Informing cancer drug development
In a recent live, interactive webinar series, Compendia Bioscience™ Translational Bioinformatics Services presented the innovative use of big data to inform cancer drug development and transform cancer clinical development. Advancing a drug development program can lead to more questions than answers. To more rapidly identify lead compounds and design successful clinical research trials, bioinformatics may help answer some critical questions.
Request the crizotinib case study
Our Compendia Bioscience™ Translational Bioinformatics Services team provides you with powerful tools to support your efforts in preclinical to companion diagnostic development.
Request this case study that includes a genomic survey of thousands of tumor samples subjected to full exome sequencing.
The use of transcriptional signatures for potential clinical trial population selection
Analysis of Oncomine Knowledgebase to identify NFE2L2 pathway as a novel therapeutic opportunity in multiple cancer types
A pan-cancer analysis of >90,000 samples identifies expanded clinical research opportunities for crizotinib
Dr. Mary Ellen Urick discussed the use of transcriptional signatures of response and resistance to support understanding mechanisms of action and building a competitive differentiation plan.
Dr. Khazanov discussed proof-of-concept identification of a potentially clinically relevant candidate driver gene using a novel systematic integrative analysis of multidimensional cancer genomic data.
Dr. Sean Eddy revealed the novel findings of this analysis, including recurrent ALK fusions in colorectal adenocarcinoma.
Download crizotinib poster
Expanded clinical research opportunities for crizotinib identified from an analysis of over 5,000 exomes.
Drawing on cancer exomes from the research domain
Drawing on cancer exomes from the research domain in the Oncomine Knowledgebase (the world’s largest curated compendium of cancer genomic information), our Compendia Bioscience™ Translational Bioinformatics Services team built algorithms to identify the likely key driver mutations in the cancer exome.