Disrupting and monitoring cancer cell viability using visualization and drug therapy

1. Disrupting and monitoring cancer cell viability using visualization and drug therapy Elinor Velasquez CS 261 Oct 21 2013

2. Our motivation: Solvecancer • Proposed way tosolvecancer: Administer cancer-killing drugs (Phase I drugs) and immune system strengthening drugs (Phase II drugs) to a patient • Issues: - Which drugs to use? - How do we know they are effective?

3. How visualization can address these issues • Visualization can identify which drugs to use • Interaction with visualization can provide scenarios for predicting drug therapy effectiveness • Visualization can monitor a patient’s progress during drug therapy

4. CS 261 project: Implement visualization for phase I drugs • Identify cancer-killing drugs for an individual patient • Predict cancer-killing drug efficacy on patient before treating with the drug • Monitor cancer-killing drug therapy response in patient

5. State of the art visualization of biological pathways • Entourage (2013) • Visualizes “focus nodes” which are genes with high standard deviation in data • User manually selects subpaths based on focus nodes • User selects drug by viewing its effect on gene (focus node) in that cell line

6. Our proposed project differs from Entourage • We implement an algorithm that ranks (by activity) all subpaths in the pathway, based on a patient’s data, literature data, biochemical flux, path topology • User interaction: Manual knocking out of alternative paths and getting cancer cell survival metric for that one patient pre-drug therapy • Animation of patient’s samples taken over time of drug therapy that monitors progress of that patient • User manually selects subpath of interest in cell line via focus gene • No metric predicting cancer cell survival in a given patient • No time-dependent or semantic attribute-dependent animation: static system Our visualization Entourage

7. Our visualization is of metabolic (energy) pathways • Cancer can be viewed as a metabolic disease • Rank metabolic activity via patient genomic/proteomic data and biochemistry and topology of path • Path activity ranking displayedusing color • Knock down/out edge or path(s) and get cancer cell survival score • Monitor patient’s progress by animation of ranked paths per sample over time of drug therapy

8. Data chosen for visualization demonstration • Study paths in the TCA cycle and glycolysis Two demos: • Snapshot data from human brain cancer cell line and healthy brain sample • Animation data from human lung cancer cells (9 time points collected over 72 hours) undergoing conversion to metastatic cells

9. Epilogue: How to cure a patient’s cancer using visualization and drug therapy • Use animation on cancer data collections (and metabolic version of Entourage) to identify all drug classes for all cancers • Given a patient’s genomic/proteomic data, use visualization to selectdrug for the patient. Ideally drug belongs to known drug class.