Precision Delivery of Human Bone Marrow-Derived Mesenchymal Stem Cells Into the Pancreas Via Intra-arterial Injection Prevents the Onset of Diabetes.

Mesenchymal stem cells (MSCs) are a promising therapy to potentially treat diabetes given their potent anti-inflammatory and immune-modulatory properties. While these regenerative cells have shown considerable promise in cell culture, their clinical translation has been challenging. In part, this can be attributed to these cells not reaching the pancreas to exert their regenerative effects following conventional intravenous (IV) injection, with the majority of cells being trapped in the lungs in the pulmonary first-pass effect. In the present study, we will therefore examine whether direct delivery of MSCs to the pancreas via an intra-arterial (IA) injection can improve their therapeutic efficacy. Using a mouse model, in which repetitive low doses of STZ induced a gentle, but progressive, hyperglycemia, we tested bone marrow-derived MSCs (BM-MSCs) which we have shown are enriched with pro-angiogenic and immunomodulatory factors. In cell culture studies, BM-MSCs were shown to preserve islet viability and function following exposure to proinflammatory cytokines (IFN-γ, IL-1ß, and TNF-α) through an increase in pAkt. When tested in our animal model, mice receiving IV BM-MSCs were not able to mitigate the effects of STZ, however those which received the same dose and batch of cells via IA injection were able to maintain basal and dynamic glycemic control, to similar levels as seen in healthy control animals, over 10 days. This study shows the importance of considering precision delivery approaches to ensure cell-based therapies reach their intended targets to enable them to exert their therapeutic effects.

AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data.

Deep learning (DL) has been proposed to automate image segmentation and provide accuracy, consistency, and efficiency. Accurate segmentation of lipomatous tumors (LTs) is critical for correct tumor radiomics analysis and localization. The major challenge of this task is data heterogeneity, including tumor morphological characteristics and multicenter scanning protocols. To mitigate the issue, we aimed to develop a DL-based Super Learner (SL) ensemble framework with different data correction and normalization methods. Pathologically proven LTs on pre-operative T1-weighted/proton-density MR images of 185 patients were manually segmented. The LTs were categorized by tumor locations as distal upper limb (DUL), distal lower limb (DLL), proximal upper limb (PUL), proximal lower limb (PLL), or Trunk (T) and grouped by 80%/9%/11% for training, validation and testing. Six configurations of correction/normalization were applied to data for fivefold-cross-validation trainings, resulting in 30 base learners (BLs). A SL was obtained from the BLs by optimizing SL weights. The performance was evaluated by dice-similarity-coefficient (DSC), sensitivity, specificity, and Hausdorff distance (HD95). For predictions of the BLs, the average DSC, sensitivity, and specificity from the testing data were 0.72 [Formula: see text] 0.16, 0.73 [Formula: see text] 0.168, and 0.99 [Formula: see text] 0.012, respectively, while for SL predictions were 0.80 [Formula: see text] 0.184, 0.78 [Formula: see text] 0.193, and 1.00 [Formula: see text] 0.010. The average HD95 of the BLs were 11.5 (DUL), 23.2 (DLL), 25.9 (PUL), 32.1 (PLL), and 47.9 (T) mm, whereas of SL were 1.7, 8.4, 15.9, 2.2, and 36.6 mm, respectively. The proposed method could improve the segmentation accuracy and mitigate the performance instability and data heterogeneity aiding the differential diagnosis of LTs in real clinical situations.

Two-group Poisson-Dirichlet mixtures for multiple testing.

The simultaneous testing of multiple hypotheses is common to the analysis of high-dimensional data sets. The two-group model, first proposed by Efron, identifies significant comparisons by allocating observations to a mixture of an empirical null and an alternative distribution. In the Bayesian nonparametrics literature, many approaches have suggested using mixtures of Dirichlet Processes in the two-group model framework. Here, we investigate employing mixtures of two-parameter Poisson-Dirichlet Processes instead, and show how they provide a more flexible and effective tool for large-scale hypothesis testing. Our model further employs nonlocal prior densities to allow separation between the two mixture components. We obtain a closed-form expression for the exchangeable partition probability function of the two-group model, which leads to a straightforward Markov Chain Monte Carlo implementation. We compare the performance of our method for large-scale inference in a simulation study and illustrate its use on both a prostate cancer data set and a case-control microbiome study of the gastrointestinal tracts in children from underdeveloped countries who have been recently diagnosed with moderate-to-severe diarrhea.

Beneficial Effects of Melatonin on Apolipoprotein-E Knockout Mice by Morphological and 18F-FDG PET/CT Assessments.

Atherosclerosis represents one of the main risk factors for the development of cardiovascular diseases. Their etiologies have been studied in recent years in order to better define therapeutic targets for intervention and to identify diagnostic methods. Two different subtypes of macrophages, M1 and M2, have been described in physiological conditions. They can also be found in the atherosclerotic process, where they both have opposite roles in disease progression. Perivascular brown adipose tissue is also involved in inflammation and endothelial damage. In this work, we provide insights into the protective role of melatonin in the atherosclerotic process by morphological and 18F-FDG-PET/CT analyses. In particular, we examined the effects of melatonin on pathways that are linked to atherosclerosis development. We showed that melatonin, by suppressing M1 activity, reduced inflammation and directed macrophage polarization toward the M2 macrophage subtype. Moreover, melatonin preserved the activity of perivascular brown adipose tissue. In addition, 18F-FDG uptake is very high in mice treated with melatonin, confirming that other factors may alter 18F-FDG distribution. In conclusion, we showed that melatonin affects inflammatory pathways that have been linked to atherosclerosis, assessed the relationships of the 18F-FDG PET/CT parameters with macrophage markers and the production of their cytokines, which that have been defined by morphological evaluations.

Qualitative evaluation of MRI features of lipoma and atypical lipomatous tumor: results from a multicenter study.

OBJECTIVES: The objectives of the study are (1) to distinguish lipoma (L) from atypical lipomatous tumor (ALT) using MRI qualitative features, (2) to assess the value of contrast enhancement, and (3) to evaluate the reproducibility and confidence level of radiological readings. MATERIALS AND METHODS: Patients with pathologically proven L or ALT, who underwent MRI within 3 months from surgical excision were included in this retrospective multicenter international study. Two radiologists independently reviewed MRI centrally. Impressions were recorded as L or ALT. A third radiologist was consulted for discordant readings. The two radiologists re-read all non-contrast sequences; impression was recorded; then post-contrast images were reviewed and any changes were recorded. RESULTS: A total of 246 patients (135 females; median age, 59 years) were included. ALT was histopathologically confirmed in 70/246 patients. In multivariable analysis, in addition to the lesion size, deep location, proximal lower limb lesions, demonstrating incomplete fat suppression, or increased architectural complexity were the independent predictive features of ALT; but not the contrast enhancement. Post-contrast MRI changed the impression in a total of 5 studies (3 for R1 and 4 for R2; 2 studies are common); all of them were incorrectly changed from Ls to ALTs. Overall, inter-reader kappa agreement was 0.42 (95% CI 0.39-0.56). Discordance between the two readers was statistically significant for both pathologically proven L (p < 0.001) and ALT (p = 0.003). CONCLUSION: Most qualitative MR imaging features can help distinguish ALTs from BLs. However, contrast enhancement may be limited and occasionally misleading. Substantial discordance on MRI readings exists between radiologists with a relatively high false positive and negative rates.

Bayesian negative binomial mixture regression models for the analysis of sequence count and methylation data.

In this article, we develop a Bayesian hierarchical mixture regression model for studying the association between a multivariate response, measured as counts on a set of features, and a set of covariates. We have available RNA-Seq and DNA methylation data measured on breast cancer patients at different stages of the disease. We account for the heterogeneity and over-dispersion of count data (here, RNA-Seq data) by considering a mixture of negative binomial distributions and incorporate the covariates (here, methylation data) into the model via a linear modeling construction on the mean components. Our modeling construction includes several innovative characteristics. First, it employs selection techniques that allow the identification of a small subset of features that best discriminate the samples while simultaneously selecting a set of covariates associated to each feature. Second, it incorporates known dependencies into the feature selection process via the use of Markov random field (MRF) priors. On simulated data, we show how incorporating existing information via the prior model can improve the accuracy of feature selection. In the analysis of RNA-Seq and DNA methylation data on breast cancer, we incorporate knowledge on relationships among genes via a gene-gene network, which we extract from the KEGG database. Our data analysis identifies genes which are discriminatory of cancer stages and simultaneously selects significant associations between those genes and DNA methylation sites. A biological interpretation of our findings reveals several biomarkers that can help understanding the effect of DNA methylation on gene expression transcription across cancer stages.

Echocardiographic measures associated with the presence of left ventricular thrombus in patients with chemotherapy-related cardiac dysfunction.

BACKGROUND: Previous studies have not evaluated the prevalence and specific risk factors for the development of left ventricular (LV) thrombus in patients with severely reduced left ventricular dysfunction due to chemotherapy-related cardiomyopathy. We sought to evaluate the prevalence and potential markers of LV thrombus in this patient population. METHODS: From January 2009 to December 2013, patients with chemotherapy-related severe LV dysfunction (LV ejection fraction [LVEF] ≤ 30%) identified from MD Anderson Cancer Center database were reviewed. Patient characteristics and echocardiographic parameters were analyzed to determine potential risk factors for LV thrombus. RESULTS: A total of 121 patients met inclusion criteria (age 54.8 ± 15.2 years; female 63.6%; LVEF 26.3 ± 4%). LV thrombus was present in 9 patients (7.4%). Patients with LV thrombus have significantly lower LVEF compared to those without (18.7 ± 3.8% vs 26.9 ± 3.4%, P < .0001). Prevalence of LV thrombus increased as LVEF decreased and was the highest in patients with LVEF < 20%. By univariate analysis, decreased LVEF, particularly LVEF < 20% (OR 36.30, 95% CI 7.35-179.25, P < .0001) and restrictive LV filling pattern (OR 18.13, 95% CI 4.17-78.89, P = .0001) were associated with presence of LV thrombus. CONCLUSION: In patients with severely reduced LV systolic function due to chemotherapy-induced cardiomyopathy, LV thrombus was found in 7.4% of subjects. Severely decreased LVEF (<20%) and restrictive LV filling pattern were associated with the presence of LV thrombus.

Spatial Bayesian modeling of GLCM with application to malignant lesion characterization.

The emerging field of cancer radiomics endeavors to characterize intrinsic patterns of tumor phenotypes and surrogate markers of response by transforming medical images into objects that yield quantifiable summary statistics to which regression and machine learning algorithms may be applied for statistical interrogation. Recent literature has identified clinicopathological association based on textural features deriving from gray-level co-occurrence matrices (GLCM) which facilitate evaluations of gray-level spatial dependence within a delineated region of interest. GLCM-derived features, however, tend to contribute highly redundant information. Moreover, when reporting selected feature sets, investigators often fail to adjust for multiplicities and commonly fail to convey the predictive power of their findings. This article presents a Bayesian probabilistic modeling framework for the GLCM as a multivariate object as well as describes its application within a cancer detection context based on computed tomography. The methodology, which circumvents processing steps and avoids evaluations of reductive and highly correlated feature sets, uses latent Gaussian Markov random field structure to characterize spatial dependencies among GLCM cells and facilitates classification via predictive probability. Correctly predicting the underlying pathology of 81% of the adrenal lesions in our case study, the proposed method outperformed current practices which achieved a maximum accuracy of only 59%. Simulations and theory are presented to further elucidate this comparison as well as ascertain the utility of applying multivariate Gaussian spatial processes to GLCM objects.

An Anthropomorphic Head and Neck Quality Assurance Phantom for Credentialing of Intensity-Modulated Proton Therapy.

PURPOSE: To design and commission a head and neck (H&N) anthropomorphic phantom that the Imaging and Radiation Oncology Core Houston (IROC-H) can use to verify the quality of intensity-modulated proton therapy H&N treatments for institutions participating in National Cancer Institute-sponsored clinical trials. MATERIALS AND METHODS: The phantom design was based on a generalized oropharyngeal tumor, including critical H&N structures (parotid glands and spinal cord). Radiochromic film and thermoluminescent dosimeter (TLD)-100 capsules were embedded in the phantom and used to evaluate dose delivery. A spot-scanning treatment plan with typical clinical constraints for H&N cancer was created by using the Eclipse analytic algorithm. The treatment plan was approved by a radiation oncologist and the phantom was irradiated 4 times. The measured dose distribution using a ±7%/4 mm gamma analysis (85% of pixels passing) and point doses were compared with the treatment planning system calculations. The prescribed target dose was 6 Gy (RBE) with 646.2 cGy (RBE) and 648.6 cGy (RBE) planned to the superior and inferior TLD, respectively. RESULTS: For point dosimetry, the average measured-to-calculated dose ratios were 0.984 and 0.986 for the superior and inferior target TLD, respectively. Dose values for the superior and inferior target TLDs were 636.1 cGy and 639.6 cGy, respectively. For the relative dose comparison, the pixel passing rates for the axial and sagittal films, respectively, were 95.5% and 94.2% for trial 1, 97.3% and 93.2% for trial 2, 93.4% and 90.0% for trial 3, and 96.2% and 92.7% for trial 4. CONCLUSION: The anthropomorphic H&N phantom was successfully designed so that TLD measured-to-calculated ratios were within IROC-H's 7% acceptance criteria, 1.6% and 1.4% lower than expected for the superior and inferior target TLDs, respectively. All trials passed the 85% pixel passing criteria established at IROC-H for the relative dose comparison performed when using a gamma index of ±7%/4 mm.

Bayesian nonparametric statistics: A new toolkit for discovery in cancer research.

Many commonly used statistical methods for data analysis or clinical trial design rely on incorrect assumptions or assume an over-simplified framework that ignores important information. Such statistical practices may lead to incorrect conclusions about treatment effects or clinical trial designs that are impractical or that do not accurately reflect the investigator's goals. Bayesian nonparametric (BNP) models and methods are a very flexible new class of statistical tools that can overcome such limitations. This is because BNP models can accurately approximate any distribution or function and can accommodate a broad range of statistical problems, including density estimation, regression, survival analysis, graphical modeling, neural networks, classification, clustering, population models, forecasting and prediction, spatiotemporal models, and causal inference. This paper describes 3 illustrative applications of BNP methods, including a randomized clinical trial to compare treatments for intraoperative air leaks after pulmonary resection, estimating survival time with different multi-stage chemotherapy regimes for acute leukemia, and evaluating joint effects of targeted treatment and an intermediate biological outcome on progression-free survival time in prostate cancer.

Testing the nicotine dependence measure mFTQ for adolescent smokers: A multinational investigation.

BACKGROUND AND OBJECTIVES: As a measure of nicotine dependence among adolescent smokers, the modified Fagerström Tolerance Questionnaire (mFTQ; seven items), has been successfully used in the United States (USA). Nonetheless, the validity and reliability of mFTQ at the international level is still needed. The current study is the first to test the validity and reliability of mFTQ in four countries: Thailand, Spain, the USA, and Russia. METHODS: In a cross-sectional survey, mFTQ, risk factors of nicotine dependence, and sociodemographic characteristics were assessed. Risk factors included age of first cigarette, frequency of alcohol use, frequency of marijuana use, and number of cigarettes smoked yesterday. Salivary cotinine was also obtained in Thailand and Spain. RESULTS: For all four countries, mFTQ exhibited a single factor structure, as supported by previous work in the USA. For all studied countries except Thailand, mFTQ presented acceptable internal reliability. Overall, risk factors of nicotine dependence have predicted mFTQ scores across countries. Frequency of alcohol use in the USA and frequency of marijuana use in Thailand and Spain were not associated with mFTQ scores. DISCUSSION AND CONCLUSIONS: mFTQ is a single-factor measure of nicotine dependence that shows acceptable internal consistency and validity across countries. Further work can advance the scale and tailor it to different cultures. SCIENTIFIC SIGNIFICANCE: mFTQ can be a clinically practical international measure of nicotine dependence. This study provides initial support for the utility of the mFTQ among Thai, Spanish, American, and Russian adolescents. Further research is needed to test and advance mFTQ across cultures. (Am J Addict 2017;26:689-696).

Quality of life among long-term survivors of advanced stage ovarian cancer: A cross-sectional approach.

PURPOSE: Long-term survival of women with advanced-stage ovarian cancer is relatively rare. Little is known about quality of life (QOL) and survivorship concerns of these women. Here, we describe QOL of women with advanced-stage ovarian cancer surviving for 8.5 years or longer and compare women with 0-1 recurrence to those with multiple recurrences. METHODS: Participants (n=56) recruited from 5 academic medical centers and the Ovarian Cancer Research Fund Alliance completed surveys regarding QOL (FACT-O), mood (CESD), social support (SPS), physical activity (IPAQ-SF), diet, and clinical characteristics. Median survival was 14.0 years (range 8.8-33.3). RESULTS: QOL and psychological adjustment of long-term survivors was relatively good, with mean FACT-G scores (multiple recurrences: 80.81±13.95; 0-1 recurrence: 89.05 ±10.80) above norms for healthy community samples (80.1±18.1). Survivors with multiple recurrences reported more compromised QOL in domains of physical and emotional well-being (p <.05), and endorsed a variety of physical and emotional concerns compared to survivors with 0-1 recurrence. Difficulties in sexual functioning were common in both groups. Almost half (43%) of the survivors reported low levels of physical activity. CONCLUSIONS: Overall, women with advanced-stage ovarian cancer who have survived at least 8.5 years report good QOL and psychological adjustment. QOL of survivors with multiple recurrences is somewhat impaired compared to those with 0-1 recurrence. Limitations include a possible bias towards participation by healthier survivors, thus under-representing the level of compromise in long-term survivors. Health care practitioners should be alert to psychosocial issues faced by these long-term survivors to provide interventions that enhance QOL.

Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients.

BACKGROUND: Understanding longitudinal variability of the microbiome in ill patients is critical to moving microbiome-based measurements and therapeutics into clinical practice. However, the vast majority of data regarding microbiome stability are derived from healthy subjects. Herein, we sought to determine intra-patient temporal microbiota variability, the factors driving such variability, and its clinical impact in an extensive longitudinal cohort of hospitalized cancer patients during chemotherapy. METHODS: The stool (n = 365) and oral (n = 483) samples of 59 patients with acute myeloid leukemia (AML) undergoing induction chemotherapy (IC) were sampled from initiation of chemotherapy until neutrophil recovery. Microbiome characterization was performed via analysis of 16S rRNA gene sequencing. Temporal variability was determined using coefficients of variation (CV) of the Shannon diversity index (SDI) and unweighted and weighted UniFrac distances per patient, per site. Measurements of intra-patient temporal variability and patient stability categories were analyzed for their correlations with genera abundances. Groups of patients were analyzed to determine if patients with adverse outcomes had significantly different levels of microbiome temporal variability. Potential clinical drivers of microbiome temporal instability were determined using multivariable regression analyses. RESULTS: Our cohort evidenced a high degree of intra-patient temporal instability of stool and oral microbial diversity based on SDI CV. We identified statistically significant differences in the relative abundance of multiple taxa amongst individuals with different levels of microbiota temporal stability. Increased intra-patient temporal variability of the oral SDI was correlated with increased risk of infection during IC (P = 0.02), and higher stool SDI CVs were correlated with increased risk of infection 90 days post-IC (P = 0.04). Total days on antibiotics was significantly associated with increased temporal variability of both oral microbial diversity (P = 0.03) and community structure (P = 0.002). CONCLUSIONS: These data quantify the longitudinal variability of the oral and gut microbiota in AML patients, show that increased variability was correlated with adverse clinical outcomes, and offer the possibility of using stabilizing taxa as a method of focused microbiome repletion. Furthermore, these results support the importance of longitudinal microbiome sampling and analyses, rather than one time measurements, in research and future clinical practice.

An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data.

BACKGROUND: The Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development. RESULTS: In this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available. CONCLUSIONS: Our method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature.

Predictors of left ventricular systolic function recovery in the setting of sinus tachycardia in patients with cancer.

OBJECTIVES: To identify unique echocardiographic features that could be used to reliably predict LVEF recovery upon resolution of sinus tachycardia in patients with cancer. BACKGROUND: Sinus tachycardia may be a manifestation of underlying cardiomyopathy or can lead to a reversible form of dilated cardiomyopathy known as tachycardia-mediated cardiomyopathy. While distinguishing the two can be challenging, predicting recovery regardless of cause can be of significant clinical importance in the cancer population. METHODS: Results of echocardiograms performed were collected. Patients with a repeat echocardiogram within 6 months of the initial echocardiogram were included. Patients with structural heart disease, acute coronary syndrome, sepsis, and pericardial disease were excluded. A comparison between baseline echocardiogram and subsequent echocardiogram was made to determine whether specific echocardiographic parameters predicted LVEF recovery. Two groups of patients were defined at the outset of the study. The recovered group was comprised of patients with reduced LVEF in the setting of sinus tachycardia and normal LVEF with resolution of tachycardia to normal sinus rhythm (NSR). The unrecovered group was comprised of subjects with low LVEF in the setting of both sinus tachycardia and NSR. RESULTS: A total of 40 patients were included in the study. LVEF in the recovered group (n=18) was 42.8% with sinus tachycardia and increased to 58.3% with NSR. Average LVEF in the unrecovered group (n=22) was 35.1% with tachycardia and improved to 38.5% with NSR. Medial TDI (E') was significantly greater in the recovered group with both tachycardia (7.95 cm/s versus 4.56 cm/s, P<.001) and NSR (8.11 cm/s versus 5.13 cm/s, P<.001). Similarly, lateral TDI (E') was significantly greater in the recovered group than in the unrecovered group during tachycardia (8.97 cm/s versus 5.13 cm/s, P<.001) and NSR (9.05 cm/s versus 5.13 cm/s, P<.001). Multivariable logistic regression analysis showed that medial TDI >6.5 cm/s (OR=30.9, P=.001) and lateral TDI >7.8 cm/s (OR=52.5, P=.002) are positively associated with the probability of LVEF recovery. CONCLUSIONS: In conclusion, TDI (medial E'>6.5 cm/s; lateral E'>7.8 cm/s) appears to predict LVEF recovery in patients with sinus tachycardia upon resolution of the tachycardia in patients with cancer.

The role of the gastrointestinal microbiome in infectious complications during induction chemotherapy for acute myeloid leukemia.

BACKGROUND: Despite increasing data on the impact of the microbiome on cancer, the dynamics and role of the microbiome in infection during therapy for acute myelogenous leukemia (AML) are unknown. Therefore, the authors sought to determine correlations between microbiome composition and infectious outcomes in patients with AML who were receiving induction chemotherapy (IC). METHODS: Buccal and fecal specimens (478 samples) were collected twice weekly from 34 patients with AML who were undergoing IC. Oral and stool microbiomes were characterized by 16S ribosomal RNA V4 sequencing using an Illumina MiSeq system. Microbial diversity and genera composition were associated with clinical outcomes. RESULTS: Baseline stool α-diversity was significantly lower in patients who developed infections during IC compared with those who did not (P = .047). Significant decreases in both oral and stool microbial α-diversity were observed over the course of IC, with a linear correlation between α-diversity change at the 2 sites (P = .02). Loss of both oral and stool α-diversity was associated significantly with the receipt of a carbapenem P < 0.001. Domination events by the majority of genera were transient (median duration, 1 sample), whereas the number of domination events by pathogenic genera increased significantly over the course of IC (P = .002). Moreover, patients who lost microbial diversity over the course of IC were significantly more likely to contract a microbiologically documented infection within the 90 days after IC neutrophil recovery (P = .04). CONCLUSIONS: The current data present the largest longitudinal analyses to date of oral and stool microbiomes in patients with AML and suggest that microbiome measurements could assist with the mitigation of infectious complications of AML therapy. Cancer 2016;122:2186-96. © 2016 American Cancer Society.

A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells.

The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems.

Salient body image concerns of patients with cancer undergoing head and neck reconstruction.

BACKGROUND: Patients with cancer undergoing head and neck reconstruction can experience significant distress from alterations in appearance and bodily functioning. We sought to delineate salient dimensions of body image concerns in this patient population preparing for reconstructive surgery. METHODS: Participants completed self-report questionnaires evaluating numerous aspects of body image. We used Bayesian factor analysis modeling methods to identify latent factors emerging from the data. RESULTS: We identified 2 latent factors: appearance distress and functional difficulties. The highest level of preoperative body image concerns were related to distress about appearance changes and its perceived social consequences. Appearance distress items displayed greater variability compared with functional difficulties. CONCLUSION: Appearance and functional changes to body image are important areas of concern for patients with head and neck cancer as they prepare for reconstructive surgery. Knowledge regarding specific body image issues can be used to guide psychosocial assessments and intervention to enhance patient care. © 2016 Wiley Periodicals, Inc. Head Neck 38: 1035-1042, 2016.

Incidence of Atypical Femur Fractures in Cancer Patients: The MD Anderson Cancer Center Experience.

Atypical femoral fractures (AFFs) are rare adverse events attributed to bisphosphonate (BP) use. Few cases of AFF in cancer have been described; the aim of this study is to identify the incidence and risk factors for AFF in a large cancer center. This retrospective study was conducted at the MD Anderson Cancer Center. The incidence rate of AFF among BP users was calculated from January 1, 2004 through December 31, 2013. The control group (n = 51) included 2 or 3 patients on BPs matched for age (≤1 year) and gender. Logistic regression analysis was used to assess the relationship between clinical characteristics and AFF. Twenty-three AFF cases were identified radiographically among 10,587 BP users, the total BP exposure was 53,789 months (4482 years), and the incidence of AFF in BP users was 0.05 cases per 100,000 person-years. Meanwhile, among 300,553 patients who did not receive BPs there were 2 cases of AFF as compared with the 23 cases noted above. The odds ratio (OR) of having AFF in BP users was 355.58 times higher (95% CI, 84.1 to 1501.4, p < 0.0001) than the risk in non-BP users. The OR of having AFF in alendronate users was 5.54 times greater (OR 5.54 [95% CI, 1.60 to 19.112, p = 0.007]) than the odds of having AFF among other BP users. Patients who were on zoledronic acid (ZOL) had smaller odds of developing AFF compared with other BP users in this matched case control sample. AFFs are rare, serious adverse events that occur in patients with cancer who receive BP therapy. Patients with cancer who receive BPs for prior osteoporosis therapy or for metastatic cancer are at higher risk of AFF. © 2016 American Society for Bone and Mineral Research.

Role of Increased n-acetylaspartate Levels in Cancer.

BACKGROUND: The clinical and biological effects of metabolic alterations in cancer are not fully understood. METHODS: In high-grade serous ovarian cancer (HGSOC) samples (n = 101), over 170 metabolites were profiled and compared with normal ovarian tissues (n = 15). To determine NAT8L gene expression across different cancer types, we analyzed the RNA expression of cancer types using RNASeqV2 data available from the open access The Cancer Genome Atlas (TCGA) website (http://www.cbioportal.org/public-portal/). Using NAT8L siRNA, molecular techniques and histological analysis, we determined cancer cell viability, proliferation, apoptosis, and tumor growth in in vitro and in vivo (n = 6-10 mice/group) settings. Data were analyzed with the Student's t test and Kaplan-Meier analysis. Statistical tests were two-sided. RESULTS: Patients with high levels of tumoral NAA and its biosynthetic enzyme, aspartate N-acetyltransferase (NAT8L), had worse overall survival than patients with low levels of NAA and NAT8L. The overall survival duration of patients with higher-than-median NAA levels (3.6 years) was lower than that of patients with lower-than-median NAA levels (5.1 years, P = .03). High NAT8L gene expression in other cancers (melanoma, renal cell, breast, colon, and uterine cancers) was associated with worse overall survival. NAT8L silencing reduced cancer cell viability (HEYA8: control siRNA 90.61% ± 2.53, NAT8L siRNA 39.43% ± 3.00, P < .001; A2780: control siRNA 90.59% ± 2.53, NAT8L siRNA 7.44% ± 1.71, P < .001) and proliferation (HEYA8: control siRNA 74.83% ± 0.92, NAT8L siRNA 55.70% ± 1.54, P < .001; A2780: control siRNA 50.17% ± 4.13, NAT8L siRNA 26.52% ± 3.70, P < .001), which was rescued by addition of NAA. In orthotopic mouse models (ovarian cancer and melanoma), NAT8L silencing reduced tumor growth statistically significantly (A2780: control siRNA 0.52 g ± 0.15, NAT8L siRNA 0.08 g ± 0.17, P < .001; HEYA8: control siRNA 0.79 g ± 0.42, NAT8L siRNA 0.24 g ± 0.18, P = .008, A375-SM: control siRNA 0.55 g ± 0.22, NAT8L siRNA 0.21 g ± 0.17 g, P = .001). NAT8L silencing downregulated the anti-apoptotic pathway, which was mediated through FOXM1. CONCLUSION: These findings indicate that the NAA pathway has a prominent role in promoting tumor growth and represents a valuable target for anticancer therapy.Altered energy metabolism is a hallmark of cancer (1). Proliferating cancer cells have much greater metabolic requirements than nonproliferating differentiated cells (2,3). Moreover, altered cancer metabolism elevates unique metabolic intermediates, which can promote cancer survival and progression (4,5). Furthermore, emerging evidence suggests that proliferating cancer cells exploit alternative metabolic pathways to meet their high demand for energy and to accumulate biomass (6-8).