Histiocytoid cardiomyopathy (Histiocytoid CM) is a rare form of cardiomyopathy observed predominantly in newborn females that is fatal unless treated early in life. We have performed whole exome sequencing on five parent-proband trios and identified nuclear-encoded mitochondrial protein mutations in three cases. Two probands had de novo non-sense mutations in the second exon of the X-linked nuclear gene NDUFB11, which has not previously been implicated in any disease, despite evidence that deficiency for other mitochondrial electron transport complex I members leads to cardiomyopathy. A third proband was doubly heterozygous for inherited rare variants in additional components of complex I, NDUFAF2 and NDUFB9, confirming that Histiocytoid CM is genetically heterogeneous. In a fourth case, the proband with Histiocytoid CM inherited a mitochondrial mutation from her heteroplasmic mother, as did her brother who presented with cardiac arrhythmia. Strong candidate recessive or compound heterozygous variants were not found for this individual or for the fifth case. Although NDUFB11 has not been implicated before in cardiac pathology, morpholino-mediated knockdown of Ndufb11 in zebrafish embryos generated defective cardiac tissue with looping defects, which confirms the causative role of NDUFB11 in cardiac pathology. Therefore, the NDUFB11 mutation represents a genetic basis of this heterogeneous disease.

Feature Selection in medical image processing is a process of selection of relevant features, which are useful in model construction, as it will lead to reduced training times and classification model designed will be easier to interrupt. In this paper a meta-heuristic algorithm Artificial Bee Colony (ABC) has been used for feature selection in Computed Tomography (CT Scan) images of cervical cancer with the objective of detecting whether the data given as input is cancerous or not. Starting with segmentation as a first step, performed by implementing Active Contour Segmentation (ACM) algorithm over the images. In this paper a semi-automated the system has been developed so as to obtain the region of interest (ROI). Further, textural features proposed by Haralick are extracted region of interest. Classification is performed using hybridization of Artificial Bee Colony (ABC) and k-Nearest Neighbors (k-NN) algorithm, ABC and Support Vector Machine (SVM). It is observed that combination of ABC with SVM (Gaussian kernel) performs better than combination of ABC with SVM (Linear Kernel) and ABC with K-NN classifier.

BackgroundPersonalized medicine is predicated on the notion that individual biochemical and genomic profiles are relatively constant in times of good health and to some extent predictive of disease or therapeutic response. We report a pilot study quantifying gene expression and methylation profile consistency over time, addressing the reasons for individual uniqueness, and its relation to N = 1 phenotypes.MethodsWhole blood samples from four African American women, four Caucasian women, and four Caucasian men drawn from the Atlanta Center for Health Discovery and Well Being study at three successive 6-month intervals were profiled by RNA-Seq, miRNA-Seq, and Illumina Methylation 450 K arrays. Standard regression approaches were used to evaluate the proportion of variance for each type of omic measure among individuals, and to quantify correlations among measures and with clinical attributes related to wellness.ResultsLongitudinal omic profiles were in general highly consistent over time, with an average of 67 % variance in transcript abundance, 42 % in CpG methylation level (but 88 % for the most differentiated CpG per gene), and 50 % in miRNA abundance among individuals, which are all comparable to 74 % variance among individuals for 74 clinical traits. One third of the variance could be attributed to differential blood cell type abundance, which was also fairly stable over time, and a lesser amount to expression quantitative trait loci (eQTL) effects. Seven conserved axes of covariance that capture diverse aspects of immune function explained over half of the variance. These axes also explained a considerable proportion of individually extreme transcript abundance, namely approximately 100 genes that were significantly up-regulated or down-regulated in each person and were in some cases enriched for relevant gene activities that plausibly associate with clinical attributes. A similar fraction of genes had individually divergent methylation levels, but these did not overlap with the transcripts, and fewer than 20 % of genes had significantly correlated methylation and gene expression.ConclusionsPeople express an “omic personality” consisting of peripheral blood transcriptional and epigenetic profiles that are constant over the course of a year and reflect various types of immune activity. Baseline genomic profiles can provide a window into the molecular basis of traits that might be useful for explaining medical conditions or guiding personalized health decisions.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0209-4) contains supplementary material, which is available to authorized users.

Background: Recent discoveries of recurrent and targetable gene fusions in breast cancer suggest the need to characterize the functional significance of such genomic aberrations within larger cohorts. We quantify fusion transcript expression in patient samples using RNASeq and evaluate their functional significance using biological pathway enrichment analysis. Methods: We sequenced transcriptomes of core biopsy RNA from 97 breast tumors obtained from brief-exposure preoperative clinical trials BrUOG 211A/211B. HER2- patients were treated with brief exposure to bevacizumab (B) or nab-paclitaxel (nP) followed by treatment with B/nP/carboplatin while HER2+ patients received brief exposure to trastuzumab (T) or nP followed by T/nP/carboplatin. Paired-end sequencing on 55 baseline biopsies and 42 post-exposure biopsies using amplified total RNA yielded 55 million reads on average per sample. We assigned RNASeq-based PAM50 subtypes for each of the samples using standard methodology. Fusion transcript abundance was evaluated using two independent pipelines, TopHat and deFuse, due to their complementary strategies in fusion detection. We eliminated fusions of genes with their respective pseudogenes as likely false positives arising due to alignment artifacts. TopHat fusion calls with total supporting reads ≥10 and deFuse calls with probability of fusion ≥0.7 were considered reliable. Results: We identified high confidence gene fusions, detected by both TopHat and deFuse, in 30 of the 55 baseline biopsies (54.4%), with 3.3 fusions on average per sample and a maximum of 10. Fusions were predominantly associated with chromosomal aberrations (75%), with putative deletions responsible for 32% of fusions and translocations responsible for 43%. We find a high level of fusion transcript heterogeneity within breast cancers, detecting a total of 80 fusions across the 30 samples with only three fusions recurrent in two samples with high expression in each: MDN1-GAS5 in two basal breast cancers, KRAS-GRIP1 and ITPR2-CCDC91 in two LumB cancers. Several cancer-related genes were found to be fusion partners: AKT3-SMYD3, CREB1-PPP1R1C, FLOT2-TOP2A and FOXC1-ARID1B. Pathway analysis of the fusion genes at baseline revealed enrichment of proteasome (p = 0.000752), tight junction (p = 0.027), insulin signaling (p = 0.0284) and melanogenesis (p = 0.05) pathways after multiple testing correction (FDR≤0.25). We looked for modulation of gene fusions upon brief exposure to therapy in 18 patients and found a majority of the baseline fusion transcripts to be present post-brief exposure in 44% of the patients, irrespective of therapy regimen. Conclusions: We find that gene fusions in breast cancer are highly heterogeneous but are enriched with cancer-related pathway genes. This is the first study to report a novel gene-lincRNA fusion transcript (MDN1-GAS5). We are currently validating the fusion calls using qRT-PCR. The heterogeneity of detected fusions suggests that multiple mechanisms could underlie the selective advantage of tumor cells expressing fusion transcripts. The brief-exposure preoperative paradigm provides a unique opportunity to evaluate modulation of fusion transcripts that can shed light on their functional importance. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P4-04-07.