Abstract 1121: Identification and <i>in vivo</i> validation of unique anti-oncogenic properties and mechanisms involving protein kinase signalling and autophagy mediated by the investigational new agent PV-10
Bibliographic record
Abstract
Abstract Introduction: PV-10 (10% Rose Bengal) is a small molecule agent previously shown to have potent immunotherapeutic and anti-tumor activities against a number of tumors including metastatic melanoma and refractory neuroblastoma, and is currently undergoing clinical testing as a single-agent for refractory metastatic neuroendocrine cancer (NCT02693067) and in combination with checkpoint inhibitors for metastatic melanoma (NCT02557321) and metastatic uveal melanoma (NCT00986661). We have previously determined that PV-10 induces cell death at pharmacologically relevant concentrations in a panel of phenotypically diverse adult solid tumor cell lines. However, the molecular consequences of this phenomenon have not yet been fully elucidated. In this study, we investigated the target validation and modulation of PV-10 on protein kinase signalling and their associated impact on specific oncogenic pathways in these tumor cells. Methods: A panel of human tumor cell lines derived from breast (MCF-7, T-47D, MDA-MB-231), colorectal (LoVo, T-84), head and neck (CAL-27, Detroit-562, FaDu, UM-SCC-1), and testicular (NCC-IT, NTERA-2, TCAM-2) tissues were treated with PV-10 and their cytotoxic and pro-apoptotic effects were described. Protein kinase profiling was performed using the human phospho-kinase antibody array (#ARY003C, R&D Systems). Western blotting was used to investigate autophagic markers and protein kinase activity. Cell migration inhibition was determined by wound healing assay following treatment with a sublethal dose of PV-10 or pan-WNK inhibitor WNK463. Tumor xenograft studies were carried out according to established protocols. Results: Treatment with PV-10 leads to consistent inhibition of WNK lysine deficient protein kinase 1 (WNK1) phosphorylation based on protein kinase profiling of drug-treated cancer cells when compared with vehicle-treated cells. WNK1 has been implicated as an inhibitor of autophagy and a promoter of cell migration and invasion in several cancers. Western blot analysis showed that PV-10 leads to the downregulation of SQSTM1/p62, upregulation of Beclin-1, and conversion of LC3B-I into LC3B-II, indicating activation of autophagy. PV-10 was also found to significantly inhibit the migration of cancer cells similar to the inhibitor WNK463. Significant tumor regression and target modulation was noted in tumor-bearing animals treated with PV-10. Conclusion: In addition to the known activity of PV-10 to mediate tumor-specific immune responses and cytotoxic effects in neoplasms, we have identified novel therapeutic targets for PV-10, such as WNK1, and provide new insights into its effect on autophagy and metastasis. Our data provide essential mechanism-based evidence and biomarkers of activity to formulate effective PV-10 backbone clinical studies in the future. Citation Format: Son Tran, Satbir Thakur, Mohit Jain, Chunfen Zhang, Aru Narendran. Identification and in vivo validation of unique anti-oncogenic properties and mechanisms involving protein kinase signalling and autophagy mediated by the investigational new agent PV-10 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1121.
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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 | 0.001 |
| 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".