- Identification of driver events based on recurrent mutations
- Project in cancer bioinformatics: Finding molecular markers for chemotherapy response in breast cancer
Identification of driver events based on recurrent mutations
Performing high-‐throughput experiments on a set of tumor samples or cell lines, one typically wants to identify which genes are recurrently mutated in many samples and how significant this is. A few methods have been proposed based on different statistics and different ways to compute a background mutation rate (Jeoblom et al., 2007; Getz et al., 2007, Comment; Rubin and Green, 2007, Comment; Dees et al., 2012).
However, there seem to be no standard method: depending on the experiments, researchers use ad-‐hoc methods combined with manual curation. We have developed a method, which compares the observed mutations with a constant mutation rate defined based on all mutations in the dataset. We would like to investigate alternative ways to define the background mutation rate. In addition, most genes have low mutation frequency. Thus it is not enough to investigate this question at the gene level. If several genes from the same pathway are frequently mutated as a group, we want to able to detect it as well. Consequently we need to define and assess methods to identify significantly mutated pathways. Finally, investigating the position of the mutations in the context of functional domains in the associated proteins can give some functional information about the consequences of the mutations.
We propose to develop methods to identify significant mutations at the domain, gene and pathway levels following the suggested plan:
- Identification of significantly mutated genes
- Propose alternative ways to define the background mutation rate
- Compare the proposed methods with existing ones
- Identification of significantly mutated pathways
- Update the methods to make them applicable to pathways
- Identification of significantly mutated protein domains
- Map mutations on functional domains
- Update the methods previously developed
- Compare the domain frequency of the mutations with existing functional scores
We are looking for a motivated student with a strong background in computational biology, statistics and the R programming language to work on this topic. There will be ample opportunity to bring forward your own ideas. The project will be carried out at the Netherlands Cancer Institute (NKI-‐AVL) in Amsterdam and the expected duration of the project will be 6 months. During this time a report has to be written about the work performed. Supervision will be performed by Magali Michaut and Lodewyk Wessels (Bioinformatics and Statistics group).
For further information contact
Magali Michaut (firstname.lastname@example.org) or Lodewyk Wessels (email@example.com)
Project in cancer bioinformatics: Finding molecular markers for chemotherapy response in breast cancer
Preoperative chemotherapy may lead to excellent responses in some tumors and to no or partial response at all in others. Therefore there is a strong need for tests that predict chemotherapy response. In the CTMM BreastCare project we are trying to develop clinically useful and feasible tests to predict the response or resistance of primary breast cancers to specific chemotherapeutic agents.
The tests to be developed will be based on DNA, RNA or protein analysis. DNA analysis is mainly done by aCGH and MLPA, RNA analysis by gene expression microarrays and real time qPCR. By now, data are available from 300 Illumina gene expression arrays (48.000 probes, RNA level) and 300 aCGH arrays (3000 probes, DNA level). By standard techniques, such as comparing responders and non-responders, we were not able to find a predictive profile (based on gene expression or aCGH). However, there are many other possibilities to explore the data and apply other techniques to find genes related to chemotherapy response. These include:
- Building a BRCA1 and BRCA2-classifier. Previously aCGH based classifiers for BRCA1 and BRCA2 mutated breast tumors were developed. It was shown that also sporadic tumors had these profiles, called BRCA1- and BRCA2- like profile. Those sporadic patients had a remarkable good response on chemotherapy. We would like to find a gene expression profile corresponding to these BRCA1 like and BRCA2 like aCGH profiles. The corresponding genes can tell us more about the biology and the pathways involved in defining the response phenotypes. Also, with the predictive genes in hand, we can go back to the aCGH profiles of the samples to determine the relationship between copy number aberrations and the observed gene expression.
- Study markers and signatures from the literature. There are now several groups which have published profiles predicting chemotherapy response, although often with limited power to discriminate groups. Those profiles could be validated on our samples. As these published profiles are often on different platforms (Agilent, Affymetrix), a first challenge is to translate them to Illumina arrays. In addition, prognostic profiles, like Mammaprint and Oncotype Dx (recurrence score), could be tested in our series, to determine if they also have predictive value.
For further information contact:
Esther Lips (firstname.lastname@example.org) or Lodewyk Wessels (email@example.com)