Objective 1: Optimization and Validation of the MeDIP technology
Objective 2: Analysis of Mechanisms of Epigenetic Deregulation in Cancer
Objective 3: Epigenetic profiling in leukemias and colon cancer
Objective 4: Development of a Cancer MeDIP Kit and Validation
Objective 5: Generation of Bioinformatic Tools for analysis of MeDIP data
1. Optimization and Validation of the MeDIP technology up
2. Analysis of Mechanisms of Epigenetic Deregulation up
- Optimization of MeDIP, through the use of different strategies to amplify the DNA, use of “spike-in” MeDIP, etc.
- Validation of MeDIP by individually analyzing two sets of sequences and procedures for the individual analysis of methylation.
- MeDIP profiling of hematopoietic cell lines: combination of MEDIP with hybridization on genomic-microarrays with leukemia cell lines.
- MeDIP profiling of solid tumour cell lines (colorectal): combination of MEDIP with hybridization on genomic-microarrays with colorectal cancer cell lines.
To optimize and validate MeDIP two groups of cell lines will be used: colorectal cell lines (HCT116, SW48 and RKO) and leukemia cell lines (NB4, U937, Raji, KG1a, HL60). Optimization will allow to standardise methods for preparation and amplification of immunoprecipitated DNA without bias for hybridization on genomic microarrays. Validation through bisulfite modification-based procedures will ensure the proper enrichment with methylated sequences of immunoprecipitated DNA. Two groups of sequences will be used in validation studies: a) genes that are known to be methylated in these cancer cell lines and b) differentially methylated regions from previous MeDIP studies. Finally, MeDIP analysis will be applied to two groups of cell lines (the above colorectal and leukemia cell lines) to generate preliminary maps of DNA methylation.
- Analysis of the molecular mechanisms implicated in targeting DNA methylation to genes that become silenced in cancer. Role of Polycomb group proteins
- Study of the mechanisms that trigger Polycomb-mediated abnormal DNA methylation in cancer.
- Analysis of the Polycomb implication in DNA methylation at a genome-wide level: ChIP-on-chip analysis
- Generation and characterisation of ChIP-on-chip antibodies for the analysis of Polycomb group protein members.
Mechanisms involved in Polycomb-associated establishment of DNA methylation patterns will be studied. In particular, implication and connections between Polycomb group proteins and elements of the DNA methylation machinery (DNA methyltransferases and methyl-CpG binding domain proteins). Polycomb group proteins and MBD proteins will be studied through the use of immunoprecipitation, chromatin immunoprecipitation assays and RNA interference experiments . In addition, we will study a novel mechanism that regulate DNA methylation and their impacts on cancerogenesis. For these analyses and studies in leukemia and colorectal cancer objectives 60 novel antibodies against Polycomb group, DNMTs, MBDs and different histone modifications will be generated and tested. The validity of the Polycomb/MBD connection and relation with patterns of DNA methylation will be investigated at a genome-wide level through the use of ChIP-on-chip and MedIP assays in the above colorectal and leukemias cell lines.
3. Epigenetic profiling in leukemias and colon cancer up
A general objective common to WP3 and WP4 is to generate DNA methylation patterns and perform correlation analysis with clinical parameters in both leukemias (hematopoietic tumor) and colorrecatl cancer (solid tumor) samples. Specific goals are:
- Mapping of the DNA methylome in leukemias and colorectal samples. For leukemias, samples containing three different chromosomal translocations will be used (10 samples of each). Also normal CD34+ cells from 10 different individuals will be compared to AML blast from the same 10 individuals. For colorectal cancer, samples from 10 individuals corresponding to normal colon, preneoplastic and neoplastic cancer tissues will be used.
- Generation of DNA methylation signature in samples from acute myeloid leukemia patients (WP3) and colorectal cancer (WP4).
- For both groups of samples, correlation analysis to determine associations between DNA hypermethylation and clinical parameters will be performed.
- Identification of altered chromatin (presence of DNMTs, MBDs or HMTs) and histone modification patterns which may have an impact on the epigenetic disregulation of the tumour cell (WP4)
.Bioinformatics analysis of validated (bisulfite sequencing) data with clinical parameters (outcome) will generate markers for various clinical applications.5. Generation of Bioinformatic Tools for analysis of MeDIP up
4. Development of a Cancer MeDIP Kit and Validation up
- Development of a kit format for the MeDIP assay.
- Kit validation on clinical samples: establishment of a list of criteria and selection of appropriate biomarkers
- Production and quality control of kits
Optimization and validation of the MeDIP technology (objective 1) will provide the necessary information in the initial set up of this new kit as well as the use of the MeDIP technology in hematopoietic and colorectal cancer cell lines. Diagenode SA (participant 6) will develop commercial kits. Development of the kit will involve:
- Selection of biomarkers,
- Test of reproducibility
- Set up of analysis modules (for the user)
- Generation of final standard protocol.
Different bioinformatics tools will be generated. The genomic location of DNA methylation, characteristics of the DNA sequence, including DNA sequence patterns, distribution of repetitive DNA and predicted DNA structure, and presence of multiple epigenetic modifications and epigenetic proteins (Polycomb, MBDs, DNMTs) will be used to generate software to predict the epigenetic status of genes. Co-localization between epigenetically modified sites and regulatory regions Genomic co-localization analysis provides a computational method for assessing associations between different types of epigenetically modified regions, based on genome-wide ChIP-on-Chip or MeDIP data. For that, a combination of epigenome prediction methods and comparison with available epigenome datasets will be used. Resulting software will be made available to all consortium members and it will be extended and customized to the special requirements of the CANCERDIP project. Finally, the use biostatistical methods such as survival analysis and Kaplan-Meier estimators will be used to estimate and evaluate individual biomarkers.
- Computational discovery and validation of hypotheses regarding functional interactions within the DNA methylation machinery (DNMTs, MBD proteins), Polycomb proteins and a number of epigenetic (histone modifications) features and chromosomal translocations (for leukemias).
- Computational ranking of all cancer-specific differentially methylation regions (based on experimental work in WP3 and WP4) according to their predicted potential as cancer biomarkers.
- Optimization and evaluation of the predictive power and robustness of selected biomarker candidates (in cooperation with experimental work in WP5).