Abstract 5146: Increasing penetration of anticancer drugs through sortilin receptor-mediated cancer therapy: A new targeted and personalized Approach in the treatment of ovarian cancer
Bibliographic record
Abstract
Abstract The development of personalized therapies against ovarian cancer remains highly challenging in current modern oncology. One way to achieve greater selectivity and better anticancer drug delivery into cancer cells is to conjugate cytotoxic agents to specific peptide ligands that will selectively target receptors abundantly and/or exclusively expressed on these cells. Increased expression of Sortilin, a scavenging receptor, has been clinically observed in several human cancers including breast, prostate, colon, pancreas, skin, and pituitary. In particular, Sortilin has also been reported to be overexpressed in ovarian cancers as compared to non-malignant ovarian tissue. In light of this, we developed a peptide conjugation platform using a new Sortilin receptor-mediated vectorization strategy to increase cell targeting selectivity and cell delivery efficacy of anticancer agents. As a proof-of-concept, Doxorubicin was conjugated to peptide sequences, termed Katana peptides (KA). In vitro, significantly increased Sortilin mediated intracellular delivery of the Doxorubicin-Katana peptide conjugate (DoxKA) was observed in SKOV3 and ES-2 ovarian cancer cell line models, with conserved efficient Doxorubicin cytotoxic mechanism. Uptake of DoxKA in ES-2 ovarian cancer cells was reduced when Sortilin expression was specifically silenced by using siRNA or upon competition with the Sortilin ligands neurotensin and progranulin. In addition, DoxKA was found to bypass the P-glycoprotein (P-gp) efflux pump since, in contrast to Doxorubicin uptake in MDCK-MDR1 cells overexpressing the P-gp efflux pump, the uptake of DoxKA was unaffected by the P-gp inhibitor Cyclosporin A. In vivo, DoxKA was better tolerated and caused a more potent inhibition of human ovarian tumor xenografts growth than did Doxorubicin alone. These results provide strong evidence for the potential of this drug development platform in the generation of novel personalized therapeutics with specific targeting of SORT1-positive cancer cells. Citation Format: Michel Demeule, Jean-Christophe Curie, Alain Larocque, Cyndia Charfi, Richard Béliveau, Claude Vezeau, Borhane Annabi. Increasing penetration of anticancer drugs through sortilin receptor-mediated cancer therapy: A new targeted and personalized Approach in the treatment of ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5146. doi:10.1158/1538-7445.AM2017-5146
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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) | 0.002 | 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".