Multicenter evaluation of parametric response mapping as an indicator of bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156134/2/ajt15814_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156134/1/ajt15814.pd

CT-Based Local Distribution Metric Improves Characterization of COPD

Parametric response mapping (PRM) of paired CT lung images has been shown to improve the phenotyping of COPD by allowing for the visualization and quantification of non-emphysematous air trapping component, referred to as functional small airways disease (fSAD). Although promising, large variability in the standard method for analyzing PRM(fSAD) has been observed. We postulate that representing the 3D PRM(fSAD) data as a single scalar quantity (relative volume of PRM(fSAD)) oversimplifies the original 3D data, limiting its potential to detect the subtle progression of COPD as well as varying subtypes. In this study, we propose a new approach to analyze PRM. Based on topological techniques, we generate 3D maps of local topological features from 3D PRM(fSAD) classification maps. We found that the surface area of fSAD (S(fSAD)) was the most robust and significant independent indicator of clinically meaningful measures of COPD. We also confirmed by micro-CT of human lung specimens that structural differences are associated with unique S(fSAD) patterns, and demonstrated longitudinal feature alterations occurred with worsening pulmonary function independent of an increase in disease extent. These findings suggest that our technique captures additional COPD characteristics, which may provide important opportunities for improved diagnosis of COPD patients

DW-MRI as a Biomarker to Compare Therapeutic Outcomes in Radiotherapy Regimens Incorporating Temozolomide or Gemcitabine in Glioblastoma

The effectiveness of the radiosensitizer gemcitabine (GEM) was evaluated in a mouse glioma along with the imaging biomarker diffusion-weighted magnetic resonance imaging (DW-MRI) for early detection of treatment effects. A genetically engineered murine GBM model [Ink4a-Arf−/− PtenloxP/loxP/Ntv-a RCAS/PDGF(+)/Cre(+)] was treated with gemcitabine (GEM), temozolomide (TMZ) +/− ionizing radiation (IR). Therapeutic efficacy was quantified by contrast-enhanced MRI and DW-MRI for growth rate and tumor cellularity, respectively. Mice treated with GEM, TMZ and radiation showed a significant reduction in growth rates as early as three days post-treatment initiation. Both combination treatments (GEM/IR and TMZ/IR) resulted in improved survival over single therapies. Tumor diffusion values increased prior to detectable changes in tumor volume growth rates following administration of therapies. Concomitant GEM/IR and TMZ/IR was active and well tolerated in this GBM model and similarly prolonged median survival of tumor bearing mice. DW-MRI provided early changes to radiosensitization treatment warranting evaluation of this imaging biomarker in clinical trials

Translational Control of Cytochrome c by RNA-Binding Proteins TIA-1 and HuR

Stresses affecting the endoplasmic reticulum (ER) globally modulate gene expression patterns by altering posttranscriptional processes such as translation. Here, we use tunicamycin (Tn) to investigate ER stress-triggered changes in the translation of cytochrome c, a pivotal regulator of apoptosis. We identified two RNA-binding proteins that associate with its ∼900-bp-long, adenine- and uridine-rich 3′ untranslated region (UTR): HuR, which displayed affinity for several regions of the cytochrome c 3′UTR, and T-cell-restricted intracellular antigen 1 (TIA-1), which preferentially bound the segment proximal to the coding region. HuR did not appear to influence the cytochrome c mRNA levels but instead promoted cytochrome c translation, as HuR silencing greatly diminished the levels of nascent cytochrome c protein. By contrast, TIA-1 functioned as a translational repressor of cytochrome c, with interventions to silence TIA-1 dramatically increasing cytochrome c translation. Following treatment with Tn, HuR binding to cytochrome c mRNA decreased, and both the presence of cytochrome c mRNA within actively translating polysomes and the rate of cytochrome c translation declined. Taken together, our data suggest that the translation rate of cytochrome c is determined by the opposing influences of HuR and TIA-1 upon the cytochrome c mRNA. Under unstressed conditions, cytochrome c mRNA is actively translated, but in response to ER stress agents, both HuR and TIA-1 contribute to lowering its biosynthesis rate. We propose that HuR and TIA-1 function coordinately to maintain precise levels of cytochrome c production under unstimulated conditions and to modify cytochrome c translation when damaged cells are faced with molecular decisions to follow a prosurvival or a prodeath path

MRI-Guided Stereotactic Biopsy of Murine GBM for Spatiotemporal Molecular Genomic Assessment

Brain tumor biopsies that are routinely performed in clinical settings significantly aid in diagnosis and staging. The aim of this study is to develop and evaluate a methodological image-guided approach that would allow for routine sampling of glioma tissue from orthotopic mouse brain tumor models. A magnetic resonance imaging-guided biopsy method is presented to allow for spatially precise stereotaxic sampling of a murine glioma coupled with genome-scale technology to provide unbiased characterization of intra- and intertumoral clonal heterogeneity. Longitudinal and multiregional sampling of intracranial tumors allows for successful collection of tumor biopsy samples, thus allowing for a pathway-enrichment analysis and a transcriptional profiling of RNA sequencing data. Spatiotemporal gene expression pattern variations revealing genomic heterogeneity were found

Negative Feedback Regulation of MKK6 mRNA Stability by p38α Mitogen-Activated Protein Kinase

p38 mitogen-activated protein (MAP) kinases play an important role in the regulation of cellular responses to all kinds of stresses. The most abundant and broadly expressed p38 MAP kinase is p38α, which can also control the proliferation, differentiation, and survival of several cell types. Here we show that the absence of p38α correlates with the up-regulation of one of its upstream activators, the MAP kinase kinase MKK6, in p38α(−/−) knockout mice and in cultured cells derived from them. In contrast, the expression levels of the p38 activators MKK3 and MKK4 are not affected in p38α-deficient cells. The increase in MKK6 protein concentration correlates with increased amounts of MKK6 mRNA in the p38α(−/−) cells. Pharmacological inhibition of p38α also up-regulates MKK6 mRNA levels in HEK293 cells. Conversely, reintroduction of p38α into p38α(−/−) cells reduces the levels of MKK6 protein and mRNA to the normal levels found in wild-type cells. Moreover, we show that the MKK6 mRNA is more stable in p38α(−/−) cells and that the 3′untranslated region of this mRNA can differentially regulate the stability of the lacZ reporter gene in a p38α-dependent manner. Our data indicate that p38α can negatively regulate the stability of the MKK6 mRNA and thus control the steady-state concentration of one of its upstream activators

Lung cancer lesion detection in histopathology images using graph‐based sparse PCA network

Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-rasG12D lung cancer mouse model using precision/recall rates, Fβ-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms