Immunostimulatory and anti-tumor metronomic cyclophosphamide regimens assessed in primary orthotopic and metastatic murine breast cancer
Bibliographic record
Abstract
Abstract The impressive successes of immune checkpoint blockade antibodies to treat various types of cancer are limited to minor subsets of patients. Combination therapy strategies, including with chemotherapy, are being explored to possibly improve the efficacy of immunotherapies. Here we report results regarding the use of an immunostimulatory regimen of metronomic cyclophosphamide (CTX). We show that in orthotopic models of syngeneic murine triple-negative breast cancer (EMT6), CTX administered at 140 mg/kg every 6 days (CTX140 1q6d) is superior at inhibiting primary tumor growth when compared to maximum tolerated dose or daily oral (continuous) low-dose CTX. In SCID or SCID beige mice, anti-tumor effects of CTX140 1q6d are reduced, reinforcing the therapeutic contribution of the adaptive and innate immune systems. In a second breast cancer model (SP1-AC2M2), CTX140 1q6d again showed clear superiority in anti-tumor effects, causing complete tumor regressions; however, these mice were not protected from subsequent tumor re-challenge, suggesting absence of immune memory. We also show that in an aggressive and metastatic cisplatin-resistant variant (EMT6-CDDP), CTX140 1q6d is superior and invokes an influx of intra-tumoral CD4+ and CD8+ T cells. CTX increases expression of tumor cell PD-L1; however, when combined with concomitant PD-L1 antibody therapy none of the CTX regimens showed increased benefit. This work sheds light on the potential use of metronomic CTX for the treatment of breast cancer, in particular using the quasi-weekly regimen, but also underscores the complexity of the anti-tumor mechanisms and potential to improve immune checkpoint therapy efficacy.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".