Evidence Implicating Immunological Host Effects in the Efficacy of Metronomic Low-Dose Chemotherapy
Bibliographic record
Abstract
Conventional chemotherapy drugs administered at a maximum tolerated dose (MTD) remains the backbone for treating most cancers. Low-dose metronomic (LDM) chemotherapy, which utilizes lower, less toxic, doses given on a close regular basis over prolonged periods, is an alternative and better tolerated potential strategy to improve chemotherapy. LDM chemotherapy has been evaluated preclinically and clinically and has shown therapeutic benefit, in both early and advanced stage metastatic disease, especially when used as a maintenance therapy. However, knowledge about the antitumor mechanisms by which LDM chemotherapy acts remain limited. Here we characterized the effects of LDM and MTD capecitabine therapy on tumor and host cells using high-throughput systems approaches involving mass spectrometry flow cytometry and automated cell imaging followed by in vivo analyses of such therapies. An increase in myeloid and T regulatory cells and a decrease in NK and T cytotoxic cells were found in MTD-capecitabine-treated tumors compared with LDM-capecitbine-treated tumors. Plasma from MTD capecitabine-treated mice induced a more tumorigenic and metastatic profile in both breast and colon carcinoma cells than plasma from mice treated with LDM capecitabine. These results correlated, in part, with in vivo studies using models of human or mouse advanced metastatic disease, where the therapeutic advantage of MTD capecitabine was limited despite a substantial initial antitumor activity found in the primary tumor setting. Overall these results implicate a possible contribution of immunologic host effects in accounting for the therapeutic limitations of MTD compared with LDM capecitabine. Cancer Res; 76(20); 5983-93. ©2016 AACR.
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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) | 0.000 | 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".