Champions' premier DLBCL Ex Vivo Screen provides a robust bank of pretreated and treatment-naïve PDX models to assess breakthrough diffuse large B-cell lymphoma therapeutics
Now Enrolling until March 27th, 2026
Clinically Relevant DLBCL Models with Key Mutations & Molecular Characteristics
Evaluate your Diffuse Large B-Cell Lymphoma drug candidates on our advanced ex vivo screening platform, which features nine pretreated and treatment-naïve patient-derived xenograft (PDX) DLBCL models. Our carefully curated collection includes models treated with advanced cancer therapies like CAR-T, RICE, and R-CHOP.
Clinically Relevant Models
Living bank of 9 clinically relevant PDX models with key mutations and molecular characteristics.
Pretreated PDX Models
Established from tumor biopsies from patients pretreated with latest generation therapies
Multi-omic Characterization
Clinical annotations coupled with molecular datasets and in vivo responses
Champions' leading DLBCL Ex Vivo Screen showcases 9 PDX models, each reflecting key clinical characteristics, mutations, and pretreatment history.
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Includes Diffuse Large B-Cell Lymphoma models pretreated with advanced cancer therapies such as CAR-T, RICE, and R-CHOP.
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Complete model characterization (clinical data, NGS analysis, proteomics, phospho-proteomics, and more).
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Several endpoint data options available including Flow Cytometry and IHC to quantify target expression and critical phenotypical changes (available upon request).
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Terminal tumor collections for target validation is available upon request (Snap Frozen/FFPE)
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No minimum to enroll & 50% off a Standard of Care Agent arm (selected by Champions)

Treatment responses of three DLBCL PDX models, CTG-3064, CTG-3793, and CTG-3803, to standards-of-care.

DLBCL Ex Vivo Screen PDX Models Mutation Clustering
Utilizing Lumin, our Clustering Heatmap tool is a web-based application designed to visually interpret and analyze complex data through interactive and shareable hierarchically clustered heatmaps.
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Visualize expression signatures and the prevalence of driver mutations in each PDX model.
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Gain profound insights by exploring RNA-seq, WES, proteomics, and phospho-proteomics datasets.
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Harness quality data from extensive datasets to drive informed decision-making.