Distinct functional and compositional properties in the gut microbiome of children with acute lymphoblastic leukaemia identified by shotgun metagenomics
Bibliographic record
Abstract
Acute lymphoblastic leukaemia (ALL) represents the most common childhood malignancy, and emerging evidence underscores the impact of the gut microbiome (GM) on its pathogenesis. In this study, we used shotgun metagenomics to investigate the GM of 30 ALL patients at diagnosis-19 with B-ALL and 11 with T-ALL-and compared them to 176 healthy controls (HCs). When considered as a single ALL group versus HCs, clear compositional differences emerged: ALL patients exhibited higher relative abundances of Enterococcus faecium, oral commensals such as Rothia dentocariosa, and multiple opportunistic species, whereas HCs were enriched in short-chain fatty acid producers like Anaerostipes hadrus and Intestinibacter bartlettii. Functionally, the ALL GM relied more on protein and amino acid catabolism, while HCs possessed enhanced pathways for carbohydrate and folate metabolism. These findings broadly align with 16S rRNA-based analyses from previous publications, though some discrepancies highlight differences in technique-driven resolution. In contrast, comparing the two major molecular phenotypes-B-ALL and T-ALL-revealed only minimal taxonomic and functional differences, primarily confined to BAs metabolism pathways. Overall, our results indicate that children with ALL at the time of diagnosis already display a dysbiotic signature, bolstering the notion that a disturbance in GM development during childhood may be linked to the multistep pathogenesis model of ALL.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".