About

The central aim of the PREDICT project is to develop a software system that enables clinicians to use the large body of data on the relationships between genetic/epigenetic alterations and treatment options/success in cancer, to support (a) the rapid development of new, targeted studies whose design essentially is based on genomic features, and to (b) enable a maximally informed and structured clinical decision process. A knowledge base will be created using advanced and innovative algorithms for knowledge extraction, semantic data integration, and biomedical text mining, and made available to the clinical oncologist through a cancer-genomic clinical workbench based. Moreover, the knowledge base will be an essential tool to initiate and support highly targeted umbrella and basket trials in which experimental drugs are administered to a typically small group of patients chosen based on their mutation status. Finally, the knowledge base will be used to develop novel algorithms to assess the effect of drugs on a patient’s tumor depending on its mutation profile.

Publications

  • Boehnke, K., Iversen, P.W. … Schäfer, R. Et al. (2016). “Assay establishment and validation of a high-throughput screening platform for three-dimensional patient-derived colon cancer organoid cultures”. J Biomol Screen
  • Griffith, M., Spies, N. C., Krysiak, K., … Rieke, D. T., et al. (2017). “CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer.” Nat Genet
  • Rieke, D. T., Lamping, M., Klauschen, F., … Keilholz, U. (2018). “Efficacy of a structured workflow for the interpretation of comprehensive genomic analysis data in clinical routine.” J Clin Oncol
  • Rieke, D. T., Lamping M., … , Keilholz U. (2018) “A comparison of treatment recommendations by molecular tumor boards worldwide”, JCO Precision Oncology, accepted
  • Schütte, M., Ogilvie L. A., Rieke DT, Lange B.M.H., Yaspo M.L., Lehrach H. (2017). “Cancer Precision Medicine: Why More is More and DNA Is Not Enough” Public Health Genomics
  • Schütte, M., Risch, T. … Schäfer, R.Keilholz, U. et al. (2017). “Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors.” Nat Commun
  • Ševa, J., Jähnichen, P., and Leser. U. (2017) “WBI@BioCreative Track 4: Mining protein interactions and mutations for precision medicine”, BioCreative VI Workshop Proceedings.
  • Ševa, J., Kittner, M., Roller, R. and Leser, U. (2017). “Multi-lingual ICD-10 coding using a hybrid rule-based and supervised classification approach at CLEF eHealth 2017”. CLEF Working Papers.
  • Ševa, J., Sänger, M. and Leser, U. (2018). “WBI at CLEF eHealth 2018 Task 1: Language-independent ICD-10 coding using multi-lingual embeddings and recurrent neural networks”, CLEF Experimental IR Meets Multilinguality.
  • Ševa, J., Wackerbauer, M. and Leser, U. (2018). “Identifying Key Sentences for Precision Oncology Using Semi-Supervised Learning”. BioNLP, Melbourne, Australia.
  • Sprenger, S., Schaefer, P. and Leser, U. (2018). “Multidimensional Range Queries on Modern Hardware”. Int. Conf. on Scientific and Statistical Database Management, Bozen, Italy.

 

Funding: BMBF, i:DSem-Program BMBF
Period: 2016 – 2019
Partnering Institutions: Charite Universitätsmedizin Berlin Charite,
Berlin Institute of Health BIH,
Humboldt-Universität zu Berlin HU-Berlin