Metabolomics of the Tumor Microenvironment in Pediatric Acute Lymphoblastic Leukemia

Stefano Tiziani, Yunyi Kang, Ricky Harjanto, Joshua Axelrod, Giovanni Paternostro, Sanford-Burnham Medical Research Institute, La Jolla, California, United States of AmericaCarlo Piermarocchi, Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, United States of AmericaWilliam Roberts, Rady Children’s Hospital, Department of Pediatrics, University of California San Diego, San Diego, California, United States of AmericaStefano Tiziani, Department of Nutritional Sciences, Dell Pediatric Research Institute, University of Texas at Austin, Austin, Texas, United States of AmericaThe tumor microenvironment is emerging as an important therapeutic target. Most studies, however, are focused on the protein components, and relatively little is known of how the microenvironmental metabolome might influence tumor survival. In this study, we examined the metabolic profiles of paired bone marrow (BM) and peripheral blood (PB) samples from 10 children with acute lymphoblastic leukemia (ALL). BM and PB samples from the same patient were collected at the time of diagnosis and after 29 days of induction therapy, at which point all patients were in remission. We employed two analytical platforms, high-resolution magnetic resonance spectroscopy and gas chromatography-mass spectrometry, to identify and quantify 102 metabolites in the BM and PB. Standard ALL therapy, which includes l-asparaginase, completely removed circulating asparagine, but not glutamine. Statistical analyses of metabolite correlations and network reconstructions showed that the untreated BM microenvironment was characterized by a significant network-level signature: a cluster of highly correlated lipids and metabolites involved in lipid metabolism (p less than 0.006). In contrast, the strongest correlations in the BM upon remission were observed among amino acid metabolites and derivatives (p less than 9.2×10-10). This study provides evidence that metabolic characterization of the cancer niche could generate new hypotheses for the development of cancer therapies.This work was funded by the National Science Foundation (Grant No. 0829891). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Nutritional SciencesDell Pediatric Research InstituteEmail: [email protected] (GP), Email: [email protected] (ST

[In Press] Severity and outcomes of admissions for gamma hydroxy-butyrate disorders before and after COVID-19 pandemic restrictions in South Western Sydney

Background: Dependence on gamma-hydroxybutyrate (GHB) is an emerging substance use disorder which can be life-threatening in overdose and withdrawal. The aim of this study was to describe rising GHB-related hospitalizations amidst the ongoing COVID-19 pandemic.Methods: A retrospective consecutive case series of adults admitted to hospitals in South Western Sydney Local Health District was identified with clinical coding of GHB-related disorder between March 20 2019 and March 20 2021. Morbidity outcomes and multivariable Kaplan-Meier survival analysis on length of hospital stay were described.Results: Sixty-nine of 84 included admissions, (82%) occurred in the 12 months following COVID-19 related border closure. Of 47 admissions for withdrawal, fifteen of 47 (32%) required intensive care, 6 (13%) intubation, 4 (9%) one-to-one ward observation, and 8 (17%) emergency calls for agitated delirium, fall, or seizure. Five cofactors were associated with longer hospital stay in the multivariable analysis: age 30 or older (p < .05), 6 months of regular GHB use (p < .01), and elective admission (p < .05), and diagnosis of psychosis rather than withdrawal (p < .05) or overdose (p < .001).Conclusions: Development of a validated GHB withdrawal severity scale based on these risk factors could help identify patients requiring close monitoring for complicated withdrawal and escalation of care

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

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

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

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

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