Taking apart pediatric cancer bit by bit 

Cancer is an elusive beast. The staggering spectrum of presentations and the diversity between and within patients make even the very definition of the disease (types) a challenge. We build upon decades of painstaking clinical and basic research. The tools are now available to go deeper than ever before and to dissect the molecular characteristics at the heart of cancer development, progression, and cure. It is our declared aim to utilize these tools to gain a better understanding of the mechanisms underlying different types of pediatric cancers and to use these insights to inspire new diagnostic and therapeutic approaches.

 

We aim to:

•    Discover molecular features of aberrant cells and disease states that serve   as biomarkers for better diagnosis, prognosis, and patient stratification
•    Identify regulators and pathways involved in disease development and progression that may be targeted with inhibitors to inspire better treatment
•    Boost effective use and re-use of multi-omics technologies and data resources by developing intuitive and assisted analysis tools powered by A.I.

 

Approach

Our research combines high-throughput analysis of clinical samples with computational, statistical, and machine learning methods to dissect the molecular framework of pediatric cancers. Some of the methods we use include:

•    High-throughput assays (“next-generation sequencing” for transcriptomic and epigenomic analysis, proteomics, imaging) on clinical samples and cell lines to compile comprehensive catalogs of the molecular features of cancer
•    Integration and interrogation of public data sources such as repositories of molecular signatures of healthy and cancer samples as well as affected pathways to rationalize pathobiology
•    Supervised and unsupervised dissection of data collections using statistical analysis and machine learning
•    Development of user-friendly software to streamline common analysis tasks and to empower experimental researchers with visual analytics and assisted discovery of features of interest

 

Open positions:

Scientific Programmer / Data Scientist

Doctoral Candidate / PhD Student

Post-Doctoral Researcher

Selected Articles

Barakat TS, Halbritter F, Zhang M, Rendeiro AR, Bock C & Chambers I. (2018) Functional dissection of the enhancer repertoire in human embryonic stem cells. Cell Stem Cell 23, 1-13

Farlik M, Halbritter F, Müller F, Choudry FA, Ebert P, Klughammer J, Farrow S, Santoro A, Ciaurro V, Mathur A, Uppal R, Stunnenberg HG, Ouwehand WH, Laurenti E, Lengauer T, Frontini M & Bock C. (2016) DNA methylation dynamics of human hematopoietic stem cell differentiation. Cell Stem Cell 19 (6), 808-822

Mass E, Ballesteros I, Farlik M, Halbritter F, Günther P, Crozet L, Jacome-Galarza CE, Händler K, Klughammer K, Kobayashi Y,  Gomez-Perdiguero E, Schultze JL, Beyer M, Bock C & Geissmann F. (2016) Specification of tissue-resident macrophages during organogenesis. Science 353 (6304), aaf4238

Bock C, Halbritter F, & The BLUEPRINT Consortium (2016) Quantitative comparison of DNA methylation assays for biomarker development and clinical applications. Nature Biotechnology 34 (6), 726-737

Halbritter F, Vaidya H & Tomlinson SR. (2011) GeneProf: analysis of high-throughput sequencing experiments. Nature Methods 9, 7-8