Q (sonnet): "Are mutations in DNA repair pathway genes (BRCA1, BRCA2, ATM, CHEK2) enriched in specific cancer types across TCGA Pan-Cancer Atlas?" ▶ read_guide { "uri": "cbioportal://mutation-frequency-guide" } ▶ read_guide { "uri": "cbioportal://gene-resolution-guide" } ◀ result # Mutation Frequency Analysis Guide ## IMPORTANT: Reporting Mutation Frequencies - **ALWAYS report frequencies as percentages**, not raw counts: `frequency = (altered_samples / total_profiled_samples) × 100` - For quick frequency lookups, **prefer the TCGA Pan-Cancer Atlas study first**, then offer to expand to other studies - When reporting across multiple studies, show **ranges** (e.g., "TP53 is mutated in 30–60% of samples") rather than a single average - **NEVER** sum mutation events across studies to compute an aggregate frequency — this can exceed 100% due to double-counting - Warn users that samples may overlap across cohorts (e.g., MSK studies may share patients) - **Choose and state the counting unit**: use patient-level frequencies for prevalence/rate questions unless the user explicitly asks for samples; use sample-level frequencies when the user asks about samples. - **For "across cancer types" questions**, jump to the [Cross-Cancer-Type Mutation Frequency](#cross-cancer-type-mutation-frequency) section below — there is one correct recipe and several common wrong ones. ## Counting Unit: Samples vs Patients Before answering any mutation count or frequency question, decide whether the unit is samples or patients and state that choice in the answer. | User wording | Counting unit | |--------------|---------------| | "prevalence", "rate", "fraction of patients", "patients with", "how common is" | Patient-level: `COUNT(DISTINCT patient_unique_id)` | | "samples", "specimens", "biopsies", sample-level cohort composition | Sample-level: `COUNT(DISTINCT sample_unique_id)` | | Ambiguous | Ask, or default to patient-level for prevalence/rate language and say so | ### Cross-study sample-count caveat When an answer touches more than one study and reports a sample count, prepend a one-line caveat: > Sample IDs are unique within cBioPortal study prefixes, not guaranteed biological-sample identifiers across studies; overlapping cohorts can count the same patient/sample more than once. Prefer one of these safer approaches: - Use a shipped `cancer_study_query_preferences` cohort such as `pan_cancer_tcga` or `all_studies_non_redundant`. - Restrict to one named study. - Aggregate by `patient_unique_id` when the biological question is patient prevalence. ## STOP rule: a frequency above 100% means your query is wrong If your query returns a frequency over 100%, **do not try to debug or explain the data inconsistency to the user**. The cause is always one of these query bugs: - Summing mutation events instead of `COUNT(DISTINCT sample_unique_id)` for the numerator - Using a study-wide sample count as the denominator instead of the gene-specific profiled count - Cross-study aggregation where the same biological sample appears under multiple `sample_unique_id` values (e.g., MSK-IMPACT and MSK-CHORD share patients) - **Joining the profiled CTE through `gene_panel` / `gene_panel_list` without a WES branch.** `gene_panel_id = 'WES'` is *not* a row in … (36467 more chars) ◀ result # Gene Resolution Guide Use this guide before querying gene expression, mutation, copy-number, methylation, or structural-variant data when the user's gene term may be ambiguous. ## Routing Triggers Read this guide when the user mentions: - a gene family shorthand: `CD3`, `HLA`, `KRT`, `MUC`, `MT-`, `IGH`, `IGK`, `IGL` - a marker name that may refer to multiple genes or proteins - a gene alias, old symbol, or informal name - a wildcard-like term such as "all CD3 genes" These examples are not exhaustive. Apply this guide to any gene term that may resolve to multiple symbols, aliases, paralogs, family members, or marker genes. ## Core Rule Do not silently aggregate multiple genes when the user names an ambiguous symbol. Either ask for clarification or choose a clearly standard marker and state the choice. For example, "CD3 expression" can refer to `CD3D`, `CD3E`, or `CD3G`; in many immune-marker contexts `CD3E` is the standard marker, but the agent must not average all CD3 genes unless the user asks for a combined signature. ## Gene Discovery Query After validating the gene table exists, search exact symbols first, then prefix/alias-like matches: ```sql SELECT hugo_gene_symbol, entrez_gene_id FROM gene WHERE upper(hugo_gene_symbol) = upper('CD3') OR upper(hugo_gene_symbol) LIKE upper('CD3%') ORDER BY hugo_gene_symbol LIMIT 50; ``` If aliases are available in this deployment, inspect the relevant alias table before assuming no match. If no alias table exists, state that alias resolution is limited to available gene symbols. ## Answer Pattern If multiple plausible genes are found: > "CD3" is ambiguous in cBioPortal gene-symbol terms. I found `CD3D`, `CD3E`, and `CD3G`. Did you mean `CD3E` as a T-cell marker, or should I analyze all three separately? If the user clearly asks for a combined family/signature: - report each gene separately by default - only compute an average/signature if the user explicitly requests it - state exactly how the combined value was calculated ## Do Not - Do not average multiple genes into one expression value without explicit permission. - Do not rewrite an ambiguous symbol to a single gene without telling the user. - Do not treat a prefix match as a validated gene symbol. ▶ clickhouse_run_select_query { "query": "SELECT 'BRCA1' AS gene, * FROM gene_mutation_frequency_by_cancer_type(preference = 'pan_cancer_tcga', gene = 'BRCA1') UNION ALL SELECT 'BRCA2' AS gene, * FROM gene_mutation_frequency_by_cancer_type(preference = 'pan_cancer_tcga', gene = 'BRCA2') UNION ALL SELECT 'ATM' AS gene, * FROM gene_mutation_frequency_by_cancer_type(preference = 'pan_cancer_tcga', gene = 'ATM') UNION ALL SELECT 'CHEK2' AS gene, * FROM gene_mutation_frequency_by_cancer_type(preference = 'pan_cancer_tcga', gene = 'CHEK2') ORDER BY gene, frequency_pct DESC", "max_rows": 200 } ◀ result {"rows":[{"gene":"BRCA1","cancer_type":"Bladder Cancer","altered_samples":21,"profiled_samples":410,"frequency_pct":5.1},{"gene":"BRCA1","cancer_type":"Renal Clear Cell Carcinoma","altered_samples":4,"profiled_samples":402,"frequency_pct":1},{"gene":"BRCA1","cancer_type":"Pancreatic Cancer","altered_samples":2,"profiled_samples":179,"frequency_pct":1.1},{"gene":"BRCA1","cancer_type":"Thyroid Cancer","altered_samples":1,"profiled_samples":490,"frequency_pct":0.2},{"gene":"BRCA1","cancer_type":"Esophagogastric Cancer","altered_samples":18,"profiled_samples":618,"frequency_pct":2.9},{"gene":"BRCA1","cancer_type":"Ovarian Epithelial Tumor","altered_samples":18,"profiled_samples":523,"frequency_pct":3.4},{"gene":"BRCA1","cancer_type":"Hepatobiliary Cancer","altered_samples":4,"profiled_samples":366,"frequency_pct":1.1},{"gene":"BRCA1","cancer_type":"Prostate Cancer","altered_samples":1,"profiled_samples":494,"frequency_pct":0.2},{"gene":"BRCA1","cancer_type":"Glioma","altered_samples":2,"profiled_samples":514,"frequency_pct":0.4},{"gene":"BRCA1","cancer_type":"Breast Cancer","altered_samples":27,"profiled_samples":1066,"frequency_pct":2.5},{"gene":"BRCA1","cancer_type":"Melanoma","altered_samples":28,"profiled_samples":440,"frequency_pct":6.4},{"gene":"BRCA1","cancer_type":"Head and Neck Cancer","altered_samples":11,"profiled_samples":515,"frequency_pct":2.1},{"gene":"BRCA1","cancer_type":"Cervical Cancer","altered_samples":14,"profiled_samples":291,"frequency_pct":4.8},{"gene":"BRCA1","cancer_type":"Colorectal Cancer","altered_samples":16,"profiled_samples":534,"frequency_pct":3},{"gene":"BRCA1","cancer_type":"Glioblastoma","altered_samples":6,"profiled_samples":397,"frequency_pct":1.5},{"gene":"BRCA1","cancer_type":"Non-Small Cell Lung Cancer","altered_samples":41,"profiled_samples":1050,"frequency_pct":3.9},{"gene":"BRCA1","cancer_type":"Renal Non-Clear Cell Carcinoma","altered_samples":3,"profiled_samples":341,"frequency_pct":0.9},{"gene":"BRCA1","cancer_type":"Endometrial Cancer","altered_samples":47,"profiled_samples":574,"frequency_pct":8.2},{"gene":"ATM","cancer_type":"Pancreatic Cancer","altered_samples":8,"profiled_samples":179,"frequency_pct":4.5},{"gene":"ATM","cancer_type":"Adrenocortical Carcinoma","altered_samples":5,"profiled_samples":91,"frequency_pct":5.5},{"gene":"ATM","cancer_type":"Endometrial Cancer","altered_samples":101,"profiled_samples":574,"frequency_pct":17.6},{"gene":"ATM","cancer_type":"Head and Neck Cancer","altered_samples":17,"profiled_samples":515,"frequency_pct":3.3},{"gene":"ATM","cancer_type":"Non-Seminomatous Germ Cell Tumor","altered_samples":1,"profiled_samples":86,"frequency_pct":1.2},{"gene":"ATM","cancer_type":"Leukemia","altered_samples":1,"profiled_samples":200,"frequency_pct":0.5},{"gene":"ATM","cancer_type":"Non-Small Cell Lung Cancer","altered_samples":76,"profiled_samples":1050,"frequency_pct":7.2},{"gene":"ATM","cancer_type":"Sarcoma","altered_samples":7,"profiled_samples":255,"frequency_pct":2.7},{"ge … (6505 more chars) ▶ clickhouse_run_select_query { "query": "SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name = 'pan_cancer_tcga' ORDER BY cancer_study_identifier", "max_rows": 50 } ▶ resolve_and_route { "studyKeywords": [ "TCGA", "PanCancer", "Atlas" ] } ◀ result {"rows":[{"cancer_study_identifier":"acc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"blca_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"brca_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"cesc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"chol_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"coadread_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"dlbc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"esca_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"gbm_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"hnsc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"kich_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"kirc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"kirp_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"laml_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"lgg_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"lihc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"luad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"meso_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"ov_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"paad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"pcpg_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"prad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"sarc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"skcm_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"stad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"tgct_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"thca_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"thym_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"ucec_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"ucs_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"uvm_tcga_pan_can_atlas_2018"}]} ◀ result {"success":false,"error":"Internal Server Error","details":{"status":500,"response":{"req":{"method":"POST","url":"https://www.cbioportal.org/api/molecular-profiles/fetch","data":{"studyIds":["ucec_tcga_pan_can_atlas_2018","hnsc_tcga_pan_can_atlas_2018","lgg_tcga_pan_can_atlas_2018","kirc_tcga_pan_can_atlas_2018","thca_tcga_pan_can_atlas_2018","prad_tcga_pan_can_atlas_2018","lusc_tcga_pan_can_atlas_2018","skcm_tcga_pan_can_atlas_2018","stad_tcga_pan_can_atlas_2018","blca_tcga_pan_can_atlas_2018","lihc_tcga_pan_can_atlas_2018","cesc_tcga_pan_can_atlas_2018","kirp_tcga_pan_can_atlas_2018","sarc_tcga_pan_can_atlas_2018","laml_tcga_pan_can_atlas_2018","paad_tcga_pan_can_atlas_2018","esca_tcga_pan_can_atlas_2018","pcpg_tcga_pan_can_atlas_2018","tgct_tcga_pan_can_atlas_2018","thym_tcga_pan_can_atlas_2018","acc_tcga_pan_can_atlas_2018","meso_tcga_pan_can_atlas_2018","uvm_tcga_pan_can_atlas_2018","kich_tcga_pan_can_atlas_2018","ucs_tcga_pan_can_atlas_2018","dlbc_tcga_pan_can_atlas_2018","chol_tcga_pan_can_atlas_2018"]},"headers":{"user-agent":"node-superagent/3.8.3","accept":"application/json","content-type":"application/json"}},"header":{"cache-control":"no-cache, no-store, max-age=0, must-revalidate","content-encoding":"gzip","content-type":"application/json","date":"Sat, 26 Sep 2026 16:45:32 GMT","elapsed-time":"1","expires":"0","pragma":"no-cache","strict-transport-security":"max-age=31536000 ; includeSubDomains","vary":"origin,access-control-request-method,access-control-request-headers,accept-encoding","x-content-type-options":"nosniff","x-frame-options":"DENY","x-xss-protection":"0","connection":"close","transfer-encoding":"chunked"},"status":500,"text":"{\"timestamp\":1790441132934,\"status\":500,\"error\":\"Internal Server Error\",\"path\":\"/api/molecular-profiles/fetch\"}"}}} ▶ resolve_and_route { "studyIds": [ "ucec_tcga_pan_can_atlas_2018" ] } ◀ result {"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"ucec_tcga_pan_can_atlas_2018","name":"Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)","sampleCount":529,"studyViewUrl":"https://www.cbioportal.org/study?id=ucec_tcga_pan_can_atlas_2018","metadata":{"clinicalAttributeIds":["AGE","AJCC_PATHOLOGIC_TUMOR_STAGE","AJCC_STAGING_EDITION","ANEUPLOIDY_SCORE","BUFFA_HYPOXIA_SCORE","CANCER_TYPE","CANCER_TYPE_ACRONYM","CANCER_TYPE_DETAILED","DAYS_LAST_FOLLOWUP","DAYS_TO_BIRTH","DAYS_TO_INITIAL_PATHOLOGIC_DIAGNOSIS","DFS_MONTHS","DFS_STATUS","DSS_MONTHS","DSS_STATUS","ETHNICITY","FORM_COMPLETION_DATE","FRACTION_GENOME_ALTERED","GENETIC_ANCESTRY_LABEL","GRADE","HISTORY_NEOADJUVANT_TRTYN","ICD_10","ICD_O_3_HISTOLOGY","ICD_O_3_SITE","INFORMED_CONSENT_VERIFIED","IN_PANCANPATHWAYS_FREEZE","MSI_SCORE_MANTIS","MSI_SENSOR_SCORE","MUTATION_COUNT","NEW_TUMOR_EVENT_AFTER_INITIAL_TREATMENT","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER_PATIENT_ID","PATH_M_STAGE","PATH_N_STAGE","PATH_T_STAGE","PERSON_NEOPLASM_CANCER_STATUS","PFS_MONTHS","PFS_STATUS","PRIMARY_LYMPH_NODE_PRESENTATION_ASSESSMENT","PRIOR_DX","RACE","RADIATION_THERAPY","RAGNUM_HYPOXIA_SCORE","SAMPLE_COUNT","SAMPLE_TYPE","SEX","SOMATIC_STATUS","SUBTYPE","TBL_SCORE","TISSUE_PROSPECTIVE_COLLECTION_INDICATOR","TISSUE_RETROSPECTIVE_COLLECTION_INDICATOR","TISSUE_SOURCE_SITE","TISSUE_SOURCE_SITE_CODE","TMB_NONSYNONYMOUS","TUMOR_TISSUE_SITE","TUMOR_TYPE","WEIGHT","WINTER_HYPOXIA_SCORE"],"molecularProfileIds":["ucec_tcga_pan_can_atlas_2018_gistic","ucec_tcga_pan_can_atlas_2018_log2CNA","ucec_tcga_pan_can_atlas_2018_mutations","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","ucec_tcga_pan_can_atlas_2018_rppa","ucec_tcga_pan_can_atlas_2018_rppa_Zscores","ucec_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["ucec_tcga_pan_can_atlas_2018_armlevel_cna","ucec_tcga_pan_can_atlas_2018_genetic_ancestry","ucec_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","ucec_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","ucec_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","ucec_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_cna","generic_assay_genetic_ancestry","generic_assay_methylation"]}}]}} ▶ navigate_to_results_view { "studyIds": [ "ucec_tcga_pan_can_atlas_2018" ], "genes": [ "BRCA1", "BRCA2", "ATM", "CHEK2" ], "tab": "oncoprint" } ▶ navigate_to_study_view { "studyIds": [ "ucec_tcga_pan_can_atlas_2018", "blca_tcga_pan_can_atlas_2018", "skcm_tcga_pan_can_atlas_2018", "coadread_tcga_pan_can_atlas_2018", "esca_tcga_pan_can_atlas_2018" ], "filterJson": { "geneFilters": [ { "molecularProfileIds": [ "mutations" ], "geneQueries": [ [ { "hugoGeneSymbol": "BRCA1" }, { "hugoGeneSymbol": "BRCA2" }, { "hugoGeneSymbol": "ATM" }, { "hugoGeneSymbol": "CHEK2" } ] ] } ] } } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=ucec_tcga_pan_can_atlas_2018&gene_list=BRCA1%0ABRCA2%0AATM&case_set_id=ucec_tcga_pan_can_atlas_2018_all","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=ucec_tcga_pan_can_atlas_2018&gene_list=BRCA1%0ABRCA2%0AATM&case_set_id=ucec_tcga_pan_can_atlas_2018_all","data":{"studyIds":["ucec_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"ucec_tcga_pan_can_atlas_2018","name":"Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)","sampleCount":529}],"genes":["BRCA1","BRCA2","ATM"],"caseSetId":"ucec_tcga_pan_can_atlas_2018_all","pageDescription":"Visual summary of alterations in queried genes across samples."}} ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/study/summary?id=ucec_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018#filterJson=%7B%22geneFilters%22%3A%5B%7B%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22BRCA1%22%7D%2C%7B%22hugoGeneSymbol%22%3A%22BRCA2%22%7D%2C%7B%22hugoGeneSymbol%22%3A%22ATM%22%7D%2C%7B%22hugoGeneSymbol%22%3A%22CHEK2%22%7D%5D%5D%2C%22molecularProfileIds%22%3A%5B%22mutations%22%5D%7D%5D%7D","url":"https://www.cbioportal.org/study/summary?id=ucec_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018#filterJson=%7B%22geneFilters%22%3A%5B%7B%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22BRCA1%22%7D%2C%7B%22hugoGeneSymbol%22%3A%22BRCA2%22%7D%2C%7B%22hugoGeneSymbol%22%3A%22ATM%22%7D%2C%7B%22hugoGeneSymbol%22%3A%22CHEK2%22%7D%5D%5D%2C%22molecularProfileIds%22%3A%5B%22mutations%22%5D%7D%5D%7D","data":{"studyIds":["ucec_tcga_pan_can_atlas_2018","blca_tcga_pan_can_atlas_2018","skcm_tcga_pan_can_atlas_2018","coadread_tcga_pan_can_atlas_2018","esca_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"ucec_tcga_pan_can_atlas_2018","name":"Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)","sampleCount":529},{"studyId":"blca_tcga_pan_can_atlas_2018","name":"Bladder Urothelial Carcinoma (TCGA, PanCancer Atlas)","sampleCount":411},{"studyId":"skcm_tcga_pan_can_atlas_2018","name":"Skin Cutaneous Melanoma (TCGA, PanCancer Atlas)","sampleCount":448},{"studyId":"coadread_tcga_pan_can_atlas_2018","name":"Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":594},{"studyId":"esca_tcga_pan_can_atlas_2018","name":"Esophageal Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":182}],"tab":"summary","hasFilters":true,"hasPlotsConfig":false,"pageDescription":"Overview of all study data displayed as interactive charts summarizing available clinical and genomic data. Click within a chart to filter. Additional charts may be available in the Charts menu."}} ═ answer (success) The oncoprint URL only kept 3 genes (dropped CHEK2) — I'll note that but proceed with the results as-is since it's still useful. ## Sample-level mutation frequencies across TCGA PanCancer Atlas (32 non-overlapping cohorts, ≥50 profiled samples/cancer type) Frequencies use gene-specific profiled-sample denominators (altered / profiled × 100), sample-level counting unit. **BRCA1** — highest in: **Endometrial Cancer** 8.2% (47/574), **Melanoma** 6.4% (28/440), **Bladder Cancer** 5.1% (21/410), Cervical Cancer 4.8% (14/291). Lowest in Prostate (0.2%), Thyroid (0.2%), Glioma (0.4%). **BRCA2** — highest in: **Endometrial Cancer** 14.5% (83/574), **Melanoma** 11.6% (51/440), **Bladder Cancer** 9.8% (40/410), Esophagogastric 7.3%, Colorectal 7.1%. **ATM** — highest in: **Endometrial Cancer** 17.6% (101/574), **Melanoma** 9.5% (42/440), Esophagogastric 8.3%, **Bladder** 13.4%, **Colorectal** 13.1%. **CHEK2** — much lower overall; highest in **Endometrial Cancer** 6.3% (36/574), Bladder 2.4%, Adrenocortical 2.2%. Most other types ≤1%. ### Pattern All four DNA-repair genes peak sharply in **Endometrial Cancer (UCEC)**, followed by **Melanoma, Bladder, and Colorectal/Esophagogastric** cancers — the classic hypermutated/MSI-high or UV-mutagenesis-driven tumor types. This is consistent with a **passenger-mutation burden effect**: these are all large genes (especially BRCA2, ATM), and UCEC/melanoma/colorectal have among the highest genome-wide mutation rates in TCGA (POLE/MSI-hypermutation in UCEC and MSI-CRC; UV signature in melanoma). Elevated frequency in these cohorts does **not by itself indicate oncogenic/driver enrichment** — I have not run a statistical test (e.g., Fisher's exact per cancer type, or a mutation-rate-normalized comparison) to establish that these genes are enriched beyond what background tumor mutational burden would predict. None of these four genes have driver annotations available for TCGA in this database, so I cannot say any specific variant is oncogenic. If you want a rigorous enrichment claim, the next step would be a Mutual Exclusivity / Group Comparison test per cancer type, or normalizing by each tumor's overall mutation count — cBioPortal's Comparison tab can run that. ### Links [OncoPrint: BRCA1/BRCA2/ATM in Endometrial Cancer (UCEC)](https://www.cbioportal.org/results/oncoprint?cancer_study_list=ucec_tcga_pan_can_atlas_2018&gene_list=BRCA1%0ABRCA2%0AATM&case_set_id=ucec_tcga_pan_can_atlas_2018_all) — the navigator tool only carried 3 of the 4 genes into this URL (CHEK2 was dropped); you can add it manually via the gene box on the page. [Study View: BRCA1/BRCA2/ATM/CHEK2-altered samples across UCEC, Bladder, Melanoma, Colorectal, and Esophagogastric cohorts](https://www.cbioportal.org/study/summary?id=ucec_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018#filterJson=%7B%22geneFilters%22%3A%5B%7B%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22BRCA1%22%7D%2C%7B%22hugoGeneSymbol%22%3A%22BRCA2%22%7D%2C%7B%22hugoGeneSymbol%22%3A%22ATM%22%7D%2C%7B%22hugoGeneSymbol%22%3A%22CHEK2%22%7D%5D%5D%2C%22molecularProfileIds%22%3A%5B%22mutations%22%5D%7D%5D%7D) — all five cancer types shown in this table, five studies, 2,164 samples combined (no overlap, since these are five distinct TCGA cohorts).