Recipe 13.7: Python Implementation Example

pip install boto3 requests gremlinpython

import boto3
import json
import requests
from datetime import datetime, timezone
from typing import Optional

# =============================================================================
# CONFIGURATION
# =============================================================================

# Neptune cluster endpoint. This is your graph database.
# In production, this comes from environment variables or SSM Parameter Store.
NEPTUNE_ENDPOINT = "your-neptune-cluster.cluster-xxxxxxxxxxxx.us-east-1.neptune.amazonaws.com"
NEPTUNE_PORT = 8182

# The base URL for openCypher queries against Neptune.
# Neptune exposes openCypher via HTTPS POST to /openCypher.
NEPTUNE_OPENCYPHER_URL = f"https://{NEPTUNE_ENDPOINT}:{NEPTUNE_PORT}/openCypher"

# S3 bucket for staging source data and graph load files.
DATA_BUCKET = "your-pharmacogenomics-knowledge-graph"

# Evidence level hierarchy. Higher index = stronger evidence.
# Only relationships at or above the configured threshold will drive clinical alerts.
EVIDENCE_LEVELS = ["4", "3", "2B", "2A", "1B", "1A"]

# Default minimum evidence level for clinical decision support queries.
# "2A" means moderate evidence or above. Level 3 and 4 are informational only.
DEFAULT_EVIDENCE_THRESHOLD = "2A"

# CPIC pharmacogenes with established guidelines.
# These are the genes where diplotype-to-phenotype translation is well-defined.
CPIC_PHARMACOGENES = [
    "CYP2D6", "CYP2C19", "CYP2C9", "CYP3A5", "CYP2B6",
    "DPYD", "TPMT", "NUDT15", "UGT1A1", "SLCO1B1",
    "VKORC1", "IFNL3", "HLA-A", "HLA-B", "CYP4F2",
    "RYR1", "CACNA1S", "G6PD", "CYP2C cluster"
]

# Phenotype categories used across pharmacogenes.
# The specific phenotypes available depend on the gene.
METABOLIZER_PHENOTYPES = [
    "Ultra-rapid Metabolizer",
    "Rapid Metabolizer",
    "Normal Metabolizer",
    "Intermediate Metabolizer",
    "Poor Metabolizer"
]

# Strong CYP2D6 inhibitors that can cause phenoconversion.
# A genetically normal metabolizer taking one of these drugs
# effectively becomes a poor metabolizer for CYP2D6 substrates.
CYP2D6_STRONG_INHIBITORS = [
    "fluoxetine", "paroxetine", "bupropion", "quinidine", "cinacalcet"
]

# CYP2D6 moderate inhibitors (less dramatic phenoconversion).
CYP2D6_MODERATE_INHIBITORS = [
    "duloxetine", "sertraline", "terbinafine", "abiraterone"
]

# =============================================================================
# STEP 1: SOURCE DATA INGESTION
# =============================================================================
# Maps to pseudocode Step 1 in the main recipe.
# Downloads source database files, validates them, and stages in S3.

s3_client = boto3.client("s3")

def ingest_source(source_name: str, source_url: str, expected_record_count_min: int) -> str:
    """
    Download a source database file and stage it in S3.

    We validate that the download is complete (not truncated) by checking
    that the record count meets a minimum threshold. A sudden drop in
    record count usually means a corrupted or partial download.

    Args:
        source_name: Identifier for the source (e.g., "pharmgkb", "clinvar")
        source_url: URL to download the source data from
        expected_record_count_min: Minimum expected records. If fewer, something is wrong.

    Returns:
        S3 key where the validated source data was stored.
    """
    # Download the source file.
    # In production, you'd handle retries, timeouts, and partial downloads.
    response = requests.get(source_url, timeout=300)
    response.raise_for_status()

    raw_data = response.content
    today = datetime.now(timezone.utc).strftime("%Y-%m-%d")

    # Basic validation: check that we got a reasonable amount of data.
    # A real implementation would also verify checksums if the source provides them.
    record_count = raw_data.count(b"\n")  # Rough line count for TSV/CSV sources
    if record_count < expected_record_count_min:
        raise ValueError(
            f"Source {source_name} returned only {record_count} records "
            f"(expected at least {expected_record_count_min}). "
            f"Possible truncated download."
        )

    # Stage in S3 with metadata for traceability.
    s3_key = f"sources/{source_name}/{today}/{source_name}-raw.tsv"
    s3_client.put_object(
        Bucket=DATA_BUCKET,
        Key=s3_key,
        Body=raw_data,
        Metadata={
            "source": source_name,
            "download-date": today,
            "record-count": str(record_count),
        },
        ServerSideEncryption="aws:kms",
    )

    print(f"[Ingest] {source_name}: {record_count} records staged at s3://{DATA_BUCKET}/{s3_key}")
    return s3_key

# =============================================================================
# STEP 2: ENTITY RESOLUTION
# =============================================================================
# Maps to pseudocode Step 2 in the main recipe.
# Resolves identifiers across sources to canonical forms.

# Cross-reference tables for entity resolution.
# In production, these come from a maintained mapping database (or a service like UMLS).
# Here we show the structure with a few examples.

GENE_XREF = {
    # Maps various gene identifiers to a canonical form.
    # Key: (id_type, id_value), Value: canonical gene dict
    ("symbol", "CYP2D6"): {
        "canonical_id": "gene:1565",
        "symbol": "CYP2D6",
        "entrez_id": "1565",
        "ensembl_id": "ENSG00000100197",
        "hgnc_id": "HGNC:2625",
    },
    ("symbol", "CYP2C19"): {
        "canonical_id": "gene:1557",
        "symbol": "CYP2C19",
        "entrez_id": "1557",
        "ensembl_id": "ENSG00000165841",
        "hgnc_id": "HGNC:2621",
    },
}

DRUG_XREF = {
    # Maps drug identifiers to canonical form.
    ("name", "tamoxifen"): {
        "canonical_id": "drug:DB00675",
        "name": "Tamoxifen",
        "drugbank_id": "DB00675",
        "rxnorm_cui": "10324",
        "atc_code": "L02BA01",
    },
    ("name", "omeprazole"): {
        "canonical_id": "drug:DB00338",
        "name": "Omeprazole",
        "drugbank_id": "DB00338",
        "rxnorm_cui": "7646",
        "atc_code": "A02BC01",
    },
}

def resolve_gene(symbol: str = None, entrez_id: str = None) -> Optional[dict]:
    """
    Resolve a gene identifier to its canonical form.

    Tries symbol first, then entrez_id. Returns None if no mapping found
    (which means we need to add it to our cross-reference table).
    """
    if symbol and ("symbol", symbol) in GENE_XREF:
        return GENE_XREF[("symbol", symbol)]
    if entrez_id and ("entrez_id", entrez_id) in GENE_XREF:
        return GENE_XREF[("entrez_id", entrez_id)]
    # Unresolved entity. In production, log this for manual review.
    print(f"[EntityResolution] WARNING: Could not resolve gene symbol={symbol} entrez={entrez_id}")
    return None

def resolve_drug(name: str = None, drugbank_id: str = None) -> Optional[dict]:
    """
    Resolve a drug identifier to its canonical form.

    Drug name matching is case-insensitive because sources are inconsistent
    about capitalization.
    """
    if name and ("name", name.lower()) in DRUG_XREF:
        return DRUG_XREF[("name", name.lower())]
    if drugbank_id and ("drugbank_id", drugbank_id) in DRUG_XREF:
        return DRUG_XREF[("drugbank_id", drugbank_id)]
    print(f"[EntityResolution] WARNING: Could not resolve drug name={name} dbid={drugbank_id}")
    return None

# =============================================================================
# STEP 3: GRAPH CONSTRUCTION
# =============================================================================
# Maps to pseudocode Step 3 in the main recipe.
# Produces Neptune bulk load CSV files from resolved entities.

import csv
import io

def build_node_csv(nodes: list) -> str:
    """
    Build a Neptune-compatible CSV for node loading.

    Neptune bulk load format for nodes:
    ~id, ~label, property1:type, property2:type, ...

    The ~id must be unique across all nodes. The ~label is the node type.
    Property types can be: String, Int, Long, Float, Double, Bool, Date.
    """
    output = io.StringIO()
    writer = csv.writer(output)

    # Header row with Neptune's special column markers
    writer.writerow([
        "~id", "~label",
        "symbol:String", "name:String", "entrez_id:String",
        "rxnorm_cui:String", "therapeutic_class:String",
        "functional_status:String", "evidence_level:String"
    ])

    for node in nodes:
        writer.writerow([
            node["id"],
            node["label"],
            node.get("symbol", ""),
            node.get("name", ""),
            node.get("entrez_id", ""),
            node.get("rxnorm_cui", ""),
            node.get("therapeutic_class", ""),
            node.get("functional_status", ""),
            node.get("evidence_level", ""),
        ])

    return output.getvalue()

def build_edge_csv(edges: list) -> str:
    """
    Build a Neptune-compatible CSV for edge loading.

    Neptune bulk load format for edges:
    ~id, ~from, ~to, ~label, property1:type, property2:type, ...

    ~from and ~to reference node ~id values.
    ~label is the relationship type.
    """
    output = io.StringIO()
    writer = csv.writer(output)

    writer.writerow([
        "~id", "~from", "~to", "~label",
        "evidence_level:String", "source:String",
        "cpic_level:String", "clinical_annotation:String"
    ])

    for edge in edges:
        writer.writerow([
            edge["id"],
            edge["from_id"],
            edge["to_id"],
            edge["label"],
            edge.get("evidence_level", ""),
            edge.get("source", ""),
            edge.get("cpic_level", ""),
            edge.get("clinical_annotation", ""),
        ])

    return output.getvalue()

def upload_graph_load_files(nodes: list, edges: list, version: str) -> dict:
    """
    Build CSV files and upload to S3 for Neptune bulk loading.

    Returns metadata about what was uploaded.
    """
    node_csv = build_node_csv(nodes)
    edge_csv = build_edge_csv(edges)

    node_key = f"graph-loads/{version}/nodes.csv"
    edge_key = f"graph-loads/{version}/edges.csv"

    s3_client.put_object(
        Bucket=DATA_BUCKET,
        Key=node_key,
        Body=node_csv.encode("utf-8"),
        ServerSideEncryption="aws:kms",
    )
    s3_client.put_object(
        Bucket=DATA_BUCKET,
        Key=edge_key,
        Body=edge_csv.encode("utf-8"),
        ServerSideEncryption="aws:kms",
    )

    print(f"[GraphBuild] Uploaded {len(nodes)} nodes and {len(edges)} edges for version {version}")
    return {"node_count": len(nodes), "edge_count": len(edges), "version": version}

# =============================================================================
# STEP 4: NEPTUNE BULK LOAD
# =============================================================================
# Maps to pseudocode Step 6d in the main recipe.
# Triggers Neptune's bulk loader to ingest graph data from S3.

import time

def trigger_neptune_bulk_load(version: str, neptune_load_role_arn: str) -> str:
    """
    Start a Neptune bulk load job from S3.

    Neptune's bulk loader reads CSV files from S3 and creates nodes/edges
    in the graph. This is orders of magnitude faster than individual inserts
    for large datasets.

    The IAM role must have:
    - S3 read access to the source bucket
    - A trust policy allowing neptune.amazonaws.com to assume it

    Args:
        version: Graph version identifier (determines S3 prefix)
        neptune_load_role_arn: IAM role ARN that Neptune assumes for S3 access

    Returns:
        Load job ID for status tracking.
    """
    load_url = f"https://{NEPTUNE_ENDPOINT}:{NEPTUNE_PORT}/loader"

    payload = {
        "source": f"s3://{DATA_BUCKET}/graph-loads/{version}/",
        "format": "csv",
        "iamRoleArn": neptune_load_role_arn,
        "region": "us-east-1",
        "failOnError": "TRUE",
        "parallelism": "MEDIUM",
        "updateSingleCardinalityProperties": "TRUE",
    }

    response = requests.post(load_url, json=payload)
    response.raise_for_status()

    load_id = response.json()["payload"]["loadId"]
    print(f"[BulkLoad] Started load job {load_id} for version {version}")
    return load_id

def wait_for_load_completion(load_id: str, timeout_seconds: int = 3600) -> dict:
    """
    Poll Neptune loader status until the job completes or times out.

    Neptune bulk loads can take minutes to hours depending on data volume.
    For our pharmacogenomics graph (~2.5M nodes, ~15M edges), expect 45-90 minutes.
    """
    status_url = f"https://{NEPTUNE_ENDPOINT}:{NEPTUNE_PORT}/loader/{load_id}"
    start_time = time.time()

    while time.time() - start_time < timeout_seconds:
        response = requests.get(status_url)
        status = response.json()["payload"]["overallStatus"]

        if status["status"] == "LOAD_COMPLETED":
            print(f"[BulkLoad] Load {load_id} completed successfully")
            return status
        elif status["status"] in ("LOAD_FAILED", "LOAD_CANCELLED_DUE_TO_ERRORS"):
            raise RuntimeError(f"Neptune load failed: {status}")

        # Poll every 30 seconds
        print(f"[BulkLoad] Status: {status['status']}. Waiting...")
        time.sleep(30)

    raise TimeoutError(f"Neptune load {load_id} did not complete within {timeout_seconds}s")

# =============================================================================
# STEP 5: PATIENT PHARMACOGENOMICS QUERY
# =============================================================================
# Maps to pseudocode Step 5 in the main recipe.
# Queries the graph for a specific patient's actionable findings.

def execute_opencypher_query(query: str, parameters: dict = None) -> list:
    """
    Execute an openCypher query against Neptune.

    Neptune's openCypher endpoint accepts queries via HTTPS POST.
    Parameters are passed separately (not interpolated into the query string)
    to prevent injection.

    In production with IAM auth enabled, you'd sign this request with SigV4.
    This example assumes VPC-only access without IAM auth for simplicity.
    """
    payload = {"query": query}
    if parameters:
        payload["parameters"] = json.dumps(parameters)

    response = requests.post(NEPTUNE_OPENCYPHER_URL, json=payload)
    response.raise_for_status()
    return response.json().get("results", [])

def get_patient_phenotype(gene: str, diplotype: str) -> Optional[dict]:
    """
    Look up the metabolizer phenotype for a given gene and diplotype.

    This traverses: Diplotype node -> results_in_phenotype -> Phenotype node.

    Example: CYP2D6 *4/*4 -> Poor Metabolizer
    """
    query = """
        MATCH (p:Phenotype)<-[:results_in_phenotype]-(d:Diplotype)
        WHERE d.gene = $gene AND d.diplotype = $diplotype
        RETURN p.phenotype AS phenotype, p.activity_score_range AS activity_range
    """
    results = execute_opencypher_query(query, {"gene": gene, "diplotype": diplotype})

    if results:
        return results[0]
    return None

def find_gene_drug_interactions(drug_rxnorm_cui: str, evidence_threshold: str) -> list:
    """
    Find all gene interactions for a given drug, filtered by evidence level.

    This traverses: Gene -[metabolizes|targets|transports]-> Drug
    and returns only relationships at or above the evidence threshold.
    """
    # Build the list of acceptable evidence levels (at or above threshold)
    threshold_index = EVIDENCE_LEVELS.index(evidence_threshold)
    acceptable_levels = EVIDENCE_LEVELS[threshold_index:]

    query = """
        MATCH (g:Gene)-[r]->(d:Drug)
        WHERE d.rxnorm_cui = $drug_cui
          AND r.evidence_level IN $levels
          AND type(r) IN ['metabolizes', 'targets', 'transports']
        RETURN g.symbol AS gene, type(r) AS relationship,
               r.evidence_level AS evidence, r.clinical_annotation AS annotation
    """
    return execute_opencypher_query(query, {
        "drug_cui": drug_rxnorm_cui,
        "levels": acceptable_levels,
    })

def get_recommendation(gene: str, phenotype: str, drug_rxnorm_cui: str) -> Optional[dict]:
    """
    Get the CPIC recommendation for a specific gene-phenotype-drug combination.

    This traverses: Phenotype -[recommendation]-> Drug
    and returns the clinical action (use_alternative, dose_adjust, standard_dose).
    """
    query = """
        MATCH (p:Phenotype)-[rec:recommendation]->(d:Drug)
        WHERE p.gene = $gene AND p.phenotype = $phenotype AND d.rxnorm_cui = $drug_cui
        RETURN rec.action AS action, rec.strength AS strength,
               rec.alternatives AS alternatives, rec.guideline_version AS guideline
    """
    results = execute_opencypher_query(query, {
        "gene": gene,
        "phenotype": phenotype,
        "drug_cui": drug_rxnorm_cui,
    })

    if results:
        return results[0]
    return None

def check_phenoconversion(current_medications: list, gene: str, genetic_phenotype: str) -> Optional[dict]:
    """
    Check if concomitant medications cause phenoconversion for a given gene.

    Phenoconversion happens when a drug inhibits an enzyme so strongly that
    the patient's effective phenotype differs from their genetic phenotype.

    Example: A CYP2D6 Normal Metabolizer taking fluoxetine (strong CYP2D6 inhibitor)
    effectively becomes a Poor Metabolizer for CYP2D6 substrates.
    """
    if gene != "CYP2D6":
        # For this example, we only model CYP2D6 phenoconversion.
        # Production systems would cover CYP2C19, CYP3A4, etc.
        return None

    med_names = [m["name"].lower() for m in current_medications]

    strong_inhibitors_present = [
        drug for drug in CYP2D6_STRONG_INHIBITORS if drug in med_names
    ]
    moderate_inhibitors_present = [
        drug for drug in CYP2D6_MODERATE_INHIBITORS if drug in med_names
    ]

    if strong_inhibitors_present:
        effective_phenotype = "Poor Metabolizer"
    elif moderate_inhibitors_present and genetic_phenotype in ("Normal Metabolizer", "Rapid Metabolizer"):
        effective_phenotype = "Intermediate Metabolizer"
    else:
        return None  # No phenoconversion detected

    if effective_phenotype != genetic_phenotype:
        return {
            "type": "phenoconversion_warning",
            "gene": gene,
            "genetic_phenotype": genetic_phenotype,
            "effective_phenotype": effective_phenotype,
            "inhibiting_drugs": strong_inhibitors_present + moderate_inhibitors_present,
            "clinical_note": (
                f"Patient is genetically {genetic_phenotype} for {gene} "
                f"but concomitant {strong_inhibitors_present or moderate_inhibitors_present} "
                f"may result in effective {effective_phenotype} status"
            ),
        }
    return None

def query_patient_pharmacogenomics(
    patient_variants: list,
    current_medications: list,
    evidence_threshold: str = DEFAULT_EVIDENCE_THRESHOLD,
) -> dict:
    """
    Main query function: given a patient's variants and medications,
    find all actionable pharmacogenomic findings.

    This is the function that a clinical decision support system would call.

    Args:
        patient_variants: List of dicts with keys: gene, diplotype
            Example: [{"gene": "CYP2D6", "diplotype": "*4/*4"}, ...]
        current_medications: List of dicts with keys: name, rxnorm_cui
            Example: [{"name": "Tamoxifen", "rxnorm_cui": "10324"}, ...]
        evidence_threshold: Minimum evidence level for findings (default "2A")

    Returns:
        Dict with findings, metadata, and confidence information.
    """
    import re

    findings = []

    # Input validation: reject malformed inputs before they reach Neptune.
    # This prevents injection, catches upstream data quality issues early,
    # and produces clean audit logs.
    # Note: In production, raw variant data (VCF) is validated at an earlier
    # pipeline step. By this point, variants are already resolved to
    # gene + diplotype form. We validate the gene is a known pharmacogene
    # and the medication has a valid RxNorm CUI format.
    validated_variants = []
    for variant in patient_variants:
        if variant.get("gene") not in CPIC_PHARMACOGENES:
            print(f"  [SKIP] Gene not in known pharmacogenes list: {variant.get('gene')}")
            continue
        validated_variants.append(variant)

    validated_medications = []
    for medication in current_medications:
        if not re.match(r"^\d+$", medication.get("rxnorm_cui", "")):
            print(f"  [SKIP] Invalid RxNorm CUI format: {medication.get('rxnorm_cui')}")
            continue
        validated_medications.append(medication)

    # Step 5a: Determine patient phenotypes from their variants
    patient_phenotypes = {}
    for variant in validated_variants:
        gene = variant["gene"]
        diplotype = variant["diplotype"]

        phenotype_result = get_patient_phenotype(gene, diplotype)
        if phenotype_result:
            patient_phenotypes[gene] = phenotype_result["phenotype"]
            print(f"  {gene} {diplotype} -> {phenotype_result['phenotype']}")

    # Step 5b: Check each medication against patient phenotypes
    for medication in validated_medications:
        interactions = find_gene_drug_interactions(
            medication["rxnorm_cui"], evidence_threshold
        )

        for interaction in interactions:
            gene = interaction["gene"]
            if gene in patient_phenotypes:
                phenotype = patient_phenotypes[gene]

                recommendation = get_recommendation(
                    gene, phenotype, medication["rxnorm_cui"]
                )

                if recommendation and recommendation["action"] != "standard_dose":
                    findings.append({
                        "medication": medication["name"],
                        "gene": gene,
                        "patient_phenotype": phenotype,
                        "recommendation": recommendation["action"],
                        "strength": recommendation["strength"],
                        "alternatives": recommendation.get("alternatives", []),
                        "evidence_level": interaction["evidence"],
                        "guideline": recommendation.get("guideline", ""),
                        "clinical_context": interaction.get("annotation", ""),
                    })

    # Step 5c: Check for phenoconversion
    for gene, phenotype in patient_phenotypes.items():
        phenoconversion = check_phenoconversion(validated_medications, gene, phenotype)
        if phenoconversion:
            findings.append(phenoconversion)

    # Sort by clinical urgency (actionable findings first, then info)
    findings.sort(key=lambda f: (
        0 if f.get("recommendation") == "use_alternative" else
        1 if f.get("recommendation") == "dose_adjust" else
        2
    ))

    return {
        "query_timestamp": datetime.now(timezone.utc).isoformat(),
        "evidence_threshold_applied": evidence_threshold,
        "genes_with_phenotype": len(patient_phenotypes),
        "findings": findings,
        "medications_without_findings": [
            m["name"] for m in validated_medications
            if not any(f.get("medication") == m["name"] for f in findings)
        ],
    }

# =============================================================================
# FULL PIPELINE
# =============================================================================

def run_patient_query_example():
    """
    Demonstrate the full patient query flow.

    This assumes the graph is already loaded (Steps 1-4 have been run).
    In production, the graph update pipeline runs on a schedule via EventBridge
    and Step Functions. The patient query runs on-demand when triggered by
    the EHR or clinical decision support system.
    """
    print("=" * 60)
    print("Disease-Gene-Drug Relationship Graph: Patient Query")
    print("=" * 60)

    # Example patient data.
    # In production, this comes from the lab's pharmacogenomic test results
    # and the patient's current medication list from the EHR.
    patient_variants = [
        {"gene": "CYP2D6", "diplotype": "*4/*4"},
        {"gene": "CYP2C19", "diplotype": "*1/*17"},
        {"gene": "CYP2C9", "diplotype": "*1/*1"},
        {"gene": "SLCO1B1", "diplotype": "*1/*1"},
        {"gene": "DPYD", "diplotype": "*1/*1"},
    ]

    current_medications = [
        {"name": "Tamoxifen", "rxnorm_cui": "10324"},
        {"name": "Omeprazole", "rxnorm_cui": "7646"},
        {"name": "Lisinopril", "rxnorm_cui": "29046"},
        {"name": "Metformin", "rxnorm_cui": "6809"},
        {"name": "Atorvastatin", "rxnorm_cui": "83367"},
    ]

    print("\nPatient Variants:")
    for v in patient_variants:
        print(f"  {v['gene']}: {v['diplotype']}")

    print("\nCurrent Medications:")
    for m in current_medications:
        print(f"  {m['name']} (RxNorm: {m['rxnorm_cui']})")

    print("\nDetermining phenotypes...")
    results = query_patient_pharmacogenomics(
        patient_variants=patient_variants,
        current_medications=current_medications,
        evidence_threshold="2A",
    )

    print(f"\nResults ({len(results['findings'])} findings):")
    print(json.dumps(results, indent=2, default=str))

    return results

def run_graph_update_example():
    """
    Demonstrate the graph update pipeline.

    This would normally be triggered by EventBridge on a schedule:
    - ClinVar: weekly
    - PharmGKB: monthly
    - CPIC: when new guidelines are published
    - DrugBank: quarterly
    """
    print("=" * 60)
    print("Disease-Gene-Drug Relationship Graph: Update Pipeline")
    print("=" * 60)

    version = datetime.now(timezone.utc).strftime("v%Y-%m-%d")
    print(f"\nBuilding graph version: {version}")

    # In production, these URLs point to the actual source database downloads.
    # PharmGKB requires registration. ClinVar is publicly accessible.
    # We skip the actual download here and show the structure.

    print("\nStep 1: Ingesting sources...")
    # ingest_source("pharmgkb", "https://api.pharmgkb.org/...", expected_record_count_min=600)
    # ingest_source("clinvar", "https://ftp.ncbi.nlm.nih.gov/pub/clinvar/...", expected_record_count_min=1000000)
    print("  (Skipped in example. Would download PharmGKB, ClinVar, DrugBank, CPIC)")

    print("\nStep 2: Resolving entities...")
    # In production, the Glue job processes millions of records.
    # Here we show a small example.
    gene = resolve_gene(symbol="CYP2D6")
    drug = resolve_drug(name="tamoxifen")
    print(f"  CYP2D6 -> {gene['canonical_id'] if gene else 'UNRESOLVED'}")
    print(f"  Tamoxifen -> {drug['canonical_id'] if drug else 'UNRESOLVED'}")

    print("\nStep 3: Building graph load files...")
    # Example nodes and edges (in production, millions of these)
    sample_nodes = [
        {"id": "gene:1565", "label": "Gene", "symbol": "CYP2D6", "entrez_id": "1565"},
        {"id": "drug:DB00675", "label": "Drug", "name": "Tamoxifen", "rxnorm_cui": "10324"},
    ]
    sample_edges = [
        {
            "id": "edge:cyp2d6-tamoxifen",
            "from_id": "gene:1565",
            "to_id": "drug:DB00675",
            "label": "metabolizes",
            "evidence_level": "1A",
            "source": "PharmGKB",
            "cpic_level": "A",
            "clinical_annotation": "CYP2D6 metabolizes tamoxifen to active metabolite endoxifen",
        },
    ]
    stats = upload_graph_load_files(sample_nodes, sample_edges, version)
    print(f"  Uploaded: {stats['node_count']} nodes, {stats['edge_count']} edges")

    print("\nStep 4: Bulk loading into Neptune...")
    # trigger_neptune_bulk_load(version, "arn:aws:iam::123456789012:role/NeptuneLoadRole")
    print("  (Skipped in example. Would trigger Neptune bulk loader)")

    print(f"\nGraph update pipeline complete for version {version}")

# Run the examples
if __name__ == "__main__":
    # Uncomment the pipeline you want to test:
    # run_graph_update_example()
    run_patient_query_example()