scientific-precision-oncology

name	scientific-precision-oncology
description	精密腫瘍学スキル。CIViC・OncoKB・cBioPortal・COSMIC・GDC/TCGA を統合し、 腫瘍ゲノムプロファイリング・分子標的選定・バイオマーカー評価・治療推奨を支援。 「がんゲノム解析して」「腫瘍プロファイリングして」「OncoKB で検索して」で発火。
tu_tools	[{"key":"oncokb","name":"OncoKB","description":"精密腫瘍学アノテーション"}]

Scientific Precision Oncology

精密腫瘍学（Precision Oncology）のための統合解析スキル。 腫瘍ゲノムデータベース（CIViC, OncoKB, cBioPortal, COSMIC, GDC/TCGA）を 横断的に活用し、分子特性に基づく治療戦略の立案を支援する。

When to Use

• 腫瘍体細胞変異のアクショナビリティ評価
• CIViC / OncoKB エビデンスレベル検索
• cBioPortal / TCGA 変異頻度・共起解析
• バイオマーカー駆動の治療推奨
• 分子標的治療のエビデンス統合
• がん種横断的なドライバー変異分析

Quick Start

精密腫瘍学パイプライン

Phase 1: Tumor Profiling
  - 体細胞変異・CNV・融合遺伝子の同定
  - TMB (Tumor Mutational Burden) 算出
  - MSI (Microsatellite Instability) 判定
    ↓
Phase 2: Variant Annotation
  - CIViC GraphQL API クエリ
  - OncoKB Annotation API
  - COSMIC / cBioPortal 変異頻度
    ↓
Phase 3: Actionability Assessment
  - OncoKB Evidence Level (1-4, R1-R2)
  - CIViC Evidence Rating (A-E)
  - AMP/ASCO/CAP Tiering (I-IV)
    ↓
Phase 4: Treatment Selection
  - 分子標的薬マッチング
  - 併用療法候補の同定
  - 耐性メカニズムの評価
    ↓
Phase 5: Clinical Trial Matching
  - ClinicalTrials.gov API 検索
  - 適格基準の自動マッチング
  - バイオマーカー駆動試験の優先順位付け
    ↓
Phase 6: Molecular Tumor Board Report
  - 統合ゲノムレポート生成
  - 治療推奨サマリー
  - エビデンステーブル

Workflow

1. CIViC (Clinical Interpretation of Variants in Cancer)

import requests
import pandas as pd

# === CIViC GraphQL API ===
CIVIC_URL = "https://civicdb.org/api/graphql"

def query_civic_variant(gene, variant_name):
    """CIViC で遺伝子バリアントのエビデンスを検索"""
    query = """
    query($gene: String!) {
      genes(name: $gene) {
        nodes {
          name
          description
          variants {
            nodes {
              name
              variantTypes { name }
              evidenceItems {
                nodes {
                  status
                  evidenceType
                  evidenceLevel
                  evidenceDirection
                  significance
                  disease { name }
                  therapies { name }
                  source { citation }
                }
              }
            }
          }
        }
      }
    }
    """
    resp = requests.post(CIVIC_URL, json={"query": query, "variables": {"gene": gene}})
    data = resp.json()["data"]["genes"]["nodes"]

    results = []
    for g in data:
        for v in g["variants"]["nodes"]:
            if variant_name.upper() in v["name"].upper():
                for ev in v["evidenceItems"]["nodes"]:
                    if ev["status"] == "accepted":
                        results.append({
                            "gene": g["name"],
                            "variant": v["name"],
                            "type": ev["evidenceType"],
                            "level": ev["evidenceLevel"],
                            "direction": ev["evidenceDirection"],
                            "significance": ev["significance"],
                            "disease": ev["disease"]["name"] if ev["disease"] else "",
                            "therapies": ", ".join(t["name"] for t in ev["therapies"]),
                            "citation": ev["source"]["citation"] if ev["source"] else "",
                        })
    return pd.DataFrame(results)

civic_results = query_civic_variant("BRAF", "V600E")
print(f"CIViC evidence items: {len(civic_results)}")
print(civic_results[["gene", "variant", "level", "significance", "therapies"]].head(10))

2. OncoKB Annotation

ONCOKB_URL = "https://www.oncokb.org/api/v1"
ONCOKB_TOKEN = "YOUR_ONCOKB_TOKEN"  # oncokb.org で取得

def annotate_oncokb(gene, variant, tumor_type=None):
    """OncoKB でバリアントをアノテーション"""
    headers = {"Authorization": f"Bearer {ONCOKB_TOKEN}"}

    # Variant annotation
    params = {"hugoSymbol": gene, "alteration": variant}
    if tumor_type:
        params["tumorType"] = tumor_type

    resp = requests.get(f"{ONCOKB_URL}/annotate/mutations/byHGVSg",
                        headers=headers, params=params)
    data = resp.json()

    return {
        "gene": gene,
        "variant": variant,
        "oncogenic": data.get("oncogenic", ""),
        "mutation_effect": data.get("mutationEffect", {}).get("knownEffect", ""),
        "highest_sensitive_level": data.get("highestSensitiveLevel", ""),
        "highest_resistance_level": data.get("highestResistanceLevel", ""),
        "treatments": [
            {
                "drugs": ", ".join(d["drugName"] for d in t.get("drugs", [])),
                "level": t.get("level", ""),
                "indication": t.get(, {}).get(, ),
            }
             t  data.get(, [])
        ],
    }


ONCOKB_LEVELS = {
    : ,
    : ,
    : ,
    : ,
    : ,
    : ,
    : ,
}

3. cBioPortal 解析

CBIOPORTAL_URL = "https://www.cbioportal.org/api"

def query_cbioportal_mutations(gene, study_ids=None):
    """cBioPortal で変異頻度を取得"""
    if study_ids is None:
        # TCGA PanCancer Atlas studies
        resp = requests.get(f"{CBIOPORTAL_URL}/studies",
                            params={"keyword": "tcga_pan_can_atlas"})
        study_ids = [s["studyId"] for s in resp.json()]

    all_mutations = []
    for study_id in study_ids:
        # Get molecular profile
        profiles = requests.get(
            f"{CBIOPORTAL_URL}/molecular-profiles",
            params={"studyId": study_id}
        ).json()

        mut_profiles = [p for p in profiles if p["molecularAlterationType"] == "MUTATION_EXTENDED"]
        if not mut_profiles:
            continue

        profile_id = mut_profiles[0]["molecularProfileId"]

        # Get mutations
        mutations = requests.get(
            f"{CBIOPORTAL_URL}/molecular-profiles/{profile_id}/mutations",
            params={"entrezGeneId": gene_to_entrez(gene)}
        ).json()

        for m in mutations:
            all_mutations.append({
                "study": study_id,
                "sample_id": m.get(, ),
                : m.get(, ),
                : m.get(, ),
                : m.get(, ),
            })

    df = pd.DataFrame(all_mutations)

    
      df.empty:
        freq = df[].value_counts().head()
        ()
        (freq)

     df

 ():
    
    mapping = {: , : , : , : , : }
     mapping.get(gene_symbol, )

4. TMB / MSI 算出

def calculate_tmb(mutations_df, exome_size_mb=38.0):
    """
    Tumor Mutational Burden (TMB) 算出
    TMB = nonsynonymous mutations / exome size (Mb)
    """
    # Nonsynonymous のみ
    nonsynonymous = mutations_df[
        mutations_df["mutation_type"].isin([
            "Missense_Mutation", "Nonsense_Mutation",
            "Frame_Shift_Del", "Frame_Shift_Ins",
            "In_Frame_Del", "In_Frame_Ins",
            "Splice_Site", "Translation_Start_Site",
        ])
    ]

    tmb = len(nonsynonymous) / exome_size_mb

    # TMB カテゴリ (FDA: ≥10 mut/Mb = TMB-High → Pembrolizumab)
    if tmb >= 20:
        category = "TMB-Very High"
    elif tmb >= 10:
        category = "TMB-High"
    elif tmb >= 5:
        category = "TMB-Intermediate"
    else:
        category = "TMB-Low"

    return {"tmb": round(tmb, 2), "category": category,
            "nonsynonymous_count": len(nonsynonymous)}


def assess_msi(microsatellite_loci_results):
    """
    MSI (Microsatellite Instability) 判定
    Bethesda Panel: BAT25, BAT26, D2S123, D5S346, D17S250
    """
    unstable_count = sum(1 for r in microsatellite_loci_results  r[] == )
    total = (microsatellite_loci_results)

     unstable_count >= :
        status =   
     unstable_count == :
        status =   
    :
        status =     

     {: status, : unstable_count, : total}

5. 分子腫瘍ボードレポート生成

import json

def generate_mtb_report(patient_id, variants, civic_data, oncokb_data,
                        tmb_result, msi_result, output_dir="results"):
    """Molecular Tumor Board (MTB) レポート生成"""
    report = {
        "patient_id": patient_id,
        "report_date": pd.Timestamp.now().isoformat(),
        "genomic_profile": {
            "tmb": tmb_result,
            "msi": msi_result,
            "variants": variants,
        },
        "actionable_findings": [],
        "clinical_trials": [],
    }

    # Actionability 統合
    for v in variants:
        finding = {
            "gene": v["gene"],
            "variant": v["variant"],
            "oncokb_level": oncokb_data.get(f"{v['gene']}_{v['variant']}", {}).get("highest_sensitive_level", ""),
            "civic_evidence": [],
            "therapies": [],
        }

        # CIViC エビデンス統合
        civic_match = civic_data[
            (civic_data["gene"] == v["gene"]) &
            (civic_data["variant"].str.contains(v["variant"], case=False))
        ]
        for _, ev in civic_match.iterrows():
            finding["civic_evidence"].append({
                "level": ev[],
                : ev[],
                : ev[],
            })

        report[].append(finding)

    
     finding  report[]:
        level = finding[]
         level  [, ]:
            finding[] = 
         level  [, ]:
            finding[] = 
         level == :
            finding[] = 
        :
            finding[] = 

    
     (, )  f:
        json.dump(report, f, indent=, default=)

    
    md = 
    md += 
    md += 
    md += 
    md += 
    md += 
    md += 
    md += 
     f_  report[]:
        therapies = .join((
            e[]  e  f_[]  e[]
        ))
        md += 

     (, )  f_out:
        f_out.write(md)

     report

---

Best Practices

1. 多データベースクロスバリデーション: CIViC + OncoKB + COSMIC の一致を重視
2. がん種特異的解釈: 同じ変異でもがん種により臨床的意義が異なる
3. エビデンスレベルの階層: Level 1 > 2 > 3A > 3B > 4 の優先順位に従う
4. 耐性変異を見逃さない: 一次耐性・獲得耐性の両方を評価
5. TMB/MSI を免疫療法判断の補助指標に: TMB-High (≥10) → Pembrolizumab 適応
6. VUS (Variant of Unknown Significance) の扱い: 機能予測ツールを補助的に使用

Completeness Checklist

• 体細胞変異リスト確定（SNV/Indel/CNV/Fusion）
• CIViC エビデンス取得
• OncoKB アノテーション完了
• cBioPortal 変異頻度確認
• TMB / MSI 判定
• AMP/ASCO/CAP Tier 分類
• 治療推奨サマリー（分子標的薬 + 免疫療法）
• 臨床試験マッチング
• MTB レポート（JSON + Markdown）生成

References

Output Files

ファイル	形式	生成タイミング
results/mtb_report.json	分子腫瘍ボードレポート（JSON）	プロファイリング完了時
results/mtb_report.md	MTB レポート（Markdown）	レポート生成時
results/variant_actionability.json	バリアント臨床的意義（JSON）	アノテーション完了時

利用可能ツール

ToolUniverse SMCP 経由で利用可能な外部ツール。

カテゴリ	主要ツール	用途
OncoKB	OncoKB_annotate_variant	体細胞変異の臨床的アノテーション
OncoKB	OncoKB_get_cancer_genes	がん遺伝子リスト取得
CIViC	civic_search_evidence_items	臨床エビデンス検索
CIViC	civic_get_variant	バリアント臨床解釈
COSMIC	COSMIC_get_mutations_by_gene	体細胞変異頻度データ
GDC	GDC_get_mutation_frequency	TCGA 変異頻度
ClinicalTrials	search_clinical_trials	腫瘍学臨床試験マッチング

参照スキル

スキル	連携
scientific-variant-interpretation	← 生殖細胞系変異の ACMG 分類
scientific-clinical-decision-support	→ 治療推奨の臨床意思決定反映
scientific-bioinformatics	← RNA-seq 発現データ解析
scientific-network-analysis	← シグナル経路解析・ドライバー予測
scientific-drug-target-profiling	← 標的ドラッガビリティ評価
scientific-disease-research	← がん種の疫学・遺伝的背景
scientific-deep-research	← 腫瘍学最新文献リサーチ
scientific-pharmacogenomics	← PGx 代謝型・投与量調整