Refining adjuvant treatment in endometrial cancer based on molecular features: the RAINBO clinical trial program

    Abstract

    Insights

    The RAINBO program uses molecular classification to tailor endometrial cancer treatments, aiming to improve cure rates and reduce side effects. This clinical trial platform investigates four distinct molecularly-guided adjuvant strategies for better patient outcomes.

    Area of Science:

    • Gynecologic Oncology
    • Translational Research
    • Clinical Trials

    Background:

    • Endometrial cancer molecular classification is now part of WHO diagnostics and European guidelines.
    • This classification guides more effective and less toxic adjuvant treatment strategies.
    • The RAINBO program builds upon this molecular understanding.

    Purpose of the Study:

    • To investigate four molecular class-directed adjuvant treatment strategies for endometrial cancer.
    • To increase cure rates by adding novel targeted therapies.
    • To safely reduce toxicity and improve quality of life through treatment de-escalation.

    Main Methods:

    • The RAINBO program is a platform of four international clinical trials (p53abn-RED, MMRd-GREEN, NSMP-ORANGE, POLEmut-BLUE).
    • Trials compare standard treatments with molecularly-guided strategies, including targeted therapies and de-escalation.
    • An overarching translational research program will analyze pooled data and tumor material.

    Main Results:

    • Primary endpoints include recurrence-free survival at 3 years (p53abn-RED, MMRd-GREEN, NSMP-ORANGE) and pelvic recurrence at 3 years (POLEmut-BLUE).
    • Total sample size across the four trials is approximately 1600 patients.
    • Main results are anticipated from 2028 onwards.

    Conclusions:

    • Molecular-directed adjuvant treatment strategies hold the potential to improve clinical outcomes in endometrial cancer.
    • These strategies aim to reduce toxicity from unwarranted therapies.
    • The RAINBO program will advance knowledge of predictive markers for improved prognostication and treatment allocation.

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