Some microDNAs are shared between and within drug groups.

<p>Number of genes where from microDNA derived shared between drug groups (Sensitive: S; Resistant: R; Treated: T; Non-Treated: NT) (<b>A</b>) Observed numbers per drug <b>Top</b>: MTX <b>Bottom</b>: ASP (<b>B</b>) Observed <i>vs</i>. expected numbers of microDNA-derived genes per group. Expected numbers were computed by generating 1000 random new microDNAs with lengths corresponding to those we identified. <b>Top</b>: MTX <b>Bottom</b>: ASP. p<0.01 (estimated by chi-square) for the difference between observed and expected numbers in all cases except MTX R_NT group.</p

MicroDNA generation in relation to treatment.

<p>Percentage (%) of unique microDNAs generated from LCL samples when treated <i>vs</i>. non-treated with (<b>A</b>) Methotrexate (MTX) or (<b>B</b>) Asparaginase (ASP). Treated <i>vs</i>. non-treated samples: <b>Left</b>, regardless of sensitivity/resistance status; <b>Center</b>, in resistant cells; <b>Right</b>, in sensitive cells. Numbers on the graph represent the number of unique microDNAs generated in each group.</p

MicroDNA length and periodicity.

<p>Size distribution in base pairs (bp) of all identified microDNAs (<b>A</b>) regardless of drug used for treatment. Vertical lines depict the 190 bp periodicity. (<b>B</b>) per drug (ASP: Asparaginase, MTX: Methotrexate) and per sensitivity status (resistant: R, sensitive: S, treated: T, non-treated: NT).</p

Percentage of shared entities between samples.

<p><b>Left</b>: Gene intersects, microDNA derivied for the same gene, shared between ≥ 2 samples. <b>Right</b>: Cluster intersects, microDNA derived from the same genomic position shared between ≥ 2 samples with > = 1 bp overlap. The number/total and (%) of intersects per drug group are indicated. Difference between groups was assessed using a two-tailed Chi-square test.</p

MicroDNA are significantly enriched in coding and active genomic regions.

<p>Fold enrichment is calculated as the ratio of the observed by expected number. Expected numbers were computed by generating 1000 random new positions with lengths corresponding to those of identified microDNAs and outputting the median. The dotted line shows a hypothetical situation where expected number would be equal to the observed number. <b>Top</b>: Methotrexate (MTX). <b>Bottom</b>: Asparaginase (ASP). Statistical significance was assessed using Fisher's Exact test (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184365#pone.0184365.s009" target="_blank">S3 Table</a>).</p

Specific expression of novel long non-coding RNAs in high-hyperdiploid childhood acute lymphoblastic leukemia

<div><p>Pre-B cell childhood acute lymphoblastic leukemia (pre-B cALL) is a heterogeneous disease involving many subtypes typically stratified using a combination of cytogenetic and molecular-based assays. These methods, although widely used, rely on the presence of known chromosomal translocations, which is a limiting factor. There is therefore a need for robust, sensitive, and specific molecular biomarkers unaffected by such limitations that would allow better risk stratification and consequently better clinical outcome. In this study we performed a transcriptome analysis of 56 pre-B cALL patients to identify expression signatures in different subtypes. In both protein-coding and long non-coding RNAs (lncRNA), we identified subtype-specific gene signatures distinguishing pre-B cALL subtypes, particularly in t(12;21) and hyperdiploid cases. The genes up-regulated in pre-B cALL subtypes were enriched in bivalent chromatin marks in their promoters. LncRNAs is a new and under-studied class of transcripts. The subtype-specific nature of lncRNAs suggests they may be suitable clinical biomarkers to guide risk stratification and targeted therapies in pre-B cALL patients.</p></div

ENCODE TF peak enrichment near TSS of dysregulated genes.

<p>The y-axis corresponds to the minimal TF expression change observed among all subtypes. The x-axis corresponds to the peak enrichment ratio for genes that are up- or down-regulated in all subtypes. All TFs are represented as dots and text labels have been added when both expression change and (positive) peak enrichment are statistically significant (FDR < 0.1).</p

Overall accuracy of 3-nearest-neighbors classification using an increasing number of top variance genes from different biotypes.

<p>(A) Multidimensional scaling plot of distances between expression profiles only for lncRNAs. The distance between each pair of samples is the Euclidean distance between expression values (logCPM) of the 500 lncRNAs with the most variance across all samples. (B) K-nearest neighbors classification accuracy comparison between lncRNA and protein-coding transcripts. The y-axis corresponds to the fraction of samples correctly classified, averaged over 100 replicates. For each replicate, we sampled 50% of available genes and ordered them according to expression variance across samples. 3-nearest-neighbors classification was then performed using an incremental number of genes and Euclidean distance between samples. The baseline accuracy corresponds to random assignment of tumor subtypes within the cohort.</p

Comparison of differentially expressed genes in our RNA-seq and public dataset.

<p>(A) Overlap between differentially expressed genes identified from microarray data (Lee et al.) and RNA-seq for the HeH versus t(12;21) comparison. The intersection of 200 genes represents a 10-fold enrichment compared to the expected intersection (20) when DEGs are picked randomly. (B) Comparison of logFCs for DEGs identified in both the microarray and RNA-seq analysis. Pearson’s product-moment correlation between log2FCs = 0.844. Spearman’s rank correlation = 0.793. We note that expression changes are coherent (in the same direction) for all DEGs identified from both datasets</p

Cohort characteristics.

<p>Cohort characteristics.</p