Tissue p53-induced glycolysis and apoptosis regulator (TIGAR) is associated with oxidative stress in benign and malignant colorectal lesions

Background: Colorectal cancer (CRC) is the fourth leading cause of cancer-mortality worldwide. Tissue p53-induced glycolysis and apoptosis regulator gene (TIGAR) has an important role in cellular glycolysis and acts as an oncogene.Objectives: We aimed to investigate the diagnostic utility of TIGAR in both CRC and benign bowel deceases.Methods: One-hundred-eighty tissue samples were recruited and classified into 3 groups: group (1) 60 CRC samples from the tumor mass of colorectal cancer patients, group (2), 60 non-neoplastic colorectal tissue samples and group (3), 60 benign bowel lesions samples (ulcerative-colitis, Chron’s disease, adenoma, and familial adenomatous polyposis). The expressions of tissue mRNA and protein levels of TIGAR were determined. Levels of malondialdehyde and reduced glutathione were also measured.Results: Our results showed upregulated expressions of TIGAR gene and protein levels in CRC tissues and benign colonic lesions compared to non-tumor tissues (p < 0.0001). Their levels were higher in inflammatory bowel diseases compared to non-inflammatory benign lesions. There were significant relations among TIGAR expression, protein levels, TNM staging, and the presence of metastasis (p<0.0001). ROC curve analysis showed that TIGAR mRNA expression and its protein can discriminate between CRC and benign lesions and between benign bowel disease and controls.Conclusions: To the best of our knowledge this is the first study to assess the level of TIGAR in different benign bowel diseases. TIGAR might be involved in the pathogenesis of benign and malignant bowel diseases and could be a potential biomarker for diagnosis

Control of replication stress and mitosis in colorectal cancer stem cells through the interplay of PARP1, MRE11 and RAD51

Cancer stem cells (CSCs) are tumor subpopulations driving disease development, progression, relapse and therapy resistance, and their targeting ensures tumor eradication. CSCs display heterogeneous replication stress (RS), but the functionality/relevance of the RS response (RSR) centered on the ATR-CHK1 axis is debated. Here, we show that the RSR is efficient in primary CSCs from colorectal cancer (CRC-SCs), and describe unique roles for PARP1 and MRE11/RAD51. First, we demonstrated that PARP1 is upregulated in CRC-SCs resistant to several replication poisons and RSR inhibitors (RSRi). In these cells, PARP1 modulates replication fork speed resulting in low constitutive RS. Second, we showed that MRE11 and RAD51 cooperate in the genoprotection and mitosis execution of PARP1-upregulated CRC-SCs. These roles represent therapeutic vulnerabilities for CSCs. Indeed, PARP1i sensitized CRC-SCs to ATRi/CHK1i, inducing replication catastrophe, and prevented the development of resistance to CHK1i. Also, MRE11i + RAD51i selectively killed PARP1-upregulated CRC-SCs via mitotic catastrophe. These results provide the rationale for biomarker-driven clinical trials in CRC using distinct RSRi combinations

BCL2 in breast cancer: a favourable prognostic marker across molecular subtypes and independent of adjuvant therapy received

Background: Breast cancer is heterogeneous and the existing prognostic classifiers are limited in accuracy, leading to unnecessary treatment of numerous women. B-cell lymphoma 2 (BCL2), an antiapoptotic protein, has been proposed as a prognostic marker, but this effect is considered to relate to oestrogen receptor (ER) status. This study aimed to test the clinical validity of BCL2 as an independent prognostic marker. Methods: Five studies of 11 212 women with early-stage breast cancer were analysed. Individual patient data included tumour size, grade, lymph node status, endocrine therapy, chemotherapy and mortality. BCL2, ER, progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) levels were determined in all tumours. A Cox model incorporating the time-dependent effects of each variable was used to explore the prognostic significance of BCL2. Results: In univariate analysis, ER, PR and BCL2 positivity was associated with improved survival and HER2 positivity with inferior survival. For ER and PR this effect was time dependent, whereas for BCL2 and HER2 the effect persisted over time. In multivariate analysis, BCL2 positivity retained independent prognostic significance (hazard ratio (HR) 0.76, 95% confidence interval (CI) 0.66-0.88, P<0.001). BCL2 was a powerful prognostic marker in ER (HR 0.63, 95% CI 0.54-0.74, P<0.001) and ER disease (HR 0.56, 95% CI 0.48-0.65, P<0.001), and in HER2 (HR 0.55, 95% CI 0.49-0.61, P<0.001) and HER2 disease (HR 0.70, 95% CI 0.57-0.85, P<0.001), irrespective of the type of adjuvant therapy received. Addition of BCL2 to the Adjuvant! Online prognostic model, for a subset of cases with a 10-year follow-up, improved the survival prediction (P<0.0039). Conclusions: BCL2 is an independent indicator of favourable prognosis for all types of early-stage breast cancer. This study establishes the rationale for introduction of BCL2 immunohistochemistry to improve prognostic stratification. Further work is now needed to ascertain the exact way to apply BCL2 testing for risk stratification and to standardise BCL2 immunohistochemistry for this application. © 2010 Cancer Research UK All rights reserved

The significance of tumour microarchitectural features in breast cancer prognosis: a digital image analysis

BACKGROUND: As only a minor portion of the information present in histological sections is accessible by eye, recognition and quantification of complex patterns and relationships among constituents relies on digital image analysis. In this study, our working hypothesis was that, with the application of digital image analysis technology, visually unquantifiable breast cancer microarchitectural features can be rigorously assessed and tested as prognostic parameters for invasive breast carcinoma of no special type. METHODS: Digital image analysis was performed using public domain software (ImageJ) on tissue microarrays from a cohort of 696 patients, and validated with a commercial platform (Visiopharm). Quantified features included elements defining tumour microarchitecture, with emphasis on the extent of tumour-stroma interface. The differential prognostic impact of tumour nest microarchitecture in the four immunohistochemical surrogates for molecular classification was analysed. Prognostic parameters included axillary lymph node status, breast cancer-specific survival, and time to distant metastasis. Associations of each feature with prognostic parameters were assessed using logistic regression and Cox proportional models adjusting for age at diagnosis, grade, and tumour size. RESULTS: An arrangement in numerous small nests was associated with axillary lymph node involvement. The association was stronger in luminal tumours (odds ratio (OR) = 1.39, p = 0.003 for a 1-SD increase in nest number, OR = 0.75, p = 0.006 for mean nest area). Nest number was also associated with survival (hazard ratio (HR) = 1.15, p = 0.027), but total nest perimeter was the parameter most significantly associated with survival in luminal tumours (HR = 1.26, p = 0.005). In the relatively small cohort of triple-negative tumours, mean circularity showed association with time to distant metastasis (HR = 1.71, p = 0.027) and survival (HR = 1.8, p = 0.02). CONCLUSIONS: We propose that tumour arrangement in few large nests indicates a decreased metastatic potential. By contrast, organisation in numerous small nests provides the tumour with increased metastatic potential to regional lymph nodes. An outstretched pattern in small nests bestows tumours with a tendency for decreased breast cancer-specific survival. Although further validation studies are required before the argument for routine quantification of microarchitectural features is established, our approach is consistent with the demand for cost-effective methods for triaging breast cancer patients that are more likely to benefit from chemotherapy

Probing exotic phenomena at the interface of nuclear and particle physics with the electric dipole moments of diamagnetic atoms: A unique window to hadronic and semi-leptonic CP violation

The current status of electric dipole moments of diamagnetic atoms which involves the synergy between atomic experiments and three different theoretical areas -- particle, nuclear and atomic is reviewed. Various models of particle physics that predict CP violation, which is necessary for the existence of such electric dipole moments, are presented. These include the standard model of particle physics and various extensions of it. Effective hadron level combined charge conjugation (C) and parity (P) symmetry violating interactions are derived taking into consideration different ways in which a nucleon interacts with other nucleons as well as with electrons. Nuclear structure calculations of the CP-odd nuclear Schiff moment are discussed using the shell model and other theoretical approaches. Results of the calculations of atomic electric dipole moments due to the interaction of the nuclear Schiff moment with the electrons and the P and time-reversal (T) symmetry violating tensor-pseudotensor electron-nucleus are elucidated using different relativistic many-body theories. The principles of the measurement of the electric dipole moments of diamagnetic atoms are outlined. Upper limits for the nuclear Schiff moment and tensor-pseudotensor coupling constant are obtained combining the results of atomic experiments and relativistic many-body theories. The coefficients for the different sources of CP violation have been estimated at the elementary particle level for all the diamagnetic atoms of current experimental interest and their implications for physics beyond the standard model is discussed. Possible improvements of the current results of the measurements as well as quantum chromodynamics, nuclear and atomic calculations are suggested.Comment: 46 pages, 19 tables and 16 figures. A review article accepted for EPJ

Evaluation of the current knowledge limitations in breast cancer research: a gap analysis

BACKGROUND A gap analysis was conducted to determine which areas of breast cancer research, if targeted by researchers and funding bodies, could produce the greatest impact on patients. METHODS Fifty-six Breast Cancer Campaign grant holders and prominent UK breast cancer researchers participated in a gap analysis of current breast cancer research. Before, during and following the meeting, groups in seven key research areas participated in cycles of presentation, literature review and discussion. Summary papers were prepared by each group and collated into this position paper highlighting the research gaps, with recommendations for action. RESULTS Gaps were identified in all seven themes. General barriers to progress were lack of financial and practical resources, and poor collaboration between disciplines. Critical gaps in each theme included: (1) genetics (knowledge of genetic changes, their effects and interactions); (2) initiation of breast cancer (how developmental signalling pathways cause ductal elongation and branching at the cellular level and influence stem cell dynamics, and how their disruption initiates tumour formation); (3) progression of breast cancer (deciphering the intracellular and extracellular regulators of early progression, tumour growth, angiogenesis and metastasis); (4) therapies and targets (understanding who develops advanced disease); (5) disease markers (incorporating intelligent trial design into all studies to ensure new treatments are tested in patient groups stratified using biomarkers); (6) prevention (strategies to prevent oestrogen-receptor negative tumours and the long-term effects of chemoprevention for oestrogen-receptor positive tumours); (7) psychosocial aspects of cancer (the use of appropriate psychosocial interventions, and the personal impact of all stages of the disease among patients from a range of ethnic and demographic backgrounds). CONCLUSION Through recommendations to address these gaps with future research, the long-term benefits to patients will include: better estimation of risk in families with breast cancer and strategies to reduce risk; better prediction of drug response and patient prognosis; improved tailoring of treatments to patient subgroups and development of new therapeutic approaches; earlier initiation of treatment; more effective use of resources for screening populations; and an enhanced experience for people with or at risk of breast cancer and their families. The challenge to funding bodies and researchers in all disciplines is to focus on these gaps and to drive advances in knowledge into improvements in patient care

Triple-negative breast cancer with brain metastases: a comparison between basal-like and non-basal-like biological subtypes

The aim of this study was to divide the group of triple-negative breast cancer patients with brain metastases into basal-like and non-basal-like biological subtypes in order to compare clinical features and survival rates in those two groups. A comprehensive analysis of 111 consecutive triple-negative breast cancer patients with brain metastases treated in the years 2003–2009 was performed. In 75 patients, immunohistochemistry was used as a surrogate of microarray in order to evaluate the expression of three basal markers: cytokeratin 5/6 (CK 5/6), EGFR/HER1 and c-KIT. The basal-like (ER/PgR/HER2-negative, CK5/6positive and/or HER1-positive) and non-basal-like (ER/PgR/HER2-negative, CK5/6-negative, HER1-negative) subsets were selected. Clinical features and survivals were compared in both groups. In the group of 111 triple-negative breast cancer patients, median DFS, OS and survival from brain metastases were 20, 29 and 4 months, respectively. In 75 patients who were evaluable for basal markers, median DFS, OS and survival from brain metastases were 18, 26 and 3.2 months, respectively. In the basal-like subtype, the survival rates were 15, 26 and 3 months, respectively, and in the non-basal-like subtypes, they were 20, 30 and 2.8 months, respectively. No statistically significant differences in survivals were detected between the basal-like and non-basal-like biological subtypes. Factors influencing survival from brain metastases were: Karnofsky performance status (KPS), the status of extracranial disease and age. Biological markers differentiating triple-negative group into basal-like and non-basal-like subtype (CK 5/6, HER1, c-KIT) had no influence on survival. In patients with triple-negative breast cancer and brain metastases, well-known clinical, but not molecular, features correlated with survival

Oxidative Modifications of Rat Liver Cell Components During Fasciola hepatica Infection

The aim of this paper was to assess the influence of Fasciola hepatica infection on oxidative modifications of rat liver cell components such as proteins and lipids. Wistar rats were infected per os with 30 metacercariae of F. hepatica. Activities and concentrations of liver damage markers were determined in the 4th, 7th, and 10th week postinfection (wpi). A decrease in antioxidant capacity of the host liver, manifested by a decrease in total antioxidant status (TAS), was observed. Diminution of antioxidant abilities resulted in enhanced oxidative modifications of lipids and proteins. F. hepatica infection enhanced lipid peroxidation, which was visible in the statistically significant increase in the level of different lipid peroxidation products such as conjugated dienes (CDs), lipid hydroperoxides (LOOHs), malondialdehyde (MDA) and 4-hydroxynonenal (4-HNE). The level of protein modification markers in the rat liver was also significantly changed and the most intensified changes were observed at seventh week postinfection. Concentration of carbonyl groups and dityrosine was significantly increased, whereas the level of tryptophan and sulfhydryl and amino groups was decreased. Changes in the antioxidant abilities of the liver and in the lipid and protein structure of the cell components resulted in destruction of the function of the liver. F. hepatica infection was accompanied by raising serum activities of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) as markers of liver damage. A significant decrease in lysosomal as well as in the total activity of cathepsin B during fasciolosis was also observed

Identifying Biological Network Structure, Predicting Network Behavior, and Classifying Network State With High Dimensional Model Representation (HDMR)

This work presents an adapted Random Sampling - High Dimensional Model Representation (RS-HDMR) algorithm for synergistically addressing three key problems in network biology: (1) identifying the structure of biological networks from multivariate data, (2) predicting network response under previously unsampled conditions, and (3) inferring experimental perturbations based on the observed network state. RS-HDMR is a multivariate regression method that decomposes network interactions into a hierarchy of non-linear component functions. Sensitivity analysis based on these functions provides a clear physical and statistical interpretation of the underlying network structure. The advantages of RS-HDMR include efficient extraction of nonlinear and cooperative network relationships without resorting to discretization, prediction of network behavior without mechanistic modeling, robustness to data noise, and favorable scalability of the sampling requirement with respect to network size. As a proof-of-principle study, RS-HDMR was applied to experimental data measuring the single-cell response of a protein-protein signaling network to various experimental perturbations. A comparison to network structure identified in the literature and through other inference methods, including Bayesian and mutual-information based algorithms, suggests that RS-HDMR can successfully reveal a network structure with a low false positive rate while still capturing non-linear and cooperative interactions. RS-HDMR identified several higher-order network interactions that correspond to known feedback regulations among multiple network species and that were unidentified by other network inference methods. Furthermore, RS-HDMR has a better ability to predict network response under unsampled conditions in this application than the best statistical inference algorithm presented in the recent DREAM3 signaling-prediction competition. RS-HDMR can discern and predict differences in network state that arise from sources ranging from intrinsic cell-cell variability to altered experimental conditions, such as when drug perturbations are introduced. This ability ultimately allows RS-HDMR to accurately classify the experimental conditions of a given sample based on its observed network state