Exploiting the DNA Damaging Activity of Liposomal Low Dose Cytarabine for Cancer Immunotherapy
Bibliographic record
Abstract
Perhaps the greatest limitation for the continually advancing developments in cancer immunotherapy remains the immunosuppressive tumor microenvironment (TME). The cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) axis is an emerging immunotherapy target, with the resulting type I interferons and transcription factors acting at several levels in both tumor and immune cells for the generation of adaptive T cell responses. The cGAS-STING axis activation by therapeutic agents that induce DNA damage, such as certain chemotherapies, continues to be reported, highlighting the importance of the interplay of this signaling pathway and the DNA damage response in cancer immunity/immunotherapy. We have developed a multi-targeted mannosylated cationic liposomal immunomodulatory system (DS) which contains low doses of the chemotherapeutic cytarabine (Ara-C). In this work, we show that entrapment of non-cytotoxic doses of Ara-C within the DS improves its ability to induce DNA double strand breaks in human ovarian and colorectal cancer cell lines, as well as in various immune cells. Importantly, for the first time we demonstrate that the DNA damage induced by Ara-C/DS translates into cGAS-STING axis activation. We further demonstrate that Ara-C/DS-mediated DNA damage leads to upregulation of surface expression of immune ligands on cancer cells, coinciding with priming of cytotoxic lymphocytes as assessed using an ex vivo model of peripheral blood mononuclear cells from colorectal cancer patients, as well as an in vitro NK cell model. Overall, the results highlight a broad immunotherapeutic potential for Ara-C/DS by enhancing tumor-directed inflammatory responses.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".