1H-MRS and GC-MS metabolic profiles of bone marrow and peripheral blood samples at the time of ALL diagnosis.

<p>Representative spectra of BM (blue line) and PB (red line) specimens. Spectra were acquired from (A) 1H-MRS analysis of filtered polar fractions, (B) 1H-MRS analysis of recovered whole lipid fractions, and (C) GC-MS analysis of FFA extracts. Metabolites with the greatest difference between BM and PB are labeled and include alanine (Ala), free cholesterol (CHOL), cholesterol esters (CHOLest), choline (Cho), formate (For), glucose (Glc), glutamate (Glu), glutamine (Gln), lactate (Lac), histidine (His), hypoxanthine (Hpx), palmitic acid, oleic acid, triacylglyceride (TAG), and uridine (Ur). Other abbreviations used are: (2HB), 2-hydroxybutyrate; (3HB), 3-hydroxybutyrate; (2Og), 2oxo-glutarate; (2Oic), 2oxo-isocaproate; (BAA), branched amino acids; (Car), carnitine; (Cho), choline; (CHOL), free cholesterol, (CHOLest), cholesterol esters; (For), formate; (Fum), fumarate; (Glyc), glycerol; (GPCho), glycero-3-phosphocholine; (Hpx), hypoxanthine; (Lac), lactate; (Niac), niacinamide; (Pglu), pyroglutamate; (Pyr), pyruvate; (Ur), uridine; (Pdx), pyridoxine; (TAG), triacylglyceride; (T-Chol), total cholesterol.</p

Network representation of correlations between metabolite pairs.

<p>Plots represent the largest connected component of the networks obtained with the ARACNE algorithm for B0-P0 (A) and B29-P29 (B). Blue nodes indicate metabolites relevant to lipid metabolism; green nodes indicate amino acids, including derivatives and analogues; red edges indicate anti-correlation; and light green edges indicate correlation. Shorter edges denote smaller p-values (higher R2). Note the presence of a community of lipid metabolites on the right side in (A) and the predominance of amino acids in (B). </p

Multivariate analysis of peripheral blood polar fractions in response to drug therapy.

<p>Untargeted mPCA was performed on 1H-MRS spectra acquired on the polar fractions of PB (A, B). (A) mPCA scores plot shows a clear separation between PB samples collected on day 0 versus day 8 (49.63% on PC1), on day 0 versus day 29 (50.85% on PC1), and on day 8 versus day 29 (38.14% on PC1). (B) Loadings plot for the first principal components depicts the most relevant discriminatory metabolites for BM before therapy (negative loadings) and during or after therapy (positive loadings).</p

Targeted metabolic analysis of bone marrow and peripheral blood samples at diagnosis and after induction therapy.

<p>Shown are the mean fold differences in metabolite concentrations in BM and PB samples collected (A) at the time of diagnosis and (B) after induction therapy (mean ± SEM, n = 10 patients). Statistical significance was assessed based on absolute metabolite concentrations (SI, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082859#pone.0082859.s001" target="_blank">Table S1</a>) using the nonparametric two-sided Wilcoxon Rank Sum Test (WRST) and p-values were corrected using false discovery rate (FDR). The bar plot is color coded according to p-values (pFDR < 10%). At day 0 (A), 22 metabolites were significantly different between BM and PB with pFDR <10%, 14 of which had pFDR <5%. In contrast, only 4 of 110 identified metabolites were found to be significantly different between BM and PB at day 29 (B). Both bar plots also show the differences in the ratios of glutamate to glutamine, aspartate to asparagine, choline to creatine, unsaturated to saturated fatty acids, and the sum of glutamine plus pyroglutamate.</p

Statistical analysis of pairwise correlations of amino acids, lipid metabolites, and other metabolites.

<p>Plots show the probability distribution function (PDF) of the p-values of edges in a relevance network of metabolites with strong correlation in B0-P0 (A) and B29-P29 (B). Edges are classified in three groups (lipid metabolism, amino acids, and others). Note the presence of the high peak at small p-values in the lipid metabolite distribution in (A) and in the amino acid (including derivatives and analogues) distribution in (B), indicating enriched correlation among lipid metabolites at day 0 and amino acids at day 29, respectively. </p

Untargeted multilevel principal component analysis of 1H-MRS spectra acquired on polar fractions of bone marrow and peripheral blood samples.

<p>(A, C) Scores plots obtained from mPCA performed on 1H MRS spectra of BM and PB samples collected at diagnosis (A, day 0) or after induction therapy (C, day 29). (B, D) Loadings plots for the first principal component depicts the most relevant discriminatory metabolites from BM (positive loadings) and PB (negative loadings) samples collected at diagnosis (B) and after induction therapy (D). Metabolites are defined in the Abbreviations section.</p

Untargeted multilevel principal component analysis performed on 1H-MRS spectra acquired on the whole lipid fraction of bone marrow and peripheral blood samples at the time of diagnosis.

<p>(A) mPCA scores plot shows a clear separation on the second principal component (PC2) between BM and PB specimens. (B) Loadings plot for the second principal component depicts the most relevant discriminatory functional groups from BM (negative loadings) and PB (positive loadings) collected at diagnosis. Red areas in (B) indicate significantly different regions of the MRS spectra according to a point-by-point nonparametric Wilcoxon Rank Sum Test (p < 0.05).</p

CAIRN analysis of co-alteration of <i>BECN1</i> and <i>BRCA1</i>.

A, Single-nucleotide variants or insertion deletion rates for the adjacent genes BECN1 and BRCA1 are compared for the SOC (OV) and breast cancer (BRCA) datasets. Monoallelic loss rates are shown for comparison. B, Ensembl display of genomic region on Chr17 encompassing BECN1 and BRCA1. C-F, A visualization tool CAIRN was developed to enable the oncology community to easily analyze and display copy-number alterations in human datasets (see Materials). Each horizontal segment is from an individual tumor and displays a continuous CNA of the chromosome. Blue segments represent copy-losses, red segments indicate copy-gains, and both are displayed in relation to the parent chromosome indicated in grey. Human TCGA tumors were tested for CNAs overlapping the genes BECN1 and BRCA1. Ovarian tumors with exclusive BRCA1 or BECN1 deletions (C) are far rarer than tumors deleted in both genes (E) as shown by CAIRN. Breast tumors followed a similar pattern (D,F). In both tumor types, BECN1 deletions without accompanying BRCA1 deletions were found.</p

Spontaneous copy-number evolution in ovarian cancer cells.

A, SKOV3 cells were stably knocked-down by shRNA lentivirus. Control, shBECN1, and shLC3B cultures were passaged 30 times (1:3 dilutions) with no selective pressure other than transgene selection. Genomic DNA was then harvested. B, SKOV3 remained knocked-down for target protein expression at the completion of the experiment. For early passage knockdown levels, please refer to Fig 2 and S6 Fig. C, Quantitation of western blot data. LC3-II is separately quantified from total LC3 to provide independent estimates of knockdown efficiency and autophagosomal LC3. D, Copy-number alteration profiles of SKOV3 transduced with shBECN1 or shLC3B, arranged by chromosome on the x-axis as indicated. Red coloration depicts copy-number gains while blue coloration depicts copy-number losses, relative to shScr controls. Hierarchical clustering of copy-number data was performed, resulting in separation of autophagy-knockdown cells from controls. E, Euclidian distance metrics of copy-number alterations is compared between the genotypes. All autophagy knockdowns were more dissimilar from one another than the scrambled controls. F, Common break points normally found in tumor samples were compared to break points of copy-number changes in SKOV3 cells. G-H, CNA edges of SKOV3 cells suppressed in autophagy (shBECN1 and shLC3B combined) were overlaid with genes and analyzed for (G) gene size and (H) expression level. While expression level did not predict gene breakage, larger genes were more often disrupted by CNA break points. However, this would be expected by chance, as shown by 1,000 randomizations of the observed CNAs (“Expected” plot). I, Fisher’s exact test tables comparing CNA segments that are shared between knockdown cells and scrambled control cells. * indicates P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ns P > 0.05.</p

CNA patterns affect SOC pathways.

A, HAPTRIG pathway analysis of SKOV3 copy-number alterations. Nominally significant (P 0.05) pathways enriched for suppression or elevation of CNAs in SKOV3 autophagy suppressed cells (shBECN1 and shLC3B combined, “shAUTO”) by the gene-set analysis tool HAPTRIG are displayed as red (elevated) or blue (suppressed). Open colored circles indicate pathways only found in SKOV3 cells, and closed colored circles indicate pathways also found to be similarly dysregulated in TCGA studied serous ovarian cancer primary tumors (OV). B-C, Select altered pathways from TCGA serous ovarian cancer data are highlighted and networks were graphed by HAPTRIG. Scores were tabulated and those gene nodes with scores one z-score away from the median node score are displayed with blue if suppressed and in red if elevated. Edges indicate physical interactions as found in BioGrid. Boxes were drawn around genes if HAPTRIG found this node to be one of the top five influential genes (suppression and elevation were separately tested) within the selected pathway for both the SKOV3 experiment and TCGA ovarian cancers.</p