PANDA: Personalized ANalysis for Drug Activity in Pancreatic Cancer
Funded by: Federal Ministry of Education and Research
Duration: 10/2024 - 03/2025 (6 months)
Aim of the project
This project aims to analyze existing drug screening data from pancreatic cancer cell lines to identify personalized treatment strategies for patients. Using advanced bioinformatics tools, particularly our previously developed MTB-Reporting framework, we will extend our current capabilities to analyze drug response data based on large drug screens and integrate these findings into our Molecular Tumor Board (MTB) reports. The outcomes of this project are expected to include actionable insights into effective treatment options pancreatic cancer and help us to define subtypes with specific treatment options. Our goal is further to extend our existing MTB-Reporting framework for interpreting biomarkers, which helps scientists and doctors understand the effects of genetic variations on cancer and identify the most effective treatment options for individual patients. This tool has a modular architecture to be open for further extensions to new methods or biomarker. Currently the focus is on a web interface designed for the preparation of an MTB. However, due to the modular character bioinformatic work flows can be build to cover new use scenaria. Pancreatic cancer is particularly challenging to treat, and having tools that can precisely predict how different drugs will work in different patients is crucial for improving outcomes. Our existing MTB-Reporting framework will be enhanced to analyze drug screening data, which involves testing how cancer cells respond to a wide range of drugs.
Workpackages
Background and Workpackages
The datasets come from the Clinical Research Group 5002 (KFO5002), focusing on pancreatic cancer (PDAC) under the leadership of Tim Beißbarth and Günter Schneider. They include RNA-seq, panel-seq, and drug screening data, which are crucial for the development of precision medicine treatment strategies. The Lower Saxony-funded MTB-Report project, led by Tim Beißbarth and Jürgen Dönitz, resulted in the development of Onkopus, a modular framework for biomarker interpretation and therapy prioritization, which has been described and validated in several publications.
Work Package 1: Data Integration and Initial Analysis
RNA-seq, panel-seq, and drug screening data from 17 pancreatic cancer CDX cell lines will be integrated. The genetic profiles will be combined with the drug response data, the datasets will be aligned, and quality checks will be conducted to ensure data integrity.
Work Package 2: Tool Development and Extension
In this work package, the MTB-Reporting framework will be expanded to analyze drug screening data. New functionalities will be developed to correlate drug responses with genetic variants and generate personalized treatment recommendations, based on the requirements of clinicians and bioinformaticians.
Work Package 3: Validation and Report
The extended MTB-reporting framework will be applied to the integrated dataset to generate personalized treatment recommendations for pancreatic cancer cell lines. These recommendations will be assessed by comparing them to clinical outcomes and literature. Comprehensive reports will also be created, summarizing potential treatment strategies for different subtypes. Where possible, a retrospective analysis will be conducted to validate the effectiveness of the recommendations.
Work Package 4: Dissemination and Future Planning
The final work package focuses on disseminating the results of the project and planning for future developments.
Project Coordination and Partners
Project coordinator:
Dr. rer nat Jürgen Dönitz, University Medical Center Göttingen,
Department of Medical Bioinformatics,
Partners:Prof. Dr. Tim Beißbarth, University Medical Center Göttingen
Department of Medical Bioinformatics
Prof. Dr. med. Günter Schneider; University Medical Center Göttingen,
Clinic for General, Visceral, and Pediatric Surgery
Publications
- Perera-Bel J, Hutter B, Heining C, Bleckmann A, Fröhlich M, Fröhling S, Glimm H, Brors B, Beißbarth T. From somatic variants towards precision oncology: Evidence-driven reporting of treatment options in molecular tumor boards. Genome Med; 2018; 10(1):18. doi:10.1186/s13073-018-0529-2.
- Kurz NS, Perera-Bel J, Höltermann C, Tucholski T, Yang J, Beißbarth T, Dönitz J. Identifying Actionable Variants in Cancer - The Dual Web and Batch Processing Tool MTB-Report. Stud Health Technol Inform; 2022; 17:296:73-80. doi:10.3233/SHTI220806.
- Yang J, Beißbarth T, Dönitz J. Onkopipe: A Snakemake Based DNA-Sequencing Pipeline for Clinical Variant Analysis in Precision Medicine. Stud Health Technol Inform; 2023; 12:307:60-68. doi:10.3233/SHTI230694.
- Schlotzig V, Kornrumpf K, König A, Tucholski T, Hügel J, Overbeck TR, Beißbarth T, Koch R, Dönitz J. Predicting the Effect of Variants of Unknown Significance in Molecular Tumor Boards with the VUS-Predict Pipeline. Stud Health Technol Inform; 2021; 283:209-216. doi:10.3233/SHTI210562.
- Kornrumpf K, Kurz NS, Drofenik K, Krauß L, Schneider C, Koch R, Beißbarth T, Dönitz J. SeqCAT: Sequence Conversion and Analysis Toolbox. Nucleic Acids Research; 2024; 52(W116–W120). doi:10.1093/nar/gkae422.
- Yang J, Chereda H, Dönitz J, Bleckmann A, Beißbarth T. Deciphering BRCAness Phenotype in Cancer: A Graph Convolutional Neural Network Approach with Layer-wise Relevance Propagation Analysis. bioRxiv; 2024; doi:10.1101/2024.06.26.600328.
- Yang J, Wang M, Dönitz J, Chapuy B, Beißbarth T. Advancing Personalized Cancer Therapy: Onko DrugCombScreen - A Novel Shiny App for Precision Drug Combination Screening. medRxiv; 2024; doi:10.1101/2024.06.20.24309094.
- Kurz NS, Kornrumpf K, Tucholski T, Drofenik K, Beißbarth T, Dönitz J. Onkopus: A Modular Biomarker Interpretation Framework for Variant Pathogenicity Prediction and Evidence-Based Prioritization of Actionable Variants. Manuscript in preparation; 2024.