Radiation metabolomics. 3. Biomarker discovery in the urine of gamma-irradiated rats using a simplified metabolomics protocol of gas chromatography-mass spectrometry combined with random forests machine learning algorithm

Abstract Radiation metabolomics employing mass spectral technologies represents a plausible means of high-throughput minimally invasive radiation biodosimetry. A simplified metabolomics protocol is described that employs ubiquitous gas chromatography-mass spectrometry and open source software including random forests machine learning algorithm to uncover latent biomarkers of 3 Gy gamma radiation in rats. Urine was collected from six male Wistar rats and six sham-irradiated controls for 7 days, 4 prior to irradiation and 3 after irradiation. Water and food consumption, urine volume, body weight, and sodium, potassium, calcium, chloride, phosphate and urea excretion showed major effects from exposure to gamma radiation. The metabolomics protocol uncovered several urinary metabolites that were significantly up-regulated (glyoxylate, threonate, thymine, uracil, p-cresol) and down-regulated (citrate, 2-oxoglutarate, adipate, pimelate, suberate, azelaate) as a result of radiation exposure. Thymine and uracil were shown to derive largely from thymidine and 2'-deoxyuridine, which are known radiation biomarkers in the mouse. The radiation metabolomic phenotype in rats appeared to derive from oxidative stress and effects on kidney function. Gas chromatography-mass spectrometry is a promising platform on which to develop the field of radiation metabolomics further and to assist in the design of instrumentation for use in detecting biological consequences of environmental radiation release.

Combining Wireless Sensor Networks and Machine Learning for Flash Flood Nowcasting

This paper addresses an investigation with machine learning (ML) classification techniques to assist in the problem of flash flood now casting. We have been attempting to build a Wireless Sensor Network (WSN) to collect measurements from a river located in an urban area. The machine learning classification methods were investigated with the aim of allowing flash flood now casting, which in turn allows the WSN to give alerts to the local population. We have evaluated several types of ML taking account of the different now casting stages (i.e. Number of future time steps to forecast). We have also evaluated different data representation to be used as input of the ML techniques. The results show that different data representation can lead to results significantly better for different stages of now casting.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.

Shallow Dialogue Processing Using Machine Learning Algorithms (or not)

This paper presents a shallow dialogue analysis model, aimed at human-human dialogues in the context of staff or business meetings. Four components of the model are defined, and several machine learning techniques are used to extract features from dialogue transcripts: maximum entropy classifiers for dialogue acts, latent semantic analysis for topic segmentation, or decision tree classifiers for discourse markers. A rule-based approach is proposed for solving cross-modal references to meeting documents. The methods are trained and evaluated thanks to a common data set and annotation format. The integration of the components into an automated shallow dialogue parser opens the way to multimodal meeting processing and retrieval applications.

Aberrant lipid metabolism in hepatocellular carcinoma revealed by plasma metabolomics and lipid profiling

There has been limited analysis of the effects of hepatocellular carcinoma (HCC) on liver metabolism and circulating endogenous metabolites. Here, we report the findings of a plasma metabolomic investigation of HCC patients by ultraperformance liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOFMS), random forests machine learning algorithm, and multivariate data analysis. Control subjects included healthy individuals as well as patients with liver cirrhosis or acute myeloid leukemia. We found that HCC was associated with increased plasma levels of glycodeoxycholate, deoxycholate 3-sulfate, and bilirubin. Accurate mass measurement also indicated upregulation of biliverdin and the fetal bile acids 7α-hydroxy-3-oxochol-4-en-24-oic acid and 3-oxochol-4,6-dien-24-oic acid in HCC patients. A quantitative lipid profiling of patient plasma was also conducted by ultraperformance liquid chromatography-electrospray ionization-triple quadrupole mass spectrometry (UPLC-ESI-TQMS). By this method, we found that HCC was also associated with reduced levels of lysophosphocholines and in 4 of 20 patients with increased levels of lysophosphatidic acid [LPA(16:0)], where it correlated with plasma α-fetoprotein levels. Interestingly, when fatty acids were quantitatively profiled by gas chromatography-mass spectrometry (GC-MS), we found that lignoceric acid (24:0) and nervonic acid (24:1) were virtually absent from HCC plasma. Overall, this investigation illustrates the power of the new discovery technologies represented in the UPLC-ESI-QTOFMS platform combined with the targeted, quantitative platforms of UPLC-ESI-TQMS and GC-MS for conducting metabolomic investigations that can engender new insights into cancer pathobiology.

The impact of neural invasion severity in gastrointestinal malignancies: a clinicopathological study.

OBJECTIVES Because neural invasion (NI) is still inconsistently reported and not well characterized within gastrointestinal malignancies (GIMs), our aim was to determine the exact prevalence and severity of NI and to elucidate the true impact of NI on patient's prognosis. BACKGROUND The union internationale contre le cancer (UICC) recently added NI as a novel parameter in the current TNM classification. However, there are only a few existing studies with specific focus on NI, so that the distinct role of NI in GIMs is still uncertain. MATERIALS AND METHODS NI was characterized in approximately 16,000 hematoxylin and eosin tissue sections from 2050 patients with adenocarcinoma of the esophagogastric junction (AEG)-I-III, squamous cell carcinoma (SCC) of the esophagus, gastric cancer (GC), colon cancer (CC), rectal cancer (RC), cholangiocellular cancer (CCC), hepatocellular cancer (HCC), and pancreatic cancer (PC). NI prevalence and severity was determined and related to patient's prognosis and survival. RESULTS NI prevalence largely varied between HCC/6%, CC/28%, RC/34%, AEG-I/36% and AEG-II/36%, SCC/37%, GC/38%, CCC/58%, and AEG-III/65% to PC/100%. NI severity score was uppermost in PC (24.9±1.9) and lowest in AEG-I (0.8±0.3). Multivariable analyses including age, sex, TNM stage, and grading revealed that the prevalence of NI was significantly associated with diminished survival in AEG-II/III, GC, and RC. However, increasing NI severity impaired survival in AEG-II/III and PC only. CONCLUSIONS NI prevalence and NI severity strongly vary within GIMs. Determination of NI severity in GIMs is a more precise tool than solely recording the presence of NI and revealed dismal prognostic impact on patients with AEG-II/III and PC. Evidently, NI is not a concomitant side feature in GIMs and, therefore, deserves special attention for improved patient stratification and individualized therapy after surgery.

The alphavbeta6 integrin is a highly specific immunohistochemical marker for cholangiocarcinoma

Hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC) are common primary hepatic malignancies. Their immunohistological differentiation using specific markers is pivotal for treatment and prognosis. We found alphavbeta6 integrin strongly upregulated in biliary fibrosis, but its expression in primary and secondary liver tumours is unknown. Here, we aimed to evaluate the diagnostic applicability of alphavbeta6 integrin in differentiating primary liver cancers.

Continuous Sunitinib treatment in patients with advanced hepatocellular carcinoma: a Swiss Group for Clinical Cancer Research (SAKK) and Swiss Association for the Study of the Liver (SASL) multicenter phase II trial (SAKK 77/06)

Sunitinib (SU) is a multitargeted tyrosine kinase inhibitor with antitumor and antiangiogenic activity. The objective of this trial was to demonstrate antitumor activity of continuous SU treatment in patients with hepatocellular carcinoma (HCC).

Everolimus augments the effects of sorafenib in a syngeneic orthotopic model of hepatocellular carcinoma

Sorafenib targets the Raf/mitogen-activated protein kinase, VEGF, and platelet-derived growth factor pathways and prolongs survival patients in advanced hepatocellular carcinoma (HCC). Everolimus inhibits the mammalian target of rapamycin, a kinase overactive in HCC. To investigate whether the antitumor effects of these agents are additive, we compared a combined and sequential treatment regimen of everolimus and sorafenib with monotherapy. After hepatic implantation of Morris Hepatoma (MH) cells, rats were randomly allocated to everolimus (5 mg/kg, 2×/week), sorafenib (7.5 mg/kg/d), combined everolimus and sorafenib, sequential sorafenib (2 weeks) then everolimus (3 weeks), or control groups. MRI quantified tumor volumes. Erk1/2, 4E-BP1, and their phosphorylated forms were quantified by immunoblotting. Angiogenesis was assessed in vitro by aortic ring and tube formation assays, and in vivo with Vegf-a mRNA and vascular casts. After 35 days, tumor volumes were reduced by 60%, 85%, and 55%, relative to controls, in everolimus, the combination, and sequential groups, respectively (P < 0.01). Survival was longest in the combination group (P < 0.001). Phosphorylation of 4E-BP1 and Erk1/2 decreased after everolimus and sorafenib, respectively. Angiogenesis decreased after all treatments (P < 0.05), although sorafenib increased Vegf-a mRNA in liver tumors. Vessel sprouting was abundant in control tumors, lower after sorafenib, and absent after the combination. Intussusceptive angiogenic transluminal pillars failed to coalesce after the combination. Combined treatment with everolimus and sorafenib exerts a stronger antitumoral effect on MH tumors than monotherapy. Everolimus retains antitumoral properties when administered sequentially after sorafenib. This supports the clinical use of everolimus in HCC, both in combination with sorafenib or after sorafenib.

The homeobox gene HLXB9 is upregulated in a morphological subset of poorly differentiated hepatocellular carcinoma

The prognostic outcome for hepatocellular carcinoma (HCC) remains poor. Disease progression is accompanied by dedifferentiation of the carcinoma, a process that is not well understood. The aim of this study was to get more insight into the molecular characteristics of dedifferentiated carcinomas using high throughput techniques. Microarray-based global gene expression analysis was performed on five poorly differentiated HCC cell lines compared with non-neoplastic hepatic controls and a set of three cholangiolar carcinoma (CC) cell lines. The gene with the highest upregulation was HLXB9. HLXB9 is a gene of the homeobox genfamily important for the development of the pancreas. RT-PCR confirmed the upregulation of HLXB9 in surgical specimens of carcinoma tissue, suggesting its biological significance. Interestingly, HLXB9 upregulation was primary observed in poorly differentiated HCC with a pseudoglandular pattern compared with a solid pattern HCC or in moderate or well-differentiated HCC. Additional the expression of translated HLXB9, the protein HB9 (NCBI: NP_001158727), was analyzed by western blotting. Expression of HB9 was only detected in the cytoplasm but not in the nuclei of the HCC cells. For validation CC were also investigated. Again, we found an upregulation of HLXB9 in CC cells accompanied by an expression of HB9 in the cytoplasms of these tumor cells, respectively. In conclusion, homeobox HLXB9 is upregulated in poorly differentiated HCC with a pseudoglandular pattern. The translated HB9 protein is found in the cytoplasm of these HCC and CC. We therefore assume HLXB9 as a possible link in the understanding of the development of HCC and CC, respectively.

Heterogeneity of patients with intermediate (BCLC B) Hepatocellular Carcinoma: proposal for a subclassification to facilitate treatment decisions

The intermediate stage of hepatocellular carcinoma (HCC) comprises a highly heterogeneous patient population and therefore poses unique challenges for therapeutic management, different from the early and advanced stages. Patients classified as having intermediate HCC by the Barcelona Clinic Liver Cancer (BCLC) staging system present with varying tumor burden and liver function. Transarterial chemoembolization (TACE) is currently recommended as the standard of care in this setting, but there is considerable variation in the clinical benefit patients derive from this treatment.In April 2012, a panel of experts convened to discuss unresolved issues surrounding the application of current guidelines when managing patients with intermediate HCC. The meeting explored the applicability of a subclassification system for intermediate HCC patients to tailor therapeutic interventions based on the evidence available to date and expert opinion. The present report summarizes the proposal of the expert panel: four substages of intermediate HCC patients, B1 to B4.