Abstract 5355: Oncolytic viral therapy with immune modulation is an effective novel treatment strategy for non-small cell lung cancer
Bibliographic record
Abstract
Abstract Background: Lung cancer is the leading cause of cancer mortality worldwide. Non-small cell lung cancer (NSCLC) represents approximately 85% of these diagnoses and despite incremental improvements with cytotoxic chemotherapy and targeted therapeutics, the five-year overall survival is only 17%. Recently improved understanding of immune checkpoint pathways and immunosuppresive cellular populations such as myeloid-derived suppressor cells (MDSCs) have led to a resurgence in immunotherapeutic strategies. Reovirus (RV) is an oncolytic virus that only targets cancer cells with certain aberrant signaling pathways such as Ras while avoiding normal cells. Furthermore, its role in stimulating an anti-tumor immune response has been recently described. Additionally, Sunitinib (S), a multitargeted tyrosine kinase inhibitor (anti-VEGFR, C-Kit and PDGFRa) also affects tumor microenvironment Tregs and MDSC numerically, which could consequentially augment RV immunotherapeutic potential. Hypothesis: RV and S combination therapy is an effective treatment modality for NSCLC via direct oncolysis, tumor immunosuppression reversal, and host anti-tumor immune response stimulation. Methods: To determine the relative susceptibility of selected NSCLC cell lines to RV, S and combination therapy, a WST-1 cell viability assay was conducted. Four human NSCLC cell lines A549, H460, H1299, and H1975 and the mouse Lewis lung carcinoma (LLC) cell line LL2 were tested, and the combination index (CI) was calculated using Calcusyn software to determine in vitro synergy. The LLC syngeneic C57BL/6 immunocompetent mouse model bearing LL2 cells in the right hind-flank was treated with PBS, S, RV, UV-inactivated RV (DV), or combination therapies (N = 6 mice/group). Mice were sacrificed on day 27. Spleens, blood, and tumors were harvested for analysis. Splenocytes were subject to flow cytometry CD11b and Gr-1 detection for MDSC quantification. Splenocyte CD8+ enrichment was performed, co-cultured with LL2, RV, DV, S or combinations and subject to IFN-γ quantification via ELISA to determine immune recognition. Results: Treatment resulted in efficacy and synergy of RV/S in A549, H460, and H1299 in vitro as indicated by CI values < 1. LL2 showed additive to synergistic responses at varying treatment concentrations. When LLC tumors in a C57BL/6 immunocompetent murine model were treated with the combination of RV/S, a significant tumor reduction occurred compared to monotherapies and untreated controls (non-overlapping 95% confidence intervals). Furthermore, an increase in anti-tumor immunity stimulating IFN-γ was detected in mice samples upon RV and combination exposure at 0.13μg/mL and 0.09μg/mL compared to PBS treated mice at 0μg/mL. Both S and RV/S treated mice had a significant splenocyte MDSC reduction. These results indicate that Reovirus and Sunitinib combination therapy holds promise as a novel treatment strategy for NSCLC. Citation Format: Jianrui Liu, Jason Spurrel, Zhong Qiao Shi, WenQian Chen, Don G. Morris. Oncolytic viral therapy with immune modulation is an effective novel treatment strategy for non-small cell lung cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5355. doi:10.1158/1538-7445.AM2015-5355
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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) | 0.002 | 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".