Airway Mucosal Host Defense Is Key to Genomic Regulation of Cystic Fibrosis Lung Disease Severity.

RATIONALE: The severity of cystic fibrosis (CF) lung disease varies widely, even for Phe508del homozygotes. Heritability studies show that more than 50% of the variability reflects non-cystic fibrosis transmembrane conductance regulator (CFTR) genetic variation; however, the full extent of the pertinent genetic variation is not known. OBJECTIVES: We sought to identify novel CF disease-modifying mechanisms using an integrated approach based on analyzing "in vivo" CF airway epithelial gene expression complemented with genome-wide association study (GWAS) data. METHODS: Nasal mucosal RNA from 134 patients with CF was used for RNA sequencing. We tested for associations of transcriptomic (gene expression) data with a quantitative phenotype of CF lung disease severity. Pathway analysis of CF GWAS data (n = 5,659 patients) was performed to identify novel pathways and assess the concordance of genomic and transcriptomic data. Association of gene expression with previously identified CF GWAS risk alleles was also tested. MEASUREMENTS AND MAIN RESULTS: Significant evidence of heritable gene expression was identified. Gene expression pathways relevant to airway mucosal host defense were significantly associated with CF lung disease severity, including viral infection, inflammation/inflammatory signaling, lipid metabolism, apoptosis, ion transport, Phe508del CFTR processing, and innate immune responses, including HLA (human leukocyte antigen) genes. Ion transport and CFTR processing pathways, as well as HLA genes, were identified across differential gene expression and GWAS signals. CONCLUSIONS: Transcriptomic analyses of CF airway epithelia, coupled to genomic (GWAS) analyses, highlight the role of heritable host defense variation in determining the pathophysiology of CF lung disease. The identification of these pathways provides opportunities to pursue targeted interventions to improve CF lung health.

Salvage therapy with nelarabine, etoposide, and cyclophosphamide in relapsed/refractory paediatric T-cell lymphoblastic leukaemia and lymphoma.

A combination of 5 d of nelarabine (AraG) with 5 d of etoposide (VP) and cyclophosphamide (CPM) and prophylactic intrathecal chemotherapy was used as salvage therapy in seven children with refractory or relapsed T-cell leukaemia or lymphoma. The most common side effects attributable to the AraG included Grade 2 and 3 sensory and motor neuropathy and musculoskeletal pain. Haematological toxicity was greater for the combination than AraG alone, although median time to neutrophil and platelet recovery was consistent with other salvage therapies. All patients had some response to the combined therapy and five of the seven went into complete remission after one or two courses of AraG/VP/CPM. Our experience supports the safety of giving AraG as salvage therapy in synchrony with etoposide and cyclophosphamide, although neurological toxicity must be closely monitored.

Ductal Carcinoma In Situ Simultaneously Involving the Breast and Epithelial Inclusions in an Ipsilateral Axillary Lymph Node.

Benign cystic epithelial inclusions with squamous, glandular, or Müllerian phenotypes are known to occur in the axillary lymph nodes of patients with benign and malignant breast disease. Careful evaluation of hematoxylin and eosin-stained slides and correlation with the histologic findings in the ipsilateral breast are paramount in evaluation of suspected benign inclusions. In this case of ductal carcinoma in situ (DCIS) of the breast in a 73-year-old woman, DCIS also involved epithelial inclusions in an ipsilateral axillary lymph node. The recognition of these benign epithelial elements, and awareness that they can be involved by DCIS, is crucial to avoid the overdiagnosis of metastatic carcinoma.

Segmenting hippocampus from infant brains by sparse patch matching with deep-learned features.

Accurate segmentation of the hippocampus from infant MR brain images is a critical step for investigating early brain development. Unfortunately, the previous tools developed for adult hippocampus segmentation are not suitable for infant brain images acquired from the first year of life, which often have poor tissue contrast and variable structural patterns of early hippocampal development. From our point of view, the main problem is lack of discriminative and robust feature representations for distinguishing the hippocampus from the surrounding brain structures. Thus, instead of directly using the predefined features as popularly used in the conventional methods, we propose to learn the latent feature representations of infant MR brain images by unsupervised deep learning. Since deep learning paradigms can learn low-level features and then successfully build up more comprehensive high-level features in a layer-by-layer manner, such hierarchical feature representations can be more competitive for distinguishing the hippocampus from entire brain images. To this end, we apply Stacked Auto Encoder (SAE) to learn the deep feature representations from both T1- and T2-weighed MR images combining their complementary information, which is important for characterizing different development stages of infant brains after birth. Then, we present a sparse patch matching method for transferring hippocampus labels from multiple atlases to the new infant brain image, by using deep-learned feature representations to measure the interpatch similarity. Experimental results on 2-week-old to 9-month-old infant brain images show the effectiveness of the proposed method, especially compared to the state-of-the-art counterpart methods.