Abstract 1061: Increasing potency of anticancer drugs through Sortilin receptor-mediated cancer therapy: A new targeted approach for the treatment of ovarian cancer
Bibliographic record
Abstract
Abstract Rational : The development of personalized therapies against ovarian cancer remains highly challenging even in current modern oncology. One strategy to achieve greater selectivity and better anticancer drug delivery into cancer cells is to conjugate cytotoxic agents to peptide ligands that selectively target receptors abundantly and/or exclusively expressed in these tumor cells. Increased expression of Sortilin (SORT1), a scavenging receptor also known as neurotensin receptor-3, has been observed in invasive ovarian cancer biopsies. In addition, increased SORT1 expression correlated with breast tumor grades. Procedures : In light of these observations, we developed a peptide conjugation platform to target SORT1 and to increase cell selectivity and internalization of anticancer agents. As a proof-of-concept, doxorubicin and docetaxel were conjugated to a Sortilin-binding proprietary peptide (TH19P01). Results : In vitro, significant intracellular delivery of the doxorubicin-TH19P01 conjugate (TH1904) and docetaxel-TH19P01 conjugate (TH1902) were observed in a ES-2 ovarian cancer cell line model while preserving efficient doxorubicin or docetaxel cytotoxic mechanism. TH19P01 and drug conjugate uptake in ovarian cancer cells assays were reduced when SORT1 expression was specifically silenced using siRNA or upon competition with the SORT1 ligands neurotensin and progranulin. Importantly, drug-TH19P01 conjugates were found to bypass the P-glycoprotein (P-gp) efflux pump in MDCK-MDR1 cells overexpressing P-gp as the uptake of conjugates was unaffected by the P-gp inhibitor Cyclosporin A. In vivo, TH1904 and TH1902 caused a more potent inhibition of human ovarian tumor xenografts grown in mice and were better tolerated than their unconjugated parent drugs. Conclusion : These results strongly support future clinical development of this targeted technology platform to generate novel personalized therapeutics with specific indications in Sortilin-positive cancers. Citation Format: Michel Demeule, Jean-Christophe Currie, Cyndia Charfi, Alain Larocque, Alain Zgheib, Sophie Kozelko, Richard Béliveau, Christian Marsolais, Borhane Annabi. Increasing potency of anticancer drugs through Sortilin receptor-mediated cancer therapy: A new targeted approach for the treatment of ovarian cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1061.
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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".