Plasma lipid levels predict chemotherapy response and survival in acute myeloid leukemia
Bibliographic record
Abstract
ABSTRACT: Acute myeloid leukemia (AML) is characterized by a low 5-year survival rate. Despite having many clinical metrics to assess patient prognosis, there remain opportunities to improve risk stratification. We hypothesized that an underexplored resource to examine the prognosis of patients with AML is plasma metabolome. Circulating metabolites are influenced by patients' clinical status and can serve as accessible cancer biomarkers. To establish a resource of circulating metabolites in genetically diverse patients with AML, we performed an unbiased metabolomic and lipidomic analysis of 231 diagnostic AML plasma samples before treatment with intensive chemotherapy. Intriguingly, circulating metabolites were highly associated with the mutation status within the AML cells. Furthermore, lipids were associated with refractory status. We established a machine learning algorithm trained on chemotherapy-refractory-associated lipids to predict patient survival. Cox regression and Kaplan-Meier analysis demonstrated that the high-risk lipid signature predicted overall survival in this patient cohort. Impressively, the top lipid in the high-risk lipid signature, sphingomyelin (d44:1), was sufficient to predict overall survival in both the original data set and an independent validation data set. Overall, this research underscores the potential of circulating metabolites to capture AML heterogeneity and lipids to be used as potential AML biomarkers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".