Multifaceted Therapeutic Targeting of Ovarian Peritoneal Carcinomatosis Through Virus-induced Immunomodulation
Bibliographic record
Abstract
Immunosuppression associated with ovarian cancer (OC) and resultant peritoneal carcinomatosis (PC) hampers the efficacy of many promising treatment options, including immunotherapies. It is hypothesized that oncolytic virus-based therapies can simultaneously kill OC and mitigate immunosuppression. Currently, reovirus-based anticancer therapy is undergoing phase I/II clinical trials for the treatment of OC. Hence, this study was focused on characterizing the effects of reovirus therapy on OC and associated immune microenvironment. Our data shows that reovirus efficiently killed OC cells and induced higher expression of the molecules involved in antigen presentation including major histocompatibility complex (MHC) class I, β2-microglobulin (β2M), TAP-1, and TAP-2. In addition, in the presence of reovirus, dendritic cells (DCs) overcame the OC-mediated phenotypic suppression and successfully stimulated tumor-specific CD8+ T cells. In animal studies, reovirus targeted local and distal OC, alleviated the severity of PC and significantly prolonged survival. These therapeutic effects were accompanied by decreased frequency of suppressive cells, e.g., Gr1.1+, CD11b+ myeloid derived suppressor cells (MDSCs), and CD4+, CD25+, FOXP3+ Tregs, tumor-infiltration of CD3+ cells and higher expression of Th1 cytokines. Finally, reovirus therapy during early stages of OC also resulted in the postponement of PC development. This report elucidates timely information on a therapeutic approach that can target OC through clinically desired multifaceted mechanisms to better the outcomes.
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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 | 0.000 | 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".