Tumour border configuration in colorectal cancer: proposal for an alternative scoring system based on the percentage of infiltrating margin.

AIMS Information on tumour border configuration (TBC) in colorectal cancer (CRC) is currently not included in most pathology reports, owing to lack of reproducibility and/or established evaluation systems. The aim of this study was to investigate whether an alternative scoring system based on the percentage of the infiltrating component may represent a reliable method for assessing TBC. METHODS AND RESULTS Two hundred and fifteen CRCs with complete clinicopathological data were evaluated by two independent observers, both 'traditionally' by assigning the tumours into pushing/infiltrating/mixed categories, and alternatively by scoring the percentage of infiltrating margin. With the pushing/infiltrating/mixed pattern method, interobserver agreement (IOA) was moderate (κ = 0.58), whereas with the percentage of infiltrating margins method, IOA was excellent (intraclass correlation coefficient of 0.86). A higher percentage of infiltrating margin correlated with adverse features such as higher grade (P = 0.0025), higher pT (P = 0.0007), pN (P = 0.0001) and pM classification (P = 0.0063), high-grade tumour budding (P < 0.0001), lymphatic invasion (P < 0.0001), vascular invasion (P = 0.0032), and shorter survival (P = 0.0008), and was significantly associated with an increased probability of lymph node metastasis (P < 0.001). CONCLUSIONS Information on TBC gives additional prognostic value to pathology reports on CRC. The novel proposed scoring system, by using the percentage of infiltrating margin, outperforms the 'traditional' way of reporting TBC. Additionally, it is reproducible and simple to apply, and can therefore be easily integrated into daily diagnostic practice.

Interplay between receptor tyrosine kinases and hypoxia signaling in cancer.

Deregulated signaling via receptor tyrosine kinase (RTK) pathways is prevalent in numerous types of human cancers and is commonly correlated with worst prognosis, resistance to various treatment modalities and increased mortality. Likewise, hypoxic tumors are often manifested by aggressive mode of growth and progression following an adaptive genetic reprogramming with consequent transcriptional activation of genes encoding proteins, which support tumor survival under low oxygen-related conditions. Consequently, both the hypoxia-inducible factor (HIF) system, which is the major mediator of hypoxia-related signaling, and numerous RTK systems are considered critical molecular targets in current cancer therapy. It is now evident that there is an intricate molecular crosstalk between RTKs and hypoxia-related signaling in the sense that hypoxia can activate expression of particular RTKs and/or their corresponding ligands, while some RTK systems have been shown to trigger activation of the HIF machinery. Moreover, signaling regulation of some RTK systems under hypoxic conditions has also been documented to take place in a HIF-independent manner. With this review we aim at overviewing the most current observations on that topic and highlight the importance of the potential co-drugging the HIF system along with particular relevant RTKs for better tumor growth control.

Biology of Bone Metastases in Prostate Cancer

Advanced-stage prostate cancer (PCa) patients are often diagnosed with bone metastases. Bone metastases remain incurable and therapies are palliative. PCa cells prevalently cause osteoblastic lesions, characterized by an excess of bone formation. The prevailing concept indicates that PCa cancer cell secrete an excess of paracrine factors stimulating osteoblasts directly or indirectly, thereby leading to an excess of bone formation. The exact mechanisms by which bone formation stimulates PCa cell growth are mostly elusive. In this review, the mechanisms of PCa cancer cell osteotropism, the cancer cell-induced response within the bone marrow/bone stroma, and therapeutic stromal targets will be summarized.

Use of cardiac glycosides for treatment of cancer: Determinants of cancer cell sensitivity

Cardiac glycoside compounds have traditionally been used to treat congestive heart failure. Recently, reports have suggested that cardiac glycosides may also be useful for treatment of malignant disease. Our research with oleandrin, a cardiac glycoside component of Nerium oleander, has shown it to be a potent inducer of human but not murine tumor cell apoptosis. Determinants of tumor sensitivity to cardiac glycosides were therefore studied in order to understand the species selective cytotoxic effects as well as explore differential sensitivity amongst a variety of human tumor cell lines. ^ An initial model system involved a comparison of human (BRO) to murine (B16) melanoma cells. Human BRO cells were found to express both the sensitive α3 as well as the less sensitive α1 isoform subunits of Na+,K +-ATPase while mouse B16 cells expressed only the α1 isoform. Drug uptake and inhibition of Na+,K+-ATPase activity were also different between BRO and B16 cells. Partially purified human Na+,K+-ATPase enzyme was inhibited by cardiac glycosides at a concentration that was 1000-fold less than that required to inhibit mouse B16 enzyme to the same extent. In addition, uptake of oleandrin and ouabain was 3–4 fold greater in human than murine cells. These data indicate that differential expression of Na+,K+-ATPase isoform composition in BRO and B16 cells as well as drug uptake and total enzyme activity may all be important determinants of tumor cell sensitivity to cardiac glycosides. ^ In a second model system, two in vitro cell culture model systems were investigated. The first consisted of HFU251 (low expression of Na+,K+-ATPase) and U251 (high Na+ ,K+-ATPase expression) cell lines. Also investigated were human BRO cells that had undergone stable transfection with the α1 subunit resulting in an increase in total Na+,K+-ATPase expression. Data derived from these model systems have indicated that increased expression of Na+,K+-ATPase is associated with an increased resistance to cardiac glycosides. Over-expression of Na +,K+-ATPase in tumor cells resulted in an increase of total Na+,K+-ATPase activity and, in turn, a decreased inhibition of Na+,K+-ATPase activity by cardiac glycosides. However, of interest was the observation that increased enzyme expression was also associated with an elevated basal level of glutathione (GSH) within cells. Both increased Na+,K+-ATPase activity and elevated GSH content appear to contribute to a delayed as well as diminished release of cytochrome c and caspase activation. In addition, we have noted an increased colony forming ability in cells with a high level of Na+,K+-ATPase expression. This suggests that Na+,K+-ATPase is actively involved in tumor cell growth and survival. ^

Toxics Release Inventory facilities and childhood cancer: Geographic information systems based approach

Purpose. To examine the association between living in proximity to Toxics Release Inventory (TRI) facilities and the incidence of childhood cancer in the State of Texas. ^ Design. This is a secondary data analysis utilizing the publicly available Toxics release inventory (TRI), maintained by the U.S. Environmental protection agency that lists the facilities that release any of the 650 TRI chemicals. Total childhood cancer cases and childhood cancer rate (age 0-14 years) by county, for the years 1995-2003 were used from the Texas cancer registry, available at the Texas department of State Health Services website. Setting: This study was limited to the children population of the State of Texas. ^ Method. Analysis was done using Stata version 9 and SPSS version 15.0. Satscan was used for geographical spatial clustering of childhood cancer cases based on county centroids using the Poisson clustering algorithm which adjusts for population density. Pictorial maps were created using MapInfo professional version 8.0. ^ Results. One hundred and twenty five counties had no TRI facilities in their region, while 129 facilities had at least one TRI facility. An increasing trend for number of facilities and total disposal was observed except for the highest category based on cancer rate quartiles. Linear regression analysis using log transformation for number of facilities and total disposal in predicting cancer rates was computed, however both these variables were not found to be significant predictors. Seven significant geographical spatial clusters of counties for high childhood cancer rates (p<0.05) were indicated. Binomial logistic regression by categorizing the cancer rate in to two groups (<=150 and >150) indicated an odds ratio of 1.58 (CI 1.127, 2.222) for the natural log of number of facilities. ^ Conclusion. We have used a unique methodology by combining GIS and spatial clustering techniques with existing statistical approaches in examining the association between living in proximity to TRI facilities and the incidence of childhood cancer in the State of Texas. Although a concrete association was not indicated, further studies are required examining specific TRI chemicals. Use of this information can enable the researchers and public to identify potential concerns, gain a better understanding of potential risks, and work with industry and government to reduce toxic chemical use, disposal or other releases and the risks associated with them. TRI data, in conjunction with other information, can be used as a starting point in evaluating exposures and risks. ^

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^

Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets

An integrated understanding of molecular and developmental biology must consider the large number of molecular species involved and the low concentrations of many species in vivo. Quantitative stochastic models of molecular interaction networks can be expressed as stochastic Petri nets (SPNs), a mathematical formalism developed in computer science. Existing software can be used to define molecular interaction networks as SPNs and solve such models for the probability distributions of molecular species. This approach allows biologists to focus on the content of models and their interpretation, rather than their implementation. The standardized format of SPNs also facilitates the replication, extension, and transfer of models between researchers. A simple chemical system is presented to demonstrate the link between stochastic models of molecular interactions and SPNs. The approach is illustrated with examples of models of genetic and biochemical phenomena where the UltraSAN package is used to present results from numerical analysis and the outcome of simulations.

In Vivo screening and discovery of novel candidate thalidomide analogs in the zebrafish embryo and chicken embryo model systems

This study was supported by a Wellcome Trust-NIH PhD Studentship to SB, WDF and NV. Grant number 098252/Z/12/Z. SB, CHC and WDF are supported by the Intramural Research Program, NCI, NIH. NHG and WL are supported by the Intramural Research Program, NIA, NIH.

bioNMF: a versatile tool for non-negative matrix factorization in biology

Medical imaging has become an absolutely essential diagnostic tool for clinical practices; at present, pathologies can be detected with an earliness never before known. Its use has not only been relegated to the field of radiology but also, increasingly, to computer-based imaging processes prior to surgery. Motion analysis, in particular, plays an important role in analyzing activities or behaviors of live objects in medicine. This short paper presents several low-cost hardware implementation approaches for the new generation of tablets and/or smartphones for estimating motion compensation and segmentation in medical images. These systems have been optimized for breast cancer diagnosis using magnetic resonance imaging technology with several advantages over traditional X-ray mammography, for example, obtaining patient information during a short period. This paper also addresses the challenge of offering a medical tool that runs on widespread portable devices, both on tablets and/or smartphones to aid in patient diagnostics.

Bifurcation Analysis of Reaction Diffusion Systems on Arbitrary Surfaces

In this article we present a computational framework for isolating spatial patterns arising in the steady states of reaction-diffusion systems. Such systems have been used to model many different phenomena in areas such as developmental and cancer biology, cell motility and material science. Often one is interested in identifying parameters which will lead to a particular pattern. To attempt to answer this, we compute eigenpairs of the Laplacian on a variety of domains and use linear stability analysis to determine parameter values for the system that will lead to spatially inhomogeneous steady states whose patterns correspond to particular eigenfunctions. This method has previously been used on domains and surfaces where the eigenvalues and eigenfunctions are found analytically in closed form. Our contribution to this methodology is that we numerically compute eigenpairs on arbitrary domains and surfaces. Here we present various examples and demonstrate that mode isolation is straightforward especially for low eigenvalues. Additionally we see that if two or more eigenvalues are in a permissible range then the inhomogeneous steady state can be a linear combination of the respective eigenfunctions. Finally we show an example which suggests that pattern formation is robust on similar surfaces in cases that the surface either has or does not have a boundary.

Biology; the key to the riddle of cancer.

Bibliography: p. 287-312.

Current cancer research [on] etiology, epidemiology, and biology of melanomas and other skin cancers.

Prepared for the ICRDB Program by the Current Cancer Research Project Analysis Center.

Current cancer research on breast cancer : etiology, endocrinology and related biology.

Includes index.

Current cancer research on endocrine neoplasms : epidemiology, etiology and related biology.

Prepared for the ICRDB Program by the Current Cancer Research Project Analysis Center.