The Biomedical Genomics group led by Núria López-Bigas at the Pompeu Fabra Unviersity have recently published a paper in Cancer Cell describing the landscape of anti-cancer targeted therapeutic opportunities across a cohort of patients of twenty eight of the most prevalent cancers. They first looked for all the driver mutations (mutations that ’cause’ the cancer) for each individual cancer, then collected information on all the existing therapeutic agents that target those mutations, and finally, combining both datasets, came up with anti-cancer targeted drugs that could potentially benefit each patient. You can read more about this paper on their blog post.
Coinciding with the publication of that paper, the lab has crafted a new IntOGen interface which presents the results of this analysis. You can see it and learn more about it here.
Cancer is generally caused by a combination of many specific mutations, called drivers. But cancer cells contain many other mutations that are not the cause of the cancer, but rather a consequence (passenger mutations). Also, high-throughput genome projects are identifying a huge number of somatic variants. Which ones are cancer-causing? How to distinguish the needle in the haystack?
A new computational method recently published in Genome Medicine by the research group led by Núria López-Bigas at the GRIB (UPF-IMIM), can help. Called transformed Functional Impact Score for Cancer (transFIC), it improves the assessment of the functional impact of tumor nonsynonymous single nucleotide variants (nsSNVs) by taking into account the baseline tolerance of genes to functional variants.
Other methods predicting the functional impact of cancer-causing somatic variants employ evolutionary information to assess the likely impact of an amino acid change on the structure or function of the altered protein. However, according to the authors, the ultimate effect of this amino acid change on the functioning of a cell depends on other factors as well, such as the particular role played by the altered protein in the cellular machinery. The more critical that role is, the less tolerant will the protein be to an amino acid change.
Their new method takes this feature into consideration, and has been shown to outperform previous ones. They distribute their new tool as a PERL script that users can download and use locally, and they have set up a web server which can be queried to obtain the transFIC of somatic cancer nsSNVs.
Gonzalez-Perez A, Deu-Pons J, Lopez-Bigas N. Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation. Genome Med. 2012 Nov 26;4(11):89
Does your research imply having to deal with a huge amount of high-throughput data? Are you worried about the interpretation of your Illumina sequencing data? Illumina’s Genome Analyzer (GA) and HiSeq instruments are currently the most widely used sequencing devices. If you use them or are thinking of using them, you might be interested in having a look at the latest paper coming from Heinz Himmelbauer and his colleagues at the CRG ultrasequencing unit and published in Genome Biology. Find out about the errors and biases they report to make sure your data analysis is of the highest quality!
Minoche AE, Dohm JC, Himmelbauer H. Evaluation of genomic high-throughput sequencing data generated on Illumina HiSeq and Genome Analyzer systems. Genome Biol. 2011 Nov 8;12(11):R112 [PDF]
The first keynote speaker at the XI Bioinformatics Symposium was Søren Brunak, director of the Centre for Biological Sequence Analysis (CBS) in Denmark. He gave an interesting overview on the need to integrate the very detailed molecular information we have with the phenotypic data we can get from the healthcare sector. He explained the best source of this type of data are the electronic patient records (EPR or EMR – for medical records), which are very well established in his country as well as other small European countries such as the Netherlands, but not at all in bigger countries such as France or Germany. Spain was halfway between both, but closer to the less advanced countries in terms of having standardized EMRs.
There are difficulties with these EMRs, not the least the barrier language amongst different countries. But there exists a unified medical language system (UMLS), a controlled vocabulary for the clinical disease descriptions which facilitates the comparison.
Brunak mentioned an interesting article by Elaine Mardis, “The $1,000 genome, the $100,000 analysis?”, which points out that even though we are quickly getting more sequence information at a lower price, the costs of analyzing it are still very high, and the task is difficult. Brunak believes the cost of these analyses will only go down if genotypic data is integrated with phenotypic information. So his take-home message was that we need to collect and analyse phenotypic data in a more fine-grained way, which will then make it easier to approach network biology from both the genotype and the phenotype data.
Report by Maruxa Martinez, Scientific Editor at the PRBB
SVGmap is a configurable image browser for experimental data, a new tool developed by the biomedical genomics group of the GRIB (UPF-IMIM) at the PRBB. According to the group “it is useful to create browsers for individualized high-quality images which change the color of some regions according to some values”. It has recently been published in Bioinformatics.
You can read more about it at Núria López-Bigas’ laboratory blog: http://bg.upf.edu/blog/