Computational Analytics

Champions Oncology is known for its well-characterized TumorGraft® models which include selected clinical data and a comprehensive profile of molecular data from Whole Exome Sequencing and RNA sequencing analyses. As a result, we have developed Lumin Bioinformatics, a dynamic analytic and visualization software that empowers cancer biologists to leverage the power of computational science in a convenient and sophisticated tool.  Access to Lumin Bioinformatics is provided with a yearly subscription, or open access to our TumorGraft® Database is available for querying for model selection.  This ensures confidence that you are selecting relevant models to run on study and can assess response data in relation to genomic profiling  We have also tested these models for sensitivity to many different reference compounds and standards of care.

The power of working with Champions Oncology is that you gain access to not only our TumorGraft® database and associated model characterization data, but you also gain access to our expert team of bioinformatic scientists who work to evaluate the data from your studies and look for key insights such as biomarkers of response/resistance that can be used to stratify clinical trial design and illustrate those insights in a publication-worthy manner.

PCA prostate

Principal Component Analysis (PCA) for gene expression across 18,000 genes demonstrating the close association of prostate PDX models developed from the same patient at different treatment stages (blue and red text) compared to unmatched prostate models (black text).



AF PDX to tumor

Comparable allele frequencies identified between mutations found in four PDX models and matched patient tumor samples.



HER2 mutation lollipop

Mutation lollipop diagram highlighting spectrum of mutations identified in ERBB2 across Champions PDX models.



HRD-PARPi response signature

Unsupervised hierarchical clustering of models based on a gene signature for PARP inhibitor response or resistance (Peng, G et al, Nat. Comm., 2014), highlighting potential responders and non-responders to PARP inhibition.

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