Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer's Disease

Background

There are various molecular hypotheses regarding Alzheimer's disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addition, genetic contribution of these molecular hypothesis is not yet established despite the high heritability of AD.

Objective

The study aims to enable the discovery of functionally connected multi-omic features through novel integration of multi-omic data and prior functional interactions.

Methods

We propose a new deep learning model MoFNet with improved interpretability to investigate the AD molecular mechanism and its upstream genetic contributors. MoFNet integrates multi-omic data with prior functional interactions between SNPs, genes, and proteins, and for the first time models the dynamic information flow from DNA to RNA and proteins.

Results

When evaluated using the ROS/MAP cohort, MoFNet outperformed other competing methods in prediction performance. It identified SNPs, genes, and proteins with significantly more prior functional interactions, resulting in three multi-omic subnetworks. SNP-gene pairs identified by MoFNet were mostly eQTLs specific to frontal cortex tissue where gene/protein data was collected. These molecular subnetworks are enriched in innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. We validated most findings in an independent dataset. One multi-omic subnetwork consists exclusively of core members of SNARE complex, a key mediator of synaptic vesicle fusion and neurotransmitter transportation.

Conclusions

Our results suggest that MoFNet is effective in improving classification accuracy and in identifying multi-omic markers for AD with improved interpretability. Multi-omic subnetworks identified by MoFNet provided insights of AD molecular mechanism with improved details.

NISNet3D: three-dimensional nuclear synthesis and instance segmentation for fluorescence microscopy images

The primary step in tissue cytometry is the automated distinction of individual cells (segmentation). Since cell borders are seldom labeled, cells are generally segmented by their nuclei. While tools have been developed for segmenting nuclei in two dimensions, segmentation of nuclei in three-dimensional volumes remains a challenging task. The lack of effective methods for three-dimensional segmentation represents a bottleneck in the realization of the potential of tissue cytometry, particularly as methods of tissue clearing present the opportunity to characterize entire organs. Methods based on deep learning have shown enormous promise, but their implementation is hampered by the need for large amounts of manually annotated training data. In this paper, we describe 3D Nuclei Instance Segmentation Network (NISNet3D) that directly segments 3D volumes through the use of a modified 3D U-Net, 3D marker-controlled watershed transform, and a nuclei instance segmentation system for separating touching nuclei. NISNet3D is unique in that it provides accurate segmentation of even challenging image volumes using a network trained on large amounts of synthetic nuclei derived from relatively few annotated volumes, or on synthetic data obtained without annotated volumes. We present a quantitative comparison of results obtained from NISNet3D with results obtained from a variety of existing nuclei segmentation techniques. We also examine the performance of the methods when no ground truth is available and only synthetic volumes were used for training.

Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images

Advances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accurate, comprehensive, interpretable, and reproducible whole slide image (WSI) feature extraction pipeline known as, IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS), is described. We used both H&E and multiplex IHC (PD-L1, CD8+, and CD163+) images, investigated whether artificial intelligence (AI)-based algorithms using automatic feature extraction methods can predict neoadjuvant chemotherapy (NAC) outcomes in HER2-positive (HER2+) and triple-negative breast cancer (TNBC) patients. Features are derived from tumor immune micro-environment and clinical data and used to train machine learning models to accurately predict the response to NAC in breast cancer patients (HER2+ AUC = 0.8975; TNBC AUC = 0.7674). The results demonstrate that this method outperforms the results trained from features that were manually generated by pathologists. The developed image features and algorithms were further externally validated by independent cohorts, yielding encouraging results, especially for the HER2+ subtype.

Integrative-omics for discovery of network-level disease biomarkers: a case study in Alzheimer's disease

A large number of genetic variations have been identified to be associated with Alzheimer's disease (AD) and related quantitative traits. However, majority of existing studies focused on single types of omics data, lacking the power of generating a community including multi-omic markers and their functional connections. Because of this, the immense value of multi-omics data on AD has attracted much attention. Leveraging genomic, transcriptomic and proteomic data, and their backbone network through functional relations, we proposed a modularity-constrained logistic regression model to mine the association between disease status and a group of functionally connected multi-omic features, i.e. single-nucleotide polymorphisms (SNPs), genes and proteins. This new model was applied to the real data collected from the frontal cortex tissue in the Religious Orders Study and Memory and Aging Project cohort. Compared with other state-of-art methods, it provided overall the best prediction performance during cross-validation. This new method helped identify a group of densely connected SNPs, genes and proteins predictive of AD status. These SNPs are mostly expression quantitative trait loci in the frontal region. Brain-wide gene expression profile of these genes and proteins were highly correlated with the brain activation map of 'vision', a brain function partly controlled by frontal cortex. These genes and proteins were also found to be associated with the amyloid deposition, cortical volume and average thickness of frontal regions. Taken together, these results suggested a potential pathway underlying the development of AD from SNPs to gene expression, protein expression and ultimately brain functional and structural changes.

Eletromagnetic Detection of Mild Brain Injury: A Novel Imaging Approach to Post Concussive Syndrome

INTRODUCTION

Mild traumatic brain injury (mTBI) is a common injury, with nearly 3 - 4 million cases annually in the United States alone. Neuroimaging in patients with mTBI provides little benefit, and is usually not indicated as the diagnosis is primarily clinical. It is theorized that microvascular trauma to the brain may be present in mTBI, that may not be captured by routine MRI and CT scans. Electromagnetic (EM) waves may provide a more sensitive medical imaging modality to provide objective data in the diagnosis of mTBI.

METHODS

COMSOL simulation software was utilized to mimic the anatomy of the human skull including skin, cranium, cerebrospinal fluid (CSF), gray-matter tissue of the brain, and microvasculature within the neural tissue. The effects of penetrating EM waves were simulated using the finite element analysis software and results were generated to identify feasibility and efficacy. Frequency ranges from 7 GHz to 15 GHz were considered, with 0.6 and 1 W power applied.

RESULTS

Variations between the differing frequency levels generated different energy levels within the neural tissue-particularly when comparing normal microvasculature versus hemorrhage from microvasculature. This difference within the neural tissue was subsequently identified, via simulation, serving as a potential imaging modality for future work.

CONCLUSION

The use of electromagnetic imaging of the brain after concussive events may play a role in future mTBI diagnosis. Utilizing the proper depth frequency and wavelength, neural tissue and microvascular trauma may be identified utilizing finite element analysis.

The wearable activity technology and action-planning trial in cancer survivors: Physical activity maintenance post-intervention

OBJECTIVES

The study objective was to assess whether moderate-to-vigorous intensity physical activity (MVPA) change in cancer survivors (n = 68, mean age = 64 years) was maintained 12-weeks following the Wearable Activity Technology and Action Planning (WATAAP) intervention. Secondary aims were to assess the effects of the intervention on blood pressure (BP) and body mass index (BMI), and to explore group differences between baseline and 24-weeks.

DESIGN

Randomized controlled trial.

METHODS

MVPA and sedentary behaviour were assessed using an accelerometer at baseline, the end of the intervention (12-weeks), and at 24-weeks. Generalised linear mixed models with random effects were used to examine between-group and within-group changes in MVPA, sedentary behaviour, BP and BMI.

RESULTS

MVPA was significantly higher in the intervention group compared with the control group at 24-weeks following adjustment for known confounders (141.4 min/wk. (95% CI = 9.1 to 273.8), p = 0.036). At 24-weeks participants in the intervention group had maintained their increased levels of MVPA (change from 12-weeks = 8.8 min/wk.; 95% CI = -43 to 61; p = 0.74). The reduction in MVPA in the control group over the first 12-weeks was also maintained at 24-weeks (5.4 min/wk.; 95% CI = -3.6 to 4.6; p = 0.80). Secondary outcomes did not differ between groups at 24-weeks.

CONCLUSIONS

Our results suggest distance-based interventions using wearable technology produce increases in MVPA that endure at least 12-weeks after the intervention is completed.

TSUNAMI: Translational Bioinformatics Tool Suite for Network Analysis and Mining

Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It enables researchers to discover latent gene/molecule interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus helping to identify disease biomarkers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis, as well as modules that may share common members. To address this need, we developed an online GCN mining tool package: TSUNAMI (Tools SUite for Network Analysis and MIning). TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data (microarray, RNA-seq, or any other numerical omics data), and then performs downstream gene set enrichment analysis for the identified modules. It has several features and advantages: 1) a user-friendly interface and real-time co-expression network mining through a web server; 2) direct access and search of NCBI Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, as well as user-input gene expression matrices for GCN module mining; 3) multiple co-expression analysis tools to choose from, all of which are highly flexible in regards to parameter selection options; 4) identified GCN modules are summarized to eigengenes, which are convenient for users to check their correlation with other clinical traits; 5) integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools; and 6) visualization of gene loci by Circos plot in any step of the process. The web service is freely accessible through URL: https://biolearns.medicine.iu.edu/. Source code is available at https://github.com/huangzhii/TSUNAMI/.

Western Australian Peritonectomy Service: a review of the first 6 years of operation of a low-volume peritonectomy centre

BACKGROUND

Cytoreductive surgery (CRS) with hyperthermic intraperitoneal early chemotherapy has gained acceptance as the standard of care for peritoneal surface malignancy over the past decade. This study reports the results of the first 6 years of activity of the Western Australian Peritonectomy Service to compare outcomes of a low-volume centre with world standards.

METHODS

An audit of all patients who received CRS ± hyperthermic intraperitoneal early chemotherapy treatment at the Western Australian Peritonectomy Service in its first 6 years of operation was performed and results were compared to the recent world literature.

RESULTS

A total of 130 patients were treated with 150 CRS procedures, including 50 pseudomyxoma peritonei (PMP), 53 colorectal cancers (CRCs), 27 appendix adenocarcinomas (AAs) and 20 other cancers. The median operating time was 12 h with median length of hospital stay of 15 days. Perioperative mortality was 0.67% and Clavien-Dindo III/IV morbidity was 20%. The mean packed red cell transfusion requirement was 0.34 units/case. Post-discharge readmission rate was 25% and the return to theatre rate was 13%. The 4-year overall survival rates for PMP, CRC and AA were 97%, 49% and 81%, respectively. The 3-year disease-free survival rates for PMP, CRC and AA were 74%, 26% and 36%, respectively.

CONCLUSION

A CRS centre averaging 25 cases per year can achieve strong outcomes in line with high-volume world centres.

Sphere estimation network: three-dimensional nuclei detection of fluorescence microscopy images

Purpose: Fluorescence microscopy visualizes three-dimensional subcellular structures in tissue with two-photon microscopy achieving deeper penetration into tissue. Nuclei detection, which is essential for analyzing tissue for clinical and research purposes, remains a challenging problem due to the spatial variability of nuclei. Recent advancements in deep learning techniques have enabled the analysis of fluorescence microscopy data to localize and segment nuclei. However, these localization or segmentation techniques would require additional steps to extract characteristics of nuclei. We develop a 3D convolutional neural network, called Sphere Estimation Network (SphEsNet), to extract characteristics of nuclei without any postprocessing steps. Approach: To simultaneously estimate the center locations of nuclei and their sizes, SphEsNet is composed of two branches to localize nuclei center coordinates and to estimate their radii. Synthetic microscopy volumes automatically generated using a spatially constrained cycle-consistent adversarial network are used for training the network because manually generating 3D real ground truth volumes would be extremely tedious. Results: Three SphEsNet models based on the size of nuclei were trained and tested on five real fluorescence microscopy data sets from rat kidney and mouse intestine. Our method can successfully detect nuclei in multiple locations with various sizes. In addition, our method was compared with other techniques and outperformed them based on object-level precision, recall, and F 1 score. Our model achieved 89.90% for F 1 score. Conclusions: SphEsNet can simultaneously localize nuclei and estimate their size without additional steps. SphEsNet can be potentially used to extract more information from nuclei in fluorescence microscopy images.

Identification of functionally connected multi-omic biomarkers for Alzheimer's disease using modularity-constrained Lasso

Large-scale genome wide association studies (GWASs) have led to discovery of many genetic risk factors in Alzheimer's disease (AD), such as APOE, TOMM40 and CLU. Despite the significant progress, it remains a major challenge to functionally validate these genetic findings and translate them into targetable mechanisms. Integration of multiple types of molecular data is increasingly used to address this problem. In this paper, we proposed a modularity-constrained Lasso model to jointly analyze the genotype, gene expression and protein expression data for discovery of functionally connected multi-omic biomarkers in AD. With a prior network capturing the functional relationship between SNPs, genes and proteins, the newly introduced penalty term maximizes the global modularity of the subnetwork involving selected markers and encourages the selection of multi-omic markers with dense functional connectivity, instead of individual markers. We applied this new model to the real data collected in the ROS/MAP cohort where the cognitive performance was used as disease quantitative trait. A functionally connected subnetwork involving 276 multi-omic biomarkers, including SNPs, genes and proteins, were identified to bear predictive power. Within this subnetwork, multiple trans-omic paths from SNPs to genes and then proteins were observed. This suggests that cognitive performance deterioration in AD patients can be potentially a result of genetic variations due to their cascade effect on the downstream transcriptome and proteome level.

Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations

BACKGROUND

Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients. Specifically, Deep Learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients.

METHODS

In this study, a broad analysis was performed on TCGA cancers using a variety of Deep Learning-based models, including Cox-nnet, DeepSurv, and a method proposed by our group named AECOX (AutoEncoder with Cox regression network). Concordance index and p-value of the log-rank test are used to evaluate the model performances.

RESULTS

All models show competitive results across 12 cancer types. The last hidden layers of the Deep Learning approaches are lower dimensional representations of the input data that can be used for feature reduction and visualization. Furthermore, the prognosis performances reveal a negative correlation between model accuracy, overall survival time statistics, and tumor mutation burden (TMB), suggesting an association among overall survival time, TMB, and prognosis prediction accuracy.

CONCLUSIONS

Deep Learning based algorithms demonstrate superior performances than traditional machine learning based models. The cancer prognosis results measured in concordance index are indistinguishable across models while are highly variable across cancers. These findings shedding some light into the relationships between patient characteristics and survival learnability on a pan-cancer level.

Differential co-expression analysis reveals early stage transcriptomic decoupling in alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is one of the leading causes of death in the US and there is no validated drugs to stop, slow or prevent AD. Despite tremendous effort on biomarker discovery, existing findings are mostly individual biomarkers and provide limited insights into the transcriptomic decoupling underlying AD. We propose to explore the gene co-expression patterns in multiple AD stages, including cognitively normal (CN), early mild cognitive impairment (EMCI), late MCI and AD.

METHODS

We modified traiditonal joint graphical lasso to model our asusmption that the co-expression networks in consecutive disease stages are largely similar with critical differences. In addition, we performed subsequent network comparison analysis for identification of stage specific transcriptomic decoupling. We focused our analysis on top AD-enriched pathways.

RESULTS

We observed that 419 edges in CN, 420 edges in EMCI, 381 edges in LMCI and 250 edges in AD were frequently estimated with non zero weights. With modified JGL, the weight of all estimated edges in CN, EMCI and LMCI are zero. In AD group, 299 edges were occasionally estimated to be nonzero and the average correlation between genes was 0.0023. For co-expression change during AD progression, there are 66 pairs of genes that demonstrated a continuously decreasing or increasing co-expression from CN to EMCI, LMCI and AD.The network level clustering coefficient remains stable from CN to LMCI and then decreases significantly when progressing to AD. When evaluating edge level differences, we identified eight gene modules with continuously decreasing or increasing co-expression patterns during AD progression. Five of them shows significant changes from CN to EMCI and thus have the potential to serve system biomarkers for early screening of AD.

CONCLUSION

We employed a modified joint graphical lasso for estimation of co-expression networks for multiple stages of AD. Comparing with graphical lasso, our modified joint graphical lasso model accounts for the similarity in consecutive disease stages. Our results on real data set revealed five gene clusters with obvious co-expression pattern change from CN to EMCI, which could be used as potential system-level biomarkers for early screening of AD.

DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data

The scale of biological microscopy has increased dramatically over the past ten years, with the development of new modalities supporting collection of high-resolution fluorescence image volumes spanning hundreds of microns if not millimeters. The size and complexity of these volumes is such that quantitative analysis requires automated methods of image processing to identify and characterize individual cells. For many workflows, this process starts with segmentation of nuclei that, due to their ubiquity, ease-of-labeling and relatively simple structure, make them appealing targets for automated detection of individual cells. However, in the context of large, three-dimensional image volumes, nuclei present many challenges to automated segmentation, such that conventional approaches are seldom effective and/or robust. Techniques based upon deep-learning have shown great promise, but enthusiasm for applying these techniques is tempered by the need to generate training data, an arduous task, particularly in three dimensions. Here we present results of a new technique of nuclear segmentation using neural networks trained on synthetic data. Comparisons with results obtained using commonly-used image processing packages demonstrate that DeepSynth provides the superior results associated with deep-learning techniques without the need for manual annotation.

Gallbladder complicating a parastomal hernia

The presence of the gallbladder in a parastomal hernia is exceeding rare. We present the case of a 75-year-old female with a parastomal hernia complicated by presence of the gallbladder. The patient was managed with surgical intervention to repair the hernia and reduce the gallbladder to its correct abdominal position without cholecystectomy. She recovered well from her surgical repair, and was recurrence free at her 6-month outpatient visit. This is the one of seven reported cases of parastomal gallbladder herniation and fits with known pre determinants of advanced age and being female. We present this case due its interesting and novel nature.

SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer

Improved cancer prognosis is a central goal for precision health medicine. Though many models can predict differential survival from data, there is a strong need for sophisticated algorithms that can aggregate and filter relevant predictors from increasingly complex data inputs. In turn, these models should provide deeper insight into which types of data are most relevant to improve prognosis. Deep Learning-based neural networks offer a potential solution for both problems because they are highly flexible and account for data complexity in a non-linear fashion. In this study, we implement Deep Learning-based networks to determine how gene expression data predicts Cox regression survival in breast cancer. We accomplish this through an algorithm called SALMON (Survival Analysis Learning with Multi-Omics Neural Networks), which aggregates and simplifies gene expression data and cancer biomarkers to enable prognosis prediction. The results revealed improved performance when more omics data were used in model construction. Rather than use raw gene expression values as model inputs, we innovatively use eigengene modules from the result of gene co-expression network analysis. The corresponding high impact co-expression modules and other omics data are identified by feature selection technique, then examined by conducting enrichment analysis and exploiting biological functions, escalated the interpretation of input feature from gene level to co-expression modules level. Our study shows the feasibility of discovering breast cancer related co-expression modules, sketch a blueprint of future endeavors on Deep Learning-based survival analysis. SALMON source code is available at https://github.com/huangzhii/SALMON/.

Electromagnetic simulation for diagnosing damage to femoral neck vasculature: A feasibility study

Background

Femoral neck fractures are common injuries managed by orthopedic surgeons across the world. From pediatrics to geriatrics, disruption of the blood supply to the femoral neck is a well-recognized source of morbidity and mortality, oftentimes resulting in avascular necrosis of the femoral head. This devastating complication occurs in 10-45% of femoral neck fractures. Therefore, it is vital for orthopedic surgeons provide efficient treatment of this injury, in order to optimize the patient's potential outcome and prevent long-term sequelae.

Methods

In this study, the anatomy of the proximal femur, including femoral metaphysis, femoral neck, vasculature, and femoral head, were simulated in COMSOL Finite Element Analysis (FEA) software. Electric fields were generated in a fashion that exploited disruptions within the vasculature of the femoral neck. This study was aimed at developing an alternative imaging modality for narrowing or disrupting the femoral neck's vasculature. The variables used for investigation included: frequency, penetration depth, and magnitude of the electrical energy. These variables, when combined, allowed for enhanced simulated visualization of the vasculature of the femoral neck and theoretically expedited diagnosis of obvious, or occult, femoral neck injury.

Results

Simulated blood vessels were developed in two-dimensions: the phi direction (circular), and z-direction. Two different frequencies, 3 GHz, and 5 GHz were considered, with 100-J energy pulses within blood vessels of 2.54 mm in diameter. The fat surrounding the bone to the outside surface body was simulated at 0.25 inch (0.65 cm). An additional model, with layered fat and skin above the vessels, was simulated at 2000J and successfully able to visualize the femoral neck's blood vessels. Results showed a distinguished E field across the blood boundary of nearly 170 V/M.

Conclusions

The electric field simulation data within the Phi and Z directions promises the feasibility of a subsequent practical model.

Heritability Estimation of Reliable Connectomic Features

Brain imaging genetics is an emerging research field to explore the underlying genetic architecture of brain structure and function measured by different imaging modalities. However, not all the changes in the brain are a consequential result of genetic effect and it is usually unknown which imaging phenotypes are promising for genetic analyses. In this paper, we focus on identifying highly heritable measures of structural brain networks derived from diffusion weighted imaging data. Using the twin data from the Human Connectome Project (HCP), we evaluated the reliability of fractional anisotropy measure, fiber length and fiber number of each edge in the structural connectome and seven network level measures using intraclass correlation coefficients. We then estimated the heritability of those reliable network measures using SOLAR-Eclipse software. Across all 64,620 network edges between 360 brain regions in the Glasser parcellation, we observed ~5% of them with significantly high heritability in fractional anisotropy, fiber length or fiber number. All the tested network level measures, capturing the network integrality, segregation or resilience, are highly heritable, with variance explained by the additive genetic effect ranging from 59% to 77%.

Wearable Activity Technology And Action-Planning (WATAAP) to promote physical activity in cancer survivors: Randomised controlled trial protocol

Background/Objective: Colorectal and gynecologic cancer survivors are at cardiovascular risk due to comorbidities and sedentary behaviour, warranting a feasible intervention to increase physical activity. The Health Action Process Approach (HAPA) is a promising theoretical framework for health behaviour change, and wearable physical activity trackers offer a novel means of self-monitoring physical activity for cancer survivors. Method: Sixty-eight survivors of colorectal and gynecologic cancer will be randomised into 12-week intervention and control groups. Intervention group participants will receive: a Fitbit Alta™ to monitor physical activity, HAPA-based group sessions, booklet, and support phone-call. Participants in the control group will only receive the HAPA-based booklet. Physical activity (using accelerometers), blood pressure, BMI, and HAPA constructs will be assessed at baseline, 12-weeks (post-intervention) and 24-weeks (follow-up). Data analysis will use the Group x Time interaction from a General Linear Mixed Model analysis. Conclusions: Physical activity interventions that are acceptable and have robust theoretical underpinnings show promise for improving the health of cancer survivors.

Increasing physical activity in cancer survivors at cardiovascular risk using Fitbits: Randomized controlled trial

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Background: Efforts to increase physical activity in cancer survivors have typically been facility-based, and results have generally not been durable. Home-based interventions provide a more feasible option, and are likely to produce longer-lasting benefits. Wearable trackers present a novel opportunity for monitoring physical activity in a practical way. The Health Action Process Approach model suggests that strategies such as action-planning and overcoming barriers may be effective for transitioning individuals from intention to behavioural change. This trial aims to increase physical activity in cancer survivors at cardiovascular risk, by implementing a 24-week self-monitoring and action-planning intervention using Fitbits. Methods: Sixty-eight cancer survivors were recruited to participate in the trial. Eligibility criteria included: insufficiently physically active, presence of cardiovascular risk factors, and completion of active cancer treatment in the five years prior to recruitment. Trial assessments are performed at baseline, 12-weeks, and 24-weeks, and measure physical activity (using 7-day accelerometer data), BMI, blood pressure, physical activity attitudes, barriers and perceived outcomes. Participants were randomly allocated to treatment and control groups, following the baseline assessment. Each participant in the treatment group was given a Fitbit Alta to monitor activity for the 24-week trial, attended two 2-hour group sessions involving goal-setting and action-planning activities, and a follow-up phone call to ensure program adherence. Results: Data collection is currently underway. Data analysis will use the Group x Time interaction from a General Linear Mixed Model analysis. Conclusions: As the population of cancer survivors is largely inactive and at cardiovascular risk, interventions are warranted. Specifically, physical activity interventions that are feasible, have robust theoretical underpinnings, and suit the preferences of cancer survivors post-treatment, show promise as a long-term solution. Clinical trial information: 2617000131358.

Antenna Design and SAR Analysis on Human Head Phantom Simulation for Future Clinical Applications

Background

The rapid development of a variety of devices that emit Radiofrequency Electromagnetic fields (RF-EMF) has sparked growing interest in their interaction with biological systems and the beneficial effects on human health. As a result, investigations have been driven by the potential for therapeutic applications, as well as concern for any possible negative health implications of these EM energies [1-4]. Recent results have indicated specific tuning of experimental and clinical RF exposure may lead to their clinical application toward beneficial health outcomes [5].

Method

In the current study, a mathematical and computer simulation model to analyze a specific RF-EMF exposure on a human head model was developed. Impetus for this research was derived from results of our previous experiments which revealed that Repeated Electromagnetic Field Stimulation (REMFS) decreased the toxic levels of beta amyloid (Aβ) in neuronal cells, thereby suggesting a new potential therapeutic strategy for the treatment of Alzheimer's disease (AD). Throughout development of the proposed device, experimental variables such as the EM frequency range, specific absorption rate (SAR), penetration depth, and innate properties of different tissues have been carefully considered.

Results

RF-EMF exposure to the human head phantom was performed utilizing a Yagi-Uda antenna type possessing high gain (in the order of 10 dbs) at a frequency of 64 MHz and SAR of 0.6 W/Kg. In order to maximize the EM power transmission in one direction, directors were placed in front of the driven element and reflectors were placed behind the driven element. So as to strategically direct the EM field into the center of the brain tissue, while providing field linearity, our analysis considered the field distribution for one versus four antennas. Within the provided dimensions of a typical human brain, results of the Bioheat equation within COMSOL Multiphysics version 5.2a software demonstrated less than a 1 m˚K increase from the absorbed EM power.

Building a Surface Atlas of Hippocampal Subfields From High Resolution T2-weighted MRI Scans Using Landmark-free Surface Registration

The hippocampus is widely studied in neuroimaging field as it plays important roles in memory and learning. However, the critical subfield information is often not explored in most hippocampal studies. We previously proposed a method for hippocampal subfield morphometry by integrating FreeSurfer, FSL, and SPHARM tools. But this method had some limitations, including the analysis of T1-weighted MRI scans without detailed subfield information and hippocampal registration without using important subfield information. To bridge these gaps, in this work, we propose a new framework for building a surface atlas of hippocampal subfields from high resolution T2-weighted MRI scans by integrating state-of-the-art methods for automated segmentation of hippocampal subfields and landmark-free, subfield-aware registration of hippocampal surfaces. Our experimental results have shown the promise of the new framework.

Dynamic thermal/acoustic response for human bone materials at different energy levels: A diagnosis approach

BACKGROUND

The non-invasive diagnostic approaches have gained high attention in recent years, utilizing high technology sensor systems, including infrared, microwave devices, acoustic transducers, etc. The patient safety, high resolution images, and reliability are among the driving forces toward high technology approaches. The thermal and acoustic responses of the materials may reflect the important research parameters such as penetration depth, power consumption, and temperature change used for the practical models of the system. This paper emphasizes the approach for orthopedic application where the bone densities were considered in simulation to designate the type of human bones.

METHODS

Thermal energy pulses were applied in order to study the penetration depth, the maximum temperature change; spatially and dynamically, and the acoustic pressure distribution over the bone thickness. The study was performed to optimize the amount of energy introduced into the materials that generate the temperature value for high resolution beyond the noise level.

RESULTS

Three different energy pulses were used; 1 J, 3 J and 5 J. The thermal energy applied to the four bone materials, cancellous bone, cortical bone, red bone marrow, and yellow bone marrow were producing relative changes in temperature. The maximum change ranges from 0.5 K to 2 K for the applied pulses. The acoustic pressure also ranges from 210 to 220 dB among the various types of bones.

CONCLUSION

The results obtained from simulation suggest that a practical model utilizing infra-red scanning probe and piezoelectric devices may serve for the orthopedic diagnostic approach. The simulations for multiple layers such as skin interfaced with bone will be reserved for future considerations.

Electromagnetic and Thermal Simulations of Human Neurons for SAR Applications

The impact of the electromagnetic waves (EM) on human neurons (HN) has been under investigation for decades, in efforts to understand the impact of cell phones (radiation) on human health, or radiation absorption by HN for medical diagnosis and treatment. Research issues including the wave frequency, power intensity, reflections and scattering, and penetration depths are of important considerations to be incorporated into the research study. In this study, computer simulation for the EM exposure to HN was studied for the purpose of determining the upper limits of the electric and magnetic field intensities, power consumption, reflections and transmissions, and the change in temperature resulting from the power absorption by human neurons. Both high frequency structural simulators (HFSS) from ANSYS software, and COMSOL multi-physics were used for the simulation of the EM transmissions and reflections, and the temperature profile within the cells, respectively. For the temperature profile estimation, the study considers an electrical source of 0.5 watt input power, 64 MHz. The EM simulation was looking into the uniformity of the fields within the sample cells. The size of the waveguide was set to be appropriate for a small animal model to be conducted in the future. The incident power was fully transmitted throughout the waveguide, and less than 1% reflections were observed from the simulation. The minimum reflected power near the sample under investigation was found to be with negligible reflected field strengths. The temperature profile resulting from the COMSOL simulation was found to be near 0.25 m°K, indicating no change in temperature on the neuro cells under the EM exposure. The paper details the simulation results for the EM response determined by HFSS, and temperature profile simulated by COMSOL.

Non-invasive photo acoustic approach for human bone diagnosis

The existing modalities of bone diagnosis including X-ray and ultrasound may cite drawback in some cases related to health issues and penetration depth, while the ultrasound modality may lack image quality. Photo acoustic approach however, provides light energy to the acoustic wave, enabling it to activate and respond according to the propagating media (which is type of bones in this case). At the same time, a differential temperature change may result in the bio heat response, resulting from the heat absorbed across the multiple materials under study. In this work, we have demonstrated the features of using photo acoustic modality in order to non-invasively diagnose the type of human bones based on their electrical, thermal, and acoustic properties that differentiate the output response of each type. COMSOL software was utilized to combine both acoustic equations and bio heat equations, in order to study both the thermal and acoustic responses through which the differential diagnosis can be obtained. In this study, we solved both the acoustic equation and bio heat equations for four types of bones, bone (cancellous), bone (cortical), bone marrow (red), and bone marrow (yellow). 1 MHz acoustic source frequency was chosen and 10(5) W/m(2) power source was used in the simulation. The simulation tested the dynamic response of the wave over a distance of 5 cm from each side for the source. Near 2.4 cm was detected from simulation from each side of the source with a temperature change of within 0.5 K for various types of bones, citing a promising technique for a practical model to detect the type of bones via the differential temperature as well as the acoustic was response via the multiple materials associated with the human bones (skin and blood). The simulation results suggest that the PA technique may be applied to non-invasive diagnosis for the different types of bones, including cancerous bones. A practical model for detecting both the temperature change via IR sensors, and acoustic wave signals may be detected via sensitive pressure transducer, which is reserved for future realization.

Surface-Based Morphometric Analysis of Hippocampal Subfields in Mild Cognitive Impairment and Alzheimer's Disease

The hippocampus is widely studied with neuroimaging techniques given its importance in learning and memory and its potential as a biomarker for Alzheimer's disease (AD). Its complex folding anatomy often presents analytical challenges. In particular, the critical subfield information is typically not addressed by the existing hippocampal shape studies. To bridge this gap, we present a computational framework for surface-based morphometric analysis of hippocampal subfields. The major strengths of this framework are as follows: (a) it performs detailed hippocampal shape analysis, (b) it embraces, rather than ignores, the important hippocampal subfield information, and (c) it analyzes regular magnetic resonance imaging scans and is applicable to large scale studies. We demonstrate its effectiveness by applying it to the identification of regional hippocampal subfield atrophy patterns associated with mild cognitive impairment and AD.

Population-based screening for Lynch syndrome in Western Australia

We showed earlier that routine screening for microsatellite instability (MSI) and loss of mismatch repair (MMR) protein expression in colorectal cancer (CRC) led to the identification of previously unrecognized cases of Lynch syndrome (LS). We report here the results of screening for LS in Western Australia (WA) during 1994-2012. Immunohistochemistry (IHC) for loss of MMR protein expression was performed in routine pathology laboratories, while MSI was detected in a reference molecular pathology laboratory. Information on germline mutations in MMR genes was obtained from the state's single familial cancer registry. Prior to the introduction of routine laboratory-based screening, an average of 2-3 cases of LS were diagnosed each year amongst WA CRC patients. Following the implementation of IHC and/or MSI screening for all younger (<60 years) CRC patients, this has increased to an average of 8 LS cases diagnosed annually. Based on our experience in WA, we propose three key elements for successful population-based screening of LS. First, for all younger CRC patients, reflex IHC testing should be carried out in accredited pathology services with ongoing quality control. Second, a state- or region-wide reference laboratory for MSI testing should be established to confirm abnormal or suspicious IHC test results and to exclude sporadic cases by carrying out BRAF mutation or MLH1 methylation testing. Finally, a state or regional LS coordinator is essential to ensure that all appropriate cases identified by laboratory testing are referred to and attend a Familial Cancer Clinic for follow-up and germline testing.

A novel suspension formulation enhances intestinal absorption of macromolecules via transient and reversible transport mechanisms

PURPOSE

Medium chain fatty acid salts promote absorption by increasing paracellular permeability of the intestinal epithelium. Novel oily suspension (OS) formulation disperses a powder containing sodium caprylate and macromolecules such as octreotide or fluorescent dextran (FD). Formulation safety, macromolecule absorption and pharmacokinetic (PK)/pharmacodynamic (PD) were evaluated.

METHODS

Octreotide/OS toxicity was evaluated in monkeys following 9 months of daily oral enteric-coated capsule administration. The OS permeation effect was also assessed in rats, using FD/OS and octreotide/OS preparations. Octreotide/OS effects on circulating growth hormone (GH) levels were also measured.

RESULTS

Safety assessment of octreotide/OS in monkeys after 9 months showed minor drug-related findings, comparable to the injectable octreotide. Octreotide exposure levels were similar across the treatment periods. In rats, OS facilitated FD permeation up to 70 kDa in a reversible, spatial and dose-dependent manner, independent of the intestinal dosing site. Following OS administration, the staining pattern of the tight-junction protein, ZO-1, changed transiently, and a paracellular penetration marker, LC-biotin, permeated between adjacent epithelial cells. Enteral octreotide/OS absorption was dose-dependent and suppressed rat GH levels.

CONCLUSIONS

Oral octreotide/OS dosing was shown to be safe in monkeys. OS enhances intestinal absorption of active octreotide, likely by transient alteration of the tight junction protein complex.

IMART software for correction of motion artifacts in images collected in intravital microscopy

Intravital microscopy is a uniquely powerful tool, providing the ability to characterize cell and organ physiology in the natural context of the intact, living animal. With the recent development of high-resolution microscopy techniques such as confocal and multiphoton microscopy, intravital microscopy can now characterize structures at subcellular resolution and capture events at sub-second temporal resolution. However, realizing the potential for high resolution requires remarkable stability in the tissue. Whereas the rigid structure of the skull facilitates high-resolution imaging of the brain, organs of the viscera are free to move with respiration and heartbeat, requiring additional apparatus for immobilization. In our experience, these methods are variably effective, so that many studies are compromised by residual motion artifacts. Here we demonstrate the use of IMART, a software tool for removing motion artifacts from intravital microscopy images collected in time series or in three dimensions.

Anastomotic leaks in colorectal surgery

BACKGROUND

Anastomotic leaks are a serious complication of bowel surgery. This study aimed to evaluate the rate and severity, and identify risk factors for leaks in patients undergoing bowel anastomoses.

METHODS

Prospective evaluation was performed on patients undergoing bowel surgery within a colorectal surgical unit. Anastomotic leak was defined and graded according to severity. A nurse independently collected the information. Stepwise logistic regression analysis was performed.

RESULTS

Two thousand three hundred and sixty-three patients underwent 2994 anastomoses. Their median age was 64 years. Seven per cent were emergency operations. Anastomotic leak occurred in 82 patients (2.7%). Sixty-three per cent of leaks were managed with drainage or re-operation. Ultra-low anterior resection (ULAR) was associated with the highest subgroup leak rate (7.3%). In multivariable analysis, independent predictors for a leak included 'other' pathologies (iatrogenic injury, ischaemia, radiation enteritis) (P=0.016, odds ratio (OR): 6.3, 95% confidence interval (CI): 1.4-28.0), ULAR (P=0.001, OR: 8.5, 95% CI: 2.3-31.2) and the surgeon (A: P<0.001, OR: 3.4, 95% CI: 2.1-5.6).

CONCLUSION

Majority of predictors for anastomotic leak were fairly intuitive. Nonetheless, it was relevant to note the importance of the individual surgeon as an independent predictor for leaks.

Stationary wavelet transform and higher order statistical analyses of intrafascicular nerve recordings

Nerve signals were recorded from the sciatic nerve of the rabbits in the acute experiments with multi-channel thin-film longitudinal intrafascicular electrodes. 5.5 s sequences of quiescent and high-level nerve activity were spectrally decomposed by applying a ten-level stationary wavelet transform with the Daubechies 10 (Db10) mother wavelet. Then, the statistical distributions of the raw and subband-decomposed sequences were estimated and used to fit a fourth-order Pearson distribution as well as check for normality. The results indicated that the raw and decomposed background and high-level nerve activity distributions were nearly zero-mean and non-skew. All distributions with the frequency content above 187.5 Hz were leptokurtic except for the first-level decomposition representing frequencies in the subband between 12 and 24 kHz, which was Gaussian. This suggests that nerve activity acts to change the statistical distribution of the recording. The results further demonstrated that quiescent recording contained a mixture of an underlying pink noise and low-level nerve activity that could not be silenced. The signal-to-noise ratios based upon the standard deviation (SD) and kurtosis were estimated, and the latter was found as an effective measure for monitoring the nerve activity residing in different frequency subbands. The nerve activity modulated kurtosis along with SD, suggesting that the joint use of SD and kurtosis could improve the stability and detection accuracy of spike-detection algorithms. Finally, synthesizing the reconstructed subband signals following denoising based upon the higher order statistics of the subband-decomposed coefficient sequences allowed us to effectively purify the signal without distorting spike shape.

FOXP3+ cell density in lymphoid follicles from histologically normal mucosa is a strong prognostic factor in early stage colon cancer

There are few clearly established prognostic factors available to guide the use of adjuvant chemotherapy in early stage colon cancer patients. Some of the most promising candidates include the invasion of extramural blood vessels by tumour cells and the densities of FOXP3+ T regulatory cells (Tregs) in tumour and adjacent normal colonic mucosal tissue. The aim of our study was to evaluate the prognostic significance of these markers in AJCC stage II colon cancer, with particular reference to lymphoid follicles in the mucosa. Histopathological review for the presence of vascular and serosal invasion was conducted on a series of 165 stage II colon cancers treated by surgery alone. Immunohistochemical staining for FOXP3 was performed on tumour tissue and histologically normal colonic mucosa from the surgical margin. Image analysis software was used to evaluate the density of FOXP3+ cells in the tumour core, invading margin and lymphoid follicles from the colonic mucosa. For survival analysis, cases were classified into high- or low-density of FOXP3+ cells according to the median value. The mean density of FOXP3+ Tregs in lymphoid follicles was twofold and fivefold higher than in the invading margin and tumour core, respectively. Multivariate analysis identified extramural vascular invasion (HR, 2.47; 95% CI: 1.00-6.07; P = 0.05) and high FOXP3+ cell density in lymphoid follicles (HR, 4.22; 95% CI: 1.49-11.91; P = 0.007) as independent factors for worse survival, whereas a high frequency of lymphoid follicles in histologically normal colonic mucosa was associated with better survival (HR, 0.31; 95% CI: 0.12-0.79; P = 0.014). Our data suggest that host factors related to the immune system have major prognostic significance in early stage colon cancer. The density of FOXP3+ cells within lymphoid follicles and the frequency of these structures in normal colonic mucosa represent novel and independent prognostic factors.

Oral octreotide absorption in human subjects: comparable pharmacokinetics to parenteral octreotide and effective growth hormone suppression

CONTEXT

Oral administration of a novel octreotide formulation enabled its absorption to the systemic circulation, exhibiting blood concentrations comparable to those observed with injected octreotide and maintaining its biological activity.

OBJECTIVES

The aim of the study was to determine oral octreotide absorption and effects on pituitary GH secretion compared to sc octreotide injection.

DESIGN

Four single-dose studies were conducted in 75 healthy volunteers.

INTERVENTION

Oral doses of 3, 10, or 20 mg octreotide and a single sc injection of 100 μg octreotide were administered.

MAIN OUTCOME MEASURE

We measured the pharmacokinetic profile of orally administrated octreotide and the effect of octreotide on basal and stimulated GH secretion.

RESULTS

Both oral and sc treatments were well tolerated. Oral octreotide absorption to the circulation was apparent within 1 h after dose administration. Escalating oral octreotide doses resulted in dose-dependent increased plasma octreotide concentrations, with an observed rate of plasma decay similar to parenteral administration. Both 20 mg oral octreotide and injection of 0.1 mg sc octreotide resulted in equivalent pharmacokinetic parameters [mean peak plasma concentration, 3.77 ± 0.25 vs. 3.97 ± 0.19 ng/ml; mean area under the curve, 16.2 ± 1.25 vs. 12.1 ± 0.45 h × ng/ml); and median time ≥ 0.5 ng/ml, 7.67 vs. 5.88 h, respectively). A single dose of 20 mg oral octreotide resulted in basal (P < 0.05) and GHRH-stimulated (P < 0.001) mean GH levels suppressed by 49 and 80%, respectively.

CONCLUSIONS

The results support an oral octreotide alternative to parenteral octreotide treatment for patients with acromegaly.

Low expression of Granzyme B in colorectal cancer is associated with signs of early metastastic invasion

AIMS

Tumour-infiltrating forkhead box P3 (FoxP3+ ) regulatory T cells (T(regs) ) have stronger prognostic significance than cytotoxic CD8+ T cells in colorectal cancer (CRC). Because there is evidence that some tumour-infiltrating CD8+ T cells may be inactive, the present study aimed to investigate the prognostic significance of Granzyme B, one of the major effector molecules of T cells.

METHODS AND RESULTS

A tissue microarray containing 963 CRCs was stained immunohistochemically for Granzyme B and the level of expression quantified by digital image analysis. Granzyme B expression was higher in tumours with microsatellite instability (P < 0.0001), a dense lymphocytic infiltrate (P < 0.0001) and location in the proximal colon (P = 0.009), but lower in tumours with vascular invasion (P = 0.007), perineural invasion (P =0.041) and positive nodal status (P < 0.001). Elevated expression of Granzyme B was associated with improved survival on univariate analysis (hazard ratio = 0.65; 95% confidence interval 0.51-0.84; P = 0.001), but not in a multivariate model that included stage, vascular invasion and FoxP3+ T(reg) cell density.

CONCLUSIONS

Low expression of Granzyme B was associated with early signs of metastasis in CRC. The stronger prognostic significance of FoxP3+ T(regs) is in keeping with animal models that suggest these cells act as gatekeepers for the release of Granzyme B from CD8+ T cells.

Digital correction of motion artefacts in microscopy image sequences collected from living animals using rigid and nonrigid registration

Digital image analysis is a fundamental component of quantitative microscopy. However, intravital microscopy presents many challenges for digital image analysis. In general, microscopy volumes are inherently anisotropic, suffer from decreasing contrast with tissue depth, lack object edge detail and characteristically have low signal levels. Intravital microscopy introduces the additional problem of motion artefacts, resulting from respiratory motion and heartbeat from specimens imaged in vivo. This paper describes an image registration technique for use with sequences of intravital microscopy images collected in time-series or in 3D volumes. Our registration method involves both rigid and nonrigid components. The rigid registration component corrects global image translations, whereas the nonrigid component manipulates a uniform grid of control points defined by B-splines. Each control point is optimized by minimizing a cost function consisting of two parts: a term to define image similarity, and a term to ensure deformation grid smoothness. Experimental results indicate that this approach is promising based on the analysis of several image volumes collected from the kidney, lung and salivary gland of living rodents.

SPARC, FOXP3, CD8 and CD45 correlation with disease recurrence and long-term disease-free survival in colorectal cancer

Background

SPARC is a matricellular protein involved in tissue remodelling, cell migration and angiogenesis, while forkhead box P3 (FOXP3) protein functions as a transcription factor involved in immune cell regulation. Both SPARC and FOXP3 can play an anti-tumorigenic role in cancer progression. The aim was to determine if SPARC, FOXP3, CD8 and CD45RO expression levels are associated with colorectal cancer (CRC) stage, disease outcome and long-term cancer-specific survival (CSS) in stage II and III CRC.

Methods and Findings

SPARC expression was initially assessed in 120 paired normal and stage I-IV CRCs. Subsequently, approximately 1000 paired patient samples of stage II or III CRCs in tissue microarrays were stained for SPARC, FOXP3, CD8 or CD45RO. Proportional hazards modelling assessed correlations between these markers and clinicopathological data, including disease outcome and cancer specific survival (CSS). Both SPARC and FOXP3 expression were significantly greater in CRC than normal colon (p<0.0001). High SPARC expression correlated with good disease outcome (≥60 mths without disease recurrence, p = 0.0039) and better long-term CSS in stage II CRC (<0.0001). In stage III CRC, high SPARC expression correlated with better long-term CSS (p<0.0001) and less adjuvant chemotherapy use (p = 0.01). High FOXP3 correlated with a good disease outcome, better long-term CSS and less adjuvant chemotherapy use in stage II (p<0.0037, <0.0001 and p = 0.04 respectively), but not in stage III CRC. High CD8 and CD45RO expression correlated with better disease outcome in stage II CRC, and better CSS, but the differences were not as marked as for SPARC and FOXP3.

Conclusions

These data suggest that high SPARC and FOXP3 are associated with better disease outcome in stage II CRC and may be prognostic indicators of CSS. Further assessment of whether these markers predict patients at high risk of recurrence with stage II CRC and functional studies of these effects are underway

Foreign-body ingestion: characteristics and outcomes in a lower socioeconomic population with predominantly intentional ingestion

BACKGROUND

Previous reports of foreign-body ingestions focused primarily on accidental ingestions.

OBJECTIVE

To describe the characteristics and management of foreign-body ingestions, with predominantly intentional ingestion, in a lower socioeconomic status population.

DESIGN

A retrospective case series.

SETTING

An urban county hospital.

PATIENTS

Patients >/=17 years old, with foreign-body ingestions between 2000 and 2006.

MAIN OUTCOME MEASUREMENTS

Characteristics of ingestion cases, endoscopic extraction, need for surgery, and complications.

RESULTS

Among 262 cases, 92% were intentional, 85% involved psychiatric patients, and 84% occurred in patients with prior ingestions. The time from ingestion to presentation was >48 hours in 168 cases (64%). The overall success rate for endoscopic extraction was 90% (165/183 cases). Surgery was performed in 30 cases (11%) and was more common for objects beyond the pylorus versus objects above the pylorus (16/43 [37%] vs 10/151 [7%], respectively) and in cases with a greater delay from ingestion to presentation (25/168 [15%] if >48 hours vs 4/77 [5%] if </=48 hours) and from presentation to intervention (15/40 [38%] if >48 hours vs 14/165 [8%] if </=48 hours). Perforation occurred in 16 cases (6%), with 6 perforations noted after endoscopy.

LIMITATION

A retrospective review of medical records.

CONCLUSIONS

Foreign-body ingestions in an urban county hospital occurred primarily in psychiatric patients who had repeated episodes of intentional ingestions. Endoscopic extraction was unsuccessful in 10% of cases. Long delays from ingestion to presentation and intervention may account for relatively high rates of surgery and perforation. Strategies to prevent ingestions and delays in endoscopic management are needed in this population.

Host response to colorectal cancer

It has long been established that inflammation and immunity play critical roles in the pathogenesis, control and eventual metastasis of cancers. With the advent of more sophisticated animal models and immunohistochemical techniques a greater understanding of the immune system and its interactions has occurred. Individual immune cells are dynamic structures that have variable behaviour controlled by complex interactions in the tumour microenvironment. In the setting of colorectal cancer it was first observed that peritumoral inflammatory infiltrates were associated with improved prognosis. Immunohistochemistry has shown the individual cells types within these infiltrates. It now appears that an adaptive immune response, differentiated along the T-helper 1 pathway controls tumour invasion and metastasis. Furthermore, the immune system exerts selection pressure leading to the evolution of tumour cell variants that can induce tolerance and disable adaptive immunity. These tumour cells then use the mechanisms of innate immunity to facilitate further growth, angiogenesis, invasion and eventual metastasis. These issues are investigated with particular relevance to colorectal cancer. Using the immune response to defeat CRC has been under intense investigation but has so far been unsuccessful. Nevertheless, researchers remain optimistic that immunotherapy will play an important role in the treatment of this common disease.

Tumor-infiltrating FOXP3+ T regulatory cells show strong prognostic significance in colorectal cancer

PURPOSE

To determine the prognostic significance of FOXP3(+) lymphocyte (Treg) density in colorectal cancer compared with conventional histopathologic features and with CD8(+) and CD45RO(+) lymphocyte densities.

PATIENTS AND METHODS

Tissue microarrays and immunohistochemistry were used to assess the densities of CD8(+), CD45RO(+), and FOXP3(+) lymphocytes in tumor tissue and normal colonic mucosa from 967 stage II and stage III colorectal cancers. These were evaluated for associations with histopathologic features and patient survival.

RESULTS

FOXP3(+) Treg density was higher in tumor tissue compared with normal colonic mucosa, whereas CD8(+) and CD45RO(+) cell densities were lower. FOXP3(+) Tregs were not associated with any histopathologic features, with the exception of tumor stage. Multivariate analysis showed that stage, vascular invasion, and FOXP3(+) Treg density in normal and tumor tissue were independent prognostic indicators, but not CD8(+) and CD45RO(+). High FOXP3(+) Treg density in normal mucosa was associated with worse prognosis (hazard ratio [HR] = 1.51; 95% CI, 1.07 to 2.13; P = .019). In contrast, a high density of FOXP3(+) Tregs in tumor tissue was associated with improved survival (HR = 0.54; 95% CI, 0.38 to 0.77; P = .001).

CONCLUSION

FOXP3(+) Treg density in normal and tumor tissue had stronger prognostic significance in colorectal cancer compared with CD8(+) and CD45RO(+) lymphocytes. The finding of improved survival associated with a high density of tumor-infiltrating FOXP3(+) Tregs in colorectal cancer contrasts with several other solid cancer types. The inclusion of FOXP3(+) Treg density may help to improve the prognostication of early-stage colorectal cancer.