Citations

AbstractBackgroundClinical diagnostic criteria for PD rely on rest tremor, bradykinesia, and rigidity. These features are non‐specific and neuropathological confirmation remains the gold standard for diagnosis. This study presents data on clinical certainty ratings in autopsy‐proven PD.MethodsSubjects were assessed annually by a movement disorders specialist and assigned to a clinical certainty group for PD based on multiple clinical features before autopsy. The three groups considered for analysis are as follows: Group I 0–49% certainty, Group II 50‐89% certainty, and Group III 90–100% certainty. All subjects were autopsied and had a standardized neuropathological assessment.Results275 subjects were assigned a PD certainty at their last visit before death. Group I had 80 subjects, Group II 56 subjects, and Group III 139 subjects. The clinical features recorded in Group I, II, and III, were as follows: rest tremor, bradykinesia, rigidity, postural instability, asymmetric onset, persistent asymmetry, current response to dopaminergic treatment, motor fluctuations, and dyskinesia. Rigidity, postural instability, asymmetric onset, current response to dopaminergic treatment, motor fluctuation, and dyskinesia were more likely to be present in the group which was rated with higher certainty. The final diagnosis of PD was confirmed by neuropathological assessment in 85% of the patients in Group III as compared to 30% in Group II and 5% in Group I.ConclusionsHigh certainty (90–100%) had strong positive predictive value (85%) for autopsy‐proven PD as compared to either lower certainty groups (0–49% and 50–89%) which had lower predictive value (5% and 30% respectively).

Objective: To determine the impact of histology on pathologic response, survival outcomes, and recurrence patterns in patients with esophageal cancer (EC) who received neoadjuvant chemoradiotherapy (CRT). Summary of Background Data: There is a paucity of data regarding comparative outcomes after neoadjuvant CRT between esophageal squamous cell carcinoma (SCC) and adenocarcinoma. Methods: Between 2002 and 2015, 895 EC patients who underwent neoadjuvant CRT followed by esophagectomy at 3 academic institutions were retrospectively reviewed, including 207 patients with SCC (23.1%) and 688 patients with adenocarcinoma (76.9%). Pathologic response, survival, recurrence pattern, and potential prognostic factors were compared. Results: Pathologic complete response (pCR) rate was significantly higher for SCC compared with adenocarcinoma (44.9% vs 25.9%, P < 0.001). After a median follow-up of 52.9 months, 71 patients (34.3%) with SCC versus 297 patients (43.2%) with adenocarcinoma had recurrent disease (P = 0.023). For patients who achieved a pCR, no significant differences were found in recurrence pattern, sites, or survival end-points between the 2 histology groups. For non-pCR patients, the SCC group demonstrated significantly higher regional and supraclavicular recurrence rates but a lower hematogenous metastasis rate than adenocarcinoma patients, whereas the adenocarcinoma patients had a more favorable locoregional failure-free survival (P = 0.005) and worse distant metastasis-free survival (P = 0.024). No differences were found in overall survival (P = 0.772) or recurrence-free survival (P = 0.696) between groups. Conclusions: SCC was associated with a significantly higher pCR rate than adenocarcinoma. Recurrence pattern and survival outcomes were significantly different between the 2 histology subtypes in non-pCR patients.

Normalization is the first critical step in microbiome sequencing data analysis used to account for variable library sizes. Current RNA-Seq based normalization methods that have been adapted for microbiome data fail to consider the unique characteristics of microbiome data, which contain a vast number of zeros due to the physical absence or under-sampling of the microbes. Normalization methods that specifically address the zero inflation remain largely undeveloped. Here we propose GMPR - a simple but effective normalization method - for zero-inflated sequencing data such as microbiome data. Simulation studies and real datasets analyses demonstrate that the proposed method is more robust than competing methods, leading to more powerful detection of differentially abundant taxa and higher reproducibility of the relative abundances of taxa.

Normalization is the first critical step in microbiome sequencing data analysis used to account for variable library sizes. Current RNA-Seq based normalization methods that have been adapted for microbiome data fail to consider the unique characteristics of microbiome data, which contain a vast number of zeros due to the physical absence or under-sampling of the microbes. Normalization methods that specifically address the zero inflation remain largely undeveloped. Here we propose GMPR - a simple but effective normalization method - for zero-inflated sequencing data such as microbiome data. Simulation studies and real datasets analyses demonstrate that the proposed method is more robust than competing methods, leading to more powerful detection of differentially abundant taxa and higher reproducibility of the relative abundances of taxa.

Abstract Motivation Recent studies have revealed a complex interplay between environment, the human microbiome and health and disease. Mediation analysis of the human microbiome in these complex relationships could potentially provide insights into the role of the microbiome in the etiology of disease and, more importantly, lead to novel clinical interventions by modulating the microbiome. However, due to the high dimensionality, sparsity, non-normality and phylogenetic structure of microbiome data, none of the existing methods are suitable for testing such clinically important mediation effect. Results We propose a distance-based approach for testing the mediation effect of the human microbiome. In the framework, the nonlinear relationship between the human microbiome and independent/dependent variables is captured implicitly through the use of sample-wise ecological distances, and the phylogenetic tree information is conveniently incorporated by using phylogeny-based distance metrics. Multiple distance metrics are utilized to maximize the power to detect various types of mediation effect. Simulation studies demonstrate that our method has correct Type I error control, and is robust and powerful under various mediation models. Application to a real gut microbiome dataset revealed that the association between the dietary fiber intake and body mass index was mediated by the gut microbiome. Availability and implementation An R package ‘MedTest’ is freely available at https://github.com/jchen1981/MedTest. Supplementary information Supplementary data are available at Bioinformatics online.

Deleterious variants in dihydropyrimidine dehydrogenase (DPD, DPYD gene) can be highly predictive of clinical toxicity to the widely prescribed chemotherapeutic 5‐fluorouracil (5‐FU). However, there are very limited data pertaining to the functional consequences of the >450 reported no‐synonymous DPYD variants. We developed a DPYD‐specific variant classifier (DPYD‐Varifier) using machine learning and in vitro functional data for 156 missense DPYD variants. The developed model showed 85% accuracy and outperformed other in silico prediction tools. An examination of feature importance within the model provided additional insight into functional aspects of the DPD protein relevant to 5‐FU toxicity. In the absence of clinical data for unstudied variants, prediction tools like DPYD‐Varifier have great potential to individualize medicine and improve the clinical decision‐making process.

ObjectivesTo prospectively evaluate the diagnostic performance of 3‐dimensional (3D) shear wave elastography (SWE) for assessing thyroid nodules.MethodsA total of 176 surgically or cytologically confirmed thyroid nodules (63 malignant and 113 benign) in 176 patients who had undergone conventional ultrasound (US), 2‐dimensional (2D) SWE, and 3D SWE examinations were included in this study. Quantitative elasticity values (mean elasticity, maximum elasticity, and standard deviation of elasticity of a large region of interest and mean elasticity of a 2‐mm region of interest) were measured on 2D and 3D SWE. Diagnostic performances of conventional US, 2D SWE, and 3D SWE were assessed. The role of 2D and 3D SWE in reducing unnecessary fine‐needle aspiration (FNA) for nodules with low suspicion was also evaluated.ResultsThe diagnostic performances in terms of the area under the receiver operating characteristic curve were 0.612 for conventional US, 0.836 for 2D SWE (P < .001 in comparison with conventional US), and 0.839 for 3D SWE (P < .001 in comparison with conventional US). The mean elasticity achieved the highest diagnostic performance in 2D SWE, whereas the standard deviation of elasticity achieved the highest performance in 3D SWE, although no significant difference was found between them (P > .05). Three‐dimensional SWE increased the specificity in comparison with 2D SWE (88.5% versus 82.3%; P = .039). For the 37 nodules with low suspicion on conventional US imaging, 2D SWE was able to avoid unnecessary FNA in 77.1% (27 of 35) of benign nodules, and 3D SWE further increased the number to 88.6% (31 of 35).ConclusionsThree‐dimensional SWE is a useful tool for predicting thyroid nodule malignancy and reducing unnecessary FNA procedures in thyroid nodules with low suspicion of malignancy.

Abstract The amplified MYCN gene serves as an oncogenic driver in approximately 20% of high-risk pediatric neuroblastomas. Here, we show that the family member MYC is a potent transforming gene in a separate subset of high-risk neuroblastoma cases (∼10%), based on (i) its upregulation by focal enhancer amplification or genomic rearrangements leading to enhancer hijacking, and (ii) its ability to transform neuroblastoma precursor cells in a transgenic animal model. The aberrant regulatory elements associated with oncogenic MYC activation include focally amplified distal enhancers and translocation of highly active enhancers from other genes to within topologically associating domains containing the MYC gene locus. The clinical outcome for patients with high levels of MYC expression is virtually identical to that of patients with amplification of the MYCN gene, a known high-risk feature of this disease. Together, these findings establish MYC as a bona fide oncogene in a clinically significant group of high-risk childhood neuroblastomas. Significance: Amplification of the MYCN oncogene is a recognized hallmark of high-risk pediatric neuroblastoma. Here, we demonstrate that MYC is also activated as a potent oncogene in a distinct subset of neuroblastoma cases through either focal amplification of distal enhancers or enhancer hijacking mediated by chromosomal translocation. Cancer Discov; 8(3); 320–35. ©2017 AACR. This article is highlighted in the In This Issue feature, p. 253