MP61-01 ANTITUMOR ACTIVITY OF SULFATED ANGIOGENIC FRAGMENTS: A NOVEL HYALURONIDASE INHIBITOR
Bibliographic record
Abstract
You have accessJournal of UrologyBladder Cancer: Basic Research & Pathophysiology II1 Apr 2016MP61-01 ANTITUMOR ACTIVITY OF SULFATED ANGIOGENIC FRAGMENTS: A NOVEL HYALURONIDASE INHIBITOR Martin J.P. Hennig, Soum D. Lokeshwar, Shenelle N. Wilson, Andre R. Jordan, Juan Chipollini, Marie C. Hupe, Mario W. Kramer, Luis E. Lopez, Axel S. Merseburger, and Vinata B. Lokeshwar Martin J.P. HennigMartin J.P. Hennig More articles by this author , Soum D. LokeshwarSoum D. Lokeshwar More articles by this author , Shenelle N. WilsonShenelle N. Wilson More articles by this author , Andre R. JordanAndre R. Jordan More articles by this author , Juan ChipolliniJuan Chipollini More articles by this author , Marie C. HupeMarie C. Hupe More articles by this author , Mario W. KramerMario W. Kramer More articles by this author , Luis E. LopezLuis E. Lopez More articles by this author , Axel S. MerseburgerAxel S. Merseburger More articles by this author , and Vinata B. LokeshwarVinata B. Lokeshwar More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.875AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES HYAL1 hyaluronidase (HAase) degrades hyaluronic acid (HA) into angiogenic fragments (AGF) that support tumor growth and metastasis by promoting epithelial mesenchymal transition (EMT). Urinary HAase levels are sensitive markers for high-grade bladder cancer (BCa) and HYAL1 expression correlates with metastasis. We evaluated if sulfated AGF (sHA-F) inhibits HAase activity and is a potential targeted therapeutic agent in preclinical models of BCa. METHODS mRNA expression of EMT genes (β-catenin, Twist and Snail) was measured by q-PCR in 66 bladder tissue specimens (27 normal; 39 tumor); follow-up: 26±4.3 months; median 20 months. Effect of sHA-F (0-40 μg/ml) on cell proliferation, apoptosis, chemotactic motility and invasion was examined in HYAL1 expressing (253J-Lung, HT1376, UMUC-3), non-expressing (T24, RT4, TCCsUP) and normal bladder (Urotsa, SV-HUC1) cells. Effect of sHA-F on apoptosis, HA receptor (CD44, RHAMM), EMT markers was evaluated by q-PCR, immunoblotting, proximal ligation and PI-3K activity assays. Mechanism of action was examined by AGF addition and mAkt transfection. Antitumor activity of sHA-F (25-50 mg/kg) was tested in a 253J-Lung xenograft model by i.p. injection. RESULTS Snail and Twist levels were elevated in BCa tissues as compared to normal bladder (P<0.001) and correlated with metastasis (2-fold increase; P=0.028). β-catenin levels correlated with survival (χ2=4.3, p=0.038). At IC50 for HAase activity inhibition (5-20 μg/ml), sHA-F inhibited proliferation, motility and invasion only in HYAL1 expressing BCa cells (p<0.002). sHA-F caused a dose dependent 3-fold induction in apoptosis by activating caspases (3,8,9), PARP-cleavage and death receptor signaling. sHA-F downregulated transcript and/or protein levels of CD44, RHAMM, pAkt, β-catenin, pβ-catenin (S552), snail and twist by 2-5-fold, but increased pβcatenin ((T41/S45), pGSK-3α/β and E-cadherin levels. sHA-F inhibited CD44/PI-3K complex formation and PI-3K activity. AGF addition or mAkt overexpression attenuated sHA-F effects, but HYAL-1 expression sensitized RT4 cells to sHA-F. In 253J-L xenograft, 2-week treatment of sHA-F starting on the day of tumor cell injection or in palpable tumors significantly inhibited tumor growth (p<0.001) by abrogating angiogenesis and HA receptor-PI-3K/Akt signaling. CONCLUSIONS This is the first investigation into the therapeutic activity of sHA-F in any system (cancer or non-cancer) and demonstrates it as a potentital targeted non-toxic agent for BCa treatment. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e803 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Martin J.P. Hennig More articles by this author Soum D. Lokeshwar More articles by this author Shenelle N. Wilson More articles by this author Andre R. Jordan More articles by this author Juan Chipollini More articles by this author Marie C. Hupe More articles by this author Mario W. Kramer More articles by this author Luis E. Lopez More articles by this author Axel S. Merseburger More articles by this author Vinata B. Lokeshwar More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".