Expression of CXCL10 is associated with response to radiotherapy and overall survival in squamous cell carcinoma of the tongue

Five-year survival for patients with oral cancer has been disappointingly stable during the last decades, creating a demand for new biomarkers and treatment targets. Lately, much focus has been set on immunomodulation as a possible treatment or an adjuvant increasing sensitivity to conventional treatments. The objective of this study was to evaluate the prognostic importance of response to radiotherapy in tongue carcinoma patients as well as the expression of the CXC-chemokines in correlation to radiation response in the same group of tumours. Thirty-eight patients with tongue carcinoma that had received radiotherapy followed by surgery were included. The prognostic impact of pathological response to radiotherapy, N-status, T-stage, age and gender was evaluated using Cox's regression models, Kaplan-Meier survival curves and chi-square test. The expression of 23 CXC-chemokine ligands and their receptors were evaluated in all patients using microarray and qPCR and correlated with response to treatment using logistic regression. Pathological response to radiotherapy was independently associated to overall survival with a 2-year survival probability of 81 % for patients showing a complete pathological response, while patients with a non-complete response only had a probability of 42 % to survive for 2 years (p = 0.016). The expression of one CXC-chemokine, CXCL10, was significantly associated with response to radiotherapy and the group of patients with the highest CXCL10 expression responded, especially poorly (p = 0.01). CXCL10 is a potential marker for response to radiotherapy and overall survival in patients with squamous cell carcinoma of the tongue

Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition

BACKGROUND: Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations. METHODS: We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty. RESULTS: Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk. CONCLUSIONS: These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types

A New Pipeline for the Normalization and Pooling of Metabolomics Data

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples' originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists

SAMHD1 is a biomarker for cytarabine response and a therapeutic target in acute myeloid leukemia.

The nucleoside analog cytarabine (Ara-C) is an essential component of primary and salvage chemotherapy regimens for acute myeloid leukemia (AML). After cellular uptake, Ara-C is converted into its therapeutically active triphosphate metabolite, Ara-CTP, which exerts antileukemic effects, primarily by inhibiting DNA synthesis in proliferating cells. Currently, a substantial fraction of patients with AML fail to respond effectively to Ara-C therapy, and reliable biomarkers for predicting the therapeutic response to Ara-C are lacking. SAMHD1 is a deoxynucleoside triphosphate (dNTP) triphosphohydrolase that cleaves physiological dNTPs into deoxyribonucleosides and inorganic triphosphate. Although it has been postulated that SAMHD1 sensitizes cancer cells to nucleoside-analog derivatives through the depletion of competing dNTPs, we show here that SAMHD1 reduces Ara-C cytotoxicity in AML cells. Mechanistically, dGTP-activated SAMHD1 hydrolyzes Ara-CTP, which results in a drastic reduction of Ara-CTP in leukemic cells. Loss of SAMHD1 activity-through genetic depletion, mutational inactivation of its triphosphohydrolase activity or proteasomal degradation using specialized, virus-like particles-potentiates the cytotoxicity of Ara-C in AML cells. In mouse models of retroviral AML transplantation, as well as in retrospective analyses of adult patients with AML, the response to Ara-C-containing therapy was inversely correlated with SAMHD1 expression. These results identify SAMHD1 as a potential biomarker for the stratification of patients with AML who might best respond to Ara-C-based therapy and as a target for treating Ara-C-refractory AML

Personer med synnedsättning och deras erfarenheter av tillgängligheten i utemiljön och på offentliga platser

The purpose of this study was to describe experiences of accessibility in outdoor environments and public places for people who are visually impaired. The authors chose an interview based methodology to answer the study´s purpose. Nine people from two different counties were interviewed with semi-structured interview questions, but with the freedom to answer the questions as they pleased. The included criteria´s for this study were acuity less than 0.3 and to be a member of the visually impaired national association (Synskadades riksförbund). The study was analyzed by a qualitative content analysis that resulted in three categories, "The difference between independence and dependence in accessible environments", "The difference between security and insecurity in accessible environments" and "General ignorance towards accessible environments". The result showed that the physical outdoor environments and public places could both inhibit and enable people with visual impairments, depending on if measures of adaptation for visual impairment had been made. The participants of this study had developed their own strategies to feel involved in activities outside their home. Conclusions that could be made from this study were that outdoor environments and public places could not be fully accessible by people with visual impairment and that there is a lack of knowledge in society regarding the measures of adaptation that is needed.Keywords: Visual impairment, environment, occupational therapy, accessibility.Validerat; 20150611 (global_studentproject_submitter

Robusta biomarkörer för prediktion av risk och sjukdom : en utvärdering av reproducerbarheten hos de stora kommersiella omik-plattformarna

I och med utveckling inom storskalig analys av blodprover har man idag insett nyttan av att omvandla biobanker med lagrade humanprover till data-banker där forskare snabbt kan få tillgång till data för att svara på forsknings-frågor. Problemet är att många av teknikerna för att skapa storskaliga data är semikvantitativa, värdena går inte att relatera till en absolut koncentration och är därmed svåra att slå samman och jämföra över tid. Randomisering, det vill säga att proverna analyseras i slumpvis inbördes ordning, är en av de viktigas-te aspekterna för att skapa data som går att slå samman och återanvända för många forskningsfrågor. Detta underlättar korrigering av oönskade analysva-riationer över tid. Utöver detta kan man använda sig av bryggningsprover, QC-prov (kvalitetskontrollprov) eller ankarprover, som analyseras upprepat både inom och mellan analystillfällen, vilket underlättar att lägga samman dataset som analyseras vid olika tillfällen. Många kommersiella analysplattformar inkluderar ett eget QC-prov i analysen och vissa delar med sig av data för dessa prover. Det vore värdefullt om alla plattformar delade dessa data för kvalitetsutvärdering och eventuell korrige-ring av analysvariationer över tid. För alla semikvantitativa plattformar som undersöktes (Olink, Somalogic, Metabolon och Biocrates) var den tekniska variabiliteten mellan QC-proverna betydligt lägre än variabiliteten mellan ana-lyserade plasmaprover. Detta var tydligast för proteomikplattformarna, vilket antyder att förutsättningarna att upptäcka biologiska skillnader är bättre i pro-teomikdata. Undantaget från detta är en femte plattform, Nightingale, en kvan-titativ men smalare metabololmikmetod som anses generera stabila mätningar. Vid all utveckling av biomarkörpaneler för att prediktera sjukdom behöver man göra upptäcktsanalyser, sedan valideringsstudier och därefter tester i den situation man tänker att testet ska fungera. De breda omikplattformarna läm-par sig för upptäckt och eventuellt validering, men för det faktiska kliniska tes-tet behövs en kvantitativ analys för att verkligen utvärdera att de proteiner eller metaboliter man vill använda är stabilt uppmätbara och fungerar för att pre-diktera sjukdom eller risk för sjukdom