Investigating Gut Microbial Taxa and Asparaginase Related Genes in Children Showing Different Direction of Change in Serum Asparaginase Activity Levels during Pegasparaginase Treatment for Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
Background: L-asparaginase (ASNase) converts Asn to Asp and at sustained high levels depletes circulating Asn, leading to leukemic cell death. This dependency has led to the use of ASNase (in a peglyated form, PEGASNase) as an important therapy in the treatment of acute lymphoblastic leukemia (ALL) and has improved survival in patients with ALL. ASNase treatment efficacy relies on significant depletion of circulating Asn for sustained periods of time. Therapeutic monitoring is therefore critical to ensure sufficient levels of ASNase activity to maintain Asn depletion. Serum ASNase activity is monitored as a proxy for Asn levels, having an inverse relationship to Asn. The predictors of serum levels of ASNase activity are not clear however with variation in levels within the same patient between doses. The gut microbiome plays a role in human health and disease, producing metabolites that could impact ASNase therapy. To date, the role of the gut microbiome community in impacting serum ASNase activity levels has not been investigated. Methods: We investigated 12 paediatric ALL patients for which serum ASNase levels were measured (7 days post treatment) for two consecutive doses of PEGASNase and a stool sample was collected between these two doses (17 samples). Change in serum ASNase activity was determined by examining the difference in consecutive serum ASNase levels. Activity was considered to have decreased when change was negative (serum ASNase levels declined from previous measurement). Gut microbial community composition of the stool samples was determined from a portion of the 16S rRNA gene. In addition whole shotgun metagenome sequencing was used to investigate the relationship between microbial ASNase and ASNS genes and changes in serum ASNase levels during treatment. We utilized a Bayesian model to examine the microbial community structure in serum ASNase decreasing (SD) vs increasing (SI) samples. We used Mann-Whitney U test to examine differences in counts of bacterial ASNase and ASNS genes in SD and SI groups. Finally we investigated counts of bacterial ASNase and ASNS genes along with age, gender, disease risk, dose number, serum ASNase level at previous dose and time between stool sample and dose at predicting change in serum ASNase activity levels using regression models after applying lasso reduction. Results: Patients in this study were 50% male and had an average age of 5 years ranging from 1 month to 14.6 years. Among samples examined 35% had decreased serum activity compared to measurements from the previous dose. We identified differing assemblages of microbial taxa prior to PEGASNase treatment. The SD community was predominated by Escherichia prior to treatment while Bacteroides and Streptococcus predominated in the SI community (Fig 1). We found that counts of microbial ASNS were significantly (p=0.003) negatively correlated with change in serum ASNase activity levels (Fig 2), however neither bacterial ASNase gene (ansA or ansB) was significant. Including covariates and applying model reduction we find that ASNS (p=0.0005), dose number (p= 0.001), age at diagnosis (p= 0.001), serum ASNase levels at previous dose (p= 0.008), and counts of ansA (p=0.04) predict change in serum ASNase levels (adjusted R2=0.826, p= 0.0002). Only dose-number was positively correlated with change in serum ASNase level. Conclusions: We found differences in the microbial community prior to PEGASNase treatment possibly suggesting that modifying the microbiome (decreasing contribution of Escherichia) prior to treatment could result in increased serum ASNase activity. This data also suggests that increased amounts of bacterial ASNS genes present may be associated with a decrease in serum ASNase activity. Future work should focus on a larger and more diverse set of samples in order to further investigate SD and SI community-level properties and the role of covariates (e.g., age and dose number), and further exam the interplay between serum ASNase activity, and bacterial ASNS. Disclosures No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".