pdx-model-analysis-agent

Core Capabilities

1. PDX-Patient Concordance: Analyze molecular similarity between PDX and donor tumor.

2. Drug Response Modeling: ML models correlating PDX drug sensitivity to patient outcomes.

3. Biomarker Discovery: Identify molecular features predicting drug response in PDX panels.

4. Combination Screening: Analyze synergy in PDX drug combination studies.

5. Translational Prediction: Project PDX findings to patient treatment selection.

6. Quality Assessment: Evaluate PDX fidelity and stability across passages.

PDX Quality Metrics

Metric	Threshold	Interpretation
Genetic concordance	>90%	Variants maintained
Expression correlation	>0.85	Transcriptome preserved
CNV fidelity	>85%	Copy number stable
Tumor take rate	Variable	Engraftment success
Passage stability	<P5 recommended	Minimal drift

Workflow

1. Input: PDX molecular data, drug response curves, patient tumor data.

2. Concordance Analysis: Compare PDX to donor tumor at molecular level.

3. Drug Response Processing: Calculate IC50, AUC, TGI from growth curves.

4. Biomarker Analysis: Correlate molecular features with drug sensitivity.

5. Patient Prediction: Project findings to patient treatment recommendations.

6. Quality Assessment: Flag PDX models with significant drift.

7. Output: Drug rankings, biomarker associations, treatment recommendations.