Sample Level Enrichment Analysis (SLEA) unravels shared stress phenotypes among multiple cancer types

We are happy to share with you the results of a new publication, which has been published today in Genome Medicine:

Gunes Gundem and Nuria Lopez-Bigas. Sample level enrichment analysis (SLEA) unravels shared stress phenotypes among multiple cancer types. Genome Medicine. 4:28 doi:10.1186/gm327

In this manuscript we introduce SLEA, which we have described earlier in this blog, and we use it to explore the interrelation of different stress phenotypes in multiple cancer types. We also ask if these phenotypes could be used to explain prognostic differences among tumor samples.

First we do SLEA using Gitools with the set of genes related to Chromosome Instability (CIN genes) in a breast cancer dataset (Ivishina et al., 2006). Next we use the result of SLEA to stratify the tumors, and find that tumors with upregulation of CIN genes have worse prognosis than the others (see figure in the left). Read the rest of this entry »

BIG12 – Bioinformatics for Integrative Genomics course in Oeiras

Michael P. Schroeder and Abel Gonzalez-Perez

This year our lab will participate again in the Gulbenkian Training Program in Bioinformatics (GTPB). Abel and Michael will give a 3 days course on Bioinformatics for Integrative Genomics – BIG12, from 7th to 9th May. The course is now open for Inscriptions, and a tentative program can be found here.

GTPB program is very well known for its practical courses on bioinformatics, which have been running since 1999. The courses are eminently practical, including few lectures and many hands on exercises. The groups are small, usually up to 20 attendees, which allows a very intense experience with the topic, the instructors and the rest of attendees. Read the rest of this entry »

Gitools 1.6.1 release

We announce today a new release of Gitools, version 1.6.1. A few bugs have been fixed and some of them are important, so we recommend to update Gitools to the latest version.

One important thing to note is, that saved color scales have to be created again. But now there should be no problem with further modifying loaded color scales.

Furthermore, we adapted the Heatmap Properties Panel to the left, so that it does not occupy too much space and is resizeable for OS X and Xfce users.

A list of all bug fixes can be found here.

Exploring the effect of cancer genomic alteration on expression with Gitools

Cancer cells often exhibit a change in number of copies of certain genomic regions when compared to normal cells (Copy Number Alterations: CNAs). Some of these CNAs may have a direct influence on the expression of genes in the affected region. The change in the number of copies of a gene may be both positive, when additional copies are gained (and the genes thus amplified) or negative, when one or more alleles of the gene are lost. The influence of CNAs on the expression of these amplified or lost genes depends on whether it occurs hetero- or homozygously and also on other regulatory factors which may override the effect of the alteration. Therefore, an essential step to verify the importance of the amplification or deletion of a given gene in the tumorigenic process is to verify if its expression tends to respond to its genomic alterations.
Effect of genomic alterations on expression

The effect of genomic alterations can be observed in the expression values. Note for example that samples with loss of CDKN2A shown lower expression values than samples without this alteration. This effect is also evident for the alteration of the other genes.

Read the rest of this entry »

Visualizing mutually exclusive alteration patterns in cancer with Gitools

On the quest to identify cancer driver genes, it has been observed that driver alterations that affect a pathway tend to be altered in a mutually exclusive manner. As far as we know this was first observed by Thomas et al., Nat Genet 2007, however from our experience this type of pattern can be observed in data from almost all cancer genomic projects. The rationale behind that observation is that once a gene involved in a particular critical pathway is altered, a second alteration affecting the same pathway does not confer a further selective advantage to the cancer cell. The concept of mutually exclusive alteration patterns has recently been exploited to identify cancer drivers (Ciriello et al, Genome Research 2011 and Vandin et al., Genome Research 2012).

Mutually exclusive sorting of p53-signalling pathway upstream genes

The heatmap in the left shows copy number alterations of TCGA Glioblastoma project in the KEGG TP53-signalling pathway. If sorted properly we can observe that the upstream genes show a mutual-exclusive alteration pattern, but not PTEN and CDK4. Loss in blue, gain in red.

Read the rest of this entry »

Exploring multiple cancer genomics alterations with Gitools.

Cancer genomics data that is produced creates multi-dimensional data sets. Gitools lets you browse all that data at once.

A typical cancer genomics project nowadays screens the cancer genome, epigenome and transcriptome of a cohort of patients and identifies various types of alterations: Copy Number changes, Somatic Mutations, Gene Expression changes and others. This is the case of projects framed within The Cancer Genome Atlas or the International Cancer Genomics Consortium, as well as many others. Each of these types of alterations is represented in different data formats and it remains a challenge to integrate them to get a unified view of the process of alterations that leads to tumorigenesis. In Gitools it is possible to explore and analyze multi-value matrices in the form of interactive heatmaps, making it possible to work with various data dimensions at once.  Read the rest of this entry »

Gitools 1.6.0 release

We are pleased to announce that as of today a new version of Gitools is available: Gitools 1.6.0.

The change from the 1.5.x to 1.6.x series promise new features, and so it is! In general, Gitools got polished to make it easier to work with multi-dimensional data. Check the list below to see what’s new


  • Color scales can be saved and loaded!
  • Display options for cell value/color scale are kept: when you define the display options of a value those are saved, so that when you switch back to see that value again the display options are kept.

Data analysis:

  • New Analysis: Group Comparison (Mann-Whitney-Wilcoxon), to compare the distribution of values between two sets of columns or rows
  • New sorting method: Mutual exclusive sorting

Plus several bugfixes.

Expect some follow-up posts explaining some of the novelties in detail.