An evolved ribosome-inactivating protein targets and kills human melanoma cells in vitro and in vivo
Bibliographic record
Abstract
BACKGROUND: Few treatment options exist for patients with metastatic melanoma, resulting in poor prognosis. One standard treatment, dacarbazine (DTIC), shows low response rates ranging from 15 to 25 percent with an 8-month median survival time. The development of targeted therapeutics with novel mechanisms of action may improve patient outcome. Ribosome-inactivating proteins (RIPs) such as Shiga-like Toxin 1 (SLT-1) represent powerful scaffolds for developing selective anticancer agents. Here we report the discovery and properties of a single chain ribosome-inactivating protein (scRIP) derived from the cytotoxic A subunit of SLT-1 (SLT-1A), harboring the 7-amino acid peptide insertion IYSNKLM (termed SLT-1A IYSNKLM) allowing the toxin variant to selectively target and kill human melanoma cells. RESULTS: SLT-1A IYSNKLM was able to kill 7 of 8 human melanoma cell lines. This scRIP binds to 518-A2 human melanoma cells with a dissociation constant of 18 nM, resulting in the blockage of protein synthesis and apoptosis in such cells. Biodistribution and imaging studies of radiolabeled SLT-1A IYSNKLM administered intravenously into SCID mice bearing a human melanoma xenograft indicate that SLT-1AI YSNKLM readily accumulates at the tumor site as opposed to non-target tissues. Furthermore, the co-administration of SLT-1A IYSNKLM with DTIC resulted in tumor regression and greatly increased survival in this mouse xenograft model in comparison to DTIC or SLT-1A IYSNKLM treatment alone (115 day median survival versus 46 and 47 days respectively; P values < 0.001). SLT-1A IYSNKLM is stable in serum and its intravenous administration resulted in modest immune responses following repeated injections in CD1 mice. CONCLUSIONS: These results demonstrate that the evolution of a scRIP template can lead to the discovery of novel cancer cell-targeted compounds and in the case of SLT-1A IYSNKLM can specifically kill human melanoma cells in vitro and in vivo.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".