Effects of Radiotherapy in the treatment of multiple myeloma: a retrospective analysis of a Single Institution

Background: Palliative irradiation of osteolytic lesions is a considerable component in the treatment for patients with multiple myeloma. In this study, we analyzed the efficacy of irradiation in these patients. Patients and methods: We retrospectively analyzed 153 patients with multiple myeloma who were admitted to our department between 1989 and 2013. According to the staging system of Durie & Salmon 116 patients were classified as stage III. 107/153 patients were treated with radiotherapy of at least one and up to 6 bony lesions at different times. In order to evaluate the effect of local radiotherapy on pain relief and bone recalcification a uni-and multivariate analysis was performed using a binary logistic regression model to correct for multiple measurements. Complete information on dose, fractionation and volume of radiotherapy was available from 81 patients treated in 136 target volumes for pain relief, and from 69 patients treated in 108 target volumes for recalcification. Total radiation doses varied between 8 Gy to 50 Gy (median dose 25 Gy in 2.5 Gy fractions, 5 times a week). Results: Radiotherapy resulted in complete local pain relief in 31% and partial local pain relief in 54% of the patients. In the univariate analysis, higher total radiation doses (p = 0.023) and higher age (p = 0.014) at the time of radiotherapy were significantly associated with a higher likelihood of pain relief, whereas no significant association was detected for concurrent systemic treatment, type and stage of myeloma and location of bone lesions. The same variables were independent predictors for pain relief in the multivariate analysis. Recalcification was observed in 48% of irradiated bone lesions. In the uni-and multivariate analysis higher radiation doses were significantly associated (p = 0.048) with an increased likelihood of recalcification. Side effects of radiotherapy were generally mild. Conclusions: Higher total biological radiation doses were associated with better pain relief and recalcification in this retrospective evaluation of multiple myeloma patients. In addition, in the elderly the therapeutic measures appear to develop a better analgesic effect

Survival models with preclustered gene groups as covariates

<p>Abstract</p> <p>Background</p> <p>An important application of high dimensional gene expression measurements is the risk prediction and the interpretation of the variables in the resulting survival models. A major problem in this context is the typically large number of genes compared to the number of observations (individuals). Feature selection procedures can generate predictive models with high prediction accuracy and at the same time low model complexity. However, interpretability of the resulting models is still limited due to little knowledge on many of the remaining selected genes. Thus, we summarize genes as gene groups defined by the hierarchically structured Gene Ontology (GO) and include these gene groups as covariates in the hazard regression models. Since expression profiles within GO groups are often heterogeneous, we present a new method to obtain subgroups with coherent patterns. We apply preclustering to genes within GO groups according to the correlation of their gene expression measurements.</p> <p>Results</p> <p>We compare Cox models for modeling disease free survival times of breast cancer patients. Besides classical clinical covariates we consider genes, GO groups and preclustered GO groups as additional genomic covariates. Survival models with preclustered gene groups as covariates have similar prediction accuracy as models built only with single genes or GO groups.</p> <p>Conclusions</p> <p>The preclustering information enables a more detailed analysis of the biological meaning of covariates selected in the final models. Compared to models built only with single genes there is additional functional information contained in the GO annotation, and compared to models using GO groups as covariates the preclustering yields coherent representative gene expression profiles.</p

Evaluation of multi-assay algorithms for cross-sectional HIV incidence estimation in settings with universal antiretroviral treatment.

BACKGROUND: Multi-assay algorithms (MAAs) are used to estimate population-level HIV incidence and identify individuals with recent infection. Many MAAs use low viral load (VL) as a biomarker for long-term infection. This could impact incidence estimates in settings with high rates of early HIV treatment initiation. We evaluated the performance of two MAAs that do not include VL. METHODS: Samples were collected from 219 seroconverters (infected  1 year) in the HPTN 071 (PopART) trial; 28.8% of seroconverter samples and 73.2% of non-seroconverter samples had VLs ≤ 400 copies/mL. Samples were tested with the Limiting Antigen Avidity assay (LAg) and JHU BioRad-Avidity assays. Antibody reactivity to two HIV peptides was measured using the MSD U-PLEX assay. Two MAAs were evaluated that do not include VL: a MAA that includes the LAg-Avidity assay and BioRad-Avidity assay (LAg + BR) and a MAA that includes the LAg-Avidity assay and two peptide biomarkers (LAg + PepPair). Performance of these MAAs was compared to a widely used MAA that includes LAg and VL (LAg + VL). RESULTS: The incidence estimate for LAg + VL (1.29%, 95% CI: 0.97-1.62) was close to the observed longitudinal incidence (1.34% 95% CI: 1.17-1.53). The incidence estimates for the other two MAAs were higher (LAg + BR: 2.56%, 95% CI 2.01-3.11; LAg + PepPair: 2.84%, 95% CI: 1.36-4.32). LAg + BR and LAg + PepPair also misclassified more individuals infected > 2 years as recently infected than LAg + VL (1.2% [42/3483 and 1.5% [51/3483], respectively, vs. 0.2% [6/3483]). LAg + BR classified more seroconverters as recently infected than LAg + VL or LAg + PepPair (80 vs. 58 and 50, respectively) and identified ~ 25% of virally suppressed seroconverters as recently infected. CONCLUSIONS: The LAg + VL MAA produced a cross-sectional incidence estimate that was closer to the longitudinal estimate than two MAAs that did not include VL. The LAg + BR MAA classified the greatest number of individual seroconverters as recently infected but had a higher false recent rate

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space

The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) was developed to address a widespread community need for a unified computing environment for genomics data storage, management, and analysis. In this perspective, we present AnVIL, describe its ecosystem and interoperability with other platforms, and highlight how this platform and associated initiatives contribute to improved genomic data sharing efforts. The AnVIL is a federated cloud platform designed to manage and store genomics and related data, enable population-scale analysis, and facilitate collaboration through the sharing of data, code, and analysis results. By inverting the traditional model of data sharing, the AnVIL eliminates the need for data movement while also adding security measures for active threat detection and monitoring and provides scalable, shared computing resources for any researcher. We describe the core data management and analysis components of the AnVIL, which currently consists of Terra, Gen3, Galaxy, RStudio/Bioconductor, Dockstore, and Jupyter, and describe several flagship genomics datasets available within the AnVIL. We continue to extend and innovate the AnVIL ecosystem by implementing new capabilities, including mechanisms for interoperability and responsible data sharing, while streamlining access management. The AnVIL opens many new opportunities for analysis, collaboration, and data sharing that are needed to drive research and to make discoveries through the joint analysis of hundreds of thousands to millions of genomes along with associated clinical and molecular data types

Novel and concordant eQTLs from analysis of iPSC-derived megakaryocytes and platelets in the GeneticStudies of Atherosclerosis Risk (GeneSTAR) project.

Kai Kammers1, Margaret A Taub2, Benjamin Rodriguez4, Ingo Ruczinski2, Lisa R Yanek3, Andrew D Johnson4, Nauder Faraday3, Lewis C Becker3, Rasika A Mathias3. 1 Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Johns Hopkins University School of Medicine, Baltimore, MD 2 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 3 The GeneSTAR Research Program, Johns Hopkins University School of Medicine, Baltimore, MD 4 NHLBI Population Sciences Branch, The Framingham Heart Study, Framingham, MA GWAS studies have identified common variants associated with platelet aggregation, but because these variants are largely intronic/intergenic, their mechanistic link to platelet function is unclear. Additionally, extensive missing heritability may be resolved by integrating genetics and transcriptomics. To better understand the transcriptome signature and its genetic regulatory landscape in platelets and megakaryocytes (MKs), we performed expression-quantitative trait locus (eQTL) analyses of RNA sequencing (RNA-seq) data on both cell types in African American (AA) and European American (EA) subjects from the Genetic Studies of Atherosclerosis Risk (GeneSTAR) project. Using genotypes from the Illumina 1M GWAS array (1,003,451 SNPs), eQTL analyses were carried out stratified by ancestry and cell type, with a 1Mb window around each gene and adjusting for relevant covariates with the R package MatrixEQTL. Significance was defined as q-value < 0.05. Genes with median FPKM<= 1 were excluded, yielding ~10,000 genes in the MKs and ~3,000 in the platelets, 94% of which are also expressed in the MKs.Non UBCUnreviewedAuthor affiliation: Johns Hopkins UniversityResearche

Standardized in vitro analysis of the degradability of hyaluronic acid fillers by hyaluronidase

Abstract Background Hyaluronidase is a hyaluronic acid (HA) metabolizing enzyme, which is approved as an adjuvant for infiltration anesthesia. The “off-label” use of hyaluronidase is regarded as gold standard for the management of HA-filler-associated complications. Yet, up to date there are only few studies that have systematically assessed the degradability of different HA-fillers by hyaluronidase. Objective To analyze the interactions of HA-fillers and hyaluronidase in a time-dependent manner using a novel standardized in vitro approach. Methods Comparable HA-fillers, Belotero Balance Lidocaine (BEL; Merz), Emervel classic (EMV; Galderma) and Juvederm Ultra 3 (JUV; Allergan), were incubated with a fluorescent dye and bovine hyaluronidase (HYAL; Hylase “Dessau”, Riemser) or control (NaCl) and monitored by time-lapse videomicroscopy. The degradation of HA-fillers was assessed as decrease in fluorescence intensity of HA-filler plus hyaluronidase vs. HA-filler plus control, quantified by computer-assisted image analysis (ImageJ). Results Hyaluronidase showed a significant degradation of the HA-fillers BEL and EMV. Degradation was measurable at 5 h (BEL) and 7 h (EMV), respectively; significance was reached at 14 h (BEL) and 13 h (EMV). No effect of hyaluronidase was observed for JUV. Conclusion Time-lapse microscopy enables systematically, standardized, comparative in vitro analyses of the interactions of hyaluronidase and HA-fillers

Importance of biomarkers in glioblastomas patients receiving local BCNU wafer chemotherapy

Abstract Background To assess the influence of molecular markers with potential prognostic value to groups of patients with newly diagnosed glioblastoma patients were examined: group A with 36 patients (surgical resection plus standard combined chemoradiotherapy) and group B with 36 patients (surgical resection, standard combined chemoradiotherapy plus carmustine wafer implantation). Our aim was to determine chromosomal alterations, methylation status of MGMT, p15, and p16 (CDKN2A) in order to analyse the influence on patient survival time as well as radio- and chemotherapy responses. Promoter hypermethylation of MGMT, p16, and p15 genes were determined by MS-PCR. Comparative genomic hybridisation (CGH) analyses were performed with isolated, labelled DNA of each tumor to detect genetic alterations. Results Age of onset of the disease showed a significant effect on overall survival (OS) (p < 0.0001). Additional treatment with carmustine wafer (group B) compared to the control group (group A) did not result in improved OS (p = 0.562). Patients with a methylated MGMT promotor showed a significant longer OS compared to those patients with unmethylated MGMT promotor (p = 0.041). Subgroup analyses revealed that patients with methylated p15 showed a significant shorter OS when administered to group B rather than in group A (p = 0.0332). In patients additionally treated with carmustine wafer an amplification of 4q12 showed a significant impact on a reduced OS (p = 0.00835). In group B, a loss of 13q was significantly associated with a longer OS (p = 0.0364). If a loss of chromosome 10 occurred, patients in group B showed a significantly longer OS (p = 0.0123). Conclusion A clinical benefit for the widespread use of additional carmustine wafer implantation could not be found. However, carmustine wafer implantation shows a significantly improved overall survival if parts of chromosome 10 or chromosome 13 are deleted. In cases of 4q12 amplification and in cases of a methylated p15 promotor, the use of carmustine wafers is especially not recommended. The MGMT promoter methylation is a strong prognostic Biomarker for benefit from temozolomide and BCNU chemotherapy