A strategy for early detection of response to chemotherapy drugs based on treatment-related changes in the metabolome
Bibliographic record
Abstract
We describe a biomarker-based approach to delivering chemotherapy that entails monitoring treatment changes in the circulating metabolome that reflect efficacy. In-vitro, multiple tumor cell lines were exposed to numerous chemotherapeutics. Supernatants were collected at baseline and 72 hours post treatment. MTT assays were used to quantify growth inhibition. Clinical samples were derived from a phase II clinical trial of second-line axitinib in patients with advanced hepatocellular carcinoma. Sera were collected at baseline and 2-4 weeks after treatment initiation. Response to therapy was estimated by CT scan at 8 weeks. Samples were analyzed by gas chromatography-mass spectrometry to identify metabolomic changes associated with response. In vitro, we found drug-specific and generalizable patterns of change in the extracellular metabolome accompany growth inhibition. A cell death signature was also identified. This approach was also applied to clinical samples. While the in vitro signatures were detectable in vivo, a more robust signal was identified clinically that appeared within 4 weeks of administering drug that distinguished individuals with a treatment response. These changes were extinguished as tumor growth resumed. Serial monitoring of the metabolome during chemotherapy is a means to follow treatment efficacy and emergence of resistance, informing the oncologist whether to modify treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".