scientific-icgc-cancer-data

name	scientific-icgc-cancer-data
description	ICGC がんゲノムデータスキル。ICGC ARGO DCC API および レガシー API による国際がんゲノムデータ検索・ドナー/ 検体/変異解析。直接 API (ToolUniverse 非連携)。
tu_tools	[]

Scientific ICGC Cancer Data

ICGC (International Cancer Genome Consortium) ARGO DCC API を 活用した国際がんゲノムデータ検索・変異統計・がん種横断解析 パイプラインを提供する。

When to Use

• 国際がんゲノムプロジェクトのデータを検索するとき
• がん種ごとの体細胞変異プロファイルを調べるとき
• ドナー・検体・変異の統計情報を取得するとき
• がんゲノムの変異シグネチャを分析するとき
• PCAWG (Pan-Cancer Analysis of Whole Genomes) データを活用するとき
• がん遺伝子変異の国際比較データが必要なとき

---

Quick Start

1. ICGC プロジェクト・ドナー検索

import requests
import pandas as pd

ICGC_BASE = "https://dcc.icgc.org/api/v1"


def icgc_search_projects(query=None, limit=50):
    """
    ICGC — がんゲノムプロジェクト検索。

    Parameters:
        query: str — 検索キーワード (例: "lung", "BRCA")
        limit: int — 最大結果数
    """
    url = f"{ICGC_BASE}/projects"
    params = {"size": limit, "from": 1}
    if query:
        params["filters"] = (
            f'{{"project":{{"primarySite":'
            f'{{"is":["{query}"]}}}}}}'
        )

    resp = requests.get(url, params=params, timeout=30)
    resp.raise_for_status()
    data = resp.json()

    results = []
    for hit in data.get("hits", []):
        results.append({
            "project_id": hit.get("id", ""),
            "project_name": hit.get("name", ""),
            "primary_site": hit.get("primarySite", ""),
            "tumour_type": hit.get("tumourType", ""),
            "tumour_subtype": hit.get("tumourSubtype", ""),
            "primary_country": "; ".join(
                hit.get("primaryCountries", [])),
            "total_donors": hit.get("totalDonorCount", 0),
            "ssm_count": hit.get("ssmCount", 0),
        })

    df = pd.DataFrame(results)
    if not df.empty:
        df = df.sort_values("total_donors", ascending=False)

    total = data.get("pagination", {}).get("total", 0)
    print(f"ICGC projects: {len(df)}/{total} "
          f"(query='{query}')")
    return df


def icgc_search_donors(project_id, limit=100):
    """
    ICGC — プロジェクト内ドナー検索。

    Parameters:
        project_id: str — プロジェクト ID (例: "BRCA-US")
        limit: int — 最大結果数
    """
    url = f"{ICGC_BASE}/donors"
    params = {
        "size": limit,
        "filters": (
            f'{{"donor":{{"projectId":'
            f'{{"is":["{project_id}"]}}}}}}'
        ),
    }

    resp = requests.get(url, params=params, timeout=30)
    resp.raise_for_status()
    data = resp.json()

    results = []
    for hit in data.get("hits", []):
        results.append({
            "donor_id": hit.get("id", ""),
            "project_id": project_id,
            "primary_site": hit.get("primarySite", ""),
            "gender": hit.get("gender", ""),
            "vital_status": hit.get("vitalStatus", ""),
            "age_at_diagnosis": hit.get("ageAtDiagnosis"),
            "disease_status": hit.get(
                "diseaseStatusLastFollowup", ""),
            "ssm_count": hit.get("ssmCount", 0),
        })

    df = pd.DataFrame(results)
    total = data.get("pagination", {}).get("total", 0)
    print(f"ICGC donors: {len(df)}/{total} "
          f"(project={project_id})")
    return df

2. 体細胞変異 (SSM) 検索

def icgc_search_mutations(gene_symbol=None,
                             project_id=None,
                             consequence_type=None,
                             limit=100):
    """
    ICGC — 体細胞変異 (Simple Somatic Mutation) 検索。

    Parameters:
        gene_symbol: str — 遺伝子シンボル (例: "TP53")
        project_id: str — プロジェクト ID
        consequence_type: str — 変異タイプ
            (例: "missense_variant")
        limit: int — 最大結果数
    """
    url = f"{ICGC_BASE}/mutations"
    filters = {}

    if gene_symbol:
        filters["gene"] = {"symbol": {"is": [gene_symbol]}}
    if project_id:
        filters["donor"] = {"projectId": {"is": [project_id]}}
    if consequence_type:
        filters["mutation"] = {
            "consequenceType": {"is": [consequence_type]}
        }

    import json
    params = {
        "size": limit,
        "filters": json.dumps(filters) if filters else "{}",
    }

    resp = requests.get(url, params=params, timeout=30)
    resp.raise_for_status()
    data = resp.json()

    results = []
    for hit in data.get("hits", []):
        # 主要な consequence 取得
        consequences = hit.get("consequences", [])
        top_cons = consequences[0] if consequences else {}

        results.append({
            "mutation_id": hit.get("id", ""),
            : hit.get(, ),
            : hit.get(),
            : hit.get(),
            : hit.get(, ),
            : hit.get(, ),
            : top_cons.get(, ),
            : top_cons.get(, ),
            : top_cons.get(, ),
            : hit.get(
                , ),
            : hit.get(
                , ),
            : hit.get(
                , ),
        })

    df = pd.DataFrame(results)
      df.empty:
        df = df.sort_values(,
                            ascending=)

    total = data.get(, {}).get(, )
    (
          )
     df

3. がん種統計・変異サマリー

def icgc_cancer_stats(project_id=None):
    """
    ICGC — がん種統計サマリー。

    Parameters:
        project_id: str — プロジェクト ID (None で全体統計)
    """
    if project_id:
        url = f"{ICGC_BASE}/projects/{project_id}"
        resp = requests.get(url, timeout=30)
        resp.raise_for_status()
        data = resp.json()

        stats = {
            "project_id": project_id,
            "project_name": data.get("name", ""),
            "primary_site": data.get("primarySite", ""),
            "total_donors": data.get("totalDonorCount", 0),
            "total_specimens": data.get(
                "totalSpecimenCount", 0),
            "ssm_count": data.get("ssmCount", 0),
            "repository": "; ".join(
                data.get("repository", [])),
        }
        print(f"ICGC stats: {project_id} — "
              f"{stats['total_donors']} donors, "
              f"{stats['ssm_count']} mutations")
        return stats
    else:
        # 全プロジェクト概要
        projects = icgc_search_projects(limit=200)
        summary = {
            "total_projects": len(projects),
            : projects[
                ].(),
            : projects[].(),
            : projects.groupby(
                )[].(
                ).sort_values(ascending=).head(
                ).to_dict(),
        }
        (
              )
         summary


 ():
    
    mutations = icgc_search_mutations(
        gene_symbol=gene_symbol, limit=)

     mutations.empty:
         pd.DataFrame()

    
    freq = mutations.groupby().agg(
        total_mutations=(, ),
        total_affected_donors=(, ),
        mutation_types=(,
                         x: .join(x.unique()[:])),
    ).reset_index()

    (
          )
     freq

4. ICGC 統合パイプライン

def icgc_pipeline(gene_symbols, cancer_site=None,
                     output_dir="results"):
    """
    ICGC 統合パイプライン。

    Parameters:
        gene_symbols: list[str] — 遺伝子リスト
        cancer_site: str — がん部位フィルタ
        output_dir: str — 出力ディレクトリ
    """
    from pathlib import Path
    output_dir = Path(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)

    # 1) プロジェクト検索
    projects = icgc_search_projects(query=cancer_site)
    projects.to_csv(output_dir / "projects.csv", index=False)

    # 2) 遺伝子別変異検索
    all_mutations = []
    for gene in gene_symbols:
        try:
            muts = icgc_search_mutations(
                gene_symbol=gene, limit=200)
            muts["query_gene"] = gene
            all_mutations.append(muts)
        except Exception as e:
            print(f"  Warning: {gene} — {e}")
            continue

    if all_mutations:
        combined = pd.concat(all_mutations,
                              ignore_index=True)
        combined.to_csv(output_dir / "mutations.csv",
                        index=False)

    # 3) がん種統計
    if not projects.empty:
        top_project = projects.iloc[0]["project_id"]
        stats = icgc_cancer_stats(project_id=top_project)
        pd.DataFrame([stats]).to_csv(
            output_dir / "cancer_stats.csv", index=False)

    print(f"ICGC pipeline: ")
     {: projects}

---

ToolUniverse 連携

TU Key	ツール名	連携内容
(direct)	ICGC DCC API	直接 REST API — TU 非連携

パイプライン統合

cancer-genomics → icgc-cancer-data → precision-oncology
  (がんゲノム全般)  (ICGC DCC API)   (精密腫瘍学)
       │                 │                 ↓
  tcga-data ────────────┘        clinical-decision-support
  (TCGA データ)      │            (臨床意思決定)
                     ↓
          variant-interpretation
          (変異臨床解釈)

パイプライン出力

ファイル	説明	次スキル
results/projects.csv	プロジェクト一覧	→ cancer-genomics
results/mutations.csv	体細胞変異	→ variant-interpretation
results/cancer_stats.csv	がん種統計	→ precision-oncology