Adaptive fractionation at the MR-linac

Objective. Fractionated radiotherapy typically delivers the same dose in each fraction. Adaptive fractionation (AF) is an approach to exploit inter-fraction motion by increasing the dose on days when the distance of tumor and dose-limiting organs at risk (OAR) is large and decreasing the dose on unfavorable days. We develop an AF algorithm and evaluate the concept for patients with abdominal tumors previously treated at the MR-linac in 5 fractions.Approach. Given daily adapted treatment plans, inter-fractional changes are quantified by sparing factorsδ$_{t}$defined as the OAR-to-tumor dose ratio. The key problem of AF is to decide on the dose to deliver in fractiont, givenδ$_{t}$and the dose delivered in previous fractions, but not knowing futureδ$_{t}$s. Optimal doses that maximize the expected biologically effective dose in the tumor (BED$_{10}$) while staying below a maximum OAR BED$_{3}$constraint are computed using dynamic programming, assuming a normal distribution overδwith mean and variance estimated from previously observed patient-specificδ$_{t}$s. The algorithm is evaluated for 16 MR-linac patients in whom tumor dose was compromised due to proximity of bowel, stomach, or duodenum.Main Results. In 14 out of the 16 patients, AF increased the tumor BED$_{10}$compared to the reference treatment that delivers the same OAR dose in each fraction. However, in 11 of these 14 patients, the increase in BED$_{10}$was below 1 Gy. Two patients with large sparing factor variation had a benefit of more than 10 Gy BED$_{10}$increase. For one patient, AF led to a 5 Gy BED$_{10}$decrease due to an unfavorable order of sparing factors.Significance. On average, AF provided only a small increase in tumor BED. However, AF may yield substantial benefits for individual patients with large variations in the geometry

miRTrail - a comprehensive webserver for analyzing gene and miRNA patterns to enhance the understanding of regulatory mechanisms in diseases

<p>Abstract</p> <p>Background</p> <p>Expression profiling provides new insights into regulatory and metabolic processes and in particular into pathogenic mechanisms associated with diseases. Besides genes, non-coding transcripts as microRNAs (miRNAs) gained increasing relevance in the last decade. To understand the regulatory processes of miRNAs on genes, integrative computer-aided approaches are essential, especially in the light of complex human diseases as cancer.</p> <p>Results</p> <p>Here, we present miRTrail, an integrative tool that allows for performing comprehensive analyses of interactions of genes and miRNAs based on expression profiles. The integrated analysis of mRNA and miRNA data should generate more robust and reliable results on deregulated pathogenic processes and may also offer novel insights into the regulatory interactions between miRNAs and genes. Our web-server excels in carrying out gene sets analysis, analysis of miRNA sets as well as the combination of both in a systems biology approach. To this end, miRTrail integrates information on 20.000 genes, almost 1.000 miRNAs, and roughly 280.000 putative interactions, for Homo sapiens and accordingly for Mus musculus and Danio rerio. The well-established, classical Chi-squared test is one of the central techniques of our tool for the joint consideration of miRNAs and their targets. For interactively visualizing obtained results, it relies on the network analyzers and viewers BiNA or Cytoscape-web, also enabling direct access to relevant literature. We demonstrated the potential of miRTrail by applying our tool to mRNA and miRNA data of malignant melanoma. MiRTrail identified several deregulated miRNAs that target deregulated mRNAs including miRNAs hsa-miR-23b and hsa-miR-223, which target the highest numbers of deregulated mRNAs and regulate the pathway "basal cell carcinoma". In addition, both miRNAs target genes like PTCH1 and RASA1 that are involved in many oncogenic processes.</p> <p>Conclusions</p> <p>The application on melanoma samples demonstrates that the miRTrail platform may open avenues for investigating the regulatory interactions between genes and miRNAs for a wide range of human diseases. Moreover, miRTrail cannot only be applied to microarray based expression profiles, but also to NGS-based transcriptomic data. The program is freely available as web-server at mirtrail.bioinf.uni-sb.de.</p

Functional Comparison of Induced Pluripotent Stem Cell- and Blood-Derived GPIIbIIIa Deficient Platelets

Human induced pluripotent stem cells (hiPSCs) represent a versatile tool to model genetic diseases and are a potential source for cell transfusion therapies. However, it remains elusive to which extent patient-specific hiPSC-derived cells functionally resemble their native counterparts. Here, we generated a hiPSC model of the primary platelet disease Glanzmann thrombasthenia (GT), characterized by dysfunction of the integrin receptor GPIIbIIIa, and compared side-by-side healthy and diseased hiPSC-derived platelets with peripheral blood platelets. Both GT-hiPSC-derived platelets and their peripheral blood equivalents showed absence of membrane expression of GPIIbIIIa, a reduction of PAC-1 binding, surface spreading and adherence to fibrinogen. We demonstrated that GT-hiPSC-derived platelets recapitulate molecular and functional aspects of the disease and show comparable behavior to their native counterparts encouraging the further use of hiPSC-based disease models as well as the transition towards a clinical application

Phocine Distemper in German Seals, 2002

Approximately 21,700 seals died during a morbillivirus epidemic in northwestern Europe in 2002. Phocine distemper virus 1 was isolated from seals in German waters. The sequence of the P gene showed 97% identity with the Dutch virus isolated in 1988. There was 100% identity with the Dutch isolate from 2002 and a single nucleotide mismatch with the Danish isolate

A hybrid flux balance analysis and machine learning pipeline elucidates the metabolic response of cyanobacteria to different growth conditions

Machine learning has recently emerged as a promising tool for inferring multi-omic relationships in biological systems. At the same time, genome-scale metabolic models (GSMMs) can be integrated with such multi-omic data to refine phenotypic predictions. In this work, we use a multi-omic machine learning pipeline to analyze a GSMM of Synechococcus sp. PCC 7002, a cyanobacterium with large potential to produce renewable biofuels. We use regularized flux balance analysis to observe flux response between conditions across photosynthesis and energy metabolism. We then incorporate principal-component analysis, k-means clustering, and LASSO regularization to reduce dimensionality and extract key cross-omic features. Our results suggest that combining metabolic modeling with machine learning elucidates mechanisms used by cyanobacteria to cope with fluctuations in light intensity and salinity that cannot be detected using transcriptomics alone. Furthermore, GSMMs introduce critical mechanistic details that improve the performance of omic-based machine learning methods

Prevalence and influence on outcome of HER2/neu, HER3 and NRG1 expression in patients with metastatic colorectal cancer

Our aim was to explore the impact of the HER2/neu, HER3 receptor as well as their ligands' neuregulin (NRG1) expression on the outcome of patients with metastatic colorectal cancer (mCRC). NRG1, HER2/neu and HER3 expression was evaluated in 208 patients with mCRC receiving 5-FU/LV plus irinotecan or irinotecan plus oxaliplatin as the first-line treatment. Biomarker expression was correlated with the outcome of patients. NRG1 (low: 192 vs. high: 16), HER2/neu (low: 201 vs. high: 7) and HER3 (low: 69 vs. high: 139) expressions were assessed in 208 patients. High versus low NRG1 expression significantly affected progression-free survival (PFS) 4.7 vs. 8.2 months, hazard ratio (HR): 2.45; 95{\%} confidence interval (CI): 1.45-4.13; P=0.001, but not overall survival (OS) (15.5 vs. 20.7 months, HR: 1.33; 95{\%} CI: 0.76-2.35; P=0.32). High versus low HER3 expression (PFS: 7.1 vs. 8.8 months, HR: 1.11; 95{\%} CI: 0.82-1.50; P=0.50; OS: 19.8 vs. 21.1 months, HR: 0.95; 95{\%} CI: 0.70-1.30; P=0.75) and high compared with low HER2/neu expression (PFS: 7.7 vs. 8.0 months, HR: 1.07; 95{\%} CI: 0.71-1.60; P=0.75; OS: 16.6 vs. 21.1 months, HR: 1.13; 95{\%} CI: 0.75-1.71; P=0.57) did not influence outcome. High NRG1 expression was associated with inferior PFS in the FIRE-1 trial. We did not detect a prognostic impact of HER2/neu and HER3 overexpression in mCRC. The frequency of overexpression was comparable with other studies

Prospecting environmental mycobacteria: combined molecular approaches reveal unprecedented diversity

Background: Environmental mycobacteria (EM) include species commonly found in various terrestrial and aquatic environments, encompassing animal and human pathogens in addition to saprophytes. Approximately 150 EM species can be separated into fast and slow growers based on sequence and copy number differences of their 16S rRNA genes. Cultivation methods are not appropriate for diversity studies; few studies have investigated EM diversity in soil despite their importance as potential reservoirs of pathogens and their hypothesized role in masking or blocking M. bovis BCG vaccine. Methods: We report here the development, optimization and validation of molecular assays targeting the 16S rRNA gene to assess diversity and prevalence of fast and slow growing EM in representative soils from semi tropical and temperate areas. New primer sets were designed also to target uniquely slow growing mycobacteria and used with PCR-DGGE, tag-encoded Titanium amplicon pyrosequencing and quantitative PCR. Results: PCR-DGGE and pyrosequencing provided a consensus of EM diversity; for example, a high abundance of pyrosequencing reads and DGGE bands corresponded to M. moriokaense, M. colombiense and M. riyadhense. As expected pyrosequencing provided more comprehensive information; additional prevalent species included M. chlorophenolicum, M. neglectum, M. gordonae, M. aemonae. Prevalence of the total Mycobacterium genus in the soil samples ranged from 2.3×107 to 2.7×108 gene targets g−1; slow growers prevalence from 2.9×105 to 1.2×107 cells g−1. Conclusions: This combined molecular approach enabled an unprecedented qualitative and quantitative assessment of EM across soil samples. Good concordance was found between methods and the bioinformatics analysis was validated by random resampling. Sequences from most pathogenic groups associated with slow growth were identified in extenso in all soils tested with a specific assay, allowing to unmask them from the Mycobacterium whole genus, in which, as minority members, they would have remained undetected

pERK, pAKT and p53 as tissue biomarkers in erlotinib-treated patients with advanced pancreatic cancer: a translational subgroup analysis from AIO-PK0104

Background: The role of pERK, pAKT and p53 as biomarkers in patients with advanced pancreatic cancer has not yet been defined. Methods: Within the phase III study AIO-PK0104 281 patients with advanced pancreatic cancer received an erlotinib-based 1st-line regimen. Archival tissue from 153 patients was available for central immunohistochemistry staining for pERK, pAKT and p53. Within a subgroup analysis, biomarker data were correlated with efficacy endpoints and skin rash using a Cox regression model. Results: Fifty-five out of 153 patients were classified as pERK(low) and 98 patients as pERK(high); median overall survival (OS) was 6.2 months and 5.7 months, respectively (HR 1.29, p = 0.16). When analysing pERK as continuous variable, the pERK score was significantly associated with OS (HR 1.06, 95% CI 1.0-1.12, p = 0.05). Twenty-one of 35 patients were pAKT(low) and 14/35 pAKT(high) with a corresponding median OS of 6.4 months and 6.8 months, respectively (HR 1.03, p = 0.93). Four out of 50 patients had a complete loss of p53 expression, 20 patients a regular expression and 26 patients had tumors with p53 overexpression. The p53 status had no impact on OS (p = 0.91); however, a significant improvement in progression-free survival (PFS) (6.0 vs 1.8 months, HR 0.24, p = 0.02) and a higher rate of skin rash (84% vs 25%, p = 0.02) was observed for patients with a regular p53 expression compared to patients with a complete loss of p53. Conclusion: pERK expression may have an impact on OS in erlotinib-treated patients with advanced pancreatic cancer; p53 should be further investigated for its potential role as a predictive marker for PFS and skin rash

Spatio-temporal Models of Lymphangiogenesis in Wound Healing

Several studies suggest that one possible cause of impaired wound healing is failed or insufficient lymphangiogenesis, that is the formation of new lymphatic capillaries. Although many mathematical models have been developed to describe the formation of blood capillaries (angiogenesis), very few have been proposed for the regeneration of the lymphatic network. Lymphangiogenesis is a markedly different process from angiogenesis, occurring at different times and in response to different chemical stimuli. Two main hypotheses have been proposed: 1) lymphatic capillaries sprout from existing interrupted ones at the edge of the wound in analogy to the blood angiogenesis case; 2) lymphatic endothelial cells first pool in the wound region following the lymph flow and then, once sufficiently populated, start to form a network. Here we present two PDE models describing lymphangiogenesis according to these two different hypotheses. Further, we include the effect of advection due to interstitial flow and lymph flow coming from open capillaries. The variables represent different cell densities and growth factor concentrations, and where possible the parameters are estimated from biological data. The models are then solved numerically and the results are compared with the available biological literature.Comment: 29 pages, 9 Figures, 6 Tables (39 figure files in total