Semi-supervised Classification of Breast Cancer Expression Profiles Using Neural Networks

In classification tasks of biological data, there are usually fewer labeled than unlabeled samples because labeling samples is costly or time-consuming. In addition, labeled data sets can be re-used in different contexts as additional unlabeled data sets. For example, when searching the Gene Expression Omnibus (GEO) repository for microarray data sets of drug sensitivity and resistance experiments, the largest one has 2,522 samples, but the median has only 12 samples. In machine learning in general, utilizing unlabeled data in classification tasks is called semi-supervised learning. Artificial neural networks can be used to pre-train on unlabeled data before fine-tuning via back-propagation with labeled data. Such artificial neural networks enabling deep learning have gained attention since around 2010, since when they have been among the best-performing algorithms in visual object recognition. We measured accuracies in the task of classifying tissue taken from breast cancer patients at reductive surgery as chemotherapy-resistant or -sensitive. Different data sets were constructed by subsampling from GEO data set GSE25055 and GSE25065. Using these data sets, we compared classification accuracy of the neural networks autoencoder, Restricted Boltzmann Machine, Deep Belief Network (DBN) and support vector machine (SVM), and Transductive SVM (TSVM). Training was done both in supervised and semi-supervised mode. For the neural networks, we tried several different network architectures. Smoothing the validation set accuracies obtained during training iterations to alleviate low sample numbers helped in model selection of the best classifier. We also investigated the effect of different normalization procedures on the classification accuracy. The data were normalized with either RMA or MAS5, followed by either no batch-effect correction or Combat batch-effect correction. Only MAS5 profited from added Combat batch-effect correction, but normalization with RMA alone yielded the best classification accuracy. We were particularly interested whether classification accuracies improve when adding unlabeled samples in semi-supervised learning. Overall, neural networks and support vector machines performed similar. We found a slight improvement of classification accuracy when the number of unlabeled samples presented to DBN and TSVM was increased to the maximal number of samples in our data sets. However, this effect was only observed when the learning algorithms were presented the expression values of all 22,283 genes, not just the 500 most variable genes

Transcript-Specific Expression Profiles Derived from Sequence-Based Analysis of Standard Microarrays

Background: Alternative mRNA processing mechanisms lead to multiple transcripts (i.e. splice isoforms) of a given gene which may have distinct biological functions. Microarrays like Affymetrix GeneChips measure mRNA expression of genes using sets of nucleotide probes. Until recently probe sets were not designed for transcript specificity. Nevertheless, the reanalysis of established microarray data using newly defined transcript-specific probe sets may provide information about expression levels of specific transcripts. Methodology/Principal Findings: In the present study alignment of probe sequences of the Affymetrix microarray HGU133A with Ensembl transcript sequences was performed to define transcript-specific probe sets. Out of a total of 247,965 perfect match probes, 95,008 were designated ‘‘transcript-specific’’, i.e. showing complete sequence alignment, no crosshybridization, and transcript-, not only gene-specificity. These probes were grouped into 7,941 transcript-specific probe sets and 15,619 gene-specific probe sets, respectively. The former were used to differentiate 445 alternative transcripts of 215 genes. For selected transcripts, predicted by this analysis to be differentially expressed in the human kidney, confirmatory real-time RT-PCR experiments were performed. First, the expression of two specific transcripts of the genes PPM1A (PP2CA_HUMAN and P35813) and PLG (PLMN_HUMAN and Q5TEH5) in human kidneys was determined by the transcriptspecific array analysis and confirmed by real-time RT-PCR. Secondly, disease-specific differential expression of single transcripts of PLG and ABCA1 (ABCA1_HUMAN and Q5VYS0_HUMAN) was computed from the available array data sets and confirmed by transcript-specific real-time RT-PCR. Conclusions: Transcript-specific analysis of microarray experiments can be employed to study gene-regulation on the transcript level using conventional microarray data. In this study, predictions based on sufficient probe set size and foldchange are confirmed by independent mean

Differences sustained between diffuse and limited forms of juvenile systemic sclerosis in expanded international cohort. www.juvenile-scleroderma.com

OBJECTIVES: To evaluate the baseline clinical characteristics of juvenile systemic sclerosis (jSSc) patients in the international Juvenile SSc Inception Cohort (jSScC), compare these characteristics between the classically defined diffuse (dcjSSc) and limited cutaneous (lcjSSc) subtypes, and among those with overlap features. METHODS: A cross-sectional study was performed using baseline visit data. Demographic, organ system evaluation, treatment, and patient and physician reported outcomes were extracted and summary statistics applied. Comparisons between dcjSSc and lcSSc subtypes and patients with and without overlap features were performed using Chi-square and Mann Whitney U-tests. RESULTS: At data extraction 150 jSSc patients were enrolled across 42 centers, 83% were Caucasian, 80% female, dcjSSc predominated (72%), and 17% of the cohort had overlap features. Significant differences were found between dcjSSc and lcjSSc regarding the modified Rodnan Skin Score, presence of Gottron's papules, digital tip ulceration, 6 Minute walk test, composite pulmonary and cardiac involvement. All more frequent in dcSSc except for cardiac involvement. DcjSSc patients had significantly worse scores for physician rated disease activity and damage. A significantly higher occurrence of Gottron's papules, musculoskeletal involvement and composite pulmonary involvement, and significantly lower frequency of Raynaud's phenomenon, were seen in those with overlap features. CONCLUSION: Results from a large international jSSc cohort demonstrate significant differences between dcjSSc and lcjSSc patients including more globally severe disease and increased frequency of ILD in dcjSSc patients, while those with lcSSc have more frequent cardiac involvement. Those with overlap features had an unexpected higher frequency of interstitial lung disease

Increased platelet reactivity is associated with circulating platelet-monocyte complexes and macrophages in human atherosclerotic plaques

Objective: Platelet reactivity, platelet binding to monocytes and monocyte infiltration play a detrimental role in atherosclerotic plaque progression. We investigated whether platelet reactivity was associated with levels of circulating platelet-monocyte complexes (PMCs) and macrophages in human atherosclerotic carotid plaques. Methods: Platelet reactivity was determined by measuring platelet P-selectin expression after platelet stimulation with increasing concentrations of adenosine diphosphate (ADP), in two independent cohorts: the Circulating Cells cohort (n = 244) and the Athero-Express cohort (n = 91). Levels of PMCs were assessed by flow cytometry in blood samples of patients who were scheduled for percutaneous coronary intervention (Circulating Cells cohort). Monocyte infiltration was semi-quantitatively determined by histological examination of atherosclerotic carotid plaques collected during carotid endarterectomy (Athero-Express cohort). Results: We found increased platelet reactivity in patients with high PMCs as compared to patients with low PMCs (median (interquartile range): 4153 (1585-11267) area under the curve (AUC) vs. 9633 (3580-21565) AUC, P<0.001). Also, we observed increased pl

Validation of a novel multivariate method of defining HIV-associated cognitive impairment

Background. The optimum method of defining cognitive impairment in virally suppressed people living with HIV is unknown. We evaluated the relationships between cognitive impairment, including using a novel multivariate method (NMM), patient– reported outcome measures (PROMs), and neuroimaging markers of brain structure across 3 cohorts. Methods. Differences in the prevalence of cognitive impairment, PROMs, and neuroimaging data from the COBRA, CHARTER, and POPPY cohorts (total n = 908) were determined between HIV-positive participants with and without cognitive impairment defined using the HIV-associated neurocognitive disorders (HAND), global deficit score (GDS), and NMM criteria. Results. The prevalence of cognitive impairment varied by up to 27% between methods used to define impairment (eg, 48% for HAND vs 21% for NMM in the CHARTER study). Associations between objective cognitive impairment and subjective cognitive complaints generally were weak. Physical and mental health summary scores (SF-36) were lowest for NMM-defined impairment (P < .05). There were no differences in brain volumes or cortical thickness between participants with and without cognitive impairment defined using the HAND and GDS measures. In contrast, those identified with cognitive impairment by the NMM had reduced mean cortical thickness in both hemispheres (P < .05), as well as smaller brain volumes (P < .01). The associations with measures of white matter microstructure and brain-predicted age generally were weaker. Conclusion. Different methods of defining cognitive impairment identify different people with varying symptomatology and measures of brain injury. Overall, NMM-defined impairment was associated with most neuroimaging abnormalities and poorer selfreported health status. This may be due to the statistical advantage of using a multivariate approac

Viral RNA and DNA Trigger Common Antiviral Responses in Mesangial Cells

Extrarenal viral infections commonly trigger glomerulonephritis, usually in association with immune complex disease. The Ig component of immune complexes can activate glomerular cell Fc receptors, but whether complexed viral nucleic acids contribute to glomerular inflammation remains unknown. Because of the types of Toll-like receptors (Tlrs) expressed by glomerular mesangial cells, we hypothesized that viral single-stranded RNA and DNA would activate mesangial cells via Tlr-independent pathways and trigger overlapping antiviral immune responses. Consistent with this hypothesis, 5′-triphosphate RNA (3P-RNA) and non-CpG DNA activated murine primary glomerular mesangial cells to secrete Cxcl10 and Il-6 even in cells derived from mice deficient in the Tlr adaptor proteins Myd88 and Trif. Transcriptome analysis revealed that 3P-RNA and non-CpG-DNA triggered almost identical gene expression programs, especially the proinflammatory cytokine Il-6, several chemokines, and genes related to type I IFN. We observed similar findings in glomerular preparations after injecting 3P-RNA and non-CpG-DNA in vivo. These effects depended on the formation of complexes with cationic lipids, which enhanced nucleic acid uptake into the cytosol of mesangial cells. Small interfering RNA studies revealed that 3P-RNA recognition involves Rig-1, whereas non-CpG-DNA did not require Rig-1 or Dai to activate glomerular mesangial cells. We conclude that 3P-RNA and double-stranded DNA trigger a common, TLR-independent, antiviral response in glomerular mesangial cells, which may promote glomerulonephritis in the setting of viral infection

Biobased Chemicals in a Carbon-Restricted World

Not Availabl

Culture-induced changes in blood-brain barrier transcriptome: implications for amino-acid transporters in vivo

Tight homeostatic control of brain amino acids (AA) depends on transport by solute carrier family proteins expressed by the blood-brain barrier (BBB) microvascular endothelial cells (BMEC). To characterize the mouse BMEC transcriptome and probe culture-induced changes, microarray analyses of platelet endothelial cell adhesion molecule-1-positive (PECAM1(+)) endothelial cells (ppMBMECs) were compared with primary MBMECs (pMBMEC) cultured in the presence or absence of glial cells and with b.End5 endothelioma cell line. Selected cell marker and AA transporter mRNA levels were further verified by reverse transcription real-time PCR. Regardless of glial coculture, expression of a large subset of genes was strongly altered by a brief culture step. This is consistent with the known dependence of BMECs on in vivo interactions to maintain physiologic functions, for example, tight barrier formation, and their consequent dedifferentiation in culture. Seven (4F2hc, Lat1, Taut, Snat3, Snat5, Xpct, and Cat1) of nine AA transporter mRNAs highly expressed in freshly isolated ppMBMECs were strongly downregulated for all cultures and two (Snat2 and Eaat3) were variably regulated. In contrast, five AA transporter mRNAs with low expression in ppMBMECs, including y(+)Lat2, xCT, and Snat1, were upregulated by culture. We hypothesized that the AA transporters highly expressed in ppMBMECs and downregulated in culture have a major in vivo function for BBB transendothelial transport.Journal of Cerebral Blood Flow & Metabolism advance online publication, 3 June 2009; doi:10.1038/jcbfm.2009.72