Microarray-based classifiers and prognosis models identify subgroups with distinct clinical outcomes and high risk of AML transformation of myelodysplastic syndrome

The diagnosis of myelodysplastic syndrome (MDS) currently relies primarily on the morphologic assessment of the patient's bone marrow and peripheral blood cells. Moreover, prognostic scoring systems rely on observer-dependent assessments of blast percentage and dysplasia. Gene expression profiling could enhance current diagnostic and prognostic systems by providing a set of standardized, objective gene signatures. Within the Microarray Innovations in LEukemia study, a diagnostic classification model was investigated to distinguish the distinct subclasses of pediatric and adult leukemia, as well as MDS. Overall, the accuracy of the diagnostic classification model for subtyping leukemia was approximately 93%, but this was not reflected for the MDS samples giving only approximately 50% accuracy. Discordant samples of MDS were classified either into acute myeloid leukemia (AML) or

Developmental care for high-risk newborns:Emerging science, clinical application, and continuity from newborn intensive care unit to community

Neonatology has optimized medical outcomes for high-risk newborns yet neurodevelopmental outcomes continue to be a concern. Basic science, clinical research, and environmental design perspectives have shown the impact of the caregiving environment on the developing brain and the role of professional caregivers in providing supportive intervention to both infants and their families. This recognition has prompted a focus on early developmentally supportive care (DSC) for high-risk newborns both in the hospital and in community follow up. DSC has emerged as a recognized standard of care in most neonatal intensive care units. Still, many questions remain and much integrative research is needed.

Eating as a neurodevelopment process for high risk newborns

Many high-risk and preterm infants have difficulty with successful feeding and subsequent optimal growth during their stay in the neonatal intensive care unit as well as in the months after discharge. Environmental, procedural, and medical issues necessary for treatment of the hospitalized infant present challenges for the development of successful eating skills. Emerging data describe eating as a predictable neurodevelopmental process that depends on the infant’s organization of physiologic processes, motor tone and movement, level of arousal, and ability to simultaneously regulate these processes.

The effect of radiation technique and bladder filling on the acute toxicity of pelvic radiotherapy for localized high risk prostate cancer

Purpose: The goal of this project was to see if using IMRT to deliver elective pelvic nodal irradiation (EPNI) for prostate cancer reduced acute treatment toxicity.

Methods: Two hundred and thirty patients were enrolled into prospective trials delivering EPNI with a concomitant hypofractionated IMRT boost to the prostate. During accrual, the method of EPNI delivery changed as new literature emerged. Three methods were used (1) 4FB, (2) IMRT with 2 cm CTV margins around the pelvic vessels as suggested by Shih et al. (2005) [7] (IMRT-Shih), and (3) IMRT with nodal volumes suggested by the RTOG (IMRT-RTOG). Initially patients were treated with an empty bladder, with the remainder treated with bladder full.

Results: Patients in the 4FB group had higher rates of grade 2 acute GI toxicities compared to the IMRT-Shih and IMRT-RTOG groups (31.9% vs 20.8% vs 7.2%, p = 0.0009). Patients in the 4FB group had higher rates of grade 3 urinary frequency compared to the two IMRT groups (8.5% vs 0% vs 0%, p = 0.027). However, multivariate analysis suggested the factor that most influenced toxicity was bladder filling followed by IMRT.

Conclusions: Bladder filling appeared to be the dominant factor which predicted for acute toxicity, followed by the use of IMRT.

Collectives of diagnostic biomarkers identify high-risk subpopulations of hematuria patients: exploiting heterogeneity in large-scale biomarker data

Background: Ineffective risk stratification can delay diagnosis of serious disease in patients with hematuria. We applied a systems biology approach to analyze clinical, demographic and biomarker measurements (n = 29) collected from 157 hematuric patients: 80 urothelial cancer (UC) and 77 controls with confounding pathologies.

Methods: On the basis of biomarkers, we conducted agglomerative hierarchical clustering to identify patient and biomarker clusters. We then explored the relationship between the patient clusters and clinical characteristics using Chi-square analyses. We determined classification errors and areas under the receiver operating curve of Random Forest Classifiers (RFC) for patient subpopulations using the biomarker clusters to reduce the dimensionality of the data.

Results: Agglomerative clustering identified five patient clusters and seven biomarker clusters. Final diagnoses categories were non-randomly distributed across the five patient clusters. In addition, two of the patient clusters were enriched with patients with ‘low cancer-risk’ characteristics. The biomarkers which contributed to the diagnostic classifiers for these two patient clusters were similar. In contrast, three of the patient clusters were significantly enriched with patients harboring ‘high cancer-risk” characteristics including proteinuria, aggressive pathological stage and grade, and malignant cytology. Patients in these three clusters included controls, that is, patients with other serious disease and patients with cancers other than UC. Biomarkers which contributed to the diagnostic classifiers for the largest ‘high cancer- risk’ cluster were different than those contributing to the classifiers for the ‘low cancer-risk’ clusters. Biomarkers which contributed to subpopulations that were split according to smoking status, gender and medication were different.

Conclusions: The systems biology approach applied in this study allowed the hematuric patients to cluster naturally on the basis of the heterogeneity within their biomarker data, into five distinct risk subpopulations. Our findings highlight an approach with the promise to unlock the potential of biomarkers. This will be especially valuable in the field of diagnostic bladder cancer where biomarkers are urgently required. Clinicians could interpret risk classification scores in the context of clinical parameters at the time of triage. This could reduce cystoscopies and enable priority diagnosis of aggressive diseases, leading to improved patient outcomes at reduced costs. © 2013 Emmert-Streib et al; licensee BioMed Central Ltd.

Identification of a high-risk haplotype for the dystrobrevin binding protein 1 (DTNBP1) gene in the Irish study of high-density schizophrenia families

A recent report showed significant associations between several SNPs in a previously unknown EST cluster with schizophrenia. (1). The cluster was identified as the human dystrobrevin binding protein 1 gene (DTNBP1) by sequence database comparisons and homology with mouse DTNBP1. (2). However, the linkage disequilibrium (LD) among the SNPs in DTNBP1 as well as the pattern of significant SNP-schizophrenia association was complex. This raised several questions such as the number of susceptibility alleles that may be involved and the size of the region where the actual disease mutation(s) could be located. To address these questions, we performed different single-marker tests on the 12 previously studied and 2 new SNPs in DTNBP1 that were re-scored using an improved procedure, and performed a variety of haplotype analyses. The sample consisted of 268 Irish multiplex families selected for high density of schizophrenia. Results suggested a simple structure where the LD in the target region could be explained by 6 haplotypes that together accounted for 96% of haplotype diversity in the whole sample. From these six, a single high-risk haplotype was identified that showed a significant association with schizophrenia and explained the pattern of significant findings in the analyses with individual markers. This haplotype was 30 kb long, had a large effect, could be measured with two tag SNPs only, had a frequency of 6% in our sample, seemed to be of relatively recent origin in evolutionary terms, and was equally distributed over Ireland. Implications of these findings for follow-up and replication studies are discussed.

Relationship between a high-risk haplotype in the DTNBP1 (dysbindin) gene and clinical features of schizophrenia

The purpose of this study was to determine whether a haplotype in the dystrobrevin binding protein 1 (DTNBP1) gene previously associated with schizophrenia not only increases the susceptibility to psychotic illness but also to a more or less clinically specific form of psychotic illness.