A Cancer Genome Interpreter to identify driver and actionable alterations in tumors

The use of genomic information is becoming a key piece of the oncology toolkit to make informed decisions aimed to improve the management of the disease and increase the cost-effectiveness of available therapies. Although in recent years the relevance of many oncogenic alterations in malignant transformation has been identified and validated across cancer types, the relevance for cancer growth of most alterations in a patient’s tumor are still of uncertain significance and their usefulness to inform the most appropriate treatment is unclear. Furthermore, mounting experimental and clinical data on tumor alterations driving the disease and influencing the response to anti-cancer therapies is currently gathered across scattered and fragmented resources,  annotated with dissimilar approaches and with no easy framework to match the knowledge they store with the alterations observed in a patient’s tumor. These problems severely limit the value that the genomic information of a tumor individual provides beyond the well-known biomarkers of drug response.

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Our paper demonstrating that proteins bound to DNA impair Nucleotide Excision Repair published in Nature

I am very happy to announce that our manuscript describing the increased mutation rate in Transcription Factor Binding Sites (TFBS) in melanomas and lung tumors has been published in today’s issue of Nature. In the manuscript we demonstrate that this accumulation is due to the impairment of Nucleotide Excision Repair (NER) activity by proteins bound to DNA.


Radhakrishnan Sabarinathan, Loris Mularoni, Jordi Deu-Pons, Abel Gonzalez-Perez & Núria López-Bigas.Nucleotide excision repair is impaired by binding of transcription factors to DNA. Nature 532, 264–267 (14 April 2016) doi:10.1038/nature17661 


Exposure to UV radiation causes specific DNA lesions, which are recognised and repaired by Nucleotide Excision Repair (NER) machinery. Non-repaired lesions may result in mutations during DNA replication. Skin tumors, such as melanomas, carry large number of mutations resulting from these unrepaired DNA lesions. Our study shows that in the DNA regions bound by proteins, such as Transcription Factors (TF), the activity of NER machinery is strongly reduced leading to an increased mutation rate in those sites. By Iris Joval


The manuscript was first published as a preprint in bioRxiv on October 2015

As many researchers have experienced, the editorial and peer-review process may be very long. We submitted the manuscript of this article for the first time to Nature on 9th June 2015, which initially rejected it without peer-review. After various transfers from one journal to another and a first round of peer-review, it went back to Nature in October 2015 for a second round of revision. At that time we felt we couldn’t wait longer to make this finding publicly available to the research community. Many research groups are analyzing tumor whole-genomes with the objective to find non-coding driver mutations. For instance, we are working together with a group of international researchers as part of the ICGC-TCGA PanCancer Analysis of Whole Genomes Project to identify driver mutations in 2800 tumor whole-genomes. The increased mutation rate in TFBS due to faulty repair may easily be mistaken by driver mutation hotspots in promoters, and thus we felt that we had the responsibility to describe our finding as soon as possible to the community. At this point, we felt we couldn’t wait for the long review process, and we decided to publish the manuscript in bioRxiv on October 13th 2015. Since then it has had more than 1000 PDF downloads.

We are grateful to the team maintaining bioRxiv and also to Nature for considering manuscripts previously posted in these repositories.


Reproducible research

To facilitate the full reproducibility of our results, we have published the data and code to carry out all the analyses and results we report in the article. These are available at http://bg.upf.edu/tfbs, where all datasets used can be downladed, and it links to the git repository with the code https://bitbucket.org/bbglab/tfbsmutrate. A sample of the code to reproduce Figure 1A is shown in the image below.



Screen Shot 2016-04-13 at 21.57.52



ERC-consolidator granted to our lab to find noncoding cancer drivers

We are happy to announce that our lab has been awarded an European Research Council Consolidator Grant.

ERC-consolidator grants are designed “To fund top researchers of any nationality and age, with over 7 and up to 12 years of experience after PhD, and a scientific track record showing great promise. The sole evaluation criterion for this grants: scientific excellence of researcher and research proposal”

The project that received the ERC,  named “NONCODRIVERS” aims at identifying mutations involved in tumour development in non-coding regions. It is set to start in 2016 and to last for five years.

If you are interested in working in our lab stay tuned: we will open Postdoc positions soon to work on the ERC project.

Just have a look below at what we intend to do within the project in the coming five years.

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In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals novel targeting opportunities

in silico drug prescription

Diagram of in silico drug prescription method


We are pleased to announce the publication of our 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.


Targeted therapies exploit tumor vulnerabilities offered by specific cancer alterations. However, not all  genomic alterations are equally relevant for a given tumor individual. Tumors are more dependent on alterations directly involved in their development and maintenance, namely driver alterations. These represent good options to develop targeted therapies. In this paper we describe an in silico prescription approach to obtain the landscape of targeted drugs against cancer driver alterations, composed of three main steps.

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How to generate mutation distribution and frequency plots?

Let’s plot some mutations!

Mutations Needle PlotEasy, right? For our next iteration of the IntOGen database, we wanted to add figures that represent the mutation distribution across the protein sequence. And we found ourselves, just as others, in the situation, that we know that there are solutions, but none is available for us to incorporate in the web portal.

We would like to produce plots that describe the mutation frequency data, are aesthetically pleasing and easily understood. The Mutation Mapper at the cBioPortal does a great job already and provides a web service. Additionally we’d like to reflect different consequence types in the same position. Thus we decided to give the all-so-famous D3 (Data Driven Documents)  a go. It’s a blast to use and in a couple of days we had our first plots as a generic library.

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OncodriveROLE predicts activating and loss of function cancer driver genes

ONCODRIVESWe are pleased to announce that the Oncodrive methods family has a new member: OncodriveROLE, an approach to classify cancer drivers into loss of function and activating roles. OncodriveROLE joins and complements the previously developed methods that identify cancer driver genes from the list of somatic mutations in cohort of tumors (OncodriveCLUST and OncodriveFM).

Cancer driver genes come in two main flavors: those that contain driver alterations which cause the loss of function (LoF) of the gene product (for instance, in tumor suppressor genes like TP53 or CDKN2A), and those with driver alterations that increase or change the activity or function of the protein product, such as oncogenes like PIK3CA or BRAF.

Distinguishing between these two classes of driver genes is very important to understand tumorigenesis in patients and has profound implications for therapeutic decision making and for the development of targeted drugs.

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Molecular subtypes of human cancer

Cancers are typically classified depending on their tissue of origin. However, novel large-scale genomic studies are providing more detailed molecular characterizations of tumors, and thus bring about the possibility of a more accurate classification based on their molecular profiling. Recently, our group has participated in the pan-cancer integrated subtypes study, published online today in Cell, in which a molecular taxonomy of cancer has been addressed by using the comprehensive multi-platform assays provided by the TCGA consortium for 12 diverse cancer types. This study represents an unprecedented effort to classify cancer by refining the molecular portrait of human malignancies. Read the rest of this entry »

How to identify oncogenic driver mutations: a review of bioinformatics approaches

Screen Shot 2014-07-16 at 5.51.18 AMRecently, at least three articles have reviewed the ensemble of bioinformatics tools developed in the past few years to understand the alterations that plague the genome and transcriptome of tumor cells. The first one (in chronological terms), published in Nature Methods one year ago –co-authored by us within the ICGC mutations consequence and pathways working subgroup– focused on the annotation of cancer variants and the tools aimed at the identification of driver mutations and driver genes. The next two, appeared in Genome Medicine and Nature Reviews Genetics covered tools involved across the whole process of cancer mutations analysis, from the calling of somatic mutations to the analysis of significantly mutated pathways or gene modules within genes interactions networks.


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Mutual exclusion statistics and data events in Gitools

We’re pleased to announce another incremental release of Gitools, version 2.2. Amongst the many improvements (listed at the bottom of this post) we’d like to highlight the effort that we put into improving performance, specifically with genomic data: mutual exclusion and co-occurrence statistics coupled with a new feature called “data events” – which helps to get a quick grasp of the data.

Low expression events ordered by mutual exclusion

Low expression data events events ordered by mutual exclusion

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