p-Medicine: From data sharing and integration via VPH models to personalized medicine

The Worldwide innovative Networking in personalized cancer medicine (WIN) initiated by the Institute Gustave Roussy (France) and The University of Texas MD Anderson Cancer Center (USA) has dedicated its 3rd symposium (Paris, 6–8 July 2011) to discussion on gateways to increase the efficacy of cancer diagnostics and therapeutics (http://www.winconsortium.org/symposium.html)

O Messianismo na Legitimação Simbólica de D. João I (1383-85/1433)

A ascensão da Dinastia de Avis em Portugal colocou no poder o bastardo D. João I, vencedor da assim chamada Revolução de Avis. Após a sua morte, para consolidar a transmissão do poder a seus descendentes, era necessário elaborar uma cuidadosa justificativa de seu governo, tornando-o, no plano simbólico, legítimo. Tal tarefa coube ao cronista régio Fernão Lopes que, na Crónica de D. João I, utilizou-se da religiosidade medieval e da expectativa de final dos tempos para justificar o novo rei. Lopes apresenta no documento uma série de sinais e milagres, acolhendo-os como confirmação da escolha divina do novo monarca, e apresenta o combate entre D. João de Portugal e D. João de Castela como a luta entre o Messias de Lisboa e o Anticristo . &nbsp

Radiogenomic analysis of primary breast cancer reveals [18F]-fluorodeoxglucose dynamic flux-constants are positively associated with immune pathways and outperform static uptake measures in associating with glucose metabolism

Background: PET imaging of 18F-fluorodeoxygucose (FDG) is used widely for tumour staging and assessment of treatment response, but the biology associated with FDG uptake is still not fully elucidated. We therefore carried out gene set enrichment analyses (GSEA) of RNA sequencing data to find KEGG pathways associated with FDG uptake in primary breast cancers. Methods: Pre-treatment data were analysed from a window-of-opportunity study in which 30 patients underwent static and dynamic FDG-PET and tumour biopsy. Kinetic models were fitted to dynamic images, and GSEA was performed for enrichment scores reflecting Pearson and Spearman coefficients of correlations between gene expression and imaging. Results: A total of 38 pathways were associated with kinetic model flux-constants or static measures of FDG uptake, all positively. The associated pathways included glycolysis/gluconeogenesis (‘GLYC-GLUC’) which mediates FDG uptake and was associated with model flux-constants but not with static uptake measures, and 28 pathways related to immune-response or inflammation. More pathways, 32, were associated with the flux-constant K of the simple Patlak model than with any other imaging index. Numbers of pathways categorised as being associated with individual micro-parameters of the kinetic models were substantially fewer than numbers associated with flux-constants, and lay around levels expected by chance. Conclusions: In pre-treatment images GLYC-GLUC was associated with FDG kinetic flux-constants including Patlak K, but not with static uptake measures. Immune-related pathways were associated with flux-constants and static uptake. Patlak K was associated with more pathways than were the flux-constants of more complex kinetic models. On the basis of these results Patlak analysis of dynamic FDG-PET scans is advantageous, compared to other kinetic analyses or static imaging, in studies seeking to infer tumour-to-tumour differences in biology from differences in imaging. Trial registration NCT01266486, December 24th 2010

A machine learning and directed network optimization approach to uncover TP53 regulatory patterns

TP53, the Guardian of the Genome, is the most frequently mutated gene in human cancers and the functional characterization of its regulation is fundamental. To address this we employ two strategies: machine learning to predict the mutation status of TP53 from transcriptomic data, and directed regulatory networks to reconstruct the effect of mutations on the transcipt levels of TP53 targets. Using data from established databases (Cancer Cell Line Encyclopedia, The Cancer Genome Atlas), machine learning could predict the mutation status, but not resolve different mutations. On the contrary, directed network optimization allowed to infer the TP53 regulatory profile across: (1) mutations, (2) irradiation in lung cancer, and (3) hypoxia in breast cancer, and we could observe differential regulatory profiles dictated by (1) mutation type, (2) deleterious consequences of the mutation, (3) known hotspots, (4) protein changes, (5) stress condition (irradiation/hypoxia). This is an important first step toward using regulatory networks for the characterization of the functional consequences of mutations, and could be extended to other perturbations, with implications for drug design and precision medicine

A multi-gene signature predicts outcome in patients with pancreatic ductal adenocarcinoma.

© 2014 Haider et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Improved usage of the repertoires of pancreatic ductal adenocarcinoma (PDAC) profiles is crucially needed to guide the development of predictive and prognostic tools that could inform the selection of treatment options

The small-nucleolar RNAs commonly used for microRNA normalisation correlate with tumour pathology and prognosis

Background:To investigate small-nucleolar RNAs (snoRNAs) as reference genes when measuring miRNA expression in tumour samples, given emerging evidence for their role in cancer.Methods:Four snoRNAs, commonly used for normalisation, RNU44, RNU48, RNU43 and RNU6B, and miRNA known to be associated with pathological factors, were measured by real-time polymerase chain reaction in two patient series: 219 breast cancer and 46 head and neck squamous cell carcinoma (HNSCC). SnoRNA and miRNA were then correlated with clinicopathological features and prognosis.Results:Small-nucleolar RNA expression was as variable as miRNA expression (miR-21, miR-210, miR-10b). Normalising miRNA PCR expression data to these recommended snoRNAs introduced bias in associations between miRNA and pathology or outcome. Low snoRNA expression correlated with markers of aggressive pathology. Low levels of RNU44 were associated with a poor prognosis. RNU44 is an intronic gene in a cluster of highly conserved snoRNAs in the growth arrest specific 5 (GAS5) transcript, which is normally upregulated to arrest cell growth under stress. Low-tumour GAS5 expression was associated with a poor prognosis. RNU48 and RNU43 were also identified as intronic snoRNAs within genes that are dysregulated in cancer.Conclusion:Small-nucleolar RNAs are important in cancer prognosis, and their use as reference genes can introduce bias when determining miRNA expression. © 2011 Cancer Research UK All rights reserved

Preclinical animal research on therapy dosimetry with dual isotopes

Preclinical research into radionuclide therapies based on radiation dosimetry will enable the use of any LET-equivalent radionuclide. Radiation dose and dose rate have significant influence on dose effects in the tumour depending on its radiation sensitivity, possibilities for repair of sublethal damage, and repopulation during or after the therapy. Models for radiation response of preclinical tumour models after peptide receptor radionuclide therapy based on the linear quadratic model are presented. The accuracy of the radiation dose is very important for observation of dose-effects. Uncertainties in the radiation dose estimation arise from incomplete assay of the kinetics, low accuracy in volume measurements and absorbed dose S-values for stylized models instead of the actual animal geometry. Normal dose uncertainties in the order of 20% might easily make the difference between seeing a dose-effect or missing it altogether. This is true for the theoretical case of a homogeneous tumour type behaving in vivo in the same way as its cells do in vitro. Heterogeneity of tumours induces variations in clonogenic cell density, radiation sensitivity, repopulation capacity and repair kinetics. The influence of these aspects are analysed within the linear quadratic model for tumour response to radionuclide therapy. Preclinical tumour models tend to be less heterogenic than the clinical conditions they should represent. The results of various preclinical radionuclide therapy experiments for peptide receptor radionuclide therapy are compared to the outcome of theoretical models and the influence of increased heterogeneity is analysed when the results of preclinical research is transferred to the clinic. When the radiation dose and radiobiology of the tumour response is known well enough it may be possible to leave the current phenomenological approach in preclinical radionuclide therapy and start basing these experiments on radiation dose. Then the use of a gamma ray-emitting radionuclides for a chemically comparable beta-particle-emitting paired isotope for therapy evaluation would be feasible