PD42-06 BOOSTING THE COMBINATION OF RADIATION AND ANTI-PD-L1 WITH STING AGONIST IN A PRECLINICAL MUSCLE INVASIVE BLADDER CANCER MURINE MODEL
Bibliographic record
Abstract
You have accessJournal of UrologyBladder Cancer: Basic Research & Pathophysiology II (PD42)1 Sep 2021PD42-06 BOOSTING THE COMBINATION OF RADIATION AND ANTI-PD-L1 WITH STING AGONIST IN A PRECLINICAL MUSCLE INVASIVE BLADDER CANCER MURINE MODEL Gautier Marcq, Jiamin Huang, Côme Tholomier, Surashri Shinde-Jadhav, Ronald Kool, Rodrigo Skowronski, Fadi Brimo, Jose Joao Mansure, and Wassim Kassouf Gautier MarcqGautier Marcq More articles by this author , Jiamin HuangJiamin Huang More articles by this author , Côme TholomierCôme Tholomier More articles by this author , Surashri Shinde-JadhavSurashri Shinde-Jadhav More articles by this author , Ronald KoolRonald Kool More articles by this author , Rodrigo SkowronskiRodrigo Skowronski More articles by this author , Fadi BrimoFadi Brimo More articles by this author , Jose Joao MansureJose Joao Mansure More articles by this author , and Wassim KassoufWassim Kassouf More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002056.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: To evaluate the efficacy of a STING agonist in the combination of radiation therapy (RT) with anti-PDL-1 in preclinical muscle invasive bladder cancer (MIBC) murine model. METHODS: A syngeneic bladder cancer model was used. MB49 murine MIBC cell line was injected (5.105 cells/200 uL) subcutaneously (s.c.) in both flanks of C57BL/6. When tumors reached 0.15 cm3, mice were randomly assigned to each of the following 8 groups (10 mice⁄group): (1) Control; (2) STING agonist alone; (3) RT alone (2 fractions of 6.25Gy in the right flank); (4) RT + STING agonist; (5) anti-PD-L1 alone; (6) RT + anti-PD-L1; (7) STING agonist + anti-PD-L1; (8) RT + STING agonist + anti-PD-L1. Tumor volume of 1.5 cm3 was used as the primary endpoint. Mice were monitored daily for 4 weeks. ANOVA for repeated measures was used to assess differences in tumor growth. Tumor microenvironment (TME) was analyzed by flow cytometry. RESULTS: The STING agonist alone showed significantly better anti-tumor activity than the control group (p<0.001). The addition of STING agonist to RT improved response to treatment (p=0.0007). Finally, the addition of STING agonist to combined RT with anti-PD-L1also improved the response to treatment (p<0.0001). Concerning the non-irradiated abscopal tumor site, STING agonist enhanced the anti-tumor response of both RT and RT + anti-PD-L1 (p<0.001) treatments with maximal response seen in the triple combination (RT, STING agonist, and anti-PD-L1, p<0.001). TME analysis revealed higher CD8 infiltration in the RT arms including a STING agonist and a better immunosuppressive profile (Figure). CONCLUSIONS: Activation of STING pathway boosts the efficacy of combined RT and anti-PD-L1 within the irradiated and the abscopal sites. Source of Funding: Canadian Institutes of Health Research PD42-06 © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e726-e726 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Gautier Marcq More articles by this author Jiamin Huang More articles by this author Côme Tholomier More articles by this author Surashri Shinde-Jadhav More articles by this author Ronald Kool More articles by this author Rodrigo Skowronski More articles by this author Fadi Brimo More articles by this author Jose Joao Mansure More articles by this author Wassim Kassouf More articles by this author Expand All Advertisement Loading ...
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".