I have a question I would like to pose to the community. I have recently received access to a bench-top ion torrent DNA sequencer. Our idea is to use this machine to sequence the DNA from patient’s tumors in order to guide treatment options. My job is to identify a list of all currently used anti-neoplastic drugs along with their known targets (i.e., specific genes and mutations) and accession numbers. I would like to put these data in a table in which each row corresponds to a different drug.

For example, a row in the table might read (column names are indicated in brackets): [disease] breast cancer, [drug] trastuzumab, [drug target] HER2/neu receptor, [gene] ERBB2, [location] chr17:37844393-37884915, [mutation type] amplification, [accession number] ENSG00000141736. The pathologists would then be able to use this database in order to select appropriate genes for sequencing whenever they receive a tumor specimen. If the patient’s tumor had an amplified ERBB2 gene, they could be given trastuzumab.

Currently our study is in pre-planning stages (i.e., we won’t actually be testing this on patients any time soon). I would appreciate it if anyone could give me on advice on how to go about creating such a database. I am aware of online databases including COSMIC, Sanger's Cancer Gene Census, and the Potential Drug Target Database (PDTD), but they don’t have everything that I’m looking for. I am familiar with R and could use it to combine data from multiple sources if necessary. If anyone else has comments or suggestions for further reading that would also be appreciated. Thanks!

Note: This question has also been posed on a Research Gate forum: http://www.researchgate.net/topic/Cancer_Biology/post/Looking_for_a_cancer_drug_target_database_to_guide_sequencing_of_patient_tumor_DNA

  • $\begingroup$ Would this question be better suited to healthcare-it? $\endgroup$
    – Rory M
    Commented Jan 24, 2012 at 14:07
  • 1
    $\begingroup$ Have you tried looking at the NCI-60? Here's a (dated) paper but might serve as a good starting point: discover.nci.nih.gov/nature2000/paper/nature_v26_3_mar_2000.pdf $\endgroup$
    – jp89
    Commented Jan 24, 2012 at 19:18
  • $\begingroup$ @RoryM I don't thinkg so, this is either our project or biostart.SE, rather the ours. $\endgroup$ Commented Jan 24, 2012 at 20:43
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    $\begingroup$ This ma be a good start pharmgkb.org. I think biostar may be able to give you a good answer. $\endgroup$
    – bobthejoe
    Commented Jan 24, 2012 at 21:52
  • $\begingroup$ Is doing whole exome sequencing out of the question for your upcoming project? I feel like it'd crank up the chances of success for your project, and ... actually ... the exome isn't all that big, right? $\endgroup$ Commented Jan 25, 2012 at 19:23

2 Answers 2


Not sure if the exome sequencing is the way to go for this kind of tasks, especially if you have an idea of the mutations you might be looking for. Current arrays are pretty performant and are much more rapid and cheap.

For the data, you might consider having a look on The Cancer Genome Atlas. Otherwise Biological Networks might provide you the API for you if you aren't afraid of doing a little bit of Java to interface it with R.


There are no perfect resources for this information in the public domain quite yet. However, there are three that are making really good progress. The first is mycancergenome at Vanderbilt. They were one of the first resources to put this type of information on the web. They tend to be pretty stringent in the level of evidence and type of aberrations that go on the Web site. However, I am not sure if you can programmatically access there information. The second resource, is the https://pct.mdanderson.org developed at MD Anderson. This is really good resources however, it is only for a handful of genes, but albeit some of the most frequently mutated. I don't see a programmatic access to the information. The third resource and most promising for the community is the CIVIC database: https://civic.genome.wustl.edu/#/home. This is crowdsourcing resource that is set up to aggregate cancer associated mutation to drugs, phenotypes, and outcomes. I highly recommend this site and encourage not only consumption of data but also to engage in comments. As someone that has been doing drug to genome matching a couple pieces of advice. DNA based alterations are the best evidence to use for matching. If you do RNA be a little bit more cautious of matching based on 'is a target' mechanism. So rank DNA based changes above RNA. Additionally, just start with drug targets as place holders but try to acquire information on alteration as it relates to drug response. So alterations in a drug target could be (and often) are passengers and not drivers and in this case will not respond to drug. Frequency in other tumors and mutations in important domains are good guides for triaging variants like this. Also, the information on the disease and the mutations is important. If a drug variant mutation is observed in one disease there maybe mechanisms intrinsic to another tumor type that would preclude acting on drug to variant association in the different tumor type. Lastly, drug mechanism is something to take into account. Certain drugs that hit the same target do so but in different mechanisms that may preclude them being given in the context of particular mutations. Good luck in your endeavors.


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