mirror of
https://github.com/mims-harvard/ToolUniverse.git
synced 2026-09-19 07:31:47 +08:00
Shanghua
130 lines
4.3 KiB
Python
130 lines
4.3 KiB
Python
"""
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Examples for using OncoKB tools in ToolUniverse.
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OncoKB is a precision oncology knowledge base that provides information about
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the effects and treatment implications of specific cancer gene alterations.
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API access requires registration: https://www.oncokb.org/apiAccess
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Set ONCOKB_API_TOKEN environment variable for full access.
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Demo mode (limited to BRAF, TP53, ROS1) available without token.
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"""
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from tooluniverse import ToolUniverse
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def main():
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# Initialize ToolUniverse
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tu = ToolUniverse()
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tu.load_tools()
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print("=" * 60)
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print("OncoKB (Precision Oncology Knowledge Base) Examples")
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print("=" * 60)
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# Example 1: Annotate BRAF V600E mutation
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print("\n1. Annotate BRAF V600E mutation:")
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print("-" * 40)
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result = tu.tools.OncoKB_annotate_variant(
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operation="annotate_variant",
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gene="BRAF",
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variant="V600E"
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)
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if result["status"] == "success":
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data = result["data"]
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print(f"API Mode: {result['metadata']['api_mode']}")
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print(f"Oncogenic: {data.get('oncogenic', 'N/A')}")
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if "mutationEffect" in data:
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print(f"Mutation Effect: {data['mutationEffect'].get('knownEffect', 'N/A')}")
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print(f"Highest Sensitive Level: {data.get('highestSensitiveLevel', 'N/A')}")
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else:
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print(f"Error: {result['error']}")
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# Example 2: Annotate BRAF V600E in melanoma specifically
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print("\n2. Annotate BRAF V600E in melanoma (tumor-specific):")
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print("-" * 40)
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result = tu.tools.OncoKB_annotate_variant(
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operation="annotate_variant",
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gene="BRAF",
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variant="V600E",
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tumor_type="MEL" # Melanoma OncoTree code
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)
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if result["status"] == "success":
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data = result["data"]
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print(f"Tumor Type: MEL (Melanoma)")
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print(f"Oncogenic: {data.get('oncogenic', 'N/A')}")
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print(f"Highest Level: {data.get('highestSensitiveLevel', 'N/A')}")
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if data.get("treatments"):
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print("Treatments:")
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for tx in data["treatments"][:3]:
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print(f" - {tx.get('drugs', 'N/A')}: Level {tx.get('level', 'N/A')}")
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else:
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print(f"Error: {result['error']}")
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# Example 3: Get gene-level information
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print("\n3. Get gene information for TP53:")
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print("-" * 40)
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result = tu.tools.OncoKB_get_gene_info(
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operation="get_gene_info",
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gene="TP53"
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)
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if result["status"] == "success":
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data = result["data"]
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print(f"Gene: {data.get('hugoSymbol', 'N/A')}")
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print(f"Oncogene: {data.get('oncogene', False)}")
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print(f"Tumor Suppressor: {data.get('tsg', False)}")
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else:
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print(f"Error: {result['error']}")
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# Example 4: Get evidence level definitions
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print("\n4. Get OncoKB evidence levels:")
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print("-" * 40)
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result = tu.tools.OncoKB_get_levels(operation="get_levels")
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if result["status"] == "success":
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for level in result["data"][:5]:
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print(f" {level.get('level', 'N/A')}: {level.get('description', 'N/A')[:60]}...")
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else:
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print(f"Error: {result['error']}")
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# Example 5: List cancer genes
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print("\n5. Get cancer genes from OncoKB:")
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print("-" * 40)
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result = tu.tools.OncoKB_get_cancer_genes(operation="get_cancer_genes")
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if result["status"] == "success":
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data = result["data"]
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print(f"Total genes in database: {data.get('total_genes', 0)}")
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print(f"Cancer-related genes: {data.get('cancer_genes_count', 0)}")
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if data.get("genes"):
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print("Sample oncogenes:")
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oncogenes = [g for g in data["genes"] if g.get("oncogene")][:5]
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for g in oncogenes:
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print(f" - {g.get('hugoSymbol', 'N/A')}")
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else:
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print(f"Error: {result['error']}")
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# Example 6: Annotate copy number alteration
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print("\n6. Annotate ERBB2 amplification:")
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print("-" * 40)
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result = tu.tools.OncoKB_annotate_copy_number(
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operation="annotate_copy_number",
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gene="ERBB2",
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copy_number_type="AMPLIFICATION"
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)
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if result["status"] == "success":
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data = result["data"]
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print(f"Gene: ERBB2")
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print(f"Alteration: Amplification")
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print(f"Oncogenic: {data.get('oncogenic', 'N/A')}")
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print(f"Highest Level: {data.get('highestSensitiveLevel', 'N/A')}")
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else:
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print(f"Error: {result['error']}")
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if __name__ == "__main__":
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main()
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