Abstract 4707: Pelareorep promotes the expression of a chemokine signature that predicts response to immunotherapy
Bibliographic record
Abstract
Abstract Introduction: It has been proposed that the presence of inflamed tumor phenotypes, characterized by the presence of infiltrating lymphocytes and the expression of specific chemokines and cytokines, can predict response to immunotherapy and result in better patient outcomes [1, 2]. We hypothesized that pelareorep, an immuno-oncolytic virus (IOV), may elicit predictive proinflammatory gene signatures in select cancer cell lines permissive to viral infection. Methods: Cell lines derived from non-small cell lung cancer (NSCLC, H522), colorectal cancer (CRC, SW-620), and hepatocellular carcinoma (HCC, SNU 387) were infected at an multiplicity of infection equal to 50. We examined changes in gene expression and conducted cell viability assays at 6, 12, and 18 hours post pelareorep infection (including a non-infected control). To monitor changes in gene expression we employed a custom 780-gene Pan Cancer Immune panel developed by nanoString Technologies and specifically monitored for changes in the expression of key interferon and NF-κB signalling genes, immune checkpoint ligands, and a 12-gene chemokine signature predictive of a positive response to immunotherapy identified by Messina et al. [2]. Results: All cell lines examined were susceptible to pelareorep induced cytopathic effect. Strikingly, principal component analysis revealed that the changes in gene expression were unique and different for each cell line. Of the cell lines examined, only HCC cells infected with pelareorep promoted an inflammatory signature, similar to the one used to predict response to immunotherapy in melanoma [2]. Conclusions: This study demonstrates that pelareorep can prime or promote a predictive inflamed tumor phenotype in HCC, which correlates with the innate response recently described in HCC- animal models treated with pelareorep [3]. The role of pelareorep in the treatment of hepatocellular carcinoma deserves further investigation, particularly in combination with other immunotherapies. References: [1] Gajewski, T.F., The Next Hurdle in Cancer Immunotherapy: Overcoming the Non-T-Cell-Inflamed Tumor Microenvironment. Semin Oncol, 2015. 42(4): p. 663-71. [2] Messina, J.L., et al., 12-Chemokine gene signature identifies lymph node-like structures in melanoma: potential for patient selection for immunotherapy? Sci Rep, 2012. 2: p. 765. [3] Samson, A., et al., Oncolytic reovirus as a combined antiviral and anti-tumour agent for the treatment of liver cancer. Gut, 2016. Citation Format: Grey A. Wilkinson, Aine Piar, Zoe Cesarz, Hue Tran, Romit Chakrabarty, Andres Gutierrez, Matt Coffey. Pelareorep promotes the expression of a chemokine signature that predicts response to immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4707.
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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) | 0.003 | 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".