One of the key reasons why oncology clinical trials fail is the limited predictive value of traditional preclinical models, such as long-passaged cell lines or CDX (cell line-derived xenografts). These systems often fail to recapitulate the complexity of the human tumor microenvironment — a limitation that becomes particularly critical in hematologic malignancies like B-cell lymphomas.
In contrast, patient-derived xenograft (PDX) models and genetically engineered mouse models (GEMMs) retain the heterogeneity, clonal evolution, and molecular signatures of primary tumors. However, their use in B-cell lymphoma remains underutilized — especially when it comes to capturing the immune tumor microenvironment (TME).
B-cell lymphomas, which account for ~85% of non-Hodgkin lymphomas, include a wide spectrum of diseases — from indolent subtypes to aggressive forms like DLBCL, mantle cell lymphoma, and follicular lymphoma. Many subtypes, such as ABC-DLBCL, remain incurable under current standard-of-care therapies, with high relapse rates and resistance.
Recent studies underscore the importance of the immune TME in DLBCL — showing that distinct immune states, from immune deserts to inflamed profiles, significantly influence disease progression and response to treatment.
At MIMO Biosciences, we are building a platform of immune-relevant GEMMs models for B-cell lymphomas. Our aim is to deliver preclinical tools that mirror both the tumor’s genetic complexity and its immune context, enabling more accurate translational studies and supporting the development of targeted, immune-informed therapies.
It’s time to move beyond flat models — and bring clinical reality into the lab.
