Pharmacometabolomics reveals urinary diacetylspermine as a biomarker of doxorubicin effectiveness in triple negative breast cancer
Bibliographic record
Abstract
Abstract Triple-negative breast cancer (TNBC) patients receive chemotherapy treatment, including doxorubicin, due to the lack of targeted therapies. Drug resistance is a major cause of treatment failure in TNBC and therefore, there is a need to identify biomarkers that determine effective drug response. Here, a pharmacometabolomics study was performed using TNBC patient-derived xenograft models to detect urinary metabolic biomarkers of doxorubicin effectiveness. Diacetylspermine was identified as a urine metabolite that robustly changed in response to effective doxorubicin treatment, which persisted after the final dose. Diacetylspermine was directly traced back to the tumor and correlated with tumor volume. Ex vivo tumor slices revealed that doxorubicin directly increases diacetylspermine production by increasing tumor spermidine/spermine N 1 -acetyltransferase 1 expression and activity, which was corroborated by elevated polyamine flux. In breast cancer patients, tumor diacetylspermine was elevated compared to matched non-cancerous tissue and increased in HER2+ and TNBC compared to ER+ subtypes. In addition, 12-hour urine diacetylspermine was associated with breast cancer tumor volume and poor tumor grade. This study describes a pharmacometabolomics strategy for identifying cancer metabolic biomarkers that indicate drug response. Our findings characterize urine diacetylspermine as a non-invasive biomarker of doxorubicin effectiveness in TNBC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".