Leukemia Cell-Rhabdovirus Vaccine: Personalized Immunotherapy for Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
PURPOSE: Acute lymphoblastic leukemia (ALL) remains incurable in most adults. It has been difficult to provide effective immunotherapy to improve outcomes for the majority of patients. Rhabdoviruses induce strong antiviral immune responses. We hypothesized that mice administered ex vivo rhabdovirus-infected ALL cells [immunotherapy by leukemia-oncotropic virus (iLOV)] would develop robust antileukemic immune responses capable of controlling ALL. EXPERIMENTAL DESIGN: Viral protein production, replication, and cytopathy were measured in human and murine ALL cells exposed to attenuated rhabdovirus. Survival following injection of graded amounts of ALL cells was compared between cohorts of mice administered γ-irradiated rhabdovirus-infected ALL cells (iLOV) or multiple control vaccines to determine key immunotherapeutic components and characteristics. Host immune requirements were assessed in immunodeficient and bone marrow-transplanted mice or by adoptive splenocyte transfer from immunized donors. Antileukemic immune memory was ascertained by second leukemic challenge in long-term survivors. RESULTS: Human and murine ALL cells were infected and killed by rhabdovirus; this produced a potent antileukemia vaccine. iLOV protected mice from otherwise lethal ALL by developing durable leukemia-specific immune-mediated responses (P < 0.0001), which required an intact CTL compartment. Preexisting antiviral immunity augmented iLOV potency. Splenocytes from iLOV-vaccinated donors protected 60% of naïve recipients from ALL challenge (P = 0.0001). Injecting leukemia cells activated by, or concurrent with, multiple Toll-like receptor agonists could not reproduce the protective effect of iLOV. Similarly, injecting uninfected irradiated viable, apoptotic, or necrotic leukemia cells with/without concurrent rhabdovirus administration was ineffective. CONCLUSION: Rhabdovirus-infected leukemia cells can be used to produce a vaccine that induces robust specific immunity against aggressive leukemia.
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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".