Comparison of the blood, bone marrow, and cerebrospinal fluid metabolomes in children with b-cell acute lymphoblastic leukemia

Metabolomics may shed light on treatment response in childhood acute lymphoblastic leukemia (ALL), however, most assessments have analyzed bone marrow or cerebrospinal fluid (CSF), which are not collected during all phases of therapy. Blood is collected frequently and with fewer risks, but it is unclear whether findings from marrow or CSF biomarker studies may translate. We profiled end-induction plasma, marrow, and CSF from N = 10 children with B-ALL using liquid chromatography-mass spectrometry. We estimated correlations between plasma and marrow/CSF metabolite abundances detected in ≥ 3 patients using Spearman rank correlation coefficients (rs). Most marrow metabolites were detected in plasma (N = 661; 81%), and we observed moderate-to-strong correlations (median rs 0.62, interquartile range [IQR] 0.29–0.83). We detected 328 CSF metabolites in plasma (90%); plasma-CSF correlations were weaker (median rs 0.37, IQR 0.07–0.70). We observed plasma-marrow correlations for metabolites in pathways associated with end-induction residual disease (pyruvate, asparagine) and plasma-CSF correlations for a biomarker of fatigue (gamma-glutamylglutamine). There is considerable overlap between the plasma, marrow, and CSF metabolomes, and we observed strong correlations for biomarkers of clinically relevant phenotypes. Plasma may be suitable for biomarker studies in B-ALL.

We evaluated plasma-marrow correlations for compounds annotated to the Kyoto Encyclopedia of Genes and Genomes (KEGG) 30 pathway "central carbon metabolism in cancer" (hsa05230), as we had previously reported an association between this pathway and end-induction minimal residual disease (MRD) in diagnostic marrow samples from children with ALL 16 . Two of these compounds, pyruvate (r s 0.89; q = 0.01) and asparagine (r s 0.87; q = 0.01), demonstrated significant plasma-marrow correlations.
Correlations between the plasma and CSF metabolomes. Both the number of compounds detected in CSF and their distribution with respect to class differed relative to marrow and plasma. In particular, we observed proportionately fewer lipids and proportionately more amino acids (Fig. 1a). Similar to marrow, the majority of compounds in CSF were also detected in blood (N = 328, 89.6%) (Fig. 1b). Plasma-CSF correlations were weaker than plasma-marrow correlations on average (median r s 0.37, IQR 0.07-0.70) (    Table S2 provides data on all metabolites common to the plasma and CSF metabolomes). Xenobiotics were overrepresented among compounds with statistically significant FDR-adjusted correlations, whereas there was some evidence that lipids were underrepresented (p = 0.09) ( Table 3) (Fig. 2b). We previously reported associations between CSF abundances of asparagine, dimethylglycine and gammaglutamylglutamine and cancer-related fatigue in children with ALL 18 . Here, we observed significant plasma-CSF correlations for dimethylglycine (r s 0.84; q = 0.04) and gamma-glutamylglutamine (r s − 0.93; q = 0.002). Interestingly, the inverse correlation between CSF and plasma abundances of gamma-glutamylglutamine was the strongest observed, and one of only two which were statistically significant.

Discussion
Overall, we found that plasma metabolomics is appropriate for estimating metabolic processes in the marrow or CSF of children with B-ALL. The majority of compounds detected in marrow or CSF were also detected in plasma and (especially for marrow) the number and type of compounds was similar. Substantial proportions (62% in marrow and 41% in CSF) demonstrated moderate or strong correlations with plasma and many (35% in marrow, 16% in CSF) remained statistically significant at q < 0.05 after multiple testing correction. Of note, we reported strong correlations for compounds previously associated with cancer-related fatigue 18 and MRD 16 , supporting that blood may be useful for biomarker studies of these compounds and endpoints. These findings have practical implications for future metabolomics studies, since blood can be collected more frequently and less invasively.
When evaluating plasma and bone marrow, we observed a robust correlation for asparagine. Asparaginase has long been utilized in ALL chemotherapy, following the discovery of its anti-leukemic effect, mediated by serum asparagine and glutamine depletion, and clinical trials demonstrating improved survival for asparaginase-treated patients 31,32 . In contrast to a previous magnetic resonance spectroscopy (MRS) and gas chromatography-mass spectrometry (GC-MS)-based untargeted metabolomics study which reported total depletion of asparagine at D29 in patients who received PEG-asparaginase between induction D4 and D6 17 , we detected asparagine in plasma and marrow samples from all children. Patients on both induction protocols (AALL0932 and AALL1131) received PEG-asparaginase on induction D4 12,33 ; our observation is consistent that of Angiolillo et al., who demonstrated that asparagine levels begin to recover 20-25 days after PEG-asparaginase administration 34 . Given the importance of asparagine depletion in ALL chemotherapy, it may be noteworthy that it was readily detected in plasma using this approach, and that plasma and marrow asparagine abundances correlated strongly. Conversely, plasma and CSF asparagine abundances were not significantly correlated. Given that CSF asparagine depletion is also essential, measurement of plasma asparagine alone may be inadequate to quantify the extent and duration of asparagine depletion in the CSF 35 .
We also observed a strong positive correlation between marrow and plasma pyruvate abundances. In a study of children with newly diagnosed ALL we reported that bone marrow pyruvate abundance at diagnosis was associated with subsequent MRD (1.9-fold increase among MRD-positive patients, p = 0.02) 16 . Pyruvate is a key intermediate in glycolysis and gluconeogenesis. Altered glucose metabolism has been described in ALL cells 36,37 , and we and others have demonstrated that inhibitors of glycolysis exert anti-leukemic effects in vitro 16,36 . Whether plasma pyruvate abundances similarly associate with treatment response is unclear, but is a promising area for future research that aims to leverage metabolomics to understand ALL outcomes.
In our analysis of plasma and CSF, we observed a strong, albeit inverse, correlation between plasma and CSF abundances of gamma-glutamylglutamine. We previously reported an inverse (cross-sectional) association of CSF gamma-glutamylglutamine abundance and fatigue scores during post-induction chemotherapy, and reported that its abundance at the time of diagnosis was inversely correlated with fatigue severity at the start of delayed intensification 18 . Gamma-glutamylglutamine is an intermediate in the gamma-glutamyl cycle, in which gammaglutamyl amino acids are formed by the transfer of glutamyl moieties (e.g., from glutathione to glutamate), then subsequently cleaved to the free amino acid and 5-oxoproline. This pathway may play a role in the regulation of amino acid transport across the blood-brain barrier 38 , which could explain the observation that plasma and CSF gamma-glutamylglutamine abundances were inversely correlated. Table 3. Number of compounds detected in ≥ 3 plasma and CSF samples, and number that were significantly correlated after FDR correction, by class. a q < 0.05 after Benjamini-Hochberg correction. www.nature.com/scientificreports/ Saito et al. investigated changes in the plasma metabolome of patients with ALL pre-and post-induction and observed that > 20% of compounds, most notably lipids, were altered 39 . The authors hypothesized that these alterations may affect the risk of adverse events in children with ALL, as previous studies suggest effects of docosahexaenoic acid (DHA) [40][41][42] and phosphatidylethanolamines 43 on asparaginase-associated pancreatitis and relapse. We found that lipids demonstrated somewhat stronger plasma-marrow correlations than other compounds.

N detected Rho, median (IQR) N correlated a p (under-representation) p (over-representation)
Tiziani et al. compared plasma and bone marrow samples from N = 10 children with ALL at diagnosis and reported differences among amino acids and ketones 17 . Collectively, these findings suggest that the plasma and marrow lipids are well correlated, but that special care may be required when comparing diagnostic samples due to the high titer of leukemic cells. These findings may have particular implications for investigators wishing to perform longitudinal assessments or utilize lipidomics approaches.
Our study should be interpreted in light of certain strengths and limitations. Our sample size was small, which limited our ability to detect statistically significant correlations and prevented us from performing analyses stratified by factors such as sex, age at diagnosis, BMI or end-induction MRD status. The study was also cross-sectional, with all samples collected at the end of induction chemotherapy. On the other hand, we performed metabolomic profiling using a well-described untargeted platform with broad coverage and an extensive reference panel, which identified > 1000 unique features. Because this platform is semi-quantitative, we present data on relative abundances rather than absolute concentrations. In future studies, targeted approaches may allow for improved quantitation of metabolite or drug concentrations. Finally, the study sample was relatively homogenous, consisting entirely of pediatric patients with newly diagnosed B-ALL treated on standard protocols. This likely reduced heterogeneity in our analysis, but we caution that it is unknown whether our findings may be applicable to T-ALL.

Conclusions
Our findings highlight that untargeted plasma metabolomics readily detects compounds associated with the clinically relevant phenotypes of end-induction MRD and fatigue in children with ALL. ALL metabolomics is a nascent field and many of the studies performed to date have used bone marrow or CSF, which may be limiting for broader applications. To accelerate translation of these findings, we have comprehensively described the correlations between the blood, marrow, and CSF metabolomes at end-induction (see Supplementary Material for correlations for all detected compounds). We observed generally strong correlations between plasma and marrow, suggesting that plasma may be an optimal and readily available source for use in future studies. In particular, quantitative evaluations of candidate biomarkers and longitudinal assessments of the plasma metabolome across therapy may be informative for precision medicine approaches, and ultimately drive improvements in survival for children with ALL. While we highlight findings for putative biomarkers of MRD and fatigue, we note that metabolomics has scarcely been applied to study other important outcomes such as cardiotoxicity, hepatotoxicity, and relapse, all of which may be promising directions for future research.

Methods
Study population. Participants (N = 10) were children diagnosed with B-lineage ALL in 2017-2018, and treated at Texas Children's Hospital on or according to Children's Oncology Group protocols appropriate for their age and disease characteristics. We obtained plasma, bone marrow and cerebrospinal fluid samples at the end of induction chemotherapy, during routine clinical care. We extracted demographic and clinical data including sex, age, race/ethnicity, height, weight, disease type, NCI risk group and end-induction MRD status from the electronic health record. We defined children as overweight if their body mass index (BMI) was ≥ 85th percentile for their age and sex, and as obese if it was ≥ 95th percentile. We defined children as NCI standard risk if they were < 10 years of age at diagnosis and had an initial white blood cell count of < 50,000/µL and high risk otherwise 44 . Children whose end-induction marrow specimen contained ≥ 0.01% leukemic blasts, measured by flow cytometry, were considered MRD-positive. This study was approved by the Baylor College of Medicine Institutional Review Board (H-29892) and performed in accordance with the Declaration of Helsinki. Informed consent was obtained from the parents/guardians of all participating children. All procedures were performed in accordance with the relevant guidelines and regulations.
Metabolomic profiling. Plasma, marrow and CSF samples were processed according to standard methods and stored at -80 °C until they were batch shipped to Metabolon Inc. (Morrisville, NC) for analysis using the Precision Metabolomics™ platform. Sample processing and analysis procedures for this platform have been described previously 16,45 . Briefly, methanol was added and samples were centrifuged to precipitate proteins. The resulting supernatant was divided into four extracts: two were analyzed by reverse phase ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) methods with positive ion mode electrospray ionization, one by UPLC-MS/MS with negative ion mode electrospray ionization, and one by hydrophilic interaction UPLC-MS/MS. Organic solvent was removed using a TurboVap® (Zymark) and samples were stored overnight under nitrogen prior to analysis. Extracts were dried, reconstituted and analyzed using untargeted, UPLC-MS/MS-based approaches on a Waters ACQUITY chromatograph (Waters, Milford, MA) and a Thermo Scientific Q-Exactive spectrometer (Thermo Fisher Scientific, Waltham, MA). Instrument variability was measured by calculating the median relative standard deviation (RSD) for internal standards added to each sample prior to analysis, and was 4% for plasma and CSF and 5% for marrow. In addition to internal standards, a small amount of each sample was pooled and used as technical replicates throughout. Total process variability (6% for plasma, 7% for marrow and 9% for CSF) was determined by calculating the median RSD for all endogenous metabolites present in these pooled matrix samples. Data extraction, peak identification and compound identification were performed by Metabolon using an in-house bioinformatics pipeline. Peaks were quantified using