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Research Summary: Epigenomics, Bioinformatics and Personalized Cancer Therapy

Christoph Bock, Ph.D.

Principal Investigator at CeMM & Visiting Professor at the Medical University of Vienna
Lab website: http://medical-epigenomics.org/, personal website: http://www.christoph-bock.org/

Scientific goals

Our long-term scientific goal is to understand and computationally model the epigenetic basis of cell fate, and to apply this knowledge in the development of epigenetic biomarkers for personalized medicine. Using a combination of bioinformatic methods and next-generation sequencing, it is now feasible to address these questions genome-wide and directly in human cells. Initially focusing on the hematopoietic system, we will: (i) investigate the dynamics of epigenome regulation during normal and aberrant stem cell differentiation; (ii) develop computational models for the interplay between genomic, epigenetic and transcriptional alternations in leukemia; and (iii) work with clinical investigators on epigenome association studies and biomarker development for hematopoietic diseases. These three biomedically driven aims are complemented by significant investment into methods and software development, which will establish a toolbox for making epigenetic biomarkers readily available in clinical research.

Significance

Epigenetic defects are a well-established cause of cancer and have been linked to other clinical conditions including autoimmune diseases and neural disorders (Esteller, 2008; Feinberg, 2007). Epigenetic mechanisms control cell fate by maintaining a delicate balance between stability and susceptibility to developmental and environmental stimuli. These characteristics make them highly promising targets for molecular diagnostics and drug discovery:

  • Epigenetic diagnostics. Environmental influences leave characteristic imprints on the human epigenome, suggesting that the epigenome provides a biochemical record of relevant life events. Epigenome association studies (EWAS) could bridge the gap between genetic risk factors and the elusive environmental contribution that has limited the risk predictiveness of genome-wide association studies. Epigenetic biomarkers are highly compatible with clinical diagnostic procedures, and they are increasingly used for informing therapeutic decision-making (Bock, 2009).
  • Epigenetic treatments. Epigenetic modifications are more readily reversible than genetic mutations and more stable over time than transcription profiles, rendering them a promising target for drug discovery. To name just one example of an increasing number of epigenetic drugs, HDAC inhibitors have a long history for the treatment of psychiatric diseases and are increasingly developed into highly specific cancer drugs (Yoo and Jones, 2006).
     

The hematopoietic system and its diseases are an ideal model for studying the epigenetic basis of cell fate, and to work toward epigenetic biomarkers that foster personalized medicine. Mechanisms of genetic, epigenetic and transcriptional regulation of the hematopoietic system are extensively studied in the mouse, and hematopoietic cells are also readily available for ex vivo studies in human. Furthermore, strong evidence from cancer genome sequencing supports that epigenetic alterations play a causal role for hematopoietic malignancies. Finally, a significant and unmet need exists for blood-based biomarkers that accurately identify suitable and personalized therapies for individual patients.

Research plan

The epigenetic alterations that are frequently observed in hematopoietic diseases emerge from the complex interplay of cell-type specific variation, genetically determined variation and disease-specific variation (Figure 1). To develop robust and accurate epigenetic biomarkers for clinical diagnostics, it is crucial to investigate the effects of all three sources of variation. Using high-throughput epigenome sequencing and computational modeling we will construct a quantitative model of epigenetic (de-) regulation in the hematopoietic system, and we will use this model to prioritize epigenetic biomarker candidates identified in epigenome association studies of hematopoetic diseases. By predicting which biomarker candidates are mechanistically linked to the disease ahead of the costly validation phase, this method will substantially improve the effectiveness and efficiency of epigenetic biomarker development.
 

Figure 1. Toward a quantitative model of epigenetic (de-) regulation in the hematopoietic system


Figure 1
. Toward a quantitative model of epigenetic (de-) regulation in the hematopoietic system

References

  • Bock, C. (2009). Epigenetic biomarker development. Epigenomics 1, 99-110.
  • Esteller, M. (2008). Epigenetics in cancer. N Engl J Med 358, 1148-1159.
  • Feinberg, A.P. (2007). Phenotypic plasticity and the epigenetics of human disease. Nature 447, 433-440.
  • Yoo, C.B., and Jones, P.A. (2006). Epigenetic therapy of cancer: past, present and future. Nat Rev Drug Discov 5, 37-50.
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