applied to experimental tumor data to study the evolution of genetic instability and infer evolutionary history. The proposed method can be used to provide new insight into the evolutionary dynamics of cancer. [ABSTRACT FROM AUTHOR]

Deconvolution of heterogeneous bulk tumor samples into distinct cellular populations is an important yet challenging problem, particularly when only partial references are available. A common approach to dealing with this problem is to deconvolve the mixed signals using available references and leverage the remaining signal as a new cell component. However, as indicated in our simulation, such an approach tends to over-estimate the proportions of known cell types and fails to detect novel cell types. Here, we propose PREDE, a partial reference-based deconvolution method using an iterative non-negative matrix factorization algorithm. Our method is verified to be effective in estimating cell proportions and expression profiles of unknown cell types based on simulated datasets at a variety of parameter settings. Applying our method to TCGA tumor samples, we found that proportions of pure cancer cells better indicate different subtypes of tumor samples. We also detected several cell types for each cancer type whose proportions successfully predicted patient survival. Our method makes a significant contribution to deconvolution of heterogeneous tumor samples and could be widely applied to varieties of high throughput bulk data. PREDE is implemented in R and is freely available from GitHub (https://xiaoqizheng.github.io/PREDE). Author summary: Tumor tissues are mixtures of different cell types. Identification and quantification of constitutional cell types within tumor tissues are important tasks in cancer research. The problem can be readily solved using regression-based methods if reference signals are available. But in most clinical applications, only partial references are available, which significantly reduces the deconvolution accuracy of the existing regression-based methods. In this paper, we propose a partial-reference based deconvolution model, PREDE, integrating the non-negative matrix factorization framework with an iterative optimization strategy. We conducted comprehensive evaluations for PREDE using both simulation and real data analyses, demonstrating better performance of our method than other existing methods. [ABSTRACT FROM AUTHOR]

Nuclear IGF1R has been linked to poor outcome in cancer. We recently showed that nuclear IGF1R phosphorylates PCNA and increases DNA damage tolerance. In this paper we aimed to describe this mechanism in cancer tissue as well as in cancer cell lines. In situ proximity ligation assay identified frequent IGF1R and PCNA colocalization in many cancer types. IGF1R/PCNA colocalization was more frequently increased in tumor cells than in adjacent normal, and more prominent in areas with dysplasia and invasion. However, the interaction was often lost in tumors with poor response to neoadjuvant treatment and most metastatic lesions. In two independent cohorts of serous ovarian carcinomas and oropharyngeal squamous cell carcinomas, stronger IGF1R/PCNA colocalization was significantly associated with a higher overall survival. Ex vivo irradiation of ovarian cancer tissue acutely induced IGF1R/PCNA colocalization together with γH2AX-foci formations. In vitro, RAD18 mediated mono-ubiquitination of PCNA during replication stress was dependent on IGF1R kinase activity. DNA fiber analysis revealed that IGF1R activation could rescue stalled DNA replication forks, but only in cancer cells with baseline IGF1R/PCNA interaction. We believe that the IGF1R/PCNA interaction is a basic cellular mechanism to increase DNA stress tolerance during proliferation, but that this mechanism is lost with tumor progression in conjunction with accumulated DNA damage and aberrant strategies to tolerate genomic instability. To exploit this mechanism in IGF1R targeted therapy, IGF1R inhibitors should be explored in the context of concomitant induction of DNA replication stress as well as in earlier clinical stages than previously tried. [ABSTRACT FROM AUTHOR]

We previously demonstrated that glycyrrhizin (GL) suppressed inflammation and carcinogenesis in an azoxymethane (AOM)/dextran sodium sulfate (DSS)-induced murine model of colorectal cancer (CC). In this study, we found an accumulation of regulatory T cells (Tregs) in the spleen and suppression by GL in model mice. ICR mice were divided into four groups: Control, GL, CC, and GL-treated CC (CC+GL), and were sacrificed 20 weeks after AOM/DSS treatment. We measured spleen weight, areas of white and red pulp, and CD8+ T cells (cytotoxic T lymphocytes, CTL), and CD11c-positive cells (dendritic cells) in splenic tissues and forkhead box protein 3 (FoxP3)-positive cells (Tregs) in colorectal and splenic tissues. In all cases, the CC group showed a significant increase compared with those in Control group, and GL administration significantly attenuated this increase. These results indicate that Tregs accumulated in the spleen may participate in inflammation-related carcinogenesis by suppressing CTL. We also suggest that GL which binds to high-mobility group box 1 (HMGB1), suppresses carcinogenesis with decreasing Tregs in the spleen. Furthermore, there was an expression of FoxP3 in cancer cells, indicating that it may be involved in the malignant transformation of cancer cells. [ABSTRACT FROM AUTHOR]

Metastasis remains the leading cause of cancer deaths worldwide and lung cancer, known for its highly metastatic progression, remains among the most lethal of malignancies. Lung cancer metastasis can selectively spread to multiple different organs, however the genetic and molecular drivers for this process are still poorly understood. Understanding the heterogeneous genomic profile of lung cancer metastases is considered key in identifying therapeutic targets that prevent its spread. Research has identified the key source for metastasis being clusters of cells rather than individual cancer cells. These clusters, known as metastatic cancer cell clusters (MCCCs) have been shown to be 100-fold more tumorigenic than individual cancer cells. Unfortunately, access to these primary drivers of metastases remains difficult and has limited our understanding of their molecular and genomic profiles. Strong evidence in the literature suggests that differentially regulated biological pathways in MCCCs can provide new therapeutic drug targets to help combat cancer metastases. In order to expand research into MCCCs and their role in metastasis, we demonstrate a novel, proof of principle technology, to capture MCCCs directly from patients' whole blood. Our platform can be readily tuned for different solid tumor types by combining a biomimicry-based margination effect coupled with immunoaffinity to isolate MCCCs. Adopting a selective capture approach based on overexpressed CD44 in MCCCs provides a methodology that preferentially isolates them from whole blood. Furthermore, we demonstrate a high capture efficiency of more than 90% when spiking MCCC-like model cell clusters into whole blood. Characterization of the captured MCCCs from lung cancer patients by immunofluorescence staining and genomic analyses, suggests highly differential morphologies and genomic profiles. This study lays the foundation to identify potential drug targets thus unlocking a new area of anti-metastatic therapeutics. [ABSTRACT FROM AUTHOR]

Women diagnosed with ovarian cancer frequently have a poor prognosis as their cancer is often diagnosed at more advanced stages when the cancer has metastasized. At this point surgery cannot remove all the tumor cells and while ovarian cancer cells often initially respond to chemotherapeutic agents like carboplatin and paclitaxel, resistance to these agents frequently occurs. Thus, novel therapies are required for the treatment of advanced stage ovarian cancer. One therapeutic option being explored is the regulation of non-coding RNAs such as microRNAs. An advantage of microRNAs is that they can regulate tens, hundreds and sometimes thousands of mRNAs in cells and thus may be more effective than chemotherapeutic agents or targeted therapies. To investigate the therapeutic potential of miR-200s in ovarian cancer, lentiviral vectors were used to overexpress both miR-200 clusters in two murine ovarian cancer cell lines, ID8 and 28–2. Overexpression of miR-200s reduced the expression of several mesenchymal genes and proteins, significantly inhibited proliferation as assessed by BrdU flow cytometry and significantly reduced invasion through Matrigel coated transwell inserts in both cell lines. Overexpression of miR-200s also increased basal apoptosis approximately 3-fold in both cell lines as determined by annexin V flow cytometry. Pathway analysis of RNA sequencing of control and miR-200 overexpressing ovarian cancer cells revealed that genes regulated by miR-200s were involved in processes like epithelial mesenchymal transition (EMT) and cell migration. Therefore, miR-200s can inhibit proliferation and increase apoptosis while suppressing tumor cell invasion and thus simultaneously target three key cancer pathways. [ABSTRACT FROM AUTHOR]

Interpreting transcriptome data is an important yet challenging aspect of bioinformatic analysis. While gene set enrichment analysis is a standard tool for interpreting regulatory changes, we utilize deep learning techniques, specifically autoencoder architectures, to learn latent variables that drive transcriptome signals. We investigate whether simple, variational autoencoder (VAE), and beta-weighted VAE are capable of learning reduced representations of transcriptomes that retain critical biological information. We propose a novel VAE that utilizes priors from biological data to direct the network to learn a representation of the transcriptome that is based on understandable biological concepts. After benchmarking five different autoencoder architectures, we found that each succeeded in reducing the transcriptomes to 50 latent dimensions, which captured enough variation for accurate reconstruction. The simple, fully connected autoencoder, performs best across the benchmarks, but lacks the characteristic of having directly interpretable latent dimensions. The beta-weighted, prior-informed VAE implementation is able to solve the benchmarking tasks, and provide semantically accurate latent features equating to biological pathways. This study opens a new direction for differential pathway analysis in transcriptomics with increased transparency and interpretability. Author summary: The ability to measure the human transcriptome has been a critical tool in studying health and disease. However, transcriptome datasets are too large and complex for direct human interpretation. Deep learning techniques such as autoencoders are capable of distilling high-level features from complex data. However, even if deep learning models find patterns, these patterns are not necessarily represented in a way that humans can easily understand. By bringing in the prior knowledge of biological pathways, we have trained the model to "speak the language" of the biologist, and represent complex transcrtomes, in simpler concepts that are already familiar to biologists. We can then apply the tool to compare for example samples from lung cancer cells to healthy cells, and show which biological processes are perturbed. [ABSTRACT FROM AUTHOR]

Cell sedimentation in 3D hydrogel cultures refers to the vertical migration of cells towards the bottom of the space. Understanding this poorly examined phenomenon may allow us to design better protocols to prevent it, as well as provide insights into the mechanobiology of cancer development. We conducted a multiscale experimental and mathematical examination of 3D cancer growth in triple negative breast cancer cells. Migration was examined in the presence and absence of Paclitaxel, in high and low adhesion environments and in the presence of fibroblasts. The observed behaviour was modeled by hypothesizing active migration due to self-generated chemotactic gradients. Our results confirmed this hypothesis, whereby migration was likely to be regulated by the MAPK and TGF-βpathways. The mathematical model enabled us to describe the experimental data in absence (normalized error<40%) and presence of Paclitaxel (normalized error<10%), suggesting inhibition of random motion and advection in the latter case. Inhibition of sedimentation in low adhesion and co-culture experiments further supported the conclusion that cells actively migrated downwards due to the presence of signals produced by cells already attached to the adhesive glass surface. Author summary: Cell sedimentation in 3D cultures can have both advantageous and unwanted effects. On the one side, the loss of the 3rd dimension is undesirable in a 3D culture. On the other side, it can reveal interesting migration phenomena given that passive mechanisms such as gravity are not potential contributors. In this work, we examined the phenomenon of sedimentation by combining 3D cultures and spatiotemporal mathematical modelling. We found that passive mechanisms such as gravity and matrix compression are not sufficient to bring the cells at the bottom. Our analysis showed that the cells actively migrate towards the bottom and in a collective manner, suggesting the presence of chemotactic gradients produced by cells already attached to the glass surface. [ABSTRACT FROM AUTHOR]

The genus Cyphostemma (Planch.) Alston (Vitaceae) includes about 150 species distributed in eastern and southern Africa and Madagascar. Some species are used in traditional medicine and their biological activities, including antiproliferative effects against cancer cell lines, have been demonstrated. To date no investigations on Cyphostemma essential oils have been carried out. Essential oils, which play important roles in plant defenses have been demonstrated to be active in the treatment of several human diseases and to enhance bioavability of other drugs. The aim of this paper was to identify the chemical composition of the essential oil of the leaves of Cyphostemma juttae (Dinter & Gilg) Desc. and to verify some biological activities on two triple negative breast cancer cell lines (MDA-MB-231, SUM 149), characterized by the over-expression of the transcription factor NF-κB. In the essential oil, obtained by hydrodistillation and analysed by gas chromatography-mass spectrometry, 39 compounds were detected and with phytol (30%) dominating the chemical composition. C. juttae essential oil reduced cell growth and showed a pro-oxidant activity in both cell lines. Moreover, C. juttae essential oil caused a substantial decrease of NF-κB activation and consequently a significant reduction of some NF-κB target genes. The present study shows for the first time the cytotoxic properties of C. juttae essential oil and highlight its availability to interfere with NF-κB pathway, suggesting a potential therapeutic use in triple negative breast cancers (TNBCs) of this essential oil. [ABSTRACT FROM AUTHOR]

Due to the growth of interest in single-cell genomics, computational methods for distinguishing true variants from artifacts are highly desirable. While special attention has been paid to false positives in variant or mutation calling from single-cell sequencing data, an equally important but often neglected issue is that of false negatives derived from allele dropout during the amplification of single cell genomes. In this paper, we propose a simple strategy to reduce the false negatives in single-cell sequencing data analysis. Simulation results show that this method is highly reliable, with an error rate of 4.94×10-5, which is orders of magnitude lower than the expected false negative rate (~34%) estimated from a single-cell exome dataset, though the method is limited by the low SNP density in the human genome. We applied this method to analyze the exome data of a few dozen single tumor cells generated in previous studies, and extracted cell specific mutation information for a small set of sites. Interestingly, we found that there are difficulties in using the classical clonal model of tumor cell growth to explain the mutation patterns observed in some tumor cells. [ABSTRACT FROM AUTHOR]

The miRNAs regulate cell functions by inhibiting expression of proteins. Research on miRNAs had usually focused on identifying targets by base pairing between miRNAs and their targets. Instead of identifying targets, this paper proposed an innovative approach, namely impact significance analysis, to study the correlation between mature sequence, expression across patient samples or time and global function on cell cycle signaling of miRNAs. With three distinct types of data: The Cancer Genome Atlas miRNA expression data for 354 human breast cancer specimens, microarray of 266 miRNAs in mouse Embryonic Stem cells (ESCs), and Reverse Phase Protein Array (RPPA) transfected by 776 miRNAs in MDA-MB-231 cell line, we linked the expression and function of miRNAs by their mature sequence and discovered systematically that the similarity of miRNA expression enhances the similarity of miRNA function, which indicates the miRNA expression can be used as a supplementary factor to predict miRNA function. The results also show that both seed region and 3' portion are associated with miRNA expression levels across human breast cancer specimens and in ESCs; miRNAs with similar seed tend to have similar 3' portion. And we discussed that the impact of 3' portion, including nucleotides , is not significant for miRNA function. These results provide novel insights to understand the correlation between miRNA sequence, expression and function. They can be applied to improve the prediction algorithm and the impact significance analysis can also be implemented to similar analysis for other small RNAs such as siRNAs. [ABSTRACT FROM AUTHOR]

Knowledge of how a population of cancerous cells progress through the cell cycle is vital if the population is to be treated effectively, as treatment outcome is dependent on the phase distributions of the population. Estimates on the phase distribution may be obtained experimentally however the errors present in these estimates may effect treatment efficacy and planning. If mathematical models are to be used to make accurate, quantitative predictions concerning treatments, whose efficacy is phase dependent, knowledge of the phase distribution is crucial. In this paper it is shown that two different transition rates at the - checkpoint provide a good fit to a growth curve obtained experimentally. However, the different transition functions predict a different phase distribution for the population, but both lying within the bounds of experimental error. Since treatment outcome is effected by the phase distribution of the population this difference may be critical in treatment planning. Using an age-structured population balance approach the cell cycle is modelled with particular emphasis on the - checkpoint. By considering the probability of cells transitioning at the - checkpoint, different transition functions are obtained. A suitable finite difference scheme for the numerical simulation of the model is derived and shown to be stable. The model is then fitted using the different probability transition functions to experimental data and the effects of the different probability transition functions on the model's results are discussed. [ABSTRACT FROM AUTHOR]

Traditionally, it has been held that a central characteristic of stem cells is their ability to divide asymmetrically. Recent advances in inducible genetic labeling provided ample evidence that symmetric stem cell divisions play an important role in adult mammalian homeostasis. It is well understood that the two types of cell divisions differ in terms of the stem cells' flexibility to expand when needed. On the contrary, the implications of symmetric and asymmetric divisions for mutation accumulation are still poorly understood. In this paper we study a stochastic model of a renewing tissue, and address the optimization problem of tissue architecture in the context of mutant production. Specifically, we study the process of tumor suppressor gene inactivation which usually takes place as a consequence of two “hits”, and which is one of the most common patterns in carcinogenesis. We compare and contrast symmetric and asymmetric (and mixed) stem cell divisions, and focus on the rate at which double-hit mutants are generated. It turns out that symmetrically-dividing cells generate such mutants at a rate which is significantly lower than that of asymmetrically-dividing cells. This result holds whether single-hit (intermediate) mutants are disadvantageous, neutral, or advantageous. It is also independent on whether the carcinogenic double-hit mutants are produced only among the stem cells or also among more specialized cells. We argue that symmetric stem cell divisions in mammals could be an adaptation which helps delay the onset of cancers. We further investigate the question of the optimal fraction of stem cells in the tissue, and quantify the contribution of non-stem cells in mutant production. Our work provides a hypothesis to explain the observation that in mammalian cells, symmetric patterns of stem cell division seem to be very common. [ABSTRACT FROM AUTHOR]

Oncolytic virus (OV) is a replication competent virus that selectively invades cancer cells; as these cells die under the viral burden, the released virus particles proceed to infect other cancer cells. Oncolytic viruses are designed to also be able to stimulate the anticancer immune response. Thus, one may represent an OV by two parameters: its replication potential and its immunogenicity. In this paper we consider a combination therapy with OV and a checkpoint inhibitor, anti-PD-1. We evaluate the efficacy of the combination therapy in terms of the tumor volume at some later time, for example, 6 months from initial treatment. Since T cells kill not only virus-free cancer cells but also virus-infected cancer cells, the following question arises: Does increasing the amount of the checkpoint inhibitor always improve the efficacy? We address this question, by a mathematical model consisting of a system of partial differential equations. We use the model to construct, by simulations, an efficacy map in terms of the doses of the checkpoint inhibitor and the OV injection. We show that there are regions in the map where an increase in the checkpoint inhibitor actually decreases the efficacy of the treatment. We also construct efficacy maps with checkpoint inhibitor vs. the replication potential of the virus that show the same antagonism, namely, an increase in the checkpoint inhibitor may actually decrease the efficacy. These results have implications for clinical trials. [ABSTRACT FROM AUTHOR]

In the present paper, we address by means of mathematical modeling the following main question: How can oncolytic virus infection of some normal cells in the vicinity of tumor cells enhance oncolytic virotherapy? We formulate a mathematical model describing the interactions between the oncolytic virus, the tumor cells, the normal cells, and the antitumoral and antiviral immune responses. The model consists of a system of delay differential equations with one (discrete) delay. We derive the model’s basic reproductive number within tumor and normal cell populations and use their ratio as a metric for virus tumor-specificity. Numerical simulations are performed for different values of the basic reproduction numbers and their ratios to investigate potential trade-offs between tumor reduction and normal cells losses. A fundamental feature unravelled by the model simulations is its great sensitivity to parameters that account for most variation in the early or late stages of oncolytic virotherapy. From a clinical point of view, our findings indicate that designing an oncolytic virus that is not 100% tumor-specific can increase virus particles, which in turn, can further infect tumor cells. Moreover, our findings indicate that when infected tissues can be regenerated, oncolytic viral infection of normal cells could improve cancer treatment. [ABSTRACT FROM AUTHOR]

Achillea fragrantissima is a shrub plant that belongs to the Asteraceae family in Arabia and Egypt. It is used as folk medicine and is a good source of phenolic acids, flavonoids, and some active compounds. To investigate the anti-cancer effect of A.fragrantissima on breast cancer MCF-7 cells and find the critical mechanism involved in apoptosis. The toxicity and pharmacokinetic studies of ethanolic extract of A.fragrantissima was examined for anti-breast cancer properties. In turn, cytotoxicity and cell viability were achieved by the MTT method. Furthermore, the trypan blue exclusion and microscopy examination proved the presence of apoptotic cells. Again, fluorescent staining such as AO/EtBr, DCFH-DA, Rho-123, and Hoechst-33342 reveals the cellular cytoplasmic disciplines upon A. fragrantissima effect. Moreover, cellular functioning tests like wound healing, colony formation, and Transwell invasion assay were demonstrated. In addition, the qRT-PCR technique authenticates the A. fragrantissima -induced apoptotic network genes (Caspase-3, Caspase-8, Caspase-9, Cytochrome c, BCL-2, BID, BAX, PARP, PTEN, PI3K, and Akt) expression were evaluated. Mainly, the Immunoblot technique proved the expressed level of apoptotic proteins such as cleaved PARP, CYCS, and FADD. This study confirmed that the A. fragrantissima exerts cytotoxicity at 20 μg/mL for 24 hrs in MCF-7 cells. Also, decreases cellular viability, producing apoptotic cells and damaged cellular surfaces with dead matter. Consequently, it creates ROS species accumulation, loss of mitochondrial membrane potential, and fragmentation of DNA in MCF-7 cells. Furthermore, it arrests cell migration, induces colony-forming ability loss, and suppresses cell invasion. In addition, A. fragrantissima significantly upregulates genes such as caspase-3, 9, cytochrome c, BID, BAX, and PTEN while downregulating the Pi3K/ Akt signaling. Nonetheless, A.fragrantissima induced cleaved PARP, CYCS, and FADD proteins in MCF-7 cells to avail apoptosis. [ABSTRACT FROM AUTHOR]

VDX-111 (also identified as AMPI-109) is a vitamin D derivative which has shown anticancer activity. To further assess the function of this compound against multiple cancer types, we examined the efficacy of VDX-111 against a panel of 30 well characterized canine cancer cell lines. Across a variety of cancer types, VDX-111 induced widely variable growth inhibition, cell death, and migration inhibition, at concentrations ranging from 10 nM to 1 μM. Growth inhibition sensitivity did not correlate strongly with tumor cell histotype; however, it was significantly correlated with the expression of genes in multiple cell signaling pathways, including the MAPK and PI3K-AKT pathways. We confirmed inhibition of these signaling pathways as likely participants in the effects of VDX-111. These results suggest that a subset of canine tumors may be sensitive to treatment with VDX-111, and suggests possible predictive markers of drug sensitivity and pharmacodynamic biomarkers of drug exposure that could be employed in future clinical trials. [ABSTRACT FROM AUTHOR]

BAK and BAX execute intrinsic apoptosis by permeabilising the mitochondrial outer membrane. Their activity is regulated through interactions with pro-survival BCL-2 family proteins and with non-BCL-2 proteins including the mitochondrial porin VDAC2. VDAC2 is important for bringing both BAK and BAX to mitochondria where they execute their apoptotic function. Despite this important function in apoptosis, while interactions with pro-survival family members are well characterised and have culminated in the development of drugs that target these interfaces to induce cancer cell apoptosis, the interaction between BAK and VDAC2 remains largely undefined. Deep scanning mutagenesis coupled with cysteine linkage identified key residues in the interaction between BAK and VDAC2. Obstructive labelling of specific residues in the BH3 domain or hydrophobic groove of BAK disrupted this interaction. Conversely, mutating specific residues in a cytosol-exposed region of VDAC2 stabilised the interaction with BAK and inhibited BAK apoptotic activity. Thus, this VDAC2–BAK interaction site can potentially be targeted to either inhibit BAK-mediated apoptosis in scenarios where excessive apoptosis contributes to disease or to promote BAK-mediated apoptosis for cancer therapy. BAK and BAX execute apoptotic cell death by increasing the permeability of the mitochondria outer membrane, and this is regulated by interactions with the mitochondrial channel protein VDAC2. This study maps the key residues involved in the BAK:VDAC2 interaction and demonstrate that this interaction can be targeted to either promote or inhibit apoptosis. [ABSTRACT FROM AUTHOR]

The biogenic approach for the synthesis of metal nanoparticles provides an efficient eco-friendly alternative to chemical synthesis. This study presents a novel route for the biosynthesis of silver nanoparticles using aqueous sandalwood (SW) leaf extract as a source of reducing and capping agents under mild, room temperature synthesis conditions. The bioreduction of Ag+ to Ago nanoparticles (SW-AgNPs) was accompanied by the appearance of brown color, with surface plasmon resonance peak at 340-360 nm. SEM, TEM and AFM imaging confirm SW-AgNP's spherical shape with size range of 10-32 nm. DLS indicates a hydrodynamic size of 49.53 nm with predominant negative Zeta potential, which can contribute to the stability of the nanoparticles. FTIR analysis indicates involvement of sandalwood leaf derived polyphenols, proteins and lipids in the reduction and capping of SW-AgNPs. XRD determines the face-centered-cubic crystalline structure of SW-AgNPs, which is a key factor affecting biological functions of nanoparticles. This study is novel in using cell culture methodologies to evaluate effects of SW-AgNPs on proliferating cells originating from plants and human cancer. Exposure of groundnut calli cells to SW-AgNPs, resulted in enhanced proliferation leading to over 70% higher calli biomass over control, enhanced defense enzyme activities, and secretion of metabolites implicated in biotic stress resistance (Crotonyl isothiocyanate, Butyrolactone, 2-Hydroxy-gamma-butyrolactone, Maltol) and plant cell proliferation (dl-Threitol). MTT and NRU were performed to determine the cytotoxicity of nanoparticles on human cervical cancer cells. SW-AgNPs specifically inhibited cervical cell lines SiHa (IC50–2.65 ppm) and CaSki (IC50–9.49 ppm), indicating potential use in cancer treatment. The opposing effect of SW-AgNPs on cell proliferation of plant calli (enhanced cell proliferation) and human cancer cell lines (inhibition) are both beneficial and point to potential safe application of SW-AgNPs in plant cell culture, agriculture and in cancer treatment. [ABSTRACT FROM AUTHOR]

Research has demonstrated that circular RNAs (circRNAs) exert critical functions in the occurrence and progression of numerous malignant tumors. CircPRMT5 was recently reported to be involved in the pathogenesis of cancers. However, the potential role of circPRMT5 in osteosarcoma needs further investigation. In present study, our results suggested that circPRMT5 was highly upregulated in osteosarcoma cells and mainly localizes in the cytoplasm. CircPRMT5 promoted the proliferation, migration and invasion capacities of osteosarcoma cells, and suppressed cell apoptosis. Knockdown of circPRMT5 exerted the opposite effects. Mechanically, circPRMT5 promoted the binding of CNBP to CDK6 mRNA, which enhanced the stability of CDK6 mRNA and facilitated its translation, thereby promoting the progression of osteosarcoma. Knockdown of CDK6 reversed the promoting effect of circPRMT5 on osteosarcoma cells. These findings suggest that circPRMT5 promotes osteosarcoma cell malignant activity by recruiting CNBP to regulate the translation and stability of CDK6 mRNA. Thus, circPRMT5 may represent a promising therapeutic target for osteosarcoma. [ABSTRACT FROM AUTHOR]

Objective: Endothelial specific molecule-1 (ESM1) is implicated as an oncogene in multiple human cancers. However, the function of ESM1 in papillary thyroid cancer (PTC) is not well understood. The current study aimed to investigate the effect of ESM1 on the growth, migration, and invasion of PTC to provide a novel perspective for PTC treatment. Methods: The expression levels of ESM1 in PTC tissues form 53 tumor tissue samples and 59 matching adjacent normal tissue samples were detected by immunohistochemical analysis. Knockdown of ESM1 expression in TPC-1 and SW579 cell lines was established to investigate its role in PTC. Moreover, cell proliferation, apoptosis, wound healing, and transwell assays were conducted in vitro to assess cell proliferation, migration and invasion. Results: The findings revealed that ESM1 expression was significantly higher in PTC tissues than that found in paraneoplastic tissues (P<0.0001). Knockdown of ESM1 expression inhibited the proliferation, migration, and invasion of TPC-1 and SW579 cells in vitro. Compared with the control group, the mRNA and protein levels of ESM1 in PTC cells were significantly reduced following knockdown of its expression (P<0.01). In addition, ESM1-knockdown cells indicated decreased proliferation and decreased migratory and invasive activities (P<0.01, P<0.01, P<0.001, respectively). Conclusions: ESM1 was identified as a major gene in the occurrence and progression of PTC, which could increase the proliferation, migration, and invasion of PTC cells. It may be a promising diagnostic and therapeutic target gene. [ABSTRACT FROM AUTHOR]

Cigarette smoke-induced protein aggregation damages the lung cells in emphysema and COPD; however, lung cancer cells continue to thrive, evolving to persist in the toxic environment. Here, we showed that upon the cigarette smoke condensate exposure, A549 lung cancer cells exhibit better survival and reduced level of protein aggregation when compared to non-cancerous Beas-2B and H-6053 cells. Our data suggests that upregulation of efflux pumps in cancer cells assists in reducing smoke toxicity. Specifically, we demonstrated that inhibition of the ABCG2 transporter in A549 by febuxostat or its downregulation by shRNA-mediated RNA interference resulted in a significant increase in protein aggregation due to smoke exposure. [ABSTRACT FROM AUTHOR]

Background: Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems. Methods: In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters. Results: Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable. Conclusion: Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset. [ABSTRACT FROM AUTHOR]

Gliomas are the most frequent type of primary brain tumours. Low grade gliomas (LGGs, WHO grade II gliomas) may grow very slowly for the long periods of time, however they inevitably cause death due to the phenomenon known as the malignant transformation. This refers to the transition of LGGs to more aggressive forms of high grade gliomas (HGGs, WHO grade III and IV gliomas). In this paper we propose a mathematical model describing the spatio-temporal transition of LGGs into HGGs. Our modelling approach is based on two cellular populations with transitions between them being driven by the tumour microenvironment transformation occurring when the tumour cell density grows beyond a critical level. We show that the proposed model describes real patient data well. We discuss the relationship between patient prognosis and model parameters. We approximate tumour radius and velocity before malignant transformation as well as estimate the onset of this process. [ABSTRACT FROM AUTHOR]

In this paper we consider a combination therapy of cancer. One drug is a vaccine which activates dendritic cells so that they induce more T cells to infiltrate the tumor. The other drug is a checkpoint inhibitor, which enables the T cells to remain active against the cancer cells. The two drugs are positively correlated in the sense that an increase in the amount of each drug results in a reduction in the tumor volume. We consider the question whether a treatment with combination of the two drugs at certain levels is preferable to a treatment by one of the drugs alone at ‘roughly’ twice the dosage level; if that is the case, then we say that there is a positive ‘synergy’ for this combination of dosages. To address this question, we develop a mathematical model using a system of partial differential equations. The variables include dendritic and cancer cells, CD4+ and CD8+ T cells, IL-12 and IL-2, GM-CSF produced by the vaccine, and a T cell checkpoint inhibitor associated with PD-1. We use the model to explore the efficacy of the two drugs, separately and in combination, and compare the simulations with data from mouse experiments. We next introduce the concept of synergy between the drugs and develop a synergy map which suggests in what proportion to administer the drugs in order to achieve the maximum reduction of tumor volume under the constraint of maximum tolerated dose. [ABSTRACT FROM AUTHOR]

This paper describes the development of a prototype of a low-cost digital fluorescent microscope built from commercial off-the-shelf (COTS) components. The prototype was tested to detect malignant tumor cells taken from a living organism in a preclinical setting. This experiment was accomplished by using Alexa Fluor 488 conjugate dye attached to the cancer cells. Our prototype utilizes a torch along with an excitation filter as a light source for fluorophore excitation, a dichroic mirror to reflect the excitation and pass the emitted green light from the sample under test and a barrier filter to permit only appropriate wavelength. The system is designed out of a microscope using its optical zooming property and an assembly of exciter filter, dichroic mirror and transmitter filter. The microscope is connected to a computer or laptop through universal serial bus (USB) that allows real-time transmission of captured florescence images; this also offers real-time control of the microscope. The designed system has comparable features of high-end commercial fluorescent microscopes while reducing cost, power, weight and size. [ABSTRACT FROM AUTHOR]

Pap test involves searching of morphological changes in cervical squamous epithelial cells by pathologists or cytotechnologists to identify potential cancerous cells in the cervix. Nuclear membrane irregularity is one of the morphological changes of malignancy. This paper proposes two novel techniques for the evaluation of nuclear membrane irregularity. The first technique, namely, penalty-driven smoothing analysis, introduces different penalty values for nuclear membrane contour with different degrees of irregularity. The second technique, which can be subdivided into mean- or median-type residual-based analysis, computes the number of points of nuclear membrane contour that deviates from the mean or median of the nuclear membrane contour. Performance of the proposed techniques was compared to three state-of-the-art techniques, namely, radial asymmetric, shape factor, and rim difference. Friedman and post hoc tests using Holm, Shaffer, and Bergmann procedures returned significant differences for all the three classes, i.e., negative for intraepithelial lesion or malignancy (NILM) versus low grade squamous intraepithelial lesion (LSIL), NILM versus high grade squamous intraepithelial lesion (HSIL), and LSIL versus HSIL when the span value equaled 3 was employed with linear penalty function. When span values equaled 5, 7, and 9, NILM versus LSIL and HSIL showed significant differences regardless of the penalty functions. In addition, the results of penalty-driven smoothing analysis were comparable with those of other state-of-the-art techniques. Residual-based analysis returned significant differences for the comparison among the three diagnostic classes. Findings of this study proved the significance of nuclear membrane irregularity as one of the features to differentiate the different diagnostic classes of cervical squamous epithelial cells. [ABSTRACT FROM AUTHOR]

One of fundamental challenges in cancer studies is that varying molecular characteristics of different tumor types may lead to resistance to certain drugs. As a result, the same drug can lead to significantly different results in different types of cancer thus emphasizing the need for individualized medicine. Individual prediction of drug response has great potential to aid in improving the clinical outcome and reduce the financial costs associated with prescribing chemotherapy drugs to which the patient’s tumor might be resistant. In this paper we develop a network based classifier (NBC) method for predicting sensitivity of cell lines to anticancer drugs from transcriptome data. In the literature, this strategy has been used for predicting cancer types. Here, we extend it to estimate sensitivity of cells from different tumor types to various anticancer drugs. Furthermore, we incorporate domain specific knowledge such as the use of apoptotic gene list and clinical dose information in our method to impart biological significance to the prediction. Our experimental results suggest that our network based classifier (NBC) method outperforms existing classifiers in estimating sensitivity of cell lines for different drugs. [ABSTRACT FROM AUTHOR]

Objective: Radiofrequency ablation is a viable option in the treatment of benign thyroid nodules. Some reports suggest that thermal ablation may also be safe for the management of low-risk thyroid cancer. In this study, we applied transient heat treatment to thyroid cancer cells to mimic clinical scenarios in which insufficient ablation leads to incomplete eradication of thyroid cancer. Methods: Differentiated thyroid cancer cell lines B-CPAP, TPC-1, and FTC-133 were subjected to heat treatment at different temperatures for 10 min. Effects on cell growth, clonogenicity, wound healing assay, and Transwell invasion were determined. Results: Heat treatment at 45°C or higher reduced cell growth, whereas viability of thyroid cancer cells was not changed after heat treatment at 37, 40, or 42°C. Heat treatment at 40°C increased the number of colony formations by 16% to 39%. Additionally, transient heat treatment at 40°C resulted in a 1.75-fold to 2.56-fold higher migratory activity than treatment at 37°C. Invasive capacity was increased after heat treatment, ranging from 115% to 126%. Expression of several epithelial-mesenchymal transition markers, including ZEB1, N-cadherin, and MMP2, was upregulated following heat treatment at 40°C. Conclusion: We for the first time demonstrate that sublethal thermal stress may increase clonogenicity, migration, and invasion of thyroid cancer cells. [ABSTRACT FROM AUTHOR]

Common genetic variants in the repressive GATA-family transcription factor (TF) TRPS1 locus are associated with breast cancer risk, and luminal breast cancer cell lines are particularly sensitive to TRPS1 knockout. We introduced an inducible degron tag into the native TRPS1 locus within a luminal breast cancer cell line to identify the direct targets of TRPS1 and determine how TRPS1 mechanistically regulates gene expression. We acutely deplete over 80 percent of TRPS1 from chromatin within 30 minutes of inducing degradation. We find that TRPS1 regulates transcription of hundreds of genes, including those related to estrogen signaling. TRPS1 directly regulates chromatin structure, which causes estrogen receptor alpha (ER) to redistribute in the genome. ER redistribution leads to both repression and activation of dozens of ER target genes. Downstream from these primary effects, TRPS1 depletion represses cell cycle-related gene sets and reduces cell doubling rate. Finally, we show that high TRPS1 activity, calculated using a gene expression signature defined by primary TRPS1-regulated genes, is associated with worse breast cancer patient prognosis. Taken together, these data suggest a model in which TRPS1 modulates the genomic distribution of ER, both activating and repressing transcription of genes related to cancer cell fitness. Author summary: Breast cancer is the most common cancer among women. The majority of cases are luminal, which tend to be estrogen receptor alpha (ER)-positive. ER is well-studied among transcription factors (TFs) because it is ligand-activated. This allows for the rapid induction of ER activity and the identification of primary estrogen-responsive genes. Most TFs have not been so extensively characterized, because their activity is not so rapidly perturbable. TRPS1 is an atypical GATA family TF that is associated with corepressor complexes and transcriptional repression. Here, we use an inducible degron tag system to rapidly deplete endogenously-tagged TRPS1 in luminal breast cancer cells within 30 minutes. We find that TRPS1 directly decreases local chromatin accessibility. This decreases ER binding intensity at TRPS1-proximal ER binding sites. As an indirect effect, ER binding intensity distal to TRPS1 increases in intensity. These effects on ER binding are associated with changes in ER target gene transcription, repressing or activating these genes in concordance with the effect on ER binding intensity. Our work is consistent with a model in which TFs either exclusively activate or exclusively repress transcription of their direct target genes, with indirect primary response genes changing due to the redistribution of limiting activating TFs or coactivators. [ABSTRACT FROM AUTHOR]

Metastatic dissemination following successful treatment of the primary tumour remains a common cause of death. There is mounting evidence that therapeutic interventions themselves may promote development of metastatic disease. We earlier reported that cell-free chromatin particles (cfChPs) released from dying cancer cells are potentially oncogenic. Based on this observation we hypothesized that therapeutic interventions may lead to the release of cfChPs from therapy induced dying cancer cells which could be carried via the blood stream to distant organs to transform healthy cells into new cancers that would masquerade as metastasis. To test this hypothesis, we generated xenografts of MDA-MB-231 human breast cancer cells in severe combined immune-deficient mice, and using immuno-fluorescence and FISH analysis looked for cfChPs in their brain cells. We detected multiple human DNA signals representing cfChPs in nuclei of brain cells of mice which co-localized with eight human onco-proteins. No intact MDA-MB-231 cells were detected. The number of co-localizing human DNA and human c-Myc signals increased dramatically following treatment with chemotherapy, localized radiotherapy or surgery, which could be prevented by concurrent treatment with three different cfChPs deactivating agents. These results suggest that therapeutic interventions lead to the release cfChPs from therapy induced dying cancer cells carrying oncogenes and are transported via the blood stream to brain cells to potentially transform them to generate new cancers that would appear as metastases. cfChPs induced metastatic spread of cancer is preventable by concurrent treatment with agents that deactivate cfChPs. [ABSTRACT FROM AUTHOR]

Colorectal cancer is the third most common cancer and the second leading cause of cancer-related deaths worldwide. The centrosome is the main microtubule-organizing center in animal cells and centrosome amplification is a hallmark of cancer cells. To investigate the importance of centrosomes in colorectal cancer, we induced centrosome loss in normal and cancer human-derived colorectal organoids using centrinone B, a Polo-like kinase 4 (Plk4) inhibitor. We show that centrosome loss represses human normal colorectal organoid growth in a p53-dependent manner in accordance with previous studies in cell models. However, cancer colorectal organoid lines exhibited different sensitivities to centrosome loss independently of p53. Centrinone-induced cancer organoid growth defect/death positively correlated with a loss of function mutation in the APC gene, suggesting a causal role of the hyperactive WNT pathway. Consistent with this notion, β-catenin inhibition using XAV939 or ICG-001 partially prevented centrinone-induced death and rescued the growth two APC-mutant organoid lines tested. Our study reveals a novel role for canonical WNT signaling in regulating centrosome loss-induced growth defect/death in a subset of APC-mutant colorectal cancer independently of the classical p53 pathway. [ABSTRACT FROM AUTHOR]

It has been well established that cancer cells can evade immune surveillance by mutating themselves. Understanding genetic alterations in cancer cells that contribute to immune regulation could lead to better immunotherapy patient stratification and identification of novel immune-oncology (IO) targets. In this report, we describe our effort of genome-wide association analyses across 22 TCGA cancer types to explore the associations between genetic alterations in cancer cells and 76 immune traits. Results showed that the tumor microenvironment (TME) is shaped by different gene mutations in different cancer types. Out of the key genes that drive multiple immune traits, top hit KEAP1 in lung adenocarcinoma (LUAD) was selected for validation. It was found that KEAP1 mutations can explain more than 10% of the variance for multiple immune traits in LUAD. Using public scRNA-seq data, further analysis confirmed that KEAP1 mutations activate the NRF2 pathway and promote a suppressive TME. The activation of the NRF2 pathway is negatively correlated with lower T cell infiltration and higher T cell exhaustion. Meanwhile, several immune check point genes, such as CD274 (PD-L1), are highly expressed in NRF2-activated cancer cells. By integrating multiple RNA-seq data, a NRF2 gene signature was curated, which predicts anti-PD1 therapy response better than CD274 gene alone in a mixed cohort of different subtypes of non-small cell lung cancer (NSCLC) including LUAD, highlighting the important role of KEAP1-NRF2 axis in shaping the TME in NSCLC. Finally, a list of overexpressed ligands in NRF2 pathway activated cancer cells were identified and could potentially be targeted for TME remodeling in LUAD. Author summary: Recent studies have found that some genetic changes help cancer cells to evade the immune surveillance. To systematically understand the impact of cancer cell genetic alterations to immune regulation, we examined 76 immune traits across 22 cancer types. The tumor microenvironment (TME), crucial for cancer development, varies based on gene mutations in different cancers. Notably, the KEAP1 gene in lung adenocarcinoma (LUAD) emerged as a key player, explaining over 10% of immune trait variations. KEAP1 mutations activate the NRF2 pathway, creating a suppressive TME in LUAD with lower T cell infiltration and heightened T cell exhaustion. Additionally, genes such as CD274 (PD-L1), associated with immune checkpoints, exhibit high expression in NRF2-activated cancer cells. By developing a NRF2 gene signature, we found that it more effectively predicts anti-PD1 therapy responses than CD274 alone in non-small cell lung cancer. Lastly, we identified ligands overexpressed in NRF2-activated cancer cells, suggesting potential targets for reshaping the LUAD microenvironment. In essence, understanding these genetic interactions helps improve lung cancer treatment and enhance the efficacy of immunotherapy. [ABSTRACT FROM AUTHOR]

It has been hypothesized that continuously releasing drug molecules into the tumor over an extended period of time may significantly improve the chemotherapeutic efficacy by overcoming physical transport limitations of conventional bolus drug treatment. In this paper, we present a generalized space- and time-dependent mathematical model of drug transport and drug-cell interactions to quantitatively formulate this hypothesis. Model parameters describe: perfusion and tissue architecture (blood volume fraction and blood vessel radius); diffusion penetration distance of drug (i.e., a function of tissue compactness and drug uptake rates by tumor cells); and cell death rates (as function of history of drug uptake). We performed preliminary testing and validation of the mathematical model using in vivo experiments with different drug delivery methods on a breast cancer mouse model. Experimental data demonstrated a 3-fold increase in response using nano-vectored drug vs. free drug delivery, in excellent quantitative agreement with the model predictions. Our model results implicate that therapeutically targeting blood volume fraction, e.g., through vascular normalization, would achieve a better outcome due to enhanced drug delivery. Author Summary: Cancer treatment efficacy can be significantly enhanced through the elution of drug from nano-carriers that can temporarily stay in the tumor vasculature. Here we present a relatively simple yet powerful mathematical model that accounts for both spatial and temporal heterogeneities of drug dosing to help explain, examine, and prove this concept. We find that the delivery of systemic chemotherapy through a certain form of nano-carriers would have enhanced tumor kill by a factor of 2 to 4 over the standard therapy that the patients actually received. We also find that targeting blood volume fraction (a parameter of the model) through vascular normalization can achieve more effective drug delivery and tumor kill. More importantly, this model only requires a limited number of parameters which can all be readily assessed from standard clinical diagnostic measurements (e.g., histopathology and CT). This addresses an important challenge in current translational research and justifies further development of the model towards clinical translation. [ABSTRACT FROM AUTHOR]

This paper addresses a problem of estimating time-varying, local concentrations of signal molecules with a carbon-nanotube (CNT)-based sensor array system, which sends signals triggered by monomolecular adsorption/desorption events of proximate molecules on the surfaces of the sensors. Such sensors work on nano-scale phenomena and show inherently stochastic non-Gaussian behavior, which is best represented by the chemical master equation (CME) describing the time evolution of the probabilities for all the possible number of adsorbed molecules. In the CME, the adsorption rate on each sensor is linearly proportional to the local concentration in the bulk phase. State estimators are proposed for these types of sensors that fully address their stochastic nature. For CNT-based sensors motivated by tumor cell detection, the particle filter, which is nonparametric and can handle non-Gaussian distributions, is compared to a Kalman filter that approximates the underlying distributions by Gaussians. In addition, the second-order generalized pseudo Bayesian estimation (GPB2) algorithm and the Markov chain Monte Carlo (MCMC) algorithm are incorporated into KF and PF respectively, for detecting latent drift in the concentration affected by different states of a cell. [ABSTRACT FROM AUTHOR]

Hodgkin’s lymphoma is an example for a tumor with an extremely tight interaction of tumor cells with cells from the tumor micro-environment. These so-called bystander cells are not inert but interact actively with the tumor cells. Some of these cells support tumor growth by delivery of co-stimulating and anti-apoptotic signals (“helper cells”). Other cells (“killer cells”) are involved in the anti-tumor immune response which is obviously not efficient enough for tumor elimination. The activity of both helper cells and killer cells is regulated by additional cells in the stroma (“regulatory cells”). The dynamic behavior of such multi-component systems is difficult to predict. In the present paper we propose a model that can be used for simulation of essential features of this system. In this model, tumor growth depends on (i) presence of few cancer stem cells, (ii) co-stimulation of cancer cells by the tumor stroma, (iii) activity of regulatory cells that suppress killer cells without suppression of helper cells. The success of cytotoxic/cytostatic therapy in this model varies depending on the therapy-related toxicity for each of the cell populations. The model also allows the analysis of immunotherapeutic interventions. Under certain conditions, paradox enhancement of tumor growth can occur after therapeutic intervention. The model might be useful for the design of new treatment strategies for Hodgkin’s lymphoma and other tumors with prominent tumor-stroma interaction. [ABSTRACT FROM AUTHOR]

Our aim was to develop a fast and accurate algorithm for constructing consensus genetic maps for chip-based SNP genotyping data with a high proportion of shared markers between mapping populations. Chip-based genotyping of SNP markers allows producing high-density genetic maps with a relatively standardized set of marker loci for different mapping populations. The availability of a standard high-throughput mapping platform simplifies consensus analysis by ignoring unique markers at the stage of consensus mapping thereby reducing mathematical complicity of the problem and in turn analyzing bigger size mapping data using global optimization criteria instead of local ones. Our three-phase analytical scheme includes automatic selection of ~100-300 of the most informative (resolvable by recombination) markers per linkage group, building a stable skeletal marker order for each data set and its verification using jackknife re-sampling, and consensus mapping analysis based on global optimization criterion. A novel Evolution Strategy optimization algorithm with a global optimization criterion presented in this paper is able to generate high quality, ultra-dense consensus maps, with many thousands of markers per genome. This algorithm utilizes "potentially good orders" in the initial solution and in the new mutation procedures that generate trial solutions, enabling to obtain a consensus order in reasonable time. The developed algorithm, tested on a wide range of simulated data and real world data (Arabidopsis), outperformed two tested state-of-the-art algorithms by mapping accuracy and computation time. [ABSTRACT FROM AUTHOR]

Surprisal analysis is increasingly being applied for the examination of transcription levels in cellular processes, towards revealing inner network structures and predicting response. But to achieve its full potential, surprisal analysis should be integrated into a wider range computational tool. The purposes of this paper are to combine surprisal analysis with other important computation procedures, such as easy manipulation of the analysis results – e.g. to choose desirable result sub-sets for further inspection –, retrieval and comparison with relevant datasets from public databases, and flexible graphical displays for heuristic thinking. The whole set of computation procedures integrated into a single practical tool is what we call Computational Surprisal Analysis. This combined kind of analysis should facilitate significantly quantitative understanding of different cellular processes for researchers, including applications in proteomics and metabolomics. Beyond that, our vision is that Computational Surprisal Analysis has the potential to reach the status of a routine method of analysis for practitioners. The resolving power of Computational Surprisal Analysis is here demonstrated by its application to a variety of cellular cancer process transcription datasets, ours and from the literature. The results provide a compact biological picture of the thermodynamic significance of the leading gene expression phenotypes in every stage of the disease. For each transcript we characterize both its inherent steady state weight, its correlation with the other transcripts and its variation due to the disease. We present a dedicated website to facilitate the analysis for researchers and practitioners. [ABSTRACT FROM AUTHOR]

HiF-1α is the central protein driving the cellular response to hypoxia. Its accumulation in cancer cells is linked to the appearance of chemoresistant and aggressive tumor phenotypes. As a consequence, understanding the regulation of HiF-1α dynamics is a major issue to design new anti-cancer therapies. In this paper, we propose a model of the hypoxia pathway, involving HiF-1α and its inhibitor pVHL. Based on data from the literature, we made the hypothesis that the regulation of HiF-1α involves two compartments (nucleus and cytoplasm) and a constitutive shuttle of the pVHL protein between them. We first show that this model captures correctly the main features of HiF-1α dynamics, including the bi-exponential degradation profile in normoxia, the kinetics of induction in hypoxia, and the switch-like accumulation. Second, we simulated the effects of a hypoxia/reoxygenation event, and show that it generates a strong instability of HiF-1α. The protein concentration rapidly increases 3 hours after the reoxygenation, and exhibits an oscillating pattern. This effect vanishes if we do not consider compartmentalization of HiF-1α. This result can explain various counter-intuitive observations about the specific molecular and cellular response to the reoxygenation process. Third, we simulated the HiF-1α dynamics in the tumor case. We considered different types of mutations associated with tumorigenesis, and we compared their consequences on HiF-1α dynamics. Then, we tested different therapeutics strategies. We show that a therapeutic decrease of HiF-1α nuclear level is not always correlated with an attenuation of reoxygenation-induced instabilities. Thus, it appears that the design of anti-HiF-1α therapies have to take into account these two aspects to maximize their efficiency. [ABSTRACT FROM AUTHOR]

Interleukin-35 (IL-35), a cytokine from the Interleukin-12 cytokine family, has been considered as an anti-inflammatory cytokine which promotes tumor progression and tumor immune evasion. It has also been demonstrated that IL-35 is secreted by regulatory T cells. Recent mouse experiments have shown that IL-35 produced by cancer cells promotes tumor growth via enhancing myeloid cell accumulation and angiogenesis, and reducing the infiltration of activated CD8 T cells into tumor microenvironment. In the present paper we develop a mathematical model based on these experimental results. We include in the model an anti-IL-35 drug as treatment. The extended model (with drug) is used to design protocols of anti-IL-35 injections for treatment of cancer. We find that with a fixed total amount of drug, continuous injection has better efficacy than intermittent injections in reducing the tumor load while the treatment is ongoing. We also find that the percentage of tumor reduction under anti-IL-35 treatment improves when the production of IL-35 by cancer is increased. [ABSTRACT FROM AUTHOR]