Epigenetics Markers of Metastasis and HPV-Induced Tumorigenesis in Penile Cancer

Purpose: Penile cancer is a rare malignancy in the developed world with just more than 1,600 new cases diagnosed in the United States per year; however, the incidence is much higher in developing countries. Although HPV is known to contribute to tumorigenesis, little is known about the genetic or epigenetic alterations defining penile cancer. / Experimental Design: Using high-density genome-wide methylation arrays, we have identified epigenetic alterations associated with penile cancer. Q-MSP was used to validate lymph node metastasis markers in 50 cases. A total of 446 head and neck squamous cell carcinoma (HNSCC) and cervical squamous cell carcinoma (CESCC) samples were used to validate HPV-associated epigenetic alterations. / Results: We defined 6,933 methylation variable positions (MVP) between normal and tumor tissue, which includes 997 hypermethylated differentially methylated regions associated with tumor supressor genes, including CDO1, AR1, and WT1. Analysis of penile cancer tumors identified a 4 gene epi-signature which accurately predicted lymph node metastasis in an independent cohort (AUC of 89%). Finally, we explored the epigenetic alterations associated with penile cancer HPV infection and defined a 30 loci lineage-independent HPV specific epi-signature which predicts HPV status and survival in independent HNSCC, CESC cohorts. Epi-signature–negative patients have a significantly worse overall survival [HNSCC P = 0.00073; 95% confidence interval (CI), 0.021–0.78; CESC P = 0.0094; HR = 3.91, 95% CI = 0.13–0.78], HPV epi-signature is a better predictor of survival than HPV status alone. / Conclusions: These data demonstrate for the first time genome-wide epigenetic events involved in an aggressive penile cancer phenotype and define the epigenetic alterations common across multiple HPV-driven malignancies

Neurophysiological modeling of bladder afferent activity in the rat overactive bladder model

The overactive bladder (OAB) is a syndrome-based urinary dysfunction characterized by “urgency, with or without urge incontinence, usually with frequency and nocturia”. Earlier we developed a mathematical model of bladder nerve activity during voiding in anesthetized rats and found that the nerve activity in the relaxation phase of voiding contractions was all afferent. In the present study, we applied this mathematical model to an acetic acid (AA) rat model of bladder overactivity to study the sensitivity of afferent fibers in intact nerves to bladder pressure and volume changes. The afferent activity in the filling phase and the slope, i.e., the sensitivity of the afferent fibers to pressure changes in the post-void relaxation phase, were found to be significantly higher in AA than in saline measurements, while the offset (nerve activity at pressure ~0) and maximum pressure were comparable. We have thus shown, for the first time, that the sensitivity of afferent fibers in the OAB can be studied without cutting nerves or preparation of single fibers. We conclude that bladder overactivity induced by AA in rats is neurogenic in origin and is caused by increased sensitivity of afferent sensors in the bladder wall

Dysregulation of the transcription factors SOX4, CBFB and SMARCC1 correlates with outcome of colorectal cancer

The aim of this study was to identify deregulated transcription factors (TFs) in colorectal cancer (CRC) and to evaluate their relation with the recurrence of stage II CRC and overall survival. Microarray-based transcript profiles of 20 normal mucosas and 424 CRC samples were used to identify 51 TFs displaying differential transcript levels between normal mucosa and CRC. For a subset of these we provide in vitro evidence that deregulation of the Wnt signalling pathway can lead to the alterations observed in tissues. Furthermore, in two independent cohorts of microsatellite-stable stage II cancers we found that high SOX4 transcript levels correlated with recurrence (HR 2.7; 95% CI, 1.2–6.0; P=0.01). Analyses of ∼1000 stage I–III adenocarcinomas, by immunohistochemistry, revealed that patients with tumours displaying high levels of CBFB and SMARCC1 proteins had a significantly better overall survival rate (P=0.0001 and P=0.0275, respectively) than patients with low levels. Multivariate analyses revealed that a high CBFB protein level was an independent predictor of survival. In conclusion, several of the identified TFs seem to be involved in the progression of CRC

Towards a Physarum learning chip

Networks of protoplasmic tubes of organism Physarum polycehpalum are macro-scale structures which optimally span multiple food sources to avoid repellents yet maximize coverage of attractants. When data are presented by configurations of attractants and behaviour of the slime mould is tuned by a range of repellents, the organism preforms computation. It maps given data configuration into a protoplasmic network. To discover physical means of programming the slime mould computers we explore conductivity of the protoplasmic tubes; proposing that the network connectivity of protoplasmic tubes shows pathway-dependent plasticity. To demonstrate this we encourage the slime mould to span a grid of electrodes and apply AC stimuli to the network. Learning and weighted connections within a grid of electrodes is produced using negative and positive voltage stimulation of the network at desired nodes; low frequency (10 Hz) sinusoidal (0.5 V peak-to-peak) voltage increases connectivity between stimulated electrodes while decreasing connectivity elsewhere, high frequency (1000 Hz) sinusoidal (2.5 V peak-to-peak) voltage stimulation decreases network connectivity between stimulated electrodes. We corroborate in a particle model. This phenomenon may be used for computation in the same way that neural networks process information and has the potential to shed light on the dynamics of learning and information processing in non-neural metazoan somatic cell networks

Efficient Network Reconstruction from Dynamical Cascades Identifies Small-World Topology of Neuronal Avalanches

Cascading activity is commonly found in complex systems with directed interactions such as metabolic networks, neuronal networks, or disease spreading in social networks. Substantial insight into a system's organization can be obtained by reconstructing the underlying functional network architecture from the observed activity cascades. Here we focus on Bayesian approaches and reduce their computational demands by introducing the Iterative Bayesian (IB) and Posterior Weighted Averaging (PWA) methods. We introduce a special case of PWA, cast in nonparametric form, which we call the normalized count (NC) algorithm. NC efficiently reconstructs random and small-world functional network topologies and architectures from subcritical, critical, and supercritical cascading dynamics and yields significant improvements over commonly used correlation methods. With experimental data, NC identified a functional and structural small-world topology and its corresponding traffic in cortical networks with neuronal avalanche dynamics

Emergence of a Small-World Functional Network in Cultured Neurons

The functional networks of cultured neurons exhibit complex network properties similar to those found in vivo. Starting from random seeding, cultures undergo significant reorganization during the initial period in vitro, yet despite providing an ideal platform for observing developmental changes in neuronal connectivity, little is known about how a complex functional network evolves from isolated neurons. In the present study, evolution of functional connectivity was estimated from correlations of spontaneous activity. Network properties were quantified using complex measures from graph theory and used to compare cultures at different stages of development during the first 5 weeks in vitro. Networks obtained from young cultures (14 days in vitro) exhibited a random topology, which evolved to a small-world topology during maturation. The topology change was accompanied by an increased presence of highly connected areas (hubs) and network efficiency increased with age. The small-world topology balances integration of network areas with segregation of specialized processing units. The emergence of such network structure in cultured neurons, despite a lack of external input, points to complex intrinsic biological mechanisms. Moreover, the functional network of cultures at mature ages is efficient and highly suited to complex processing tasks