Abstract C031: Ras processing by RRSP protease as a strategy to inhibit Ras-driven tumors
Bibliographic record
Abstract
Abstract RAS genes are the most frequently mutated oncogenes in human cancers encoding four highly-related proteins that are critical molecular hubs for cancer cell proliferation and survival. Although promising results have been recently reported in early trials for KRas G12C inhibitors, there are no FDA-approved drugs that are effective against Ras-driven cancers, and Ras is still considered one of the most elusive targets in cancer research. We discovered the Ras-Rap1-Specific Protease (RRSP) from bacterium Vibrio vulnificus. RRSP is a naturally occurring endopeptidase that site-specifically cleaves the major Ras isoforms (H, N and K) and the most common mutated Ras oncoproteins (G12V, G13D and Q61R) within the Switch I region. In order to bring RRSP into cancer cells, RRSP was fused to diphtheria toxin binding subunit B (DTB). DTB delivers RRSP into the cytosol of targeted cells via binding to its receptor hHB-EGF (human heparin-binding epidermal growth factor-like growth factor). KRAS mutations occur in up to 50% of colorectal cancer (CRC) and in approximately 2% of triple-negative breast cancer (TNBC). However, 50% of TNBC show Ras upregulation via mutation-independent activation mechanisms. We investigated the anticancer potential of RRSP in CRC HCT-116 KRAS G13D as well as TNBC MDA-MB-231 KRAS G13D and MDA-MB-436 KRAS WT cell lines. RRSP was successfully delivered via DTB into CRC and TNBC cancer cell lines as indicated by Ras cleavage and pERK1/2 dephosphorylation at concentrations as low as 10 pM. As a consequence of Ras processing, a strong decrease in cell viability and proliferation was observed in all cell lines tested as per crystal violet staining and CellTiter Glo assay. A catalytically-dead mutant of RRSP carrying an amino acid substitution in its active site (RRSP*-DTB) did not show Ras processing nor reduced cell viability. Interestingly, our in vivo xenograft data demonstrated that, unlike RRSP*-DTB, RRSP-DTB effectively inhibited TNBC MDA-MB-436 and MDA-MB-231 tumor growth with no obvious toxicity. Proof of RRSP-target engagement was confirmed by loss of pERK1/2 phosphorylation via immunohistochemical analysis of tumor sections. We also found that RRSP-DTB strongly reduced the size and viability of 3-dimensional CRC HCT-116 spheroids in a time and dose-dependent manner. Moreover, an independent experiment at Charles River showed significant inhibition of tumor growth in a CRC HCT-116 xenograft model. Altogether our results revealed that, by physically processing Ras, RRSP strongly inhibits tumor growth in subcutaneously implanted human CRC and TNBC Ras-dependent tumors bearing either mutant or wild-type KRAS. This work supports further development of RRSP as an anticancer therapeutic for a broad spectrum of Ras-driven tumors and has the potential to take anti-Ras therapy to the next level. Citation Format: Vania Vidimar, Greg L Beilhartz, Roman A Melnyk, Karla JF Satchell. Ras processing by RRSP protease as a strategy to inhibit Ras-driven tumors [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr C031. doi:10.1158/1535-7163.TARG-19-C031
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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) | 0.001 | 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".