An in-silico study examining the induction of apoptosis by Cryptotanshinone in metastatic melanoma cell lines.

BACKGROUND: Metastatic melanoma is an aggressive form of skin cancer that evades various anti-cancer treatments including surgery, radio-,immuno- and chemo-therapy. TRAIL-induced apoptosis is a desirable method to treat melanoma since, unlike other treatments, it does not harm non-cancerous cells. The pro-inflammatory response to melanoma by nF κB and STAT3 pathways makes the cancer cells resist TRAIL-induced apoptosis. We show that due to to its dual action on DR5, a death receptor for TRAIL and on STAT3, Cryptotanshinone can be used to increase sensitivity to TRAIL. METHODS: The development of chemoresistance and invasive properties in melanoma cells involves several biological pathways. The key components of these pathways are represented as a Boolean network with multiple inputs and multiple outputs. RESULTS: The possible mutations in genes that can lead to cancer are captured by faults in the combinatorial circuit and the model is used to theoretically predict the effectiveness of Cryptotanshinone for inducing apoptosis in melanoma cell lines. This prediction is experimentally validated by showing that Cryptotanshinone can cause enhanced cell death in A375 melanoma cells. CONCLUSION: The results presented in this paper facilitate a better understanding of melanoma drug resistance. Furthermore, this framework can be used to detect additional drug intervention points in the pathway that could amplify the action of Cryptotanshinone.

Determinants of patient screen failures in Phase 1 clinical trials.

OBJECTIVE: Certain eligibility criteria for Phase 1 cancer clinical trials may impede successful patient enrollment onto a study. We evaluated patient-specific or study-specific reasons for screen failures on Phase 1 oncology clinical trials and discuss factors which may inhibit subject enrollment. METHODS: Thirty-eight Phase 1 clinical trials for solid tumors meeting eligibility criteria and opened for enrollment between February 2006 and February 2011 at one oncology Phase 1 program were examined. Categorical reasons for screen failures and patients' demographics were examined and compared to characteristics of patients that successfully enrolled on a Phase 1 trial. RESULTS: There were a total of 583 successful Phase 1 enrollment and dose administration events out of 773 Phase 1 consent events (75.4 % dose success rate). The three most common reasons for screen failure were: out of protocol-specified range for chemistry, development of an interval medical issue that precluded proceeding with study participation, and subject declining participation after signing consent. Living further away from the Phase 1 program and receipt of fewer prior lines of systemic chemotherapy were significantly associated with increased screen failures. CONCLUSION: Screen failures for Phase 1 studies are not uncommon (24.6 %). When a protocol required tumor or host analyte is not required, most screen failures are due to out of protocol-specified range for chemistry or the development of an interval medical issue. Screen failure rates were increased when patients had longer travel distances and fewer prior lines of systemic chemotherapy.

High-dimensional bolstered error estimation.

MOTIVATION: In small-sample settings, bolstered error estimation has been shown to perform better than cross-validation and competitively with bootstrap with regard to various criteria. The key issue for bolstering performance is the variance setting for the bolstering kernel. Heretofore, this variance has been determined in a non-parametric manner from the data. Although bolstering based on this variance setting works well for small feature sets, results can deteriorate for high-dimensional feature spaces. RESULTS: This article computes an optimal kernel variance depending on the classification rule, sample size, model and feature space, both the original number and the number remaining after feature selection. A key point is that the optimal variance is robust relative to the model. This allows us to develop a method for selecting a suitable variance to use in real-world applications where the model is not known, but the other factors in determining the optimal kernel are known. AVAILABILITY: Companion website at http://compbio.tgen.org/paper_supp/high_dim_bolstering. CONTACT: edward@mail.ece.tamu.edu.

Anti-tumor effects of cryptotanshinone (C19H20O3) in human osteosarcoma cell lines.

Osteosarcoma is the most prevalent malignant bone tumor and occurs most commonly in the adolescent and young adult population. Despite the recent advances in surgeries and chemotherapy, the overall survival in patients with resectable metastases is around 20%. This challenge in osteosarcoma is often attributed to the drastic differences in the tumorigenic profiles and mutations among patients. With diverse mutations and multiple oncogenes, it is necessary to identify the therapies that can attack various mutations and simultaneously have minor side-effects. In this paper, we constructed the osteosarcoma pathway from literature and modeled it using ordinary differential equations. We then simulated this network for every possible gene mutation and their combinations and ranked different drug combinations based on their efficacy to drive a mutated osteosarcoma network towards cell death. Our theoretical results predict that drug combinations with Cryptotanshinone (C19H20O3), a traditional Chinese herb derivative, have the best overall performance. Specifically, Cryptotanshinone in combination with Temsirolimus inhibit the JAK/STAT, MAPK/ERK, and PI3K/Akt/mTOR pathways and induce cell death in tumor cells. We corroborated our theoretical predictions using wet-lab experiments on SaOS2, 143B, G292, and HU03N1 human osteosarcoma cell lines, thereby demonstrating the potency of Cryptotanshinone in fighting osteosarcoma.

In Silico Modeling of the Induction of Apoptosis by Cryptotanshinone in Osteosarcoma Cell Lines.

Osteosarcoma (OS) is the most common primary malignant bone tumor of both children and pet canines. Its characteristic genomic instability and complexity coupled with the dearth of knowledge about its etiology has made improvement in the current treatment difficult. We use the existing literature about the biological pathways active in OS and combine it with the current research involving natural compounds to identify new targets and design more effective drug therapies. The key components of these pathways are modeled as a Boolean network with multiple inputs and multiple outputs. The combinatorial circuit is employed to theoretically predict the efficacies of various drugs in combination with Cryptotanshinone. We show that the action of the herbal drug, Cryptotanshinone on OS cell lines induces apoptosis by increasing sensitivity to TNF-related apoptosis-inducing ligand (TRAIL) through its multi-pronged action on STAT3, DRP1 and DR5. The Boolean framework is used to detect additional drug intervention points in the pathway that could amplify the action of Cryptotanshinone.

Reporting bias when using real data sets to analyze classification performance.

MOTIVATION: It is commonplace for authors to propose a new classification rule, either the operator construction part or feature selection, and demonstrate its performance on real data sets, which often come from high-dimensional studies, such as from gene-expression microarrays, with small samples. Owing to the variability in feature selection and error estimation, individual reported performances are highly imprecise. Hence, if only the best test results are reported, then these will be biased relative to the overall performance of the proposed procedure. RESULTS: This article characterizes reporting bias with several statistics and computes these statistics in a large simulation study using both modeled and real data. The results appear as curves giving the different reporting biases as functions of the number of samples tested when reporting only the best or second best performance. It does this for two classification rules, linear discriminant analysis (LDA) and 3-nearest-neighbor (3NN), and for filter and wrapper feature selection, t-test and sequential forward search. These were chosen on account of their well-studied properties and because they were amenable to the extremely large amount of processing required for the simulations. The results across all the experiments are consistent: there is generally large bias overriding what would be considered a significant performance differential, when reporting the best or second best performing data set. We conclude that there needs to be a database of data sets and that, for those studies depending on real data, results should be reported for all data sets in the database. AVAILABILITY: Companion web site at http://gsp.tamu.edu/Publications/supplementary/yousefi09a/

Small-sample precision of ROC-related estimates.

MOTIVATION: The receiver operator characteristic (ROC) curves are commonly used in biomedical applications to judge the performance of a discriminant across varying decision thresholds. The estimated ROC curve depends on the true positive rate (TPR) and false positive rate (FPR), with the key metric being the area under the curve (AUC). With small samples these rates need to be estimated from the training data, so a natural question arises: How well do the estimates of the AUC, TPR and FPR compare with the true metrics? RESULTS: Through a simulation study using data models and analysis of real microarray data, we show that (i) for small samples the root mean square differences of the estimated and true metrics are considerable; (ii) even for large samples, there is only weak correlation between the true and estimated metrics; and (iii) generally, there is weak regression of the true metric on the estimated metric. For classification rules, we consider linear discriminant analysis, linear support vector machine (SVM) and radial basis function SVM. For error estimation, we consider resubstitution, three kinds of cross-validation and bootstrap. Using resampling, we show the unreliability of some published ROC results. AVAILABILITY: Companion web site at http://compbio.tgen.org/paper_supp/ROC/roc.html CONTACT: edward@mail.ece.tamu.edu.

Comprehensive live-cell imaging analysis of cryptotanshinone and synergistic drug-screening effects in various human and canine cancer cell lines.

BACKGROUND: Several studies have highlighted both the extreme anticancer effects of Cryptotanshinone (CT), a Stat3 crippling component from Salvia miltiorrhiza, as well as other STAT3 inhibitors to fight cancer. METHODS: Data presented in this experiment incorporates 2 years of in vitro studies applying a comprehensive live-cell drug-screening analysis of human and canine cancer cells exposed to CT at 20 µM concentration, as well as to other drug combinations. As previously observed in other studies, dogs are natural cancer models, given to their similarity in cancer genetics, epidemiology and disease progression compared to humans. RESULTS: Results obtained from several types of human and canine cancer cells exposed to CT and varied drug combinations, verified CT efficacy at combating cancer by achieving an extremely high percentage of apoptosis within 24 hours of drug exposure. CONCLUSIONS: CT anticancer efficacy in various human and canine cancer cell lines denotes its ability to interact across different biological processes and cancer regulatory cell networks, driving inhibition of cancer cell survival.

Targeting oncogenic mutations in colorectal cancer using cryptotanshinone.

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

RNAi phenotype profiling of kinases identifies potential therapeutic targets in Ewing's sarcoma.

BACKGROUND: Ewing's sarcomas are aggressive musculoskeletal tumors occurring most frequently in the long and flat bones as a solitary lesion mostly during the teen-age years of life. With current treatments, significant number of patients relapse and survival is poor for those with metastatic disease. As part of novel target discovery in Ewing's sarcoma, we applied RNAi mediated phenotypic profiling to identify kinase targets involved in growth and survival of Ewing's sarcoma cells. RESULTS: Four Ewing's sarcoma cell lines TC-32, TC-71, SK-ES-1 and RD-ES were tested in high throughput-RNAi screens using a siRNA library targeting 572 kinases. Knockdown of 25 siRNAs reduced the growth of all four Ewing's sarcoma cell lines in replicate screens. Of these, 16 siRNA were specific and reduced proliferation of Ewing's sarcoma cells as compared to normal fibroblasts. Secondary validation and preliminary mechanistic studies highlighted the kinases STK10 and TNK2 as having important roles in growth and survival of Ewing's sarcoma cells. Furthermore, knockdown of STK10 and TNK2 by siRNA showed increased apoptosis. CONCLUSION: In summary, RNAi-based phenotypic profiling proved to be a powerful gene target discovery strategy, leading to successful identification and validation of STK10 and TNK2 as two novel potential therapeutic targets for Ewing's sarcoma.

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

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

A Gaussian Mixture-Model Exploiting Pathway Knowledge for Dissecting Cancer Heterogeneity.

In this work, we develop a systematic approach for applying pathway knowledge to a multivariate Gaussian mixture model for dissecting a heterogeneous cancer tissue. The downstream transcription factors are selected as observables from available partial pathway knowledge in such a way that the subpopulations produce some differential behavior in response to the drugs selected in the upstream. For each subpopulation, each unique (drug, observable) pair is considered as a unique dimension of a multivariate Gaussian distribution. Expectation-maximization (EM) algorithm with hill-climbing is then used to rank the most probable estimates of the mixture composition based on the log-likelihood value. A major contribution of this work is to examine the efficacy of the EM based approach in estimating the composition of experimental mixture sets from cell-by-cell measurements collected on a dynamic cell imaging platform. Towards this end, we apply the algorithm on hourly data collected for two different mixture compositions of A2058, HCT116, and SW480 cell lines for three scenarios: untreated, Lapatinib-treated, and Temsirolimus-treated. Additionally, we show how this methodology can provide a basis for comparing the killing rate of different drugs for a heterogeneous cancer tissue. This obviously has important implications for designing efficient drugs for treating heterogeneous malignant tumors.

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

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

Dose and Dose-Rate Effects in a Mouse Model of Internal Exposure to 137Cs. Part 1: Global Transcriptomic Responses in Blood.

Internal contamination by radionuclides may constitute a major source of exposure and biological damage after radiation accidents and potentially in a dirty bomb or improvised nuclear device scenario. We injected male C57BL/6 mice with radiolabeled cesium chloride solution (137CsCl) to evaluate the biological effects of varying cumulative doses and dose rates in a two-week study. Injection activities of 137CsCl were 5.71, 6.78, 7.67 and 9.29 MBq, calculated to achieve a target dose of 4 Gy at days 14, 7, 5 and 3, respectively. We collected whole blood samples at days 2, 3, 5, 7 and 14 so that we can publish the issue in Decemberfrom all injection groups and measured gene expression using Agilent Mouse Whole Genome microarrays. We identified both dose-rate-independent and dose-rate-dependent gene expression responses in the time series. Gene Ontology analysis indicated a rapid and persistent immune response to the chronic low-dose-rate irradiation, consistent with depletion of radiosensitive B cells. Pathways impacting platelet aggregation and TP53 signaling appeared activated, but not consistently at all times in the study. Clustering of genes by pattern and identification of dose-rate-independent and -dependent genes provided insight into possible drivers of the dynamic transcriptome response in vivo, and also indicated that TP53 signaling may be upstream of very different transcript response patterns. This characterization of the biological response of blood cells to internal radiation at varying doses and dose rates is an important step in understanding the effects of internal contamination after a nuclear event.

Bayesian Inference Identifies Combination Therapeutic Targets in Breast Cancer.

OBJECTIVE: Breast cancer is the second leading cause of cancer death among US women; hence, identifying potential drug targets is an ever increasing need. In this paper, we integrate existing biological information with graphical models to deduce the significant nodes in the breast cancer signaling pathway. METHODS: We make use of biological information from the literature to develop a Bayesian network. Using the relevant gene expression data we estimate the parameters of this network. Then, using a message passing algorithm, we infer the network. The inferred network is used to quantitatively rank different interventions for achieving a desired phenotypic outcome. The particular phenotype considered here is the induction of apoptosis. RESULTS: Theoretical analysis pinpoints to the role of Cryptotanshinone, a compound found in traditional Chinese herbs, as a potent modulator for bringing about cell death in the treatment of cancer. CONCLUSION: Using a mathematical framework, we showed that the combination therapy of mTOR and STAT3 genes yields the best apoptosis in breast cancer. SIGNIFICANCE: The computational results we arrived at are consistent with the experimental results that we obtained using Cryptotanshinone on MCF-7 breast cancer cell lines and also by the past results of others from the literature, thereby demonstrating the effectiveness of our model.

Candidate gene biodosimetry markers of exposure to external ionizing radiation in human blood: A systematic review.

PURPOSE: To compile a list of genes that have been reported to be affected by external ionizing radiation (IR) and to assess their performance as candidate biomarkers for individual human radiation dosimetry. METHODS: Eligible studies were identified through extensive searches of the online databases from 1978 to 2017. Original English-language publications of microarray studies assessing radiation-induced changes in gene expression levels in human blood after external IR were included. Genes identified in at least half of the selected studies were retained for bio-statistical analysis in order to evaluate their diagnostic ability. RESULTS: 24 studies met the criteria and were included in this study. Radiation-induced expression of 10,170 unique genes was identified and the 31 genes that have been identified in at least 50% of studies (12/24 studies) were selected for diagnostic power analysis. Twenty-seven genes showed a significant Spearman's correlation with radiation dose. Individually, TNFSF4, FDXR, MYC, ZMAT3 and GADD45A provided the best discrimination of radiation dose < 2 Gy and dose ≥ 2 Gy according to according to their maximized Youden's index (0.67, 0.55, 0.55, 0.55 and 0.53 respectively). Moreover, 12 combinations of three genes display an area under the Receiver Operating Curve (ROC) curve (AUC) = 1 reinforcing the concept of biomarker combinations instead of looking for an ideal and unique biomarker. CONCLUSION: Gene expression is a promising approach for radiation dosimetry assessment. A list of robust candidate biomarkers has been identified from analysis of the studies published to date, confirming for example the potential of well-known genes such as FDXR and TNFSF4 or highlighting other promising gene such as ZMAT3. However, heterogeneity in protocols and analysis methods will require additional studies to confirm these results.

Phenotype Classification Using Moment Features of Single-Cell Data.

Features for standard expression microarray and RNA-Seq classification are expression averages over collections of cells. Single cell provides expression measurements for individual cells in a collection of cells from a particular tissue sample. Hence, it can yield feature vectors consisting of higher order and mixed moments. This article demonstrates the advantage of using these expression moments in cancer-related classification. We use synthetic data generated from 2 real networks, the mammalian cell cycle network and a melanoma-related pathway network, and real single-cell data generated via fluorescent protein reporters from 2 cell lines, HT-29 and HCT-116. The networks consist of hidden binary regulatory networks with Gaussian observations. The steady-state distributions of both the original and mutated networks are found, and data are drawn from these for moment-based classification using the mean, variance, skewness, and mixed moments. For the real data, we only observe 1 gene at a time, so that only the mean, variance, and skewness are considered, the analysis being done for 2 genes, EGFR and ERRB2. For the synthetic data, classification improves as we move from just the mean to mean, variance, and skewness and then to these plus the mixed moments. Comparisons are done with 3, 4, or 5 features, using feature selection. Sample size effects are considered. For the real data, we only consider mean, variance, and skewness, with results improving when the higher order moments are used as features.

What should be expected from feature selection in small-sample settings.

MOTIVATION: High-throughput technologies for rapid measurement of vast numbers of biological variables offer the potential for highly discriminatory diagnosis and prognosis; however, high dimensionality together with small samples creates the need for feature selection, while at the same time making feature-selection algorithms less reliable. Feature selection must typically be carried out from among thousands of gene-expression features and in the context of a small sample (small number of microarrays). Two basic questions arise: (1) Can one expect feature selection to yield a feature set whose error is close to that of an optimal feature set? (2) If a good feature set is not found, should it be expected that good feature sets do not exist? RESULTS: The two questions translate quantitatively into questions concerning conditional expectation. (1) Given the error of an optimal feature set, what is the conditionally expected error of the selected feature set? (2) Given the error of the selected feature set, what is the conditionally expected error of the optimal feature set? We address these questions using three classification rules (linear discriminant analysis, linear support vector machine and k-nearest-neighbor classification) and feature selection via sequential floating forward search and the t-test. We consider three feature-label models and patient data from a study concerning survival prognosis for breast cancer. With regard to the two focus questions, there is similarity across all experiments: (1) One cannot expect to find a feature set whose error is close to optimal, and (2) the inability to find a good feature set should not lead to the conclusion that good feature sets do not exist. In practice, the latter conclusion may be more immediately relevant, since when faced with the common occurrence that a feature set discovered from the data does not give satisfactory results, the experimenter can draw no conclusions regarding the existence or nonexistence of suitable feature sets. AVAILABILITY: http://ee.tamu.edu/~edward/feature_regression/

Assessing Combinational Drug Efficacy in Cancer Cells by Using Image-based Dynamic Response Analysis.

The landscape of translational research has been shifting toward drug combination therapies. Pairing of drugs allows for more types of drug interaction with cells. In order to accurately and comprehensively assess combinational drug efficacy, analytical methods capable of recognizing these alternative reactions will be required to prioritize those drug candidates having better chances of delivering appreciable therapeutic benefits. Traditional efficacy measures are primarily based on the "extent" of drug inhibition, which is the percentage of cells being killed after drug exposure. Here, we introduce a second dimension of evaluation criterion, speed of killing, based on a live cell imaging assay. This dynamic response trajectory approach takes advantage of both "extent" and "speed" information and uncovers synergisms that would otherwise be missed, while also generating hypotheses regarding important mechanistic modes of drug action.

Desmoplasia in Primary Tumors and Metastatic Lesions of Pancreatic Cancer.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is characterized by high levels of fibrosis, termed desmoplasia, which is thought to hamper the efficacy of therapeutics treating PDAC. Our primary focus was to evaluate differences in the extent of desmoplasia in primary tumors and metastatic lesions. As metastatic burden is a primary cause for mortality in PDAC, the extent of desmoplasia in metastases may help to determine whether desmoplasia targeting therapeutics will benefit patients with late-stage, metastatic disease. EXPERIMENTAL DESIGN: We sought to assess desmoplasia in metastatic lesions of PDAC and compare it with that of primary tumors. Fifty-three patients' primaries and 57 patients' metastases were stained using IHC staining techniques. RESULTS: We observed a significant negative correlation between patient survival and extracellular matrix deposition in primary tumors. Kaplan-Meier curves for collagen I showed median survival of 14.6 months in low collagen patients, and 6.4 months in high-level patients (log rank, P < 0.05). Low-level hyaluronan patients displayed median survival times of 24.3 months as compared with 9.3 months in high-level patients (log rank, P < 0.05). Our analysis also indicated that extracellular matrix components, such as collagen and hyaluronan, are found in high levels in both primary tumors and metastatic lesions. The difference in the level of desmoplasia between primary tumors and metastatic lesions was not statistically significant. CONCLUSIONS: Our results suggest that both primary tumors and metastases of PDAC have highly fibrotic stroma. Thus, stromal targeting agents have the potential to benefit PDAC patients, even those with metastatic disease.