Cancer driver results

The analysis identified 459 genes with a combination of signals of positive selection in the pattern of somatic mutations, indicating that they drive tumorigenesis in these samples upon somatic mutations. All results presented in the paper can be now explored at www.intogen.org. The  cancer driver genes, their signals of positive selection and their mutational frequency can be viewed at three levels: 1) Global (all mutations & cancer types), 2) Cancer type and 3) Project. Similarly, details about a driver gene can be explored at the same three levels, since their mutational frequency and clusterization may depend on cancer type.

Reports

On each page for a gene, cancer type or a project you will find multiple reports, such as a cancers driver list, the mutational frequency of a gene or the distribution of mutations across the sequence of a cancer driver gene. Below, we present plots downloaded from the Breast Carcinoma view and the PIK3CA global view. The data on each report can be explored both through a plot and a table view, and the user can switch between both. The tables and plots are also downloadable as text files and .png files respectively.

Breast Carcinoma driver genes cloud

IntOGen PIK3CA mutation distribution plot

IntOGen PIK3CA mutation frequency plot

Search

You may search for a cancer type and gene of interest through the same search box. If you are looking for something specific, you may even specify keywords like ‘distribution plot’ or ‘driver table’. Take a look at the search examples on the initial search page.

Analysis

The cancer driver genes list used in the analysis of the user’s own data, has also been updated according to the new results.

We hope this new crafted web is of use to the community. Let us know your opinion.

• Quality control: we have included a report that provides information on how the mutations and genes travel through the different steps of the pipeline (see screenshot at the end of the post). With this report users can now monitor the number of mutations included in the input file, the number of those that were correctly parsed and correctly mapped to genome annotations (by Variant Effect Predictor). This helps to detect errors in the list of somatic mutations used as input. In addition the report also provides information on the number of genes analyzed by OncodriveFM and OncodriveCLUST, and the number of those that appear significant given different qvalue cutoffs. This information can be used to monitor if the run of the pipeline was correct and to fine tune the parameters, for instance the minimum number of mutations required for a gene to enter OncodriveFM and OncodriveCLUST analysis, or the qvalue cutoff to consider a gene as a candidate driver.

• MAF parser: We have corrected the parser of mutations from MAF files that was not working properly. This in turn allowed us to replace TCGA datasets by the original MAF files, thus correcting errors in mutations occurring in genes coding in the reverse strand.
• Parsing indels: We have fixed a problem with parsing some insertions in which a shift of one base position was introduced.
• Functional impact calculation: We have introduced slight changes on how functional impacts are calculated. In particular we now consider splice donor variant and splice acceptor variants as high impacting, similarly as STOP gains, STOP loss and frameshift.
• Stability: This new version also improves stability.

Note that we have re-run all the 4623 tumor genomes with the new version of the pipeline, and thus the results have changed slightly, mainly due to the correction of the errors mentioned above.

Related posts:

IntOGen-mutations: the analysis of cancer genomes published in Nature Methods

We have been interested for quite a while in the study of patterns of genomics alterations in cancer across tumor types. Thus a project like the TCGA Pan-Cancer provided a unique opportunity to apply our tools and expertise to a unique collection of data.

In the past few years we have developed computational methodologies to identify cancer drivers by analyzing the patterns of somatic mutations across tumors (i.e OncodriveFM and OncodriveCLUST) as well as tools to facilitate the visual exploration of multidimensional cancer genomics datasets (i.e. Gitools, IntOGen, see our review on this topic if you are interested in this), we now had the opportunity to apply those tools to TCGA Pan-Cancer data.

What does TCGA Pan-Cancer data consist of?

Integrated data set for comparing and contrasting multiple tumor types. Figure from Nature Genetics article. See details in NG.

The TCGA Pan-Cancer project assembled data from more than 3000 patients with primary tumors from different organs, covering 12 tumor types. In each of these tumors a number of omics technologies were applied to obtain complete genomics, transcriptomics, proteomics and epigenomics profiles of the tumors.

Collaboration and teleconferences

David Tamborero, Nuria Lopez-Bigas and Abel Gonzalez-Perez

The TCGA Pan-Cancer collaborative project works through regular teleconferences (usually on Thursdays at 2pm ET) with all the members of the consortium and collaborators. In each teleconference different groups present the results of their analyses. In our case, being in Barcelona, the time of the teleconference was quite inconvenient (8pm) for work-life balance, but alternating between David, Abel and myself we managed to attend most of the teleconferences and to present the progress of our work to other researchers several times.

Data and intermediate results generated by different groups are shared through the Synapse platform. There is a nice paper describing the use of Synapse for the collaborative work within TCGA Pan-Cancer (Omberg et al., Nature Genetics 45, 1125-1126).

Our Contribution

We tried, as much as we could, to use our tools and expertise to extract interesting knowledge from the valuable data generated by the TCGA consortium. In total we contributed to the project with 4 different results:

IntOGen-mutations

Authors of IntOGen-mutations. From left to right, Michael P. Schroeder, David Tamborero, Nuria Lopez-Bigas, Abel Gonzalez-Perez, Jordi Deu-Pons and Christian Perez-Llamas. Two more authors of IntOGen-mutations missing in the picture are Alba Jene-Sanz and Alberto Santos.

IntOGen-mutations is a web platform for cancer genomes interpretation. It not only analyses TCGA Pan-Cancer data but also additional datasets generated by other initiatives such as those included within the International Cancer Genome Consortium. In the current version users can retrieve driver mutations, genes and pathways acting on 4623 tumors covering 13 cancer sites. They are also able and to analyze newly sequenced tumor genomes and identify relevant mutations by putting them in the context of the accumulated knowledge. 

Probably the most interesting feature of IntOGen-mutations is that it provides a comprehensive view of cancer vulnerabilities across cancer types, which was not available before. Tumor re-sequencing projects usually report a list of cancer drivers identified with differing criteria and methodologies, which make it difficult to have a complete view of which genes are drivers in each cancer type. It is now possible to have this comprehensive view with IntOGen-mutations.

We have designed the IntOGen-mutations to be updated regularly and to be scalable to the analysis of much larger cohorts of tumors, so that we can keep up with the expected increase in the number of sequenced tumor genomes/exomes available. Thus with each update we will obtain a more complete view of cancer drivers across tumor types.

To know more about this project you can read this previous Blog Post.

Article: Abel Gonzalez-Perez, Christian Perez-Llamas, Jordi Deu-Pons, David Tamborero, Michael P Schroeder, Alba Jene-Sanz, Alberto Santos and Nuria Lopez-Bigas. IntOGen-mutations identifies cancer drivers across tumor types. Nature Methods, doi:10.1038/nmeth.2642 (2013)

TCGA data visualization using Interactive Heat-maps (Gitools)

One important challenge posed by the large and complex data generated by the TCGA Pan-Cancer project is how to provide access to researchers to explore it and extract useful knowledge from it. We have been working previously on this topic and we propose the use of Interactive Heat-maps (read more on that). We have prepared all TCGA data ready to be navigated with Gitools interactive heat-maps. See video below to learn how to use it.

Comprehensive identification of mutational cancer driver genes

One of the main advantages of analyzing the aggregated data of more than 3000 tumors across 12 tumor types is that it provides increased statistical power to distinguish driver mutations from passenger ones. In collaboration with other researchers of the Pan-Cancer project we have analyzed the mutational patterns of genes across tumors in the search of signals for positive selection that points to candidate cancer drivers. Integrating also additional data generated by the consortium has allowed us to obtain a reliable list of 291 mutational drivers acting in one or more of the 12 cancer types -accounting for 3,205 tumors. We have confirmed and extended the role of known cancer genes and we have identified novel candidates that complete the mutational landscape of these diseases. The article describing this work will be published next week. You can browse the results of this project in IntOGen (at http://www.intogen.org/tcga) and also using Gitools (at http://www.gitools.org/datasets). In addition the results are also available in Synapse (syn1962006).

Article: David Tamborero, Abel Gonzalez-Perez, Christian Perez-Llamas, Jordi Deu-Pons, Cyriac Kandoth, Jüri Reimand, Michael S. Lawrence, Gad Getz, Gary D. Bader, Li Ding i Nuria Lopez-Bigas. Comprehensive identification of mutational cancer driver genes across 12 tumor types, Nature Scientific Reports, 2n October, DOI: 10.1038/srep02650.

The mutational landscape of chromatin regulatory factors across 4623 tumor samples

Chromatin regulatory factors are emerging as important genes in cancer development and are regarded as interesting candidates for novel targets for cancer treatment. For this reason, and also due to previous interest in our group, we focused our effort to the study of the mutational landscape of these class of genes. For this we used all TCGA Pan-Cancer mutational data and additional datasets, summing up to 4623 tumors. You can read more on that in a recent post.

Article: Gonzalez-Perez A#, Jene-Sanz A# & Lopez-Bigas N. The mutational landscape of chromatin regulatory factors across 4623 tumor samples. Genome Biology 2013, 14:r106. # Equal contribution

What’s next?

The current state-of-the-art of the technology provides an unprecedented opportunity for the understanding of tumor biology. Most importantly, this should eventually lead to develop better treatments for the disease. At this moment, the bottleneck of oncogenomics is not to produce the data but to interpret it in order to retrieve useful knowledge. Initiatives as the Pan-Cancer project succesfully deal with the front-line of these analyses by sifting the important information from the huge amount of alterations that are observed in a tumor cell. Many challenges must be solved before all this information may improve the clinical management of cancer patients. Although many histories of success have been already incorporated to the clinical practice, now we are understanding the complexity of the disease and how many efforts are required to disentangle its mechanisms. It’s a long way to go, but we believe that we are walking in the good direction.

One of these novel hallmarks has to do with the alteration of general mechanisms of chromatin regulation and maintenance. It is now clear that these mechanisms become altered in one way or another across many tumor types, and that their alteration in principle could lead to the de-regulation of several cellular functions that promote tumorigenesis. With this in mind, we have examined the mutations that occur in chromatin regulatory factors (CRFs) across 4623 tumor samples representing 31 cancer genome re-sequencing projects from 13 anatomical sites. The results of this study have just been published in Genome Biology.

I want to highlight here the main findings of our study.

• CRFs are overrepresented amongst driver genes. From IntOGen-mutations, we obtained the list of putative driver genes across 4623 tumor samples. While 34 out of 183 CRFs (manually compiled from the literature) probably act as drivers in these tumors, the total number of drivers found in them amounts to 348 within 22696 human genes. This underlines the relevance of CRFs’ alteration in tumorigenesis in all tumor types with data in IntOGen-mutations.
  

  Word cloud of the 34 CRFs identified as candidate drivers across the 4623 tumors from 13 sites. The size of the word indicates the mutation frequency across all tumors.

• Multi-proetin complexes rather than individual CRFs drive carcinogenesis in these tumors. Several complexes of CRFs that act together exhibit both a clear bias towards the accumulation of high-impact mutations and a pattern of mutual exclusivity in the genes where those mutations occur within the samples of a tumor type.

CRFs within their context of functional interactions. Square nodes represent likely driver CRFs, circle nodes other CRFs within the catalog, and diamond nodes
represent linker genes. CRFs functions are color-coded, and genes in the same complex are grouped and circled.

• The outcome of CRFs’ mutations is possibly the de-regulation of the expression of broad genesets. The pattern of mutations of two CRFs across almost 1000 cancer cell lines correlates with the de-regulation of genes marked by H3K27me3, H3K9ac, as well as late replicating genes.
• The approach can be applied to other sets of drivers. For the first time –thanks to the IntOGen-mutations web discovery tool— it is possible to assess the likely involvement of groups of genes in tumorigenesis across more than 4500 samples. The process is straightforward and may shed light on the relevance of other cellular mechanisms, genesets or pathways in tumorigenesis (watch the video tutorial below to learn how to get information for a set of genes in IntOGen-mutations).

You can explore our results through the IntOGen-mutations web discovery tool and through Gitools interactive heatmaps. So, browse away!

Article:

Gonzalez-Perez A#, Jene-Sanz A# & Lopez-Bigas N. The mutational landscape of chromatin regulatory factors across 4623 tumor samples. Genome Biology 2013, 14:r106

# Equal contribution

It all started few years ago with the development of the first IntOGen (Nature Methods in 2010), which focuses on the analysis of genes and pathways affected by expression and copy number changes in tumors across projects and cancer types. We had decided to extend this analysis to tumor somatic mutations, following a similar concept. Therefore, we started by developing OncodriveFM algorithm to detect genes that accumulate functional mutations across tumor samples. To validate the algorithm we tested it on the glioblastoma and ovarian carcinoma datasets published by TCGA and on the chronic lymphocytic leukemia published by the ICGC. We were ready to continue analyzing other publicly available somatic mutations datasets and compile all candidate driver genes picked up by OncodriveFM in different malignancies into an unbiased catalog of cancer genes. I then started the job of collecting the datasets and writing the scripts (I was still a PERList at the time) to transform the list of mutations into functional impact scores that could be used by OncodriveFM. The task required transforming all lists of mutations to the coordinates of the hg19 assembly of the human genome (thank you, UCSC, for all the liftover!) and welding together the Ensembl Variant Effect Predictor and tools that predict the functional impact of mutations.

At first sight, the job seemed like a piece of cake: but like always, the devil hid behind the details. I kept being bugged by small abnormalities in the intermediate files of the incipient pipeline that could be traced back to minor differences in annotation formats employed by different projects. Some of them, for example didn’t annotate the strand of the mutation; different notations were used to code for indels. Eventually, I became overwhelmed with the task of coding the pipeline with anything close to execution efficiency, and it became crystal clear that the project required the deeds of a software engineer. And that is how, first Alberto, and then Christian got involved in the project with the task of developing an pipeline based in Wok to efficiently analyze dataset of cancer somatic mutations and detect in them putative driver genes. Once Christian finally had the pipeline up and running it was easy to start incorporating new analysis and pieces of software to what we were beginning to call IntOGen-mutations. The most important was OncodriveCLUST, a new member of the family of drivers nominators. Finally, it was time to work on the visualization of the data generated by the pipeline. Jordi took care of it with Onexus, and patiently accommodated every one of our endless requests, big and small, to make the interface simpler and friendlier.

Some views of IntOGen-mutations browser

So, in the end, we finished the platform, called IntOGen-mutations, which is composed of an automatic pipeline to analyze somatic mutations detected either in a single patient or across a cohort of tumors, and a web discovery tool containing the results of analyzing mutations across 4623 tumor samples employing that pipeline. The web discovery tool, thus contains information on the genes and pathways that drive tumorigenesis in tumor types from 13 anatomical sites. The platform may be useful, primarily to three types of research:

• First, cancer researchers with interest in one gene or group of genes, may query the web discovery tool (see video tutorial) and find out tumor types where they act as drivers

• Second, groups sequencing cohorts of cancer genomes may use the pipeline to detect genes that act as drivers in their tumor samples, browse the results through a private IntOGen-like website (see video tutorial), and compare them to the knowledge accumulated in the web discovery tool

• Third, clinically-oriented researchers who sequence the tumor of a patient, may use the pipeline to rank its mutations by putative functional impact, browse the results through a private IntOGen-like website (see video tutorial), and compare them to the knowledge accumulated in the web discovery tool

So, without further ado, we give you IntOGen-mutations where you can browse a catalog of cancer somatic mutations, analyze your own datasets, check plots and visualize as heatmaps. Enjoy!

Related posts:

How to identify functional genetic variants in cancer genomes?

How to identify cancer drivers from tumor somatic mutations?

The poster describes IntOGen project, focusing on the analysis of Somatic Mutations detected by tumour genome re-sequencing projects. IntOGen aims to integrate data across projects and cancer sites to identify genes and pathways involved in cancer, in a systematic way. We have developed methods able to perform the analyses in a effective and scalable way (namely, OncodriveFM, OncodriveCLUST and transFIC), we have analysed more than 4500 tumors from 13 different cancer sites and we have identified many known and novel cancer driver genes across cancer types. The results are presented in the IntOGen web discovery tool (currently at http://beta.intogen.org).

The complete pipeline we use for the analysis of somatic mutations in IntOGen is available for other researchers to process their own data here, which allows to compare the results of user’s data with the accumulated knownledge about cancer mutations and cancer driver genes in IntOGen.

For those interested in knowing more about it, I post here the slides about this project that I prepared recently for a talk at the Oncogenomics Workshop in Hinxton.

Related posts:

How to prioritize cancer somatic mutations?

How to identify cancer drivers from tumors somatic mutations?

I explained the IntOGen project, focusing on the analysis of Somatic Mutations detected by tumour genome re-sequencing projects. IntOGen aims to integrate data across projects and cancer sites to identify genes and pathways involved in cancer. I briefly exposed how we obtain the data, the methods that we have developed to perform the analyses (namely, OncodriveFM, OncodriveCLUST and transFIC) and how we present the results in the IntOGen web discovery tool (currently at http://beta.intogen.org).

A key feature for a project like IntOGen is that it uses methods that are scalable for the analysis of a large number of tumours. For that, we have developed novel methods that can identify cancer driver genes solely from the list of tumour somatic mutations, eliminating the need to download and process large and protected files (eg. BAM files), which would be impractical for a project like IntOGen and not be scalable for the analysis of much larger cohorts of tumours.

I also noted that the complete pipeline we use for the analysis of somatic mutations in IntOGen is available for other researchers to process their own data here.

Related posts:

New IntOGen somatic mutations analysis version available

How to prioritize cancer somatic mutations?

IntOGen v.04: with somatic mutations from cancer genome sequencing projects

How to identify cancer drivers from tumors somatic mutations?

The IntOGen SM pipeline addresses the challenge of identifying which somatic mutations are important for the development of tumors. The input for the analysis is a list of somatic mutations detected in a cohort of tumors.

The analysis follows several steps. First, the list of mutations have to be read and parsed, as several files and formats can be used; then follows the identification of the effect that mutations may have on transcripts and regulatory regions using the Variant Effect Predictor (VEP) and subsequently the identification of their functional impact from the scores computed by the tools SIFT, PolyPhen2 and Mutation Assessor (MA) and transformed with TransFIC. The following steps perform the calculation of the recurrence of mutations, genes and pathways across tumors, the identification of cancer drivers genes and pathways using OncodriveFM, and the identification of clustered mutations in genes that would confer an adaptive advantage to the cancer cells by using OncodriveCLUST. Finally several datasets are generated with all the results.

The source code is freely available under the Affero GPL 3.0 license. We encourage people to test it and report suggestions and failures as issues on the Bitbucket project site where you can download it.

It is compatible with MacOS X and Linux and it can be run in different ways:

• By using the online demo: We provide an online demo for fast evaluation without having to install anything (just clicking on the Analysis tab). We encourage people to install it in their servers if they find it useful. Here you will find also the documentation.
• By running a local web interface: The best solution for novel users and for institutions wanting to provide a common service for all the researchers.
• By running the command on a unix terminal: for advanced users and people wanting to embed the pipeline within their own workflows.

We hope you find it a useful tool for your daily analyses but you can also become indirect user without having to execute anything if you just browse the data available in the IntOGen Web. The data there has been analysed with the IntOGen SM pipeline from 26 cancer somatic mutations analysis projects obtained from different sources such as the ICGC, TCGA and the literature.

Thanks to the rapid advances in sequencing technologies, cancer research projects are now able to sequence the genome (or exome) of thousand of tumors and rapidly obtain a big amount of data comprising the catalogs of somatic mutations in those tumors. Our aim is to be able to analyse hundred of thousand of tumours in the near future with the lowest possible cost in infrastructure and time.

The implementation of the steps is quite more complex than the simplified schema shown before and require some kind of orchestra director. We use Wok for that purpose. Wok is a workflow management system that is in development in our group. It is based on message passing and allows to separate the logic of the analyses from the complexity of the execution. It can be run on platforms as simple as a laptop or as complex as a cluster of thousand of computers. Its main features are a powerful configuration system, automatic and transparent partitioning of data and parallelization of execution, and a web interface for management and monitoring.

We feel very happy with this release and expect to release new versions in the near future, so keep in touch with us through any of the available channels: the blog, our twitters, the web …

See you soon.