Proteasome inhibition enhances oncolytic reovirus therapy in multiple myeloma independently of its direct cytotoxic effects
Bibliographic record
Abstract
BACKGROUND: Reovirus (RV) is an oncolytic virus with natural tropism for cancer cells. We previously showed that RV administration in multiple myeloma (MM) patients was safe, but disease control associated with viral replication in the cancer cells was not observed. The combination with proteasome inhibitors (PIs) has shown to enhance RV therapeutic activity, but the mechanisms of action have not been fully elucidated. METHODS: Electron microscopy, q-RT-PCR, single-cell mass cytometry (CyTOF), flow cytometry, plaque assays, immunohistochemistry, and Western blot analysis were used to assess RV infection of both myeloma and immune cells. Immune fluorescence, flow cytometry, and luciferase reporter assays were used to assess NF-κB pathway activation upon RV treatments. Immune profiling changes, both ex vivo and in MM patients, were analyzed by flow cytometry and CyTOF analysis. T-cell receptor (TCR) sequencing was also conducted both in immune competent MM mice and in patients enrolled in a phase 1b trial per a standard 3 + 3 dose escalation schedule. RESULTS: Here we show ex vivo and in vivo that proteasome inhibitors (PIs) potentiate reovirus replication in circulating classical monocytes, increasing viral delivery to myeloma cells. We found that the anti-viral signals in monocytes primarily rely on NF-κB activation and that this effect is impaired by the addition of PIs. Conversely, the addition of PIs to RV therapy supports immune activation and killing of MM, independently of direct PI sensitivity. To validate the importance of PIs in enhancing oncolytic viral therapy independently of their killing activity on cancer cells, we then conducted a phase 1b trial of the reovirus Pelareorep together with the PI carfilzomib in 13 heavily pretreated PI-resistant MM patients. Objective responses, which were associated with active reovirus replication in MM cells, T cell activation, and monocytic expansion, were noted in 70% of patients. CONCLUSIONS: Although characterized as immunosuppressive drugs, PIs improved RV delivery to MM cells but also enhanced anti-MM efficacy through immune-mediated killing of myeloma cells, independently of their PI sensitivity. These results highlight a more generalizable use of PIs as therapeutic companions to support oncolytic-based therapies in cancers. TRIAL REGISTRATION: clinicaltrials.gov, NCT02101944.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".