Abstract B101: Novel oncolytic vaccinia virus platform for systemic delivery of immunotherapeutic payloads
Bibliographic record
Abstract
Abstract The treatment paradigm for patients with metastatic cancer has evolved rapidly with the approval of agents targeting CTLA-4 and the PD-1/L1 immune checkpoint axis. Despite the profound impact these agents have had, they are minimally effective in the majority of cancer patients. Rational combinations of complementary immune modulating agents have thus far not led to clear patient benefit, and newer technologies that are better able to safely combine multiple modes of action could well prove to be vital. Oncolytic viruses (OVs) have the capacity to be the ideal therapeutic partner for immune checkpoint therapeutics in several ways. First, on their own OVs can “heat-up” immunologically “cold” tumors by initiating a pro-inflammatory infection within the tumor microenvironment (TME). Second, some OVs can be engineered to strategically express one or more immune-modulating molecules. Finally, certain OVs have the capacity to be delivered systemically and thus enhance immune cell recruitment and activation in all metastatic sites. We have selected a novel vaccinia virus as our therapeutic OV platform and are using it to engineer multi-mechanistic cancer therapeutics. Previously it has been demonstrated that certain oncolytic vaccinia viruses can be delivered systemically and spread within metastatic lesions. These early clinical viruses, however, contain multiple potent immune suppressive genes and are not ideal for the generation of antitumor immune responses “in situ.” Furthermore, in clinical studies some of these therapeutics exhibited off-tumor infections (e.g., pox lesions), which may ultimately limit their ability to be used to deliver potent immune modulators. We used a combination of functional genomics and bio-selection strategies to optimize the vaccinia virus platform. Initially we developed a fitness assay to identify the vaccinia strain with the best ability to replicate in and kill both established cancer cell lines and cancer patient tumor explants. Next, we used a transposon insertion strategy and deep sequencing of viral populations to systematically examine the role of each vaccinia virus gene in its ability to be an anticancer therapeutic. Ultimately, we identified large regions (25Kb) of the vaccinia genome that when deleted, augment the oncolytic activity of a newly generated vaccinia backbone termed SKV. Our new best-in-class vaccinia, SKV, robustly stimulates anti-immune responses, rapidly spreads within and between tumors and has a substantially improved preclinical safety profile when compared to other vaccinia clinical candidates. As predicted, SKV synergizes well with immune checkpoint inhibitor antibodies and potently activates human immune cells. Due to the exquisite tumor selectivity of SKV, we have been able to engineer and express from the backbone a combination of very potent immune modulators that are safest and most effective when expressed within the TME. These include an immune checkpoint inhibitor, a membrane tethered cytokine and antigen-presenting cell activating ligand in a single virus. Ongoing toxicity and efficacy studies are being carried out to prepare our novel virus construct for clinical launch. Citation Format: John C. Bell, Adrian Pelin, Michael Huh, Matthew Tang, Fabrice Le Boeuf, Brian Keller, Jessie Duong, Katherine Clark-Knowles, Julia Petryk, Victoria A. Jennings, Alan Melcher, Mathieu Crupi, Larissa Pikor, Caroline Breitbach, Steven Bernstein, Michael Burgess. Novel oncolytic vaccinia virus platform for systemic delivery of immunotherapeutic payloads [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B101.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".