Synergistic anti-tumor efficacy of oncolytic influenza viruses and B7-H3 immune- checkpoint inhibitors against IC-resistant lung cancers
Bibliographic record
Abstract
Non-small cell lung cancers (NSCLCs) establish a highly immunosuppressive tumor microenvironment supporting cancer growth. To interfere with cancer-mediated immunosuppression, selective immune-checkpoint inhibitors (ICIs) have been approved as a standard-of-care treatment for NSCLCs. However, the majority of patients poorly respond to ICI-based immunotherapies. Oncolytic viruses are amongst the many promising immunomodulatory treatments tested as standalone therapy or in combination with ICIs to improve therapeutic outcome. Previously, we demonstrated the oncolytic and immunomodulatory efficacy of low-pathogenic influenza Aviruses (IAVs) against NSCLCs in immunocompetent transgenic mice with alung-specific overexpression of active Raf kinase (Raf-BxB). IAV infection not only resulted in significant primary virus-induced oncolysis, but also caused afunctional reversion of tumor-associated macrophages (TAMs) comprising additional anti-cancer activity. Here we show that NSCLCs as well as TAMs and cytotoxic immune cells overexpress IC molecules of the PD-L2/PD-1 and B7-H3 signaling axes. Thus, we aimed to combine oncolytic IAV-infection with ICIs to exploit the benefits of both anti-cancer approaches. Strikingly, IAV infection combined with the novel B7-H3 ICI led to increased levels of M1-polarized alveolar macrophages and increased lung infiltration by cytotoxic Tlymphocytes, which finally resulted in significantly improved oncolysis of about 80% of existing tumors. In contrast, application of clinically approved α-PD-1 IC antibodies alone or in combination with oncolytic IAV did not provide additional oncolytic or immunomodulatory efficacy. Thus, individualized therapy with synergistically acting oncolytic IAV and B7-H3 ICI might be an innovative future approach to target NSCLCs that are resistant to approved ICIs in patients.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".