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Scientists have created an online database of mutations that have been shown to drive cancers in preclinical or clinical research.
The database, called the Cancer Driver Log (CanDL), currently includes mutations in 62 genes, with hundreds of distinct variants across multiple cancers.
Sameek Roychowdhury, MD, PhD, of The Ohio State University in Columbus, and his colleauges described CanDL in the Journal of Molecular Diagnostics.
“CanDL is a database of gene mutations that have been functionally characterized or have been targeted clinically or preclinically with approved or investigational agents,” Dr Roychowdhury explained.
“Currently, pathology laboratories that sequence tumor tissue must manually research the scientific literature for individual mutations to determine whether they are considered a driver or a passenger to facilitate clinical interpretation.”
“CanDL expedites this time-consuming process by placing key information about known and possible driver mutations that might be effective targets for drug development at their fingertips.”
CanDL entries can be searched by gene or amino acid variants, and they can be downloaded for custom analyses.
The database also includes a mechanism for users to contribute novel driver mutations in open collaboration with the Roychowdhury lab. The team plans to update the database quarterly. 
Photo by Darren Baker
Scientists have created an online database of mutations that have been shown to drive cancers in preclinical or clinical research.
The database, called the Cancer Driver Log (CanDL), currently includes mutations in 62 genes, with hundreds of distinct variants across multiple cancers.
Sameek Roychowdhury, MD, PhD, of The Ohio State University in Columbus, and his colleauges described CanDL in the Journal of Molecular Diagnostics.
“CanDL is a database of gene mutations that have been functionally characterized or have been targeted clinically or preclinically with approved or investigational agents,” Dr Roychowdhury explained.
“Currently, pathology laboratories that sequence tumor tissue must manually research the scientific literature for individual mutations to determine whether they are considered a driver or a passenger to facilitate clinical interpretation.”
“CanDL expedites this time-consuming process by placing key information about known and possible driver mutations that might be effective targets for drug development at their fingertips.”
CanDL entries can be searched by gene or amino acid variants, and they can be downloaded for custom analyses.
The database also includes a mechanism for users to contribute novel driver mutations in open collaboration with the Roychowdhury lab. The team plans to update the database quarterly.
Photo by Darren Baker
Scientists have created an online database of mutations that have been shown to drive cancers in preclinical or clinical research.
The database, called the Cancer Driver Log (CanDL), currently includes mutations in 62 genes, with hundreds of distinct variants across multiple cancers.
Sameek Roychowdhury, MD, PhD, of The Ohio State University in Columbus, and his colleauges described CanDL in the Journal of Molecular Diagnostics.
“CanDL is a database of gene mutations that have been functionally characterized or have been targeted clinically or preclinically with approved or investigational agents,” Dr Roychowdhury explained.
“Currently, pathology laboratories that sequence tumor tissue must manually research the scientific literature for individual mutations to determine whether they are considered a driver or a passenger to facilitate clinical interpretation.”
“CanDL expedites this time-consuming process by placing key information about known and possible driver mutations that might be effective targets for drug development at their fingertips.”
CanDL entries can be searched by gene or amino acid variants, and they can be downloaded for custom analyses.
The database also includes a mechanism for users to contribute novel driver mutations in open collaboration with the Roychowdhury lab. The team plans to update the database quarterly.