Predicting personalized cancer drug combinations by integrating drug screening and single-cell lineage tracing data

Project Details

Description (abstract)

Chemotherapy is one of the primary treatments for cancer, yet the efficacy is often limited by drug resistance. Emerging evidence suggests that non-genetic transient states in cancer cells contribute to drug resistance. This project aims to develop computational tools to understand the transient cellular states in chemoresistance and rationalize drug combination strategies for personalized medicine.

To achieve this goal​​, we are developing a single-cell based lineage tracing method (ResisTrace) to capture the primed resistant cells (i.e. pre-resistant cells) in treatment-naïve samples. I will build a probabilistic model to facilitate the experiment design and a data analysis pipeline to characterize the gene expression profiles for pre-resistant cells. We have performed successful pilot studies on an ovarian cancer cell line and identified the cells that are resistant to olaparib, carboplatin, or natural killer cells. Potential transcriptomics biomarkers for pre-resistant cells have been identified for further validation.

Furthermore, I will develop machine learning and network modelling approaches to predict synergistic drug combinations to treat multiple cell lineages effectively. To facilitate the model development, I have updated the DrugComb database by integrating publicly available drug screening and molecular profiling data for cancer cells. Furthermore, I have developed SynergyFinderPlus, a computational tool for drug combination data analysis. These tools shall help the rationalization of drug combinations from drug screening and ResisTrace data.

The computational tools will be tested initially on cancer cell lines and then applied on patient-derived samples from high-grade serous ovarian cancer (HGSOC) and pancreatic ductal adenocarcinoma (PDAC). In the end, all the tools will be integrated and published as web applications to facilitate the development of new combinatorial therapies for cancer patients who have become resistant to standard treatments.
Effective start/end date01/05/202031/12/2024

Fields of Science

  • 3122 Cancers
  • drug resistance
  • drug combination
  • 3111 Biomedicine
  • bioinformatics