12. 臨床応用システム
12.1 がんゲノム医療
体細胞変異解析パイプライン:
class CancerGenomicsPipeline:
"""がんゲノム解析パイプライン"""
def __init__(self, tumor_sample, normal_sample):
self.tumor = tumor_sample
self.normal = normal_sample
def identify_driver_mutations(self):
"""
ドライバー変異の同定
"""
# 体細胞変異の検出
somatic_variants = self.call_somatic_variants()
# 既知のがん遺伝子との照合
cancer_genes = self.load_cancer_gene_census()
driver_candidates = []
for variant in somatic_variants:
if variant['gene'] in cancer_genes:
# 機能的影響の評価
impact = self.assess_functional_impact(variant)
if impact == 'HIGH':
driver_candidates.append(variant)
return driver_candidates
def tumor_mutational_burden(self):
"""腫瘍変異負荷(TMB)の計算"""
total_mutations = len(self.somatic_variants)
genome_size_mb = 3000 # ヒトゲノムサイズ(Mb)
tmb = total_mutations / genome_size_mb
return tmb
精密医療への応用:
- 分子標的薬の選択
- 免疫チェックポイント阻害薬の適応判定
- 臨床試験へのマッチング
12.2 希少疾患診断
エクソーム/ゲノム解析:
class RareDiseaseAnalyzer:
"""希少疾患原因遺伝子探索"""
def __init__(self, patient_vcf, parent_vcfs=None):
self.patient_vcf = patient_vcf
self.parent_vcfs = parent_vcfs
def trio_analysis(self):
"""
トリオ解析(患者+両親)
"""
if not self.parent_vcfs:
return None
# de novo変異の検出
de_novo = []
for variant in self.patient_vcf:
if (variant not in self.parent_vcfs['mother'] and
variant not in self.parent_vcfs['father']):
de_novo.append(variant)
return de_novo
def prioritize_variants(self, variants):
"""
変異の優先順位付け
"""
scored_variants = []
for variant in variants:
score = 0
# ACMG基準による評価
if self.is_null_variant(variant):
score += 10
if self.in_disease_gene(variant):
score += 5
if self.rare_in_population(variant):
score += 3
scored_variants.append((variant, score))
return sorted(scored_variants, key=lambda x: x[1], reverse=True)
12.3 薬理ゲノミクス
薬剤応答予測:
class PharmacogenomicsPredictor:
"""薬理ゲノミクス予測"""
def __init__(self):
self.pgx_database = self.load_pharmgkb()
def predict_drug_response(self, genotype, drug):
"""
薬剤応答の予測
"""
# 関連する薬理遺伝子変異の確認
relevant_variants = self.pgx_database.query(drug)
response_profile = {
'efficacy': 'normal',
'toxicity_risk': 'low',
'dosage_adjustment': 1.0
}
for variant in relevant_variants:
if self.has_variant(genotype, variant):
# 応答プロファイルの更新
response_profile = self.update_profile(
response_profile, variant
)
return response_profile
12.4 臨床レポート生成
自動レポート作成:
class ClinicalReportGenerator:
"""臨床レポート自動生成"""
def generate_report(self, analysis_results):
"""
解析結果から臨床レポートを生成
"""
report = {
'patient_id': analysis_results['patient_id'],
'test_date': datetime.now(),
'findings': [],
'recommendations': []
}
# 臨床的に重要な所見
for variant in analysis_results['pathogenic_variants']:
finding = self.format_clinical_finding(variant)
report['findings'].append(finding)
# 治療推奨
recommendations = self.generate_recommendations(
analysis_results
)
report['recommendations'] = recommendations
return self.format_as_pdf(report)
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