Cancer is widely recognized as a genetic disease in which somatic mutations are sequentially accumulated to drive tumor progression. Although genomic landscape studies are informative for individual cancer types, a comprehensive comparative study of tumorigenic mutations across cancer types based on integrative data sources is still a pressing need. We systematically analyzed ~106 non-synonymous mutations extracted from COSMIC, involving ~8000 genome-wide screened samples across 23 major human cancers at both the amino acid and gene levels. Our analysis identified cancer-specific heterogeneity that traditional nucleotide variation analysis alone usually overlooked. Particularly, the amino acid arginine (R) turns out to be the most favorable target of amino acid alteration in most cancer types studied (P < 10−9, binomial test), reflecting its important role in cellular physiology. The tumor suppressor gene TP53 is mutated exclusively with the HYDIN, KRAS, and PTEN genes in large intestine, lung, and endometrial cancers respectively, indicating that TP53 takes part in different signaling pathways in different cancers. While some of our analyses corroborated previous observations, others indicated relevant candidates with high priority for further experimental validation. Our findings have many ramifications in understanding the etiology of cancer and the underlying molecular mechanisms in particular cancers.
Motivation: p38 mitogen-activated protein kinase activation plays an important role in resistance to chemotherapeutic cytotoxic drugs in treating multiple myeloma (MM). However, how the p38 mitogen-activated protein kinase signaling pathway is involved in drug resistance, in particular the roles that the various p38 isoforms play, remains largely unknown.
Method: To explore the underlying mechanisms, we developed a novel systems biology approach by integrating liquid chromatography–mass spectrometry and reverse phase protein array data from human MM cell lines with computational pathway models in which the unknown parameters were inferred using a proposed novel algorithm called modularized factor graph.
Results: New mechanisms predicted by our models suggest that combined activation of various p38 isoforms may result in drug resistance in MM via regulating the related pathways including extracellular signal-regulated kinase (ERK) pathway and NFкB pathway. ERK pathway regulating cell growth is synergistically regulated by p38δ isoform, whereas nuclear factor kappa B (NFкB) pathway regulating cell apoptosis is synergistically regulated by p38α isoform. This finding that p38δ isoform promotes the phosphorylation of ERK1/2 in MM cells treated with bortezomib was validated by western blotting. Based on the predicted mechanisms, we further screened drug combinations in silico and found that a promising drug combination targeting ERK1/2 and NFκB might reduce the effects of drug resistance in MM cells. This study provides a framework of a systems biology approach to studying drug resistance and drug combination selection.
Availability and implementation: RPPA experimental Data and Matlab source codes of modularized factor graph for parameter estimation are freely available online at http://ctsb.is.wfubmc.edu/publications/modularized-factor-graph.php
firstname.lastname@example.org or email@example.com
Supplementary data are available at Bioinformatics online.
Multiple myeloma (MM) is a B lymphocyte malignancy that remains incurable despite extensive research efforts. This is due, in part, to frequent disease recurrences associated with the persistence of myeloma cancer stem cells (mCSCs). Bone marrow mesenchymal stromal cells (BMSCs) play critical roles in supporting mCSCs through genetic or biochemical alterations. Previously, we identified mechanical distinctions between BMSCs isolated from MM patients (mBMSCs) and those present in the BM of healthy individuals (nBMSCs). These properties of mBMSC contributed to their ability to preferentially support mCSCs. To further illustrate mechanisms underlying the differences between mBMSCs and nBMSCs, here we report that (i) mBMSCs express an abnormal, constitutively high level of phosphorylated Myosin II, which leads to stiffer membrane mechanics, (ii) mBMSCs are more sensitive to SDF-1α-induced activation of MYL2 through the G(i./o)-PI3K-RhoA-ROCK-Myosin II signaling pathway, affecting Young’s modulus in BMSCs and (iii) activated Myosin II confers increased cell contractile potential, leading to enhanced collagen matrix remodeling and promoting the cell–cell interaction between mCSCs and mBMSCs. Together, our findings suggest that interfering with SDF-1α signaling may serve as a new therapeutic approach for eliminating mCSCs by disrupting their interaction with mBMSCs.
multiple myeloma; SDF-1α; mesenchymal stromal cells; RhoA-ROCK-Myosin II
Transcription factors (TFs) and epigenetic modifications play crucial roles in the regulation of gene expression, and correlations between the two types of factors have been discovered. However, methods for quantitatively studying the correlations remain limited. Here, we present a computational approach to systematically investigating how epigenetic changes in chromatin architectures or DNA sequences relate to TF binding. We implemented statistical analyses to illustrate that epigenetic modifications are predictive of TF binding affinities, without the need of sequence information. Intriguingly, by considering genome locations relative to transcription start sites (TSSs) or enhancer midpoints, our analyses show that different locations display various relationship patterns. For instance, H3K4me3, H3k9ac and H3k27ac contribute more in the regions near TSSs, whereas H3K4me1 and H3k79me2 dominate in the regions far from TSSs. DNA methylation plays relatively important roles when close to TSSs than in other regions. In addition, the results show that epigenetic modification models for the predictions of TF binding affinities are cell line-specific. Taken together, our study elucidates highly coordinated, but location- and cell type-specific relationships between epigenetic modifications and binding affinities of TFs.
Mathematical modeling of influenza epidemic is important for analyzing the main cause of the epidemic and finding effective interventions towards it. The epidemic is a dynamic process. In this process, daily infections are caused by people's contacts, and the frequency of contacts can be mainly influenced by their cognition to the disease. The cognition is in turn influenced by daily illness attack rate, climate, and other environment factors. Few existing methods considered the dynamic process in their models. Therefore, their prediction results can hardly be explained by the mechanisms of epidemic spreading. In this paper, we developed a heterogeneous graph modeling approach (HGM) to describe the dynamic process of influenza virus transmission by taking advantage of our unique clinical data. We built social network of studied region and embedded an Agent-Based Model (ABM) in the HGM to describe the dynamic change of an epidemic. Our simulations have a good agreement with clinical data. Parameter sensitivity analysis showed that temperature influences the dynamic of epidemic significantly and system behavior analysis showed social network degree is a critical factor determining the size of an epidemic. Finally, multiple scenarios for vaccination and school closure strategies were simulated and their performance was analyzed.
The genetic risk factors for susceptibility to chronic obstructive
pulmonary disease (COPD) are still largely unknown. Additional genetic
variants are likely to be identified by genome-wide association studies in
larger cohorts or specific subgroups.
Genome-wide association analysis in COPDGene (non-Hispanic whites and
African-Americans) was combined with existing data from the ECLIPSE,
NETT/NAS, and GenKOLS (Norway) studies. Analyses were performed both using
all moderate-to-severe cases and the subset of severe cases. Top loci not
previously described as genome-wide significant were genotyped in the ICGN
study, and results combined in a joint meta-analysis.
Analysis of a total of 6,633 moderate-to-severe cases and 5,704
controls confirmed association at three known loci:
CHRNA3/CHRNA5/IREB2, FAM13A, and HHIP
(10−12 < P < 10−14),
and also showed significant evidence of association at a novel locus near
RIN3 (overall P, including ICGN =
5•4×10−9). In the severe COPD analysis
(n=3,497), the effects at two of three previously described loci were
significantly stronger; we also identified two additional loci previously
reported to affect gene expression of MMP12 and
TGFB2 (overall P = 2•6x10−9
and 8•3×10−9). RIN3 and
TGFB2 expression levels were reduced in a set of Lung
Tissue Research Consortium COPD lung tissue samples compared with
In a genome-wide study of COPD, we confirmed associations at three
known loci and found additional genome-wide significant associations with
moderate-to-severe COPD near RIN3 and with severe COPD near
MMP12 and TGFB2. Genetic variants,
apart from alpha-1 antitrypsin deficiency, increase the risk of COPD. Our
analysis of severe COPD suggests additional genetic variants may be
identified by focusing on this subgroup.
National Heart, Lung, and Blood Institute; the COPD Foundation
through contributions from AstraZeneca, Boehringer Ingelheim, Novartis, and
Sepracor; GlaxoSmithKline; Centers for Medicare and Medicaid Services;
Agency for Healthcare Research and Quality; US Department of Veterans
The HHIP gene, encoding Hedgehog interacting protein, has been implicated in chronic obstructive pulmonary disease (COPD) by genome-wide association studies (GWAS), and our subsequent studies identified a functional upstream genetic variant that decreased HHIP transcription. However, little is known about how HHIP contributes to COPD pathogenesis.
We exposed Hhip haploinsufficient mice (Hhip+/-) to cigarette smoke (CS) for 6 months to model the biological consequences caused by CS in human COPD risk-allele carriers at the HHIP locus. Gene expression profiling in murine lungs was performed followed by an integrative network inference analysis, PANDA (Passing Attributes between Networks for Data Assimilation) analysis.
We detected more severe airspace enlargement in Hhip+/- mice vs. wild-type littermates (Hhip+/+) exposed to CS. Gene expression profiling in murine lungs suggested enhanced lymphocyte activation pathways in CS-exposed Hhip+/- vs. Hhip+/+ mice, which was supported by increased numbers of lymphoid aggregates and enhanced activation of CD8+ T cells after CS-exposure in the lungs of Hhip+/-mice compared to Hhip+/+ mice. Mechanistically, results from PANDA network analysis suggested a rewired and dampened Klf4 signaling network in Hhip+/- mice after CS exposure.
In summary, HHIP haploinsufficiency exaggerated CS-induced airspace enlargement, which models CS-induced emphysema in human smokers carrying COPD risk alleles at the HHIP locus. Network modeling suggested rewired lymphocyte activation signaling circuits in the HHIP haploinsufficiency state.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-015-0137-3) contains supplementary material, which is available to authorized users.
RNA-protein complexes are essential in mediating important fundamental cellular processes, such as transport and localization. In particular, ncRNA-protein interactions play an important role in post-transcriptional gene regulation like mRNA localization, mRNA stabilization, poly-adenylation, splicing and translation. The experimental methods to solve RNA-protein interaction prediction problem remain expensive and time-consuming. Here, we present the RPI-Pred (RNA-protein interaction predictor), a new support-vector machine-based method, to predict protein-RNA interaction pairs, based on both the sequences and structures. The results show that RPI-Pred can correctly predict RNA-protein interaction pairs with ∼94% prediction accuracy when using sequence and experimentally determined protein and RNA structures, and with ∼83% when using sequences and predicted protein and RNA structures. Further, our proposed method RPI-Pred was superior to other existing ones by predicting more experimentally validated ncRNA-protein interaction pairs from different organisms. Motivated by the improved performance of RPI-Pred, we further applied our method for reliable construction of ncRNA-protein interaction networks. The RPI-Pred is publicly available at: http://ctsb.is.wfubmc.edu/projects/rpi-pred.
Engineered functional organs or tissues, created with autologous somatic cells and seeded on biodegradable or hydrogel scaffolds, have been developed for use in individuals with tissue damage suffered from congenital disorders, infection, irradiation, or cancer. However, in those patients, abnormal cells obtained by biopsy from the compromised tissue could potentially contaminate the engineered tissues. Thus, an alternative cell source for construction of the neo-organ or functional recovery of the injured or diseased tissues would be useful. Recently, we have found stem cells existing in the urine. These cells are highly expandable, and have self-renewal capacity, paracrine properties, and multi-differentiation potential. As a novel cell source, urine-derived stem cells (USCs) provide advantages for cell therapy and tissue engineering applications in regeneration of various tissues, particularly in the genitourinary tract, because they originate from the urinary tract system. Importantly, USCs can be obtained via a non-invasive, simple, and low-cost approach and induced with high efficiency to differentiate into three dermal cell lineages.
Cell therapy; Genitourinary tract; Stem cells; Tissue regeneration; Urine
To design an algorithm for optimizing pulsed high intensity focused ultrasound (p-HIFU) treatment parameters to maximize tissue transport while minimizing thermal necrosis based on MR image guidance.
Materials and Methods
P-HIFU power, duty cycle and treatment duration were varied to generate different levels of thermal and mechanical deposition in rabbit muscle. Changes in T2-weighted and T1 contrast-enhanced (CE) signal were assessed immediately following treatment and at 24 hrs. Transport parameters were extracted via T1-weighted dynamic contrast-enhanced MRI (DCE-MRI) technique at 0 and 24 hr time points.
Successful p-HIFU treatment was indicated by focal hyperintensity on the T2-weighted image immediately post-treatment, suggesting increased fluid (edema), with little intensity change in CE image. After 24h, the affected region expanded along the muscle fiber accompanied by clear hyperintensity in CE image (contrast uptake). Quantitative DCE-MRI analysis revealed statistically significant increases in both leakage rate and extracellular space, accompanied by a decrease in clearance rate.
Successful p-HIFU treatment was mainly correlated to tissue heating. Tissue transport properties following treatment success would result in improved contact between drug and targets in both time and space. MRI is the key to controlling treatment via thermometry and also monitoring efficacy via T2-weighted imaging.
pulsed high-intensity focused ultrasound; MRI-guided focused ultrasound; drug delivery; dynamic contrast enhanced-MRI; tissue permeability; clearance rate
Cancer initiating cells have been documented in multiple myeloma and believed to be a key factor that initiates and drives tumor growth, differentiation, metastasis, and recurrence of the diseases. Although myeloma initiating cells (MICs) are likely to share many properties of normal stem cells, the underlying mechanisms regulating the fate of MICs are largely unknown. Studies designed to explore such communication are urgently needed to enhance our ability to predict the fate decisions of MICs (self-renewal, differentiation, and proliferation). In this study, we developed a novel system to understand the intercellular communication between MICs and their niche by seamlessly integrating experimental data and mathematical model. We first designed dynamic cell culture experiments and collected three types of cells (side population cells, progenitor cells, and mature myeloma cells) under various cultural conditions with flow cytometry. Then we developed a lineage model with ordinary differential equations by considering secreted factors, self-renewal, differentiation, and other biological functions of those cells, to model the cell–cell interactions among the three cell types. Particle swarm optimization was employed to estimate the model parameters by fitting the experimental data to the lineage model. The theoretical results show that the correlation coefficient analysis can reflect the feedback loops among the three cell types, the intercellular feedback signaling can regulate cell population dynamics, and the culture strategies can decide cell growth. This study provides a basic framework of studying cell–cell interactions in regulating MICs fate.
Cancer initiating cell; lineage model; mathematical modeling; multiple myeloma (MM); parameter estimation
The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be used to elucidate potential mechanisms of a compound's efficacy.
Lymphangioleiomyomatosis (LAM) is a destructive lung disease primarily affecting women. Genetic studies indicate that LAM cells carry inactivating tuberous sclerosis complex (TSC)–2 mutations, and metastasize to the lung. We previously discovered that estradiol increases the metastasis of TSC2-deficient cells in mice carrying xenograft tumors. Here, we investigate the molecular basis underlying the estradiol-induced lung metastasis of TSC2-deficient cells, and test the efficacy of Faslodex (an estrogen receptor antagonist) in a preclinical model of LAM. We used a xenograft tumor model in which estradiol induces the lung metastasis of TSC2-deficient cells. We analyzed the impact of Faslodex on tumor size, the extracellular matrix organization, the expression of matrix metalloproteinase (MMP)–2, and lung metastasis. We also examined the effects of estradiol and Faslodex on MMP2 expression and activity in tuberin-deficient cells in vitro. Estradiol resulted in a marked reduction of Type IV collagen deposition in xenograft tumors, associated with 2-fold greater MMP2 concentrations compared with placebo-treated mice. Faslodex normalized the Type IV collagen changes in xenograft tumors, enhanced the survival of the mice, and completely blocked lung metastases. In vitro, estradiol enhanced MMP2 transcripts, protein accumulation, and activity. These estradiol-induced changes in MMP2 were blocked by Faslodex. In TSC2-deficient cells, estradiol increased MMP2 concentrations in vitro and in vivo, and induced extracellular matrix remodeling. Faslodex inhibits the estradiol-induced lung metastasis of TSC2-deficient cells. Targeting estrogen receptors with Faslodex may be of efficacy in the treatment of LAM.
tuberin; estrogen receptor antagonist; matrix metalloproteinase; extracellular matrix
The investigation of microcirculation is a critical task in biomedical and physiological research. In order to monitor human’s condition and develop effective therapies of some diseases, the microcirculation information, such as flow velocity and vessel density, must be evaluated in a noninvasive manner. As one of the tasks of microcirculation investigation, automatic blood cell tracking presents an effective approach to estimate blood flow velocity. Currently, the most common method for blood cell tracking is based on spatiotemporal image analysis, which has lots of limitations, such as the diameter of microvesssels cannot be too larger than blood cells or tracers, cells or tracers should have fixed velocity, and it requires the image with high qualification. In this paper, we propose an optical flow method for automatic cell tracking. The key algorithm of the method is to align an image to its neighbors in a large image collection consisting of a variety of scenes. Considering the method cannot solve the problems in all cases of cell movement, another optical flow method, SIFT (Scale Invariant Feature Transform) flow, is also presented. The experimental results show that both methods can track the cells accurately. Optical flow is specially robust to the case where the velocity of cell is unstable, while SIFT flow works well when there are large displacement of cell between two adjacent frames. Our proposed methods outperform other methods when doing in vivo cell tracking, which can be used to estimate the blood flow directly and help to evaluate other parameters in microcirculation.
Optical flow; SIFT Flow; blood cell; in vivo cell tracking; microscopy imaging
We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, http://www.SCILHS.org) clinical data research network, which leverages the $48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative ‘apps’ to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components.
Electronic Health Record; Learning Health System; Clinical Trials; Patient Engagement; Distributed Computing
Hedgehog Interacting Protein (HHIP) was implicated in chronic obstructive pulmonary disease (COPD) by genome-wide association studies (GWAS). However, it remains unclear how HHIP contributes to COPD pathogenesis. To identify genes regulated by HHIP, we performed gene expression microarray analysis in a human bronchial epithelial cell line (Beas-2B) stably infected with HHIP shRNAs. HHIP silencing led to differential expression of 296 genes; enrichment for variants nominally associated with COPD was found. Eighteen of the differentially expressed genes were validated by real-time PCR in Beas-2B cells. Seven of 11 validated genes tested in human COPD and control lung tissues demonstrated significant gene expression differences. Functional annotation indicated enrichment for extracellular matrix and cell growth genes. Network modeling demonstrated that the extracellular matrix and cell proliferation genes influenced by HHIP tended to be interconnected. Thus, we identified potential HHIP targets in human bronchial epithelial cells that may contribute to COPD pathogenesis.
Hedgehog interacting protein (HHIP); Gene expression profiling; COPD (Chronic obstructive pulmonary disease); extracellular matrix (ECM); network modeling
Ovarian carcinoma immunoreactive antigen-like protein 2 (OCIAD2) is a protein with unknown function. Frequently methylated or downregulated, OCIAD2 has been observed in kinds of tumors, and TGFβ signaling has been proved to induce the expression of OCIAD2. However, current pathway analysis tools do not cover the genes without reported interactions like OCIAD2 and also miss some significant genes with relatively lower expression. To investigate potential biological milieu of OCIAD2, especially in cancer microenvironment, a nova approach pbMOO was created to find the potential pathways from TGFβ to OCIAD2 by searching on the pathway bridge, which consisted of cancer enriched looping patterns from the complicated entire protein interactions network. The pbMOO approach was further applied to study the modulator of ligand TGFβ1, receptor TGFβR1, intermediate transfer proteins, transcription factor, and signature OCIAD2. Verified by literature and public database, the pathway TGFβ1- TGFβR1- SMAD2/3- SMAD4/AR-OCIAD2 was detected, which concealed the androgen receptor (AR) which was the possible transcription factor of OCIAD2 in TGFβ signal, and it well explained the mechanism of TGFβ induced OCIAD2 expression in cancer microenvironment, therefore providing an important clue for the future functional analysis of OCIAD2 in tumor pathogenesis.
Amyotrophic lateral sclerosis (ALS) is a devastating and chronic neurodegenerative disease without any known cure. In the brain and spinal cord of both patients and animal models with ALS, neuroinflammation is a prominent pathological hallmark which is characterized by infiltrating T cells at sites of motor neurons injury. Their presence in mutant Cu2+/Zn2+ superoxide dismutase (mSOD1) induced ALS plays an important role in shifting the response of microglia from neuroprotective into neurotoxic. In order to better understand how these cells and their communication network collectively modulate the disease progress, we have established a mathematical model integrating diverse cells and cytokines. According to the experimental data sets, we first refined this model by identifying a link between TGFβ and M1 microglia which can produce an optimized model to fit data sets better. Then based on this model, parameters were estimated using genetic algorithm. Sensitivity analysis of these parameters identified several factors such as release rate of IFNγ by T helper 1 (Th1) cells, which may be related to the heterogeneity between the patients with different survival time. Furthermore, the tests on T cell based therapeutic strategies indicated that elimination of Th1 cells is the most effective approach extending survival time. This confirmed the dominant role of Th1 cells in leading the rapid disorder in the later stage of ALS. For the therapies targeting cytokines, injection of IL6 can essentially augment the neuroprotective response and extend the life effectively by elevating the level of IL4, a neuroprotective cytokine, while direct injected IL4 will decay rapidly in ALS microenvironment and cannot provide a persistent protective effect. On the other hand, in spite of the attractive effect of direct elimination of mSOD1 or self-antigen, it is difficult to implement in CNS. As an alternative, elimination of IFNγ can be chosen as another effective therapy. In the future, if we combine the side effect of different therapies, this model can be used to optimize the therapeutic strategies so that they can effectively improve survival rates and quality of life for patients with ALS.
Multiple myeloma, the second most common hematological cancer, is currently incurable due to refractory disease relapse and development of multiple drug resistance. We and others recently established the biophysical model that myeloma initiating (stem) cells (MICs) trigger the stiffening of their niches via SDF-1/CXCR4 paracrine; The stiffened niches then promote the colonogenesis of MICs and protect them from drug treatment. In this work we examined in silico the pharmaceutical potential of targeting MIC niche stiffness to facilitate cytotoxic chemotherapies. We first established a multi-scale agent-based model using the Markov Chain Monte Carlo approach to recapitulate the niche stiffness centric, pro-oncogenetic positive feedback loop between MICs and myeloma-associated bone marrow stromal cells (MBMSCs), and investigated the effects of such intercellular chemo-physical communications on myeloma development. Then we used AMD3100 (to interrupt the interactions between MICs and their stroma) and Bortezomib (a recently developed novel therapeutic agent) as representative drugs to examine if the biophysical properties of myeloma niches are drugable. Results showed that our model recaptured the key experimental observation that the MBMSCs were more sensitive to SDF-1 secreted by MICs, and provided stiffer niches for these initiating cells and promoted their proliferation and drug resistance. Drug synergism analysis suggested that AMD3100 treatment undermined the capability of MICs to modulate the bone marrow microenvironment, and thus re-sensitized myeloma to Bortezomib treatments. This work is also the first attempt to virtually visualize in 3D the dynamics of the bone marrow stiffness during myeloma development. In summary, we established a multi-scale model to facilitate the translation of the niche-stiffness centric myeloma model as well as experimental observations to possible clinical applications. We concluded that targeting the biophysical properties of stem cell niches is of high clinical potential since it may re-sensitize tumor initiating cells to chemotherapies and reduce risks of cancer relapse.
Solid tumors develop abnormally at spatial and temporal scales, giving rise to biophysical barriers that impact anti-tumor chemotherapy. This may increase the expenditure and time for conventional drug pharmacokinetic and pharmacodynamic studies. In order to facilitate drug discovery, we propose a mathematical model that couples three-dimensional tumor growth and angiogenesis to simulate tumor progression for chemotherapy evaluation. This application-oriented model incorporates complex dynamical processes including cell- and vascular-mediated interstitial pressure, mass transport, angiogenesis, cell proliferation, and vessel maturation to model tumor progression through multiple stages including tumor initiation, avascular growth, and transition from avascular to vascular growth. Compared to pure mechanistic models, the proposed empirical methods are not only easy to conduct but can provide realistic predictions and calculations. A series of computational simulations were conducted to demonstrate the advantages of the proposed comprehensive model. The computational simulation results suggest that solid tumor geometry is related to the interstitial pressure, such that tumors with high interstitial pressure are more likely to develop dendritic structures than those with low interstitial pressure.
The purpose of this prospective multicenter study was to assess the accuracy of a computer-aided surgical simulation (CASS) protocol for orthognathic surgery.
Materials and Methods
The accuracy of the CASS protocol was assessed by comparing planned and postoperative outcomes of 65 consecutive patients enrolled from 3 centers. Computer-generated surgical splints were used for all patients. For the genioplasty, one center utilized computer-generated chin templates to reposition the chin segment only for patients with asymmetry. Standard intraoperative measurements were utilized without the chin templates for the remaining patients. The primary outcome measurements were linear and angular differences for the maxilla, mandible and chin when the planned and postoperative models were registered at the cranium. The secondary outcome measurements were: maxillary dental midline difference between the planned and postoperative positions; and linear and angular differences of the chin segment between the groups with and without the use of the template. The latter was measured when the planned and postoperative models were registered at mandibular body. Statistical analyses were performed, and the accuracy was reported using root mean square deviation (RMSD) and Bland and Altman's method for assessing measurement agreement.
In the primary outcome measurements, there was no statistically significant difference among the 3 centers for the maxilla and mandible. The largest RMSD was 1.0mm and 1.5° for the maxilla, and 1.1mm and 1.8° for the mandible. For the chin, there was a statistically significant difference between the groups with and without the use of the chin template. The chin template group showed excellent accuracy with largest positional RMSD of 1.0mm and the largest orientational RSMD of 2.2°. However, larger variances were observed in the group not using the chin template. This was significant in anteroposterior and superoinferior directions, as in pitch and yaw orientations. In the secondary outcome measurements, the RMSD of maxillary dental midline positions was 0.9mm. When registered at the body of the mandible, the linear and angular differences of the chin segment between the groups with and without the use of the chin template were consistent with the results found in the primary outcome measurements.
Using the CASS protocol, the computerized plan can be accurately and consistently transferred to the patient to position the maxilla and mandible at the time of surgery. The computer-generated chin template provides more accuracy in repositioning the chin segment than the intraoperative measurements.
computer-aided surgical simulation; orthognathic surgery; computer-generated surgical splint; computer-generated chin template; intraoperative measurements; dentofacial deformity
To provide site-specific delivery and targeted release of growth factors to implanted urine-derived stem cells (USCs), we prepared microbeads of alginate containing growth factors. The growth factors included VEGF, IGF-1, FGF-1, PDGF, HGF and NGF. Radiolabeled growth factors were loaded separately and used to access the in vitro release from the microbeads with a gamma counter over 4 weeks. In vitro endothelial differentiation of USCs by the released VEGF from the microbeads in a separate experiment confirmed that the released growth factors from the microbeads were bioactive. USCs and microbeads were mixed with the collagen gel type 1 (2 mg/ml) and used for in vivo studies through subcutaneous injection into nude mice. Four weeks after subcutaneous injection, we found that grafted cell survival was improved and more cells expressed myogenic and endothelial cell transcripts and markers compared to controls. More vessel formation and innervations were observed in USCs combined with six growth factors cocktail incorporated in microbeads compared to controls. In conclusion, a combination of growth factors released locally from the alginate microbeads induced USCs to differentiate into a myogenic lineage, enhanced revascularization and innervation, and stimulated resident cell growth in vivo. This approach could potentially be used for cell therapy in the treatment of stress urinary incontinence.
Drug delivery; Stem cells; Alginate microbeads; Controlled release; Growth factors
Prostate cancer patients often have increased levels of psychological stress or anxiety, but the molecular mechanisms underlying the interaction between psychological stress and prostate cancer as well as therapy resistance have been rarely studied and remain poorly understood. Recent reports show that stress inhibits apoptosis in prostate cancer cells via epinephrine/beta2 adrenergic receptor/PKA/BAD pathway. In this study, we used experimental data on the signaling pathways that control BAD phosphorylation to build a dynamic network model of apoptosis regulation in prostate cancer cells. We then compared the predictive power of two different models with or without the role of Mcl-1, which justified the role of Mcl-1 stabilization in anti-apoptotic effects of emotional stress. Based on the selected model, we examined and quantitatively evaluated the induction of apoptosis by drug combination therapies. We predicted that the combination of PI3K inhibitor LY294002 and inhibition of BAD phosphorylation at S112 would produce the best synergistic effect among 8 interventions examined. Experimental validation confirmed the effectiveness of our predictive model. Moreover, we found that epinephrine signaling changes the synergism pattern and decreases efficacy of combination therapy. The molecular mechanisms responsible for therapeutic resistance and the switch in synergism were explored by analyzing a network model of signaling pathways affected by psychological stress. These results provide insights into the mechanisms of psychological stress signaling in therapy-resistant cancer, and indicate the potential benefit of reducing psychological stress in designing more effective therapies for prostate cancer patients.
Psychological stress and anxiety are often experienced by prostate cancer patients, but the underlying mechanisms of interactions between psychological stress and cancer development, as well as drug resistance, are unclear. Here, we employed a systems biology approach to study interactions between stress-activated epinephrine/beta2 adrenergic receptor/protein kinase A signaling and a regulatory network that controls apoptosis in prostate cancer cells. We developed a dynamic network model of signaling pathways that control apoptosis in prostate cancer cells and quantitatively evaluated the effects of stress-activated signaling on apoptosis induced by drug combinations. Experimental data were used to guide modeling, to fit the unknown parameters and validate the model. Based on our model we found that epinephrine/beta2 adrenergic receptor/protein kinase A signaling can decrease drug efficiency, and can shift the effect of drug combination from synergy to antagonism. We also predicted that in addition to BAD phosphorylation Mcl-1 expression could be upregulated by stress/epinephrine signaling to inhibit apoptosis. This study provides insights into the mechanisms of psychological stress signaling in therapy-resistant cancer, and suggests that reducing psychological stress could help to make prostate cancer treatment more effective.
Substantial effort in recent years has been devoted to analyzing data based large-scale biological networks, which provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or compounds. In this work, we proposed a novel strategy to investigate kinase inhibitor induced pathway signatures by integrating multiplex data in Library of Integrated Network-based Cellular Signatures (LINCS), e.g. KINOMEscan data and cell proliferation/mitosis imaging data. Using this strategy, we first established a PC9 cell line specific pathway model to investigate the pathway signatures in PC9 cell line when perturbed by a small molecule kinase inhibitor GW843682. This specific pathway revealed the role of PI3K/AKT in modulating the cell proliferation process and the absence of two anti-proliferation links, which indicated a potential mechanism of abnormal expansion in PC9 cell number. Incorporating the pathway model for side effects on primary human hepatocytes, it was used to screen 27 kinase inhibitors in LINCS database and PF02341066, known as Crizotinib, was finally suggested with an optimal concentration 4.6 uM to suppress PC9 cancer cell expansion while avoiding severe damage to primary human hepatocytes. Drug combination analysis revealed that the synergistic effect region can be predicted straightforwardly based on a threshold which is an inherent property of each kinase inhibitor. Furthermore, this integration strategy can be easily extended to other specific cell lines to be a powerful tool for drug screen before clinical trials.
Motivation: It becomes widely accepted that human cancer is a disease involving dynamic changes in the genome and that the missense mutations constitute the bulk of human genetic variations. A multitude of computational algorithms, especially the machine learning-based ones, has consequently been proposed to distinguish missense changes that contribute to the cancer progression (‘driver’ mutation) from those that do not (‘passenger’ mutation). However, the existing methods have multifaceted shortcomings, in the sense that they either adopt incomplete feature space or depend on protein structural databases which are usually far from integrated.
Results: In this article, we investigated multiple aspects of a missense mutation and identified a novel feature space that well distinguishes cancer-associated driver mutations from passenger ones. An index (DX score) was proposed to evaluate the discriminating capability of each feature, and a subset of these features which ranks top was selected to build the SVM classifier. Cross-validation showed that the classifier trained on our selected features significantly outperforms the existing ones both in precision and robustness. We applied our method to several datasets of missense mutations culled from published database and literature and obtained more reasonable results than previous studies.
Availability: The software is available online at http://www.methodisthealth.com/software and https://sites.google.com/site/drivermutationidentification/.
Supplementary information: Supplementary data are available at Bioinformatics online.