Q (haiku): Can you help me write a production-ready Python script using Bokeh to build an interactive clinicogenomic dashboard to analyzing the MSK-CHORD dataset? I have some specific requirements I can give you

═ answer (success)
I'd be happy to help you build a production-ready Bokeh dashboard for the MSK-CHORD dataset! Before I start coding, let me understand your requirements better.

Please share:

1. **Data source & scope**
   - Are you pulling directly from cBioPortal's REST API, or do you have a local export of MSK-CHORD data?
   - Which clinical and genomic attributes do you want to visualize (e.g., cancer type, mutations, CNAs, clinical outcomes)?

2. **Dashboard layout & widgets**
   - What are the main views? (e.g., cohort overview, patient-level drill-down, gene alteration frequency, survival curves, treatment response)
   - Do you need filtering/interaction (e.g., by cancer type, mutation status, clinical stage)?

3. **Interactivity & performance**
   - Are we talking ~500 samples, ~5000, or larger?
   - Do you need real-time updates or a static snapshot?
   - Should filters cross-update (linked brushing)?

4. **Output & deployment**
   - Standalone HTML file, Bokeh server app, or embedded in a web framework?
   - Any authentication or access control?

5. **Specific visualizations**
   - OncoPrint-style mutation matrix?
   - Survival plots, clinical timelines, expression scatter plots?
   - Summary statistics tables?

Once you give me those details, I'll write a modular, production-ready script with:
- Clean architecture (data pipeline → Bokeh layout)
- Error handling & logging
- Configurable parameters
- Comments & docstrings
- Example deployment instructions

Go ahead with your requirements—I'll build exactly what you need.