--- name: cbioportal-database description: 查询cBioPortal获取癌症基因组学数据，包括体细胞突变、拷贝数改变、基因表达和数百项癌症研究的生存数据。对于癌症靶点验证、癌基因/抑癌基因分析和患者水平基因组分析至关重要。 license: LGPL-3.0 metadata: skill-author: Kuan-lin Huang --- # cBioPortal 数据库 ## 概述 cBioPortal 癌症基因组学(https://www.cbioportal.org/)是一个开放获取的资源,用于探索、可视化和分析多维癌症基因组学数据。它托管了来自癌症基因组图谱(TCGA)、AACR GENIE 项目、MSK-IMPACT 和数百个其他癌症研究的数据——涵盖数千个癌症样本的突变、拷贝数变异(CNA)、结构变异、mRNA/蛋白质表达、甲基化和临床数据。 **关键资源:** - cBioPortal 网站: https://www.cbioportal.org/ - REST API: https://www.cbioportal.org/api/ - API 文档(Swagger): https://www.cbioportal.org/api/swagger-ui/index.html - Python 客户端: `bravado` 或 `requests` - GitHub: https://github.com/cBioPortal/cbioportal ## 何时使用此技能 在以下情况下使用 cBioPortal: - **突变谱系**: 特定癌症类型中有多少比例的样本在特定基因中发生突变? - **癌基因/TSG 验证**: 某基因在癌症中是否频繁突变、扩增或缺失? - **共突变模式**: 基因 A 和基因 B 的突变是否互斥或共存? - **生存分析**: 某基因的突变是否与更好的或更差的患者预后相关? - **变异谱系**: 哪些类型的变异(错义、截短、扩增、缺失)影响某个基因? - **泛癌分析**: 比较不同癌症类型的变异频率 - **临床关联**: 将基因组变异与临床变量(分期、分级、治疗反应)联系起来 - **TCGA/GENIE 探索**: 系统访问 TCGA 和临床测序数据集 ## 核心功能 ### 1. cBioPortal REST API 基础 URL: `https://www.cbioportal.org/api` API 是 RESTful 的,返回 JSON,公共数据无需 API 密钥。 ```python import requests BASE_URL = "https://www.cbioportal.org/api" HEADERS = {"Accept": "application/json", "Content-Type": "application/json"} def cbioportal_get(endpoint, params=None): url = f"{BASE_URL}/{endpoint}" response = requests.get(url, params=params, headers=HEADERS) response.raise_for_status() return response.json() def cbioportal_post(endpoint, body): url = f"{BASE_URL}/{endpoint}" response = requests.post(url, json=body, headers=HEADERS) response.raise_for_status() return response.json() ``` ### 2. 浏览研究 ```python def get_all_studies(): """列出所有可用的癌症研究。""" return cbioportal_get("studies", {"pageSize": 500}) # 每个研究包含: # studyId: 唯一标识符(例如 "brca_tcga") # name: 人类可读的名称 # description: 数据集描述 # cancerTypeId: 癌症类型缩写 # referenceGenome: GRCh37 或 GRCh38 # pmid: 相关发表文献 studies = get_all_studies() print(f"研究总数: {len(studies)}") # 常见的 TCGA 研究 ID: # brca_tcga, luad_tcga, coadread_tcga, gbm_tcga, prad_tcga, # skcm_tcga, blca_tcga, hnsc_tcga, lihc_tcga, stad_tcga # 筛选 TCGA 研究 tcga_studies = [s for s in studies if "tcga" in s["studyId"]] print([s["studyId"] for s in tcga_studies[:10]]) ``` ### 3. 分子谱系 每个研究有多个分子谱系(突变、CNA、表达等): ```python def get_molecular_profiles(study_id): """获取研究的所有分子谱系。""" return cbioportal_get(f"studies/{study_id}/molecular-profiles") profiles = get_molecular_profiles("brca_tcga") for p in profiles: print(f" {p['molecularProfileId']}: {p['name']} ({p['molecularAlterationType']})") # 变异类型: # MUTATION_EXTENDED — 体细胞突变 # COPY_NUMBER_ALTERATION — CNA (GISTIC) # MRNA_EXPRESSION — mRNA 表达 # PROTEIN_LEVEL — RPPA 蛋白质表达 # STRUCTURAL_VARIANT — 融合/重排 ``` ### 4. 突变数据 ```python def get_mutations(molecular_profile_id, entrez_gene_ids, sample_list_id=None): """获取分子谱系中指定基因的突变。""" body = { "entrezGeneIds": entrez_gene_ids, "sampleListId": sample_list_id or molecular_profile_id.replace("_mutations", "_all") } return cbioportal_post( f"molecular-profiles/{molecular_profile_id}/mutations/fetch", body ) # BRCA1 的 Entrez ID 是 672,TP53 是 7157,PTEN 是 5728 mutations = get_mutations("brca_tcga_mutations", entrez_gene_ids=[7157]) # TP53 # 每个突变记录包含: # patientId, sampleId, entrezGeneId, gene.hugoGeneSymbol # mutationType (Missense_Mutation, Nonsense_Mutation, Frame_Shift_Del 等) # proteinChange (例如 "R175H") # variantClassification, variantType # ncbiBuild, chr, startPosition, endPosition, referenceAllele, variantAllele # mutationStatus (Somatic/Germline) # alleleFreqT (肿瘤 VAF) import pandas as pd df = pd.DataFrame(mutations) print(df[["patientId", "mutationType", "proteinChange", "alleleFreqT"]].head()) print(f"\n突变类型:\n{df['mutationType'].value_counts()}") ``` ### 5. 拷贝数变异数据 ```python def get_cna(molecular_profile_id, entrez_gene_ids): """获取离散 CNA 数据(GISTIC: -2, -1, 0, 1, 2)。""" body = { "entrezGeneIds": entrez_gene_ids, "sampleListId": molecular_profile_id.replace("_gistic", "_all").replace("_cna", "_all") } return cbioportal_post( f"molecular-profiles/{molecular_profile_id}/discrete-copy-number/fetch", body ) # GISTIC 值: # -2 = 深度缺失(纯合子丢失) # -1 = 浅度缺失(杂合子丢失) # 0 = 二倍体(中性) # 1 = 低水平获得 # 2 = 高水平扩增 cna_data = get_cna("brca_tcga_gistic", entrez_gene_ids=[1956]) # EGFR df_cna = pd.DataFrame(cna_data) print(df_cna["value"].value_counts()) ``` ### 6. 变异频率(OncoPrint 风格) ```python def get_alteration_frequency(study_id, gene_symbols, alteration_types=None): """计算癌症研究中基因的变异频率。""" import requests, pandas as pd # 获取样本列表 samples = requests.get( f"{BASE_URL}/studies/{study_id}/sample-lists", headers=HEADERS ).json() all_samples_id = next( (s["sampleListId"] for s in samples if s["category"] == "all_cases_in_study"), None ) total_samples = len(requests.get( f"{BASE_URL}/sample-lists/{all_samples_id}/sample-ids", headers=HEADERS ).json()) # 获取基因 Entrez ID gene_data = requests.post( f"{BASE_URL}/genes/fetch", json=[{"hugoGeneSymbol": g} for g in gene_symbols], headers=HEADERS ).json() entrez_ids = [g["entrezGeneId"] for g in gene_data] # 获取突变 mutation_profile = f"{study_id}_mutations" mutations = get_mutations(mutation_profile, entrez_ids, all_samples_id) freq = {} for g_symbol, e_id in zip(gene_symbols, entrez_ids): mutated = len(set(m["patientId"] for m in mutations if m["entrezGeneId"] == e_id)) freq[g_symbol] = mutated / total_samples * 100 return freq # 示例 freq = get_alteration_frequency("brca_tcga", ["TP53", "PIK3CA", "BRCA1", "BRCA2"]) for gene, pct in sorted(freq.items(), key=lambda x: -x[1]): print(f" {gene}: {pct:.1f}%") ``` ### 7. 临床数据 ```python def get_clinical_data(study_id, attribute_ids=None): """获取患者级临床数据。""" params = {"studyId": study_id} all_clinical = cbioportal_get( "clinical-data/fetch", params ) # 返回 {patientId, studyId, clinicalAttributeId, value} 列表 # 临床属性包括: # OS_STATUS, OS_MONTHS, DFS_STATUS, DFS_MONTHS (生存) # TUMOR_STAGE, GRADE, AGE, SEX, RACE # 研究特定属性各不相同 def get_clinical_attributes(study_id): """列出研究的所有可用临床属性。""" return cbioportal_get(f"studies/{study_id}/clinical-attributes") ``` ## 查询工作流程 ### 工作流程 1: 癌症类型中的基因变异谱系 ```python import requests, pandas as pd def alteration_profile(study_id, gene_symbol): """癌症研究中基因的完整变异谱系。""" # 1. 获取基因 Entrez ID gene_info = requests.post( f"{BASE_URL}/genes/fetch", json=[{"hugoGeneSymbol": gene_symbol}], headers=HEADERS ).json()[0] entrez_id = gene_info["entrezGeneId"] # 2. 获取突变 mutations = get_mutations(f"{study_id}_mutations", [entrez_id]) mut_df = pd.DataFrame(mutations) if mutations else pd.DataFrame() # 3. 获取 CNA cna = get_cna(f"{study_id}_gistic", [entrez_id]) cna_df = pd.DataFrame(cna) if cna else pd.DataFrame() # 4. 汇总 n_mut = len(set(mut_df["patientId"])) if not mut_df.empty else 0 n_amp = len(cna_df[cna_df["value"] == 2]) if not cna_df.empty else 0 n_del = len(cna_df[cna_df["value"] == -2]) if not cna_df.empty else 0 return {"mutations": n_mut, "amplifications": n_amp, "deep_deletions": n_del} result = alteration_profile("brca_tcga", "PIK3CA") print(result) ``` ### 工作流程 2: 泛癌基因突变频率 ```python import requests, pandas as pd def pan_cancer_mutation_freq(gene_symbol, cancer_study_ids=None): """多个癌症类型中基因的突变频率。""" studies = get_all_studies() if cancer_study_ids: studies = [s for s in studies if s["studyId"] in cancer_study_ids] results = [] for study in studies[:20]: # 演示时限制 try: freq = get_alteration_frequency(study["studyId"], [gene_symbol]) results.append({ "study": study["studyId"], "cancer": study.get("cancerTypeId", ""), "mutation_pct": freq.get(gene_symbol, 0) }) except Exception: pass df = pd.DataFrame(results).sort_values("mutation_pct", ascending=False) return df ``` ### 工作流程 3: 基于突变状态的生存分析 ```python import requests, pandas as pd def survival_by_mutation(study_id, gene_symbol): """获取按突变状态分组的生存数据。""" # 此工作流程获取临床和突变数据用于下游分析 gene_info = requests.post( f"{BASE_URL}/genes/fetch", json=[{"hugoGeneSymbol": gene_symbol}], headers=HEADERS ).json()[0] entrez_id = gene_info["entrezGeneId"] mutations = get_mutations(f"{study_id}_mutations", [entrez_id]) mutated_patients = set(m["patientId"] for m in mutations) clinical = cbioportal_get("clinical-data/fetch", {"studyId": study_id}) clinical_df = pd.DataFrame(clinical) os_data = clinical_df[clinical_df["clinicalAttributeId"].isin(["OS_MONTHS", "OS_STATUS"])] os_wide = os_data.pivot(index="patientId", columns="clinicalAttributeId", values="value") os_wide["mutated"] = os_wide.index.isin(mutated_patients) return os_wide ``` ## 关键 API 端点摘要 | 端点 | 描述 | |----------|-------------| | `GET /studies` | 列出所有研究 | | `GET /studies/{studyId}/molecular-profiles` | 研究的分子谱系 | | `POST /molecular-profiles/{profileId}/mutations/fetch` | 获取突变数据 | | `POST /molecular-profiles/{profileId}/discrete-copy-number/fetch` | 获取 CNA 数据 | | `POST /molecular-profiles/{profileId}/molecular-data/fetch` | 获取表达数据 | | `GET /studies/{studyId}/clinical-attributes` | 可用的临床变量 | | `GET /clinical-data/fetch` | 临床数据 | | `POST /genes/fetch` | 按符号或 Entrez ID 获取基因元数据 | | `GET /studies/{studyId}/sample-lists` | 样本列表 | ## 最佳实践 - **了解研究 ID**: 使用 Swagger UI 或 `GET /studies` 查找正确的研究 ID - **使用样本列表**: 每个研究有一个 `all` 样本列表和子集;始终指定合适的列表 - **TCGA vs. GENIE**: TCGA 数据全面但较旧;GENIE 有更新的临床测序数据 - **Entrez 基因 ID**: API 使用 Entrez ID — 使用 `/genes/fetch` 从符号转换 - **处理 404 错误**: 某些分子谱系可能不存在于所有研究 - **速率限制**: 批量查询时添加延迟;大规模分析考虑下载数据文件 ## 数据下载 对于大规模分析,直接下载研究数据: ```bash # 下载 TCGA BRCA 数据 wget https://cbioportal-datahub.s3.amazonaws.com/brca_tcga.tar.gz ``` ## 其他资源 - **cBioPortal 网站**: https://www.cbioportal.org/ - **API Swagger UI**: https://www.cbioportal.org/api/swagger-ui/index.html - **文档**: https://docs.cbioportal.org/ - **GitHub**: https://github.com/cBioPortal/cbioportal - **数据中心**: https://datahub.cbioportal.org/ - **引用**: Cerami E et al. (2012) Cancer Discovery. PMID: 22588877 - **API 客户端**: https://docs.cbioportal.org/web-api-and-clients/