Abstract C003: Neonatal Dried Blood Spot Metabolomics Reveals Signatures of Early-Onset Myeloproliferative Neoplasms
Bibliographic record
Abstract
Abstract INTRODUCTION Myeloproliferative Neoplasms (MPN) are a group of hematopoietic malignancies generally seen in older adults, but more cases are being diagnosed in younger patients (ranging from 0 to ∼30 years) for whom the etiology is poorly understood. Assessing early-life exposures and their potential effects on the biology of the individual is complex. However dried blood spots (DBS) which are collected at birth, years preceding the development of cancer, provide a resource to retrospectively analyze this critical window of exposure. Metabolomics analysis of these DBS offer a unique opportunity to measure both exposures and biological effect, through the examination of metabolites, including those derived from environmental sources, the microbiome, and endogenous processes. MATERIAL AND METHOD This study investigates the role of early-life exposures in the pathogenesis of early-onset MPN (EO-MPN) through untargeted metabolomics-based mass spectrometry on neonatal DBS samples. We combine a broad analysis of cases versus matched controls with a focused investigation of cohort-specific factors within the cases. This multi-step approach allows for the identification of potential confounding factors related to the general population and isolates those that are likely to contribute to the disease. RESULT AND DISCUSSION Multivariate statistical analyses such as PCA, PLS-DA and Uniform Manifold Approximation and Projection (UMAP), revealed subtle metabolic differences between cases and control particularly when stratifying by age of diagnosis and birth cohort. Despite the modest fold changes resulting from the comparison, key metabolic alterations in the cases were measured for fatty acids, amino acids and metabolites involved in redox pathways. CONCLUSION This study represents the first application of a comprehensive metabolomics analysis in the context of early-onset MPN. The findings from this preliminary analysis suggest that early life metabolic shifts may play a role in MPN development, and lay the groundwork for future research to explore these metabolites as potential biomarkers and therapeutic targets for EO-MPN. Citation Format: Domenica Berardi. Neonatal Dried Blood Spot Metabolomics Reveals Signatures of Early-Onset Myeloproliferative Neoplasms [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr C003.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".