Engineering a juxtamembrane-targeting CAR T-cell against mesothelin: a novel binder resilient to shed antigen for enhanced efficacy against ovarian and pancreatic cancer - Takeaways - MDSpire

Engineering a juxtamembrane-targeting CAR T-cell against mesothelin: a novel binder resilient to shed antigen for enhanced efficacy against ovarian and pancreatic cancer

  • By

  • John Scholler

  • Ai Song

  • Mansi Deshmukh

  • Decheng Song

  • Khatuna Gabunia

  • Mei Ji

  • Shimin Liu

  • Ting-Jia Fan

  • Gayathri Gulendran

  • Carl H. June

  • Don L. Siegel

  • July 1, 2026

  • 0 min

Share

  • 1

    Mesothelin is a promising target for CAR T therapy in ovarian and pancreatic cancers but faces challenges due to antigen shedding.

  • 2

    Shed mesothelin can act as a decoy, reducing the efficacy of CAR T cells targeting membrane distal regions of mesothelin.

  • 3

    Novel CAR T cells targeting the juxtamembrane region of mesothelin, specifically CAR 422, show comparable cytotoxicity to existing therapies.

  • 4

    CAR 422 demonstrated effectiveness against tumor cells resistant to conventional anti-mesothelin CAR T cells with reduced off-target toxicity.

  • 5

    The study utilized a canine phage display library to isolate scFvs targeting the mesothelin stump, enhancing therapeutic potential.

Original Source(s)

Engineering a juxtamembrane-targeting CAR T-cell against mesothelin: a novel binder resilient to shed antigen for enhanced efficacy against ovarian and pancreatic cancer

  • by John Scholler, Ai Song, Mansi Deshmukh, Decheng Song, Khatuna Gabunia, Mei Ji, Shimin Liu, Ting-Jia Fan, Gayathri Gulendran, Carl H. June, Don L. Siegel

  • July 1, 2026

Related Content

Bystander and abscopal effects of boron neutron capture therapy: radiobiological mechanisms and immunotherapeutic strategies

Development and application of a prognostic model based on radiomics and artificial intelligence for patients with lung adenocarcinoma brain metastasis

Predicting overall survival in synchronous metastatic nasopharyngeal carcinoma using a stacking ensemble machine learning model: a multicenter retrospective study