Potentiating Cancer Immunotherapy with Antigen-Agnostic Therapies
Bibliographic record
Abstract
Cancer remains a lead health concern for human and veterinary patients. We are entering a new era of cancer therapy, driven by extensive evidence that the patient’s immune system holds the key to beating cancer. Immunotherapy encompasses any therapeutic modality with the explicit goal of activating the host immune response against malignancy. Immunotherapy has already produced therapies that are being used clinically. Oncolytic viruses (OV) are immunotherapeutics that use a diverse set of cancer-targeting viruses to engage host anticancer immunity. OVs are multi-mechanistic tools, each with unique biological characteristics. A key component of OVs is the ability to generate inflammation in the tumor microenvironment, recruiting effector cells and driving tumor-specific adaptive immune responses. In this thesis, two methods for evaluating the adaptive immune response to cancer are developed. These methods enable the quantification of tumor-specific T-lymphocyte and tumor-directed antibody responses without the need to identify a specific target antigen. Next, we demonstrate that fever can dramatically impact the oncolytic efficacy of two intensely studied OVs, vesicular stomatitis virus and Maraba virus, both from the Rhabdoviridae family. We developed a heat-adapted Maraba virus functional at fever-grade temperatures, and make recommendations for preclinical and clinical evaluation and implementation of OVs with regards to temperature. The final two research chapters evaluate the poxvirusParapoxvirus ovis (OrfV) as an oncolytic virus in two challenging models of late-stage ovarian cancer and osteosarcoma lung metastases. In both models, OrfV was a dramatic immune-stimulating OV platform that could circumvent the host antiviral interferon response and kill cancer cells by immunogenic cell death. OrfV massively recruited tumoricidal natural killer cells that directly kill tumor cells, release tumor antigen and guide the development of adaptive antitumor immune responses. The work in this thesis provides tools for researchers to detect anticancer immune responses in hosts, uncovers fever as a previously under-appreciated Achilles heel of some OVs and presents OrfV as a multi-functional immunogenic OVs deserving of further investigation and clinical translation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".